Latest open access articles published in International Journal of Knowledge and Innovation Studies at https://www.acadlore.com/journals/IJKIS https://www.acadlore.com/journals/IJKIS Acadlore en Creative Commons Attribution(CC - BY) IJKIS support@acadlore.com https://www.acadlore.com/article/IJKIS/2025_3_3/ijkis030303 Entity–relation extraction constitutes a fundamental step in the construction of domain-specific knowledge graphs. In fault analysis of transmission systems, this task is complicated by extensive entity–relation overlap, nested structures, and strong semantic dependencies in technical texts. To address these challenges, an entity–relation joint extraction framework integrating reinforcement learning with a global pointer network (GPN) is developed (joint extraction model based on GPN and reinforcement learning, RL-BGPNet). A fault-oriented dataset is first established from helicopter transmission system maintenance manuals and related technical documents. Global semantic associations are then captured through a relation-aware attention mechanism, while parallel decoding is achieved using a GPN to accommodate overlapping and nested entities. The extraction of entity–relation triplets is further formulated as a multi-step decision process under a reinforcement learning paradigm, enabling coordinated optimization of entity recognition and relation classification and alleviating error accumulation caused by task interference. Experimental evaluations demonstrate that the proposed framework maintains stable performance under complex semantic conditions and exhibits satisfactory generalization, supporting its application to knowledge extraction and preliminary knowledge graph construction in the helicopter transmission system fault domain. 09-04-2025 <![CDATA[ <p>Entity–relation extraction constitutes a fundamental step in the construction of domain-specific knowledge graphs. In fault analysis of transmission systems, this task is complicated by extensive entity–relation overlap, nested structures, and strong semantic dependencies in technical texts. To address these challenges, an entity–relation joint extraction framework integrating reinforcement learning with a global pointer network (GPN) is developed (joint extraction model based on GPN and reinforcement learning, RL-BGPNet). A fault-oriented dataset is first established from helicopter transmission system maintenance manuals and related technical documents. Global semantic associations are then captured through a relation-aware attention mechanism, while parallel decoding is achieved using a GPN to accommodate overlapping and nested entities. The extraction of entity–relation triplets is further formulated as a multi-step decision process under a reinforcement learning paradigm, enabling coordinated optimization of entity recognition and relation classification and alleviating error accumulation caused by task interference. Experimental evaluations demonstrate that the proposed framework maintains stable performance under complex semantic conditions and exhibits satisfactory generalization, supporting its application to knowledge extraction and preliminary knowledge graph construction in the helicopter transmission system fault domain.</p> ]]> Entity–Relation Joint Extraction Method Based on Reinforcement Learning and Global Pointer Network weidong pan yijie li doi: 10.56578/ijkis030303 International Journal of Knowledge and Innovation Studies 09-04-2025 International Journal of Knowledge and Innovation Studies 09-04-2025 2025 3 3 Article 158 10.56578/ijkis030303 https://www.acadlore.com/article/IJKIS/2025_3_3/ijkis030303 https://www.acadlore.com/article/IJKIS/2025_3_3/ijkis030302 The strategic siting of a military airport constitutes a high-stakes planning problem characterized by complex trade-offs, long-term operational consequences, and pronounced uncertainty in expert judgment. In contrast to civilian airport planning, where economic efficiency and environmental externalities are typically prioritized, military airport location decisions are governed by additional requirements related to operational security, survivability, logistical resilience, and future capacity expansion. To address these challenges, a hybrid Multi-Criteria Decision-Making (MCDM) framework is proposed for the systematic evaluation and selection of military airport locations under uncertainty. Six core criteria and their associated sub-criteria, reflecting operational, strategic, technical, and infrastructural considerations, were identified through expert consultation and domain analysis. Criteria weights were derived using the Defining Interrelationships Between Ranked Criteria II (DIBR II) method and its Fuzzy, Grey, and Rough extensions, enabling the explicit modelling of vagueness, incompleteness, and ambiguity inherent in subjective assessments. Expert evaluations were aggregated using the Einstein Weighted Arithmetic Average (EWAA) operator, which accommodates heterogeneous levels of expertise and mitigates dominance bias. Alternative locations were subsequently ranked using the Weighted Aggregated Sum Product Assessment (WASPAS) method, allowing for flexible integration of additive and multiplicative aggregation schemes. The robustness of the obtained rankings was examined through a sensitivity analysis of the WASPAS aggregation parameter $\lambda$, confirming that variations in the aggregation structure do not alter the identification of the optimal and least-preferred alternatives. Furthermore, a comparative analysis with five established MCDM techniques revealed a high degree of rank correlation, thereby reinforcing the internal consistency and reliability of the proposed framework. The results demonstrate that the integration of uncertainty theories with advanced MCDM techniques provides a rigorous and adaptable decision-support tool for military infrastructure planning. Owing to its modular structure and methodological generality, the proposed framework can be readily adapted to diverse geographical settings, operational doctrines, and security environments, offering practical value for strategic decision-making in the defense sector. 08-16-2025 <![CDATA[ The strategic siting of a military airport constitutes a high-stakes planning problem characterized by complex trade-offs, long-term operational consequences, and pronounced uncertainty in expert judgment. In contrast to civilian airport planning, where economic efficiency and environmental externalities are typically prioritized, military airport location decisions are governed by additional requirements related to operational security, survivability, logistical resilience, and future capacity expansion. To address these challenges, a hybrid Multi-Criteria Decision-Making (MCDM) framework is proposed for the systematic evaluation and selection of military airport locations under uncertainty. Six core criteria and their associated sub-criteria, reflecting operational, strategic, technical, and infrastructural considerations, were identified through expert consultation and domain analysis. Criteria weights were derived using the Defining Interrelationships Between Ranked Criteria II (DIBR II) method and its Fuzzy, Grey, and Rough extensions, enabling the explicit modelling of vagueness, incompleteness, and ambiguity inherent in subjective assessments. Expert evaluations were aggregated using the Einstein Weighted Arithmetic Average (EWAA) operator, which accommodates heterogeneous levels of expertise and mitigates dominance bias. Alternative locations were subsequently ranked using the Weighted Aggregated Sum Product Assessment (WASPAS) method, allowing for flexible integration of additive and multiplicative aggregation schemes. The robustness of the obtained rankings was examined through a sensitivity analysis of the WASPAS aggregation parameter $\lambda$, confirming that variations in the aggregation structure do not alter the identification of the optimal and least-preferred alternatives. Furthermore, a comparative analysis with five established MCDM techniques revealed a high degree of rank correlation, thereby reinforcing the internal consistency and reliability of the proposed framework. The results demonstrate that the integration of uncertainty theories with advanced MCDM techniques provides a rigorous and adaptable decision-support tool for military infrastructure planning. Owing to its modular structure and methodological generality, the proposed framework can be readily adapted to diverse geographical settings, operational doctrines, and security environments, offering practical value for strategic decision-making in the defense sector. ]]> Strategic Location Selection of Military Airports under Uncertainty: A Hybrid Multi-Criteria Decision-Making Approach duško tešić darko božanić doi: 10.56578/ijkis030302 International Journal of Knowledge and Innovation Studies 08-16-2025 International Journal of Knowledge and Innovation Studies 08-16-2025 2025 3 3 Article 142 10.56578/ijkis030302 https://www.acadlore.com/article/IJKIS/2025_3_3/ijkis030302 https://www.acadlore.com/article/IJKIS/2025_3_3/ijkis030301 Virtual communities function as large-scale knowledge interaction systems in which users jointly produce, exchange, and validate knowledge resources. However, not all interactions contribute positively to system performance. This study examined how different forms of value co-destruction behavior degrade knowledge interaction processes and user-level value outcomes in virtual communities. Drawing on survey data from 530 users of firm-hosted virtual communities, a structural equation modeling approach was employed to analyze the effects of five negative interaction behaviors—irresponsible behavior, knowledge hiding, avoidance, conflict, and negative information interaction—on three dimensions of user value: practical, entertainment, and social value. The results indicate that avoidance, conflict, and negative information interaction significantly reduce practical value by impairing knowledge accessibility and information reliability. Knowledge hiding, avoidance, and conflict significantly reduce entertainment and social value by weakening interaction quality and relational embeddedness. Interestingly, irresponsible behavior increases individual entertainment and social value while simultaneously posing systemic risks to collective knowledge quality. These findings suggest that value co-destruction is not merely a behavioral problem but a systemic phenomenon that degrades knowledge flow efficiency, information quality, and collaborative stability in digital knowledge ecosystems. The study contributes to knowledge engineering research by identifying key failure mechanisms in knowledge interaction systems and offers governance implications for designing resilient and sustainable online knowledge platforms. 08-04-2025 <![CDATA[ Virtual communities function as large-scale knowledge interaction systems in which users jointly produce, exchange, and validate knowledge resources. However, not all interactions contribute positively to system performance. This study examined how different forms of value co-destruction behavior degrade knowledge interaction processes and user-level value outcomes in virtual communities. Drawing on survey data from 530 users of firm-hosted virtual communities, a structural equation modeling approach was employed to analyze the effects of five negative interaction behaviors—irresponsible behavior, knowledge hiding, avoidance, conflict, and negative information interaction—on three dimensions of user value: practical, entertainment, and social value. The results indicate that avoidance, conflict, and negative information interaction significantly reduce practical value by impairing knowledge accessibility and information reliability. Knowledge hiding, avoidance, and conflict significantly reduce entertainment and social value by weakening interaction quality and relational embeddedness. Interestingly, irresponsible behavior increases individual entertainment and social value while simultaneously posing systemic risks to collective knowledge quality. These findings suggest that value co-destruction is not merely a behavioral problem but a systemic phenomenon that degrades knowledge flow efficiency, information quality, and collaborative stability in digital knowledge ecosystems. The study contributes to knowledge engineering research by identifying key failure mechanisms in knowledge interaction systems and offers governance implications for designing resilient and sustainable online knowledge platforms. ]]> Knowledge Interaction Failure in Virtual Communities: Modeling the Impact of Value Co-Destruction on Knowledge System Performance zhaohui li bing cao qingjuan bu fenghua xiao doi: 10.56578/ijkis030301 International Journal of Knowledge and Innovation Studies 08-04-2025 International Journal of Knowledge and Innovation Studies 08-04-2025 2025 3 3 Article 129 10.56578/ijkis030301 https://www.acadlore.com/article/IJKIS/2025_3_3/ijkis030301 https://www.acadlore.com/article/IJKIS/2025_3_2/ijkis030205 In the context of technological development and digitalization of agriculture, mobile applications are playing an increasingly essential role in the management of small farms located in countries with fragmented agricultural structures. The aim of this research is to evaluate the most widely adopted mobile applications for monitoring and managing agricultural activities in areas with high agricultural potential such as Myzeqe, Korça, and Saranda in Albania. In order to achieve an impartial and sustainable assessment, multi-criteria decision-making (MCDM) methods integrated with fuzzy logic helped address the uncertainties and subjectivity in the evaluation process. The fuzzy CRiteria Importance through Intercriteria Correlation (CRITIC) method was employed to objectively determine the weights of the criteria based on the variability and contradiction between them. The fuzzy Combined Compromise Solution (CoCoSo) method was then adopted to rank the mobile applications. As revealed from the findings in this study, the most highly-rated criteria by experts, i.e., criterion C1-Ease of use and criterion C6-Integration with other technologies had the highest weight. The least rated criterion by experts was criterion C7-Technical support and training. AgriApp (A7) was the mobile application identified with the best performance. The contribution of this research lied in the building of a structured and objective framework to evaluate mobile technologies applied in agriculture, thus enabling more informed decisions for their adoption at the local and regional level. 06-29-2025 <![CDATA[ In the context of technological development and digitalization of agriculture, mobile applications are playing an increasingly essential role in the management of small farms located in countries with fragmented agricultural structures. The aim of this research is to evaluate the most widely adopted mobile applications for monitoring and managing agricultural activities in areas with high agricultural potential such as Myzeqe, Korça, and Saranda in Albania. In order to achieve an impartial and sustainable assessment, multi-criteria decision-making (MCDM) methods integrated with fuzzy logic helped address the uncertainties and subjectivity in the evaluation process. The fuzzy CRiteria Importance through Intercriteria Correlation (CRITIC) method was employed to objectively determine the weights of the criteria based on the variability and contradiction between them. The fuzzy Combined Compromise Solution (CoCoSo) method was then adopted to rank the mobile applications. As revealed from the findings in this study, the most highly-rated criteria by experts, i.e., criterion C1-Ease of use and criterion C6-Integration with other technologies had the highest weight. The least rated criterion by experts was criterion C7-Technical support and training. AgriApp (A7) was the mobile application identified with the best performance. The contribution of this research lied in the building of a structured and objective framework to evaluate mobile technologies applied in agriculture, thus enabling more informed decisions for their adoption at the local and regional level. ]]> Objective Evaluation of Mobile Applications for Small Farm Management in Albania with Fuzzy Methods arianit peci blerina zanaj doi: 10.56578/ijkis030205 International Journal of Knowledge and Innovation Studies 06-29-2025 International Journal of Knowledge and Innovation Studies 06-29-2025 2025 3 2 Article 118 10.56578/ijkis030205 https://www.acadlore.com/article/IJKIS/2025_3_2/ijkis030205 https://www.acadlore.com/article/IJKIS/2025_3_2/ijkis030204 The extent to which emotional perception shapes the acquisition, analysis, and presentation of knowledge within human–machine communicative interaction remains insufficiently understood. In this study, the principles of emotion artificial intelligentce (AI) (also referred to as affective computing) were integrated with trust as a socio-technical construct to investigate the mediating role of emotional expression in cognitive processing. A mixed-methods design was adopted, drawing on structured questionnaires and open-ended responses collected from 50 participants over a five-year period. Statistical modelling revealed that system quality significantly enhanced perceived ease of use when emotional signals were effectively encoded and decoded by both humans and machines. Trust was found to exert a positive influence on perceived usefulness, credibility, and user satisfaction, although it did not directly predict behavioural intention. In contrast, perceived ease of use demonstrated a strong positive association with intention in emotion-driven contexts, thereby rendering human–machine interaction more engaging, reliable, and trustworthy. These findings indicate that the tension between emotional and rational dimensions of higher cognitive processes within knowledge systems is shaped less by individual reluctance than by systemic and institutional determinants. The contribution of this work lies in the development of a conceptual framework for emotion-aware knowledge presentation, offering design implications for intelligent systems in education, public administration, business applications, and conversational AI. By demonstrating how emotion-aware mechanisms enhance both cognitive efficiency and affective engagement, the study advances understanding of human–machine cooperation and provides actionable guidance for the construction of more adaptive and trustworthy knowledge systems. 