Journal of Computational and Cognitive Engineering
The Journal of Computational and Cognitive Engineering (JCCE) is an international, peer-reviewed, interdisciplinary journal that publishes cutting-edge articles on the recent trends in computational and cognitive engineering. The goal of the journal is to provide a new platform for the dissemination of the research and current practices in the emerging disciplines of the cognitive and computational engineering to solve the real-world problems. The journal welcomes submission related to various computational and cognitive modelling approaches in the areas of health, education, finance, environment, engineering, commerce and industry. The journal is a Gold Open Access journal, online readers don't have to pay any fees.
Adaptive Client-Specific Augmentation (ACSA) for Mitigating Data Heterogeneity in Federated Learning
Federated learning (FL) enables the training of deep learning models across multiple distributed clients while maintaining data privacy. Although this approach effectively protects sensitive information, it often yields lower accuracy compared to conventional centralized training. This performance gap is primarily due to the non-independent and non-identically distributed nature of client data—a central challenge in FL known as data heterogeneity. Because each client's dataset follows a distinct distribution, local models may diverge during training, which can degrade the performance of the aggregated global model. To address this issue, various data augmentation techniques have been proposed within the FL framework. However, many of these methods still require sharing of raw data or intermediate features among clients, potentially compromising privacy and conflicting with the fundamental principles of FL. To overcome this limitation, we introduce an additional module, Adaptive Client-Specific Augmentation, within the FedHetero, a refined framework that aims to identify the nature of heterogeneity present in the data and provides recommendations of augmentation policies instead of any data or feature-level information, which enhances privacy. Based on these shared policies, each client autonomously performs a dynamic data augmentation search to determine and apply the strategy most appropriate for its local dataset. This client-specific adaptation not only improves local and global model performance but also ensures strict privacy preservation. Experimental results on heterogeneous FL benchmarks demonstrate that our approach outperforms existing state-of-the-art methods in both communication efficiency and overall accuracy. Received: 13 November 2025 | Revised: 28 January 2026 | Accepted: 13 March 2026 Conflicts of Interest The authors declare that they have no conflicts of interest to this work. Data Availability Statement The CIFAR-10 and CIFAR-100 data that support the findings of this study are openly available at https://www.cs.toronto.edu/~kriz/cifar.html; The Riga data that support the findings of this study are openly available at https://deepblue.lib.umich.edu/data/concern/data_sets/3b591905z; The Dhristi data that support the findings of this study are openly available at https://www.kaggle.com/datasets/lokeshsaipureddi/drishtigs-retina-dataset-for-onh-segmentation; The Rim-one data that support the findings of this study are openly available at https://github.com/miag-ull/rim-one-dl; and The HRF data that support the findings of this study are openly available at https://www5.cs.fau.de/research/data/fundus-images. Author Contribution Statement Aiswariya Milan Kummaya: Conceptualization, Methodology, Software, Investigation, Resources, Data curation, Writing - original draft, Visualization. Amudha Joseph: Conceptualization, Methodology, Validation, Formal analysis, Writing – review & editing, Supervision, Project administration. George Ghinea: Supervision, Project administration.
Copyright (c) 2026 Authors https://creativecommons.org/licenses/by/4.0/
Enhancing Cardiovascular Disease Risk Prediction: A Comparative Analysis of Machine Learning Techniques
Cardiovascular disease (CVD) remains a global health threat. Accurately assessing CVD risk is crucial for preventative measures and interventions. This paper explores advancements in CVD risk prediction by examining several approaches and trials designed with the aim of diagnosing myocardial infarction (MI), which is generally known as a heart attack. MI is a critical medical condition in which a blocked artery cuts off blood flow and oxygen to a specific part of the heart muscle. This starves the heart tissue, leading to permanent damage and ranking as a top cause of mortality globally. By analyzing vast amounts of patient data, machine learning (ML) algorithms can predict the probability of a heart attack, pinpointing high-risk individuals. This allows for preventative measures and early intervention. The study in this paper utilizes tabular data for risk factors and includes an examination of multiple ML models to improve diagnostics, particularly for high-risk MI. These ML models include random forest classifiers, decision tree classifiers, support vector machines, logistic regression, and gradient boosting (GB). The experimental results reveal that GB has achieved higher accuracy than other models. This provides insights into enhancing cardiovascular health monitoring and diagnosis in clinical settings. Received: 8 July 2025 | Revised: 15 December 2025 | Accepted: 5 March 2026 Conflicts of Interest The authors declare that they have no conflicts of interest to this work. Data Availability Statement The data that support the findings of this study are openly available in Kaggle at https://www.kaggle.com/datasets/ritwikb3/heart-disease-cleveland. Author Contribution Statement Esraa Eldesouky: Conceptualization, Project administration, Funding acquisition. Walaa H. Elashmawi: Conceptualization, Software, Formal analysis, Resources, Supervision. Ahmed S. Salama: Data curation, Writing – review & editing. Ahmed Ali: Methodology, Investigation, Writing – original draft. Magi Mahfouz: Validation, Visualization.
Deep Learning Approaches for Detecting Cyberbullying on Social Media
The widespread use of social media has brought many challenges, mainly due to a misconstrued interpretation of the right to freedom of expression. Cyberbullying is a particularly noteworthy issue with far-reaching global implications for both its victims and the wider community. It takes the form of bullying that happens on several social media websites. This paper's goal is to develop a deep learning model capable of recognizing cases of cyberbullying on social media. Four models, such as bidirectional long short-term memory (BiLSTM), convolutional neural network with bidirectional long short-term memory (CNN-BiLSTM), bidirectional long short-term memory with gated recurrent unit (BiLSTM-GRU), and artificial neural network (ANN), will be evaluated in a multiclass classification difficulty context. The results showed that the BiLSTM model outperformed the other models by achieving the highest accuracy in 91% of cases, while the CNN-BiLSTM and ANN models demonstrated relatively lower performance. In addition to determining the efficacy of the deep learning techniques, the work highlights the urgent requirement for strong systems to resist cyberbullying. By enhancing detection accuracy, the proposed model can contribute significantly to providing a safer digital environment for further studies in this field. Received: 24 August 2024 | Revised: 16 December 2024 | Accepted: 25 February 2025 Conflicts of Interest The authors declare that they have no conflicts of interest to this work. Data Availability Statement Data are available on request from the corresponding author upon reasonable request. Author Contribution Statement Ghaith Jaradat: Conceptualization, Methodology, Formal analysis, Data curation, Writing – original draft, Writing – review & editing, Visualization, Project administration. Mohammad Shehab: Conceptualization, Methodology, Software, Validation, Formal analysis, Data curation, Writing – original draft, Writing – review & editing, Supervision, Project administration. Dyala Ibrahim: Software, Validation, Formal analysis, Investigation, Writing – original draft, Writing – review & editing, Project administration. Saif Najdawi: Methodology, Validation, Writing – original draft, Writing – review & editing, Visualization. Rami Sihwail: Software, Investigation, Data curation, Writing – original draft, Writing – review & editing, Visualization.
