Journal of Computational and Cognitive Engineering
The Journal of Computational and Cognitive Engineering (JCCE) is an international, peer-reviewed, interdisciplinary journal that publishes cutting-edge articles on the recent trends in computational and cognitive engineering. The goal of the journal is to provide a new platform for the dissemination of the research and current practices in the emerging disciplines of the cognitive and computational engineering to solve the real-world problems. The journal welcomes submission related to various computational and cognitive modelling approaches in the areas of health, education, finance, environment, engineering, commerce and industry. The journal is a Gold Open Access journal, online readers don't have to pay any fees.
A Context-Aware Intelligent Scheduling Framework for Modern Operating Systems Using Hybrid Machine Learning Models
In modern computing environments, the operating systems are required to handle dynamic and complex applications with heterogeneous workloads. These workloads include interactive, data-centric, and high-computing applications. Interactive tasks require low latency and high response, while data-centric transactions demand high resource utilization and input/output. Likewise, high-computing processes including machine learning tasks require sustained throughput. Traditional CPU scheduling, like round-robin and priority scheduling, highly relies on static and rule-based systems. Generally, these give equal weightage to all processes. They are not paying attention to the contextual variations and working patterns that impact the responsiveness, efficiency, and overall performance. There is an evident need for a context-aware and adaptive CPU scheduling mechanism that will handle applications and processes based on context. To address this gap, this paper introduces an application-aware intelligent scheduling mechanism that integrates a random forest (RF) classifier and deep Q-network (DQN) reinforcement learning. The RF classifies the processes based on their working and behavioral patterns, and then DQN has system state and queue dynamics to make adaptive scheduling decisions. It can be continuously learned and adjusted based on the user's needs; it is responsive and achieves high throughput. Experimental evaluations are conducted on generative datasets and show remarkable performance improvements. The proposed framework achieves up to 42.2% responsive time reduction on interactive application workloads, 29.2% throughput gain against the machine learning training workloads, and 22.8% for database transactions. The architecture is computationally sparse and uses the existing Linux scheduling system. This renders it a convenient and scalable method of CPU scheduling in contemporary multicore systems. Received: 2 July 2025 | Revised: 17 December 2025 | Accepted: 6 January 2026 Conflicts of Interest The authors declare that they have no conflicts of interest to this work. Data Availability Statement Data are available from the corresponding author upon reasonable request. Author Contribution Statement Pandikumar Savarimalai: Conceptualization, Validation, Project administration. Menaka Chellappan: Conceptualization, Formal analysis, Writing – review & editing, Supervision. Murugaiyan Swaminathan Nidhya: Methodology, Investigation, Resources. Margaret Mary Thomas: Validation, Data curation. Sevugapandi Nallathambi: Software, Writing – review & editing. Manjula Selvaraj: Investigation, Writing – original draft, Visualization.
Copyright (c) 2026 Authors https://creativecommons.org/licenses/by/4.0/
Advanced Residual Network Architecture for Automated Brain Tumor Diagnosis: The MesResNet50 Approach
Identifying tumors in the brain is very crucial for early diagnosis that supports in developing successful therapeutic treatments. Traditional methods were based on manual feature extraction, and classic machine learning techniques lack the ability to explain the intricate variations found in the morphology and positioning of brain tumors. In this paper, we propose MesResNet50, a modified residual network architecture for classifying different brain tumor types from magnetic resonance imaging (MRI) images. MesResNet50 utilizes transfer learning like partial layer freezing and possesses a strong classification head in an effort to improve generalization and performance. The dataset consists of 7023 MRI scans. These scans depicted four types of tumors: no tumor, glioma, meningioma, and pituitary tumor. MesResNet50 performed better than other models like CNN, AlexNet, VGG16, and ResNet50 on major evaluation metrics. With a test accuracy of 96.80%, an F1-score of 96.69%, and a ROC-AUC of 0.9949, MesResNet50 clearly demonstrates the capabilities to discriminate between classes and has a well-balanced performance on sensitivity and specificity metrics. Such effectiveness is due to residual architecture enabling greater depth and, hence, improved feature extraction and generalization. The improved residual architecture has much greater computational cost, which can be a burden in low-resource environments. The findings demonstrate the potential of utilizing MesResNet50 as a reliable and good approach for automated diagnosis of brain tumors for clinical decision support. Future work will focus on improving efficiency for rapid inference, evaluating simpler architectures to reduce resource usage, and testing the model on multicenter datasets to enhance clinical utility. Received: 31 August 2025 | Revised: 4 February 2026 | Accepted: 3 April 2026 Conflicts of Interest The authors declare that they have no conflicts of interest to this work. Data Availability Statement The Brain Tumor MRI datasets that support the findings of this study are openly available in Kaggle at https://www.kaggle.com/datasets/masoudnickparvar/brain-tumor-mri-dataset. The data that support the findings of this study are openly available in GitHub at https://github.com/vasanthbhagawat-source/Ensembled.git. Author Contribution Statement Vasanth C. Bhagawat: Conceptualization, Methodology, Software, Formal analysis, Investigation, Data curation, Writing – original draft, Writing – review & editing, Visualization. Pravinth Raja Suthkar: Validation, Resources, Supervision, Project administration.
Multi-Source Data Fusion and Machine Learning for Soybean Crop Price Forecasting in India
Soybean is a major oilseed crop in India, and its market prices have exhibited significant volatility in recent years. Such price fluctuations create serious challenges for small and medium-scale farmers. Accurate price forecasting is essential to support informed decision-making by farmers and agri-business stakeholders. This study focuses on forecasting soybean prices in the Indian market using data spanning January 2015 to June 2025. The dataset integrates multiple heterogeneous sources, including daily market data from Agmarknet, weather information from the India Meteorological Department, regional production, and trade statistics. Extensive exploratory data analysis is conducted to examine price distributions, temporal trends, regional variations, and inter-variable relationships. Several predictive approaches are evaluated, including traditional time-series models (Autoregressive Integrated Moving Average), ensemble machine learning models, and deep learning models. In addition, a hybrid Long Short-Term Memory–Gated Recurrent Unit framework (AgroNET) is proposed to effectively model complex temporal dependencies across heterogeneous data sources. Model performance is assessed using k-fold cross-validation and evaluated through root mean squared error, mean squared error, MAPE, and R² metrics in a Python-based implementation. The results demonstrate that deep learning-based models outperform conventional approaches, with AgroNET achieving the highest R² and lowest error values. To enhance model transparency, explainable artificial intelligence using the local interpretable model-agnostic explanation technique is incorporated to identify key factors influencing individual price predictions. Overall, the proposed framework offers an effective and interpretable solution for soybean price forecasting in India and supports future research on multi-source integration and real-time agricultural price prediction. Received: 9 September 2025 | Revised: 12 January 2026 | Accepted: 11 March 2026 Conflicts of Interest The authors declare that they have no conflicts of interest to this work. Data Availability Statement Data are available from the corresponding author upon reasonable request. Author Contribution Statement Vilas Damodhar Ghonge: Conceptualization, Methodology, Software, Validation, Formal analysis, Investigation, Resources, Data curation, Writing – original draft, Writing – review & editing, Visualization. Yogesh Kulkarni: Conceptualization, Methodology, Investigation, Resources, Supervision.
