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176 results for “Machine Learning Models”
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Research ArticleOpen accessIn the abstractEarly Diagnosis of Parkinson's Disease Through Voice Analysis and Machine LearningAmrita Naik, Mohd Sahil Khan, Akhilesh Manoj Saraf et al.Parkinson’s Disease (PD) is a progressive neurological disorder, and its prevalence has increased considerably in recent years. Early and accurate diagnosis plays a crucial role in effective treatment, but identifying the disease in its early stages remains challenging, especially for non-specialist clinicians. This study presents a predictive analytics framework for detecting Parkinson's disease using voice-based features. To improve the diversity … …Journal of Computer Science, Vol. 22, Issue 11 · 9 Oct 2026 · 652 accessesView PDFCite
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 · 382 accessesView PDFCite
Research ArticleOpen accessIn the abstractDeep Learning Analysis of Drug Reviews: Identifying Patient Conditions, Predicting Drug Ratings, and Sentiment AnalysisRafat Hammad, Mohammad Ashraf Ottom, Ayah Bani BakerUnderstanding patients' experiences with medications is essential for improving drug effectiveness, detecting potential side effects, and enhancing overall patient satisfaction. This research investigates applying machine learning to patient drug reviews across three main tasks: Identifying patient conditions, predicting drug ratings, and performing sentiment analysis. We implemented and evaluated Convolutional Neural Networks (CNNs), Artificial Neural Networks (ANNs), and Recurrent Neural Networks … …Journal of Computer Science, Vol. 22, Issue 9 · 7 Sep 2026 · 747 accessesView PDFCite
Research ArticleOpen accessIn the abstractHMA-RAG: A Hierarchical Memory Augmented Retrieval Augmented Generation Framework for Fake News DetectionAfnan Altarjami, Sawsan AlshattnawiExisting methods for detecting fake news, such as traditional machine learning and transformer-based models, often work as separate classifiers that do not use outside evidence, historical knowledge, or ways to make the results easier to understand. This paper presents a framework that combines retrieval augmentation and hierarchical memory to overcome three primary constraints: Insufficient external evidence, the absence of memory, … …Journal of Computer Science, Vol. 22, Issue 9 · 7 Sep 2026 · 747 accessesView PDFCite
Research ArticleOpen accessIn the abstractMachine Learning Based Prediction of Catch Yields in Small Scale FisheriesEpifelward Niño Olaivar Amora, Patrick D. CernaSmall-scale fisheries are critical for food security and livelihoods but increasingly face threats from overfishing and environmental change. This study employed machine learning to predict catch yields in Cogtong Bay, Philippines, using 3,478 records collected in 2020–2021 that included species, gear, location, season, and weather data. Models developed in Python with scikit-learn and XGBoost compared Random Forest (RF), Extreme Gradient … …Journal of Computer Science, Vol. 22, Issue 8 · 24 Aug 2026 · 1,117 accessesView PDFCite
Research ArticleOpen accessIn the abstractEnhancing SQL Injection Detection: A Machine Learning Approach Using Network Flow DataP. Vinoth, K. Muthamil Sudar, S. MuthukumarBackground: SQL injection is one of the cyberattacks which occurs in web application vulnerabilities. It targets the poor input validation and enables the attackers to inject malicious queries into the database fields in any application. It will grant access to unauthorized users and data modification, or they will take control over the complete database. To prevent this, there are several … …Journal of Computer Science, Vol. 22, Issue 8 · 18 Aug 2026 · 1,059 accessesView PDFCite
Research ArticleOpen accessIn the abstractEW-GAT: Edge-Weighted Graph Attention Framework for Patient-Aware Heart Disease Classification Using Feature-to-Feature Similarity GraphsRamakrishna Reddy Badveli, Nijaguna Gollara Siddappa, Sundeep Kumar KanipakapatnamHeart disease remains an important cause of global death, requiring accurate and clinically interpretable prediction models. However, traditional Machine Learning (ML) methods often fail to capture meaningful dependencies among clinical features and lack robustness across diverse patient populations. To overcome these issues, this research introduces an Edge-Weighted Graph Attention Network (EW-GAT) for patient-aware heart disease classification. The approach first models … …Journal of Computer Science, Vol. 22, Issue 7 · 25 Jul 2026 · 920 accessesView PDFCite
Research ArticleOpen accessIn the abstractForecasting Corporate Financial Distress Using Explainable Machine Learning: Implications for Risk Management TechnologyKanjana Hinthaw, Warawut NarkbunnumFinancial distress prediction is a critical task in financial risk management, yet it remains challenging due to class imbalance, complex financial structures, and limited model interpretability. This study develops an integrated machine learning framework that combines feature selection, class imbalance handling, threshold optimization, and Explainable Artificial Intelligence (XAI) to improve both predictive performance and decision-making reliability. Using a dataset of … …Journal of Computer Science, Vol. 22, Issue 7 · 22 Jul 2026 · 1,135 accessesView PDFCite
Research ArticleOpen accessIn the abstractEpileptic Seizure Detection from EEG Signal Using Progressive Channel Selection and Deep LearningSuraiya Akter Mumu, Shupta Das, M. A. H. Akhand et al.Epileptic Seizures (ES), marked by abrupt abnormal electrical discharges in the brain, represent the primary symptom of epilepsy, making timely and accurate detection critical because of their unpredictable recurrence and potentially severe consequences. The Electroencephalogram (EEG), a widely used, non-invasive, and economical method for monitoring brain activity, plays a key role in automated ES detection through Machine Learning (ML) and … …Journal of Computer Science, Vol. 22, Issue 6 · 8 Jul 2026 · 1,409 accessesView PDFCite
Research ArticleOpen accessIn the abstractA Misuse and Anomaly Intrusion Detection System Using Hybrid Supervised and Unsupervised Data Mining ApproachesHoma Molavi, Mohammad KhanbabaeiIntrusion Detection Systems (IDSs) play a crucial role in monitoring computer network security. Data mining and machine learning techniques facilitate the identification of intrusion patterns within large volumes of data. Hybrid data mining models have become increasingly popular in IDSs due to their enhanced effectiveness. This study presents a novel hybrid IDS that combines misuse and anomaly detection by integrating … …Journal of Computer Science, Vol. 22, Issue 6 · 6 Jul 2026 · 1,011 accessesView PDFCite