Journal of Computational and Cognitive Engineering
type: channel
description: 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.
publisher: Bon View Publishing Ptd., Ltd
language: en
publication name: Journal of Computational and Cognitive Engineering
issn: 2810-9570
items: AI-Driven Diagnosis of Autism Spectrum Disorder Using Retinal Fundus Imaging: A Comparison of Traditional and Deep Learning Feature Extraction Methods
items: Research on Microvideo Character Perception and Recognition Based on Target Detection Technology
items: NCOHA-WSC: Handling Noise and Class Overlap in Web Service Classification
items: Explainable Fuzzy Modeling Through Antecedent Reduction and Antecedent Association in Type-1 ANFIS System
items: Adaptive Client-Specific Augmentation (ACSA) for Mitigating Data Heterogeneity in Federated Learning
items: An IoT-Based Intelligent Monitoring System for Cargo Loss Detection in FPSO-Tanker Oil Transfer
items: Enhancing Cardiovascular Disease Risk Prediction: A Comparative Analysis of Machine Learning Techniques
items: Infection Management in Hospitals Using a Two-Stage Fuzzy Decision-Making Approach
items: Radio Antennas in Sixth-Generation Mobile Wireless Systems Using Carbon Nanotubes
items: Multi-agent Reinforcement Learning with Clustering and Forecasting for Optimized Energy Sharing in Microgrids
items: Deep Learning Approaches for Detecting Cyberbullying on Social Media
items: Strengthening Security in Clouds Through Cloud Computing Authentication Using Facial Image Forensics
items: Adaptive Gaussian-Based Kernel K-Means: Scalable Adaptive Kernel-Based Clustering for Big Data
items: A Novel Approach to Model the Economic Characteristics of an Organization by Interval-Valued Complex Pythagorean Fuzzy Information
items: Intuitionistic Fuzzy Rough Mutual Information Aided Feature Subset Selection and Its Applications
items: Switching-Algebraic Calculation of Banzhaf Voting Indices
items: Digital Transformation and Corporate ESG Performance – Evidence from China
items: Performance Analysis of Block–Internet of Things–Chain (BIoTC) Framework for Smart Home
items: Deep Learning for Medicinal Plant Classification: A Comprehensive Review of Recent Advances and Challenges
items: Explainable AI Framework for Automated Lesion Segmentation and Severity Assessment in Neonatal Hypoxic-Ischemic Encephalopathy
items: A Weighted Ensemble of EfficientNetV2 Variants with Optimizer Tuning for Tomato Leaf Disease Classification
items: Embedded Sparsity and Ensemble Learning for Real-Time Textual Anomaly Detection: A SCADA-Inspired Benchmark on Lecture Transcript Data
items: Real-Time Drowsiness Detection Using YOLOv11 in Driver Monitoring Systems
items: Multi-Source Data Fusion and Machine Learning for Soybean Crop Price Forecasting in India
items: Advanced Residual Network Architecture for Automated Brain Tumor Diagnosis: The MesResNet50 Approach
items: An Innovative Exponential Distribution for Modeling Various Risk Trends: Theory, Bayesian Inference, and Applications in Partially Accelerated Life Tests with Dispersion Data
items: AI Integration and Digital Communication: The Cognitive Power of Brand Equity in the Jordanian Banking Industry
items: Optimization of Electroencephalogram Feature Based on Multi-Band Feature Matrix and Relative Difference for Imagined Motor Movement Pattern Classification
items: FedProx-Enhanced Federated Transfer Learning for Heterogeneous 3D Medical Image Classification
items: Beta-Transformed Inverse Length-Biased Exponential Distribution: Properties and Applications

AI-Driven Diagnosis of Autism Spectrum Disorder Using Retinal Fundus Imaging: A Comparison of Traditional and Deep Learning Feature Extraction Methods
type: item
description: 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, Visualizat…
subject: Research Articles
  topic: section
subject: ASD detection
  topic: keywords
subject: deep learning approaches
  topic: keywords
subject: retinal fundus images
  topic: keywords
subject: traditional feature extraction
  topic: keywords
subject: classification models
  topic: keywords
creator: Ayain John
creator: S Santhanalakshmi
rights: Copyright (c) 2025 Authors https://creativecommons.org/licenses/by/4.0/
date: 2025-08-18
publication date: 2025-08-18
doi: 10.47852/bonviewJCCE52026045