Real-Time Drowsiness Detection Using YOLOv11 in Driver Monitoring Systems
One of the main causes of traffic accidents that frequently result in fatalities, serious injuries, and large property losses is driver fatigue. Therefore, enhancing driver safety and preventing such incidents depend heavily on the early, nonintrusive detection of fatigue. A real-time driver monitoring system based on the most recent YOLOv11 framework is presented in this paper. By examining visual indicators like prolonged eye closure and abnormal head movements, the system can identify early indicators of drowsiness. We acquired a purpose-designed dataset that covered a range of driving behaviors and environmental changes in order to improve the framework's flexibility and dependability. The effectiveness of the proposed model was extensively compared with several existing detection methods, and the results indicated that YOLOv11 achieved better accuracy in terms of various evaluation metrics. This improvement is due to its better feature extraction pipeline, attention modules, and computationally efficient architecture, which make it very appealing for real-time applications even inside the vehicle. Overall, the system provides a practical and reliable early warning system for drivers and could greatly reduce the risks associated with fatigue to improve safety in transportation. However, extreme lighting variations and face occlusions may impact the model's performance. To improve robustness, future research will concentrate on expanding the dataset andincorporating multimodal inputs. Received: 9 September 2025 | Revised: 4 December 2025 | Accepted: 6 February 2026 Conflicts of Interest The authors declare that they have no conflicts of interest to this work. Data Availability Statement The data that support the findings of this study are openly available in Roboflow website at https://universe.roboflow.com/santhosh-kumar-k-l/drowsiness-detection-koqqc. Author Contribution Statement Santhosh Kumar Kadur Lokeshappa: Conceptualization, Methodology, Software, Validation, Formal analysis, Investigation, Data curation, Writing – original draft. Pravinth Raja Suthakar: Resources, Writing – review & editing, Visualization, Supervision.
A Weighted Ensemble of EfficientNetV2 Variants with Optimizer Tuning for Tomato Leaf Disease Classification
Tomatoes are one of the most widely grown horticultural crops but are highly susceptible to leaf diseases that could significantly affect yield and quality. Early and accurate disease detection is necessary to enhance crop productivity and food security. The present study presents a deep learning system for automatic tomato leaf disease classification using a weighted ensemble of EfficientNetV2 models. Seven variants of EfficientNetV2 (B0-B3, S, M, and L) were fine-tuned on an augmented version of the filtered PlantVillage dataset. To improve feature visibility under varying lighting conditions, images were preprocessed using contrast-limited adaptive histogram equalization. Five optimization algorithms, which include Adam, Adamax, AdamW, Nadam, and RMSProp, were tested to assess their impact on model convergence and generalization. Among individual models, Adam-optimized EfficientNetV2L performed the best in accuracy with a measure of 99.50%. For classifiability resilience improvement, 16 ensemble configurations (eight unweighted and eight weighted ensembles using different combinations of EfficientNetV2-S, M, and L variants and five optimizers) were explored, with the best weights discovered using brute-force grid search. The best-performing weighted ensemble achieved 99.89% in accuracy, precision, recall, and F1-score, which was evaluated on a held-out test set not used during training or validation, demonstrating its strong generalizability. The proposed framework offers a scalable and reliable solution for early tomato disease detection with significant potential for real-time plant health monitoring in precision agriculture. Received: 10 September 2025 | Revised: 22 December 2025 | Accepted: 6 February 2026 Conflicts of Interest The authors declare that they have no conflicts of interest to this work. Data Availability Statement Data are available from the corresponding author upon reasonable request. Author Contribution Statement Aliyu Tetengi Ibrahim: Conceptualization, Methodology, Formal analysis, Writing – original draft. Ibrahim Hayatu Hassan: Conceptualization, Methodology, Formal analysis, Resources, Data curation, Writing – original draft. Mohammed Abdullahi: Validation, Formal analysis, Resources, Data curation, Writing – review & editing, Visualization. Abeer Rashad Mirdad: Conceptualization, Software, Validation, Investigation, Writing – original draft, Writing – review & editing, Supervision, Project administration. Muhammad I. Khan: Methodology, Validation, Investigation, Resources, Data curation, Writing – review & editing, Visualization, Project administration. Saeed Ali Bahaj: Conceptualization, Software, Writing – original draft. Fatima Khan Nayer: Investigation, Visualization, Supervision, Project administration.
Deep Learning for Medicinal Plant Classification: A Comprehensive Review of Recent Advances and Challenges
Deep learning classifies medicinal plants, driven by the need to preserve traditional knowledge and automate identification for practical uses. This review extensively summarizes 30 recent studies (2021–June 2025) on applying deep learning, primarily using image data, to classify medicinal plants. This review analyzes research distribution, dataset preparation, image preprocessing, augmentation, and deep learning architectures like convolutional neural networks, Vision Transformers, and hybrid models. Our analysis reveals a strong geographic focus, with 50% of the selected studies originating from India and Bangladesh. The focus is overwhelmingly on leaf imagery, with 29 out of the 30 studies relying on this approach. The field is also characterized by its dependence on existing data, as 56.6% of studies utilized public datasets and another 26.6% employed a hybrid of public and private data, with dataset sizes ranging from a minimum of 637 to a maximum of 13,500 images. Methodologically, the vast majority of studies rely on a transfer learning approach (36.7%), achieving robust accuracy rates between 74% and 99.9%. Furthermore, we recognize significant limitations, such as the absence of standardized and diverse datasets, insufficient inclusion of uncommon or endangered species, and inadequate representation of whole-plant imaging. The research underscores the necessity for collaborative, multidisciplinary initiatives to develop centralized, high-quality, and geographically comprehensive datasets. We delineate prospective avenues, including multimodal feature integration, the development of real-world applications, and optimization for privacy-preserving frameworks such as federated learning. This study guides academics advancing deep learning for medicinal plant classification and biodiversity conservation. Received: 13 September 2025 | Revised: 8 December 2025 | Accepted: 5 March 2026 Conflicts of Interest The authors declare that they have no conflicts of interest to this work. Data Availability Statement Data sharing is not applicable to this article as no new data were created or analyzed in this study. Author Contribution Statement Monir Hossain: Conceptualization, Methodology, Software, Validation, Formal analysis, Investigation, Data curation, Writing – original draft, Writing – review & editing, Visualization, Supervision. Fahmid Al Farid: Validation, Formal analysis, Investigation, Resources, Writing – review & editing, Visualization. Momotaz Begum: Conceptualization, Methodology, Validation, Formal analysis, Investigation, Resources, Data curation, Writing – review & editing, Visualization. Jia Uddin: Conceptualization, Validation, Formal analysis, Investigation, Resources, Writing – review & editing, Visualization, Supervision, Project administration. Hezerul Bin Abdul Karim: Validation, Formal analysis, Investigation, Resources, Writing – review & editing, Visualization, Funding acquisition.
Robust and Interpretable Deep Learning on EEG Spectrograms for Autism Spectrum Disorder Detection
Autism spectrum disorder (ASD) manifests early alterations in neural dynamics that often precede observable behavioral symptoms, yet current diagnostic practices rely predominantly on subjective clinical assessments. In this study, we present a robust, image-based convolutional framework for automated ASD detection from resting-state electroencephalography (EEG), validated across two independent cohorts: the King Abdulaziz University ASD dataset and the Autism Centre of Excellence (ACE) dataset. Raw EEG recordings were transformed into time–frequency spectrograms via continuous wavelet transforms and then fed into three deep architectures—a custom 4-layer convolutional neural network (CNN), ResNet50, and EfficientNet—to learn discriminative features of ASD versus neurotypical patterns. To enhance clinical trust and interpretability, we integrated Smooth Grad-CAM++ to generate high-resolution activation maps that pinpoint critical spectral–temporal regions driving each classification. On the King Abdulaziz dataset, our custom 4-layer CNN achieved a test accuracy of 98.59%, with an average F1-score of 0.99, precision of 0.97, recall of 0.98, and specificity of 95.36%. On the ACE dataset, ResNet50 yielded a test accuracy of 95.23%, F1-score of 0.96, precision of 0.97, recall of 0.94, and specificity of 97.43%. Across both cohorts, all models consistently exceeded 95% accuracy and demonstrated balanced sensitivity and specificity, underscoring their generalizability. These results establish a high-performance, explainable computer-aided diagnostic system for early ASD detection using EEG spectrograms. The conjunction of deep feature learning and explainable AI visualization not only accelerates diagnostic workflows but also offers actionable insights into the neural substrates of ASD, paving the way for timely, objective interventions. Received: 28 September 2025 | Revised: 16 March 2026 | Accepted: 27 April 2026 Conflicts of Interest The authors declare that they have no conflicts of interest to this work. Data Availability Statement The Dataset I (KAU dataset) that supports the findings of this study is openly available at https://www.earticle.net/Article/A207042, Reference number [23]. The Dataset II (ACE dataset) that supports the findings of this study is openly available in the NIH National Database for Autism Research (NDA) at https://nda.nih.gov/edit_collection.html?id=2021. Author Contribution Statement Andrew Jeyabose: Conceptualization, Methodology, Software, Validation, Formal analysis, Investigation, Writing – original draft, Writing – review & editing, Visualization, Supervision. Arav Chadda: Methodology, Software, Writing – original draft, Writing – review & editing, Visualization. Venkatesh Bhandage: Conceptualization, Software, Formal analysis, Resources, Data curation, Visualization, Supervision.
