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298 results for “Neural Networks”
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Research ArticleOpen accessPhrase in the titleA New Data Base Scheme Arabic Handwriting Recognition by Hopfield Neural Networks AlgorithmHasan M. Al Shalabi, Mowafak F. Hasan, Abbas M. AliA new scheme for Arabic handwriting recognition by means of mat lab software is proposed. It can be seen as construction of neural networks that associate the hand writing Arabic text with the stored Arabic text in a specific data base are nearly equivalent. This result indicates that the new scheme have the potential to be robust in the presence … …Journal of Computer Science, Vol. 1, Issue 2 · 30 Jun 2005 · 6,784 accessesView PDFCite
Research ArticleOpen accessPhrase in the titleA Hybrid Fuzzy Neural Networks for the Detection of Tumors in Medical ImagesN. Benamrane, A. Freville, R. NekkacheIn this study, we propose an approach to detect suspect zones or tumors in medical images. The idea is to define with precision the existence of different kinds of lesions using a hybrid system, which combines Fuzzy Neural Networks and Expert System. After applying a method of image segmentation to extract regions (by region growing algorithm or by mathematical morphology … …American Journal of Applied Sciences, Vol. 2, Issue 4 · 30 Apr 2005 · 6,873 accessesView PDFCite
Research ArticleOpen accessPhrase in the titleLeveraging 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 accessPhrase in the titleEfficient Rainfall Forecasting Using Sequential Momentum-Based Artificial Neural Networks for Urban Flood ManagementSherin K K, G. SuganthiUrban floods cause severe disruption in densely populated cities and damage essential public infrastructure. Rapid urbanization and weak drainage systems further increase the risk during heavy rainfall. Accurate short-term rainfall forecasting is required to support early warning and reduce losses. This research focuses on improving rainfall prediction accuracy using an Artificial Neural Network trained with a new optimizer named Sequential … …Journal of Computer Science, Vol. 22, Issue 4 · 9 Apr 2026 · 1,539 accessesView PDFCite
Research ArticleOpen accessPhrase in the titlePhishing Website Detection Using Improved Multilayered Convolutional Neural NetworksHadia Bibi, Syed Rehan Shah, Mirza Murad Baig et al.The internet has become an essential part of many fields: Communication, entertainment, commerce, industrial production, agriculture, etc. Unfortunately, online users are vulnerable to various attacks; this could lead to financial damages and loss of personal information. Phishing is seen as an internet threat and a cybercrime where anyone can capture personal information and data by posing as a reliable source. … …Journal of Computer Science, Vol. 20, Issue 9 · 6 Jul 2024 · 6,578 accessesView PDFCite
Research ArticleOpen accessPhrase in the titleA Robust Ensemble Convolutional Neural Networks for Diagnosing Chest DiseasesMansour Ali Alhlalat, Ahmad Abdel-Aziz Sharieh, Mohammed Belal Al-ZoubiRadiologists employ X-ray images to differentiate various chest diseases. Given the intricate and meticulous nature of this diagnostic procedure, the assistance of automated models becomes imperative in detecting and diagnosing diseases from X-ray images. This research paper proposed a novel approach called Ensemble Convolutional Neural Network for Diagnosing Chest Diseases (ECDCNet), aimed at accurately and efficiently diagnosing fifteen different chest … …Journal of Computer Science, Vol. 19, Issue 12 · 27 Oct 2023 · 5,104 accessesView PDFCite
Research ArticleOpen accessPhrase in the titleA Comprehensive Review on Skin Cancer Detection Strategies using Deep Neural NetworksAkepati Sankar Reddy, Gopinath M. PSkincancer is a deadly malignancy. Incomplete D.N.A. repair in skin cells causeshereditary mutations and cancer. Early skin cancer is easier to treat since itspreads slowly to other body areas. As a result, the optimal time to find it isduring its infancy. Because of the rising frequency of skin cancer, the highmortality rate, and the high cost of medical treatment, early … …Journal of Computer Science, Vol. 18, Issue 10 · 5 Oct 2022 · 6,400 accessesView PDFCite
Research ArticleOpen accessPhrase in the titleDiagnosing Alzheimer’s Disease using Convolution Neural NetworksSarita, Saurabh Mukherjee, Tanupriya Choudhury et al.Alzheimer’s is a disease wherein constant degeneration of neurons and their synapses result in impaired brain functioning which leads to personality changes, memory loss, thinking and speech disorder. So, there is a requirement of an automated and early diagnosis of this disease to decrease the death rate. The proposed work is coupled with deep learning techniques to predict the Alzheimer’s … …Journal of Computer Science, Vol. 18, Issue 2 · 21 Feb 2022 · 7,344 accessesView PDFCite
Research ArticleOpen accessPhrase in the titleOptimization 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 accessPhrase in the titleCrime Rate Analysis and Mapping from Socio-Economic Data Using Deep Neural NetworksSanthosh Sankara, Nadeson SugithaCrime prediction is the attempt to identify and mitigate future criminal activity. Crime is typically "unpredictable"; it cannot be predicted in advance. Addressing the roots of crime has long been a priority for researchers. Extracting causality from data is difficult if the relevant data aspects are not selected. In this research activity, an innovative classification framework is defined which is … …Journal of Computer Science, Vol. 20, Issue 10 · 17 Aug 2024 · 5,089 accessesView PDFCite