Research on Microvideo Character Perception and Recognition Based on Target Detection Technology
type: item
description: 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.
subject: Research Articles
  topic: section
subject: target detection
  topic: keywords
subject: microvideo
  topic: keywords
subject: face recognition
  topic: keywords
subject: Gabor
  topic: keywords
subject: 2DPCA algorithm
  topic: keywords
creator: Yue Lei
rights: Copyright (c) 2022 Author https://creativecommons.org/licenses/by/4.0/
date: 2022-01-11
publication date: 2022-01-11
starting page: 83
ending page: 87
doi: 10.47852/bonviewJCCE19522514

NCOHA-WSC: Handling Noise and Class Overlap in Web Service Classification
type: item
description: 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 & edi…
subject: Research Articles
  topic: section
subject: Web service classification
  topic: keywords
subject: noise handling
  topic: keywords
subject: class overlap
  topic: keywords
subject: confidence learning
  topic: keywords
subject: prior distribution
  topic: keywords
creator: Feng Zhang
creator: Lin Xue
creator: Huiling Li
creator: Cong Liu
rights: Copyright (c) 2026 Authors https://creativecommons.org/licenses/by/4.0/
date: 2026-05-12
publication date: 2026-05-12
doi: 10.47852/bonviewJCCE62028810

Explainable Fuzzy Modeling Through Antecedent Reduction and Antecedent Association in Type-1 ANFIS System
type: item
description: 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, Methodolo…
subject: ICON-AI 2025
  topic: section
subject: explainable artificial intelligence (XAI)
  topic: keywords
subject: Adaptive Neuro-Fuzzy Inference System (ANFIS)
  topic: keywords
subject: fuzzy logic system
  topic: keywords
subject: Fuzzy C-Means clustering
  topic: keywords
subject: antecedent reduction
  topic: keywords
creator: Muhammad Hamza Azam
creator: Mohd Hilmi Hasan
creator: Saima Hassan
creator: Noureen Talpur
creator: Muhammad Huzaifa Azam
rights: Copyright (c) 2026 Authors https://creativecommons.org/licenses/by/4.0/
date: 2026-05-07
publication date: 2026-05-07
doi: 10.47852/bonviewJCCE62028575

Adaptive Client-Specific Augmentation (ACSA) for Mitigating Data Heterogeneity in Federated Learning
type: item
description: 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 suppo…
subject: Research Articles
  topic: section
subject: federated learning
  topic: keywords
subject: deep learning
  topic: keywords
subject: data heterogeneity
  topic: keywords
subject: data augmentation
  topic: keywords
creator: Aiswariya Milan Kummaya
creator: Amudha Joseph
creator: George Ghinea
rights: Copyright (c) 2026 Authors https://creativecommons.org/licenses/by/4.0/
date: 2026-04-13
publication date: 2026-04-13
doi: 10.47852/bonviewJCCE62028199

An IoT-Based Intelligent Monitoring System for Cargo Loss Detection in FPSO-Tanker Oil Transfer
type: item
description: 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…
subject: Research Articles
  topic: section
subject: IoT
  topic: keywords
subject: cargo loss detection
  topic: keywords
subject: intelligent monitoring system
  topic: keywords
subject: FPSO-tanker offloading
  topic: keywords
subject: maritime safety
  topic: keywords
creator: Habibi Palippui
creator: Daniel Mohammad Rosyid
creator: Silvianita Silvianita
creator: Juswan Sade
rights: Copyright (c) 2026 Authors https://creativecommons.org/licenses/by/4.0/
date: 2026-05-14
publication date: 2026-05-14
doi: 10.47852/bonviewJCCE62028129

Enhancing Cardiovascular Disease Risk Prediction: A Comparative Analysis of Machine Learning Techniques
type: item
description: 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 dra…
subject: Research Articles
  topic: section
subject: machine learning (ML)
  topic: keywords
subject: cardiovascular disease
  topic: keywords
subject: myocardial infarction (MI)
  topic: keywords
subject: coronary heart disease (CHD)
  topic: keywords
subject: classification approaches
  topic: keywords
creator: Esraa Eldesouky
creator: Walaa H. Elashmawi
creator: Ahmed S. Salama
creator: Ahmed Ali
creator: Magi Mahfouz
rights: Copyright (c) 2026 Authors https://creativecommons.org/licenses/by/4.0/
date: 2026-04-28
publication date: 2026-04-28
starting page: 354
ending page: 370
doi: 10.47852/bonviewJCCE62026735