Multi-agent Reinforcement Learning with Clustering and Forecasting for Optimized Energy Sharing in Microgrids
The increasing prevalence of renewable energy and the evolving nature of energy consumption have motivated the need for more complex and dynamic microgrid energy management systems. Recent advances in artificial intelligence (AI) address these challenges by learning, predicting, and optimizing based on the large volumes of data generated by microgrid systems and related operations. Drawing on this context, the article proposes a novel framework for multi-agent reinforcement learning (MARL) with clustering and forecasting for optimized energy sharing in a microgrid environment with renewables and battery storage integration. The framework consists of three components: first, a structure-adapting unsupervised learning approach for creating clusters of prosumer energy consumption and generation patterns; second, a time-series forecasting ensemble for predicting future behaviors of the prosumers; and third, a continuous internal auction with a MARL for optimized energy sharing within the microgrid that collectively leads to reduced dependence on external energy sources. The proposed framework is empirically evaluated in the microgrid setting of a large multi-campus tertiary education institution. The results of this evaluation include stabilization of mean reward gain between independent agent and multi-agent models, impact of forecasting on MARL across seasonal variation, performance gains of 10–15% of MARL against heuristics, and scalability of the framework against cost, stability, reward, and convergence metrics. These results confirm the effectiveness of this AI framework for optimized prosumer energy sharing in microgrids with renewables and battery storage integration. Received: 9 October 2025 | Revised: 14 January 2026 | Accepted: 25 March 2026 Conflicts of Interest The authors declare that they have no conflicts of interest to this work. Data Availability Statement The data that support the findings of this study are openly available at https://github.com/CDAC-lab/UNICON. Author Contribution Statement Daswin De Silva: Conceptualization, Methodology, Formal analysis, Writing – original draft, Supervision, Funding acquisition. Thimal Kempitiya: Software, Validation, Formal analysis, Investigation, Data curation, Writing – original draft. Nuwan Madhusanka: Software, Validation, Resources, Data curation, Writing – original draft. Prabod Rathnayaka: Formal analysis, Investigation, Resources, Writing – original draft, Visualization. Nishan Mills: Methodology, Validation, Investigation, Writing – original draft, Supervision. Andrew Jennings: Conceptualization, Formal analysis, Resources, Writing – original draft, Supervision. Milos Manic: Conceptualization, Methodology, Validation, Writing – original draft, Supervision.
An IoT-Based Intelligent Monitoring System for Cargo Loss Detection in FPSO-Tanker Oil Transfer
This study presents the development of an Internet of Things-based monitoring system for detecting cargo loss during oil transfer operations between Floating Production Storage and Offloading units and receiving tankers. The proposed system integrates an ESP32S microcontroller, ultrasonic sensors, GPS modules, and a web-based dashboard to enable real-time monitoring and detect anomalies. The system is designed to reduce measurement errors and response delays that may lead to cargo discrepancies during transfer operations. Performance evaluation was conducted using prototype-based simulations to assess the system accuracy, responsiveness, and communication reliability. The results demonstrate high measurement accuracy (≥99.8%), fast response time (~180 ms), low latency (~210 ms), and reliable data transmission with a success rate of 98.7%. The system is also capable of detecting abnormal flow conditions or potential leakage within less than 1 min. Compared with conventional SCADA-based systems, the proposed approach offers improved real-time responsiveness, modularity, and adaptability to dynamic offshore environments. The developed system provides a scalable and flexible platform for maritime monitoring applications and establishes a foundation for the future integration of machine learning techniques to enable predictive and adaptive anomaly detection. Received: 8 November 2025 | Revised: 9 March 2026 | Accepted: 15 April 2026 Conflicts of Interest The authors declare that they have no conflicts of interest to this work. Data Availability Statement Data are available from the corresponding author upon reasonable request. Author Contribution Statement Habibi Palippui: Conceptualization, Methodology, Software, Validation, Formal analysis, Investigation, Data curation, Writing – original draft, Visualization, Funding acquisition. Daniel Mohammad Rosyid: Conceptualization, Resources, Writing – review & editing, Supervision, Project administration. Silvianita Silvianita: Methodology, Validation, Formal analysis, Writing – review & editing. Juswan Sade: Resources, Writing – review & editing.
NCOHA-WSC: Handling Noise and Class Overlap in Web Service Classification
With the rapid growth in the number of Web services, accurate and efficient Web service classification has become crucial for improving the quality-of-service discovery. However, existing classification approaches often overlook the issues of noise and class overlap inherent in Web service data, which leads to degraded classification precision. To address these challenges, this paper proposes NCOHA-WSC, an approach for Web service classification designed to handle both noise and class overlap and to be easily integrated with existing machine learning–based service classification models. Specifically, noisy samples in the training data are filtered using confidence learning and information entropy, thereby reducing the negative impact of noise on the classification model during preprocessing. In addition, during the testing phase, the prediction results for overlapping services are corrected based on the label prior distribution, further improving classification precision. Experiments conducted on the real-world ProgrammableWeb dataset demonstrate that NCOHA-WSC is compatible with mainstream Web service classification models and can enhance the Macro-F1 performance of models such as ServeNet and CARL-Net to varying degrees. These results indicate that the proposed approach effectively mitigates the impact of noisy data on Web service classification and improves the precision of existing models in the presence of overlapping service classes. Received: 16 December 2025 | Revised: 12 February 2026 | Accepted: 3 April 2026 Conflicts of Interest The authors declare that they have no conflicts of interest to this work. Data Availability Statement The data that support the findings of this study are openly available on GitHub at https://github.com/HIT-ICES/Correted-ProgrammableWeb-dataset.git. Author Contribution Statement Feng Zhang: Writing – original draft, Writing – review & editing, Visualization, Project administration, Formal analysis. Lin Xue: Writing – review & editing, Software, Validation, Project administration. Huiling Li: Writing – original draft, Writing – review & editing, Methodology, Investigation. Cong Liu: Writing – review & editing, Conceptualization, Resources, Data curation, Supervision, Funding acquisition.