06-29-2025 <![CDATA[ The extent to which emotional perception shapes the acquisition, analysis, and presentation of knowledge within human–machine communicative interaction remains insufficiently understood. In this study, the principles of emotion artificial intelligentce (AI) (also referred to as affective computing) were integrated with trust as a socio-technical construct to investigate the mediating role of emotional expression in cognitive processing. A mixed-methods design was adopted, drawing on structured questionnaires and open-ended responses collected from 50 participants over a five-year period. Statistical modelling revealed that system quality significantly enhanced perceived ease of use when emotional signals were effectively encoded and decoded by both humans and machines. Trust was found to exert a positive influence on perceived usefulness, credibility, and user satisfaction, although it did not directly predict behavioural intention. In contrast, perceived ease of use demonstrated a strong positive association with intention in emotion-driven contexts, thereby rendering human–machine interaction more engaging, reliable, and trustworthy. These findings indicate that the tension between emotional and rational dimensions of higher cognitive processes within knowledge systems is shaped less by individual reluctance than by systemic and institutional determinants. The contribution of this work lies in the development of a conceptual framework for emotion-aware knowledge presentation, offering design implications for intelligent systems in education, public administration, business applications, and conversational AI. By demonstrating how emotion-aware mechanisms enhance both cognitive efficiency and affective engagement, the study advances understanding of human–machine cooperation and provides actionable guidance for the construction of more adaptive and trustworthy knowledge systems. ]]> Emotion-Aware Knowledge Acquisition, Perception and Presentation: An Empirical Study Across Pragmatics, Cognitive Linguistics and Human-Machine Communicative Interaction anna rostomyan doi: 10.56578/ijkis030204 International Journal of Knowledge and Innovation Studies 06-29-2025 International Journal of Knowledge and Innovation Studies 06-29-2025 2025 3 2 Article 103 10.56578/ijkis030204 https://www.acadlore.com/article/IJKIS/2025_3_2/ijkis030204 https://www.acadlore.com/article/IJKIS/2025_3_2/ijkis030203 The macroeconomic performance of nations provides valuable insights into the knowledge economy and the governance structures that sustain its development. This study formalizes a framework for evaluating knowledge flows and innovation capacity through multi-criteria decision analysis (MCDA) using open World Bank data. The analysis employs the Logarithmic Decomposition of Criteria Importance (LODECI) method in conjunction with the Preference Selection Index (PSI) to determine objective weights, while the Weighted Euclidean Distance-Based Approach (WEDBA) is applied to rank the G7 countries and Türkiye in 2023. Knowledge flows, as represented by exports and foreign direct investment (FDI), serve as proxies for cross-border knowledge exchange, while inflation, unemployment, and economic growth are assessed within a reproducible, policy-driven framework. The weighting procedure assigns the greatest aggregate importance to inflation and the least to unemployment. The resulting rankings place the United States first, followed by Japan in second place, Türkiye fourth, and the United Kingdom last. The analysis further highlights how factors such as price stability, external openness, and investment dynamics shape national knowledge creation, diffusion, and organizational learning processes. By focusing on the utilization of open data, explicit knowledge representation, and transparent multi-criteria methodologies, the proposed framework strengthens digital knowledge infrastructures and facilitates actionable cross-country benchmarking. The findings have important policy implications, particularly in understanding how national macroeconomic variables influence innovation capacity. The framework is designed to be extensible, allowing for future adaptation to evaluate additional indicators, such as R&amp;D intensity, high-tech export shares, and patenting activity. Furthermore, the approach is structured to support replication across various regions and timeframes, ensuring its broad applicability and scalability. 06-28-2025 <![CDATA[ The macroeconomic performance of nations provides valuable insights into the knowledge economy and the governance structures that sustain its development. This study formalizes a framework for evaluating knowledge flows and innovation capacity through multi-criteria decision analysis (MCDA) using open World Bank data. The analysis employs the Logarithmic Decomposition of Criteria Importance (LODECI) method in conjunction with the Preference Selection Index (PSI) to determine objective weights, while the Weighted Euclidean Distance-Based Approach (WEDBA) is applied to rank the G7 countries and Türkiye in 2023. Knowledge flows, as represented by exports and foreign direct investment (FDI), serve as proxies for cross-border knowledge exchange, while inflation, unemployment, and economic growth are assessed within a reproducible, policy-driven framework. The weighting procedure assigns the greatest aggregate importance to inflation and the least to unemployment. The resulting rankings place the United States first, followed by Japan in second place, Türkiye fourth, and the United Kingdom last. The analysis further highlights how factors such as price stability, external openness, and investment dynamics shape national knowledge creation, diffusion, and organizational learning processes. By focusing on the utilization of open data, explicit knowledge representation, and transparent multi-criteria methodologies, the proposed framework strengthens digital knowledge infrastructures and facilitates actionable cross-country benchmarking. The findings have important policy implications, particularly in understanding how national macroeconomic variables influence innovation capacity. The framework is designed to be extensible, allowing for future adaptation to evaluate additional indicators, such as R&amp;D intensity, high-tech export shares, and patenting activity. Furthermore, the approach is structured to support replication across various regions and timeframes, ensuring its broad applicability and scalability. ]]> Knowledge Flows and Innovation Capacity: A Reproducible Multi-Criteria Decision Analysis of the G7 and Türkiye salim üre ali aygün yürüyen alptekin ulutaş muzaffer demirbaş ali oğuz bayrakçıl doi: 10.56578/ijkis030203 International Journal of Knowledge and Innovation Studies 06-28-2025 International Journal of Knowledge and Innovation Studies 06-28-2025 2025 3 2 Article 89 10.56578/ijkis030203 https://www.acadlore.com/article/IJKIS/2025_3_2/ijkis030203 https://www.acadlore.com/article/IJKIS/2025_3_2/ijkis030202 Accurate selective image segmentation continues to pose substantial challenges, particularly under conditions of noise interference, intensity inhomogeneity, and irregular object boundaries. To address these complexities, a novel framework is introduced that integrates fuzzy Einstein–Dombi (ED) operators with level set energy minimization, guided by marker-based initialization. The proposed approach departs from traditional intensity-driven models by jointly incorporating intensity, texture, and gradient-based features, thereby facilitating improved boundary delineation and enhanced regional homogeneity. A spatially adaptive regularization term has been embedded within the level set formulation to reinforce contour stability and robustness in the presence of artefacts and signal degradation. The fuzzy ED operators enable nuanced fusion of multiple features through non-linear aggregation, yielding a more expressive and resilient energy functional. In contrast to conventional segmentation schemes, the developed method achieves superior convergence and delineation accuracy, particularly within complex grayscale and noisy medical image datasets. Experimental validation has been conducted across a range of imaging conditions, with performance quantitatively assessed using established metrics, including segmentation accuracy (0.95), intersection over union (IoU: 0.89), and Dice similarity coefficient (DSC: 0.94). These results demonstrate statistically significant improvements over comparative models. Additionally, qualitative evaluations reveal enhanced contour fidelity and resistance to local intensity fluctuations. The methodological simplicity and computational efficiency of the framework render it highly suitable for real-time applications in medical imaging diagnostics, object detection, and related image analysis tasks. By offering a robust, interpretable, and generalizable solution, this work establishes a new reference point for selective image segmentation under non-ideal conditions, and paves the way for further exploration of fuzzy operator integration within variational segmentation paradigms. 05-29-2025 <![CDATA[ Accurate selective image segmentation continues to pose substantial challenges, particularly under conditions of noise interference, intensity inhomogeneity, and irregular object boundaries. To address these complexities, a novel framework is introduced that integrates fuzzy Einstein–Dombi (ED) operators with level set energy minimization, guided by marker-based initialization. The proposed approach departs from traditional intensity-driven models by jointly incorporating intensity, texture, and gradient-based features, thereby facilitating improved boundary delineation and enhanced regional homogeneity. A spatially adaptive regularization term has been embedded within the level set formulation to reinforce contour stability and robustness in the presence of artefacts and signal degradation. The fuzzy ED operators enable nuanced fusion of multiple features through non-linear aggregation, yielding a more expressive and resilient energy functional. In contrast to conventional segmentation schemes, the developed method achieves superior convergence and delineation accuracy, particularly within complex grayscale and noisy medical image datasets. Experimental validation has been conducted across a range of imaging conditions, with performance quantitatively assessed using established metrics, including segmentation accuracy (0.95), intersection over union (IoU: 0.89), and Dice similarity coefficient (DSC: 0.94). These results demonstrate statistically significant improvements over comparative models. Additionally, qualitative evaluations reveal enhanced contour fidelity and resistance to local intensity fluctuations. The methodological simplicity and computational efficiency of the framework render it highly suitable for real-time applications in medical imaging diagnostics, object detection, and related image analysis tasks. By offering a robust, interpretable, and generalizable solution, this work establishes a new reference point for selective image segmentation under non-ideal conditions, and paves the way for further exploration of fuzzy operator integration within variational segmentation paradigms. ]]> Selective Image Segmentation Through Fuzzy Einstein–Dombi Operators and Level Set Energy Minimization uzair ahmad doi: 10.56578/ijkis030202 International Journal of Knowledge and Innovation Studies 05-29-2025 International Journal of Knowledge and Innovation Studies 05-29-2025 2025 3 2 Article 74 10.56578/ijkis030202 https://www.acadlore.com/article/IJKIS/2025_3_2/ijkis030202 https://www.acadlore.com/article/IJKIS/2025_3_2/ijkis030201 For reducing uncertainty in data gathered from real-world scenarios, the picture fuzzy rough set (PFRS) framework is a reliable resource. This article presents new aggregation operators (AOs) based on the Schweizer-Sklar t-conorm (SS-TC) and Schweizer-Sklar t-norm (SS-TN). They present the PFRS framework with SS, which aims to handle the intricacies in contexts where decision-making is marked by ambiguity and uncertainty. In the context of Green Supply Chain Management (GSCM), where supply chain procedures incorporate sustainability considerations, this framework is especially pertinent. GSCM places a strong emphasis on minimizing environmental impacts by employing techniques such as effective resource management and sustainable sourcing. The adaptability and versatility required to assess and optimize these inexperienced practices are significantly improved with the aid of our expert PFRS framework. Businesses can keep operational efficiency and align their supply chain operations with environmental desires with the aid of using this framework. By considering both the blessings and disadvantages of environmental sustainability, using PFRS in GSCM enhances decision-making and promotes environmental sustainability. To handle picture fuzzy rough values (PFRVs), these operators include picture fuzzy rough weighted averaging (PFRSSWA) and picture fuzzy rough weighted geometric (PFRSSWG) operators. We investigate these recently created AOs' basic characteristics and use them to solve multi-attribute group decision-making (MAGDM) issues under the framework of picture fuzzy (PF) data. Our results demonstrate how the outcomes in SS-TN and SS-TC vary with varying parameter values. We also contrast these outcomes with the ones obtained from pre-existing AOs. In addition, we provide a graphic representation of all observations and findings to show how flexible and successful the suggested operators are at handling MAGDM problems. 05-22-2025 <![CDATA[ For reducing uncertainty in data gathered from real-world scenarios, the picture fuzzy rough set (PFRS) framework is a reliable resource. This article presents new aggregation operators (AOs) based on the Schweizer-Sklar t-conorm (SS-TC) and Schweizer-Sklar t-norm (SS-TN). They present the PFRS framework with SS, which aims to handle the intricacies in contexts where decision-making is marked by ambiguity and uncertainty. In the context of Green Supply Chain Management (GSCM), where supply chain procedures incorporate sustainability considerations, this framework is especially pertinent. GSCM places a strong emphasis on minimizing environmental impacts by employing techniques such as effective resource management and sustainable sourcing. The adaptability and versatility required to assess and optimize these inexperienced practices are significantly improved with the aid of our expert PFRS framework. Businesses can keep operational efficiency and align their supply chain operations with environmental desires with the aid of using this framework. By considering both the blessings and disadvantages of environmental sustainability, using PFRS in GSCM enhances decision-making and promotes environmental sustainability. To handle picture fuzzy rough values (PFRVs), these operators include picture fuzzy rough weighted averaging (PFRSSWA) and picture fuzzy rough weighted geometric (PFRSSWG) operators. We investigate these recently created AOs' basic characteristics and use them to solve multi-attribute group decision-making (MAGDM) issues under the framework of picture fuzzy (PF) data. Our results demonstrate how the outcomes in SS-TN and SS-TC vary with varying parameter values. We also contrast these outcomes with the ones obtained from pre-existing AOs. In addition, we provide a graphic representation of all observations and findings to show how flexible and successful the suggested operators are at handling MAGDM problems. ]]> Multi-Attribute Green Supplier Decision-Making Using Picture Fuzzy Rough Schweizer-Sklar Aggregation Operators rizwan gul faiza tufail doi: 10.56578/ijkis030201 International Journal of Knowledge and Innovation Studies 05-22-2025 International Journal of Knowledge and Innovation Studies 05-22-2025 2025 3 2 Article 60 10.56578/ijkis030201 https://www.acadlore.com/article/IJKIS/2025_3_2/ijkis030201 https://www.acadlore.com/article/IJKIS/2025_3_1/ijkis030105 Image segmentation plays a crucial role in medical imaging, remote sensing, and object detection. However, challenges persist due to uncertainty in region classification, sensitivity to noise, and discontinuities in object boundaries. To address these issues, a novel segmentation framework is proposed, integrating Complex Pythagorean Fuzzy Aggregation Operators (CPFAs) with a level-set-based optimization strategy to enhance both precision and adaptability. The proposed model leverages complex Pythagorean fuzzy membership functions, incorporating both magnitude and phase components, to effectively manage overlapping intensity distributions and classification uncertainty. Additionally, geometric constraints, including gradient and curvature-based regularization, are employed to refine boundary evolution, ensuring accurate edge delineation in noisy and complex imaging conditions. A key contribution of this work is the formulation of a complex fuzzy energy functional, which synergistically integrates fuzzy region classification, phase-aware boundary refinement, and geometric constraints to guide segmentation. The level-set method is utilized to iteratively minimize this functional, facilitating smooth transitions between segmented regions while preserving structural integrity. Experimental evaluations conducted across diverse imaging domains demonstrate the robustness and versatility of the proposed approach, highlighting its efficacy in medical image segmentation, remote sensing analysis, and object detection. The integration of complex fuzzy logic with geometric optimization not only enhances segmentation accuracy but also improves resilience to noise and irregular boundary structures, making this framework particularly suitable for applications requiring high-precision image analysis. 