Copyright (c) 2025 Authors https://creativecommons.org/licenses/by/4.0/
Strengthening Security in Clouds Through Cloud Computing Authentication Using Facial Image Forensics
Numerous cyber threats may succeed in cloud platforms owing to unproductive authentication methods. Multiple credential authentication is a vital precautionary measure that helps reinforce cloud security for warding off imminent data breaches and illegal access. This kind of authentication approach strongly guarantees that trustworthy clients are only ratified to get cloud services, making it less tiresome for clients and more secure for organizations. This study proposes an Image Forensics-based Dual Credential Authentication (IF-DCA) based on username (i.e., preferably email ID to avoid replica) and client photographs as password. Along with a username, a rapid and acquainted user action of capturing a photograph using a web camera is adequate for this method; hence it does not need any expensive, special hardware devices. During the registration phase, along with the username, the user's face image is stored on the cloud server as a password. When registered clients need to access cloud resources or data, they should log in on the server with their username and their photograph. To improve the authentication approach, this work proposes an Attribute-controlled Conditional Generative Adversarial Network (ACC-GAN) to generate face images of the same user at various age groups. ACC-GAN includes an additional attribute control unit and an age prediction unit to synthesize photo-realistic face images with aging effects. After matching the username and face image with the registered username and ACC-GAN-generated images, the face identification module provides access to the authorized cloud user. The performance of IF-DCA is assessed through the Cross-Age Celebrity Dataset using the MATLAB R2018b/deep learning toolbox. The empirical analysis reveals that the ACC-GAN achieves better performance measures such as 98.40% accuracy, 99.3% sensitivity, 90.0% specificity, and 95.7% precision. The performance of IF-DCA is analyzed using the verification time for username, user image, and complete authentication against the number of users. Received: 4 September 2024 | Revised: 14 October 2024 | Accepted: 7 November 2024 Conflicts of Interest The authors declare that they have no conflicts of interest to this work. Data Availability Statement The Cross-Age Celebrity Datasets that support the findings of this study are openly available at https://paperswithcode.com/dataset/cacd. Author Contribution Statement Pranali Dahiwal: Conceptualization, Software, Formal analysis, Resources, Writing – original draft, Visualization, Project administration. Vijay Khare: Methodology, Validation, Investigation, Data curation, Writing – review & editing, Supervision.
Adaptive Gaussian-Based Kernel K-Means: Scalable Adaptive Kernel-Based Clustering for Big Data
In the field of big data, clustering remains a fundamental component of data mining and knowledge discovery, particularly in high-volume, heterogeneous environments. Although traditional clustering algorithms have been widely used, they often exhibit poor scalability, limited robustness, and sensitivity to noise when applied to large industrial-scale datasets. To address these limitations, this paper proposes a Spark-based clustering framework that incorporates an Adaptive Gaussian-Based Kernel K-Means (A-GBK-Means) algorithm, designed to mitigate performance degradation in the presence of noisy and nonhomogeneous data. The A-GBK-Means algorithm integrates fuzzy logic with adaptively determined kernel widths, using local density estimation, thereby enhancing both clustering accuracy and flexibility. Extensive experiments were conducted on real-world industrial sensor data as well as synthetic benchmarks, and performance was evaluated using multiple metrics, including execution time, Silhouette Score, Davies–Bouldin Index, and noise resilience. The comparative analysis demonstrates that the A-GBK-Means algorithm consistently outperforms classical approaches—namely K-Means, Density-Based Spatial Clustering of Applications with Noise, Fuzzy C-Means, and standard GBK-Means—in terms of clustering quality and computational efficiency. Furthermore, the proposed framework exhibits superior scalability, owing to its distributed architecture built on Apache Spark. This study provides practical insights into scalable and interpretable clustering methods for intelligent manufacturing and sensor-based systems and highlights the effectiveness of adaptive kernel learning in modern big data applications. Received: 18 June 2025 | Revised: 8 August 2025 | Accepted: 16 September 2025 Conflicts of Interest The authors declare that they have no conflicts of interest to this work. Data Availability Statement The data that support the findings of this study are openly available in Kaggle at https://www.kaggle.com/datasets/dnkumars/industrial-equipment-monitoring-dataset. The data that support the findings of this study are openly available in Kaggle at https://www.kaggle.com/datasets/mohammedarfathr/sensor-data-from-an-industrial-machine. Author Contribution Statement Bakhshali Bakhtiyarov: Investigation, Data curation, Writing – original draft, Project administration. Aynur Jabiyeva: Conceptualization, Validation, Formal analysis. Anakhanim Mutallimova: Software, Resources. Rukhsara Novruzova: Methodology, Writing – review & editing, Supervision. Mahabbat Khudaverdiyeva: Visualization.
A Novel Approach to Model the Economic Characteristics of an Organization by Interval-Valued Complex Pythagorean Fuzzy Information
The success of a nation mainly depends on its economic power. The economic growth plays an important place in enhancing the caliber of life as it produces job opportunities. In this research, the effect of uncertainty on economic growth and investments is examined with gross domestic product (GDP). Currently, the relationship between financial market and economic growth is one of the most important issues in economics. These departments are often accompanied by uncertainty. Through that regard, the idea of interval-valued complex Pythagorean fuzzy relation (IVCPFR) is introduced that is handy in such situations. Moreover, the Cartesian product between interval-valued complex Pythagorean fuzzy sets and verities of IVCPFRs was described. Furthermore, these IVCPFRs are applied to analyze GDP, unemployment, price, demand, supply, interest rate, investment, and money supply effecting business markets. The possibility of the Hasse diagram is more suitable to make sense of the relationship between various economic factors. IVCPFRs will clearly help to set up the impact of one component on the other and change the grades of supportive and distinctive outcomes concerning the time. IVCPFR is used for tracking various multidimensional issues and produces quick and better outcomes rather than the previous methodologies. Finally, the proposed method is the best method to model uncertainty in economics rather than previous methods. Received: 12 May 2022 | Revised: 12 August 2022 | Accepted: 17 August 2022 Conflicts of Interest The authors declare that they have no conflicts of interest to this work.
