Journal of Industrial Intelligence

Journal of Industrial Intelligence, 2026, Volume 4, Issue 1, Pages undefined: A Case-Density-Driven Closed-Loop Intelligent Strategy for Air-Ground-Human Collaborative Disinfection: An End-to-End Framework from Case Perception to Task Scheduling

liuhua zhang — 01-30-2026

Traditional public health disinfection tasks relying on fixed-area coverage often suffer from resource waste, delayed intervention, and low response efficiency. This study proposes a case-density-driven closed-loop intelligent strategy for air-ground-human collaborative disinfection, establishing an end-to-end framework from case perception to task scheduling. Firstly, a spatiotemporal risk field is constructed based on reported case data and population mobility information, and high-risk areas are adaptively identified and prioritized through dynamic evaluation. Secondly, for coordinated execution by unmanned aerial vehicles (UAVs), ground vehicles, and personnel, a multi-objective coupled optimization model is designed, targeting coverage efficiency, suppression timeliness, path conflicts, and resource cost to generate executable collaborative schedules. Furthermore, a closed-loop execution mechanism is developed, enabling real-time rolling re-planning and adaptive strategy correction in response to task feedback, unexpected disturbances (area lockdown, equipment failure, chemical shortage), and risk field updates. Experimental results demonstrate that the proposed closed-loop approach significantly improves coverage, suppression time, and resource utilization compared with traditional static scheduling and single-entity planning methods across multiple scenarios, and exhibits robustness against environmental uncertainties and resource disturbances. This framework provides a feasible theoretical and methodological foundation for intelligent, precise, and resilient public health disinfection operations.

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Journal of Industrial Intelligence, 2026, Volume 4, Issue 1, Pages undefined: Unmanned Aerial Vehicle Selection with Different MCDM Methods in Defense Industry

galip cihan yalçın — 01-24-2026

Unmanned aerial vehicles (UAVs) have gained increasing importance due to their expanding application areas and operational flexibility. Selecting the most suitable UAV, however, represents a complex multi-criteria decision-making (MCDM) problem that involves numerous technical and performance-related factors. This study addresses the UAV selection problem by employing four distinct MCDM approaches: Evidential fuzzy MCDM based on Belief Entropy, Intuitionistic Fuzzy Dempster-Shafer Theory (DST), Spherical Fuzzy Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS), and Type-2 Neutrosophic Fuzzy CRITIC-MABAC. Each method incorporates different fuzzy set theories, while a common seven-point linguistic scale is utilized to ensure consistency across models. The evaluation criteria were determined through a comprehensive literature review, and expert opinions were collected from experienced UAV pilots and technical personnel. The analysis identified the most suitable UAV alternative among the considered options. Sensitivity analyses were conducted to assess the robustness of the obtained results. The findings demonstrate that the proposed framework enables a simultaneous comparison of different fuzzy set environments on a unified linguistic scale. Overall, the results are consistent, reliable, and practically applicable, offering valuable insights and methodological contributions to the field of UAV selection and fuzzy MCDM applications.

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Journal of Industrial Intelligence, 2025, Volume 3, Issue 2, Pages undefined: AG-CLRNet: A Real-time Industrial Lane Perception Framework for Intelligent Driving Systems

weiguo ding — 06-29-2025

Reliable lane perception is a core enabling function in industrial intelligent driving systems, providing essential structural constraints for downstream tasks such as lane keeping assistance, trajectory planning, and vehicle control. In real-world deployments, lane detection remains challenging due to complex road geometries, illumination variations, occlusions, and the limited computational resources of on-board platforms. This study presents Attention-Guided Cross-Layer Refinement Network (AG-CLRNet), a real-time lane perception framework designed for industrial intelligent driving applications. Built upon an anchor-based detection paradigm, the framework integrates adaptive multi-scale contextual fusion, channel–spatial attention refinement, and long-range dependency modeling to improve feature discrimination and structural continuity while maintaining computational efficiency. The proposed design strengthens the representation of distant and slender lane markings, suppresses background interference caused by shadows and pavement textures, and enhances global geometric consistency in curved and fragmented scenarios. Extensive experiments conducted on the CULane benchmark demonstrate that AG-CLRNet achieves consistent improvements in precision, recall, and F1 score over representative state-of-the-art methods, while sustaining real-time inference performance suitable for practical deployment. Ablation studies further confirm the complementary contributions of the proposed modules to robustness and structural stability under challenging conditions. Overall, AG-CLRNet provides a practical and deployable lane perception solution for industrial intelligent driving systems, offering a balanced trade-off between accuracy, robustness, and real-time performance in complex road environments.

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Journal of Industrial Intelligence, 2025, Volume 3, Issue 2, Pages undefined: Optimizing Time-Based Heuristics for Resilient VMI Replenishment: A Simulation-Optimization Approach

jamal musbah — 06-24-2025

In critical supply chains like pharmaceuticals, balancing operational cost with service resilience is paramount. While complex adaptive models dominate academic literature on inventory routing, the potential of simpler, managerially intuitive heuristics remains underexplored, creating a gap between theory and practice. This study investigates whether a rigorously optimized, simple time-based heuristic can achieve superior performance and robustness compared to a state-of-the-art, multi-parameter adaptive policy within a stochastic Vendor-Managed Inventory (VMI) system. We formalize a time-to-stockout rule into a novel, single-parameter metaheuristic called the Optimized Urgency Threshold (OUT) policy. Using a simulation-optimization framework powered by a Genetic Algorithm, we benchmarked the OUT policy against a non-optimized heuristic and a complex Dynamic Inertial policy across five problem instances subjected to environmental shocks. The OUT policy demonstrated superior performance, achieving the lowest average total cost (€ 58,595.46) and reducing stockouts by 66.3% compared to the Dynamic Inertial model. Sensitivity analysis confirmed the OUT policy's balanced robustness to demand and capacity shocks, whereas the complex policy exhibited service failures under demand surges. Our findings show that a parsimonious, optimized heuristic can outperform a complex adaptive model, challenging the assumption that parametric complexity is necessary for high performance in stochastic IRPs. The OUT policy provides a transparent, effective, and easily implementable solution for enhancing supply chain resilience and mitigating stockouts.

