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: A Context-Aware Intelligent Scheduling Framework for Modern Operating Systems Using Hybrid Machine Learning Models
items: Expression of Concern: Examining Cognitive Shifts Through EEG: Insights from Resting State to Neurofeedback Game Engagement
items: Advanced Residual Network Architecture for Automated Brain Tumor Diagnosis: The MesResNet50 Approach
items: Multi-Source Data Fusion and Machine Learning for Soybean Crop Price Forecasting in India
items: Real-Time Drowsiness Detection Using YOLOv11 in Driver Monitoring Systems
items: A Weighted Ensemble of EfficientNetV2 Variants with Optimizer Tuning for Tomato Leaf Disease Classification
items: Deep Learning for Medicinal Plant Classification: A Comprehensive Review of Recent Advances and Challenges
items: Robust and Interpretable Deep Learning on EEG Spectrograms for Autism Spectrum Disorder Detection
items: Multi-agent Reinforcement Learning with Clustering and Forecasting for Optimized Energy Sharing in Microgrids
items: An IoT-Based Intelligent Monitoring System for Cargo Loss Detection in FPSO-Tanker Oil Transfer
items: NCOHA-WSC: Handling Noise and Class Overlap in Web Service Classification
items: Optimization of Electroencephalogram Feature Based on Multi-Band Feature Matrix and Relative Difference for Imagined Motor Movement Pattern Classification
items: Automated Classification of Self-Admitted Technical Debt Using Advanced Word Embedding Techniques
items: Adaptive Fault-Tolerant Scheduling in Dynamic Clouds
items: Manipulator Control Using DRL: Sim-to-Real Validation on a 3-DoF Arm
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: Bibliometric Analysis of Artificial Intelligence in Team Sports: Research Trends, Collaborations, and Creative Insights
items: A Hybrid Transformer–Ontology Framework for Halal Food Classification Using Multilingual Ingredient Label Analysis
items: ChatGPT as an Artificial Intelligence Tool to Provide an Analytical Boost to Text Mining: An Application to Tucker Models for Multiway Tables
items: Lesion-Conditioned Fundus Image Synthesis for Diabetic Retinopathy Using Pix2Pix Conditional GANs
items: MOD-NAV: Scalable Modular Inertial Navigation Framework for Resource-Constrained Embedded Hardware
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: HistoPath AI: An Edge-Powered Web Platform for Real-Time Lung Cancer Diagnosis
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: Deep Learning Approaches for Detecting Cyberbullying on Social Media
items: Strengthening Security in Clouds Through Cloud Computing Authentication Using Facial Image Forensics

A Context-Aware Intelligent Scheduling Framework for Modern Operating Systems Using Hybrid Machine Learning Models
type: item
description: 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 …
subject: Research Articles
  topic: section
subject: intelligence CPU scheduling
  topic: keywords
subject: context-aware scheduling
  topic: keywords
subject: reinforcement learning
  topic: keywords
subject: random forest classification
  topic: keywords
subject: deep Q-network
  topic: keywords
creator: Pandikumar Savarimalai
creator: Menaka Chellappan
creator: Murugaiyan Swaminathan Nidhya
creator: Margaret Mary Thomas
creator: Sevugapandi Nallathambi
creator: Manjula Selvaraj
rights: Copyright (c) 2026 Authors https://creativecommons.org/licenses/by/4.0/
date: 2026-02-24
publication date: 2026-02-24
starting page: 321
ending page: 332
doi: 10.47852/bonviewJCCE62026664