interval-valued complex Pythagorean fuzzy set
interval-valued complex Pythagorean fuzzy relation
Copyright (c) 2022 Authors https://creativecommons.org/licenses/by/4.0/
Intuitionistic Fuzzy Rough Mutual Information Aided Feature Subset Selection and Its Applications
Feature selection plays an essential role in solving the challenges of the "curse of dimensionality" in data analysis, aiming to increase the learning algorithms' performance. A key challenge in this field is achieving accurate attribute selection when handling both numerical and nominal attributes. To address this problem, we demonstrate a hybrid intuitionistic fuzzy (IF) similarity-based approach that flexibly handles mixed types of data for more precise attribute selection. The study shows an IF granular structure to manage noise in heterogeneous data and enhances the concepts of IF rough entropy, joint entropy, and conditional entropy to provide a comprehensive framework to deal with uncertainty. Moreover, IF rough mutual information is implemented to extract both uncertainty and the association between conditional attributes and the decision class, forming the basis of a novel attribute selection approach. The proposed algorithm contains intuitionistic fuzzification, evaluation of fuzzy mutual information to compute the significance of features, and recursive selection of the most important attributes, thus effectively reducing dimensionality. We set up a theoretical foundation using IF sets, IF information system, IF relation theory, and hybrid similarity relations, which ensures the robustness of the approach. The method is rigorously evaluated on seven benchmark datasets, showing superior performance on various metrics, including accuracy, sensitivity, and specificity, when compared with existing attribute selection approaches. Results demonstrate increased prediction of phospholipidosis-positive molecules with a sensitivity of 89.56%, a specificity of 92.63%, an accuracy of 95.98%, an AUC of 0.968, and an MCC of 0.908, which represents the strong class differentiation ability of the model. These findings underscore the effectiveness of the hybrid IF similarity-based attribute selection approach, which makes it a valuable tool for managing high-dimensional datasets and advancing the field of attribute selection. Received: 14 May 2025 | Revised: 30 October 2025 | Accepted: 21 November 2025 Conflicts of Interest The authors declare that they have no conflicts of interest to this work. Data Availability Statement The data that support the findings of this study are openly available at https://archive.ics.uci.edu/. Author Contribution Statement Aneesh Kumar Mishra: Conceptualization, Methodology, Validation, Data curation, Writing – original draft, Writing – review & editing. Neelesh Kumar Jain: Software, Formal analysis, Supervision, Project administration. Ravindra Kumar Singh Jain: Investigation, Resources, Visualization.
Switching-Algebraic Calculation of Banzhaf Voting Indices
This paper employs switching-algebraic techniques for the calculation of a fundamental index of voting powers, namely, the total Banzhaf power. This calculation involves two distinct operations: (a) Boolean differencing or differentiation, and (b) computation of the weight (the number of true vectors or minterms) of a switching function. Both operations can be considerably simplified and facilitated if the pertinent switching function is symmetric or it is expressed in a disjoint sum-of-products form. We provide a tutorial exposition on how to implement these two operations, with a stress on situations in which partial symmetry is observed among certain subsets of a set of arguments. We introduce novel Boolean-based symmetry-aware techniques for computing the Banzhaf index by way of two prominent voting systems. These are scalar systems involving six variables and nine variables, respectively. The paper is a part of our on- going effort for transforming the methodologies and concepts of voting systems to the switching-algebraic domain, and subsequently utilizing switching-algebraic tools in the calculation of pertinent quantities in voting theory. Received: 13 February 2023 | Revised: 5 April 2023 | Accepted: 19 May 2023 Conflicts of Interest The authors declare that they have no conflicts of interest to this work. Data Availability Statement Data sharing is not applicable to this article as no new data were created or analyzed in this study.
Copyright (c) 2023 Authors https://creativecommons.org/licenses/by/4.0/
Digital Transformation and Corporate ESG Performance – Evidence from China
The focal point of this paper is to study environmental, social, and governance (ESG) performance consequences of digital transformation for companies, as well as green technological innovation, corporate social responsibility, and internal control as mediating variables. A set of digital transformation indicators was developed utilizing data from 355 listed industrial companies in China between 2011 and 2019 and vector arithmetic. The study’s findings suggested that digital transformation has considerably raised ESG performance, with green technological innovation, corporate social responsibility, and internal control as significant mediating variables. The paper has several contributions; for example, it provides theoretical and empirical evidence for ESG improvement in Chinese firms and an essential factor associated with ESG performance, among others. The non-economic impacts of digital transformation on ESG performance are essential insights offered by this study. They also provide insight into these impacts' mechanisms, including green technology, corporate social responsibility, and internal control. Received: 9 May 2024 | Revised: 5 August 2024 | Accepted: 31 August 2024 Conflicts of Interest The authors declare that they have no conflicts of interest to this work. Data Availability Statement Data sharing is not applicable to this article as no new data were created or analyzed in this study. Author Contribution Statement Wenxin Zhang: Conceptualization, Methodology, Investigation, Resources, Data curation, Writing – original draft. Xiangyu Li: Software, Writing – review & editing.
Copyright (c) 2024 Authors https://creativecommons.org/licenses/by/4.0/
Performance Analysis of Block–Internet of Things–Chain (BIoTC) Framework for Smart Home
Smart-home systems built on Internet of Things (IoT) devices offer convenience, but they also introduce gaps in security and privacy that are often overlooked. Traditional protective measures such as lightweight encryption, access control rules, and intrusion alerts do help, but they do not fully address issues such as traceability, proof of device identity, or resistance to more adaptive attacks. In response to this, the research proposes a blockchain-supported security model called Block–Internet of Things–Chain (BIoTC), designed specifically for smart-home IoT networks. This work developed the setup using Cisco Packet Tracer to model a basic smart home. In this environment, the study carried out on four forms of attacks: Man in the Middle (12 attempts), RFID cloning (6 attempts), unauthorized access (1 case), and social engineering (1 case). Each attack was performed on a regular IoT configuration, and then the same test was repeated after introducing the BIoTC layer. The blockchain component, implemented through a lightweight Python module, maintains immutability and coordinates identity checks to confirm that a device should be allowed in the network. The distinct contribution of research comes from grounding the evaluation in actual experimental observation rather than theoretical claims. The results show that BIoTC is successful in the reduction of attack success rate with acceptable performance overhead for a smart-home environment. Using a real-world example, the mathematical validation was performed. This research paper presents verified and statistical methods for the security of IoT smart-home systems. The results demonstrate that integrating blockchains enhances defense against security threats in the IoT environment. Received: 15 September 2025 | Revised: 12 November 2025 | Accepted: 6 February 2026 Conflicts of Interest The authors declare that they have no conflicts of interest to this work. Data Availability Statement Data sharing is not applicable to this article as no new data were created or analyzed in this study. Author Contribution Statement Gaurav Vats: Conceptualization, Methodology, Software, Formal analysis, Investigation, Writing – original draft, Visualization. Sarvesh Tanwar: Validation, Investigation, Resources, Writing – review & editing, Visualization, Supervision, Project administration. Pankaj Kumar Sharma: Resources, Data curation, Supervision.