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Journal of Industrial Intelligence, 2025, Volume 3, Issue 2, Pages undefined: A Decision-Making Framework for Early-Stage Industrial Intelligent Engineering Innovation Based on Extenics and TRIZ

fan jiang — 06-21-2025

The early stages of engineering innovation are typically characterized by high levels of uncertainty, strong dependence on expert experience, and complex coupling among design objectives, manufacturing constraints, and solution maturity. These characteristics make the associated decision-making processes difficult to formalize and reproduce. To address this challenge, an industrial intelligence framework integrating Extenics and the Theory of Inventive Problem Solving (TRIZ) was proposed to support structured reasoning and consistent decision-making in the early phase of engineering innovation. Within the proposed framework, engineering objectives, constraint conditions, and solution maturity are represented as structured industrial knowledge elements, enabling unified processes of conflict identification, rule-based reasoning, and multi-criteria evaluation. Extenics is employed to construct formal representations of problem elements and their interrelationships, while TRIZ is utilized to support systematic principle-based resolution of contradictions. Through this integration, engineering decision-making is shifted from reliance on implicit experiential knowledge toward an explicit, knowledge-driven paradigm. The applicability and effectiveness of the framework were demonstrated through a conceptual design case study of a household product. The results indicate that the proposed approach enhances the transparency and consistency of early-stage engineering decisions, reduces dependence on individual expertise, and provides an interpretable industrial intelligence solution for supporting knowledge-intensive engineering innovation.

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Journal of Industrial Intelligence, 2025, Volume 3, Issue 2, Pages undefined: Identification of Delays and Bottlenecks in Manufacturing Processes Through Process Mining

safiye turgay — 06-17-2025

In the highly competitive landscape of modern manufacturing, the efficient and timely operation of production processes is paramount for sustaining productivity and ensuring customer satisfaction. Delays and latent bottlenecks, however, often hinder optimal performance. A data-driven methodology for identifying these inefficiencies is presented, employing process mining techniques. By analyzing event logs from Manufacturing Execution Systems (MES) and Enterprise Resource Planning (ERP) systems, the actual execution flow of production processes is reconstructed and compared against the designed process models. Through process discovery, conformance checking, and performance analysis, the underlying causes of delays and capacity bottlenecks are pinpointed. A case study from a manufacturing facility is used to demonstrate the effectiveness of process mining in uncovering critical areas for process improvement. The findings indicate that process mining not only enhances transparency but also provides actionable insights for optimizing resource planning, reducing cycle times, and maximizing overall operational effectiveness. The approach is demonstrated to facilitate the identification of inefficiencies, leading to targeted interventions that significantly improve process performance and business outcomes.

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Journal of Industrial Intelligence, 2025, Volume 3, Issue 2, Pages undefined: Systemic Determinants of Equipment Failure in Paper Mills: A Hybrid FAST-PCA Approach for Maintenance Optimization

tefy rabarinjatovo — 06-12-2025

Equipment failure in paper mills represents a critical barrier to operational efficiency and the adoption of Industry 4.0 principles. To address this, a systematic literature review was conducted to identify the multifactorial determinants of such failures. A novel hybrid methodology was proposed, integrating the Functional Analysis Systems Technique (FAST), enhanced by Lean 5S (Sort “Seiri”, Set in Order “Seiton”, Shine “Seiso”, Standardize “Seiketsu”, Sustain “Shitsuke”) principles, to structure the qualitative data collection. The analysis was performed using a Pugh matrix, followed by a Principal Component Analysis (PCA) to extract knowledge systematically. This approach facilitated the development of a conceptual model for downtime causation. The PCA results indicate that two principal components collectively explain 58.5% of the observed variance in failure data. The f irst component was strongly correlated with maintenance practices and operational errors, while the second was associated with intrinsic equipment characteristics and their operating conditions. This data-driven modeling elucidates underlying correlations between disparate factors, providing a robust foundation for prioritizing targeted maintenance optimization actions. This research contributes to the field of industrial intelligence by demonstrating an original methodology for transforming qualitative systematic review data into a quantifiable analytical framework. The application of PCA to this corpus enables the identification of multidimensional interactions that are frequently overlooked in conventional analyses, thereby enriching root-cause failure analysis and informing strategic decision making for predictive maintenance. The identified factors underscore the imperative of a balanced integration between technical data and human factors for the successful digital transformation of production systems.