Expression of Concern: Examining Cognitive Shifts Through EEG: Insights from Resting State to Neurofeedback Game Engagement
type: item
description: The Editors are issuing an Expression of Concern to alert readers that, after the publication of this article, concerns have been raised regarding the integrity and reliability of aspects of the research and reporting. Specifically, these concerns relate to the description of EEG data acquisition; inconsistencies between reported statistical results and narrative interpretations; discrepancies in classification accuracy values; incomplete or inconsistent sample and protocol information; insufficient details of ethical approval; and limited information on the deep learning methodology and data transparency. The Editorial Office investigated these concerns in accordance with the journal’s publication ethics policies and relevant guidance. The authors were asked to provide clarifications and supporting documentation, including original data, device information, ethical approval documentation, statistical outputs, and model or code information where available. The authors cooperated with these requests and supplied responses and supporting materials. However, the information and documentation provided have not fully resolved the concerns. Sufficient doubts remain regarding the reliability and interpretation of the reported methods, analyses, and conclusions. Readers are therefore advised to interpret the findings of this article with caution. The article record may be updated should further editorial action be warranted. Author Saikat Gochhait disagrees with this Expression of Concern and requested that no statement be placed on the article pending further laboratory testing and validation. The remaining authors did not explicitly state whether or not they agree with this Expression of Concern. DOl of original article: https://doi.org/10.47852/bonviewJCCE52025505
subject: Expression of Concern
  topic: section
creator: Saikat Gochhait
creator: Irina Leonova
creator: Prabha Kiran
creator: Ayodeji Olalekan Salau
creator: Aitizaz Ali
creator: Tin Tin Ting
rights: Copyright (c) 2026 Authors https://creativecommons.org/licenses/by/4.0/
date: 2026-07-13
publication date: 2026-07-13
doi: 10.47852/bonviewJCCE620211314

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

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

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

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

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

Robust and Interpretable Deep Learning on EEG Spectrograms for Autism Spectrum Disorder Detection
type: item
description: 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 …
subject: Research Articles
  topic: section
subject: autism spectrum disorder (ASD)
  topic: keywords
subject: EEG spectrogram analysis
  topic: keywords
subject: convolutional neural networks (CNN)
  topic: keywords
subject: continuous wavelet transform
  topic: keywords
subject: explainable AI
  topic: keywords
creator: Andrew Jeyabose
creator: Arav Chadda
creator: Venkatesh Bhandage
rights: Copyright (c) 2026 Authors https://creativecommons.org/licenses/by/4.0/
date: 2026-06-16
publication date: 2026-06-16
doi: 10.47852/bonviewJCCE62027780

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

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

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

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

Automated Classification of Self-Admitted Technical Debt Using Advanced Word Embedding Techniques
type: item
description: 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.
subject: Research Articles
  topic: section
subject: self-admitted technical debt
  topic: keywords
subject: n-gram IDF
  topic: keywords
subject: word embeddings
  topic: keywords
subject: feature extraction
  topic: keywords
subject: natural language processing
  topic: keywords
creator: Satya Mohan Chowdary Gorripati
creator: Ali Altalbe
creator: Prasanna Kumar Rangarajan
rights: Copyright (c) 2025 Authors https://creativecommons.org/licenses/by/4.0/
date: 2025-11-21
publication date: 2025-11-21
doi: 10.47852/bonviewJCCE52025976

Adaptive Fault-Tolerant Scheduling in Dynamic Clouds
type: item
description: 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, Reso…
subject: Research Articles
  topic: section
subject: fault tolerance
  topic: keywords
subject: real-time cloud computing
  topic: keywords
subject: hybrid reinforcement learning
  topic: keywords
subject: Double Deep Q-Learning (DDQL)
  topic: keywords
subject: Proximal Policy Optimization (PPO)
  topic: keywords
subject: Soft Actor-Critic (SAC)
  topic: keywords
creator: Chetankumar Kalaskar
creator: Thangam Somasundaram
rights: Copyright (c) 2025 Authors https://creativecommons.org/licenses/by/4.0/
date: 2025-12-12
publication date: 2025-12-12
doi: 10.47852/bonviewJCCE52026266