Infection Management in Hospitals Using a Two-Stage Fuzzy Decision-Making Approach
type: item
description: 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: Conce…
subject: Research Articles
  topic: section
subject: infection management
  topic: keywords
subject: health policy
  topic: keywords
subject: health services
  topic: keywords
subject: multi-criteria decision-making
  topic: keywords
subject: fuzzy number
  topic: keywords
creator: Sefer Aygün
creator: Sadiq Hussain
creator: Yaşar Gökalp
creator: Serkan Eti
creator: Umutcan Altun
creator: Berkay Alikan
rights: Copyright (c) 2026 Authors https://creativecommons.org/licenses/by/4.0/
date: 2026-05-15
publication date: 2026-05-15
doi: 10.47852/bonviewJCCE62027891

Radio Antennas in Sixth-Generation Mobile Wireless Systems Using Carbon Nanotubes
type: item
description: 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 –…
subject: Research Articles
  topic: section
subject: sixth-generation networks
  topic: keywords
subject: carbon nanotube antennas
  topic: keywords
subject: next-generation wireless systems
  topic: keywords
subject: advanced antenna design
  topic: keywords
subject: CNT-Ti composite antennas
  topic: keywords
creator: Jafaar Fahad A. Rida
creator: Basim Abood
rights: Copyright (c) 2026 Authors https://creativecommons.org/licenses/by/4.0/
date: 2026-05-08
publication date: 2026-05-08
doi: 10.47852/bonviewJCCE62027867

Multi-agent Reinforcement Learning with Clustering and Forecasting for Optimized Energy Sharing in Microgrids
type: item
description: 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-…
subject: Research Articles
  topic: section
subject: multi-agent reinforcement learning
  topic: keywords
subject: deep learning
  topic: keywords
subject: demand forecasting
  topic: keywords
subject: microgrid auction
  topic: keywords
subject: artificial intelligence
  topic: keywords
creator: Daswin De Silva
creator: Thimal Kempitiya
creator: Nuwan Madhusanka
creator: Prabod Rathnayaka
creator: Nishan Mills
creator: Andrew Jennings
creator: Milos Manic
rights: Copyright (c) 2026 Authors https://creativecommons.org/licenses/by/4.0/
date: 2026-05-11
publication date: 2026-05-11
starting page: 187
ending page: 201
doi: 10.47852/bonviewJCCE62027858

Deep Learning Approaches for Detecting Cyberbullying on Social Media
type: item
description: 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 …
subject: Research Articles
  topic: section
subject: cyberbullying
  topic: keywords
subject: social media
  topic: keywords
subject: tweets
  topic: keywords
subject: machine learning
  topic: keywords
subject: deep learning
  topic: keywords
subject: classification
  topic: keywords
creator: Ghaith Jaradat
creator: Mohammad Shehab
creator: Dyala Ibrahim
creator: Saif Najdawi
creator: Rami Sihwail
rights: Copyright (c) 2025 Authors https://creativecommons.org/licenses/by/4.0/
date: 2025-03-31
publication date: 2025-03-31
starting page: 451
ending page: 465
doi: 10.47852/bonviewJCCE52024162

Strengthening Security in Clouds Through Cloud Computing Authentication Using Facial Image Forensics
type: item
description: 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 …
subject: Research Articles
  topic: section
subject: authentication mechanism
  topic: keywords
subject: face recognition
  topic: keywords
subject: generative adversarial network
  topic: keywords
subject: multiple credential authentication
  topic: keywords
subject: password
  topic: keywords
creator: Pranali Dahiwal
creator: Vijay Khare
rights: Copyright (c) 2025 Authors https://creativecommons.org/licenses/by/4.0/
date: 2025-01-08
publication date: 2025-01-08
starting page: 151
ending page: 160
doi: 10.47852/bonviewJCCE52024248

Adaptive Gaussian-Based Kernel K-Means: Scalable Adaptive Kernel-Based Clustering for Big Data
type: item
description: 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 …
subject: Research Articles
  topic: section
subject: big data clustering
  topic: keywords
subject: Apache Spark
  topic: keywords
subject: K-Means
  topic: keywords
subject: DBSCAN
  topic: keywords
subject: Fuzzy C-Means
  topic: keywords
subject: GBK-Means
  topic: keywords
subject: industrial sensor data
  topic: keywords
creator: Bakhshali Bakhtiyarov
creator: Aynur Jabiyeva
creator: Anakhanim Mutallimova
creator: Rukhsara Novruzova
creator: Mahabbat Khudaverdiyeva
rights: Copyright (c) 2025 Authors https://creativecommons.org/licenses/by/4.0/
date: 2025-10-16
publication date: 2025-10-16
starting page: 258
ending page: 273
doi: 10.47852/bonviewJCCE52026511