A Weighted Ensemble of EfficientNetV2 Variants with Optimizer Tuning for Tomato Leaf Disease Classification
Tomatoes are one of the most widely grown horticultural crops but are highly susceptible to leaf diseases that could significantly affect yield and quality. Early and accurate disease detection is necessary to enhance crop productivity and food security. The present study presents a deep learning system for automatic tomato leaf disease classification using a weighted ensemble of EfficientNetV2 models. Seven variants of EfficientNetV2 (B0-B3, S, M, and L) were fine-tuned on an augmented version of the filtered PlantVillage dataset. To improve feature visibility under varying lighting conditions, images were preprocessed using contrast-limited adaptive histogram equalization. Five optimization algorithms, which include Adam, Adamax, AdamW, Nadam, and RMSProp, were tested to assess their impact on model convergence and generalization. Among individual models, Adam-optimized EfficientNetV2L performed the best in accuracy with a measure of 99.50%. For classifiability resilience improvement, 16 ensemble configurations (eight unweighted and eight weighted ensembles using different combinations of EfficientNetV2-S, M, and L variants and five optimizers) were explored, with the best weights discovered using brute-force grid search. The best-performing weighted ensemble achieved 99.89% in accuracy, precision, recall, and F1-score, which was evaluated on a held-out test set not used during training or validation, demonstrating its strong generalizability. The proposed framework offers a scalable and reliable solution for early tomato disease detection with significant potential for real-time plant health monitoring in precision agriculture. Received: 10 September 2025 | Revised: 22 December 2025 | Accepted: 6 February 2026 Conflicts of Interest The authors declare that they have no conflicts of interest to this work. Data Availability Statement Data are available from the corresponding author upon reasonable request. Author Contribution Statement Aliyu Tetengi Ibrahim: Conceptualization, Methodology, Formal analysis, Writing – original draft. Ibrahim Hayatu Hassan: Conceptualization, Methodology, Formal analysis, Resources, Data curation, Writing – original draft. Mohammed Abdullahi: Validation, Formal analysis, Resources, Data curation, Writing – review & editing, Visualization. Abeer Rashad Mirdad: Conceptualization, Software, Validation, Investigation, Writing – original draft, Writing – review & editing, Supervision, Project administration. Muhammad I. Khan: Methodology, Validation, Investigation, Resources, Data curation, Writing – review & editing, Visualization, Project administration. Saeed Ali Bahaj: Conceptualization, Software, Writing – original draft. Fatima Khan Nayer: Investigation, Visualization, Supervision, Project administration.
Embedded Sparsity and Ensemble Learning for Real-Time Textual Anomaly Detection: A SCADA-Inspired Benchmark on Lecture Transcript Data
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 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 GitHub at https://github.com/rosewang2008/sight. Author Contribution Statement Ayoub Alsarhan: Conceptualization, Resources, Writing – original draft, Visualization. Saja Jaradat: Conceptualization, Resources, Writing – original draft, Visualization. Suhaila Abuowaida: Validation, Writing – review & editing, Funding acquisition. Sami Aziz Alshammari: Methodology, Investigation, Data curation, Supervision. Nayef Hmoud Alshammari: Software, Project administration. Khalid Hamad Alnafisah: Software, Formal analysis, Project administration.
Real-Time Drowsiness Detection Using YOLOv11 in Driver Monitoring Systems
One of the main causes of traffic accidents that frequently result in fatalities, serious injuries, and large property losses is driver fatigue. Therefore, enhancing driver safety and preventing such incidents depend heavily on the early, nonintrusive detection of fatigue. A real-time driver monitoring system based on the most recent YOLOv11 framework is presented in this paper. By examining visual indicators like prolonged eye closure and abnormal head movements, the system can identify early indicators of drowsiness. We acquired a purpose-designed dataset that covered a range of driving behaviors and environmental changes in order to improve the framework's flexibility and dependability. The effectiveness of the proposed model was extensively compared with several existing detection methods, and the results indicated that YOLOv11 achieved better accuracy in terms of various evaluation metrics. This improvement is due to its better feature extraction pipeline, attention modules, and computationally efficient architecture, which make it very appealing for real-time applications even inside the vehicle. Overall, the system provides a practical and reliable early warning system for drivers and could greatly reduce the risks associated with fatigue to improve safety in transportation. However, extreme lighting variations and face occlusions may impact the model's performance. To improve robustness, future research will concentrate on expanding the dataset andincorporating multimodal inputs. Received: 9 September 2025 | Revised: 4 December 2025 | Accepted: 6 February 2026 Conflicts of Interest The authors declare that they have no conflicts of interest to this work. Data Availability Statement The data that support the findings of this study are openly available in Roboflow website at https://universe.roboflow.com/santhosh-kumar-k-l/drowsiness-detection-koqqc. Author Contribution Statement Santhosh Kumar Kadur Lokeshappa: Conceptualization, Methodology, Software, Validation, Formal analysis, Investigation, Data curation, Writing – original draft. Pravinth Raja Suthakar: Resources, Writing – review & editing, Visualization, Supervision.
Multi-Source Data Fusion and Machine Learning for Soybean Crop Price Forecasting in India
Soybean is a major oilseed crop in India, and its market prices have exhibited significant volatility in recent years. Such price fluctuations create serious challenges for small and medium-scale farmers. Accurate price forecasting is essential to support informed decision-making by farmers and agri-business stakeholders. This study focuses on forecasting soybean prices in the Indian market using data spanning January 2015 to June 2025. The dataset integrates multiple heterogeneous sources, including daily market data from Agmarknet, weather information from the India Meteorological Department, regional production, and trade statistics. Extensive exploratory data analysis is conducted to examine price distributions, temporal trends, regional variations, and inter-variable relationships. Several predictive approaches are evaluated, including traditional time-series models (Autoregressive Integrated Moving Average), ensemble machine learning models, and deep learning models. In addition, a hybrid Long Short-Term Memory–Gated Recurrent Unit framework (AgroNET) is proposed to effectively model complex temporal dependencies across heterogeneous data sources. Model performance is assessed using k-fold cross-validation and evaluated through root mean squared error, mean squared error, MAPE, and R² metrics in a Python-based implementation. The results demonstrate that deep learning-based models outperform conventional approaches, with AgroNET achieving the highest R² and lowest error values. To enhance model transparency, explainable artificial intelligence using the local interpretable model-agnostic explanation technique is incorporated to identify key factors influencing individual price predictions. Overall, the proposed framework offers an effective and interpretable solution for soybean price forecasting in India and supports future research on multi-source integration and real-time agricultural price prediction. Received: 9 September 2025 | Revised: 12 January 2026 | Accepted: 11 March 2026 Conflicts of Interest The authors declare that they have no conflicts of interest to this work. Data Availability Statement Data are available from the corresponding author upon reasonable request. Author Contribution Statement Vilas Damodhar Ghonge: Conceptualization, Methodology, Software, Validation, Formal analysis, Investigation, Resources, Data curation, Writing – original draft, Writing – review & editing, Visualization. Yogesh Kulkarni: Conceptualization, Methodology, Investigation, Resources, Supervision.
