Research ArticleOpen accessIn the abstractActive Reset and Scheduling in Fault-Tolerant Quantum-Classical Cloud ComputingMohamed El-DosukyQuantum-classical cloud computing refers to a hybrid computing model that integrates quantum computing with classical computing resources, typically hosted on the cloud. The objective of this paper is threefold: First, to scrutinize the computing paradigm of quantum-classical cloud computing; second, to study the three pillars of the underpinning techniques: Active reset, scheduling, and fault tolerance; third, to demonstrate the realization … …Journal of Computer Science, Vol. 22, Issue 7 · 1 Sep 2026 · 775 accessesView PDFCite
Research ArticleOpen accessIn the abstractVision Tune: A Deep Learning Framework for Sentiment Driven Video, Image and Music CreationLukesh Rameshpant Kadu, Manoj Deshpande, Vijaykumar PawarArtificial intelligence has enabled powerful generative models for text, images, video, and music, yet most tools still operate independently without a unified, multi-modal workflow. This article proposes an integrated AI framework, Vision Tune, that consolidates these isolated capabilities into a single, sentiment-aware platform for end-to-end media creation. The system leverages deep learning and multi-scope AI models to automatically generate written … …Journal of Computer Science, Vol. 22, Issue 4 · 29 Apr 2026 · 1,502 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 · 1,853 accessesView PDFCite
Research ArticleOpen accessIn the abstractMANAT: A Filtering-Based Method for Denoising Nonuniform Photogrammetric Point CloudsYun Sin Chong, Hui Hui Wang, Yin Chai WangThree-dimensional point clouds reconstructed from photogrammetry often exhibit noise and non-uniform sampling density, which challenges existing denoising methods that rely on precise normal estimation or extensive parameter tuning. This study presents the Multi Attribute Neighbour Attraction Technique (MANAT), a novel single-stage, density-adaptive filtering method that jointly leverages spatial position, surface normals, and color as inherent photogrammetric attributes for unified noise … …Journal of Computer Science, Vol. 22, Issue 4 · 28 Apr 2026 · 1,531 accessesView PDFCite
Research ArticleOpen accessIn the abstractCascaded MNetV3UNet: A Lightweight Two-Stage Architecture for High-Precision Brain Tumor Segmentation in MRIMayuri Popat, Sanskruti PatelPrecise brain tumor segmentation is an essential but quite challenging process in MRI images because of their irregular shapes, heterogeneous appearance, and low contrast with surrounding tissues. While U-Net-based architectures have achieved significant success, their high computational complexity limits deployment on resource-constrained systems. In this study, a Novel two-stage cascaded architecture, MNetV3UNet, is introduced, which employs the light-weight MobileNetV3-Large as … …Journal of Computer Science, Vol. 22, Issue 2 · 23 Feb 2026 · 1,793 accessesView PDFCite
Research ArticleOpen accessIn the abstractA Multiphase Zero-Trust Authentication Framework Using Replicated and Homomorphic EncryptionModisaotsile Marope, Venumadhav Kuthadi, Rajalakshmi Selvaraj et al.In response to the increasing complexity and vulnerability of traditional authentication techniques, this paper proposes the Multiphase Zero-Trust Authentication Framework (MZTAF), which combines device-level and user-level authentication to enhance security in zero-trust environments. Phase I leverages replicated key-based authentication to ensure fault tolerance and reduce the risk of device-level compromise. Phase II introduces homomorphic encryption for user authentication, securely verifying … …Journal of Computer Science, Vol. 22, Issue 2 · 19 Feb 2026 · 1,665 accessesView PDFCite
Research ArticleOpen accessIn the abstractFine-Tuned and Optimized Transformer-Based Model for EEG Seizure DetectionPuspanjali Mallik, Ajit Kumar Nayak, Kumar Janardan Patra et al.Epilepsy, a widely recognized neurological disorder, results from irregularities in the transmission of electrical impulses among neurons in the brain. Over the last two decades, significant efforts have been made by researchers and clinicians to develop effective methods for its early detection and management. The electroencephalogram (EEG), a non-invasive tool used to monitor brainwave activity, has become a central device … …Journal of Computer Science, Vol. 21, Issue 11 · 11 Feb 2026 · 1,424 accessesView PDFCite
Research ArticleOpen accessIn the abstractAn Optimized Multi-Layer Perceptron Framework for Detecting and Classifying IoT AttacksSanchit Vashisht, Shalli RaniThe Internet of Things (IoT) is revolutionizing industries by connecting billions of smart devices, enabling automation and information exchange. The expansion of IoT ecosystems has simultaneously increased the surface area for cyberattacks. These environments are particularly vulnerable to a wide range of threats, such as Distributed Denial-of-Service (DDoS), poisoning, brute-force SSH intrusions, and various network reconnaissance techniques. The dynamic nature … …Journal of Computer Science, Vol. 21, Issue 12 · 11 Feb 2026 · 1,341 accessesView PDFCite
Research ArticleOpen accessIn the abstractTowards an Explainable Approach: Hybrid Residual Network and SVM for Automated Brain Tumor Detection and ClassificationKamini Lamba, Shalli RaniThe abnormal growth of brain cells leads to tumor formation, which can be fatal if not detected and treated promptly. Given the complexity of brain tumors, early detection is critical in healthcare. Traditional radiology-based tumor detection is prone to human error and delays. Hence, a computer- assisted method is needed for accurate and efficient diagnosis. With the rapid advancements in … …Journal of Computer Science, Vol. 22, Issue 1 · 11 Feb 2026 · 1,435 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 · 2,120 accessesView PDFCite