03-30-2025 <![CDATA[ Image segmentation plays a crucial role in medical imaging, remote sensing, and object detection. However, challenges persist due to uncertainty in region classification, sensitivity to noise, and discontinuities in object boundaries. To address these issues, a novel segmentation framework is proposed, integrating Complex Pythagorean Fuzzy Aggregation Operators (CPFAs) with a level-set-based optimization strategy to enhance both precision and adaptability. The proposed model leverages complex Pythagorean fuzzy membership functions, incorporating both magnitude and phase components, to effectively manage overlapping intensity distributions and classification uncertainty. Additionally, geometric constraints, including gradient and curvature-based regularization, are employed to refine boundary evolution, ensuring accurate edge delineation in noisy and complex imaging conditions. A key contribution of this work is the formulation of a complex fuzzy energy functional, which synergistically integrates fuzzy region classification, phase-aware boundary refinement, and geometric constraints to guide segmentation. The level-set method is utilized to iteratively minimize this functional, facilitating smooth transitions between segmented regions while preserving structural integrity. Experimental evaluations conducted across diverse imaging domains demonstrate the robustness and versatility of the proposed approach, highlighting its efficacy in medical image segmentation, remote sensing analysis, and object detection. The integration of complex fuzzy logic with geometric optimization not only enhances segmentation accuracy but also improves resilience to noise and irregular boundary structures, making this framework particularly suitable for applications requiring high-precision image analysis. ]]> Intelligent Image Segmentation via Complex Pythagorean Fuzzy Sets and Level-Set Optimization muhammad shahkar khan doi: 10.56578/ijkis030105 International Journal of Knowledge and Innovation Studies 03-30-2025 International Journal of Knowledge and Innovation Studies 03-30-2025 2025 3 1 Article 50 10.56578/ijkis030105 https://www.acadlore.com/article/IJKIS/2025_3_1/ijkis030105 https://www.acadlore.com/article/IJKIS/2025_3_1/ijkis030104 Transformer-based language models have demonstrated remarkable success in few-shot text classification; however, their effectiveness is often constrained by challenges such as high intraclass diversity and interclass similarity, which hinder the extraction of discriminative features. To address these limitations, a novel framework, Adaptive Masking Bidirectional Encoder Representations from Transformers with Dynamic Weighted Prototype Module (AMBERT-DWPM), is introduced, incorporating adaptive masking and dynamic weighted prototypical learning to enhance feature representation and classification performance. The standard BERT architecture is refined by integrating an adaptive masking mechanism based on Layered Integrated Gradients (LIG), enabling the model to dynamically emphasize salient text segments and improve feature discrimination. Additionally, a DWPM is designed to assign adaptive weights to support samples, mitigating inaccuracies in prototype construction caused by intraclass variability. Extensive evaluations conducted on six publicly available benchmark datasets demonstrate the superiority of AMBERT-DWPM over existing few-shot classification approaches. Notably, under the 5-shot setting on the DBpedia14 dataset, an accuracy of 0.978±0.004 is achieved, highlighting significant advancements in feature discrimination and generalization capabilities. These findings suggest that AMBERT-DWPM provides an efficient and robust solution for few-shot text classification, particularly in scenarios characterized by limited and complex textual data. 03-30-2025 <![CDATA[ <p>Transformer-based language models have demonstrated remarkable success in few-shot text classification; however, their effectiveness is often constrained by challenges such as high intraclass diversity and interclass similarity, which hinder the extraction of discriminative features. To address these limitations, a novel framework, Adaptive Masking Bidirectional Encoder Representations from Transformers with Dynamic Weighted Prototype Module (AMBERT-DWPM), is introduced, incorporating adaptive masking and dynamic weighted prototypical learning to enhance feature representation and classification performance. The standard BERT architecture is refined by integrating an adaptive masking mechanism based on Layered Integrated Gradients (LIG), enabling the model to dynamically emphasize salient text segments and improve feature discrimination. Additionally, a DWPM is designed to assign adaptive weights to support samples, mitigating inaccuracies in prototype construction caused by intraclass variability. Extensive evaluations conducted on six publicly available benchmark datasets demonstrate the superiority of AMBERT-DWPM over existing few-shot classification approaches. Notably, under the 5-shot setting on the DBpedia14 dataset, an accuracy of 0.978±0.004 is achieved, highlighting significant advancements in feature discrimination and generalization capabilities. These findings suggest that AMBERT-DWPM provides an efficient and robust solution for few-shot text classification, particularly in scenarios characterized by limited and complex textual data.</p> ]]> AMBERT-DWPM: An Adaptive Masking and Dynamic Prototype Learning Framework for Few-Shot Text Classification junyu li jialin ma ashim khadka doi: 10.56578/ijkis030104 International Journal of Knowledge and Innovation Studies 03-30-2025 International Journal of Knowledge and Innovation Studies 03-30-2025 2025 3 1 Article 37 10.56578/ijkis030104 https://www.acadlore.com/article/IJKIS/2025_3_1/ijkis030104 https://www.acadlore.com/article/IJKIS/2025_3_1/ijkis030103 Accurate estimation of tree height is fundamental to sustainable forest management, particularly in regions such as Kumrat Valley, Pakistan, where Deodar Cedar (Cedrus deodara) serves as a vital ecological and economic resource. Conventional height estimation models often exhibit limitations in capturing the inherent complexity of forest ecosystems, where multiple environmental factors interact non-linearly. To address this challenge, a hybrid predictive framework integrating fuzzy inference systems (FIS) and multiple linear regression (MLR) has been developed to enhance the accuracy of height estimation. The FIS model incorporates key environmental and physiological parameters, including trunk diameter, soil quality, temperature, and rainfall, which are classified into fuzzy sets—low, medium, and high—corresponding to distinct growth rates (slow, normal, fast) and developmental stages (early, average, late). This classification enables a nuanced representation of environmental variability and tree growth dynamics. Complementarily, the MLR model quantifies the statistical relationships between these variables and tree height, yielding an R² value of 0.85, an adjusted R² of 0.64, and a statistically significant p-value of 0.04. The integration of fuzzy logic with regression analysis offers a robust, data-driven approach to height prediction, effectively addressing the uncertainties associated with environmental fluctuations. By leveraging both rule-based inference and quantitative modeling, this method provides valuable insights for precision forestry, contributing to the sustainable management and conservation of Deodar Cedar in Kumrat Valley. 03-23-2025 <![CDATA[ Accurate estimation of tree height is fundamental to sustainable forest management, particularly in regions such as Kumrat Valley, Pakistan, where Deodar Cedar (Cedrus deodara) serves as a vital ecological and economic resource. Conventional height estimation models often exhibit limitations in capturing the inherent complexity of forest ecosystems, where multiple environmental factors interact non-linearly. To address this challenge, a hybrid predictive framework integrating fuzzy inference systems (FIS) and multiple linear regression (MLR) has been developed to enhance the accuracy of height estimation. The FIS model incorporates key environmental and physiological parameters, including trunk diameter, soil quality, temperature, and rainfall, which are classified into fuzzy sets—low, medium, and high—corresponding to distinct growth rates (slow, normal, fast) and developmental stages (early, average, late). This classification enables a nuanced representation of environmental variability and tree growth dynamics. Complementarily, the MLR model quantifies the statistical relationships between these variables and tree height, yielding an R² value of 0.85, an adjusted R² of 0.64, and a statistically significant p-value of 0.04. The integration of fuzzy logic with regression analysis offers a robust, data-driven approach to height prediction, effectively addressing the uncertainties associated with environmental fluctuations. By leveraging both rule-based inference and quantitative modeling, this method provides valuable insights for precision forestry, contributing to the sustainable management and conservation of Deodar Cedar in Kumrat Valley. ]]> Integration of Fuzzy Inference Systems and Linear Regression for Enhanced Height Prediction of Deodar Cedar Trees in Kumrat Valley muhammad zeeshan naeem doi: 10.56578/ijkis030103 International Journal of Knowledge and Innovation Studies 03-23-2025 International Journal of Knowledge and Innovation Studies 03-23-2025 2025 3 1 Article 26 10.56578/ijkis030103 https://www.acadlore.com/article/IJKIS/2025_3_1/ijkis030103 https://www.acadlore.com/article/IJKIS/2025_3_1/ijkis030102 With the rapid advancement of modern robotics and artificial intelligence, intelligent picking robots have been widely adopted in agricultural production. Global path planning techniques have been applied to crop harvesting, such as oranges, apples, tea leaves, and tomatoes, yielding promising results. This study focuses on the path planning problem for a robotic arm used in premium tea leaf picking. Experimental simulations reveal that the Ant Colony Optimization (ACO) algorithm performs particularly well in solving small-scale Traveling Salesman Problems (TSP), as it can incrementally construct initial paths and, with properly tuned parameters, produce higher-quality solutions and achieve faster convergence compared to other algorithms. However, the traditional ACO algorithm tends to fall into local optima and suffers from slow convergence. To address these challenges, this paper proposes a dynamically optimized ACO algorithm that enhances the pheromone update rules and optimizes the $\alpha$ and $\beta$ parameters during the search process. These parameters are updated according to the optimization results, and a ranking factor is introduced to prevent the optimal picking path from being overlooked. The proposed method demonstrates superior performance over the traditional ACO algorithm in terms of path quality and convergence speed. 03-20-2025 <![CDATA[ With the rapid advancement of modern robotics and artificial intelligence, intelligent picking robots have been widely adopted in agricultural production. Global path planning techniques have been applied to crop harvesting, such as oranges, apples, tea leaves, and tomatoes, yielding promising results. This study focuses on the path planning problem for a robotic arm used in premium tea leaf picking. Experimental simulations reveal that the Ant Colony Optimization (ACO) algorithm performs particularly well in solving small-scale Traveling Salesman Problems (TSP), as it can incrementally construct initial paths and, with properly tuned parameters, produce higher-quality solutions and achieve faster convergence compared to other algorithms. However, the traditional ACO algorithm tends to fall into local optima and suffers from slow convergence. To address these challenges, this paper proposes a dynamically optimized ACO algorithm that enhances the pheromone update rules and optimizes the $\alpha$ and $\beta$ parameters during the search process. These parameters are updated according to the optimization results, and a ranking factor is introduced to prevent the optimal picking path from being overlooked. The proposed method demonstrates superior performance over the traditional ACO algorithm in terms of path quality and convergence speed. ]]> Tea Leaf Picking Path Planning Based on an Improved Ant Colony Optimization Algorithm luqi zhang doi: 10.56578/ijkis030102 International Journal of Knowledge and Innovation Studies 03-20-2025 International Journal of Knowledge and Innovation Studies 03-20-2025 2025 3 1 Article 12 10.56578/ijkis030102 https://www.acadlore.com/article/IJKIS/2025_3_1/ijkis030102 https://www.acadlore.com/article/IJKIS/2025_3_1/ijkis030101 This research examines customer relationship management (CRM) systems using multi-criteria decision-making (MCDM) methods, with the intention of selecting the most suitable solution for small companies. The main goal of this research is to make a decision when choosing a CRM system by applying an objective approach. For this purpose, objective criteria were used, and an objective evaluation of the observed CRM systems was conducted. By using the MEREC (MEthod based on the Removal Effects of Criteria) method, the importance of the criteria was determined, while the CORASO (COmpromise Ranking from Alternative SOlutions) method was applied to rank the CRM systems. These methods were combined using a methodology into a hybrid approach. The results of this approach indicate that CRM systems with a higher degree of integration and automation achieved a higher rank, while systems with limited functionalities and longer implementation times received a lower ranking. This analysis confirms the importance of CRM systems in optimizing business processes, improving customer satisfaction, and enhancing marketing activities in companies. The results of the research can assist small companies in making decisions when selecting a CRM system. The contribution of this research is to provide efficient decision-making in the selection of a CRM system, thereby strengthening the companies' operations and improving their performance. 02-11-2025 <![CDATA[ <p style="text-align: justify">This research examines customer relationship management (CRM) systems using multi-criteria decision-making (MCDM) methods, with the intention of selecting the most suitable solution for small companies. The main goal of this research is to make a decision when choosing a CRM system by applying an objective approach. For this purpose, objective criteria were used, and an objective evaluation of the observed CRM systems was conducted. By using the MEREC (MEthod based on the Removal Effects of Criteria) method, the importance of the criteria was determined, while the CORASO (COmpromise Ranking from Alternative SOlutions) method was applied to rank the CRM systems. These methods were combined using a methodology into a hybrid approach. The results of this approach indicate that CRM systems with a higher degree of integration and automation achieved a higher rank, while systems with limited functionalities and longer implementation times received a lower ranking. This analysis confirms the importance of CRM systems in optimizing business processes, improving customer satisfaction, and enhancing marketing activities in companies. The results of the research can assist small companies in making decisions when selecting a CRM system. The contribution of this research is to provide efficient decision-making in the selection of a CRM system, thereby strengthening the companies' operations and improving their performance.</p> ]]> Selection of CRM Systems Using Objective Criteria for the Needs of Small Companies adis puška doi: 10.56578/ijkis030101 International Journal of Knowledge and Innovation Studies 02-11-2025 International Journal of Knowledge and Innovation Studies 02-11-2025 2025 3 1 Article 1 10.56578/ijkis030101 https://www.acadlore.com/article/IJKIS/2025_3_1/ijkis030101 https://www.acadlore.com/article/IJKIS/2024_2_4/ijkis020405 As a critical component of mechanical transmission systems, gears play a vital role in ensuring industrial production runs smoothly. Undetected gear failures can lead to mechanical breakdowns, production interruptions, and even safety hazards. Therefore, an efficient gear fault detection method is essential for maintaining industrial continuity and safety. This paper proposes a hybrid model that integrates convolutional neural networks (CNN) and support vector machines (SVM) for gear fault detection. The model leverages CNNs to automatically extract key features from vibration signals, while SVMs enhance classification accuracy, resulting in a high-precision fault diagnosis system. On a publicly available gear fault dataset, the proposed model achieved an impressive accuracy of 0.9922, significantly outperforming single-classifier models. Moreover, the model exhibits a short training time, demonstrating its computational efficiency. This research provides an effective and automated approach to gear fault detection, offering significant potential for industrial applications. 12-30-2024 <![CDATA[ As a critical component of mechanical transmission systems, gears play a vital role in ensuring industrial production runs smoothly. Undetected gear failures can lead to mechanical breakdowns, production interruptions, and even safety hazards. Therefore, an efficient gear fault detection method is essential for maintaining industrial continuity and safety. This paper proposes a hybrid model that integrates convolutional neural networks (CNN) and support vector machines (SVM) for gear fault detection. The model leverages CNNs to automatically extract key features from vibration signals, while SVMs enhance classification accuracy, resulting in a high-precision fault diagnosis system. On a publicly available gear fault dataset, the proposed model achieved an impressive accuracy of 0.9922, significantly outperforming single-classifier models. Moreover, the model exhibits a short training time, demonstrating its computational efficiency. This research provides an effective and automated approach to gear fault detection, offering significant potential for industrial applications. ]]> Gear Fault Detection Based on Convolutional Neural Networks and Support Vector Machines yeqi fei ziyang meng tianxi chen pengfei zhao doi: 10.56578/ijkis020405 International Journal of Knowledge and Innovation Studies 12-30-2024 International Journal of Knowledge and Innovation Studies 12-30-2024 2024 2 4 Article 259 10.56578/ijkis020405 https://www.acadlore.com/article/IJKIS/2024_2_4/ijkis020405 https://www.acadlore.com/article/IJKIS/2024_2_4/ijkis020404 The objective of this work is to analyze the environmental sustainability performance of deposit banks traded in Borsa Istanbul (BIST) through the application a novel integrated grey Multi-Criteria Decision-Making (MCDM) approach. The grey combined model proposed for the assessment of environmental performance in the banking sector integrates the Logarithmic Objective Weighting Based on Percentage Change (LOPCOW) and Proximity Indexed Value (PIV) algorithms. In the first stage, the importance weights of the criteria were determined using the Grey LOPCOW objective weighting technique, which enables a comprehensive and robust weighting system. Following this, the Grey PIV method was employed to assess the banks' environmental sustainability performance. To demonstrate the robustness and applicability of the suggested MCDM framework, several sensitivity analyses and comparative assessments were conducted. The empirical findings imply that the most significant environmental performance indicator affecting the environmental sustainability performance of deposit banks is “amount of disposed waste”. Moreover, Yapı Kredi was identified to be the bank with the highest environmental sustainability performance compared to its competitors in the BIST banking industry. The findings obtained through sensitivity and comparative analyses indicate that the introduced hybrid decision model in the existing work constitutes a robust, defendable, and effective framework for assessing the environmental sustainability performance of banking institutions. Lastly, the findings have important implications for bank management, regulators, and policymakers, offering valuable insights for the enhancement of sustainability practices within the banking industry. This work contributes to the growing body of literature on environmental performance measurement in the financial sector and provides a methodological foundation for future sustainability assessments in similar contexts. 