AI Integration and Digital Communication: The Cognitive Power of Brand Equity in the Jordanian Banking Industry
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 curation, Writing – review & editing, Visualization. Mohammed Binkhamis: Validation, Resources, Data curation, Writing – original draft. Raed Momani: Software, Investigation, Resources. Mohammad Alshinwan: Data curation, Writing – original draft, Writing – review & editing, Visualization.
FedProx-Enhanced Federated Transfer Learning for Heterogeneous 3D Medical Image Classification
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/dataset. Author Contribution Statement Manjunath Naganna: Conceptualization, Methodology, Software, Resources, Writing – original draft, Writing – review & editing, Visualization, Project administration. Guru Ramachandra Nayaka: Formal analysis, Supervision. Natesh Mahadev: Validation, Investigation, Resources, Data curation. Mayura Tapkire: Conceptualization, Validation, Data curation, Writing – review & editing.
Beta-Transformed Inverse Length-Biased Exponential Distribution: Properties and Applications
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, Supervision. Mohamed Nejib Ouertani: Software, Validation, Formal analysis, Data curation. Hanene Hamdani: Software, Validation, Formal analysis, Data curation. Fasna Kottakkunnan: Conceptualization, Methodology, Software, Resources. Mohammed Elgarhy: Validation, Investigation, Writing – original draft, Writing – review & editing, Project administration, Funding acquisition.
inverse length-biased exponential distribution
Exploring Image Encryption Techniques: Challenges and Real-Life Application Insights with AI Influence
In today's rapidly evolving digital landscape, the importance of data security cannot be overstated. Every day, vast amounts of information are generated and shared, with each data type (whether text, image, audio, or video) possessing its own unique characteristics and vulnerabilities. Among these, image data stand out due to their intricate structure and the sheer volume of sensitive information that they can contain. Images are different from text data. Text data are straightforward data and are easy to protect. Images are difficult to protect because they are not straightforward. Ways used for encryption have long been considered suitable for encrypting image data. However, currently, they are not. Criminals are developing techniques every day to defeat the right-spotting methods of the business world. Further, this prompts scientists and professionals to find effective solutions. This is where artificial intelligence (AI) comes into play. Techniques for securing images enhanced by AI have the potential to improve image encryption significantly in a manner that increases both protective security efficiency and processing efficiency. This paper discusses various image encryption techniques in detail, including both ancient and modern methods for protecting visual information. It also discusses AI that helps in enhancing the accuracy and efficacy of these encryption techniques. When it comes to encryption of images, AI makes it more powerful. It can also counter newer threats that are emerging. Therefore, security is more dependable and efficient. This paper gives a detailed review of state-of-the-art methods and their effectiveness over traditional methods. By delving into various AI techniques for image encryption, this paper shows that the incorporation of these techniques makes image encryption more secure and efficient. Received: 25 July 2025 | Revised: 18 September 2025 | Accepted: 11 October 2025 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 Dyala Ibrahim: Conceptualization, Methodology, Software, Validation, Investigation, Writing – original draft, Writing – review & editing, Supervision, Project administration. Omar Isam Al Mrayat: Methodology, Software, Validation, Investigation, Writing – original draft, Supervision, Project administration. Ahmad Reda Alzighaibi: Formal analysis, Writing – original draft, Visualization. Hasan Hashim: Writing – original draft, Visualization. El-Sayed Atlam: Writing – original draft, Visualization.
Lightweight CNN-Enabled Framework for Automated Detection of Scoliosis and Spondylosis Using Spine X-rays
The spine serves as a fundamental structure that supports bodily functions, movements, protection of vital neural pathways, and overall well-being. Any abnormalities or injuries to the spine can significantly impact a person's quality of life. Early detection of these conditions is crucial for effective treatment and management. Hence, this study focuses on the detection and classification of spine abnormalities using advanced deep learning algorithms. In recent years, deep learning methods, particularly convolutional neural networks (CNNs), have demonstrated promising performance in medical image processing applications. This study investigates the effectiveness of advanced CNN architectures, specifically AlexNet and ResNet, in detecting spine abnormalities and distinguishing scoliosis, spondylosis, and normal spine. Leveraging a dataset comprising 3341 diverse spine X-rays images, this study aims to not only compare the effectiveness of AlexNet and ResNet but also determine the most accurate model for deployment in clinical settings. The results demonstrate the comparable performance of AlexNet and ResNet in detecting spine abnormalities. Insights gained from this comparative analysis can inform healthcare practitioners and researchers on the optimal choice of deep learning architecture for spine abnormality detection, ultimately contributing to improved diagnostic accuracy and patient outcomes. Received: 25 July 2025 | Revised: 28 September 2025 | Accepted: 15 November 2025 Conflicts of Interest The authors declare that they have no conflicts of interest to this work. Data Availability Statement The data that support the findings of this study are openly available in Mendeley Data at http://doi.org/10.17632/xkt857dsxk.1, reference number [23]. Author Contribution Statement Gobalakrishnan Natesan: Conceptualization, Methodology, Software, Validation, Formal analysis, Investigation, Resources, Data curation, Writing – original draft, Project administration. Anbarasan Murugesan: Software, Formal analysis, Investigation, Resources, Data curation, Writing – review & editing, Visualization, Supervision. Ramshankar Nagarajan: Software, Formal analysis, Investigation, Resources, Data curation, Writing – review & editing, Visualization.
Measuring Satisfaction of Security Requirements Using Fuzzy Soft Set Theory
In recent years, a range of innovative approaches have been developed to tackle decision-making challenges more effectively. These approaches include concepts such as probability theory, fuzzy set theory, intuitionistic fuzzy sets, vague sets, interval mathematics, and rough set theory. These tools each offer unique approaches to managing uncertainty and imprecision, which are common when working with complex systems. One major issue in software development is failure to meet security requirements. This can be caused by a variety of factors, such as unclear security specifications, evolving threat landscapes, or gaps in communication among stakeholders. To address this persistent problem, this paper introduces a new method designed to quantify security requirements more accurately and streamline the decision-making process for assessing security risks. This approach is based on fuzzy soft set theory, which allows for more flexible and precise handling of uncertain or ambiguous information. By using this method, software engineers and security professionals can gain a clearer understanding of security risks and make better-informed decisions when building secure systems. To demonstrate the practical value of the proposed method, a case study is conducted to evaluate its effectiveness. The results of this case study suggest that security administrators can use this method to implement more targeted and efficient security measures, ensuring that the systems they develop are more resilient to potential threats. Received: 20 July 2025 | Revised: 20 October 2025 | Accepted: 31 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 Dyala Ibrahim: Conceptualization, Software, Resources, Data curation, Writing – review & editing, Supervision, Project administration. Omar Isam Al Mrayat: Conceptualization, Methodology, Software, Resources, Data curation, Writing – review & editing, Supervision, Project administration. Lial Raja Alzabin: Software, Resources, Data curation, Visualization. Ahmad Alajarmeh: Validation, Visualization. Ahmad Reda Alzighaibi: Validation, Formal analysis, Writing – original draft. Hasan Hashim: Investigation. El-Sayed Atlam: Investigation. Malik Jawarneh: Investigation.
