Research ArticleOpen accessIn the keywordsComparative Analysis on Prediction of Chronic KidneyDisease by various Machine Learning ModelsNikhil Verma, Tripti Sharma, Bobbinpreet Kaur, Ayush DograChronic Kidney Disease (CKD) is a prevalent global healthconcern, which is affecting a number of people in the whole world. Earlydetection & precise checking of CKD are really important for essentialsupervision & intervention to prevent disease progression and associatedcomplications. Machine learning (ML) models have emerged as favourabletools for CKD detection due to their ability to analyse complex data patternsand provide … …Journal of Computer Science, Vol. 21, Issue 6 · 24 May 2025 · 5,939 accessesView PDFCite
Research ArticleOpen accessIn the keywordsSentiment Analysis on User Reviews of Snapchat in IndonesiaEvaristus Didik Madyatmadja, Brigita Christabel Surya Winata, Evelyn Pradhan et al.This research explores the sentiment expressed in Snapchat user reviews within the Indonesian context, leveraging advanced natural language processing techniques and classification models. With a focus on the Indonesian user base, 8,015 reviews from the Google Play Store were analyzed using naive bayes, Support Vector Machines (SVM), and random forest models. The results indicated that the random forest model outperformed … …Journal of Computer Science, Vol. 21, Issue 1 · 2 Jan 2025 · 5,591 accessesView PDFCite
Research ArticleOpen accessIn the keywordsStroke Risk Assessment with Classical ML MethodsMohammad Aref Alshraideh, Najwan Alshraideh, Abedalrahman Alshraideh et al.Stroke, often caused by a disruption in the supply of essential oxygen, blood, and nutrients to the brain, represents a significant global health challenge. Due to limited resources, developing countries like Ethiopia face unique obstacles in identifying and treating strokes. This study explores the potential of Machine Learning (ML) techniques to predict stroke risk and facilitate early detection and intervention. … …Journal of Computer Science, Vol. 21, Issue 1 · 18 Dec 2024 · 4,649 accessesView PDFCite
Research ArticleOpen accessIn the keywordsBig Data Framework for Predicting Infectious Diseases to Improve Healthcare by Discovering New Symptom PatternsAmal Mohamed Mounir, Mohamed Ibrahim Marie, Laila Abd-ElhamidThe utilization of big data in infectious disease control represents a captivating opportunity, as these novel data streams offer the potential to enhance the timeliness of preventive measures. Various healthcare providers in both the public and private sectors generate, store, and analyse extensive datasets to enhance the quality of services they deliver. Recently, the outbreak of the new coronavirus, COVID-19, … …Journal of Computer Science, Vol. 20, Issue 10 · 2 Aug 2024 · 4,915 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
Review ArticleOpen accessIn the keywordsCrime Prediction Using Machine Learning: A Comparative AnalysisAbdulrahman Alsubayhin, Muhammad Ramzan, Bander AlzahraniIn the past few years, Machine Learning (ML) methods have acquired extensive attention from researchers and even criminology practitioners for their potential in crime prediction and crime forecast. To date, many research studies have already been performed on crime prediction using machine learning techniques. Some common methods that have been used include KNNs, Decision trees, Naïve Bayes, and random forest, … …Journal of Computer Science, Vol. 19, Issue 9 · 29 Aug 2023 · 8,063 accessesView PDFCite
Research ArticleOpen accessIn the keywordsStock Price Classification Based on Hybrid Feature Selection MethodSrivinay, Manujakshi B. C., Mohan Govindsa Kabadi et al.In recent years, investors and traders have used Technical Indicators (TIs) to forecast the stock market. An accurate classification model is required in the stock market to gain more profit. Selecting relevant TIs for the stock market remains a hot research topic. The proposed work aims to identify important technical indicators. Therefore, the proposed work considers a hybrid feature selection … …Journal of Computer Science, Vol. 19, Issue 2 · 10 Feb 2023 · 6,101 accessesView PDFCite
Research ArticleOpen accessIn the keywordsSentiment Analysis on User Reviews of Mutual Fund ApplicationsEvaristus Didik Madyatmadja, Shinta, Devi Susanti et al.The primary goal of this study is to compare the accuracyof the results of sentiment analysis using the Naive Bayes, Support VectorMachine (SVM), and Random Forest methods on one of the mutual fund application’s user reviews.The second goal is to identify user reviews of the mutual fund app to gaininsight into the topics covered by each sentiment. The user reviews … …Journal of Computer Science, Vol. 18, Issue 10 · 26 Sep 2022 · 6,441 accessesView PDFCite
Research ArticleOpen accessIn the keywordsIntelligent Digital Signal Modulation Recognition using Machine LearningMudhafar Haji Mala Abd, Sadegh AminifarInrecent years, modulation type recognition has received a lot of attentionacross the board. There are several methods to detect modulation types, butthere are only a few effective methods to handle signals with a higher level ofnoise. This study introduces an approach to verify the ability of differentmachine learning algorithms to automatically manage noise in detecting digitalmodulations. This research examines two … …Journal of Computer Science, Vol. 18, Issue 10 · 26 Sep 2022 · 6,095 accessesView PDFCite
Research ArticleOpen accessIn the keywordsPredicting the Remaining Lifetime of Distribution Transformers using Machine LearningNtiminity Abontakoyah Enoch, Puguo Gbene George, Justice AningDistribution Transformer is a crucial element in deciding the power flow in large power systems. Their better performance implies high power system efficiency and enhanced power transfer capability. However, various Distribution Transformer failures in the recent past lead to power supply disturbance and have acquired much attention from the electrical intellectuals. It is of considerable significance to accurately get the … …American Journal of Engineering and Applied Sciences, Vol. 13, Issue 4 · 1 Jan 2021 · 6,644 accessesView PDFCite