Manipulator Control Using DRL: Sim-to-Real Validation on a 3-DoF Arm
type: item
description: Deep reinforcement learning is emerging as a powerful alternative to traditional inverse kinematics for controlling robotic manipulators. By learning optimal actions through interaction with the environment, it enables adaptable and precise control in complex continuous workspaces, making it suitable for dynamic manipulator operations. This paper proposes an actor–critic deep reinforcement learning framework for manipulator control in a continuous workspace. Conventional inverse kinematics solutions can become computationally complex for high-degree-of-freedom manipulators or complex kinematic structures. Accurately generating joint angles by deriving configuration-specific equations therefore remains a persistent challenge. This study employs deep deterministic policy gradient and twin delayed deep deterministic policy gradient algorithms to directly predict joint angles for target-reaching tasks, eliminating reliance on conventional inverse kinematics formulations. A key contribution is a simple yet effective linear reward function that supports stable convergence in continuous action spaces. The proposed framework is implemented using ROS2 and Gazebo simulation and validated on a custom-built physical manipulator. The work addresses complex equation-based control through reinforcement learning and demonstrates proof of concept on a 3-degree-of-freedom manipulator. Experimental results show high task success rates of 95.77% for the deep deterministic policy gradient algorithm and 94.71% for the twin delayed deep deterministic policy gradient algorithm, with accurate end-effector positioning within 0.01 m. These results confirm the effectiveness of the proposed framework for accurate and reliable manipulator control with reduced model complexity and improved real-world applicability. Received: 13 September 2025 | Revised: 14 April 2026 | Accepted: 10 June 2026 Conflicts of Interest The authors declare that they have no conflicts of interest to this work. Data Avail…
subject: Research Articles
  topic: section
subject: deep reinforcement learning
  topic: keywords
subject: DDPG
  topic: keywords
subject: TD3
  topic: keywords
subject: manipulator control
  topic: keywords
subject: simulation-to-real transfer
  topic: keywords
creator: Shivkumar Sankaralingam
creator: Nippun Kumaar Arulmani Angamuthu
creator: Suja Palaniswamy
rights: Copyright (c) 2026 Authors https://creativecommons.org/licenses/by/4.0/
date: 2026-07-17
publication date: 2026-07-17
doi: 10.47852/bonviewJCCE62027656

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

Bibliometric Analysis of Artificial Intelligence in Team Sports: Research Trends, Collaborations, and Creative Insights
type: item
description: 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 Inte…
subject: Research Articles
  topic: section
subject: artificial intelligence in team sports
  topic: keywords
subject: bibliometric analysis
  topic: keywords
subject: deep learning
  topic: keywords
subject: player tracking
  topic: keywords
subject: VOSviewer
  topic: keywords
creator: Doha Lefhal
creator: Ali Ouacha
creator: Abdeslam El Harraj
creator: Soumia Ziti
rights: Copyright (c) 2026 Authors https://creativecommons.org/licenses/by/4.0/
date: 2026-06-29
publication date: 2026-06-29
doi: 10.47852/bonviewJCCE62028489

A Hybrid Transformer–Ontology Framework for Halal Food Classification Using Multilingual Ingredient Label Analysis
type: item
description: Ensuring halal compliance within global food supply chains has become increasingly challenging due to multilingual ingredient labeling, ambiguous additive terminology, and heterogeneous sourcing practices. Existing rule-based systems or sequential machine learning models have limited capacity to resolve semantic ambiguity, address cross-lingual variation, and meet the interpretability requirements imposed by halal certification authorities. A hybrid neural–symbolic classification framework is proposed that integrates a multilingual transformer-based semantic encoder (XLM-R) with ontology-driven lexical reasoning for automated halal ingredient assessment. The proposed architecture leverages contextual embeddings learned from Malay, Arabic, and English ingredient descriptions, while a structured halal ontology encodes domain knowledge on ingredient origin, permissibility, and confidence levels. A probabilistic fusion mechanism combines transformer prediction scores with weighted ontological evidence to produce confidenceaware classifications into halal, haram, and syubhah categories. An experimental evaluation was conducted on a dataset of 10,000 product records using an 80:20 stratified train-test split. The proposed XLM-R + Ontology model achieves consistently superior performance, achieving overall accuracy of 0.81–1.00, recall of 0.90–1.00, and macro-averaged F1 of 0.85–0.90, outperforming Naive Bayes, support vector machine, and LSTM baselines. The hybrid framework demonstrates substantial improvements in detecting ambiguous syubhah cases and low-resource haram indicators, which remain problematic for conventional classifiers. Attention-based interpretability and performance attribution analyses indicate that integrating contextual semantic learning with structured symbolic knowledge enhances both reliability and transparency. These findings underscore the effectiveness of hybrid neural-symbolic architectures for multilingual, domain-specific food classification …
subject: Research Articles
  topic: section
subject: multilingual transformer
  topic: keywords
subject: halal food classification
  topic: keywords
subject: domain adaptation
  topic: keywords
subject: semantic reasoning
  topic: keywords
subject: halal ingredient ontology
  topic: keywords
creator: Mohd Azmi Al Betar
creator: Noorrezam Yusop
creator: Tao Hai
creator: Nor Aiza Moketar
creator: Nuridawati Mustafa
creator: Mohd Nazrien Zaraini
creator: Massila Kamalrudin
rights: Copyright (c) 2026 Authors https://creativecommons.org/licenses/by/4.0/
date: 2026-07-24
publication date: 2026-07-24
doi: 10.47852/bonviewJCCE62029040