A Novel Approach to Model the Economic Characteristics of an Organization by Interval-Valued Complex Pythagorean Fuzzy Information
type: item
description: 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.
subject: Research Articles
  topic: section
subject: interval-valued complex Pythagorean fuzzy set
  topic: keywords
subject: interval-valued complex Pythagorean fuzzy relation
  topic: keywords
subject: economics
  topic: keywords
subject: GDP
  topic: keywords
subject: Hasse diagram
  topic: keywords
creator: Hawraa Akram Yazbek
creator: Fatima Surriya
creator: Sami Ullah Khan
creator: Naeem Jan
creator: Dragan Marinkovic
rights: Copyright (c) 2022 Authors https://creativecommons.org/licenses/by/4.0/
date: 2022-08-22
publication date: 2022-08-22
starting page: 75
ending page: 87
doi: 10.47852/bonviewJCCE2202249

Intuitionistic Fuzzy Rough Mutual Information Aided Feature Subset Selection and Its Applications
type: item
description: 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 d…
subject: Research Articles
  topic: section
subject: feature selection
  topic: keywords
subject: intuitionistic fuzzy set
  topic: keywords
subject: mutual information
  topic: keywords
subject: rough set
  topic: keywords
subject: similarity relation
  topic: keywords
subject: granular structure
  topic: keywords
creator: Aneesh Kumar Mishra
creator: Neelesh Kumar Jain
creator: Ravindra Kumar Singh
rights: Copyright (c) 2026 Authors https://creativecommons.org/licenses/by/4.0/
date: 2026-01-30
publication date: 2026-01-30
doi: 10.47852/bonviewJCCE62026152

Switching-Algebraic Calculation of Banzhaf Voting Indices
type: item
description: 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.
subject: Research Articles
  topic: section
subject: voting system
  topic: keywords
subject: Banzhaf index
  topic: keywords
subject: voting power
  topic: keywords
subject: coalition
  topic: keywords
subject: disjoint sum-of-products
  topic: keywords
subject: Boolean differentiation
  topic: keywords
subject: weight of a switching function
  topic: keywords
creator: Ali Muhammad Rushdi
creator: Muhammad Ali Rushdi
rights: Copyright (c) 2023 Authors https://creativecommons.org/licenses/by/4.0/
date: 2023-06-05
publication date: 2023-06-05
starting page: 274
ending page: 284
doi: 10.47852/bonviewJCCE3202746

Digital Transformation and Corporate ESG Performance – Evidence from China
type: item
description: 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.
subject: Research Articles
  topic: section
subject: digital transformation
  topic: keywords
subject: corporate ESG performance
  topic: keywords
subject: green technology innovation
  topic: keywords
subject: corporate social responsibility
  topic: keywords
subject: corporate internal control
  topic: keywords
subject: ESG development
  topic: keywords
creator: Wenxin Zhang
creator: Xiangyu Li
rights: Copyright (c) 2024 Authors https://creativecommons.org/licenses/by/4.0/
date: 2024-09-16
publication date: 2024-09-16
starting page: 195
ending page: 207
doi: 10.47852/bonviewJCCE42023387

Performance Analysis of Block–Internet of Things–Chain (BIoTC) Framework for Smart Home
type: item
description: 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 …
subject: Research Articles
  topic: section
subject: IoT security
  topic: keywords
subject: smart homes
  topic: keywords
subject: blockchain
  topic: keywords
subject: cybersecurity attacks
  topic: keywords
subject: security frameworks
  topic: keywords
creator: Gaurav Vats
creator: Sarvesh Tanwar
creator: Pankaj Kumar Sharma
rights: Copyright (c) 2026 Authors https://creativecommons.org/licenses/by/4.0/
date: 2026-03-20
publication date: 2026-03-20
doi: 10.47852/bonviewJCCE62027670

Deep Learning for Medicinal Plant Classification: A Comprehensive Review of Recent Advances and Challenges
type: item
description: 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 interes…
subject: Research Articles
  topic: section
subject: medicinal plant classification
  topic: keywords
subject: plant image dataset
  topic: keywords
subject: deep learning
  topic: keywords
subject: feature extraction
  topic: keywords
subject: image preprocessing
  topic: keywords
creator: Monir Hossain
creator: Fahmid Al Farid
creator: Momotaz Begum
creator: Jia Uddin
creator: Hezerul Bin Abdul Karim
rights: Copyright (c) 2026 Authors https://creativecommons.org/licenses/by/4.0/
date: 2026-05-20
publication date: 2026-05-20
doi: 10.47852/bonviewJCCE62027652