12-30-2024 <![CDATA[ The objective of this work is to analyze the environmental sustainability performance of deposit banks traded in Borsa Istanbul (BIST) through the application a novel integrated grey Multi-Criteria Decision-Making (MCDM) approach. The grey combined model proposed for the assessment of environmental performance in the banking sector integrates the Logarithmic Objective Weighting Based on Percentage Change (LOPCOW) and Proximity Indexed Value (PIV) algorithms. In the first stage, the importance weights of the criteria were determined using the Grey LOPCOW objective weighting technique, which enables a comprehensive and robust weighting system. Following this, the Grey PIV method was employed to assess the banks' environmental sustainability performance. To demonstrate the robustness and applicability of the suggested MCDM framework, several sensitivity analyses and comparative assessments were conducted. The empirical findings imply that the most significant environmental performance indicator affecting the environmental sustainability performance of deposit banks is “amount of disposed waste”. Moreover, Yapı Kredi was identified to be the bank with the highest environmental sustainability performance compared to its competitors in the BIST banking industry. The findings obtained through sensitivity and comparative analyses indicate that the introduced hybrid decision model in the existing work constitutes a robust, defendable, and effective framework for assessing the environmental sustainability performance of banking institutions. Lastly, the findings have important implications for bank management, regulators, and policymakers, offering valuable insights for the enhancement of sustainability practices within the banking industry. This work contributes to the growing body of literature on environmental performance measurement in the financial sector and provides a methodological foundation for future sustainability assessments in similar contexts. ]]> Assessing the Environmental Sustainability Performance of the Banking Sector: A Novel Integrated Grey Multi-Criteria Decision-Making (MCDM) Approach osman yavuz akbulut doi: 10.56578/ijkis020404 International Journal of Knowledge and Innovation Studies 12-30-2024 International Journal of Knowledge and Innovation Studies 12-30-2024 2024 2 4 Article 239 10.56578/ijkis020404 https://www.acadlore.com/article/IJKIS/2024_2_4/ijkis020404 https://www.acadlore.com/article/IJKIS/2024_2_4/ijkis020403 This study addresses the issues of fragmentation, unstructured information, and low reusability in the process knowledge management of aircraft engine component manufacturing. A process knowledge modeling method based on ontology is proposed. By constructing an ontology knowledge base tailored for the aircraft engine manufacturing domain, an improved top-down approach is employed. This method introduces feature-based constraints on process parameters and uses tools to create a Web Ontology Language (OWL) model. The manufacturing of a long tension bolt is chosen as the case study, and application verification is carried out based on the Model-Based Definition (MBD) model. The results demonstrate that the proposed method significantly improves the sharing and reusability of process knowledge, providing theoretical support for the intelligent process design of aircraft engine components. 11-28-2024 <![CDATA[ This study addresses the issues of fragmentation, unstructured information, and low reusability in the process knowledge management of aircraft engine component manufacturing. A process knowledge modeling method based on ontology is proposed. By constructing an ontology knowledge base tailored for the aircraft engine manufacturing domain, an improved top-down approach is employed. This method introduces feature-based constraints on process parameters and uses tools to create a Web Ontology Language (OWL) model. The manufacturing of a long tension bolt is chosen as the case study, and application verification is carried out based on the Model-Based Definition (MBD) model. The results demonstrate that the proposed method significantly improves the sharing and reusability of process knowledge, providing theoretical support for the intelligent process design of aircraft engine components. ]]> Ontology-Based Method for Constructing Process Knowledge Models of Aircraft Engine Components tichun wang yuxin chen doi: 10.56578/ijkis020403 International Journal of Knowledge and Innovation Studies 11-28-2024 International Journal of Knowledge and Innovation Studies 11-28-2024 2024 2 4 Article 222 10.56578/ijkis020403 https://www.acadlore.com/article/IJKIS/2024_2_4/ijkis020403 https://www.acadlore.com/article/IJKIS/2024_2_4/ijkis020402 The theory of Complex T-Spherical Fuzzy Sets (CTSpFSs) is introduced along with their Einstein operational methods under induced variables. This research aims to extend the theoretical framework of complex fuzzy sets (CFSs) by exploring fundamental Einstein operational laws and proposing two novel aggregation operators: the induced complex T-spherical fuzzy Einstein ordered weighted averaging (I-CTSpFEOWA) operator and the induced complex T-spherical fuzzy Einstein hybrid averaging (I-CTSpFEHA) operator. Aggregation operators serve as powerful tools in data analysis, decision-making, and understanding complex systems by enabling the extraction of meaningful insights from large, multidimensional datasets. These operators contribute to the simplification of information, ultimately enhancing decision support in complex decision-making processes. The proposed operators, designed to handle complex and multidimensional fuzzy information, enhance the ability to refine these decision-making processes. Their effectiveness is demonstrated through the development of a numerical example, which illustrates their potential application in real-world scenarios. The proposed techniques not only improve the clarity and relevance of the aggregated information but also provide an efficient methodology for managing complex fuzzy environments, thus refining decision-making across diverse domains. By demonstrating the utility of the I-CTSpFEOWA and I-CTSpFEHA operators, the research highlights their practical application in systems where traditional fuzzy aggregation methods may fall short. This work contributes significantly to the field of fuzzy set theory by presenting advanced aggregation methods that support improved decision-making in environments characterised by uncertainty and complexity. 11-24-2024 <![CDATA[ The theory of Complex T-Spherical Fuzzy Sets (CTSpFSs) is introduced along with their Einstein operational methods under induced variables. This research aims to extend the theoretical framework of complex fuzzy sets (CFSs) by exploring fundamental Einstein operational laws and proposing two novel aggregation operators: the induced complex T-spherical fuzzy Einstein ordered weighted averaging (I-CTSpFEOWA) operator and the induced complex T-spherical fuzzy Einstein hybrid averaging (I-CTSpFEHA) operator. Aggregation operators serve as powerful tools in data analysis, decision-making, and understanding complex systems by enabling the extraction of meaningful insights from large, multidimensional datasets. These operators contribute to the simplification of information, ultimately enhancing decision support in complex decision-making processes. The proposed operators, designed to handle complex and multidimensional fuzzy information, enhance the ability to refine these decision-making processes. Their effectiveness is demonstrated through the development of a numerical example, which illustrates their potential application in real-world scenarios. The proposed techniques not only improve the clarity and relevance of the aggregated information but also provide an efficient methodology for managing complex fuzzy environments, thus refining decision-making across diverse domains. By demonstrating the utility of the I-CTSpFEOWA and I-CTSpFEHA operators, the research highlights their practical application in systems where traditional fuzzy aggregation methods may fall short. This work contributes significantly to the field of fuzzy set theory by presenting advanced aggregation methods that support improved decision-making in environments characterised by uncertainty and complexity. ]]> Selection of Enhanced Security Systems Using Complex T-Spherical Fuzzy Models Within a Complex Fuzzy Environment muhammad sajjad ali khan doi: 10.56578/ijkis020402 International Journal of Knowledge and Innovation Studies 11-24-2024 International Journal of Knowledge and Innovation Studies 11-24-2024 2024 2 4 Article 208 10.56578/ijkis020402 https://www.acadlore.com/article/IJKIS/2024_2_4/ijkis020402 https://www.acadlore.com/article/IJKIS/2024_2_4/ijkis020401 In recent years, e-commerce has emerged as a dominant sales channel, with an increasing number of large-scale companies exclusively operating online. The substantial growth of e-commerce has been paralleled by the growing importance of efficient logistics, as the flow of goods in international trade demands sophisticated planning and execution. Following the purchase stage, logistics plays a pivotal role in ensuring timely delivery to end customers, with final distribution being one of the most critical aspects. The optimization of the distribution process is particularly challenging due to the complexities involved in the selection of transport modes, optimal routing, and the appropriate types of vehicles. This study investigates the parcel distribution process in the Serbian logistics sector, providing a comprehensive analysis of e-commerce flows during the initial stages of goods movement. A decision-making model based on the Stepwise Weight Assessment Ratio Analysis (SWARA) and Weighted Aggregated Sum Product Assessment (WASPAS) methods is proposed to optimize vehicle selection for parcel distribution. The model evaluates ten vehicle alternatives across nine distinct criteria: delivery volume ($\mathrm{C}_1$), average number of parcels per delivery ($\mathrm{C}_2$), vehicle fleet size ($\mathrm{C}_3$), payload capacity ($\mathrm{C}_4$), number of customer complaints ($\mathrm{C}_5$), cargo volume ($\mathrm{C}_6$), incidence of damaged shipments ($\mathrm{C}_7$), loss of shipments ($\mathrm{C}_8$), and vehicle height limitations ($\mathrm{C}_9$). Sensitivity analysis is conducted to test the robustness and stability of the proposed model, ensuring that the selected vehicle configurations are resilient under varying operational conditions. The findings contribute to the broader understanding of logistics optimization in e-commerce, offering insights into the effective selection of transport vehicles that can enhance the efficiency and reliability of the final distribution phase. 11-12-2024 <![CDATA[ <p>In recent years, e-commerce has emerged as a dominant sales channel, with an increasing number of large-scale companies exclusively operating online. The substantial growth of e-commerce has been paralleled by the growing importance of efficient logistics, as the flow of goods in international trade demands sophisticated planning and execution. Following the purchase stage, logistics plays a pivotal role in ensuring timely delivery to end customers, with final distribution being one of the most critical aspects. The optimization of the distribution process is particularly challenging due to the complexities involved in the selection of transport modes, optimal routing, and the appropriate types of vehicles. This study investigates the parcel distribution process in the Serbian logistics sector, providing a comprehensive analysis of e-commerce flows during the initial stages of goods movement. A decision-making model based on the Stepwise Weight Assessment Ratio Analysis (SWARA) and Weighted Aggregated Sum Product Assessment (WASPAS) methods is proposed to optimize vehicle selection for parcel distribution. The model evaluates ten vehicle alternatives across nine distinct criteria: delivery volume ($\mathrm{C}_1$), average number of parcels per delivery ($\mathrm{C}_2$), vehicle fleet size ($\mathrm{C}_3$), payload capacity ($\mathrm{C}_4$), number of customer complaints ($\mathrm{C}_5$), cargo volume ($\mathrm{C}_6$), incidence of damaged shipments ($\mathrm{C}_7$), loss of shipments ($\mathrm{C}_8$), and vehicle height limitations ($\mathrm{C}_9$). Sensitivity analysis is conducted to test the robustness and stability of the proposed model, ensuring that the selected vehicle configurations are resilient under varying operational conditions. The findings contribute to the broader understanding of logistics optimization in e-commerce, offering insights into the effective selection of transport vehicles that can enhance the efficiency and reliability of the final distribution phase.</p> ]]> Strategic Optimization of Parcel Distribution in E-Commerce: A Comprehensive Analysis of Logistic Flows and Vehicle Selection Using SWARA-WASPAS Methods aleksa maravić milan andrejić vukašin pajić doi: 10.56578/ijkis020401 International Journal of Knowledge and Innovation Studies 11-12-2024 International Journal of Knowledge and Innovation Studies 11-12-2024 2024 2 4 Article 190 10.56578/ijkis020401 https://www.acadlore.com/article/IJKIS/2024_2_4/ijkis020401 https://www.acadlore.com/article/IJKIS/2024_2_3/ijkis020305 Urban competitiveness is an essential determinant of the long-term sustainability and economic development of cities, influencing not only local prosperity but also national growth. The accurate measurement of urban competitiveness is critical for policymakers, as it provides insights into the strengths and weaknesses of cities, informing strategic development. This study evaluates the competitiveness of 17 European cities through an integrated Multi-Criteria Decision-Making (MCDM) framework, combining the Logarithmic Percentage Change-driven Objective Weighting (LOPCOW) method for criteria weighting with the Ranking of Alternatives with Weights of Criterion (RAWEC) method for city ranking. The dataset utilised in this analysis was derived from the 2024 Global Power City Index (GPCI), a comprehensive report assessing various urban performance dimensions. The LOPCOW methodology revealed that the livability (L) criterion holds the highest weight in determining urban competitiveness, whereas research and development (R&amp;D) emerged as the least influential factor. Using the RAWEC method, cities were ranked based on their overall competitiveness, with London identified as the most competitive urban centre, while Istanbul was ranked lowest. The findings highlight the importance of livability in enhancing urban competitiveness and suggest that cities should prioritise improvements in R&amp;D to foster more balanced and sustainable competitiveness. This research contributes to the growing body of literature on urban performance measurement, offering a novel methodological approach that integrates both objective weighting and ranking techniques, which can be applied to further studies on global urban competitiveness. 09-29-2024 <![CDATA[ Urban competitiveness is an essential determinant of the long-term sustainability and economic development of cities, influencing not only local prosperity but also national growth. The accurate measurement of urban competitiveness is critical for policymakers, as it provides insights into the strengths and weaknesses of cities, informing strategic development. This study evaluates the competitiveness of 17 European cities through an integrated Multi-Criteria Decision-Making (MCDM) framework, combining the Logarithmic Percentage Change-driven Objective Weighting (LOPCOW) method for criteria weighting with the Ranking of Alternatives with Weights of Criterion (RAWEC) method for city ranking. The dataset utilised in this analysis was derived from the 2024 Global Power City Index (GPCI), a comprehensive report assessing various urban performance dimensions. The LOPCOW methodology revealed that the livability (L) criterion holds the highest weight in determining urban competitiveness, whereas research and development (R&amp;D) emerged as the least influential factor. Using the RAWEC method, cities were ranked based on their overall competitiveness, with London identified as the most competitive urban centre, while Istanbul was ranked lowest. The findings highlight the importance of livability in enhancing urban competitiveness and suggest that cities should prioritise improvements in R&amp;D to foster more balanced and sustainable competitiveness. This research contributes to the growing body of literature on urban performance measurement, offering a novel methodological approach that integrates both objective weighting and ranking techniques, which can be applied to further studies on global urban competitiveness. ]]> Assessing the Urban Competitiveness of European Cities Using LOPCOW-RAWEC Methodologies ali aygün yürüyen alptekin ulutaş doi: 10.56578/ijkis020305 International Journal of Knowledge and Innovation Studies 09-29-2024 International Journal of Knowledge and Innovation Studies 09-29-2024 2024 2 3 Article 179 10.56578/ijkis020305 https://www.acadlore.com/article/IJKIS/2024_2_3/ijkis020305 https://www.acadlore.com/article/IJKIS/2024_2_3/ijkis020304 This study examines the role of Open Innovation (OI) in facilitating the adoption of Industry 4.0 (I4.0) technologies by small manufacturing enterprises in the non-energy sector of Caribbean Small Island Developing States (SIDS). These firms encounter significant challenges, including limited resources, inadequate infrastructure, and underdeveloped innovation ecosystems, which necessitate the adoption of tailored OI practices. A comprehensive literature review was conducted to identify the key enablers of OI, which led to the development of a conceptual framework. Insights gained from structured interviews with industry experts were used to assess the influence of these enablers on I4.0 adoption. Pairwise comparisons were employed to explore the interrelationships among these factors, culminating in the construction of a reachability matrix and a hierarchical model through Interpretive Structural Modelling (ISM) to analyse the dependencies and causal relationships among them. The study identified “Competitive Pressure,” “Customer Pressure,” and “Managerial Dynamic Capabilities” as the primary enablers driving OI and influencing the adoption of I4.0 technologies. Intermediate factors, such as “Digital Trust,” “R&amp;D Investment Capabilities,” and “Collaborative Networks,” were found to mediate the relationship between the primary enablers and the outcome of “Adaptation to Global Best Practices.” Despite the fact that OI practices are often driven by external pressures, the adoption of I4.0 technologies was found to be strongly supported by managerial dynamic capabilities, highlighting the importance of both push and pull factors. The adaptation to global best practices is significantly shaped by managerial capabilities, competitive pressures, and customer demands. Furthermore, environmental scanning was identified as an essential tool for aligning managerial dynamic capabilities with market conditions, facilitating agile decision-making for technology adoption through collaboration. Strategic interventions to support intermediary factors are crucial for small firms to navigate external pressures, sustain innovation, and build internal capabilities for I4.0. The findings contribute to the development of a networked ecosystem framework, which offers a pathway to strengthening stakeholder alliances, implementing customer-centric open OI practices, and enhancing management effectiveness. It is concluded that the successful adoption of I4.0 technologies is achievable through strategic, managerial, and policy-driven frameworks that align with global standards and address competitive and customization demands. 