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Journal of Industrial Intelligence, 2025, Volume 3, Issue 1, Pages undefined: Enhancing Material Extrusion Additive Manufacturing with Sensor Fusion and Machine Learning

touqeer ahmad — 03-30-2025

Material extrusion additive manufacturing (MEX-AM) has emerged as a transformative technology with the potential to redefine industrial production; however, persistent challenges remain regarding variability in part quality, the absence of robust in-process defect detection, and limited capacity for process optimization. To address these limitations, an integrated multi-sensor and machine learning (ML) framework was developed to enhance real-time monitoring and defect detection during MEX-AM. Data were acquired from thermocouples, accelerometers, and high-resolution cameras, and subsequently processed through a multi-sensor data fusion pipeline to ensure robustness against noise and variability. A Multi-Criteria Decision Analysis (MCDA) framework was employed to evaluate candidate ML algorithms based on accuracy, computational cost, and interpretability. Random Forest (RF) and Artificial Neural Network (ANN) models were identified as the most suitable approaches for MEX-AM applications. Validation experiments demonstrated a 92% success rate in corrective interventions, with a reduction of defective components by 38% compared with conventional monitoring methods. The integration of sensor fusion with advanced learning models provided improved predictive capability, enhanced process stability, and significant progress toward intelligent, self-optimizing manufacturing systems. The proposed methodology advances statistical quality control and reduces material waste while aligning with the objectives of Industry 4.0 and smart manufacturing. By demonstrating the efficacy of multi-sensor fusion and ML in real-world AM environments, this study highlights a pathway toward scalable, autonomous, and sustainable industrial production.

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Journal of Industrial Intelligence, 2025, Volume 3, Issue 1, Pages undefined: Machine Learning-Driven IDPS in IIoT Smart Metering Networks

qutaiba i. ali — 03-30-2025

The proliferation of the Industrial Internet of Things (IIoT) has transformed energy distribution infrastructures through the deployment of smart metering networks, enhancing operational efficiency while concurrently expanding the attack surface for sophisticated cyber threats. In response, a wide range of Machine Learning (ML)–based Intrusion Detection and Prevention Systems (IDPS) have been proposed to safeguard these networks. In this study, a systematic review and comparative analysis were conducted across seven representative implementations targeting the Internet of Things (IoT), IIoT, fog computing, and smart metering contexts. Detection accuracies reported in these studies range from 90.00% to 99.95%, with models spanning clustering algorithms, Support Vector Machine (SVM), and Deep Neural Network (DNN) architectures. It was observed that hybrid Deep Learning (DL) models, particularly those combining the Convolutional Neural Network and the Long Short-Term Memory (CNN-LSTM), achieved the highest detection accuracy (99.95%), whereas unsupervised approaches such as K-means clustering yielded comparatively lower performance (93.33%). Datasets utilized included NSL-KDD, CICIDS2017, and proprietary smart metering traces. Despite notable classification accuracy, critical evaluation metrics—such as False Positive Rate (FPR), inference latency, and computational resource consumption—were frequently underreported or omitted, thereby impeding real-world applicability, especially in edge computing environments with limited resources. To address this deficiency, a unified benchmarking framework was proposed, incorporating precision-recall analysis, latency profiling, and memory usage evaluation. Furthermore, strategic directions for future research were outlined, including the integration of federated learning to preserve data privacy and the development of lightweight hybrid models tailored for edge deployment. This review provides a data-driven foundation for the design of scalable, resource-efficient, and privacy-preserving IDPS solutions within next-generation IIoT smart metering systems.

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Journal of Industrial Intelligence, 2025, Volume 3, Issue 1, Pages undefined: Industrial Food Process Improvement by Waste Minimization in Pasta Packaging Using DMAIC Methodology

svetlana dabic-miletic — 03-30-2025

Journal of Industrial Intelligence, 2025, Volume 3, Issue 1, Pages undefined: Optimization of Industrial Process Management in Postal and Logistics Centers Based on Transit Time Quality Standards

momčilo dobrodolac — 03-30-2025

Journal of Industrial Intelligence, 2025, Volume 3, Issue 1, Pages undefined: Benzene Pollution Forecasting by Recurrent Neural Networks Tuned with Adapted Elk Heard Optimizer

dejan bulaja — 03-30-2025

Journal of Industrial Intelligence, 2024, Volume 2, Issue 4, Pages undefined: YOLOv8n-AM: Enhanced Real-Time Smoke Detection via Attention-Based Feature Interaction and Multi-Scale Downsampling

zijun yao — 12-30-2024

Accurate smoke detection in complex industrial environments, such as chemical plants, remains a significant challenge due to the inherently low contrast, transparency, and weak texture features of smoke, which often exhibits blurred boundaries and diverse spatial scales. To address these limitations, YOLOv8n-AM, an enhanced lightweight detection framework belonging to the YOLO (You Only Look Once) series, was developed by integrating advanced architectural components into the baseline YOLOv8n model. Specifically, the conventional Spatial Pyramid Pooling-Fast (SPPF) module was replaced with an Attention-based Intra-scale Feature Interaction (AIFI) Convolution Synergistic Feature Processing Module (SFPM), i.e., AIFC-SFPM, enabling more effective semantic feature representation and an improvement in detection accuracy. In parallel, the original convolutional module was optimized using a Multi-Scale Downsampling (MSDown) module, which reduces model redundancy and computational overhead, increasing the detection speed. Experimental evaluations demonstrate that the YOLOv8n-AM model achieves a 1.7% improvement in mean Average Precision (mAP), accompanied by a 9.1% reduction in Giga Floating-point Operations Per Second (GFLOPs) and a 15.4% decrease in parameter count when compared to the original YOLOv8n framework. These improvements collectively underscore the model’s suitability for real-time deployment in resource-constrained industrial settings where rapid and reliable smoke detection is critical. The proposed architecture thus provides a computationally efficient and high-precision solution for safety-critical visual monitoring applications.