Optimization of Electroencephalogram Feature Based on Multi-Band Feature Matrix and Relative Difference for Imagined Motor Movement Pattern Classification
Electroencephalogram (EEG)-based imagined motor movement recognition plays a crucial role in brain–computer interface applications and medical rehabilitation systems. However, EEG signals inherently exhibit high levels of noise, are nonlinear and nonstationary, contain spatial information, and show significant signal pattern variance across individuals, complicating feature extraction and classification. This study proposes a novel framework that integrates the relative difference method with a multi-band feature matrix, explicitly incorporating four EEG frequency bands (theta, alpha, beta, and gamma) mapped according to the international system 10–20. The extracted features are processed by a three-layer convolutional neural network (CNN) for classification. The proposed method is evaluated on the MIMED dataset for six-class imagined motor movement recognition and validated on the AMIGOS dataset for four-class emotion recognition. The experimental evaluation on the MIMED dataset demonstrates consistently strong classification performance, achieving 99.36% accuracy, 99.37% precision, 99.37% recall, and an F1-score of 99.36%. These results significantly outperform those without baseline reduction, the difference, and the fractional methods. Further validation on AMIGOS shows that the proposed model achieves an accuracy of 99.64%. This result surpasses the previously reported state of the art. These findings confirm that combining relative difference and multi-band feature matrices produces more stable, representative, and discriminative EEG features, making them readily recognizable by CNN methods. The proposed approach has strong potential for real-time brain–computer interfaces, clinical neurorehabilitation, and emotion-sensitive human–computer interaction systems Received: 10 August 2025 | Revised: 14 February 2026 | Accepted: 10 April 2026 Conflicts of Interest The authors declare that they have no conflicts of interest to this work. Data Availability Statement The MIMED dataset that supports the findings of this study is openly available in Mendeley Data at https://data.mendeley.com/datasets/zs25xxjkm9/3. The AMIGOS dataset that supports the findings of this study is openly available at https://doi.org/10.1109/TAFFC.2018.2884461, reference number [18]. Author Contribution Statement I Made Agus Wirawan: Conceptualization, Methodology, Software, Validation, Formal analysis, Investigation, Resources, Data curation, Writing – original draft, Writing – review & editing, Visualization, Supervision, Project administration, Funding acquisition. I Nyoman Sukajaya: Methodology, Software, Validation, Formal analysis, Investigation, Resources, Data curation, Writing – original draft, Writing – review & editing, Visualization, Project administration. Ni Nyoman Mestri Agustini: Methodology, Validation, Formal analysis, Investigation, Writing – original draft, Writing – review & editing, Visualization, Supervision, Project administration.
Automated Classification of Self-Admitted Technical Debt Using Advanced Word Embedding Techniques
This research uses advanced word embedding techniques to improve the automatic classification of Self-Admitted Technical Debt (SATD). We evaluate how successfully n-gram Inverse Document Frequency (IDF) creates machine learning classifier-friendly feature sets. A publicly available dataset including Java source code comments from 10 open-source projects was used to assess SATD classification methods. This category included Random Forest, SVM, Logistic Regression, and XGBoost. We used instance hardness undersampling to handle the imbalance in the SATD dataset. We tested the classifier using accuracy, recall, F1-score, and Macro-Averaged Mean Cost-Error (MMCE). The Random Forest classifier with n-gram IDF features achieved an average accuracy of 87%. It performed similarly to the traditional TF-IDF and Bag-of-Words methods on average, and on certain projects and MMCE values, it performed better. In rare circumstances, n-gram IDF may reveal contextual phrase patterns and improve SATD recognition, especially when combined with ensemble learning. To enhance generalisability, future research will expand the dataset, investigate hybrid and deep learning models, and increase applicability across various programming languages and project areas. Received: 21 April 2025 | Revised: 12 September 2025 | Accepted: 7 October 2025 Conflicts of Interest The authors declare that they have no conflicts of interest to this work. Data Availability Statement Data are available on request from the corresponding author upon reasonable request. Author Contribution Statement Satya Mohan Chowdary Gorripati: Methodology, Software, Formal analysis, Investigation, Resources, Data curation, Writing – original draft. Ali Altalbe: Writing – review & editing. Prasanna Kumar Rangarajan: Conceptualization, Validation, Visualization, Supervision, Project administration.
Copyright (c) 2025 Authors https://creativecommons.org/licenses/by/4.0/
Adaptive Fault-Tolerant Scheduling in Dynamic Clouds
The transition from product-centric to service-driven paradigms in cloud computing has enabled the on-demand provisioning of scalable resources. However, the extensive operational complexity and interdependent infrastructure of cloud environments make them highly susceptible to failures, posing significant challenges to ensuring robust fault tolerance in dynamic and distributed systems. This study introduces a novel Hybrid Value-Policy Double Deep Q-Learning (DDQL) Scheduling approach, integrating value-based DDQL with policy-based reinforcement learning techniques, which are Proximal Policy Optimization and Soft Actor-Critic, to enhance fault-tolerant scheduling dynamically. Combining the advantages of both learning paradigms, the proposed method improves decision-making efficiency, enhances system resilience against failures, and optimizes scheduling performance in real-time cloud environments. Improved mean time between failures, mean time to recovery, failure recovery time, and fault tolerance success rate are guaranteed by the Hybrid DDQL scheduler's dynamic adjustments to scheduling and resource allocation algorithms. Also, it reduces the ratio of task deadline miss, energy consumption, and response time and maximizes the throughput. These results confirm the flexibility and strength of the model in dynamic and unreliable real-time cloud computing systems. Received: 27 May 2025 | Revised: 30 September 2025 | Accepted: 16 October 2025 Conflicts of Interest The authors declare that they have no conflicts of interest to this work. Data Availability Statement Data are available from the corresponding author upon reasonable request. Author Contribution Statement Chetankumar Kalaskar: Conceptualization, Methodology, Software, Validation, Formal analysis, Investigation, Resources, Data curation, Writing – original draft, Writing – review & editing, Visualization, Supervision, Project administration. Thangam Somasundaram: Methodology, Validation, Formal analysis, Resources, Data curation, Writing – original draft, Writing – review & editing, Visualization, Supervision, Project administration, Funding acquisition.
AI-Driven Diagnosis of Autism Spectrum Disorder Using Retinal Fundus Imaging: A Comparison of Traditional and Deep Learning Feature Extraction Methods
Autism spectrum disorder (ASD) is a complicated neurodevelopmental disorder. There is no definitive or easily interpretable medical test that aids in the early diagnosis of ASD, which leads to delays in its detection. In this study, we compared deep learning and traditional feature extraction methods used to detect ASD using retinal fundus images. The authors implemented convolutional neural networks (CNNs) such as ResNet50, EfficientNet, and vision transformers (ViTs), apart from the hybrid CNN + ViT model, for automated feature extraction. In addition, classic methods such as the gray level co-occurrence matrix for texture analysis, Frangi filters for measuring vessel density, and cup-to-disc ratio estimation were used to extract clinically relevant retinal features. To evaluate the discriminative power of the features obtained by each technique, classification models such as support vector machines, random forest, and XGBoost were implemented. Among the models used, hybrid CNN + ViT obtained the highest accuracy, which suggests that combining spatial and contextual retinal information enhances the detection of ASD. This study examined various feature extraction approaches in detail and elucidated the advantages of deep-learning-based approaches to enhance ASD diagnosis using retinal images. The results contribute to ongoing research on AI-supported ASD detection and provide crucial insights into the selection of optimal feature representation methods for future clinical applications. Received: 29 April 2025 | Revised: 14 July 2025 | Accepted: 18 July 2025 Conflicts of Interest The authors declare that they have no conflicts of interest to this work. Data Availability Statement Data are available on request from the corresponding author upon reasonable request. Author Contribution Statement Ayain John: Conceptualization, Methodology, Software, Formal analysis, Investigation, Resources, Data curation, Writing – original draft, Writing – review & editing, Visualization, Project administration. Santhanalakshmi S.: Validation, Formal analysis, Resources, Writing – review & editing, Supervision, Project administration.
