Research ArticleOpen accessIn the abstractA Dynamically Zero Trust and Risk Aware Cryptographic Orchestration Framework of Secure Multi Cloud Electronic Health RecordsD. Veerabhadra Rao, G. Appa Rao, S. AnuradhaThe adoption of multi-cloud infrastructures for Electronic Health Records (EHRs) improves scalability and accessibility but introduces security challenges such as fragmented trust management, high cryptographic overhead, and inefficient integrity verification. Existing frameworks rely on static authorization and uniform encryption mechanisms, which fail to dynamically evaluate behavioural risks associated with user access requests. Consequently, Distributed Trust Inconsistency, Cryptographic Scalability Saturation, and … …Journal of Computer Science, Vol. 22, Issue 10 · 7 Oct 2026 · 453 accessesView PDFCite
Research ArticleOpen accessIn the abstractOptimization of Non-Technical Electricity Loss Detection Using Artificial Neural Networks: Case of Cameroon Distribution NetworkLekini Nkodo Claude Bernard, Bell Serge Samuel, Nyemb Nsoga Valjacques et al.Non-Technical Losses (NTL) represent a critical challenge for power utilities, particularly in developing countries. This paper proposes an unsupervised deep learning approach based on an autoencoder to detect abnormal electricity consumption patterns in the Cameroonian distribution network. The model learns normal customer behavior using historical consumption data and identifies anomalies through reconstruction error analysis. Statistical thresholds are applied to classify … …American Journal of Engineering and Applied Sciences, Vol. 19, Issue 1 · 23 Jul 2026 · 1,225 accessesView PDFCite
Research ArticleOpen accessIn the abstractHybrid Soft Voting Ensemble of XGBoost and DNN for At-Risk Student Performance PredictionEugene Wan, Po Chan Chiu, Mohammad bin Hossin et al.Early identification of at-risk students in higher education is important for timely academic intervention, yet conventional prediction methods often struggle with data imbalance and limited model precision. This study proposes a hybrid soft voting ensemble model that integrates Extreme Gradient Boosting (XGBoost) and Deep Neural Network (DNN) to enhance multi-class student grade prediction (A-F classification) and at-risk student identification. This … …Journal of Computer Science, Vol. 22, Issue 5 · 28 May 2026 · 1,390 accessesView PDFCite
Research ArticleOpen accessIn the abstractA Coding Schema-Based Mobile System for Children With ASD in MauritaniaCheikhne Mohamed Mahmoud Seyidi, Ahmed Mohameden, Mohamed Lamine Diakité, Mohamedade Farouk NanneChildren with Autism Spectrum Disorders (ASD) in many African countries face persistent barriers, including limited diagnostic awareness, scarce specialized resources, and the absence of educational interventions adapted to local contexts. In Mauritania, these challenges are exacerbated by the extreme centralization of autism services, with only two care centers available nationwide. Unlike existing ASD interventions that rely on imported pedagogical models … …Journal of Computer Science, Vol. 22, Issue 5 · 25 May 2026 · 1,312 accessesView PDFCite
Review ArticleOpen accessIn the abstractImproving Classification and Clustering Accuracy of Palembang Duku Fruit Quality via CNN-K-Means IntegrationHenny Hartono, Francka Sakti Lee, Herlina Herlina et al.Duku Palembang (Lansium domesticum Corr.) is a leading commodity from South Sumatra with high economic value. The quality assessment process for duku has traditionally relied on manual visual inspection, which is subjective, time-consuming, and inconsistent, especially at large production scales. This study proposes an integrative framework combining Convolutional Neural Network (CNN) and K-Means Clustering to improve the accuracy of classification … …OnLine Journal of Biological Sciences, Vol. 26, Issue 2 · 8 May 2026 · 1,475 accessesView PDFCite
Research ArticleOpen accessIn the abstractIntelligent Multi Model Ensemble for Engagement PredictionFahmida Begum, K Ulaga PriyaFor intelligent educational systems, the ability to monitor and respond to student engagement in real time is essential for enhancing learning outcomes. However, existing models often lack adaptability and practical deployment potential, as they depend on single data modalities, rigid ensemble mechanisms, and post-session analysis. This study introduces an intelligent multimodal ensemble framework designed to address these challenges by predicting … …Journal of Computer Science, Vol. 22, Issue 4 · 28 Apr 2026 · 1,844 accessesView PDFCite
Research ArticleOpen accessIn the abstractA Comprehensive Review of Medical Image Denoising: Techniques, Challenges, Applications and Future DirectionsMohit Sharma, Ayush Dogra, Anita Gupta et al.The concept of noise reduction in medical images plays a vital role in improving image quality and accuracy associated with diagnosis, and facilitating reliable automated analysis. This survey presents a structured overview of medical image denoising, beginning with foundational definitions and noise models for modalities such as MRI and CT. We critically evaluate persistent gaps, including the limited generalizability of … …Journal of Computer Science, Vol. 22, Issue 3 · 28 Mar 2026 · 1,849 accessesView PDFCite
Research ArticleOpen accessIn the abstractPredictive Mathematical Modeling and Classification of Retail Sales Orders Using AI Machine Learning TechniquesMohammad Subhi Al-Batah, Mowafaq Salem Alzboon, Hamzeh ZureigatThis study presents a systematic mathematical model known as a low-code approach to classifying retail sales orders by size using AI machine learning techniques within the Orange Data Mining platform. Leveraging a real-world sales dataset sourced from Kaggle, we implemented and evaluated ten classification models, including ensemble learners (AdaBoost, Gradient Boosting), probabilistic classifiers (Naïve Bayes), distance-based methods (kNN), and interpretable … …Journal of Computer Science, Vol. 22, Issue 3 · 10 Mar 2026 · 1,567 accessesView PDFCite
Research ArticleOpen accessIn the abstractLSTM-Based AI Model for Sinkhole Attack Detection With Legal Basis in an Ecuadorian Public InstitutionEstefanía Alejandra Mora Parra, Rubén Nogales Portero, Moisés Toapanta T. et al.Wireless Sensor Networks (WSN) are an essential component of the Internet of Things (IoT). However, their decentralized nature, data transmission over unencrypted channels, and the physical exposure of nodes make them especially vulnerable to attacks, among which the sinkhole attack stands out. This research aims to develop a machine learning–based model to detect sinkhole‐type attacks in wireless sensor networks, with … …Journal of Computer Science, Vol. 22, Issue 3 · 4 Mar 2026 · 1,648 accessesView PDFCite
Research ArticleOpen accessIn the abstractDeep Learning Synergy: CNNs and Transformers for Epidemic Outbreak ForecastingAlexander R., Shakthi Priya V., Sumathi G. et al.An Accurate prediction of outbreaks is extremely crucial for taking proactive public health interventions, distributing limited resources, and controlling a disease. This paper assesses and also compares the performances of the available deep learning models, namely, Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM) networks, and Bidirectional Long Short-Term Memory (BiLSTM) networks, in predicting. CNNs are best for feature extraction … …Journal of Computer Science, Vol. 21, Issue 12 · 23 Jan 2026 · 2,113 accessesView PDFCite