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Journal of Industrial Intelligence, 2024, Volume 2, Issue 4, Pages undefined: Click Fraud Detection with Recurrent Neural Networks Optimized by Adapted Crayfish Optimization Algorithm

lepa babic — 12-30-2024

Click fraud is a deceptive malicious strategy that relies on repetitive mimicking of human clicking on online advertisements, without actual intention to complete a purchase. This fraud can result in significant financial loses for both advertising companies and marketers, and at the same time destroying their public images. Nevertheless, detection of these illegitimate clicks is very challenging as they closely resemble to authentic human engagement. This study examines the utilization of artificial intelligence approaches to detect deceptive clicks, by identifying subtle correlations between the timing of the clicks, taking into account their geographical or network sources and linked application sources as indicators to separate legitimate from malicious activity. This study highlights the application of recurrent neural networks (RNNs) for this task, keeping in mind that the process of selection and tuning of the model's hyperparameters plays a vital role in the performance. An adapted implementation of crayfish optimization algorithm (COA) was consequently proposed in this paper, and used to optimize RNN models to enhance their general performance. The developed framework was evaluated utilizing actual operational datasets and yielded encouraging outcomes.

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Journal of Industrial Intelligence, 2024, Volume 2, Issue 4, Pages undefined: Enhanced Low-Illumination Image Defect Detection Using Machine Vision

li yan — 12-30-2024

Journal of Industrial Intelligence, 2024, Volume 2, Issue 4, Pages undefined: A Characterization Model for Vibration Hazards in Mining Equipment Based on Time-Frequency Mask and Sparse Representation

yunbo li — 12-30-2024

Journal of Industrial Intelligence, 2024, Volume 2, Issue 4, Pages undefined: Improved Relay Selection Strategy for 5G Non-Orthogonal Multiple Access Cooperative Communication Systems

ren jin — 12-30-2024

Journal of Industrial Intelligence, 2024, Volume 2, Issue 3, Pages undefined: Rapid Fault Detection for Exhibition Light Box Groups Using PCI Bus Structure

gong chen — 09-29-2024

The internal translucent color light box serves as both a display and behavioral guidance tool in exhibitions. However, its functionality can be compromised by variations in light intensity, temperature, humidity, bolt fastening integrity, and door lock status. Conventional Internet of Things (IoT)-based systems, while effective, often involve the installation of expensive sensors, control units, and network infrastructure within each light box. Given the large number of light boxes typically used in exhibitions, the high cost and slow response time of such systems remain significant limitations. This study proposes a novel approach utilizing a Peripheral Component Interconnect (PCI) bus structure to form a network of interconnected light boxes. By sequentially collecting voltage and current data from photosensitive resistors across adjacent groups of four light boxes, faults can be rapidly identified through a hierarchical comparison method. This method enables precise fault localization with minimal cost and at significantly reduced time. Simulations and physical prototypes were developed using Multisim to model the changes in light intensity during the fault detection process. Experimental results demonstrate the system's ability to accurately pinpoint malfunctioning light boxes when light levels fall below 1000 lx. The detection accuracy reaches 100% under these conditions. Notably, the proposed system requires no complex control processing, and offers an over 90% reduction in detection time and cost compared to traditional manual inspections and IoT-based fault detection systems. This approach presents a highly cost-effective and efficient solution for exhibition light box fault localization, facilitating maintenance by enabling visual identification of malfunctioning units.

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Journal of Industrial Intelligence, 2024, Volume 2, Issue 3, Pages undefined: Strategies for Improving Maintenance Efficiency and Reliability Through Wrench Time Optimization

mohammad rahman — 09-29-2024

This study investigates the optimization of wrench time to improve maintenance efficiency and reliability within a chemical processing plant. Wrench time, defined as the proportion of time spent directly performing maintenance tasks, was quantified through random observations of maintenance technicians. The findings revealed an average wrench time of 28% across the site, with variations between individual crew groups ranging from 20% to 35% and craft-specific wrench times varying from 13.3% to 45.5%. Several inefficiencies were identified, including prolonged wait times for equipment isolation, safety clearance, job planning, and parts procurement. Key contributing factors to these inefficiencies were found to include poor coordination between maintenance and production, insufficient work prioritization, inadequate adherence to schedules, a high volume of emergency tasks, and the absence of essential tools such as bills of materials (BOMs), equipment data, and troubleshooting checklists. To address these challenges, a range of improvement initiatives were implemented. These included enhancing coordination between maintenance and production by refining process steps, introducing additional planning tools for effective work prioritization, providing job aids, developing generic troubleshooting checklists, leveraging Industrial Internet of Things (IIoT) technologies, and establishing metrics to monitor progress. Early indications suggest that these initiatives have led to a reduction in maintenance backlog and gradual improvements in overall equipment effectiveness (OEE). It is anticipated that these changes will result in increased wrench time, enhanced maintenance quality and reliability, reduced downtime, and lower operational costs. For maintenance managers and engineers, the findings offer actionable insights into optimizing workflows and resource allocation, thereby contributing to the improvement of operational efficiency and reliability.

