Javascript is required
Search

Acadlore takes over the publication of JORIT from 2025 Vol. 4, No. 3. The preceding volumes were published under a CC-BY 4.0 license by the previous owner, and displayed here as agreed between Acadlore and the owner.

This issue/volume is not published by Acadlore.
Volume 4, Issue 1, 2025

Abstract

Full Text|PDF|XML

This paper presents two synthetic estimations of the Gini coefficient at a municipality level for Colombia in the years 2000-2020. The methodology relies on several machine learning models to select the best model for imputation of the data. This derives in two Random Forest models where the first is characterized by containing Dominant Fixed Effects, while the second contains a set of Dominant Varying Factors. Upon these estimations, the Synthetic Gini Coefficients for both models are inspected, and public links are generated to access them. The Dominant Fixed Effects models is rather “stiff” in contrast to the Varying Factor model. Hence, for researchers it is recommended to use the Synthetic Gini Coefficient with Varying Factors because it contains greater variability across time than the Dominant Fixed Effects models.

Abstract

Full Text|PDF|XML

The domination of cyberspace technologies in inter-human communications is obvious because of their ‎ultra-rapidness and enormous data capacity. Human-intensive ‎use of cyberspace increased the magnitude of streamed data through its nodes, created by two sources: human users and AI. While humans can control their generated data, ‎it proves impossible to control AI due to its super intelligence along with their self-developing ‎abilities, enabling it to produce unlimited volumes of data. It is known that cyberspace depends on physical infrastructure, which is inherently limited. Despite investments to expand capacity, overloading this infrastructure with unlimited data creates critical functionality issues. Additionally, the presence of uncontrollable AI elements leads to unpredictable outcomes. Ultimately, this results in AI dominating cyberspace, a phenomenon known as cyber singularity.

The ultimate consequences of AI cyber singularity motivated the study to recall a similar phenomenon in astrophysics: gravitational singularity. Using general relativity theory, the ‎research analyses the dilemma of data overload in cyberspace and its effects, drawing parallels ‎between outer space and cyberspace‎. It aims to illustrate AI's acquisition of cyber singularity according to astrophysics laws on gravitational singularity, providing an innovative perspective for scientists and scholars studying cyberspace.

Open Access
Research article
Knowledge Management in Virtual Organisations Using Mobile Agents
laura nicola gavrilă ,
claudiu ionuț popîrlan
|
Available online: 03-29-2025

Abstract

Full Text|PDF|XML

This paper presents a conceptual framework for enhancing knowledge management (KM) processes in virtual organizations through the integration of mobile agents. With the growing digitization of workplaces and the proliferation of distributed teams, managing and leveraging knowledge efficiently has become critical. Mobile agents offer promising features such as autonomy, adaptability, and mobility, making them suitable for dynamic knowledge environments. The paper outlines the architecture of a multi-agent system for KM and discusses its potential impact on organizational performance. Emphasis is placed on the role of intelligent agents in collecting, filtering, and disseminating relevant knowledge across virtual settings. The proposed model aims to support decision-making, reduce information overload, and facilitate knowledge sharing among members of decentralized organizations.

Abstract

Full Text|PDF|XML

Speaker identification among identical twins remains a significant challenge in voice-based biometric systems, particularly under emotional variability. Emotions dynamically alter speech characteristics, reducing the effectiveness of conventional identification algorithms. To address this, we propose a hybrid deep learning architecture that integrates gender and emotion classification with speaker Identification, tailored specifically to the complexity of identical twin voices. The system combines Emphasized Channel Attention Propagation and Aggregation in Time Delay Neural Network (ECAPA-TDNN) embeddings for speaker-specific representations, Power Normalized Cepstral Coefficients (PNCC) for noise-robust spectral features, and Maximal Overlap Discrete Wavelet Transform (MODWT) for effective time-frequency denoising. A Radial Basis Function Neural Network (RBFNN) is employed to refine and fuse feature vectors, enhancing the discrimination of emotion-related cues. An attention mechanism further emphasizes emotionally salient patterns, followed by a Multi-Layer Perceptron (MLP) for final classification. The model is evaluated on speech datasets from RAVDESS, Google Research, and a proprietary corpus of identical twin voices. Results demonstrate significant improvements in speaker and emotion recognition accuracy, especially under low signal-to-noise ratio (SNR) conditions, outperforming traditional Mel Cepstral-based methods. The proposed system’s integration of robust audio fingerprinting, feature refinement, and attention-guided.

Open Access
Research article
The Use of Adaptive Artificial Intelligence (AI) Learning Models in Decision Support Systems for Smart Regions
pavlo fedorka ,
roman buchuk ,
mykhailo klymenko ,
fedir saibert ,
andrii petrushyn
|
Available online: 03-29-2025

Abstract

Full Text|PDF|XML

The purpose of this study is to analyse the effectiveness of implementing adaptive AI learning models in decision support systems to optimise the functioning of smart regions. The study provides a detailed examination of the application of machine learning algorithms, deep learning, and reinforcement learning across various sectors, such as urban management, energy resources, and security. The results revealed that the implementation of these models enhances the efficiency of urban system management, reduces costs, and increases the flexibility of decision-making processes. In particular, adaptive models in energy resource management optimise decision-making processes, leading to more rational resource use and substantial cost reductions. In the security field, adaptive AI models show improvements in predicting and preventing incidents, ensuring more reliable and stable system performance. Moreover, the results include the implementation of adaptive models based on programming languages such as TypeScript and JavaScript. The study demonstrated that the use of TypeScript reduces errors and improves system scalability due to strict typing, as shown in the implementation of a reinforcement learning model. Meanwhile, the use of JavaScript enabled the effective adaptation of models to new data through dynamic updates of regression coefficients, leading to improved prediction accuracy.

- no more data -