Research on Microvideo Character Perception and Recognition Based on Target Detection Technology
With the rapid development of the Internet short-video platform, microvideo is increasingly attracting users because of its small traffic and time period. However, the content of microvideo on the Internet is complex, with illegal information. This research is an improved algorithm based on target detection technology, aiming at the characteristics of difficult character recognition in microvideo. The algorithm combines Gabor wavelet transform algorithm and 2D principal component analysis (PCA) algorithm and makes full use of video perception technology to realize character recognition in microvideo. Through MATLAB simulation analysis, it can be seen that the accuracy of the optimization algorithm proposed in this paper is 86.34% and the recognition time is 34.28s. Compared with the traditional PCA algorithm and artificial neural network (ANN) algorithm, the optimization algorithm proposed in this paper has better recognition rate and recognition efficiency. Received: 28 October 2021 | Revised: 15 December 2021 | Accepted: 21 December 2021 Conflicts of Interest The author declares that he has no conflicts of interest to this work.
Copyright (c) 2022 Author https://creativecommons.org/licenses/by/4.0/
Bibliometric Analysis of Artificial Intelligence in Team Sports: Research Trends, Collaborations, and Creative Insights
This paper presents a bibliometric analysis of Scopus-indexed publications from 2000 to 2024 to examine the development of deep learning in team sports, particularly in real-time player tracking, performance analytics, and wearable-based monitoring of athletes. The publication trends, collaboration networks, and thematic structures were explored using Bibliometrix (R) and VOSviewer based on the co-occurrence of keywords, co-citation, and clustering. The findings indicate that after 2016, research production has increased significantly, with convolutional neural networks, temporal models, and new transformer architectures emerging as prominent approaches. The analysis identified three predominant research streams: computer vision–based detection and tracking, machine learning–driven performance analytics, and wearable-sensor applications. Object detectors and multi-object tracking frameworks used together to create a computer vision pipeline are now the backbone of real-time sports analytics, and predictive models are increasingly used to support tactical decision-making and workload evaluation. In spite of these developments, there are still issues concerning model interpretability, resiliency to dynamic environments, data privacy, and incorporation into the coaching workflows. It is recommended that future studies focus on multimodal systems combining visual, temporal, and physiological cues, as well as explainable artificial intelligence methods to enhance transparency and trust among practitioners. Further, this paper describes a system-based AI architecture that integrates vision-based analysis, time-based learning, and wearable sensing to support data-driven decision-making. Overall, this bibliometric mapping offers a systematic understanding of the new research areas and sets the stage toward building operational, reliable, and human-centric AI systems in team sports. Received: 28 November 2025 | Revised: 15 April 2026 | Accepted: 25 May 2026 Conflicts of Interest The authors declare that they have no conflicts of interest to this work. Data Availability Statement Data are available from the corresponding author upon reasonable request. Author Contribution Statement Doha Lefhal: Conceptualization, Methodology, Software, Formal analysis, Investigation, Data curation, Writing – original draft, Visualization, Project administration, Funding acquisition. Ali Ouacha: Validation, Supervision. Abdeslam El Harraj: Resources. Soumia Ziti: Supervision.
Explainable Fuzzy Modeling Through Antecedent Reduction and Antecedent Association in Type-1 ANFIS System
The increasing demand for interpretable artificial intelligence (AI) has underscored the importance of explainable fuzzy models, particularly those based on Adaptive Neuro-Fuzzy Inference Systems. However, standard grid-based initialization often leads to exponential rule proliferation, significantly compromising model transparency and computational efficiency. To address this challenge, this study proposes an interpretable Type-1 fuzzy framework that utilizes Fuzzy C-Means clustering for data-driven membership function generation, followed by a systematic two-stage reduction strategy. The methodology integrates antecedent merging based on Euclidean distance to consolidate overlapping clusters and an activation-based pruning approach to eliminate inactive logic. Validated on the Fisher Iris dataset and Banknote Authentication dataset, the framework successfully reduced the rule base from 81 to 44 and 81 to 42 rules, achieving a 45.68% and 48.15% reduction in complexity, respectively. Crucially, this optimization enhanced classification accuracy from 76.00% to 93.33% for the Iris dataset and 44.08% to 51.13% for the Banknote Authentication dataset, demonstrating that removing redundant rules actively reduces logical noise. These results confirm that the proposed paradigm effectively balances parsimony with performance, offering a robust solution for explainable AI applications, with future extensions envisioned toward Type-2 systems for handling higher-order uncertainty. Received: 30 November 2025 | Revised: 2 March 2026 | Accepted: 18 March 2026 Conflicts of Interest The authors declare that they have no conflicts of interest to this work. Data Availability Statement The data that support the findings of this study are openly available in the UCI Machine Learning Repository at https://archive.ics.uci.edu/dataset/53/iris and https://archive.ics.uci.edu/dataset/267/banknote+authentication. Author Contribution Statement Muhammad Hamza Azam: Conceptualization, Methodology, Software, Validation, Formal analysis, Investigation, Resources, Data curation, Writing – original draft, Writing – review & editing, Visualization. Mohd Hilmi Hasan: Conceptualization, Resources, Supervision, Project administration, Funding acquisition. Saima Hassan: Writing – review & editing, Supervision. Noureen Talpur: Validation, Formal analysis, Writing – review & editing. Muhammad Huzaifa Azam: Writing – review & editing, Visualization.
explainable artificial intelligence (XAI)
Adaptive Neuro-Fuzzy Inference System (ANFIS)
Adaptive Client-Specific Augmentation (ACSA) for Mitigating Data Heterogeneity in Federated Learning
Federated learning (FL) enables the training of deep learning models across multiple distributed clients while maintaining data privacy. Although this approach effectively protects sensitive information, it often yields lower accuracy compared to conventional centralized training. This performance gap is primarily due to the non-independent and non-identically distributed nature of client data—a central challenge in FL known as data heterogeneity. Because each client's dataset follows a distinct distribution, local models may diverge during training, which can degrade the performance of the aggregated global model. To address this issue, various data augmentation techniques have been proposed within the FL framework. However, many of these methods still require sharing of raw data or intermediate features among clients, potentially compromising privacy and conflicting with the fundamental principles of FL. To overcome this limitation, we introduce an additional module, Adaptive Client-Specific Augmentation, within the FedHetero, a refined framework that aims to identify the nature of heterogeneity present in the data and provides recommendations of augmentation policies instead of any data or feature-level information, which enhances privacy. Based on these shared policies, each client autonomously performs a dynamic data augmentation search to determine and apply the strategy most appropriate for its local dataset. This client-specific adaptation not only improves local and global model performance but also ensures strict privacy preservation. Experimental results on heterogeneous FL benchmarks demonstrate that our approach outperforms existing state-of-the-art methods in both communication efficiency and overall accuracy. Received: 13 November 2025 | Revised: 28 January 2026 | Accepted: 13 March 2026 Conflicts of Interest The authors declare that they have no conflicts of interest to this work. Data Availability Statement The CIFAR-10 and CIFAR-100 data that support the findings of this study are openly available at https://www.cs.toronto.edu/~kriz/cifar.html; The Riga data that support the findings of this study are openly available at https://deepblue.lib.umich.edu/data/concern/data_sets/3b591905z; The Dhristi data that support the findings of this study are openly available at https://www.kaggle.com/datasets/lokeshsaipureddi/drishtigs-retina-dataset-for-onh-segmentation; The Rim-one data that support the findings of this study are openly available at https://github.com/miag-ull/rim-one-dl; and The HRF data that support the findings of this study are openly available at https://www5.cs.fau.de/research/data/fundus-images. Author Contribution Statement Aiswariya Milan Kummaya: Conceptualization, Methodology, Software, Investigation, Resources, Data curation, Writing - original draft, Visualization. Amudha Joseph: Conceptualization, Methodology, Validation, Formal analysis, Writing – review & editing, Supervision, Project administration. George Ghinea: Supervision, Project administration.