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Journal of Industrial Intelligence, 2024, Volume 2, Issue 3, Pages undefined: Fuzzy Control of Active Vehicle Suspensions for Enhanced Safety in Goods Transport

georgios k. tairidis — 09-29-2024

Journal of Industrial Intelligence, 2024, Volume 2, Issue 3, Pages undefined: Computational Fluid Dynamics Evaluation of Nitrogen and Hydrogen for Enhanced Air Conditioning Efficiency

yuki trisnoaji — 09-29-2024

This study evaluates the potential of nitrogen and hydrogen as alternative working fluids in air conditioning systems to improve thermal comfort and optimize energy efficiency, using computational fluid dynamics (CFD) simulations. A controlled indoor environment measuring 6 m $\times$ 4.5 m $\times$ 3 m was simulated, with nitrogen and hydrogen tested at inlet velocities of 0.7 m/s, 0.8 m/s, 0.9 m/s, 1.0 m/s, and 1.1 m/s, and an inlet temperature fixed at 293 K (20℃). The analysis focused on the impact of these gases on room and outlet temperatures to assess airflow distribution, heat transfer, and thermal comfort compared to traditional air-based systems. Results indicated that nitrogen improved airflow uniformity and facilitated heat transfer but exhibited limitations in effectively reducing room temperature due to its thermal properties. In contrast, hydrogen demonstrated stable outlet temperatures across all velocities, benefiting from its higher thermal conductivity; however, room temperatures showed significant variation, particularly at higher inlet velocities. Temperature prediction errors in the CFD model ranged from 0.003% to 2.78%, suggesting high accuracy yet underscoring the need for refinement in simulation methods. The findings highlight the promise of nitrogen and hydrogen in optimizing air conditioning system performance but emphasize the necessity for further investigation into the practical implications, specifically regarding operational safety, energy efficiency, and environmental impacts.

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Journal of Industrial Intelligence, 2024, Volume 2, Issue 3, Pages undefined: Development of a Hybrid Model for a Single-Machine Scheduling Using Expert Systems and Search Algorithms: A Simulation Study

abdurrahman zubia — 09-29-2024

Journal of Industrial Intelligence, 2024, Volume 2, Issue 2, Pages undefined: The Challenges of Integrating AI and Robotics in Sustainable WMS to Improve Supply Chain Economic Resilience

svetlana dabic-miletic — 06-29-2024

Journal of Industrial Intelligence, 2024, Volume 2, Issue 2, Pages undefined: Competitive Supply Chain Strategy Optimization Based on Game Model and NSGA-II Algorithm

fangfang guo — 06-29-2024

Journal of Industrial Intelligence, 2024, Volume 2, Issue 2, Pages undefined: Challenges and Opportunities in Implementing Smart Grid Technologies in Kurdistan: A Comprehensive Review

emad hussen sadiq — 06-29-2024

Journal of Industrial Intelligence, 2024, Volume 2, Issue 2, Pages undefined: Exploring the Impact of Artificial Intelligence Integration on Cybersecurity: A Comprehensive Analysis

shankha shubhra goswami — 05-23-2024

Journal of Industrial Intelligence, 2024, Volume 2, Issue 2, Pages undefined: Fuzzy Logic-Based Fault Detection in Industrial Production Systems: A Case Study

imen driss — 05-20-2024

Journal of Industrial Intelligence, 2024, Volume 2, Issue 1, Pages undefined: Lifetime Extension of Wireless Sensor Networks by Perceptive Selection of Cluster Head Using K-Means and Einstein Weighted Averaging Aggregation Operator under Uncertainty

supriyan sen — 03-30-2024

In the realm of Wireless Sensor Networks (WSNs), energy efficiency emerges as a paramount concern due to the inherent limitations in the energy capacity of sensor nodes. The extension of network lifespan is critically dependent on the strategic selection of Cluster Heads (CHs), a process that necessitates a nuanced approach to optimize communication, resource allocation, and network performance overall. This study proposes a novel methodology for CH selection, integrating Multiple Criteria Decision Making (MCDM) with the K-Means algorithm to facilitate a more discerning aggregation and forwarding of data to the network sink. Central to this approach is the application of the Einstein Weighted Averaging Aggregation (EWA) operator, which introduces a layer of sophistication in handling the uncertainties inherent in WSN deployments. The efficiency of CH selection is vital, as CHs serve as pivotal nodes within the network, their selection and operational efficiency directly influencing the network's energy consumption and data processing capabilities. By employing a meticulously designed clustering process via the K-Means algorithm and selecting CHs based on a comprehensive set of parameters, including, but not limited to, residual energy and node proximity, this methodology seeks to substantially enhance the energy efficiency of WSNs. Comparative analysis with the Low-Energy Adaptive Cluster Hierarchy (LEACH)-Fuzzy Clustering (FC) algorithm underscores the efficacy of the proposed approach, demonstrating a 15% improvement in network lifespan. This advancement not only ensures optimal utilization of limited resources but also promotes the sustainability of WSN deployments, a critical consideration for the widespread application of these networks in various fields. The findings of this study underscore the significance of adopting sophisticated, algorithmically driven strategies for CH selection, highlighting the potential for significant enhancements in WSN longevity through methodical, data-informed decision-making processes.