09-24-2024 <![CDATA[ This study examines the role of Open Innovation (OI) in facilitating the adoption of Industry 4.0 (I4.0) technologies by small manufacturing enterprises in the non-energy sector of Caribbean Small Island Developing States (SIDS). These firms encounter significant challenges, including limited resources, inadequate infrastructure, and underdeveloped innovation ecosystems, which necessitate the adoption of tailored OI practices. A comprehensive literature review was conducted to identify the key enablers of OI, which led to the development of a conceptual framework. Insights gained from structured interviews with industry experts were used to assess the influence of these enablers on I4.0 adoption. Pairwise comparisons were employed to explore the interrelationships among these factors, culminating in the construction of a reachability matrix and a hierarchical model through Interpretive Structural Modelling (ISM) to analyse the dependencies and causal relationships among them. The study identified “Competitive Pressure,” “Customer Pressure,” and “Managerial Dynamic Capabilities” as the primary enablers driving OI and influencing the adoption of I4.0 technologies. Intermediate factors, such as “Digital Trust,” “R&amp;D Investment Capabilities,” and “Collaborative Networks,” were found to mediate the relationship between the primary enablers and the outcome of “Adaptation to Global Best Practices.” Despite the fact that OI practices are often driven by external pressures, the adoption of I4.0 technologies was found to be strongly supported by managerial dynamic capabilities, highlighting the importance of both push and pull factors. The adaptation to global best practices is significantly shaped by managerial capabilities, competitive pressures, and customer demands. Furthermore, environmental scanning was identified as an essential tool for aligning managerial dynamic capabilities with market conditions, facilitating agile decision-making for technology adoption through collaboration. Strategic interventions to support intermediary factors are crucial for small firms to navigate external pressures, sustain innovation, and build internal capabilities for I4.0. The findings contribute to the development of a networked ecosystem framework, which offers a pathway to strengthening stakeholder alliances, implementing customer-centric open OI practices, and enhancing management effectiveness. It is concluded that the successful adoption of I4.0 technologies is achievable through strategic, managerial, and policy-driven frameworks that align with global standards and address competitive and customization demands. ]]> An ISM-Based Framework for Open Innovation Enablers Driving the Adoption of Industry 4.0 in Small Manufacturing Firms in Caribbean Small Island Developing States annusha bridglal kit fai pun doi: 10.56578/ijkis020304 International Journal of Knowledge and Innovation Studies 09-24-2024 International Journal of Knowledge and Innovation Studies 09-24-2024 2024 2 3 Article 155 10.56578/ijkis020304 https://www.acadlore.com/article/IJKIS/2024_2_3/ijkis020304 https://www.acadlore.com/article/IJKIS/2024_2_3/ijkis020303 The challenge of executing iterative big data analysis algorithms within the Google Cloud MapReduce environment has been addressed by developing a parallel K-means algorithm capable of leveraging the distributed computing power of the platform. Traditional K-means, which requires iterative steps, is adapted into a parallel version using MapReduce to enhance computational efficiency. This parallel algorithm is structured into multiple super-steps, each of which executes in parallel but is processed sequentially across super-steps. Each super-step corresponds to one iteration of the serial K-means algorithm, with parallel computation carried out at each node to determine the mean of each cluster center. Experimental evaluations have demonstrated that the parallel K-means algorithm performs effectively and accurately. Notably, for a dataset of 450 water samples, a parallel speedup factor of 20.8 was achieved, significantly reducing the time required for data analysis. This substantial reduction in processing time is critical in time-sensitive applications, such as coal mine rescue operations, where quick decision-making is essential. The results indicate that the proposed parallel K-means algorithm is both a feasible and efficient solution for handling large-scale datasets within cloud environments, providing substantial benefits in both computational speed and practical application. 08-22-2024 <![CDATA[ The challenge of executing iterative big data analysis algorithms within the Google Cloud MapReduce environment has been addressed by developing a parallel K-means algorithm capable of leveraging the distributed computing power of the platform. Traditional K-means, which requires iterative steps, is adapted into a parallel version using MapReduce to enhance computational efficiency. This parallel algorithm is structured into multiple super-steps, each of which executes in parallel but is processed sequentially across super-steps. Each super-step corresponds to one iteration of the serial K-means algorithm, with parallel computation carried out at each node to determine the mean of each cluster center. Experimental evaluations have demonstrated that the parallel K-means algorithm performs effectively and accurately. Notably, for a dataset of 450 water samples, a parallel speedup factor of 20.8 was achieved, significantly reducing the time required for data analysis. This substantial reduction in processing time is critical in time-sensitive applications, such as coal mine rescue operations, where quick decision-making is essential. The results indicate that the proposed parallel K-means algorithm is both a feasible and efficient solution for handling large-scale datasets within cloud environments, providing substantial benefits in both computational speed and practical application. ]]> Development and Evaluation of a Parallel K-means Algorithm for Big Data Analysis in Google MapReduce Environment junwei zhao xuexu yuan qingtao hou hanyu gao chunyu gao yuanyuan zhang doi: 10.56578/ijkis020303 International Journal of Knowledge and Innovation Studies 08-22-2024 International Journal of Knowledge and Innovation Studies 08-22-2024 2024 2 3 Article 147 10.56578/ijkis020303 https://www.acadlore.com/article/IJKIS/2024_2_3/ijkis020303 https://www.acadlore.com/article/IJKIS/2024_2_3/ijkis020302 Digital ink Chinese character recognition (DICCR) systems have predominantly been developed using datasets composed of native language writers. However, the handwriting of foreign students, who possess distinct writing habits and often make errors or deviations from standard forms, poses a unique challenge to recognition systems. To address this issue, a robust and adaptable approach is proposed, utilizing a residual network augmented with multi-scale dilated convolutions. The proposed architecture incorporates convolutional kernels of varying scales, which facilitate the extraction of contextual information from different receptive fields. Additionally, the use of dilated convolutions with varying dilation rates allows the model to capture long-range dependencies and short-range features concurrently. This strategy mitigates the gridding effect commonly associated with dilated convolutions, thereby enhancing feature extraction. Experiments conducted on a dataset of digital ink Chinese characters (DICCs) written by foreign students demonstrate the efficacy of the proposed method in improving recognition accuracy. The results indicate that the network is capable of more effectively handling the non-standard writing styles often encountered in such datasets. This approach offers significant potential for the error extraction and automatic evaluation of Chinese character writing, especially in the context of non-native learners. 07-31-2024 <![CDATA[ <p style="text-align: justify">Digital ink Chinese character recognition (DICCR) systems have predominantly been developed using datasets composed of native language writers. However, the handwriting of foreign students, who possess distinct writing habits and often make errors or deviations from standard forms, poses a unique challenge to recognition systems. To address this issue, a robust and adaptable approach is proposed, utilizing a residual network augmented with multi-scale dilated convolutions. The proposed architecture incorporates convolutional kernels of varying scales, which facilitate the extraction of contextual information from different receptive fields. Additionally, the use of dilated convolutions with varying dilation rates allows the model to capture long-range dependencies and short-range features concurrently. This strategy mitigates the gridding effect commonly associated with dilated convolutions, thereby enhancing feature extraction. Experiments conducted on a dataset of digital ink Chinese characters (DICCs) written by foreign students demonstrate the efficacy of the proposed method in improving recognition accuracy. The results indicate that the network is capable of more effectively handling the non-standard writing styles often encountered in such datasets. This approach offers significant potential for the error extraction and automatic evaluation of Chinese character writing, especially in the context of non-native learners.</p> ]]> A Residual Network with Multi-Scale Dilated Convolutions for Enhanced Recognition of Digital Ink Chinese Characters by Non-Native Writers huafen xu xiwen zhang doi: 10.56578/ijkis020302 International Journal of Knowledge and Innovation Studies 07-31-2024 International Journal of Knowledge and Innovation Studies 07-31-2024 2024 2 3 Article 130 10.56578/ijkis020302 https://www.acadlore.com/article/IJKIS/2024_2_3/ijkis020302 https://www.acadlore.com/article/IJKIS/2024_2_3/ijkis020301 The accurate estimation of the age of orange trees is a critical task in orchard management, providing valuable insights into tree growth, yield prediction, and the implementation of optimal agricultural practices. Traditional methods, such as counting growth rings, while precise, are often labor-intensive and invasive, requiring tree cutting or core sampling. These techniques are impractical for large-scale application, as they are time-consuming and may cause damage to the trees. A novel non-invasive system based on fuzzy logic, combined with linear regression analysis, has been developed to estimate the age of orange trees using easily measurable parameters, including trunk diameter and height. The fuzzy inference system (FIS) offers an adaptive, intuitive, and accurate model for age estimation by incorporating these key variables. Furthermore, a multiple linear regression analysis was performed, revealing a statistically significant correlation between the predictor variables (trunk diameter and height) and tree age. The regression coefficients for diameter (p = 0.0134) and height (p = 0.0444) demonstrated strong relationships with tree age, and an R-squared value of 0.9800 indicated a high degree of model fit. These results validate the effectiveness of the proposed system, highlighting the potential of combining fuzzy logic and regression techniques to achieve precise and scalable age estimation. The model provides a valuable tool for orchard managers, agronomists, and environmental scientists, offering an efficient method for monitoring tree health, optimizing fruit production, and promoting sustainable agricultural practices. 07-08-2024 <![CDATA[ The accurate estimation of the age of orange trees is a critical task in orchard management, providing valuable insights into tree growth, yield prediction, and the implementation of optimal agricultural practices. Traditional methods, such as counting growth rings, while precise, are often labor-intensive and invasive, requiring tree cutting or core sampling. These techniques are impractical for large-scale application, as they are time-consuming and may cause damage to the trees. A novel non-invasive system based on fuzzy logic, combined with linear regression analysis, has been developed to estimate the age of orange trees using easily measurable parameters, including trunk diameter and height. The fuzzy inference system (FIS) offers an adaptive, intuitive, and accurate model for age estimation by incorporating these key variables. Furthermore, a multiple linear regression analysis was performed, revealing a statistically significant correlation between the predictor variables (trunk diameter and height) and tree age. The regression coefficients for diameter (p = 0.0134) and height (p = 0.0444) demonstrated strong relationships with tree age, and an R-squared value of 0.9800 indicated a high degree of model fit. These results validate the effectiveness of the proposed system, highlighting the potential of combining fuzzy logic and regression techniques to achieve precise and scalable age estimation. The model provides a valuable tool for orchard managers, agronomists, and environmental scientists, offering an efficient method for monitoring tree health, optimizing fruit production, and promoting sustainable agricultural practices. ]]> Advanced Estimation of Orange Tree Age Using Fuzzy Inference and Linear Regression Models muhammad shahkar khan doi: 10.56578/ijkis020301 International Journal of Knowledge and Innovation Studies 07-08-2024 International Journal of Knowledge and Innovation Studies 07-08-2024 2024 2 3 Article 119 10.56578/ijkis020301 https://www.acadlore.com/article/IJKIS/2024_2_3/ijkis020301 https://www.acadlore.com/article/IJKIS/2024_2_2/ijkis020205 This study introduces novel algebraic techniques within the framework of complex Fermatean fuzzy sets (CFFSs) by incorporating confidence levels, presenting a suite of operators tailored for advanced decision-making. Specifically, the confidence complex Fermatean fuzzy weighted geometric (CCFFWG) operator, the confidence complex Fermatean fuzzy ordered weighted geometric (CCFFOWG) operator, and the confidence complex Fermatean fuzzy hybrid geometric (CCFFHG) operator are developed to address multi-attribute group decision-making (MCGDM) challenges. These methodologies are designed to enhance decision-making in scenarios where decision-makers provide asymmetric or imprecise information, often encountered in environmental and industrial contexts. To validate the applicability of the proposed approach, a practical case study involving the selection of an optimal fire extinguisher from several alternatives is conducted. The performance of the newly developed operators is benchmarked against established methods from prior studies, with results demonstrating superior decision outcomes in terms of precision and reliability. By embedding confidence levels into complex Fermatean fuzzy operations, the proposed techniques offer greater robustness in managing uncertainty and variability across multiple attributes. These findings suggest that the advanced algebraic framework contributes significantly to improving decision quality in complex group decision-making environments. 06-27-2024 <![CDATA[ This study introduces novel algebraic techniques within the framework of complex Fermatean fuzzy sets (CFFSs) by incorporating confidence levels, presenting a suite of operators tailored for advanced decision-making. Specifically, the confidence complex Fermatean fuzzy weighted geometric (CCFFWG) operator, the confidence complex Fermatean fuzzy ordered weighted geometric (CCFFOWG) operator, and the confidence complex Fermatean fuzzy hybrid geometric (CCFFHG) operator are developed to address multi-attribute group decision-making (MCGDM) challenges. These methodologies are designed to enhance decision-making in scenarios where decision-makers provide asymmetric or imprecise information, often encountered in environmental and industrial contexts. To validate the applicability of the proposed approach, a practical case study involving the selection of an optimal fire extinguisher from several alternatives is conducted. The performance of the newly developed operators is benchmarked against established methods from prior studies, with results demonstrating superior decision outcomes in terms of precision and reliability. By embedding confidence levels into complex Fermatean fuzzy operations, the proposed techniques offer greater robustness in managing uncertainty and variability across multiple attributes. These findings suggest that the advanced algebraic framework contributes significantly to improving decision quality in complex group decision-making environments. ]]> Enhanced Decision-Making with Advanced Algebraic Techniques in Complex Fermatean Fuzzy Sets under Confidence Levels jan muhammad nisar gul rifaqat ali doi: 10.56578/ijkis020205 International Journal of Knowledge and Innovation Studies 06-27-2024 International Journal of Knowledge and Innovation Studies 06-27-2024 2024 2 2 Article 107 10.56578/ijkis020205 https://www.acadlore.com/article/IJKIS/2024_2_2/ijkis020205 https://www.acadlore.com/article/IJKIS/2024_2_2/ijkis020204 Self-regulated learning (SRL) is conceptualized as a series of interrelated cognitive and affective processes rather than as isolated events. To elucidate the relationship between students' cognitive engagement and their comprehension of self-regulation strategies, a conceptual model was developed to examine learner engagement during a hypothetical learning scenario. The model posits that the learning environment can be represented as a social network in which the mechanisms of knowledge diffusion significantly influence a learner's adoption of self-regulatory processes. The results obtained from this model corroborate the modes of cognitive engagement as predicted by the Interactive, Constructive, Active, and Passive (ICAP) framework, manifesting as absorbing-state phase transitions. These transitions are interpreted as self-tuned phase changes associated with self-schema and personal adaptive and reflexive learning thresholds. This framework suggests that learners engage in retrospective monitoring processes that activate SRL mechanisms. It is inferred that learning occurs through continuous change; wherein self-regulated practices can be viewed as processes leading to specific events that subsequently trigger further learning. This conceptualization underscores the dynamic nature of SRL and highlights the potential for computer simulations to model and understand these processes. 06-09-2024 <![CDATA[ Self-regulated learning (SRL) is conceptualized as a series of interrelated cognitive and affective processes rather than as isolated events. To elucidate the relationship between students' cognitive engagement and their comprehension of self-regulation strategies, a conceptual model was developed to examine learner engagement during a hypothetical learning scenario. The model posits that the learning environment can be represented as a social network in which the mechanisms of knowledge diffusion significantly influence a learner's adoption of self-regulatory processes. The results obtained from this model corroborate the modes of cognitive engagement as predicted by the Interactive, Constructive, Active, and Passive (ICAP) framework, manifesting as absorbing-state phase transitions. These transitions are interpreted as self-tuned phase changes associated with self-schema and personal adaptive and reflexive learning thresholds. This framework suggests that learners engage in retrospective monitoring processes that activate SRL mechanisms. It is inferred that learning occurs through continuous change; wherein self-regulated practices can be viewed as processes leading to specific events that subsequently trigger further learning. This conceptualization underscores the dynamic nature of SRL and highlights the potential for computer simulations to model and understand these processes. ]]> Understanding Self-Regulated Learning Dynamics Through Computer Simulation: A Model-Based Approach kyffin bradshaw doi: 10.56578/ijkis020204 International Journal of Knowledge and Innovation Studies 06-09-2024 International Journal of Knowledge and Innovation Studies 06-09-2024 2024 2 2 Article 92 10.56578/ijkis020204 https://www.acadlore.com/article/IJKIS/2024_2_2/ijkis020204 https://www.acadlore.com/article/IJKIS/2024_2_2/ijkis020203 Multi-Criteria Decision-Making (MCDM) represents a critical area of research, particularly in artificial intelligence, through the modeling of real-world decision-making scenarios. Numerous methods have been developed to address the challenges of integrating non-quantitative, incomplete, and imprecise information under conditions of uncertainty. This paper presents the enhancement of the Defining Interrelationships Between Ranked Criteria II (DIBR II) method by incorporating interval grey numbers, in accordance with the principles of Grey theory, its arithmetic operations, and the DIBR II methodology. The enhancement includes the introduction of a conviction degree to reflect decision-makers' or experts' confidence in their assertions. The application of this enhanced method is demonstrated through an illustrative example, following the procedural steps. Additionally, its efficacy is validated in a real-world scenario involving the selection of Lean organization system management techniques, utilizing the Rough Multi-Attributive Border Approximation Area Comparison (Rough MABAC) method. The results indicate that the enhanced DIBR II method is effective in determining criteria weight coefficients, offering a more nuanced distribution compared to traditional crisp methods. Furthermore, when implemented in a multi-criteria model, it yields a more refined ranking of alternatives, contingent on the degree of confidence in the given claims. 