HistoPath AI: An Edge-Powered Web Platform for Real-Time Lung Cancer Diagnosis
Lung cancer remains a major cause of mortality worldwide, underscoring the importance of accurate and timely diagnosis. Manual evaluation of histopathology slides is labor-intensive and error-prone, particularly in resource-constrained settings. This work introduces HistoPath AI, an edge-based web application for real-time lung cancer detection. The system employs a fine-tuned EfficientNetB7 model to classify histopathology images into benign, adenocarcinoma, and squamous cell carcinoma, achieving 99.8% validation accuracy with strong precision and recall. A novel Computational Severity Index approach stratifies disease progression into four levels, supporting clinical decision-making. To ensure practical deployment, the model is optimized into FP32, FP16, and INT8 formats using TensorFlow Lite and integrated on a Raspberry Pi 4. The framework includes benchmarking of latency, resource utilization, and thermal stability, alongside a Flask-based role-driven interface for secure access, patient registration, and automated reporting. HistoPath AI demonstrates a scalable, privacy-preserving, and deployable solution for point-of-care cancer diagnostics. The proposed framework emphasizes offline inference and local data processing, enabling privacy-preserving operation without reliance on cloud infrastructure. The results highlight the feasibility of deploying deep learning-based histopathology analysis on low-cost embedded hardware for real-time use. This study establishes a strong engineering foundation for future extensions involving clinically informed validation and real-world deployment in resource-limited healthcare settings. Received: 8 November 2025 | Revised: 5 February 2026 | Accepted: 25 May 2026 Conflicts of Interest The authors declare that they have no conflicts of interest to this work. Data Availability Statement The data that support the findings of this study are openly available at https://doi.org/10.48550/arXiv.1912.12142, reference number [25]. Author Contribution Statement Rajkumar Maharaju: Conceptualization, Methodology, Software, Validation, Formal analysis, Investigation, Resources, Data curation, Writing – original draft, Writing – review & editing, Visualization, Supervision, Project administration. Sai Prasad Ellaboina: Conceptualization, Methodology, Software, Validation, Resources. Rama Valupadasu: Conceptualization, Writing – review & editing, Supervision.
Enhancing Cardiovascular Disease Risk Prediction: A Comparative Analysis of Machine Learning Techniques
Cardiovascular disease (CVD) remains a global health threat. Accurately assessing CVD risk is crucial for preventative measures and interventions. This paper explores advancements in CVD risk prediction by examining several approaches and trials designed with the aim of diagnosing myocardial infarction (MI), which is generally known as a heart attack. MI is a critical medical condition in which a blocked artery cuts off blood flow and oxygen to a specific part of the heart muscle. This starves the heart tissue, leading to permanent damage and ranking as a top cause of mortality globally. By analyzing vast amounts of patient data, machine learning (ML) algorithms can predict the probability of a heart attack, pinpointing high-risk individuals. This allows for preventative measures and early intervention. The study in this paper utilizes tabular data for risk factors and includes an examination of multiple ML models to improve diagnostics, particularly for high-risk MI. These ML models include random forest classifiers, decision tree classifiers, support vector machines, logistic regression, and gradient boosting (GB). The experimental results reveal that GB has achieved higher accuracy than other models. This provides insights into enhancing cardiovascular health monitoring and diagnosis in clinical settings. Received: 8 July 2025 | Revised: 15 December 2025 | Accepted: 5 March 2026 Conflicts of Interest The authors declare that they have no conflicts of interest to this work. Data Availability Statement The data that support the findings of this study are openly available in Kaggle at https://www.kaggle.com/datasets/ritwikb3/heart-disease-cleveland. Author Contribution Statement Esraa Eldesouky: Conceptualization, Project administration, Funding acquisition. Walaa H. Elashmawi: Conceptualization, Software, Formal analysis, Resources, Supervision. Ahmed S. Salama: Data curation, Writing – review & editing. Ahmed Ali: Methodology, Investigation, Writing – original draft. Magi Mahfouz: Validation, Visualization.
Infection Management in Hospitals Using a Two-Stage Fuzzy Decision-Making Approach
Effective infection management is important for health systems. However, many factors influence infection management. This study aims to determine the most suitable strategies for effective infection management. Factors affecting infection management were determined based on the literature. The Criteria Importance Assessment (CIMAS) method is used for weighting the criteria, while Ranking of Alternatives through Functional mapping of criterion Subintervals into a single Interval (RAFSI) method is used to analyze the most effective alternative. Fuzzy numbers are used to minimize uncertainty during the analysis. Expert selection is based on Z-score weighting. The results of the analysis showed that the most important factors affecting infection management are compliance with patient safety (w = 0.363). The results of the alternative analysis show that the most effective strategy is hygiene and handwashing training (w = 0.826). In this regard, hospital administrators should ensure that hand hygiene, isolation, and sterilization protocols are implemented within the framework of quality standards. Politicians should establish an audit mechanism for infection management and invest in health infrastructure. The healthcare delivery process is important in terms of sustainability and is mostly provided in hospitals. Digital systems are recommended for monitoring hand hygiene and sterilization. One of the important original contributions of this study is the introduction of a set of factors affecting infection management into the literature. Furthermore, it is the first study to develop strategies for infection management using the CIMAS and RAFSI methods. Received: 13 October 2025 | Revised: 3 December 2025 | Accepted: 10 April 2026 Conflicts of Interest The authors declare that they have no conflicts of interest to this work. Data Availability Statement Data are available from the corresponding author upon reasonable request. Author Contribution Statement Sefer Aygün: Conceptualization, Investigation, Data curation, Writing – original draft, Writing – review & editing, Visualization, Supervision, Project administration. Sadiq Hussain: Validation, Resources, Writing – review & editing, Visualization, Supervision, Project administration. Yaşar Gökalp: Conceptualization, Investigation, Writing – original draft, Writing – review & editing, Visualization, Project administration. Serkan Eti: Methodology, Formal analysis, Writing – review & editing, Visualization. Umutcan Altun: Writing – original draft, Investigation. Berkay Alikan: Writing – original draft, Investigation, Writing – review & editing.
Radio Antennas in Sixth-Generation Mobile Wireless Systems Using Carbon Nanotubes
The development of sixth-generation (6G) networks operating in the terahertz (THz) and sub-THz frequency bands, where exceptionally large data rates, ultra-low latency, and dependable connectivity are necessary, is being propelled by the quick evolution of wireless communication systems. Conventional metallic antennas are severely limited at these frequencies because of significant surface losses, low radiation efficiency, and problematic shrinking. As a result, new materials are needed to overcome these constraints and enable high-performance antenna designs for next-generation wireless systems. This work introduces a carbon nanotube titanium (CNT-Ti) composite microstrip patch antenna as a potential contender for 6G applications. The suggested antenna provides reduced ohmic losses, improved impedance matching, and wideband performance by using carbon nanotubes' outstanding electrical conductivity, mechanical strength, and thermal stability, as well as titanium's structural strength. Full-wave electromagnetic simulations with CST Studio Suite show that the CNT-Ti patch antenna beats traditional copper and aluminum counterparts in terms of reflection coefficient, bandwidth, radiation efficiency, and signal transmission quality. The suggested CNT-Ti antenna has a minimum reflection coefficient of −30 dB, a VSWR of 1.03, and a radiation efficiency of around 94% at 180 GHz. These findings demonstrate that CNT-Ti composite antennas are an extremely appealing option for ultra-wideband, high-speed, and high-frequency 6G wireless communication systems. Received: 10 October 2025 | Revised: 3 December 2025 | Accepted: 5 March 2026 Conflicts of Interest The authors declare that they have no conflicts of interest to this work. Data Availability Statement Data are available from the corresponding author upon reasonable request. Author Contribution Statement Jafaar Fahad A. Rida: Conceptualization, Methodology, Validation, Formal analysis, Investigation, Data curation, Writing – original draft, Writing – review & editing, Visualization, Project administration. Basim Abood: Conceptualization, Methodology, Software, Investigation, Resources, Data curation, Writing – review & editing, Supervision, Project administration.