Explainable AI Framework for Automated Lesion Segmentation and Severity Assessment in Neonatal Hypoxic-Ischemic Encephalopathy
type: item
description: Neonatal hypoxic-ischemic encephalopathy (HIE) is a critical brain injury in newborns resulting from perinatal oxygen deprivation, often leading to long-term neurological impairments and developmental delays. Early and accurate diagnosis is essential for improving clinical outcomes. In this work, we propose a multimodal framework for automated HIE lesion segmentation and severity classification from magnetic resonance imaging scans. A SegResNet architecture was employed to segment lesions from apparent diffusion coefficient (ADC) and Z-scored ADC maps. In the subsequent stage, an early fusion strategy was adopted to integrate features derived from the predicted lesion masks with clinical metadata. The fused representation was classified into mild, moderate, and severe HIE categories using an XGBoost classifier. The Boston Neonatal Brain Injury Dataset for Hypoxic-Ischemic Encephalopathy was used for the experiments. The severity classification received an accuracy of 88.64%, and the segmentation model received a Dice score of 0.758 ± 0.29. Explainable AI with Gradient-weighted Class Activation Mapping was also used to improve the clinical dependability. These results demonstrate the effectiveness of the proposed system in both precise lesion localization and clinically meaningful severity stratification, highlighting its potential to support early clinical decision-making in neonatal care. Received: 12 September 2025 | Revised: 24 March 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 data that support the findings of this study are openly available in the Zenodo repository at https://zenodo.org/records/10602767. Author Contribution Statement Athira Chandran: Conceptualization, Methodology, Software, Validation, Formal analysis, Investigation, Data curation, Writing – original draft, Writing – review & editing, Visualization. Lekshmi Chandrika Reghunath: Con…
subject: Research Articles
  topic: section
subject: hypoxic-ischemic encephalopathy (HIE)
  topic: keywords
subject: magnetic resonance imaging (MRI)
  topic: keywords
subject: medical image segmentation
  topic: keywords
subject: SegResNet
  topic: keywords
subject: multimodal fusion
  topic: keywords
creator: Athira Chandran
creator: Lekshmi Chandrika Reghunath
rights: Copyright (c) 2026 Authors https://creativecommons.org/licenses/by/4.0/
date: 2026-05-18
publication date: 2026-05-18
doi: 10.47852/bonviewJCCE62027647

A Weighted Ensemble of EfficientNetV2 Variants with Optimizer Tuning for Tomato Leaf Disease Classification
type: item
description: 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: Concep…
subject: Research Articles
  topic: section
subject: EfficientNetV2
  topic: keywords
subject: weighted ensemble learning
  topic: keywords
subject: tomato leaf disease detection
  topic: keywords
subject: transfer learning
  topic: keywords
subject: technological development
  topic: keywords
creator: Aliyu Tetengi Ibrahim
creator: Ibrahim Hayatu Hassan
creator: Mohammed Abdullahi
creator: Abeer Rashad Mirdad
creator: Muhammad I. Khan
creator: Saeed Ali Bahaj
creator: Fatima Khan Nayer
rights: Copyright (c) 2026 Authors https://creativecommons.org/licenses/by/4.0/
date: 2026-03-06
publication date: 2026-03-06
starting page: 292
ending page: 303
doi: 10.47852/bonviewJCCE62027632