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Journal of Industrial Intelligence, 2024, Volume 2, Issue 1, Pages undefined: Intelligent Risk Analysis of Investment Projects in the Extractive Industry

abdullah m. al-ansi — 03-30-2024

Journal of Industrial Intelligence, 2024, Volume 2, Issue 1, Pages undefined: An Advanced YOLOv5s Approach for Vehicle Detection Integrating Swin Transformer and SimAM in Dense Traffic Surveillance

yi zhang — 03-30-2024

In the realm of high-definition surveillance for dense traffic environments, the accurate detection and classification of vehicles remain paramount challenges, often hindered by missed detections and inaccuracies in vehicle type identification. Addressing these issues, an enhanced version of the You Only Look Once version v5s (YOLOv5s) algorithm is presented, wherein the conventional network structure is optimally modified through the partial integration of the Swin Transformer V2. This innovative approach leverages the convolutional neural networks' (CNNs) proficiency in local feature extraction alongside the Swin Transformer V2's capability in global representation capture, thereby creating a symbiotic system for improved vehicle detection. Furthermore, the introduction of the Similarity-based Attention Module (SimAM) within the CNN framework plays a pivotal role, dynamically refocusing the feature map to accentuate local features critical for accurate detection. An empirical evaluation of this augmented YOLOv5s algorithm demonstrates a significant uplift in performance metrics, evidencing an average detection precision (mAP@0.5:0.95) of 65.7%. Specifically, in the domain of vehicle category identification, a notable increase in the true positive rate by 4.48% is observed, alongside a reduction in the false negative rate by 4.11%. The culmination of these enhancements through the integration of Swin Transformer and SimAM within the YOLOv5s framework marks a substantial advancement in the precision of vehicle type recognition and reduction of target miss detection in densely populated traffic flows. The methodology's success underscores the efficacy of this integrated approach in overcoming the prevalent limitations of existing vehicle detection algorithms under complex surveillance scenarios.

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Journal of Industrial Intelligence, 2024, Volume 2, Issue 1, Pages undefined: Enhanced Signal Processing Through FPGA-Based Digital Downconversion via the CORDIC Algorithm

ming yin — 03-28-2024

Journal of Industrial Intelligence, 2024, Volume 2, Issue 1, Pages undefined: Benefits and Challenges of Implementing Autonomous Technology for Sustainable Material Handling in Industrial Processes

svetlana dabic-miletic — 03-18-2024

Journal of Industrial Intelligence, 2023, Volume 1, Issue 4, Pages undefined: Aerodynamic Performance of High-Speed Maglev Trains Under Crosswind Conditions: A Computational Simulation Study

yifan yan — 12-30-2023

Journal of Industrial Intelligence, 2023, Volume 1, Issue 4, Pages undefined: Modeling and Coefficient Identification of Cortical Bone Milling Forces of Ball-End Milling Cutter for Orthopaedic Robot

heqiang tian — 12-30-2023

Journal of Industrial Intelligence, 2023, Volume 1, Issue 4, Pages undefined: Navigating Complexity: A Multidimensional Neutrosophic Fuzzy Hypersoft Approach to Empowering Decision-Makers

muhammad saeed — 12-30-2023

Journal of Industrial Intelligence, 2023, Volume 1, Issue 4, Pages undefined: Analyzing Traceability Models in E-Commerce Logistics: A Multi-Channel Approach

fan jiang — 12-30-2023

Journal of Industrial Intelligence, 2023, Volume 1, Issue 4, Pages undefined: Numerical Analysis of Two-Phase Flow in Serrated Minichannels Using COMSOL Multiphysics

razieh abbasgholi rezaei — 12-17-2023

Journal of Industrial Intelligence, 2023, Volume 1, Issue 3, Pages undefined: Text Readability Evaluation in Higher Education Using CNNs

muhammad zulqarnain — 09-29-2023

The paramountcy of English in the contemporary global landscape necessitates the enhancement of English language proficiency, especially in academic settings. This study addresses the disparate levels of English proficiency among college students by proposing a novel approach to evaluate English text readability, tailored for the higher education context. Employing a deep learning (DL) framework, the research focuses on developing a model based on convolutional neural networks (CNNs) to assess the readability of English texts. This model diverges from traditional methods by evaluating the difficulty of individual sentences and extending its capability to ascertain the readability of entire texts through adaptive weight learning. The methodology's effectiveness is underscored by an impressive 72% accuracy rate in readability assessment, demonstrating its potential as a transformative tool in English language education. The application of this DL-based text readability evaluation model in college English training is explored, highlighting its potential to facilitate a more nuanced understanding of text complexity. Furthermore, the study contributes to the broader discourse on enhancing English language instruction in higher education, proposing a method that not only evaluates text comprehensibility but also aligns with diverse educational needs. The findings suggest that this approach could significantly support the enhancement of English teaching methodologies, thereby promoting a deeper, more accessible learning experience for students with varying levels of proficiency.

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Journal of Industrial Intelligence, 2023, Volume 1, Issue 3, Pages undefined: Material Impact on Performance of Suction Cups: A Finite Element Analysis

obusitswe makgotla seretse — 09-29-2023

Journal of Industrial Intelligence, 2023, Volume 1, Issue 3, Pages undefined: Numerical Analysis of Viscosity and Surface Tension on Microdroplet Dynamics in Microelectromechanical Systems Applications

somaiyeh alizadeh — 09-29-2023

Journal of Industrial Intelligence, 2023, Volume 1, Issue 3, Pages undefined: Advanced Technologies in Smart Factories: A Cornerstone of Industry 4.0

svetlana dabic-miletic — 09-29-2023

Journal of Industrial Intelligence, 2023, Volume 1, Issue 3, Pages undefined: Iris Detection for Attendance Monitoring in Educational Institutes Amidst a Pandemic: A Machine Learning Approach

hafiz burhan ul haq — 09-24-2023

Amid the COVID-19 pandemic, the imperative for alternative biometric attendance systems has arisen. Traditionally, fingerprint and facial recognition have been employed; however, these methods posed challenges in adherence to Standard Operational Procedures (SOPs) set during the pandemic. In response to these limitations, iris detection has been advanced as a superior alternative. This research introduces a novel machine learning approach to iris detection, tailored specifically for educational environments. Addressing the restrictions posed by COVID-19 SOPs, which permitted only 50% of student occupancy, an automated e-attendance mechanism has been proposed. The methodology comprises four distinct phases: initial registration of the student's iris, subsequent identity verification upon institutional entry, evaluation of individual attendance during examinations to assess exam eligibility, and the maintenance of a defaulter list. To validate the efficiency and accuracy of the proposed system, a series of experiments were conducted. Results indicate that the proposed system exhibits remarkable accuracy in comparison to conventional methods. Furthermore, a desktop application was developed to facilitate real-time iris detection.

