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93 results for “Detection Methods\Techniques”
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Research ArticleOpen accessEvery word in the titleComparative Analysis of Fraud Detection Methods in Banking Using Machine Learning TechniquesYoussef Tounsi, Ennouri TaziFraud detection in banking requires algorithms that balance classification performance, computational efficiency, and regulatory interpretability, criteria that are rarely benchmarked together. We present a comprehensive evaluation of nine machine learning approaches (Logistic Regression, Decision Tree, Random Forest, SVM, SGD, XGBoost, CatBoost, LightGBM, and MLP) across three datasets differing in size, imbalance severity, and feature type (synthetic, real-world PCA-anonymized, and large-scale … …Journal of Computer Science, Vol. 22, Issue 6 · 22 Jun 2026 · 1,341 accessesView PDFCite
Review ArticleOpen accessIn the keywordsSurvey on Representation Techniques for Malware Detection SystemGamal Abdel Nassir Mohamed, Norafida Bte IthninMalicious programs are malignant software’s designed by hackers or cyber offenders with a harmful intent to disrupt computer operation. In various researches, we found that the balance between designing an accurate architecture that can detect the malware and track several advanced techniques that malware creators apply to get variants of malware are always a difficult line. Hence the study of … …American Journal of Applied Sciences, Vol. 14, Issue 11 · 10 Jul 2017 · 8,582 accessesView PDFCite
Review ArticleOpen accessIn the keywordsA Survey on Event Detection Models for Text Data StreamsWafa Zubair AL-Dyani, Farzana Kabir Ahmad, Siti Sakira KamaruddinEvent Detection (ED) is a study area that attracts the attention of decision-makers from various disciplines in order to help them in taking the right decision. ED has been examined on various text streams like Twitter, Facebook, Emails, Blogs, Web Forums and newswires. Many ED models have been proposed in literature. In general, ED model consists of six main phases: … …Journal of Computer Science, Vol. 16, Issue 7 · 14 Jul 2020 · 7,038 accessesView PDFCite
Research ArticleOpen accessIn the abstractExplainability Aware Heterogeneous Temporal Graph Transformer for Automated Genetic Disorder Detection from Genomic DataPuvaneswari Subramaniapillai, Indirani Govindasamy, Revathy GurumurthyGenetic disorders affect millions worldwide, posing major challenges for accurate diagnosis and effective treatments. New developments in genomic technologies, especially Next-Generation Sequencing (NGS), have enhanced the classification of genetic modification and improved diagnostic abilities. Moreover, the Machine Learning (ML) and Deep Learning (DL) methods emerged as efficient tools for evaluating the intricate genomic data and determining the patterns, which can … …Journal of Computer Science, Vol. 22, Issue 11 · 14 Oct 2026 · 566 accessesView PDFCite
Research ArticleOpen accessIn the abstractEnhancing Kidney Stone Detection Accuracy Using Image Processing and a Hybrid Convolutional Deep Belief NetworkGeetha M. N, Barkathulla A, S. Nandhini Devi et al.Automated CKD is a rapidly expanding non-communicable ailment that significantly contributes to global mortality and morbidity. CKD impacts more than 10% of the worldwide population, leading to millions of deaths annually. This paper proposes an enhanced deep learning model for renal disease detection that integrates convolutional and involution layers to enhance classification accuracy. CKD is a worldwide health issue characterised … …Journal of Computer Science, Vol. 22, Issue 10 · 9 Oct 2026 · 749 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 · 372 accessesView PDFCite
Research ArticleOpen accessIn the abstractAn Explainable AI (XAI) Framework for Intrusion Detection and Identification to Secure IoT NetworksVibhor Harit, Rajeev Dahiya, Umang Garg, Anil KumarThe Internet of Things (IoT) encompasses a wide range of applications from wearable devices to smart homes, as well as industrial automation and smart cities. As IoT networks and devices continue to grow rapidly, security has become crucial. There are many techniques utilized to provide security aspects in IoT networks. These security aspects focus on mitigating risks and fixing vulnerabilities … …Journal of Computer Science, Vol. 22, Issue 7 · 23 Jul 2026 · 1,005 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 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 abstractImplementation and Performance Evaluation of YOLOv8 for Wheat Fungal Disease DetectionShivani Sood, Shallu Duggal, Monika Sethi et al.Among the many difficulties in producing wheat, a vital food source for the world, there are biotic variables like fungal diseases, which drastically lower crop output. Around the world, biotrophic fungi particularly, leaf rust, powdery mildew, and yellow rusts-have become a constant threat to wheat production. Traditional methods of identification and mitigation are labor-intensive, slow, and imprecise, limiting therefore early … …Journal of Computer Science, Vol. 22, Issue 4 · 2 May 2026 · 1,599 accessesView PDFCite