Embedded Sparsity and Ensemble Learning for Real-Time Textual Anomaly Detection: A SCADA-Inspired Benchmark on Lecture Transcript Data
type: item
description: This study presents a comprehensive benchmark of feature selection methods for real-time text classification, evaluating the computational trade-offs between embedded sparsity techniques (Lasso regularization), filter methods (ANOVA, mutual information), and wrapper approaches (recursive feature elimination). Using a dataset of 15,746 educational comments with TF-IDF representations, we systematically compare five feature selection methods paired with both classical machine learning and shallow neural network classifiers. Our experimental model evaluates both classification performance and computational latency in the context of binary and multiclass sentiment classification. Results show that the Lasso-based feature selection, combined with XGBoost, achieves F1-scores of 0.859 for binary classification and 0.699 for multiclass classification, with inference times of less than 1 s. Recursive feature elimination takes 284 s to do similar performance. Shallow neural networks achieve higher accuracy (F1 = 0.910 for binary, 0.841 for multiclass) at the cost of 8-s training times. In contrast, convolutional neural networks (CNNs) applied directly to TF-IDF vectors perform poorly (F1 = 0.646) with excessive training overhead (126 s), indicating that standard CNN architectures are unsuitable for sparse text representations. Our findings offer practical guidance for selecting feature reduction and classification methods based on latency requirements: Lasso with ensemble methods for real-time applications that require sub-second response and shallow neural networks for batch processing, where higher accuracy justifies the additional computational cost. Although this work uses educational text data as a testbed, the methodology and findings are applicable to any high-dimensional text classification scenario requiring efficient feature selection. Received: 10 September 2025 | Revised: 3 December 2025 | Accepted: 6 January 2026 Conflicts of Interest The authors declare that the…
subject: Research Articles
  topic: section
subject: feature selection
  topic: keywords
subject: intrusion detection
  topic: keywords
subject: SCADA systems
  topic: keywords
subject: text classification
  topic: keywords
subject: gradient boosting
  topic: keywords
creator: Ayoub Alsarhan
creator: Saja Jaradat
creator: Suhaila Abuowaida
creator: Sami Aziz Alshammari
creator: Nayef Hmoud Alshammari
creator: Khalid Hamad Alnafisah
rights: Copyright (c) 2026 Authors https://creativecommons.org/licenses/by/4.0/
date: 2026-02-24
publication date: 2026-02-24
doi: 10.47852/bonviewJCCE62027629

Real-Time Drowsiness Detection Using YOLOv11 in Driver Monitoring Systems
type: item
description: 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, Valida…
subject: Research Articles
  topic: section
subject: real-time monitoring
  topic: keywords
subject: road safety
  topic: keywords
subject: drowsiness detection
  topic: keywords
subject: YOLOv11
  topic: keywords
creator: Santhosh Kumar Kadur Lokeshappa
creator: Pravinth Raja Suthakar
rights: Copyright (c) 2026 Authors https://creativecommons.org/licenses/by/4.0/
date: 2026-03-05
publication date: 2026-03-05
doi: 10.47852/bonviewJCCE62027625

Multi-Source Data Fusion and Machine Learning for Soybean Crop Price Forecasting in India
type: item
description: 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 |…
subject: Research Articles
  topic: section
subject: soybean price prediction
  topic: keywords
subject: time-series forecasting
  topic: keywords
subject: agricultural market
  topic: keywords
subject: Indian agriculture
  topic: keywords
subject: machine learning
  topic: keywords
creator: Vilas Damodhar Ghonge
creator: Yogesh Kulkarni
rights: Copyright (c) 2026 Authors https://creativecommons.org/licenses/by/4.0/
date: 2026-04-13
publication date: 2026-04-13
doi: 10.47852/bonviewJCCE62027619

Advanced Residual Network Architecture for Automated Brain Tumor Diagnosis: The MesResNet50 Approach
type: item
description: 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 Bra…
subject: Research Articles
  topic: section
subject: brain tumor detection
  topic: keywords
subject: deep learning
  topic: keywords
subject: ResNet50
  topic: keywords
subject: AlexNet
  topic: keywords
subject: medical imaging
  topic: keywords
creator: Vasanth C. Bhagawat
creator: Pravinth Raja Suthkar
rights: Copyright (c) 2026 Authors https://creativecommons.org/licenses/by/4.0/
date: 2026-05-15
publication date: 2026-05-15
doi: 10.47852/bonviewJCCE62027511