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Journal of Industrial Intelligence, 2023, Volume 1, Issue 2, Pages undefined: Algorithmic Approach for the Confluence of Lean Methodology and Industry 4.0 Technologies: Challenges, Benefits, and Practical Applications

dragana stojanović — 06-29-2023

This study focuses on formulating an integration algorithm for manufacturing firms aiming to infuse the immense potential of Industry 4.0 technologies into lean manufacturing systems. The goal is to unlock and harness the advantages offered by these advanced technologies in an economically efficient manner. An analytic approach has been implemented in this investigation, examining a broad array of relevant empirical research. This comprehensive analysis serves to derive a universal algorithm predicated on the principles of both lean methodology and Industry 4.0. The complexities and challenges of amalgamating lean methodology and Industry 4.0 have been scrutinized meticulously in this study. The study elaborates on the extent to which Industry 4.0 technologies can augment lean production practices, delves into the difficulties encountered by corporations during the integration process, and suggests measures to surmount these obstacles. Moreover, potential benefits realized through this integration are explored. The algorithm proffered in this study permits a phased integration approach. Firms have the flexibility to adopt the integration in specific production segments or processes initially and progressively expand, aligning with their capabilities, resources, and the level of process maturity. Such an integration strategy allows companies to leverage Industry 4.0 in overcoming restrictions traditionally associated with the lean approach.

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Journal of Industrial Intelligence, 2023, Volume 1, Issue 2, Pages undefined: Interval-Valued Picture Fuzzy Uncertain Linguistic Dombi Operators and Their Application in Industrial Fund Selection

chiranjibe jana — 06-29-2023

This study presents an advanced generalization of uncertain linguistic numbers (ULNs) and interval-valued intuitionistic uncertain linguistic numbers (IVIULNs) through the development of interval-valued picture fuzzy numbers (IVPFNs). Firstly, the IVPFUL weighted average and IVPFUL weighted geometric operators, denoted as IVPFULWA and IVPFULWG, have been introduced. Furthermore, the IVPFUL Dombi weighted average and geometric operators, represented by IVPFULDWA and IVPFULDWG, are also proposed in the same context. These operators are utilized to establish a multi-attribute decision-making (MADM) approach with IVPFUL data. Finally, the proposed methodology is applied to a mutual fund selection problem through a demonstrative example.

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Journal of Industrial Intelligence, 2023, Volume 1, Issue 2, Pages undefined: Evaluating Free Zone Industrial Plant Proposals Using a Combined Full Consistency Method-Grey-CoCoSo Model

ibrahim badi — 06-25-2023

Libya's strategic position at the crossroads of Europe and Africa offers access to abundant raw materials, labor, and extensive land for establishing free trade zones. The primary objective of this research is to determine the key advantages and opportunities that Libya could potentially leverage as a transit trade hub in the Mediterranean region through the establishment of free trade zones. This study investigates the extent to which Libya facilitates the expansion of commerce between Europe and Africa via initiatives related to free trade zones. Six criteria were employed in the present research, including economic, social, financial, environmental, quality, and demand factors. A panel of experts evaluated these criteria. The Full Consistency Method (FUCOM) was utilized to derive the criteria weights, with the economic criterion identified as the most significant. The Grey-CoCoSo (Combined Compromise Solution) methodology was applied to rank the industries eligible for investment within Libya's free zones. According to the findings, the food sector holds the highest importance in relation to investment.

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Journal of Industrial Intelligence, 2023, Volume 1, Issue 2, Pages undefined: Strategies for Enhancing Industry 4.0 Adoption in East Africa: An Integrated Spherical Fuzzy SWARA-WASPAS Approach

yanjun qiu — 06-25-2023

Developed countries have successfully implemented various Industry 4.0 (I4.0) initiatives, showcasing their ability to reap the benefits of this new industrial revolution. Active pursuit of excellence in Industry 4.0 is evident in these nations. However, in Africa, many countries still lack a clear understanding of Industry 4.0, with some remaining trapped in Industry 1.0 and others facing challenges in transitioning to Industry 2.0. Moreover, a significant number of these African countries continue to grapple with limited access to reliable electricity. To address the issue, this study examines seven strategies identified as criteria for enhancing the adoption of Industry 4.0 within the East African Community (EAC). These strategies are derived from observations of Industry 4.0 initiatives implemented in developed countries. Subsequently, the criteria are used to evaluate and rank the level of Industry 4.0 adoption in two specific East African countries. To tackle the challenges of complex group decision-making, the study integrates the Weighted Aggregated Sum Product Assessment (WASPAS) technique with the Step-Wise Weight Assessment Ratio Analysis (SWARA) within a spherical fuzzy (SF) framework. The SF-SWARA approach is applied to determine the weight and importance of the criteria, while SF-WASPAS is employed to rank the countries based on the criteria weighted by SF-SWARA. According to the findings, it was revealed that education and training, research, development, and innovation, as well as public-private partnerships and policy innovation, are the three most influential strategies for significantly improving the adoption of Industry 4.0 within the East African community. Furthermore, the results indicate that Rwanda stands out as the leading country in terms of implementing these strategies to enhance the adoption of Industry 4.0 technology. To verify the reliability and suitability of the proposed methodology, a sensitivity analysis was conducted, which affirmed the stability and practicality of the suggested approach.