Layered Resource Sharing for Distributed Computing in IoT Using Hybrid MILP-Chameleon Swarm Optimization
The emergence of edge computing and IoT has allowed computational resources closer to the end users, hence improving applications that are latency-sensitive and demand significant resources. Distributed computing is a viable method for advancing computations through parallel tasking across multiple computation nodes. Research has been predominantly focused on the masters and worker paradigm, wherein resource sharing is confined to one-hop neighborhoods. This limitation can disrupt the efficiency of distributed computing, particularly when local resources are scarce or the connectivity is inconsistent. This study offers a unique distributed computing paradigm that expands resource-sharing capabilities beyond local one-hop neighbors by leveraging layered network topologies. We propose a hybrid centralized mixed-integer linear programming optimization method that incorporates a chameleon swarm optimization approach to derive the optimal solutions. The efficacy of the proposed scheme was assessed through theoretical analyses and extensive simulation experiments, which demonstrated its advantages over traditional distributed computing and computation offloading schemes. Received: 20 July 2025 | Revised: 4 November 2025 | Accepted: 24 November 2025 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 Sathishkumar Samiappan: Software, Validation, Investigation, Resources, Data curation, Writing – original draft, Writing – review & editing, Visualization, Supervision, Project administration. Anand R: Conceptualization, Methodology, Software, Validation, Formal analysis, Investigation, Resources, Data curation, Writing – original draft, Writing – review & editing, Visualization, Supervision, Project administration.
Killer Whale Algorithm for Solving the Maximum Flow Problem on Transportation Networks
The most extreme stream issue (MFP) may be a principal subject in the chart hypothesis with applications in computer science, operations inquiry, and designing. It plays a key part in ranges such as activity control, communication frameworks, and the dissemination of assets. Classical calculations, especially Ford–Fulkerson and its expansions, have given the establishment of the means for tackling this issue. In any case, when systems develop bigger, more complex, or dynamically alter, these approaches regularly battle to preserve effectiveness and accuracy. Much like transportation frameworks, each course in an arrangement encompasses a capacity restraint, confining how much stream can move between hubs. Conventional strategies, which accept settled structures and unsurprising imperatives, are not continuously suited to taking care of these challenges. To deal with these limits, this think piece shows the Killer Whale Optimization (KWO) calculation; it is pushed by the chasing methods of killer whales. The strategy was based on multi-flow problems using real and made-up datasets. To show its useful meaning, a case study was made on a project stream in Kota Kinabalu, where made-up systems of different sizes were used to test flexibility and reliability. The result seems like KWO usually beats the Ford–Fulkerson method, especially because it does faster merging and shows more flexibility when dealing with complicated stream situations. These advantages make KWO a good choice for real world stream optimization problems. Beyond theoretical interest, the results suggest that the calculation may be used for project management, transport planning, and resource assignment in various organized systems. Received: 19 July 2025 | Revised: 18 September 2025 | Accepted: 22 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 Afaf Edinat: Conceptualization, Methodology, Software, Validation, Formal analysis, Investigation, Data curation, Writing – original draft, Writing – review & editing, Visualization, Supervision, Project administration. Mohammad Shehab: Conceptualization, Methodology, Software, Validation, Formal analysis, Investigation, Data curation, Writing – original draft, Writing – review & editing, Supervision, Project administration. Fatima Haimour: Methodology, Validation, Formal analysis, Data curation, Writing – original draft, Writing – review & editing, Supervision. Hanaa Fathi: Conceptualization, Software, Formal analysis, Data curation, Writing – original draft, Writing – review & editing, Visualization, Supervision. Katrina Sundus: Software, Validation, Writing – original draft, Writing – review & editing.
Data-Driven Churn Prediction: Evaluating ML Models for Business Retention Strategies
Customer churn, which is the loss of customers to a service, is a major threat to business profits, especially in competitive industries like telecommunications, finance, and e-commerce. Identification of at-risk customers early enough helps organizations to take proactive retention efforts, thus minimizing loss of revenues. The research study will focus on forecasting customer churn through the application of a machine learning (ML) algorithm to provide strategies to enhance customer retention strategies. A comparative analysis was done using five ML models: logistic regression, K-Nearest Neighbors, support vector machine (SVM), decision tree, and random forest (RF). To have a better understanding of the performance of the models, several metrics were applied, namely, accuracy, sensitivity (recall), specificity, precision, and F1 score. In terms of performance, RF showed the highest performance and the best accuracy (80.74%) and F1 score (61.34%), and the specificity and precision were high. SVM also demonstrated stable results, especially in sensitivity, which makes it appropriate for detecting churn-prone customers. The results indicate the advantage of ensemble algorithms such as RF because of their strong nature and generalization on complex data forms. This study addresses existing research gaps by standardizing data preprocessing, evaluation metrics, and model benchmarking, while incorporating multiple performance indicators beyond accuracy to effectively handle class imbalance and real-world decision-making trade-offs. The study further provides insights into model interpretability, deployment feasibility, and computational cost, enabling organizations to select churn prediction models based on both predictive performance and practical applicability. Received: 19 July 2025 | Revised: 28 October 2025 | Accepted: 6 January 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/blastchar/telco-customer-churn/data. Author Contribution Statement Ramshankar Nagarajan: Methodology, Software, Validation, Resources, Visualization. Siva Subramanian Raju: Conceptualization, Formal analysis, Investigation, Data curation, Writing – original draft, Writing – review & editing, Supervision, Project administration. Athiraja Atheeswaran: Methodology, Software, Validation, Visualization.
Sharded Fog Architecture for Scalable CP-ABE with Minimized Policy Trees in Blockchain Systems
The protection of Personally Identifiable Information (PII) remains a significant challenge, particularly within financial transactions. Current systems often fail to strike an optimal balance between security, performance, fine-grained access control, and distributed processing. A common dilemma is that some solutions prioritize security at the cost of efficiency, while others struggle with the complexity of managing detailed access controls in decentralized environments. Blockchain technology is widely recognized for its auditability, tamper resistance, and decentralization; however, the limitations of this method are computational efficiency of cryptographic operations and latency. Thus, to solve such limitations, this paper proposes an innovative approach that combines Ciphertext-Policy Attribute-Based Encryption (CP-ABE) with a lightweight fog layer. This hybrid design offloads resource-intensive cryptographic operations to the fog layer, effectively reducing the complexity of the policy tree while also protecting against potential side-channel attacks through a multi-layer encryption strategy. The proposed method not only strengthens data confidentiality but also ensures that the impact on transaction performance is minimal. This approach has been proven to be more secure, efficient, and a significantly distributed solution for managing sensitive information. Additionally, the research validates the effectiveness of this design through practical implementation and experimental comparisons with state-of-the-art systems, conducted across various settings. These experiments highlight the advantages of the proposed approach in terms of both security and operational efficiency regarding decryption and encryption, as experimental results show that this paper achieves near-constant encryption and decryption times at the Data Owner and Data User sides, reducing latency by over 80% compared to existing schemes. Received: 12 July 2025 | Revised: 10 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 The data that support the findings of this study are openly available in GitHub at https://github.com/Fraunhofer-AISEC/rabe. Author Contribution Statement Leon Wirz: Conceptualization, Methodology, Software, Formal analysis, Investigation, Resources, Data curation, Writing – original draft, Writing – review & editing, Visualization. Pattarasinee Bhattarakosol: Validation, Writing – review & editing, Supervision, Project administration.