Deep Learning Approaches for Detecting Cyberbullying on Social Media
The widespread use of social media has brought many challenges, mainly due to a misconstrued interpretation of the right to freedom of expression. Cyberbullying is a particularly noteworthy issue with far-reaching global implications for both its victims and the wider community. It takes the form of bullying that happens on several social media websites. This paper's goal is to develop a deep learning model capable of recognizing cases of cyberbullying on social media. Four models, such as bidirectional long short-term memory (BiLSTM), convolutional neural network with bidirectional long short-term memory (CNN-BiLSTM), bidirectional long short-term memory with gated recurrent unit (BiLSTM-GRU), and artificial neural network (ANN), will be evaluated in a multiclass classification difficulty context. The results showed that the BiLSTM model outperformed the other models by achieving the highest accuracy in 91% of cases, while the CNN-BiLSTM and ANN models demonstrated relatively lower performance. In addition to determining the efficacy of the deep learning techniques, the work highlights the urgent requirement for strong systems to resist cyberbullying. By enhancing detection accuracy, the proposed model can contribute significantly to providing a safer digital environment for further studies in this field. Received: 24 August 2024 | Revised: 16 December 2024 | Accepted: 25 February 2025 Conflicts of Interest The authors declare that they have no conflicts of interest to this work. Data Availability Statement Data are available on request from the corresponding author upon reasonable request. Author Contribution Statement Ghaith Jaradat: Conceptualization, Methodology, Formal analysis, Data curation, Writing – original draft, Writing – review & editing, Visualization, Project administration. Mohammad Shehab: Conceptualization, Methodology, Software, Validation, Formal analysis, Data curation, Writing – original draft, Writing – review & editing, Supervision, Project administration. Dyala Ibrahim: Software, Validation, Formal analysis, Investigation, Writing – original draft, Writing – review & editing, Project administration. Saif Najdawi: Methodology, Validation, Writing – original draft, Writing – review & editing, Visualization. Rami Sihwail: Software, Investigation, Data curation, Writing – original draft, Writing – review & editing, Visualization.
Strengthening Security in Clouds Through Cloud Computing Authentication Using Facial Image Forensics
Numerous cyber threats may succeed in cloud platforms owing to unproductive authentication methods. Multiple credential authentication is a vital precautionary measure that helps reinforce cloud security for warding off imminent data breaches and illegal access. This kind of authentication approach strongly guarantees that trustworthy clients are only ratified to get cloud services, making it less tiresome for clients and more secure for organizations. This study proposes an Image Forensics-based Dual Credential Authentication (IF-DCA) based on username (i.e., preferably email ID to avoid replica) and client photographs as password. Along with a username, a rapid and acquainted user action of capturing a photograph using a web camera is adequate for this method; hence it does not need any expensive, special hardware devices. During the registration phase, along with the username, the user's face image is stored on the cloud server as a password. When registered clients need to access cloud resources or data, they should log in on the server with their username and their photograph. To improve the authentication approach, this work proposes an Attribute-controlled Conditional Generative Adversarial Network (ACC-GAN) to generate face images of the same user at various age groups. ACC-GAN includes an additional attribute control unit and an age prediction unit to synthesize photo-realistic face images with aging effects. After matching the username and face image with the registered username and ACC-GAN-generated images, the face identification module provides access to the authorized cloud user. The performance of IF-DCA is assessed through the Cross-Age Celebrity Dataset using the MATLAB R2018b/deep learning toolbox. The empirical analysis reveals that the ACC-GAN achieves better performance measures such as 98.40% accuracy, 99.3% sensitivity, 90.0% specificity, and 95.7% precision. The performance of IF-DCA is analyzed using the verification time for username, user image, and complete authentication against the number of users. Received: 4 September 2024 | Revised: 14 October 2024 | Accepted: 7 November 2024 Conflicts of Interest The authors declare that they have no conflicts of interest to this work. Data Availability Statement The Cross-Age Celebrity Datasets that support the findings of this study are openly available at https://paperswithcode.com/dataset/cacd. Author Contribution Statement Pranali Dahiwal: Conceptualization, Software, Formal analysis, Resources, Writing – original draft, Visualization, Project administration. Vijay Khare: Methodology, Validation, Investigation, Data curation, Writing – review & editing, Supervision.
Adaptive Gaussian-Based Kernel K-Means: Scalable Adaptive Kernel-Based Clustering for Big Data
In the field of big data, clustering remains a fundamental component of data mining and knowledge discovery, particularly in high-volume, heterogeneous environments. Although traditional clustering algorithms have been widely used, they often exhibit poor scalability, limited robustness, and sensitivity to noise when applied to large industrial-scale datasets. To address these limitations, this paper proposes a Spark-based clustering framework that incorporates an Adaptive Gaussian-Based Kernel K-Means (A-GBK-Means) algorithm, designed to mitigate performance degradation in the presence of noisy and nonhomogeneous data. The A-GBK-Means algorithm integrates fuzzy logic with adaptively determined kernel widths, using local density estimation, thereby enhancing both clustering accuracy and flexibility. Extensive experiments were conducted on real-world industrial sensor data as well as synthetic benchmarks, and performance was evaluated using multiple metrics, including execution time, Silhouette Score, Davies–Bouldin Index, and noise resilience. The comparative analysis demonstrates that the A-GBK-Means algorithm consistently outperforms classical approaches—namely K-Means, Density-Based Spatial Clustering of Applications with Noise, Fuzzy C-Means, and standard GBK-Means—in terms of clustering quality and computational efficiency. Furthermore, the proposed framework exhibits superior scalability, owing to its distributed architecture built on Apache Spark. This study provides practical insights into scalable and interpretable clustering methods for intelligent manufacturing and sensor-based systems and highlights the effectiveness of adaptive kernel learning in modern big data applications. Received: 18 June 2025 | Revised: 8 August 2025 | Accepted: 16 September 2025 Conflicts of Interest The authors declare that they have no conflicts of interest to this work. Data Availability Statement The data that support the findings of this study are openly available in Kaggle at https://www.kaggle.com/datasets/dnkumars/industrial-equipment-monitoring-dataset and https://www.kaggle.com/datasets/mohammedarfathr/sensor-data-from-an-industrial-machine. Author Contribution Statement Bakhshali Bakhtiyarov: Investigation, Data curation, Writing – original draft, Project administration. Aynur Jabiyeva: Conceptualization, Validation, Formal analysis. Anakhanim Mutallimova: Software, Resources. Rukhsara Novruzova: Methodology, Writing – review & editing, Supervision. Mahabbat Khudaverdiyeva: Visualization.