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Journal of Industrial Intelligence, 2023, Volume 1, Issue 2, Pages undefined: A Numerically Validated Approach to Modeling Water Hammer Phenomena Using Partial Differential Equations and Switched Differential-Algebraic Equations

rukhsana kausar — 06-25-2023

Water distribution networks are susceptible to abrupt pressure fluctuations and spikes due to rapid adjustments in valve and pump settings. A common occurrence resulting from the sudden closure of a valve, known as water hammer, can potentially cause damage to various components within the network if not adequately addressed. Traditionally, water hammer phenomena have been modeled using a set of hyperbolic partial differential equations (PDEs). This study introduces a simplified model that employs switched differential-algebraic equations (DAEs). Recognized for their capacity to generate infinite peaks in response to sudden structural changes, switched DAEs provide mathematical representations of infinite peaks, manifested as Dirac impulses. This modeling approach offers the potential for more straightforward analyses of complex water networks in future research. To validate the proposed technique, a numerical comparison was conducted between the PDE- and DAE-based models, using a basic configuration consisting of two reservoirs, a pipe, and a valve.

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Journal of Industrial Intelligence, 2023, Volume 1, Issue 1, Pages undefined: Optimization of Magnetically Coupled Resonant Wireless Power Transfer Based on Improved Whale Optimization Algorithm

yi du — 03-30-2023

This study aimed to address the optimization of magnetically coupled resonant wireless power transfer. An equivalent circuit for the wireless power transfer was established and the factors affecting the transmission efficiency were analyzed. To optimize the system, an improved whale optimization algorithm (WOA) was proposed and applied to optimize the optimal matching values of resonant frequency and load resistance. Performance of the improved WOA was tested using different test functions, and the optimized parameters were applied to the transmission efficiency test of the wireless power transfer system. Experiments demonstrated that the improved WOA effectively optimized the transmission efficiency and achieved good application results in the intelligent transfer system.

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Journal of Industrial Intelligence, 2023, Volume 1, Issue 1, Pages undefined: Autonomous Vehicles as an Essential Component of Industry 4.0 for Meeting Last-Mile Logistics Requirements

svetlana dabic-miletic — 03-30-2023

Journal of Industrial Intelligence, 2023, Volume 1, Issue 1, Pages undefined: Enhancing Multi-Attribute Decision Making with Pythagorean Fuzzy Hamacher Aggregation Operators

tapas kumar paul — 03-30-2023

The attention of many researchers has been drawn to Pythagorean fuzzy information, which involves Pythagorean fuzzy numbers and their aggregation operators. In this study, the concept of the Pythagorean fuzzy set is discussed, along with the Hamacher t-norm and t-conorm operators. Furthermore, novel aggregation operators are developed using the operational rules of the Hamacher t-norm and t-conorm. The primary objective of this article is to develop a multi-attribute decision-making method in a Pythagorean fuzzy environment using Pythagorean fuzzy Hamacher aggregation operators. It is noted that the Hamacher operator, which is a generalization of the algebraic Einstein operator and contains a parameter, is more potent than some existing operators. Finally, an example of an enterprise application software selection problem is presented to demonstrate the proposed method.

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Journal of Industrial Intelligence, 2023, Volume 1, Issue 1, Pages undefined: Enhancing Green Supply Chain Efficiency Through Linear Diophantine Fuzzy Soft-Max Aggregation Operators

muhammad riaz — 03-30-2023

Improving the effectiveness of green supply chains is a critical step towards minimizing waste, optimizing resource use, and reducing the environmental impact of business operations. Sustainable practices should be implemented throughout the entire supply chain, from product design and procurement to production and transportation, in order to achieve these goals. By doing so, businesses can not only improve their environmental performance but also reduce costs, increase customer satisfaction, and gain a competitive advantage in the market. However, due to the existence of competing characteristics, imprecise information, and a lack of knowledge, selecting the appropriate green provider is a complex and unpredictable decision-making issue. The primary objective of a linear-diophantine fuzzy (LiDF) framework is to assist decision makers in selecting the optimal course of action. This paper introduces several novel aggregation operators (AOs), namely the linear Diophantine fuzzy soft-max average (LiDFSMA) and the linear Diophantine fuzzy soft-max geometric (LiDFSMG) operators. The proposed method is then demonstrated through a simple example of a green supplier optimization technique containing linear Diophantine fuzzy content, showing the utility and applicability of the approach. Overall, the proposed LiDF framework and AOs can aid decision makers in selecting the most suitable green provider, thereby enhancing the efficiency of green supply chains.

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Journal of Industrial Intelligence, 2023, Volume 1, Issue 1, Pages undefined: Intuitionistic Fuzzy Multi-Index Multi-Criteria Decision-Making for Smart Phone Selection Using Similarity Measures in a Fuzzy Environment

jogjiban chakraborty — 03-30-2023