ChatGPT as an Artificial Intelligence Tool to Provide an Analytical Boost to Text Mining: An Application to Tucker Models for Multiway Tables
type: item
description: In this comprehensive study, we meticulously investigated multidimensional data analysis techniques, particularly focusing on Tucker decomposition methods, spanning the period from 2000 to 2025. Our primary objective was to discern trends, advancements, and applications of these techniques across various domains of knowledge and how they have evolved over time. An extensive corpus of 288 scientific articles related to tensor decompositions, Tucker models, and applications was previously reviewed. Multivariate methods such as text mining using IraMuteq software and MANOVA-Biplot were employed to visualize identified data patterns, and the analytical capability of ChatGPT artificial intelligence was assessed to provide contextual insights and add another layer of information to the research. Our conclusions underscore the importance of blending traditional statistical approaches with natural language processing prowess to achieve a profound understanding of the data. This analysis offers a comprehensive perspective on the evolution and application of multidimensional data analysis techniques, with a special emphasis on the enduring relevance of Tucker techniques in this new millennium. Received: 24 July 2025 | Revised: 22 May 2026 | Accepted: 10 June 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 Roberto Cascante-Yarlequé: Conceptualization, Methodology, Validation, Investigation, Resources, Data curation, Writing –riginal draft, Writing – review & editing, Visualization, Supervision, Project administration. Purificación Galindo-Villardón: Software, Validation, Formal analysis, Resources, Data curation, Writing – review & editing. Fabricio Guevara-Viejó: Software, Formal analysis, Investigation, Visualization, Writing – original draft, Writing – review & editing.
subject: Research Articles
  topic: section
subject: Tucker decomposition
  topic: keywords
subject: Canonical Biplot
  topic: keywords
subject: ChatGPT
  topic: keywords
subject: neural networks
  topic: keywords
subject: scientific literature review
  topic: keywords
creator: Roberto Cascante-Yarlequé
creator: Purificación Galindo-Villardón
creator: Fabricio Guevara-Viejó
rights: Copyright (c) 2026 Authors https://creativecommons.org/licenses/by/4.0/
date: 2026-07-28
publication date: 2026-07-28
doi: 10.47852/bonviewJCCE62026916