05-05-2024 <![CDATA[ <p>Multi-Criteria Decision-Making (MCDM) represents a critical area of research, particularly in artificial intelligence, through the modeling of real-world decision-making scenarios. Numerous methods have been developed to address the challenges of integrating non-quantitative, incomplete, and imprecise information under conditions of uncertainty. This paper presents the enhancement of the Defining Interrelationships Between Ranked Criteria II (DIBR II) method by incorporating interval grey numbers, in accordance with the principles of Grey theory, its arithmetic operations, and the DIBR II methodology. The enhancement includes the introduction of a conviction degree to reflect decision-makers' or experts' confidence in their assertions. The application of this enhanced method is demonstrated through an illustrative example, following the procedural steps. Additionally, its efficacy is validated in a real-world scenario involving the selection of Lean organization system management techniques, utilizing the Rough Multi-Attributive Border Approximation Area Comparison (Rough MABAC) method. The results indicate that the enhanced DIBR II method is effective in determining criteria weight coefficients, offering a more nuanced distribution compared to traditional crisp methods. Furthermore, when implemented in a multi-criteria model, it yields a more refined ranking of alternatives, contingent on the degree of confidence in the given claims.</p> ]]> Enhancement of the Defining Interrelationships Between Ranked Criteria II Method Using Interval Grey Numbers for Application in the Grey-Rough MCDM Model duško tešić darko božanić mohammad khalilzadeh doi: 10.56578/ijkis020203 International Journal of Knowledge and Innovation Studies 05-05-2024 International Journal of Knowledge and Innovation Studies 05-05-2024 2024 2 2 Article 81 10.56578/ijkis020203 https://www.acadlore.com/article/IJKIS/2024_2_2/ijkis020203 https://www.acadlore.com/article/IJKIS/2024_2_2/ijkis020202 Blockchain has attracted widespread attention due to its unique features such as decentralization, traceability, and tamper resistance. With the rapid development of blockchain technology, an increasing number of industries are gradually applying blockchain technology to various fields such as the Internet of Things, healthcare, finance, agriculture, and government affairs. However, there are certain differences in the underlying architecture, data structures, consensus algorithms, and other aspects of blockchain technology across different sectors, which restrict transactions to occur within a single blockchain. Achieving interoperability between different blockchains is challenging, hindering data exchange and collaborative business to some extent, inevitably leading to the problem of “data silo”. Against this backdrop, this study aims to explore a cross-chain solution based on relay technology to address the current challenges of interoperability between blockchain systems. By employing relay-based cross-chain technology, a blockchain cross-chain collaboration platform is established to simulate the construction of a real cross-chain network. By deploying business contracts, data and resources between heterogeneous blockchains can seamlessly communicate, resolving the challenge of cross-chain interoperability. The research findings demonstrate that the blockchain cross-chain solution based on relay technology can effectively enhance interoperability between different blockchain systems, enabling cross-chain asset circulation and information transmission, highlighting the practical applicability and scalability of this study. 04-14-2024 <![CDATA[ <p>Blockchain has attracted widespread attention due to its unique features such as decentralization, traceability, and tamper resistance. With the rapid development of blockchain technology, an increasing number of industries are gradually applying blockchain technology to various fields such as the Internet of Things, healthcare, finance, agriculture, and government affairs. However, there are certain differences in the underlying architecture, data structures, consensus algorithms, and other aspects of blockchain technology across different sectors, which restrict transactions to occur within a single blockchain. Achieving interoperability between different blockchains is challenging, hindering data exchange and collaborative business to some extent, inevitably leading to the problem of “data silo”. Against this backdrop, this study aims to explore a cross-chain solution based on relay technology to address the current challenges of interoperability between blockchain systems. By employing relay-based cross-chain technology, a blockchain cross-chain collaboration platform is established to simulate the construction of a real cross-chain network. By deploying business contracts, data and resources between heterogeneous blockchains can seamlessly communicate, resolving the challenge of cross-chain interoperability. The research findings demonstrate that the blockchain cross-chain solution based on relay technology can effectively enhance interoperability between different blockchain systems, enabling cross-chain asset circulation and information transmission, highlighting the practical applicability and scalability of this study.</p> ]]> A Blockchain Cross-Chain Solution Based on Relays wanshu fu jiaqi du yi zhang ziqi wang doi: 10.56578/ijkis020202 International Journal of Knowledge and Innovation Studies 04-14-2024 International Journal of Knowledge and Innovation Studies 04-14-2024 2024 2 2 Article 70 10.56578/ijkis020202 https://www.acadlore.com/article/IJKIS/2024_2_2/ijkis020202 https://www.acadlore.com/article/IJKIS/2024_2_2/ijkis020201 This study introduces logarithmic operations tailored to intuitionistic fuzzy sets (IFSs) aimed at mitigating uncertainty in decision-making processes. Through logarithmic transformations, the membership and non-membership degrees are effectively scaled, thereby enhancing interpretability and facilitating the assessment of uncertainty. Advanced logarithmic aggregation operators have been developed, specifically the Induced Confidence Logarithmic Intuitionistic Fuzzy Einstein Ordered Weighted Geometric Aggregation (ICLIFEOWGA) operator and the Induced Confidence Logarithmic Intuitionistic Fuzzy Einstein Hybrid Geometric Aggregation (ICLIFEHGA) operator. These operators serve as versatile tools, providing robust frameworks for integrating diverse information sources in decision-making and assessment processes. The versatility of the operators is demonstrated through their application across various industries and domains, where they support the integration of multiple criteria in complex decision-making scenarios. An algorithm for the decision-making process is presented, and the effectiveness and efficiency of the proposed techniques are illustrated through a case study on laptop selection. 04-11-2024 <![CDATA[ This study introduces logarithmic operations tailored to intuitionistic fuzzy sets (IFSs) aimed at mitigating uncertainty in decision-making processes. Through logarithmic transformations, the membership and non-membership degrees are effectively scaled, thereby enhancing interpretability and facilitating the assessment of uncertainty. Advanced logarithmic aggregation operators have been developed, specifically the Induced Confidence Logarithmic Intuitionistic Fuzzy Einstein Ordered Weighted Geometric Aggregation (ICLIFEOWGA) operator and the Induced Confidence Logarithmic Intuitionistic Fuzzy Einstein Hybrid Geometric Aggregation (ICLIFEHGA) operator. These operators serve as versatile tools, providing robust frameworks for integrating diverse information sources in decision-making and assessment processes. The versatility of the operators is demonstrated through their application across various industries and domains, where they support the integration of multiple criteria in complex decision-making scenarios. An algorithm for the decision-making process is presented, and the effectiveness and efficiency of the proposed techniques are illustrated through a case study on laptop selection. ]]> Advanced Logarithmic Aggregation Operators for Enhanced Decision-Making in Uncertain Environments quaid iqbal shazia kalsoom doi: 10.56578/ijkis020201 International Journal of Knowledge and Innovation Studies 04-11-2024 International Journal of Knowledge and Innovation Studies 04-11-2024 2024 2 2 Article 57 10.56578/ijkis020201 https://www.acadlore.com/article/IJKIS/2024_2_2/ijkis020201 https://www.acadlore.com/article/IJKIS/2024_2_1/ijkis020105 To facilitate early intervention and control efforts, this study proposes a soybean leaf disease detection method based on an improved Yolov5 model. Initially, image preprocessing is applied to two datasets of diseased soybean leaf images. Subsequently, the original Yolov5s network model is modified by replacing the Spatial Pyramid Pooling (SPP) module with a simplified SimSPPF for more efficient and precise feature extraction. The backbone Convolutional Neural Network (CNN) is enhanced with the Bottleneck transformer (BotNet) self-attention mechanism to accelerate detection speed. The Complete Intersection over Union (CIoU) loss function is replaced by EIoU-Loss to increase the model's inference speed, and Enhanced Intersection over Union (EIoU)-Non-Maximum Suppression (NMS) is used instead of traditional NMS to optimize the handling of prediction boxes. Experimental results demonstrate that the modified Yolov5s model increases the mean Average Precision (mAP) value by 4.5% compared to the original Yolov5 network model for the detection and identification of soybean leaf diseases. Therefore, the proposed method effectively detects and identifies soybean leaf diseases and can be validated for practicality in actual production environments. 03-30-2024 <![CDATA[ <p>To facilitate early intervention and control efforts, this study proposes a soybean leaf disease detection method based on an improved Yolov5 model. Initially, image preprocessing is applied to two datasets of diseased soybean leaf images. Subsequently, the original Yolov5s network model is modified by replacing the Spatial Pyramid Pooling (SPP) module with a simplified SimSPPF for more efficient and precise feature extraction. The backbone Convolutional Neural Network (CNN) is enhanced with the Bottleneck transformer (BotNet) self-attention mechanism to accelerate detection speed. The Complete Intersection over Union (CIoU) loss function is replaced by EIoU-Loss to increase the model's inference speed, and Enhanced Intersection over Union (EIoU)-Non-Maximum Suppression (NMS) is used instead of traditional NMS to optimize the handling of prediction boxes. Experimental results demonstrate that the modified Yolov5s model increases the mean Average Precision (mAP) value by 4.5% compared to the original Yolov5 network model for the detection and identification of soybean leaf diseases. Therefore, the proposed method effectively detects and identifies soybean leaf diseases and can be validated for practicality in actual production environments.</p> ]]> Enhanced Detection of Soybean Leaf Diseases Using an Improved Yolov5 Model shiqin peng guiqing xi yongshun wei ling yu doi: 10.56578/ijkis020105 International Journal of Knowledge and Innovation Studies 03-30-2024 International Journal of Knowledge and Innovation Studies 03-30-2024 2024 2 1 Article 45 10.56578/ijkis020105 https://www.acadlore.com/article/IJKIS/2024_2_1/ijkis020105 https://www.acadlore.com/article/IJKIS/2024_2_1/ijkis020104 The T-spherical fuzzy set (T-SFS), an advancement over the spherical fuzzy set (SFS), offers a refined approach for addressing contradictions and ambiguities in data. In this context, similarity measures (SMs) serve as critical tools for quantifying the resemblance between fuzzy values, traditionally relying on the calculation of distances between these values. Nevertheless, existing methodologies often encounter irrational outcomes due to certain characteristics and complex operations involved. To surmount these challenges, a novel parametric similarity measure is proposed, grounded in three adjustable parameters. This enables decision-makers to tailor the SM to suit diverse decision-making styles, thereby circumventing the aforementioned irrationalities. An analytical comparison with existing SM reveals the superiority of the proposed measure through mathematical validation. Furthermore, the utility of this measure is demonstrated in the resolution of multi-attribute decision-making (MADM) problems, highlighting its efficacy over several existing approaches within the domain of T-SFS. The implementation of the proposed SM not only enhances the precision of similarity assessment in fuzzy sets but also significantly contributes to the optimization of decision-making processes. 03-30-2024 <![CDATA[ The T-spherical fuzzy set (T-SFS), an advancement over the spherical fuzzy set (SFS), offers a refined approach for addressing contradictions and ambiguities in data. In this context, similarity measures (SMs) serve as critical tools for quantifying the resemblance between fuzzy values, traditionally relying on the calculation of distances between these values. Nevertheless, existing methodologies often encounter irrational outcomes due to certain characteristics and complex operations involved. To surmount these challenges, a novel parametric similarity measure is proposed, grounded in three adjustable parameters. This enables decision-makers to tailor the SM to suit diverse decision-making styles, thereby circumventing the aforementioned irrationalities. An analytical comparison with existing SM reveals the superiority of the proposed measure through mathematical validation. Furthermore, the utility of this measure is demonstrated in the resolution of multi-attribute decision-making (MADM) problems, highlighting its efficacy over several existing approaches within the domain of T-SFS. The implementation of the proposed SM not only enhances the precision of similarity assessment in fuzzy sets but also significantly contributes to the optimization of decision-making processes. ]]> Parametric Similarity Measurement of T-Spherical Fuzzy Sets for Enhanced Decision-Making mehwish sarfraz muhammad azeem doi: 10.56578/ijkis020104 International Journal of Knowledge and Innovation Studies 03-30-2024 International Journal of Knowledge and Innovation Studies 03-30-2024 2024 2 1 Article 29 10.56578/ijkis020104 https://www.acadlore.com/article/IJKIS/2024_2_1/ijkis020104 https://www.acadlore.com/article/IJKIS/2024_2_1/ijkis020103 The regulatory system for hazardous materials is complex, with poor inter-departmental communication and low levels of data sharing, making effective regulation challenging. Blockchain technology, known for its decentralization, traceability, and secure and trustworthy information, is widely applied in data sharing. Concurrently, attribute-based encryption (ABE), a novel encryption technique, offers high security and fine-grained access control, providing technical support for secure data access and privacy protection. However, existing attribute encryption algorithms do not consider the hierarchical relationship of access structures among data files during data sharing. Moreover, the immutable nature of blockchain means that access policies stored on it cannot be altered, leading to a lack of flexibility in data sharing. To address these issues, this paper proposes a blockchain and attribute-based dynamic layered access scheme for hazardous materials circulation data sharing. By constructing a Linear Secret-Sharing Scheme (LSSS) matrix, layered access control is achieved, allowing data decryption related to the matching parts of a user's attributes with the access structure. Additionally, through the design of a policy update algorithm, the blockchain structure is organized into transaction blocks and policy blocks, storing the encrypted symmetric keys separately to enable dynamic updates of access policies. Security analysis and experimental comparisons demonstrate the scheme's effectiveness and security in hazardous materials circulation data sharing. 03-30-2024 <![CDATA[ The regulatory system for hazardous materials is complex, with poor inter-departmental communication and low levels of data sharing, making effective regulation challenging. Blockchain technology, known for its decentralization, traceability, and secure and trustworthy information, is widely applied in data sharing. Concurrently, attribute-based encryption (ABE), a novel encryption technique, offers high security and fine-grained access control, providing technical support for secure data access and privacy protection. However, existing attribute encryption algorithms do not consider the hierarchical relationship of access structures among data files during data sharing. Moreover, the immutable nature of blockchain means that access policies stored on it cannot be altered, leading to a lack of flexibility in data sharing. To address these issues, this paper proposes a blockchain and attribute-based dynamic layered access scheme for hazardous materials circulation data sharing. By constructing a Linear Secret-Sharing Scheme (LSSS) matrix, layered access control is achieved, allowing data decryption related to the matching parts of a user's attributes with the access structure. Additionally, through the design of a policy update algorithm, the blockchain structure is organized into transaction blocks and policy blocks, storing the encrypted symmetric keys separately to enable dynamic updates of access policies. Security analysis and experimental comparisons demonstrate the scheme's effectiveness and security in hazardous materials circulation data sharing. ]]> A Blockchain and Attribute-Based Encryption Scheme for Hazardous Materials Circulation Data Sharing xuewei li jialin ma ashim khadka doi: 10.56578/ijkis020103 International Journal of Knowledge and Innovation Studies 03-30-2024 International Journal of Knowledge and Innovation Studies 03-30-2024 2024 2 1 Article 19 10.56578/ijkis020103 https://www.acadlore.com/article/IJKIS/2024_2_1/ijkis020103 https://www.acadlore.com/article/IJKIS/2024_2_1/ijkis020102 This study introduces novel aggregation operators aimed at enhancing data analysis and decision-making processes through the induction of confidence levels into complex polytopic fuzzy systems. Specifically, the induced confidence complex polytopic fuzzy ordered weighted averaging aggregation (ICCPoFOWAA) operator and the induced confidence complex polytopic fuzzy hybrid averaging aggregation (ICCPoFHAA) operator are proposed. By integrating confidence levels into the aggregation process, these operators facilitate a more nuanced interpretation of fuzzy data, allowing for the incorporation of expert judgment and uncertainty in decision-making frameworks. A practical demonstration is provided to validate the efficacy and proficiency of these innovative techniques. Through a comprehensive example, the ability of the ICCPoFOWAA and ICCPoFHAA operators to enhance decision-making accuracy and reliability is substantiated, showcasing their potential as powerful tools in the realms of data analysis and complex decision-making scenarios. The incorporation of confidence levels into fuzzy aggregation processes represents a significant advancement in the field, offering a sophisticated approach to handling uncertainty and expert opinions in multi-criteria decision-making problems. This work not only introduces groundbreaking aggregation operators but also sets a new standard for research in fuzzy decision-making, underscoring the importance of confidence levels in the analytical process. 