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202 results for “classification techniques”
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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 abstractLeveraging Transformer Model for Data-Driven Requirement Engineering: A Case Study of NLP4RE E-Commerce Business Apps in Malaysia and IndonesiaDevi Yurisca Bernanda, Dayang Norhayati Abang Jawawi, Shahliza Abd Halim et al.Data-Driven Requirement Engineering (DDRE) marks a departure from conventional methods, embracing dynamic, user-centric strategies with the help of machine learning and artificial intelligence. As software systems grow more intricate and demand adaptability, a continuous software engineering approach becomes crucial. However, this shift poses challenges in handling data inconsistencies during the elicitation process, leading to software development shortcomings. This study tackles … …Journal of Computer Science, Vol. 20, Issue 11 · 2 Oct 2024 · 5,131 accessesView PDFCite
Research ArticleOpen accessIn the abstractPersonalized Recommendation System: Web of Things Using Modular Density-Based Community DiscoveryVinod Patidar, Jayashree A. Patil, Sonika Thapak et al.Community discovery is the cornerstone and core of study in customized recommendation, assembling group features and social network analysis on the web of things. Conventional community discovery methods, however, struggle with difficulties including low accuracy, delayed convergence, modularity resolution limits and more when dealing with more complicated social networks. Because of this, differential evolution and module density are included in … …Journal of Computer Science, Vol. 20, Issue 9 · 21 Jun 2024 · 5,628 accessesView PDFCite
Research ArticleOpen accessIn the abstractCardiovascular Diseases Identification Using Wavelet Optimization and Modify Cuckoo Search AlgorithmBanit Negi, Abhilekh Bartwal, Agya Ram Verma et al.The problem the author aims to solve is the extraction of discriminatory features in ECG (Electrocardiogram) signals for classification purposes. The scope of the work is to propose a new method for building wavelets that best reflect the discriminatory capacity of ECG signals. The approach involves optimizing the wavelets specifically for the classification function under consideration. To address the problem, … …Journal of Computer Science, Vol. 19, Issue 8 · 3 Aug 2023 · 5,403 accessesView PDFCite
Research ArticleOpen accessIn the abstractNon-Decimated Wavelet Transform and Vector Quantization for Lossy Medical Images CompressionHend A. Elsayed, Qusay E. Majeed, Mohammed M. El SherbinyThis study presents a new approach for lossy medical image compression using vector quantization. Recently, the digital image has been a reliable replacement for a hard copy of medical images, therefore, an effort has been made to ensure maintaining high-quality images to use for archiving, classification, or automated diagnostics support. Although the medical application contains all sorts of the images … …Journal of Computer Science, Vol. 19, Issue 3 · 24 Feb 2023 · 5,403 accessesView PDFCite
Research ArticleOpen accessIn the abstractClassify Breast Cancer Patients using Hybrid Data-Mining TechniquesFaris E. Mohammed, Nadia Smaoui Zghal, Dalinda Ben Aissa, Mostafa Mahmoud El-GayarAccording to the World Health Organization (WHO), breast cancer is a disease that leads to death, especially for women who have neglected or ignored the risk factors. Doctors can classify patients according to clinical information, famous disease symptoms, or similar cases. But, some cases are difficult to detect early or diagnose accurately. Therefore, the most important challenge faced by researchers … …Journal of Computer Science, Vol. 18, Issue 4 · 12 May 2022 · 6,526 accessesView PDFCite
Research ArticleOpen accessIn the abstractA CNN-KNN Based Recognition of Online Handwritten Symbols within Physics Expressions Using Contour-Based Bounding Box (CBBS) Segmentation TechniqueUjwala Kolte, Sachin Naik, Vidya KumbharArtieres, T., Marukatat, S., & Gallinari, P. (2007). Online Handwritten Shape Recognition Using Segmental Hidden Markov Models. IEEE Transactions on Pattern Analysis and Machine Intelligence, 29(2), 205–217. https://doi.org/10.1109/tpami.2007.38 Baró, A., Riba, P., Calvo-Zaragoza, J., & Fornés, A. (2019). From Optical Music Recognition to Handwritten Music Recognition: A baseline. Pattern Recognition Letters, 123, 1–8. https://doi.org/10.1016/j.patrec.2019.02.029 Bresler, M., Prua, D., & Hlavác, … …Journal of Computer Science, Vol. 20, Issue 7 · 7 May 2024 · 6,278 accessesView PDFCite
Research ArticleOpen accessIn the abstractBreast Cancer Grading using Machine Learning Approach AlgorithmsHiba Nabeel Zalloum, Saada Al Zeer, Amir Manassra et al.Recently, Breast Cancer (BC) becomes a more common cancer disease in women and it considers the most important sign which leads to death among women. Therefore, it requires efficient methods for detecting it to reduce the risk of death. A positive prognosis and greater chances of survival are improved if the BC is detected early. Currently, machine learning plays an … …Journal of Computer Science, Vol. 18, Issue 12 · 22 Dec 2022 · 6,610 accessesView PDFCite
Research ArticleOpen accessIn the abstractA Hybrid Model of Bidirectional Long-Short Term Memory and CNN for Multivariate Time Series Classification of Remote Sensing DataSawsan Morkos GharghoryClassification of multivariate time series has got massive attention in the last decade. The traditional modeling classifiers are complicated patterns and are incompetent to capture the dependencies of multivariate time series data. To include both of the effective features and the embedded relationships in the multivariate time series, a new hybrid model which incorporates both Convolutional Neural Network (CNN) and … …Journal of Computer Science, Vol. 17, Issue 9 · 29 Sep 2021 · 6,686 accessesView PDFCite