An Improved Computational Model Based on Cardiac Imaging Data for the Quantitative and Qualitative Assessment of the Mechanical Properties of the Human Left Ventricle
Computational modeling has contributed to many fields of medicine and has been proven very useful for diagnosing complex diseases. A special case is that of myocardial infarction (MI), a prevalent cardiovascular disease where computational modeling approaches have been useful for detecting abnormalities. The aim of this paper is to propose enhanced computational models for the human left ventricle (LV) to estimate the strain and stress distributions in healthy and diseased subjects. Computational models were developed and evaluated using human LV of 20 patients with MI and 20 healthy controls using cardiac magnetic resonance imaging acquisitions and simulation tools. The finite element technique was employed for LV modeling. Comparative analysis revealed higher global strains in healthy subjects compared to MI patients, particularly global circumferential, longitudinal, and radial strains. The average stress distributions were 67.9 ± 5.01 kPa in healthy models and 78.3 ± 8.21 kPa in infarcted regions. Model-derived strain data indicated an overall average of −0.15 ± 0.06 for healthy models and 0.2 ± 0.04 for infarcted regions. LV strain values were compared with those obtained from two feature-tracking algorithms to validate the proposed models, resulting in very promising findings. The work presented here highlights the importance of computational modeling for quantitative and qualitative analyses of heart disease and the potential of using such models in other organs. When combined with imaging data, the proposed models can have significant implications for improved patient care and treatment strategies. Received: 3 June 2025 | Revised: 30 July 2025 | Accepted: 8 September 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 Rania Awadi: Conceptualization, Methodology, Software, Validation, Formal analysis, Investigation, Data curation, Writing – original draft, Writing – review & editing, Visualization. Narjes Benameur: Methodology, Validation, Investigation, Writing – review & editing, Visualization, Supervision, Project administration. Wafa Baccouch: Formal analysis, Writing – review & editing, Visualization. Mohamed Deriche: Methodology, Writing – review & editing, Visualization, Project administration, Funding acquisition. Arnab Palit: Validation, Visualization. Moncef Aloui: Resources, Data curation. Nadjia Kachenoura: Software, Validation, Resources, Data curation, Writing – review & editing, Visualization. Salam Labidi: Methodology, Investigation, Writing – review & editing, Visualization, Supervision, Project administration.
AI-Driven Prediction of Greenhouse Gas Emissions in Livestock Supply Chains: Towards a Data-Driven Model for Sustainable Agri-food Systems
This greenhouse gas (GHG) emissions mainly from enteric fermentation and manure management in livestock are mostly from copious methane (CH₄) and some nitrous oxide (N₂O). IPCC (International Panel on Climate Change) Tier 1 and Tier 2 emission prediction methods cannot provide details about where the emissions come from and are ineffective for different forms of agriculture. In our research, a machine learning model based on national agricultural data forecasts how much CH₄ and N₂O will be emitted from U.S. manure management. The performance of Random Forest Regression (RFR), Extreme Gradient Boosting (XGBoost) and Support Vector Regression (SVR) was tested by using RMSE, MAE and R² as performance metrics. XGBoost performed better than SVR since its predictive results were better than reaching R² = 0.98. The analysis of feature importance found that livestock type, methods of managing manure and population density are the main factors leading to emissions. The models resulted in information that communities in various locations could use to improve their sustainability. An adaptable decision-making procedure is proposed by the research to assist environmental planning in the agri-food sector and to ensure that intelligent agricultural platforms can better manage GHG emissions. More research is needed to improve the model by studying additional aspects from the supply chain, covering both its upstream and downstream operations, to obtain a complete analysis of environmental results. Future work should aim to incorporate additional stages of the livestock supply chain and adopt explainable AI techniques to improve transparency and support realtime decision-making. Received: 22 April 2025 | Revised: 31 July 2025 | Accepted: 3 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 USDA at https://agdatacommons.nal.usda.gov/search?itemTypes=3&categories=30761,30746,30689,30821,33323. Author Contribution Statement Baha M. Mohsen: Conceptualization, Methodology, Software, Formal analysis, Investigation, Resources, Data curation, Writing – original draft, Writing – review & editing, Supervision, Project administration. Mohamad Mohsen: Methodology, Validation, Resources, Data curation, Writing – original draft, Writing – review & editing, Visualization.
Performance Optimization of Naive Bayes Classification Using Filter-Based Feature Selection
In machine learning (ML), feature selection (FS) is considered an important preprocessing step that helps to find and pick the most relevant attributes from a dataset. FS minimizes computational complexity, enhances model efficiency, and improves generalization performance by removing unnecessary features. With an important emphasis on the use of filter FS techniques in the research, this study examines the importance and effects of FS in the context of ML. FS is important in ML because it maximizes the interpretability and performance of the ML models. This study explores how well the performance of the naive Bayes (NB) classifier is influenced by filter FS methods such as Symmetrical Uncertainty, Information Gain, Gain Ratio, Chi-Square (CHISQUARE), and RELIEFF. These filter FS techniques are selected because they make ranking of features according to certain criteria and are also computationally efficient. The results captured are projected using the validity scores like accuracy, specificity, precision, false negative rate, and false positive rate using the selected features. Further, using with FS and without FS, the classifier's performance is analyzed using these validity scores. The results represent the RELIEFF approach with NBs getting the best superior results with regards to accuracy, specificity, and precision when compared to other filter FS and NB without FS strategies. Finally, the study reveals the importance of filter FS techniques in maximizing the performance and efficacy of ML models. It also provides useful information on the subtle effects of FS strategies on classification performance, offering practitioners and academics useful knowledge that will further help them optimize their models for practical uses. Received: 5 July 2025 | Revised: 29 October 2025 | Accepted: 8 November 2025 Conflicts of Interest The authors declare that they have no conflicts of interest to this work. Data Availability Statement The data that support the findings of this study are openly available in the UCI repository at https://doi.org/10.1016/j.dss.2014.03.001, reference number [6]. Author Contribution Statement Nalini Manogaran: Methodology, Resources. Kalpana Vadivelu: Formal analysis, Writing – original draft, Visualization. Siva Subramanian Raju: Conceptualization, Investigation, Writing – review &; editing. Yamini Bhavani Shankar: Software, Data curation, Visualization. Balamurugan Balusamy: Software, Validation, Data curation. Sumendra Yogarayan: Formal analysis, Writing – review & editing, Supervision, Project administration.