An Innovative Exponential Distribution for Modeling Various Risk Trends: Theory, Bayesian Inference, and Applications in Partially Accelerated Life Tests with Dispersion Data
type: item
description: This paper presents a new asymmetric generalization of the exponential distribution tailored for non-negative datasets that display unimodal or bimodal characteristics. A shape-controlling parameter substantially improves the model's flexibility, enabling it to accommodate a broader spectrum of distributional shapes compared to the traditional exponential distribution. The theoretical study offers a thorough derivation of essential features, encompassing the moment-generating function, raw moments, mean deviation, entropy measures, and stochastic representations. An in-depth examination of the hazard rate demonstrates its capacity to represent both increasing and bathtub-shaped functions, rendering it especially appropriate for dependability and lifespan data analysis. Additionally, multiple characterization outcomes are produced to reinforce the theoretical foundation, and a modeling framework for partially accelerated life-test conditions is created. Parameter estimation is performed utilizing both conventional and Bayesian inferential methodologies. A thorough simulation analysis assesses the efficacy of these estimators in terms of bias and mean squared error. The model’s practical applicability is illustrated through two real-world asymmetric datasets: walking dimension metrics and breast cancer survival durations. Ultimately, likelihood ratio tests are utilized to evaluate the proposed distribution against nested alternatives, consistently demonstrating its enhanced performance and effectiveness in hypothesis testing. Received: 29 August 2025 | Revised: 16 April 2026 | Accepted: 18 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/10.1109/TR.1986.4335370, reference number [29], and at https://doi.org/10.21608/cjmss.2022.271186, reference number [30]. Author Contribution Statement Mohamed S. …
subject: Research Articles
  topic: section
subject: statistical model
  topic: keywords
subject: failure analysis
  topic: keywords
subject: simulation
  topic: keywords
subject: Bayesian inference
  topic: keywords
subject: data analysis
  topic: keywords
creator: Mohamed S. Eliwa
creator: Mohamed F. Abouelenein
creator: Jondeep Das
creator: Lakhimi Doley
creator: Partha Jyoti Hazarika
creator: Gholamhossein G. Hamedani
creator: Ehab M. Almetwally
rights: Copyright (c) 2026 Authors https://creativecommons.org/licenses/by/4.0/
date: 2026-06-10
publication date: 2026-06-10
doi: 10.47852/bonviewJCCE62027466

AI Integration and Digital Communication: The Cognitive Power of Brand Equity in the Jordanian Banking Industry
type: item
description: This paper investigates how brand equity mediates the association between digital marketing communication and the adoption of AI technologies by consumers in the banking industry in Jordan. A self-administered online questionnaire was used to collect data on 203 bank customers. Partial least squares structural equation modeling (PLS-SEM) was used to assess the effects of six latent constructs. The results show that brand equity mediates the effects of digital marketing channels, i.e., email marketing (EM), online advertising (OA), and mobile marketing (MM) on AI adoption in the financial services industry. The difference was statistically significant (b = 0.113, t = 3.195, p = 0.001; b = 0.064, t = 2.318, p = 0.028; b = 0.054, t = 2.412, p = 0.018). These findings indicate that brand equity is a strategic factor in enhancing the effectiveness of digital marketing and promoting the adoption of AI. The research has provided a contribution not only to theory but also to practice, especially in emerging markets like Jordan. It implies that, with the help of brand equity, the banks can maximize digital transformation strategies and increase customer engagement through AI-based financial services. Received: 11 August 2025 | Revised: 30 October 2025 | Accepted: 15 November 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 Ahmed Alamro: Conceptualization, Methodology, Validation, Writing – original draft, Writing – review & editing, Supervision. Jassim Al-Gasawneh: Conceptualization, Methodology, Software, Validation, Formal analysis, Data curation, Writing – original draft. Abdullah Al Sokkar: Conceptualization, Methodology, Software, Formal analysis, Investigation, Writing – review & editing, Visualization, Supervision, Project administration. Mohammed Otair: Investigation, Resources, Data cu…
subject: Research Articles
  topic: section
subject: digital communication
  topic: keywords
subject: artificial intelligence (AI)
  topic: keywords
subject: digital marketing
  topic: keywords
subject: brand equity
  topic: keywords
subject: applications in finance
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subject: banking sector
  topic: keywords
creator: Ahmed Alamro
creator: Jassim Al-Gasawneh
creator: Abdullah Al Sokkar
creator: Mohammed Otair
creator: Mohammed Binkhamis
creator: Raed Momani
creator: Mohammad Alshinwan
rights: Copyright (c) 2025 Authors https://creativecommons.org/licenses/by/4.0/
date: 2025-12-30
publication date: 2025-12-30
doi: 10.47852/bonviewJCCE52027154

Optimization of Electroencephalogram Feature Based on Multi-Band Feature Matrix and Relative Difference for Imagined Motor Movement Pattern Classification
type: item
description: 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 T…
subject: Research Articles
  topic: section
subject: electroencephalogram
  topic: keywords
subject: relative difference
  topic: keywords
subject: multi-band feature matrix
  topic: keywords
subject: brain-computer interface
  topic: keywords
subject: imagined motor movement
  topic: keywords
creator: I Made Agus Wirawan
creator: I Nyoman Sukajaya
creator: Ni Nyoman Mestri Agustini
rights: Copyright (c) 2026 Authors https://creativecommons.org/licenses/by/4.0/
date: 2026-05-18
publication date: 2026-05-18
doi: 10.47852/bonviewJCCE62027142

