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Research ArticleOpen accessIn the keywordsMedical Image Compression with Lossless Region of Interest Using Adaptive Active ContourR. Loganathan, Y. S. KumaraswamyProblem statement: With increasing bandwidth, digital medical image storage and transmission is a boon to patients and health professionals alike. Medical images are available instantly and avoid the need to carry the data physically. Popular imaging techniques extensively used in medicine include X-Ray, Magnetic Resonance Imaging (MRI), Ultrasound and Computed Tomography (CT). The images produced from the above techniques can … …Journal of Computer Science, Vol. 8, Issue 5 · 1 Mar 2012 · 7,723 accessesView PDFCite
Research ArticleOpen accessIn the abstractApplication of Multi Task Analysis Model Based on EEG Features in the Study of the Correlation between Depression and Sleep Disorders in College StudentsFeng MaWith the improvement of multi-modal technology, research on depression recognition combining multiple biological signal features has been increasing. Electroencephalogram signals reflect brain activity, and by analyzing their characteristics, the accuracy and stability of depression recognition can be effectively improved. Therefore, this study proposes a correlation analysis between depression and sleep disorders in college students based on electroencephalogram feature analysis, innovatively … …American Journal of Biochemistry and Biotechnology, Vol. 21, Issue 4 · 2 Feb 2026 · 1,994 accessesView PDFCite
Research ArticleOpen accessIn the abstractA Vertical Stacking-Based Ensemble Deep Learning Model for Early Diagnosis of Alzheimer's Disease Using Multimodal MRI ScansParvatham Niranjan Kumar, Lakshmana Phaneendra MaguluriEarly detection of Alzheimer's Disease (AD) is crucial for timely interventions that can slow disease progression, enhance quality of life, and assist with future planning. Convolutional Neural Networks (CNNs) are an efficient method for processing image-based data. In this work, we used CNN-based deep learning models to extract structural information from structural MRI (sMRI) and brain neuron connectivity patterns from … …Journal of Computer Science, Vol. 21, Issue 6 · 14 Jun 2025 · 3,099 accessesView PDFCite
Research ArticleOpen accessIn the abstractLeveraging Modified Cascaded Fully Convolutional Neural Networks and Optimized Swin U-Net for Liver Lesion PredictionSugandha Chakraverti, Tejaswi Khanna, Vijay ShuklaLiver lesions are abnormal growths that can develop for various reasons, some of which are noncancerous (benign) while others are cancerous. Machine learning and, deep learning techniques are employed for liver health assessment and lesion detection, but the quality and, diversity of training data can hinder the performance, leading to overfitting or underfitting. These models often need extensive pre-processing and … …Journal of Computer Science, Vol. 22, Issue 10 · 8 Oct 2026 · 372 accessesView PDFCite
Research ArticleOpen accessIn the abstractAn Efficient Cluster Head Selection in IoV-Enabled VANETs Using a Federated Diffusion Convolutional Recurrent Neural Networks FrameworkP Subhash, Madana Srinivas, A. Ranjith et al.The IoV enables the formation of VANETs that require a strong selection of CHs for effective communication within high-velocity changing traffic scenarios. Existing approaches often face issues related to scalability, privacy, and adaptability. To meet the aforementioned urgent needs, we design a decentralized scheme for intelligent CH selection based on Federated Learning with Diffusion Convolutional Recurrent Neural Networks (DCRNNs). According … …Journal of Computer Science, Vol. 22, Issue 10 · 1 Oct 2026 · 599 accessesView PDFCite
Research ArticleOpen accessIn the abstractDynamic Knowledge Driven Multi-Scale Spatiotemporal Fusion Framework for Reliable Multi-Horizon Traffic Flow ForecastingDeepika M S, P. Deepa Shenoy, Venugopal K RAccurate traffic flow forecasting remains a core challenge in intelligent transportation systems. Urban traffic patterns are characterised by complex spatiotemporal dependencies and inherent uncertainties, which complicate traffic forecasting. This study proposes a Dynamic Knowledge-Driven Spatiotemporal Fusion (DKDMSF) architecture to extract features of Multi-Scale traffic dynamics across various temporal traffic horizons. DKDMSF integrates Dynamic Spatial Attention (DSA) and Adaptive Graph Learning … …Journal of Computer Science, Vol. 22, Issue 9 · 30 Sep 2026 · 413 accessesView PDFCite
Research ArticleOpen accessIn the abstractDesign and Implementation of a Smart File Cabinet System With Enhanced Security and Remote AccessAdebayo Afeez Oluwagbenga, Mustapha Favour, Adebiyi Temidayo Daniel et al.In an era increasingly defined by digital interconnectivity, the physical security of sensitive hard-copy documents remains a persistent vulnerability in organizational infrastructure. Traditional filing cabinets, relying on passive mechanical locks, lack the capability for real-time monitoring, remote access control, or active intrusion detection. This research presents the design and validation of a Smart File Cabinet (SFC) system that bridges this … …Journal of Mechatronics and Robotics, Vol. 10, Issue 1 · 25 May 2026 · 1,696 accessesView PDFCite
Research ArticleOpen accessIn the abstractA Hybrid Grey Wolf Optimizer and Deep Transfer Learning-Based Intrusion Detection System for IoTKapil Shrivastava, Manish Tiwari, Prasun ChakrabartiThe rapid proliferation of Internet of Things (IoT) devices has driven significant advancements in connectivity and automation, but it has also introduced substantial cybersecurity risks, including intrusions and cyberattacks. To address these challenges, this study proposes a hybrid intrusion detection framework that combines advanced optimization techniques with ensemble deep learning to enhance detection accuracy and computational efficiency. The proposed framework … …Journal of Computer Science, Vol. 22, Issue 2 · 17 Feb 2026 · 1,506 accessesView PDFCite
Research ArticleOpen accessIn the abstractEnergy and Distance Aware Multi-Objective Firebug Swarm Optimization Based Clustering and Routing in Wireless Sensor NetworksGundeboyina Srinivasalu, Hanumanthappa UmadeviIn recent years, Wireless Sensor Networks (WSNs) have received increased international attention due to advancements in the communication, electronics, and information fields. These sensor networks integrate a huge number of sensor nodes to track rapidly changing physical events. The key improvement is that these nodes can be easily positioned in any environment due to their small size. Therefore, maintaining network … …Journal of Computer Science, Vol. 19, Issue 3 · 10 Feb 2023 · 6,029 accessesView PDFCite
Review ArticleOpen accessIn the abstractA Systematic Review on Wake-up Radios Applied to Wireless Sensor NetworksMarco Aurélio Spohn, Caetano Mazzoni Ranieri, Agnelo Rocha da Silva, Jó UeyamaWireless Sensor Networks (WSNs) are the backbone of many monitoring applications, especially in the Internet of Things (IoT) context. However, efficient power management becomes a critical challenge when sensor nodes rely on disposable batteries. The deployment of WSNs must ensure coverage and connectivity, but the resulting distribution of nodes and underlying protocols directly impact the network’s lifetime. The concept of … …Journal of Computer Science, Vol. 21, Issue 3 · 4 Feb 2025 · 6,201 accessesView PDFCite