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24 results for “Hybrid Machine Learning Models”
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Research ArticleOpen accessIn the keywordsSpatio-Temporal Anomaly Detection in Groundwater Electrical Conductivity Using a Hybrid Framework of Isolation Forest and AutoencoderEunji Lee, Seunghyun Lim, Seojun Lee, Abhijit DebnathMonitoring groundwater quality is vital for environmental safety and resource sustainability. This study combines Isolation Forest and Autoencoder models to detect anomalies in Electrical Conductivity (EC) and temperature, using monthly data collected in South Korea between 2006 and 2023. Linear regression and the Mann-Kendall test reveal a weak, episodic downward EC trend. Seasonal decomposition indicates annual cyclicality, while residual analysis … …Journal of Computer Science, Vol. 22, Issue 1 · 14 Feb 2026 · 1,918 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 · 999 accessesView PDFCite
Research ArticleOpen accessIn the abstractA Machine Learning-Based Sentiment Analysis of Article 370 Tweets to Support Government Policy DecisionsSubhasis Mohapatra, Sudhir Kumar Mohapatra, Sweta Samantaray et al.This study proposes a robust sentiment analysis framework to evaluate public opinion on the abrogation of Article 370 using Twitter data. The methodology begins with tweet collection through the Twitter API, followed by systematic preprocessing. Sentiment labels were generated using the Text Blob lexicon-based polarity scoring approach to facilitate the construction of a large-scale sentiment dataset. Features are extracted, and … …Journal of Computer Science, Vol. 22, Issue 6 · 2 Jul 2026 · 914 accessesView PDFCite
Research ArticleOpen accessIn the abstractEnsemble Learning-Based Hybrid Explainable Intrusion Detection System With SMOTE for Enhanced Detection and Interpretability in CybersecurityMohammad Subhi Al-Batah, Taqieddin AldrousWith cyberattacks becoming increasingly sophisticated, there is a growing need for Intrusion Detection Systems (IDS) that are both accurate and interpretable. This study introduces a hybrid IDS framework that integrates Random Forest (RF) and Light Gradient Boosting Machine (LGBM) classifiers with the Synthetic Minority Over-sampling Technique (SMOTE) to address class imbalance. To enhance transparency and trust, the system also incorporates … …Journal of Computer Science, Vol. 22, Issue 5 · 19 May 2026 · 1,277 accessesView PDFCite
Research ArticleOpen accessIn the abstractArabic Fake News Detection Across Generational Text Representations: From Traditional Models to Transformer-Based MethodologiesNoor M. AlkudahThe rapid proliferation of fake news on Arabic social media has amplified societal and political risks, yet research on automatic detection in Arabic remains limited due to scarce datasets, morphological complexity, and underexplored preprocessing strategies. This study presents a comprehensive benchmark for Arabic fake news detection, unifying seven Machine Learning (ML) algorithms, three Deep Learning (DL) models, and a transformer-based … …Journal of Computer Science, Vol. 22, Issue 4 · 17 Apr 2026 · 1,873 accessesView PDFCite
Research ArticleOpen accessIn the abstractMoney Laundering Detection in Financial Institutions Using Machine LearningNoor Samer Masood, Rasha Hassan Sakr, Amal Abou EleneenThis research aims to develop a system based on machine learning techniques for the accurate detection and classification of money laundering transactions. This system utilizes real and synthetic financial data, including the IBM AML dataset and synthetic financial databases. The methodology included data collection and cleaning, feature extraction from transaction behavior, and training several classification algorithms, including Random Forest, Support … …Journal of Computer Science, Vol. 22, Issue 4 · 16 Apr 2026 · 2,325 accessesView PDFCite
Research ArticleOpen accessIn the abstractA Comprehensive Review of Medical Image Denoising: Techniques, Challenges, Applications and Future DirectionsMohit Sharma, Ayush Dogra, Anita Gupta et al.The concept of noise reduction in medical images plays a vital role in improving image quality and accuracy associated with diagnosis, and facilitating reliable automated analysis. This survey presents a structured overview of medical image denoising, beginning with foundational definitions and noise models for modalities such as MRI and CT. We critically evaluate persistent gaps, including the limited generalizability of … …Journal of Computer Science, Vol. 22, Issue 3 · 28 Mar 2026 · 1,849 accessesView PDFCite
Research ArticleOpen accessIn the abstractA Comparative Study of Resampling, Cost-Sensitive, and Ensemble Techniques for Handling Class Imbalance in Indonesian Financial DataGunawan Kurnia, Ditdit Nugeraha UtamaHandling class imbalance is a critical challenge in machine learning applications, particularly in financial domains where minority instances often represent significant anomalies such as fraud or audit risks. Various oversampling and undersampling methods were tested, alongside cost-sensitive adjustments and ensemble models including Random Forest, AdaBoost, Gradient Boosting, and XGBoost. The evaluation, based on 10-fold stratified cross-validation and performance metrics such … …Journal of Computer Science, Vol. 22, Issue 2 · 27 Feb 2026 · 1,874 accessesView PDFCite
Research ArticleOpen accessIn the abstractA Novel Hybrid Machine and Deep Learning Model for Detecting Arabic Phishing EmailsFahad GhabbanPhishing emails are becoming an increasingly popular type of cybercrime on the internet, affecting both businesses and individuals. The attackers generally use various methods to trick victims and extract personal information from them, such as bank details, home addresses, and account information. Many attempts have been proposed to tackle this issue by using filtering mechanisms or automated classification methods which … …Journal of Computer Science, Vol. 22, Issue 1 · 9 Feb 2026 · 1,897 accessesView PDFCite
Research ArticleOpen accessIn the abstractImplementing a Hybrid Parallel Framework Utilizing Machine and Deep Learning for Rapid Rumor Detection in Social MediaNeetu Rani, Amit Kumar Bhardwaj, Shaily Jain et al.Numerous businesses have faced significant repercussions due to the widespread dissemination of false information and rumors across social media platforms. The impact of fake news extends to tarnishing public perception, damaging corporate reputations, disrupting communities, undermining governmental integrity, exposing companies to risks, and posing a grave threat to social cohesion. This research article delves into the endeavours of prominent researchers … …Journal of Computer Science, Vol. 21, Issue 12 · 27 Jan 2026 · 1,814 accessesView PDFCite