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121 articles for “Deep Networks”
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Research ArticleOpen accessIn the keywordsMachine Learning and Deep Learning for Phishing Email Classification using One-Hot EncodingSikha Bagui, Debarghya Nandi, Subhash Bagui, Robert Jamie WhiteRepresentation of text is a significant task in Natural Language Processing (NLP) and in recent years Deep Learning (DL) and Machine Learning (ML) have been widely used in various NLP tasks like topic classification, sentiment analysis and language translation. Until very recently, little work has been devoted to semantic analysis in phishing detection or phishing email detection. The novelty of … …Journal of Computer Science, Vol. 17, Issue 7 · 23 Jul 2021 · 10,936 accessesView PDFCite
Research ArticleOpen accessIn the keywordsEffectual Deep Learning Based Digital Image Forgery Detection Model Using Ameliorate CNN, VGG 16 and VGG 19 for Copy Move and Image Splicing Classification With Enhanced PerformancePreeti Dhiman, Usha ChauhanIn this research, a pioneer passive approach for detecting and localizing tampering in images is proposed. In the past, image forgeries have created a huge impact in the world. Developments of various image processing technologies made it easier to tamper images with the objective to cover up or hide a significant feature in an image. In some situation, such as … …Journal of Computer Science, Vol. 22, Issue 10 · 3 Oct 2026 · 568 accessesView PDFCite
Research ArticleOpen accessIn the keywordsDengue Fever Prediction Empowered by Radial Basis Function Networks, Dynamic Mode Decomposition, and Learning-Based Foraging AlgorithmArchana T, Faritha Banu JDengue fever is presently considered a major health threat that must be addressed. External and internal factors that induce nonlinear oscillations in the occurrence of dengue disease have made optimal resource allocation stimulating. Public health initiatives aimed at eradicating this vector use accurate and timely data through field processes. In dengue-endemic nations, early dengue prediction remains a main concern for … …Journal of Computer Science, Vol. 22, Issue 4 · 16 Apr 2026 · 1,496 accessesView PDFCite
Research ArticleOpen accessIn the keywordsUsing Deep Learning to Support Clinical Decision-Making: The Case of Alzheimer’s Disease DiagnosisNawal Mohamed Bahy Eldin, Ghada A. El Khayat, Abeer A. AmerAlzheimer’s disease is a chronic, progressive brain disorder that leads to a gradual decline in memory and cognitive functions. In this study, N-VGG16, an advanced deep learning model, is proposed. The model builds upon the VGG16 architecture, incorporating key enhancements to improve its ability to classify neurodegenerative conditions. The model processes structural neuroimaging data using a refined pipeline that applies … …Journal of Computer Science, Vol. 21, Issue 10 · 11 Dec 2025 · 3,383 accessesView PDFCite
Research ArticleOpen accessIn the keywordsTransforming Retinal Diagnostics: Advanced Detection of Diabetic Retinopathy Using Vision Transformers and Capsule NetworksVishal Sharma, Rishu, Vinay Kukreja et al.Diabetic Retinopathy (DR), nowadays is one of the leading causes of blindness worldwide, it is a severe complication of diabetes mellitus that affects the retina blood vessels. Accurate diagnosis depends on early detection of DR. The study aims to develop a hybrid model that is the combination of a Vision Transformer and Capsule Network (ViT-CapsNet) to classify the DR at … …Journal of Computer Science, Vol. 21, Issue 2 · 14 Jan 2025 · 6,755 accessesView PDFCite
Research ArticleOpen accessIn the keywordsWhale Optimized Deep Generative Adversarial Network Based Alzheimer's Stages Detection Using 3D MRI Brain NeuroimagingR. Sampath, M. BaskarAlzheimer's Disease (AD), a common, chronic neurodegenerative condition, is characterized by the loss of neurons and synapses in the cerebral cortex and specific subcortical regions. According to claims from a recent study, AD has a 20% misdiagnosis rate. Therefore, it is essential to create a useful tool to recognize the stages of AD with a lower prediction error rate to … …Journal of Computer Science, Vol. 19, Issue 8 · 4 Aug 2023 · 5,544 accessesView PDFCite
Research ArticleOpen accessIn the keywordsAdvancing Digital Forgery Detection: A Comparative Analysis of U-Net, Buster Net, Efficient B0+ U-Net, and VGG 16+ U-Net ModelsPreeti Dhiman, Usha ChauhanForgery Detection in the area of digital images is indispensable for insuring authenticity, particularly in detecting Copy-Move, Splicing. In this research, a meticulous analysis of latest learning architectures i.e. U-Net, Buster Net, Efficient B0+U-Net and VGG16+U-Net are compared for the detecting these types of forgeries. U-Net is popular due to its potent segmentation capabilities to provide pixel-level forgery localization while … …Journal of Computer Science, Vol. 22, Issue 10 · 8 Oct 2026 · 383 accessesView PDFCite
Research ArticleOpen accessIn the keywordsA Distillation GAN Model for Network Intrusion DetectionChandrakala C B, Sai Sreevall, Raghudathesh G PThis study presents a distillation-based Generative Adversarial Network (GAN) model designed for intrusion detection in network systems. A Teacher-Student architecture is proposed, in which a single teacher generator is utilized to train both the teacher discriminator and the student discriminator. The model was trained to identify anomalies present in network traffic intrusions by tuning the min-max loss function in GANs. … …Journal of Computer Science, Vol. 22, Issue 2 · 5 Mar 2026 · 1,549 accessesView PDFCite
Research ArticleOpen accessIn the keywordsApplication of Data Enhancement Method Based on Generative Adversarial Networks for Soybean Leaf Disease IdentificationXiao Yu, Cong Chen, Qi Gong, Lina LuSoybean leaf disease data collection is an expensive and time-consuming task. Convolutional neural network training requires a large amount of data, but traditional data enhancement methods (such as rotation, flipping, translation) are restricted by fixed rules and cannot generate images with diversity and variability. Aiming at the problem of the lack of soybean leaf disease data set, this study proposes … …American Journal of Biochemistry and Biotechnology, Vol. 18, Issue 4 · 26 Nov 2022 · 5,207 accessesView PDFCite
Research ArticleOpen accessIn the keywordsOptiCNN: Local thresholding segmentation and CNN with SVM Approach for Diabetic Retinopathy Detection and Classification of Fundus ImagesAnju Mishra, Mrinal Pandey, Laxman SinghDiabetic Retinopathy (DR) is a progressive eye disease that can lead to vision loss and blindness if left untreated. Ophthalmologists diagnose DR using medical imaging modalities such as fundus photography and optical coherence tomography (OCT); however, manual interpretation of these images is time-consuming and subject to inter-observer variability. While DR is irreversible, vision loss can be prevented through early detection … …Journal of Computer Science, Vol. 21, Issue 10 · 15 Dec 2025 · 2,153 accessesView PDFCite