A Context-Aware Intelligent Scheduling Framework for Modern Operating Systems Using Hybrid Machine Learning Models
In modern computing environments, the operating systems are required to handle dynamic and complex applications with heterogeneous workloads. These workloads include interactive, data-centric, and high-computing applications. Interactive tasks require low latency and high response, while data-centric transactions demand high resource utilization and input/output. Likewise, high-computing processes including machine learning tasks require sustained throughput. Traditional CPU scheduling, like round-robin and priority scheduling, highly relies on static and rule-based systems. Generally, these give equal weightage to all processes. They are not paying attention to the contextual variations and working patterns that impact the responsiveness, efficiency, and overall performance. There is an evident need for a context-aware and adaptive CPU scheduling mechanism that will handle applications and processes based on context. To address this gap, this paper introduces an application-aware intelligent scheduling mechanism that integrates a random forest (RF) classifier and deep Q-network (DQN) reinforcement learning. The RF classifies the processes based on their working and behavioral patterns, and then DQN has system state and queue dynamics to make adaptive scheduling decisions. It can be continuously learned and adjusted based on the user's needs; it is responsive and achieves high throughput. Experimental evaluations are conducted on generative datasets and show remarkable performance improvements. The proposed framework achieves up to 42.2% responsive time reduction on interactive application workloads, 29.2% throughput gain against the machine learning training workloads, and 22.8% for database transactions. The architecture is computationally sparse and uses the existing Linux scheduling system. This renders it a convenient and scalable method of CPU scheduling in contemporary multicore systems. Received: 2 July 2025 | Revised: 17 December 2025 | Accepted: 6 January 2026 Conflicts of Interest The authors declare that they have no conflicts of interest to this work. Data Availability Statement Data are available from the corresponding author upon reasonable request. Author Contribution Statement Pandikumar Savarimalai: Conceptualization, Validation, Project administration. Menaka Chellappan: Conceptualization, Formal analysis, Writing – review & editing, Supervision. Murugaiyan Swaminathan Nidhya: Methodology, Investigation, Resources. Margaret Mary Thomas: Validation, Data curation. Sevugapandi Nallathambi: Software, Writing – review & editing. Manjula Selvaraj: Investigation, Writing – original draft, Visualization.
Early-Stage Diabetic Retinopathy Diagnosis with Feature Pyramid Networks and Spatial Pyramid Pooling Utilizing Full-Field Optical Coherence Tomography (FF-OCT)
Diabetes is a disease that is growing at the highest rate globally, and it has several impacts. One of such complications is diabetic retinopathy (DR), which causes damage to the retina and vision impairment. DR can be mild, and it can also be severe, and there are many shades in between. Therefore, it is possible to avoid vision loss if DR is diagnosed and treated in its early stages. Diagnosis of DR today is still a tedious process as only ophthalmologists can diagnose it using digitized colorful retinal fundus images. This article describes a new strategy for the early identification of DR that combines full-field optical coherence tomography (FF-OCT) with sophisticated deep learning (DL) algorithms, notably Feature Pyramid Networks (FPNs) linked with spatial pyramid pooling (SPP). FF-OCT produces high-resolution, cross-sectional pictures of the retina, revealing precise structural changes that occur before apparent symptoms of DR. We created a multiscale DL framework that takes advantage of the capabilities of FPNs and SPP to efficiently process and evaluate the fine features of FF-OCT images. The FPN design detects changes in the retinal structure at different scales, which improves the network's performance in detecting early signs of DR. On the other hand, the SPP module collects contextual information from several sub-regions of the image to provide a stable and accurate feature representation regardless of the size and location of the lesion. These models were trained and validated on this dataset using performance indicators such as sensitivity, specificity, and area under the ROC curve (AUC). The findings of this paper suggest that the FPNs with the SPP model are superior to the traditional image analysis methods and the standard Convolutional Neural Network in diagnosing early-stage DR. Received: 9 November 2024 | Revised: 10 March 2025 | Accepted: 3 April 2025 Conflicts of Interest The authors declare that they have no conflicts of interest to this work. Data Availability Statement The data that support the findings of this study are openly available in Kaggle at https://www.kaggle.com/datasets/paultimothymooney/kermany2018. Author Contribution Statement Anitha Jaikumar: Conceptualization, Methodology, Software, Validation, Formal analysis, Investigation, Resources, Data curation, Writing – original draft, Visualization. Sreenivasa Chakravarthi Sangapu: Conceptualization, Formal analysis, Investigation, Resources, Writing – review & editing, Supervision, Project administration.
full-field optical coherence tomography (FF-OCT)
An Explainable AI for Stock Market Prediction: A Machine Learning Approach with XAI and Deep Neural Networks
Forecasting stock prices remains a complex challenge due to the inherent nonlinearity and volatility of financial markets. This study proposes a deep learning framework that integrates a long short-term memory (LSTM) network with a multiplicative attention mechanism to dynamically prioritize informative time steps in historical data. To improve robustness during training, a hybrid loss function combining mean absolute error (MAE) and mean absolute percentage error (MAPE) is employed, effectively penalizing both absolute and relative prediction errors. The framework incorporates sentiment signals and technical indicators from historical stock data to enrich the feature set. Explainable artificial intelligence (XAI) techniques, including SHAP and LIME, are integrated to ensure the global and local interpretability of the model's decisions. Experimental evaluation across 25 independent runs demonstrates that the proposed model consistently outperforms baseline approaches such as XGBoost, random forest, and conventional LSTM, achieving a low MSE of 2.45, an RMSE of 1.56, an MAE of 1.08, and an R² score of 0.88. These results validate the model's effectiveness in delivering highly accurate and interpretable stock price forecasts, thereby supporting more transparent and informed financial decision-making in real-world applications. Received: 10 June 2025 | Revised: 30 September 2025 | Accepted: 11 October 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 Yahoo Finance at https://finance.yahoo.com/quote/AAPL/history/?utm_source=chatgpt.com&period1=1577836800&period2=1735603200. Author Contribution Statement Kangana Wallapure Manikrao: Conceptualization, Methodology, Software, Validation, Formal analysis, Investigation, Resources, Data curation, Writing – original draft, Writing – review & editing, Visualization. Shridhar Allagi: Validation, Resources, Writing –review & editing, Supervision, Project administration. Wai Yie Leong: Supervision, Project administration. Mahantesh Laddi: Visualization.