A Novel Approach to Model the Economic Characteristics of an Organization by Interval-Valued Complex Pythagorean Fuzzy Information
The success of a nation mainly depends on its economic power. The economic growth plays an important place in enhancing the caliber of life as it produces job opportunities. In this research, the effect of uncertainty on economic growth and investments is examined with gross domestic product (GDP). Currently, the relationship between financial market and economic growth is one of the most important issues in economics. These departments are often accompanied by uncertainty. Through that regard, the idea of interval-valued complex Pythagorean fuzzy relation (IVCPFR) is introduced that is handy in such situations. Moreover, the Cartesian product between interval-valued complex Pythagorean fuzzy sets and verities of IVCPFRs was described. Furthermore, these IVCPFRs are applied to analyze GDP, unemployment, price, demand, supply, interest rate, investment, and money supply effecting business markets. The possibility of the Hasse diagram is more suitable to make sense of the relationship between various economic factors. IVCPFRs will clearly help to set up the impact of one component on the other and change the grades of supportive and distinctive outcomes concerning the time. IVCPFR is used for tracking various multidimensional issues and produces quick and better outcomes rather than the previous methodologies. Finally, the proposed method is the best method to model uncertainty in economics rather than previous methods. Received: 12 May 2022 | Revised: 12 August 2022 | Accepted: 17 August 2022 Conflicts of Interest The authors declare that they have no conflicts of interest to this work.
interval-valued complex Pythagorean fuzzy set
interval-valued complex Pythagorean fuzzy relation
Copyright (c) 2022 Authors https://creativecommons.org/licenses/by/4.0/
Intuitionistic Fuzzy Rough Mutual Information Aided Feature Subset Selection and Its Applications
Feature selection plays an essential role in solving the challenges of the "curse of dimensionality" in data analysis, aiming to increase the learning algorithms' performance. A key challenge in this field is achieving accurate attribute selection when handling both numerical and nominal attributes. To address this problem, we demonstrate a hybrid intuitionistic fuzzy (IF) similarity-based approach that flexibly handles mixed types of data for more precise attribute selection. The study shows an IF granular structure to manage noise in heterogeneous data and enhances the concepts of IF rough entropy, joint entropy, and conditional entropy to provide a comprehensive framework to deal with uncertainty. Moreover, IF rough mutual information is implemented to extract both uncertainty and the association between conditional attributes and the decision class, forming the basis of a novel attribute selection approach. The proposed algorithm contains intuitionistic fuzzification, evaluation of fuzzy mutual information to compute the significance of features, and recursive selection of the most important attributes, thus effectively reducing dimensionality. We set up a theoretical foundation using IF sets, IF information system, IF relation theory, and hybrid similarity relations, which ensures the robustness of the approach. The method is rigorously evaluated on seven benchmark datasets, showing superior performance on various metrics, including accuracy, sensitivity, and specificity, when compared with existing attribute selection approaches. Results demonstrate increased prediction of phospholipidosis-positive molecules with a sensitivity of 89.56%, a specificity of 92.63%, an accuracy of 95.98%, an AUC of 0.968, and an MCC of 0.908, which represents the strong class differentiation ability of the model. These findings underscore the effectiveness of the hybrid IF similarity-based attribute selection approach, which makes it a valuable tool for managing high-dimensional datasets and advancing the field of attribute selection. Received: 14 May 2025 | Revised: 30 October 2025 | Accepted: 21 November 2025 Conflicts of Interest The authors declare that they have no conflicts of interest to this work. Data Availability Statement The data that support the findings of this study are openly available at https://archive.ics.uci.edu/. Author Contribution Statement Aneesh Kumar Mishra: Conceptualization, Methodology, Validation, Data curation, Writing – original draft, Writing – review & editing. Neelesh Kumar Jain: Software, Formal analysis, Supervision, Project administration. Ravindra Kumar Singh Jain: Investigation, Resources, Visualization.
Switching-Algebraic Calculation of Banzhaf Voting Indices
This paper employs switching-algebraic techniques for the calculation of a fundamental index of voting powers, namely, the total Banzhaf power. This calculation involves two distinct operations: (a) Boolean differencing or differentiation, and (b) computation of the weight (the number of true vectors or minterms) of a switching function. Both operations can be considerably simplified and facilitated if the pertinent switching function is symmetric or it is expressed in a disjoint sum-of-products form. We provide a tutorial exposition on how to implement these two operations, with a stress on situations in which partial symmetry is observed among certain subsets of a set of arguments. We introduce novel Boolean-based symmetry-aware techniques for computing the Banzhaf index by way of two prominent voting systems. These are scalar systems involving six variables and nine variables, respectively. The paper is a part of our on- going effort for transforming the methodologies and concepts of voting systems to the switching-algebraic domain, and subsequently utilizing switching-algebraic tools in the calculation of pertinent quantities in voting theory. Received: 13 February 2023 | Revised: 5 April 2023 | Accepted: 19 May 2023 Conflicts of Interest The authors declare that they have no conflicts of interest to this work. Data Availability Statement Data sharing is not applicable to this article as no new data were created or analyzed in this study.
Copyright (c) 2023 Authors https://creativecommons.org/licenses/by/4.0/
Digital Transformation and Corporate ESG Performance – Evidence from China
The focal point of this paper is to study environmental, social, and governance (ESG) performance consequences of digital transformation for companies, as well as green technological innovation, corporate social responsibility, and internal control as mediating variables. A set of digital transformation indicators was developed utilizing data from 355 listed industrial companies in China between 2011 and 2019 and vector arithmetic. The study’s findings suggested that digital transformation has considerably raised ESG performance, with green technological innovation, corporate social responsibility, and internal control as significant mediating variables. The paper has several contributions; for example, it provides theoretical and empirical evidence for ESG improvement in Chinese firms and an essential factor associated with ESG performance, among others. The non-economic impacts of digital transformation on ESG performance are essential insights offered by this study. They also provide insight into these impacts' mechanisms, including green technology, corporate social responsibility, and internal control. Received: 9 May 2024 | Revised: 5 August 2024 | Accepted: 31 August 2024 Conflicts of Interest The authors declare that they have no conflicts of interest to this work. Data Availability Statement Data sharing is not applicable to this article as no new data were created or analyzed in this study. Author Contribution Statement Wenxin Zhang: Conceptualization, Methodology, Investigation, Resources, Data curation, Writing – original draft. Xiangyu Li: Software, Writing – review & editing.
Copyright (c) 2024 Authors https://creativecommons.org/licenses/by/4.0/
Performance Analysis of Block–Internet of Things–Chain (BIoTC) Framework for Smart Home
Smart-home systems built on Internet of Things (IoT) devices offer convenience, but they also introduce gaps in security and privacy that are often overlooked. Traditional protective measures such as lightweight encryption, access control rules, and intrusion alerts do help, but they do not fully address issues such as traceability, proof of device identity, or resistance to more adaptive attacks. In response to this, the research proposes a blockchain-supported security model called Block–Internet of Things–Chain (BIoTC), designed specifically for smart-home IoT networks. This work developed the setup using Cisco Packet Tracer to model a basic smart home. In this environment, the study carried out on four forms of attacks: Man in the Middle (12 attempts), RFID cloning (6 attempts), unauthorized access (1 case), and social engineering (1 case). Each attack was performed on a regular IoT configuration, and then the same test was repeated after introducing the BIoTC layer. The blockchain component, implemented through a lightweight Python module, maintains immutability and coordinates identity checks to confirm that a device should be allowed in the network. The distinct contribution of research comes from grounding the evaluation in actual experimental observation rather than theoretical claims. The results show that BIoTC is successful in the reduction of attack success rate with acceptable performance overhead for a smart-home environment. Using a real-world example, the mathematical validation was performed. This research paper presents verified and statistical methods for the security of IoT smart-home systems. The results demonstrate that integrating blockchains enhances defense against security threats in the IoT environment. Received: 15 September 2025 | Revised: 12 November 2025 | Accepted: 6 February 2026 Conflicts of Interest The authors declare that they have no conflicts of interest to this work. Data Availability Statement Data sharing is not applicable to this article as no new data were created or analyzed in this study. Author Contribution Statement Gaurav Vats: Conceptualization, Methodology, Software, Formal analysis, Investigation, Writing – original draft, Visualization. Sarvesh Tanwar: Validation, Investigation, Resources, Writing – review & editing, Visualization, Supervision, Project administration. Pankaj Kumar Sharma: Resources, Data curation, Supervision.