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96 results for “Deep Neural Networks”
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Research ArticleOpen accessIn the keywordsConvolution Neural Networks of Dynamically Sized Filters with Modified Stochastic Gradient Descent Optimizer for Sound ClassificationManu Pratap Singh, Pratibha RashmiDeep Neural Networks (DNNs), specifically Convolution Neural Networks (CNNs) are found well suited to address the problem of sound classification due to their ability to capture the pattern of time and frequency domain. Mostly the convolutional neural networks are trained and tested with time-frequency patches of sound samples in the form of 2D pattern vectors. Generally, existing pre-trained convolutional neural … …Journal of Computer Science, Vol. 20, Issue 1 · 13 Dec 2023 · 5,781 accessesView PDFCite
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 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 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 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
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 keywordsA Convolutional Neural Network Approach for Skin Lesion Classification Using Imbalanced Dataset with Image AugmentationZhyar Yassin Abdalla, Nor Hazlyna Harun, Mohammed Shihab AhmedA significant threat to people's health all over the world is skin cancer. The purpose of this research is to improve the detection of skin cancer by utilizing a CNN classification model that makes use of preprocessing and augmentation techniques. The HAM10000 dataset is used, and the imbalance it contains is addressed by resizing the images to 120×120 pixels and … …Journal of Computer Science, Vol. 20, Issue 11 · 10 Sep 2024 · 5,427 accessesView PDFCite
Research ArticleOpen accessIn the keywordsSentence Classification Using Attention Model for E-Commerce Product ReviewNagendra N, Chandra JThe importance of aspect extraction in text classification, particularly in the e-commerce sector. E-commerce platforms generate vast amounts of textual data, such as comments, product descriptions, and customer reviews, which contain valuable information about various aspects of products or services. Aspect extraction involves identifying and classifying individual traits or aspects mentioned in textual reviews to understand customer opinions, improve products, … …Journal of Computer Science, Vol. 20, Issue 5 · 28 Feb 2024 · 5,855 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 keywordsThermal Face Authentication with Convolutional Neural NetworkMohamed Sayed, Faris BakerMatching thermal face images as a method of biometric authentication has gained increasing interest because of its advantage of tracking a target object at night and in total darkness. Therefore, for security purposes, it has become highly favourable and has extensive applications, for instance, in video surveillance at night. The aim of this study is to present a simple and … …Journal of Computer Science, Vol. 14, Issue 12 · 10 Dec 2018 · 7,854 accessesView PDFCite