02-21-2024 <![CDATA[ <p>This study introduces novel aggregation operators aimed at enhancing data analysis and decision-making processes through the induction of confidence levels into complex polytopic fuzzy systems. Specifically, the induced confidence complex polytopic fuzzy ordered weighted averaging aggregation (ICCPoFOWAA) operator and the induced confidence complex polytopic fuzzy hybrid averaging aggregation (ICCPoFHAA) operator are proposed. By integrating confidence levels into the aggregation process, these operators facilitate a more nuanced interpretation of fuzzy data, allowing for the incorporation of expert judgment and uncertainty in decision-making frameworks. A practical demonstration is provided to validate the efficacy and proficiency of these innovative techniques. Through a comprehensive example, the ability of the ICCPoFOWAA and ICCPoFHAA operators to enhance decision-making accuracy and reliability is substantiated, showcasing their potential as powerful tools in the realms of data analysis and complex decision-making scenarios. The incorporation of confidence levels into fuzzy aggregation processes represents a significant advancement in the field, offering a sophisticated approach to handling uncertainty and expert opinions in multi-criteria decision-making problems. This work not only introduces groundbreaking aggregation operators but also sets a new standard for research in fuzzy decision-making, underscoring the importance of confidence levels in the analytical process.</p> ]]> Enhanced Decision-Making Through Induced Confidence-Level Complex Polytopic Fuzzy Aggregation Operators khaista rahman jan muhammad doi: 10.56578/ijkis020102 International Journal of Knowledge and Innovation Studies 02-21-2024 International Journal of Knowledge and Innovation Studies 02-21-2024 2024 2 1 Article 11 10.56578/ijkis020102 https://www.acadlore.com/article/IJKIS/2024_2_1/ijkis020102 https://www.acadlore.com/article/IJKIS/2024_2_1/ijkis020101 In the era of branding, the design of plush toy brands often faces a contradiction with the needs of target user groups. Addressing the brand transformation challenges faced by small and micro enterprises in the plush toy industry, this paper proposes a method for generating creative design schemes for plush toy brands based on extension theory. This method involves introducing the theory of primitives, utilizing extension primitives to construct problem models, employing extension diamond thinking for ideation and divergence, and using extension analysis for a comprehensive description of brand design elements. Subsequently, the method involves transforming these elements through extension transformation to generate innovative brand design schemes. 01-14-2024 <![CDATA[ In the era of branding, the design of plush toy brands often faces a contradiction with the needs of target user groups. Addressing the brand transformation challenges faced by small and micro enterprises in the plush toy industry, this paper proposes a method for generating creative design schemes for plush toy brands based on extension theory. This method involves introducing the theory of primitives, utilizing extension primitives to construct problem models, employing extension diamond thinking for ideation and divergence, and using extension analysis for a comprehensive description of brand design elements. Subsequently, the method involves transforming these elements through extension transformation to generate innovative brand design schemes. ]]> A Method for Creative Scheme Generation for Brand Design of Plush Toys Based on Extension Theory lei wang tao hu doi: 10.56578/ijkis020101 International Journal of Knowledge and Innovation Studies 01-14-2024 International Journal of Knowledge and Innovation Studies 01-14-2024 2024 2 1 Article 1 10.56578/ijkis020101 https://www.acadlore.com/article/IJKIS/2024_2_1/ijkis020101 https://www.acadlore.com/article/IJKIS/2023_1_2/ijkis010205 Recent advancements in non-destructive testing methodologies have significantly propelled the efficiency of bearing defect detection, vital for maintaining optimal final quality standards. This study introduces a novel approach, integrating an Optimized Continuous Wavelet Transform (OCWT) and a Non-Local Convolutional Block Attention Module (NCBAM), to elevate fault diagnosis in motor bearings. The OCWT, central to this methodology, undergoes fine-tuning through a newly formulated metaheuristic algorithm, the Skill Optimization Algorithm (SOA). This algorithm bifurcates into two critical components: the acquisition of expertise (exploration) and the enhancement of individual capabilities (exploitation). The NCBAM, proposed for classification, adeptly captures long-range dependencies across spatial and channel dimensions. Furthermore, the model employs a learning matrix, adept at synthesizing spatial, channel, and temporal data, thus effectively balancing diverse data contributions by extracting intricate interrelations. The model's efficacy is rigorously validated using a gearbox dataset and a motor bearing dataset. The outcomes reveal superior performance, with the model achieving an average accuracy of 94.17% on the bearing dataset and 95.77% on the gearbox dataset. These results demonstrably surpass those of existing alternatives, underscoring the model's potential in enhancing fault diagnosis accuracy in motor bearings. 12-30-2023 <![CDATA[ <p>Recent advancements in non-destructive testing methodologies have significantly propelled the efficiency of bearing defect detection, vital for maintaining optimal final quality standards. This study introduces a novel approach, integrating an Optimized Continuous Wavelet Transform (OCWT) and a Non-Local Convolutional Block Attention Module (NCBAM), to elevate fault diagnosis in motor bearings. The OCWT, central to this methodology, undergoes fine-tuning through a newly formulated metaheuristic algorithm, the Skill Optimization Algorithm (SOA). This algorithm bifurcates into two critical components: the acquisition of expertise (exploration) and the enhancement of individual capabilities (exploitation). The NCBAM, proposed for classification, adeptly captures long-range dependencies across spatial and channel dimensions. Furthermore, the model employs a learning matrix, adept at synthesizing spatial, channel, and temporal data, thus effectively balancing diverse data contributions by extracting intricate interrelations. The model's efficacy is rigorously validated using a gearbox dataset and a motor bearing dataset. The outcomes reveal superior performance, with the model achieving an average accuracy of 94.17% on the bearing dataset and 95.77% on the gearbox dataset. These results demonstrably surpass those of existing alternatives, underscoring the model's potential in enhancing fault diagnosis accuracy in motor bearings.</p> ]]> Enhanced Fault Diagnosis in Motor Bearings: Leveraging Optimized Wavelet Transform and Non-Local Attention syed ziaur rahman ramesh vatambeti doi: 10.56578/ijkis010205 International Journal of Knowledge and Innovation Studies 12-30-2023 International Journal of Knowledge and Innovation Studies 12-30-2023 2023 1 2 Article 127 10.56578/ijkis010205 https://www.acadlore.com/article/IJKIS/2023_1_2/ijkis010205 https://www.acadlore.com/article/IJKIS/2023_1_2/ijkis010204 In the field of computer vision and digital image processing, the division of images into meaningful segments is a pivotal task. This paper introduces an innovative global image segmentation model, distinguished for its ability to segment pixels with intensity inhomogeneity and robustly handle noise. The proposed model leverages a combination of randomness measurement and spatial techniques to accurately segment regions within and outside contours in challenging conditions. Its efficacy is demonstrated through rigorous testing with images from the Berkeley image database. The results significantly surpass existing methods, particularly in the context of noisy and intensity inhomogeneous images. The model's proficiency lies in its unique ability to differentiate between minute, yet crucial, details and outliers, thus enhancing the precision of global segmentation in complex scenarios. This advancement is particularly relevant for images plagued by unknown noise distributions, overcoming limitations such as the inadequate handling of convex images at local minima and the segmentation of images corrupted by additive and multiplicative noise. The model's design integrates a region-based active contour method, refined through the incorporation of a local similarity factor, level set method, partial differential equations, and entropy considerations. This approach not only addresses the technical challenges posed by image segmentation but also sets a new benchmark for accuracy and reliability in the field. 12-30-2023 <![CDATA[ In the field of computer vision and digital image processing, the division of images into meaningful segments is a pivotal task. This paper introduces an innovative global image segmentation model, distinguished for its ability to segment pixels with intensity inhomogeneity and robustly handle noise. The proposed model leverages a combination of randomness measurement and spatial techniques to accurately segment regions within and outside contours in challenging conditions. Its efficacy is demonstrated through rigorous testing with images from the Berkeley image database. The results significantly surpass existing methods, particularly in the context of noisy and intensity inhomogeneous images. The model's proficiency lies in its unique ability to differentiate between minute, yet crucial, details and outliers, thus enhancing the precision of global segmentation in complex scenarios. This advancement is particularly relevant for images plagued by unknown noise distributions, overcoming limitations such as the inadequate handling of convex images at local minima and the segmentation of images corrupted by additive and multiplicative noise. The model's design integrates a region-based active contour method, refined through the incorporation of a local similarity factor, level set method, partial differential equations, and entropy considerations. This approach not only addresses the technical challenges posed by image segmentation but also sets a new benchmark for accuracy and reliability in the field. ]]> Enhanced Global Image Segmentation: Addressing Pixel Inhomogeneity and Noise with Average Convolution and Entropy-Based Local Factor ibrar hussain jan muhammad rifaqat ali doi: 10.56578/ijkis010204 International Journal of Knowledge and Innovation Studies 12-30-2023 International Journal of Knowledge and Innovation Studies 12-30-2023 2023 1 2 Article 116 10.56578/ijkis010204 https://www.acadlore.com/article/IJKIS/2023_1_2/ijkis010204 https://www.acadlore.com/article/IJKIS/2023_1_2/ijkis010203 In this study, a novel methodology is proposed for ranking the knowledge economies of European Union (EU) countries, leveraging their positioning within the global knowledge index (GKI). The GKI, encompassing seven pivotal indicators, serves as a benchmark for assessing a nation's knowledge economy. The EU, a prominent political and economic conglomerate, forms the focal point of this analysis. A multi-criteria analysis approach is adopted, wherein the Entropy method is utilized to determine the significance of individual GKI indicators. Additionally, the CRADIS (Compromise Ranking of Alternatives from Distance to Ideal Solution) method is employed for the ranking of these nations. The Entropy method, renowned for its efficacy in subjective weight determination, and the CRADIS method, a novel multi-criteria analysis tool yielding results based on deviations from the ideal and anti-ideal solutions, are integrated. This integration is pivotal, as it offers results comparable with other multi-criteria methodologies. The analysis reveals that Research Development and Innovation emerges as the most critical indicator. According to the CRADIS method, Sweden is identified as the leading country in terms of GKI indicators, followed by Finland and Denmark. This trend underscores a superior performance of the northern EU countries. Conversely, Eastern EU countries are observed to lag in their GKI standings. These findings are corroborated through comparative and sensitivity analyses, highlighting the influence of normalization on country rankings and pinpointing specific indicators necessitating enhancement for bolstering the knowledge economy. This research not only aids EU countries in identifying their strengths and weaknesses in the realm of knowledge economy but also serves as a strategic guide for policymakers. It provides actionable insights for fostering knowledge economy development, emphasizing the need for strengthening existing advantages and addressing shortcomings. Such strategic initiatives are crucial for enhancing global market competitiveness. The study's outcomes, therefore, offer valuable resources for decision-making in policy and economic development contexts. 12-30-2023 <![CDATA[ In this study, a novel methodology is proposed for ranking the knowledge economies of European Union (EU) countries, leveraging their positioning within the global knowledge index (GKI). The GKI, encompassing seven pivotal indicators, serves as a benchmark for assessing a nation's knowledge economy. The EU, a prominent political and economic conglomerate, forms the focal point of this analysis. A multi-criteria analysis approach is adopted, wherein the Entropy method is utilized to determine the significance of individual GKI indicators. Additionally, the CRADIS (Compromise Ranking of Alternatives from Distance to Ideal Solution) method is employed for the ranking of these nations. The Entropy method, renowned for its efficacy in subjective weight determination, and the CRADIS method, a novel multi-criteria analysis tool yielding results based on deviations from the ideal and anti-ideal solutions, are integrated. This integration is pivotal, as it offers results comparable with other multi-criteria methodologies. The analysis reveals that Research Development and Innovation emerges as the most critical indicator. According to the CRADIS method, Sweden is identified as the leading country in terms of GKI indicators, followed by Finland and Denmark. This trend underscores a superior performance of the northern EU countries. Conversely, Eastern EU countries are observed to lag in their GKI standings. These findings are corroborated through comparative and sensitivity analyses, highlighting the influence of normalization on country rankings and pinpointing specific indicators necessitating enhancement for bolstering the knowledge economy. This research not only aids EU countries in identifying their strengths and weaknesses in the realm of knowledge economy but also serves as a strategic guide for policymakers. It provides actionable insights for fostering knowledge economy development, emphasizing the need for strengthening existing advantages and addressing shortcomings. Such strategic initiatives are crucial for enhancing global market competitiveness. The study's outcomes, therefore, offer valuable resources for decision-making in policy and economic development contexts. ]]> Evaluating the Knowledge Economies within the European Union: A Global Knowledge Index Ranking via Entropy and CRADIS Methodologies adis puška ilhana hodžić anđelka štilić doi: 10.56578/ijkis010203 International Journal of Knowledge and Innovation Studies 12-30-2023 International Journal of Knowledge and Innovation Studies 12-30-2023 2023 1 2 Article 103 10.56578/ijkis010203 https://www.acadlore.com/article/IJKIS/2023_1_2/ijkis010203 https://www.acadlore.com/article/IJKIS/2023_1_2/ijkis010202 Twitter, a predominant platform for instantaneous communication and idea dissemination, is often exploited by cybercriminals for victim harassment through sexism, racism, hate speech, and trolling using pseudony-mous accounts. The propagation of racially charged online discourse poses significant threats to the social, political, and cultural fabric of many societies. Monitoring and prompt eradication of such content from social media, a breeding ground for racist ideologies, are imperative. This study introduces an advanced hybrid forecasting model, utilizing convolutional neural networks (CNNs) and long-short-term memory (LSTM) neural networks, for the efficient and accurate detection of racist and hate speech in English on Twitter. Unlabelled tweets, collated via the Twitter API, formed the basis of the initial investigation. Feature vectors were extracted from these tweets using the TF-IDF (Term Frequency-Inverse Document Frequency) feature extraction technique. This research contrasts the proposed model with existing intelligent classification algorithms in supervised learning. The HateMotiv corpus, a publicly available dataset annotated with types of hate crimes and ideological motivations, was employed, emphasizing Twitter as the primary social media context. A novel aspect of this study is the introduction of a revised artificial hummingbird algorithm (AHA), supplemented by quantum-based optimization (QBO). This quantum-based artificial hummingbird algorithm (QAHA) aims to augment exploration capabilities and reveal potential solution spaces. Employing QAHA resulted in a detection accuracy of approximately 98%, compared to 95.97% without its application. The study's principal contribution lies in the significant advancements achieved in the field of racism and hate speech detection in English through the application of hybrid deep learning methodologies. 