Lesion-Conditioned Fundus Image Synthesis for Diabetic Retinopathy Using Pix2Pix Conditional GANs
type: item
description: Deep learning models for diabetic retinopathy (DR) demonstrate superior performance when trained on an extensive, classbalanced fundus dataset. However, widely used collections, such as EyePACS, exhibit significant class imbalances, particularly with the underrepresentation of severe stages. We propose a lesion-conditioned, pathology-aware synthesis framework to augment DR fundus images using a Pix2Pix-based conditional generative adversarial network. Lesion structures are incorporated through segmentation masks for four clinically significant lesion types—microaneurysms, hemorrhages, hard exudates, and soft exudates—derived from pixellevel annotations and/or a trained U-Net segmenter, which serves as conditioning inputs for the generator. To enhance data availability for minority grades, we implemented a minimum-real subsampling baseline and augmented each grade with lesion-fidelity-filtered synthetic images using a mask cycling strategy. We present qualitative results that demonstrate spatial consistency between the conditioned lesion masks and the synthesized appearance, and we include quantitative image-quality descriptors (Fréchet Inception Distance), discussing their limitations in small, low-diversity medical datasets. Finally, we evaluated the downstream utility through a real-only versus real + synthetic robustness study across three random seeds, reporting mean ± std and explicitly noting the small held-out test subset used in this paper. Overall, the proposed framework facilitates controlled, lesion-preserving synthesis for class balancing and offers an auditable augmentation protocol for DR model development. Received: 5 December 2025 | Revised: 27 March 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 IDRiD datasets that support the findings of this study are openly available at https://idrid.grand-challenge.org/Data/. The EyePACS datasets that su…
subject: Research Articles
  topic: section
subject: diabetic retinopathy
  topic: keywords
subject: fundus image synthesis
  topic: keywords
subject: Pix2Pix
  topic: keywords
subject: conditional GAN
  topic: keywords
subject: data augmentation
  topic: keywords
creator: Anju Asokan
creator: Manju Bhaskara Panickar Radha
rights: Copyright (c) 2026 Authors https://creativecommons.org/licenses/by/4.0/
date: 2026-07-23
publication date: 2026-07-23
doi: 10.47852/bonviewJCCE62028693

MOD-NAV: Scalable Modular Inertial Navigation Framework for Resource-Constrained Embedded Hardware
type: item
description: Inertial navigation algorithms are inherently computationally intensive, often necessitating high-performance embedded processors for real-time execution. This requirement poses a significant challenge for deploying such algorithms on resource-constrained, small-scale embedded platforms commonly used in cost-sensitive and power-limited applications. To address this limitation, this paper presents a scalable and modular inertial navigation framework designed to adapt to varying application requirements. The proposed framework decomposes the navigation system into independent functional modules, enabling selective inclusion or exclusion based on specific operational needs. This modular architecture allows developers to tailor the computational load by integrating only the essential components required for a given application, thereby reducing unnecessary processing overhead. As a result, the framework significantly lowers computational demand, memory usage, and power consumption. Furthermore, the scalability of the framework ensures that it can be efficiently deployed across a wide spectrum of hardware platforms, ranging from low-power microcontrollers to high-end processors. The design also supports ease of maintenance, extensibility, and system upgrades, making it suitable for diverse navigation applications such as unmanned systems and portable devices. Experimental validation demonstrates that the proposed approach achieves a substantial reduction in computational complexity while maintaining the accuracy of the navigation output. This makes it a practical and efficient solution for implementing inertial navigation systems on resource-constrained embedded hardware. Received: 29 July 2025 | Revised: 17 April 2026 | Accepted: 11 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 Mahe…
subject: Research Articles
  topic: section
subject: inertial navigation
  topic: keywords
subject: embedded systems
  topic: keywords
subject: modular software
  topic: keywords
subject: resource-constrained embedded hardware
  topic: keywords
creator: Mahender Katukuri
creator: Satyanarayana JV
creator: Ruchir Gupta
creator: Bhaskar Biswas
rights: Copyright (c) 2026 Authors https://creativecommons.org/licenses/by/4.0/
date: 2026-07-17
publication date: 2026-07-17
doi: 10.47852/bonviewJCCE62026975

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

HistoPath AI: An Edge-Powered Web Platform for Real-Time Lung Cancer Diagnosis
type: item
description: 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]. Autho…
subject: Research Articles
  topic: section
subject: histopathology
  topic: keywords
subject: Computational Severity Index (CSI)
  topic: keywords
subject: adenocarcinoma
  topic: keywords
subject: squamous cell carcinoma
  topic: keywords
subject: Edge AI
  topic: keywords
creator: Rajkumar Maharaju
creator: Sai Prasad Ellaboina
creator: Rama Valupadasu
rights: Copyright (c) 2026 Authors https://creativecommons.org/licenses/by/4.0/
date: 2026-06-22
publication date: 2026-06-22
doi: 10.47852/bonviewJCCE62028130

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

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