FedProx-Enhanced Federated Transfer Learning for Heterogeneous 3D Medical Image Classification
type: item
description: Artificial intelligence has revolutionized the analysis of medical images, but the creation of robust models is still problematic because of the strict regulation of data privacy and the very nature of medical imaging data heterogeneity. Federated learning (FL) is a privacy-preserving method with convergence instability and worse performance, particularly with non-Independent and Identically Distributed (non-IID) data. In addressing these disadvantages, this paper suggests a new FL framework based on the integration of transfer learning (TL) and FedProx to increase the resilience and stability of 3D medical image classification models in the distributed hospital context. TL offers a knowledgeable starting point to local models that enhances adaptation to different client data, whereas FedProx presents a proximal term to decrease the effects of data variability. The proposed framework was tested on three 3D medical imaging datasets, namely, OrganMNIST, FractureMNIST, and NoduleMNIST, with non-IID data distributions of 10 clients. It has been experimentally demonstrated that the performance is significantly improved, with an accuracy improvement of up to 18.2 and higher precision, recall, and F1 scores than current FL methods. All in all, the suggested solution offers a good and privacy-aware solution to collaborative learning in heterogeneous 3D medical imaging settings. Received: 4 August 2025 | Revised: 4 Novemeber 2025 | Accepted: 16 January 2026 Conflicts of Interest The authors declare that there are no conflicts of interest to this work. Data Availability Statement The OrganMNIST, FractureMNIST, and NoduleMNIST datasets used in this study are part of the MedMNIST collection, a standardized benchmark suite of biomedical images for machine learning tasks. The data that support the findings of this study are openly available at https://medmnist.com/, https://github.com/MedMNIST/experiments/tree/main/MedMNIST3D, and https://github.com/TsingZ0/PFLlib/tree/master/dat…
subject: Research Articles
  topic: section
subject: federated learning
  topic: keywords
subject: medical imaging
  topic: keywords
subject: data heterogeneity
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subject: healthcare
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creator: Manjunath Naganna
creator: Guru Ramachandra Nayaka
creator: Natesh Mahadev
creator: Mayura Tapkire
rights: Copyright (c) 2026 Authors https://creativecommons.org/licenses/by/4.0/
date: 2026-02-11
publication date: 2026-02-11
doi: 10.47852/bonviewJCCE62027069

Beta-Transformed Inverse Length-Biased Exponential Distribution: Properties and Applications
type: item
description: In this article, we introduce a novel probability distribution, the beta-transformed inverse length-biased exponential (BTILBE) distribution, derived from the exponential distribution using the beta transformation technique. This approach is advantageous because it introduces no additional parameters beyond those of the baseline distribution. The BTILBE distribution features a highly flexible hazard function and exhibits unique statistical properties that we comprehensively analyze. We explore key statistical characteristics, including moments, inverted moments, incomplete moments, generating functions, mean residual life, and mean inactivity times. Additionally, we provide numerical illustrations of moments, skewness, kurtosis, and the coefficient of variation to offer a thorough understanding of the proposed model. We also derive expressions for entropy measures, such as Shannon entropy, Tsallis entropy, and Renyi entropy, accompanied by numerical examples. The parameters of the BTILBE distribution are estimated using maximum likelihood estimation (MLE), and a simulation study validates their accuracy. To demonstrate the model's practical applicability, we apply it to two real-world datasets, where the BTILBE distribution exhibits exceptional flexibility and outperforms several established distributions, establishing it as a robust and valuable tool for statistical modeling. Received: 1 August 2025 | Revised: 10 November 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 supporting the findings of this study are openly availableat https://doi.org/10.1007/0-387-21645-6_3, reference number [45], and https://doi.org/10.1175/1520-0493(1973)101<0701:TKDMLE>2.3.CO;2, reference number [46]. Author Contribution Statement Jabir Bengalath: Conceptualization, Methodology, Software, Formal analysis, Investigation, Writing – review & editing, Visualization, Super…
subject: Research Articles
  topic: section
subject: beta transformation
  topic: keywords
subject: inverse length-biased exponential distribution
  topic: keywords
subject: statistical properties
  topic: keywords
subject: maximum likelihood estimation
  topic: keywords
subject: simulation
  topic: keywords
subject: applications
  topic: keywords
creator: Jabir Bengalath
creator: Mohamed Nejib Ouertani
creator: Hanene Hamdani
creator: Fasna Kottakkunnan
creator: Mohammed Elgarhy
rights: Copyright (c) 2025 Authors https://creativecommons.org/licenses/by/4.0/
date: 2025-12-11
publication date: 2025-12-11
doi: 10.47852/bonviewJCCE52027030