12-07-2023 <![CDATA[ <p>Twitter, a predominant platform for instantaneous communication and idea dissemination, is often exploited by cybercriminals for victim harassment through sexism, racism, hate speech, and trolling using pseudony-mous accounts. The propagation of racially charged online discourse poses significant threats to the social, political, and cultural fabric of many societies. Monitoring and prompt eradication of such content from social media, a breeding ground for racist ideologies, are imperative. This study introduces an advanced hybrid forecasting model, utilizing convolutional neural networks (CNNs) and long-short-term memory (LSTM) neural networks, for the efficient and accurate detection of racist and hate speech in English on Twitter. Unlabelled tweets, collated via the Twitter API, formed the basis of the initial investigation. Feature vectors were extracted from these tweets using the TF-IDF (Term Frequency-Inverse Document Frequency) feature extraction technique. This research contrasts the proposed model with existing intelligent classification algorithms in supervised learning. The HateMotiv corpus, a publicly available dataset annotated with types of hate crimes and ideological motivations, was employed, emphasizing Twitter as the primary social media context. A novel aspect of this study is the introduction of a revised artificial hummingbird algorithm (AHA), supplemented by quantum-based optimization (QBO). This quantum-based artificial hummingbird algorithm (QAHA) aims to augment exploration capabilities and reveal potential solution spaces. Employing QAHA resulted in a detection accuracy of approximately 98%, compared to 95.97% without its application. The study's principal contribution lies in the significant advancements achieved in the field of racism and hate speech detection in English through the application of hybrid deep learning methodologies.</p> ]]> Racism and Hate Speech Detection on Twitter: A QAHA-Based Hybrid Deep Learning Approach Using LSTM-CNN praveen kumar jayapal kumar raja depa ramachandraiah kranthi kumar lella doi: 10.56578/ijkis010202 International Journal of Knowledge and Innovation Studies 12-07-2023 International Journal of Knowledge and Innovation Studies 12-07-2023 2023 1 2 Article 89 10.56578/ijkis010202 https://www.acadlore.com/article/IJKIS/2023_1_2/ijkis010202 https://www.acadlore.com/article/IJKIS/2023_1_2/ijkis010201 This study, rooted in extension theory and the principles of knowledge engineering, explores and formulates a novel method for generating sports protective gear designs. Given the critical role of sports protective gear in safeguarding athletes from injuries, coupled with escalating demands for product quality, the aim is to uncover a more effective approach to innovative design. This method involves formalizing modeling of various elements in the design process and representing this information in the elemental form of knowledge engineering. Through the related analysis, divergent analysis, as well as permutation and conduction transformations of these elements, innovative design schemes for sports protective gear are generated. This process not only optimizes design schemes in depth but also ventures into new design methods and processes. The objective is to offer a novel perspective in integrating extension theory and knowledge engineering in the design of sports protective gear, aspiring to provide more effective strategies to enhance existing design workflows. The goal of this new design method is to produce sports protective gear that is both practical and innovative, thereby enhancing the safety and enjoyment of athletes. 12-06-2023 <![CDATA[ <p>This study, rooted in extension theory and the principles of knowledge engineering, explores and formulates a novel method for generating sports protective gear designs. Given the critical role of sports protective gear in safeguarding athletes from injuries, coupled with escalating demands for product quality, the aim is to uncover a more effective approach to innovative design. This method involves formalizing modeling of various elements in the design process and representing this information in the elemental form of knowledge engineering. Through the related analysis, divergent analysis, as well as permutation and conduction transformations of these elements, innovative design schemes for sports protective gear are generated. This process not only optimizes design schemes in depth but also ventures into new design methods and processes. The objective is to offer a novel perspective in integrating extension theory and knowledge engineering in the design of sports protective gear, aspiring to provide more effective strategies to enhance existing design workflows. The goal of this new design method is to produce sports protective gear that is both practical and innovative, thereby enhancing the safety and enjoyment of athletes.</p> ]]> Application of Knowledge Engineering in Sports Protective Gear Design: A Study on Innovative Methods Based on Extension Theory chenglong xu zdravko nunić tichun wang doi: 10.56578/ijkis010201 International Journal of Knowledge and Innovation Studies 12-06-2023 International Journal of Knowledge and Innovation Studies 12-06-2023 2023 1 2 Article 73 10.56578/ijkis010201 https://www.acadlore.com/article/IJKIS/2023_1_2/ijkis010201 https://www.acadlore.com/article/IJKIS/2023_1_1/ijkis010105 This paper aims to introduce the concepts of complex Polytopic fuzzy sets (CPoFSs) and complex Polytopic fuzzy numbers (CPoFNs), advancing the field of fuzzy logic. Three innovative aggregation operators based on CPoFNs are presented: The complex Polytopic fuzzy weighted averaging aggregation (CPoFWAA) operator, the complex Polytopic fuzzy ordered weighted averaging aggregation (CPoFOWAA) operator, and the complex Polytopic fuzzy hybrid averaging aggregation (CPoFHAA) operator. A significant application of these complex Polytopic fuzzy sets is their integration into decision-making processes, particularly in identifying the most suitable COVID-19 vaccines for patients. This application highlights the practical relevance and the innovative nature of the proposed methods. The paper further demonstrates the efficacy and efficiency of these methods through a comprehensive example provided towards the end, underscoring their potential in real-world scenarios. 09-29-2023 <![CDATA[ This paper aims to introduce the concepts of complex Polytopic fuzzy sets (CPoFSs) and complex Polytopic fuzzy numbers (CPoFNs), advancing the field of fuzzy logic. Three innovative aggregation operators based on CPoFNs are presented: The complex Polytopic fuzzy weighted averaging aggregation (CPoFWAA) operator, the complex Polytopic fuzzy ordered weighted averaging aggregation (CPoFOWAA) operator, and the complex Polytopic fuzzy hybrid averaging aggregation (CPoFHAA) operator. A significant application of these complex Polytopic fuzzy sets is their integration into decision-making processes, particularly in identifying the most suitable COVID-19 vaccines for patients. This application highlights the practical relevance and the innovative nature of the proposed methods. The paper further demonstrates the efficacy and efficiency of these methods through a comprehensive example provided towards the end, underscoring their potential in real-world scenarios. ]]> Application of Complex Polytopic Fuzzy Information Systems in Knowledge Engineering: Decision Support for COVID-19 Vaccine Selection khaista rahman doi: 10.56578/ijkis010105 International Journal of Knowledge and Innovation Studies 09-29-2023 International Journal of Knowledge and Innovation Studies 09-29-2023 2023 1 1 Article 60 10.56578/ijkis010105 https://www.acadlore.com/article/IJKIS/2023_1_1/ijkis010105 https://www.acadlore.com/article/IJKIS/2023_1_1/ijkis010104 The evaluation of the Logistics Performance Index (LPI), as computed by the World Bank, incorporates six equally weighted criteria to ascertain the overall performance scores of countries globally. This study aims to scrutinize the impact of the weighting coefficients of criteria on the computation of the total LPI scores, employing a selection of Multi-Criteria Decision Making (MCDM) methods. The Criteria Importance Through Intercriteria Correlation (CRITIC) and Full Consistency Method (FUCOM) methods were utilized to determine the weighting coefficients, while the Measurement of Alternatives and Ranking according to Compromise Solution (MARCOS) method was employed for ranking the European Union member states. The findings reveal that Finland emerges as the top-ranked nation upon application of the integrated MCDM model. A comparative analysis was conducted, incorporating three additional MCDM methods to assess the robustness of the ranking. Furthermore, a sensitivity analysis was performed, generating sixty novel scenarios to examine the effects of variations in the criteria weighting coefficients. This analysis confirmed the influence of these coefficients on the ultimate ranking of the nations. The research underscores the significance of criteria weightings in the evaluation of the LPI and provides insights into the stability of the rankings under different weighting scenarios. 09-29-2023 <![CDATA[ The evaluation of the Logistics Performance Index (LPI), as computed by the World Bank, incorporates six equally weighted criteria to ascertain the overall performance scores of countries globally. This study aims to scrutinize the impact of the weighting coefficients of criteria on the computation of the total LPI scores, employing a selection of Multi-Criteria Decision Making (MCDM) methods. The Criteria Importance Through Intercriteria Correlation (CRITIC) and Full Consistency Method (FUCOM) methods were utilized to determine the weighting coefficients, while the Measurement of Alternatives and Ranking according to Compromise Solution (MARCOS) method was employed for ranking the European Union member states. The findings reveal that Finland emerges as the top-ranked nation upon application of the integrated MCDM model. A comparative analysis was conducted, incorporating three additional MCDM methods to assess the robustness of the ranking. Furthermore, a sensitivity analysis was performed, generating sixty novel scenarios to examine the effects of variations in the criteria weighting coefficients. This analysis confirmed the influence of these coefficients on the ultimate ranking of the nations. The research underscores the significance of criteria weightings in the evaluation of the LPI and provides insights into the stability of the rankings under different weighting scenarios. ]]> Evaluating the Logistics Performance Index of European Union Countries: An Integrated Multi-Criteria Decision-Making Approach Utilizing the Bonferroni Operator almedina hadžikadunić željko stević ibrahim badi violeta roso doi: 10.56578/ijkis010104 International Journal of Knowledge and Innovation Studies 09-29-2023 International Journal of Knowledge and Innovation Studies 09-29-2023 2023 1 1 Article 44 10.56578/ijkis010104 https://www.acadlore.com/article/IJKIS/2023_1_1/ijkis010104 https://www.acadlore.com/article/IJKIS/2023_1_1/ijkis010103 The quest for heightened precision in fuzzy system predictions has culminated in the development of an innovative model that integrates a Fuzzy K-Clustering (FKC) algorithm with a fuzzy neural network (FNN). In this approach, the novel FKC algorithm, herein introduced, undertakes the clustering of sample data. Subsequently, the clustering outcomes inform the configuration of the FNN, specifically guiding the determination of node quantities across its layers and the initial network parameters. A distinctive hybrid learning algorithm, designated as the Conjugate Recursive Least Squares (CRLS), facilitates the optimization of network parameters via distinct methods tailored to parameter types. This model underwent empirical validation using 2-minute interval average wind speed data from surface meteorological stations in China. Analytical comparisons between model predictions and actual wind speed data revealed an average absolute error of 0.2764m/s, an average absolute percentage error of 2.33%, and a maximum error of 0.6035m/s. The findings substantiate the model's superior predictive capability. This study thus presents a significant advancement in fuzzy system prediction methodologies, underscoring the potential of the FKC and FNN in complex data analysis. 09-29-2023 <![CDATA[ <p>The quest for heightened precision in fuzzy system predictions has culminated in the development of an innovative model that integrates a Fuzzy K-Clustering (FKC) algorithm with a fuzzy neural network (FNN). In this approach, the novel FKC algorithm, herein introduced, undertakes the clustering of sample data. Subsequently, the clustering outcomes inform the configuration of the FNN, specifically guiding the determination of node quantities across its layers and the initial network parameters. A distinctive hybrid learning algorithm, designated as the Conjugate Recursive Least Squares (CRLS), facilitates the optimization of network parameters via distinct methods tailored to parameter types. This model underwent empirical validation using 2-minute interval average wind speed data from surface meteorological stations in China. Analytical comparisons between model predictions and actual wind speed data revealed an average absolute error of 0.2764m/s, an average absolute percentage error of 2.33%, and a maximum error of 0.6035m/s. The findings substantiate the model's superior predictive capability. This study thus presents a significant advancement in fuzzy system prediction methodologies, underscoring the potential of the FKC and FNN in complex data analysis.</p> ]]> Enhanced Prediction Accuracy in Complex Systems: An Approach Integrating Fuzzy K-Clustering and Fuzzy Neural Network tichun wang xianwei wang hao li doi: 10.56578/ijkis010103 International Journal of Knowledge and Innovation Studies 09-29-2023 International Journal of Knowledge and Innovation Studies 09-29-2023 2023 1 1 Article 30 10.56578/ijkis010103 https://www.acadlore.com/article/IJKIS/2023_1_1/ijkis010103 https://www.acadlore.com/article/IJKIS/2023_1_1/ijkis010102 Fermatean fuzzy set (FRFS) is very helpful in representing vague information that occurs in real world circumstances. Their eminent characteristic of FRFS is that the degree of membership $\Im^{\ell}$ and degree of nonmembership $\beth^\gamma$ satisfy the condition $0 \leq \Im^{\ell^3}(x)+\Im^{\ell^3}(x) \leq 1$, so the space of vague information they can describe is broader. This study introduces the concept of generalized parameters into the FRFS framework and proposes a set of generalized Fermatean fuzzy average aggregation operators for the purpose of information aggregation. Subsequently, the operators are expanded to encompass a generalized parameter based on group consensus, which is derived from the perspectives of numerous experienced senior experts and observers. The present study offers a multi-criteria decision-making (MCDM) methodology, which is demonstrated using a numerical example to successfully showcase the suggested technique. In conclusion, a comparative study is undertaken to validate the efficacy of the suggested technique in relation to existing methodologies. 09-29-2023 <![CDATA[ <p>Fermatean fuzzy set (FRFS) is very helpful in representing vague information that occurs in real world circumstances. Their eminent characteristic of FRFS is that the degree of membership $\Im^{\ell}$ and degree of nonmembership $\beth^\gamma$ satisfy the condition $0 \leq \Im^{\ell^3}(x)+\Im^{\ell^3}(x) \leq 1$, so the space of vague information they can describe is broader. This study introduces the concept of generalized parameters into the FRFS framework and proposes a set of generalized Fermatean fuzzy average aggregation operators for the purpose of information aggregation. Subsequently, the operators are expanded to encompass a generalized parameter based on group consensus, which is derived from the perspectives of numerous experienced senior experts and observers. The present study offers a multi-criteria decision-making (MCDM) methodology, which is demonstrated using a numerical example to successfully showcase the suggested technique. In conclusion, a comparative study is undertaken to validate the efficacy of the suggested technique in relation to existing methodologies.</p> ]]> Generalized and Group-Generalized Parameter Based Fermatean Fuzzy Aggregation Operators with Application to Decision-Making ali aslam khan ling wang doi: 10.56578/ijkis010102 International Journal of Knowledge and Innovation Studies 09-29-2023 International Journal of Knowledge and Innovation Studies 09-29-2023 2023 1 1 Article 10 10.56578/ijkis010102 https://www.acadlore.com/article/IJKIS/2023_1_1/ijkis010102 https://www.acadlore.com/article/IJKIS/2023_1_1/ijkis010101 Amidst a transformative economic milieu in China, domestic enterprises are venturing into the global market, exposing them to intensified perils in international trade and investment. This research elucidates the international trade and investment (ITI) context within China, establishing criteria for ITI risk evaluation through an analytical exploration of international trade interactions. A methodology has been developed to quantify ITI risk, employing deep neural networks (DNNs), with a particular focus on the potential impact of edge cloud computing on China's trading economy. Through the utilization of convolutional neural networks (CNN), risks in China's trade and investment are appraised across various dimensions, exhibiting a noteworthy accuracy rate of 90.38%. It is identified that while CNN exhibits exemplary performance in estimating severe and high-risk scenarios, its efficacy diminishes when discerning general investment perils. The analysis underscores that a substantial portion of investments, constituting 14.8%, emanates from The Association of Southeast Asian Nations (ASEAN) and China, with market dynamics and macroeconomic conditions markedly influencing the risk associated with Chinese investments. By extending the utilization of deep learning (DL) in financial investments and integrating edge cloud computing, this investigation augments the capabilities for assessing China's ITI risk, providing a valuable resource for comprehending the ITI landscape within China. 09-29-2023 <![CDATA[ <p>Amidst a transformative economic milieu in China, domestic enterprises are venturing into the global market, exposing them to intensified perils in international trade and investment. This research elucidates the international trade and investment (ITI) context within China, establishing criteria for ITI risk evaluation through an analytical exploration of international trade interactions. A methodology has been developed to quantify ITI risk, employing deep neural networks (DNNs), with a particular focus on the potential impact of edge cloud computing on China's trading economy. Through the utilization of convolutional neural networks (CNN), risks in China's trade and investment are appraised across various dimensions, exhibiting a noteworthy accuracy rate of 90.38%. It is identified that while CNN exhibits exemplary performance in estimating severe and high-risk scenarios, its efficacy diminishes when discerning general investment perils. The analysis underscores that a substantial portion of investments, constituting 14.8%, emanates from The Association of Southeast Asian Nations (ASEAN) and China, with market dynamics and macroeconomic conditions markedly influencing the risk associated with Chinese investments. By extending the utilization of deep learning (DL) in financial investments and integrating edge cloud computing, this investigation augments the capabilities for assessing China's ITI risk, providing a valuable resource for comprehending the ITI landscape within China.</p> ]]> Utilizing Edge Cloud Computing and Deep Learning for Enhanced Risk Assessment in China’s International Trade and Investment muhammad abid muhammad saqlain doi: 10.56578/ijkis010101 International Journal of Knowledge and Innovation Studies 09-29-2023 International Journal of Knowledge and Innovation Studies 09-29-2023 2023 1 1 Article 1 10.56578/ijkis010101 https://www.acadlore.com/article/IJKIS/2023_1_1/ijkis010101