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92 results for “Imbalance”
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Review ArticleOpen accessIn the keywordsAn Insight into Rare Class Problem: Analysis and Potential SolutionsSatyam Maheshwari, R.C. Jain, R.S. JadonThe class imbalance problem presents an important challenge to the data mining community, in which the number of examples of one class is more than the others. This problem is characterized by a different distribution of cases between all the classes. In this paper, our goal is to study the various challenges of class imbalance problem and provide a comparative … …Journal of Computer Science, Vol. 14, Issue 6 · 9 May 2018 · 8,393 accessesView PDFCite
Research ArticleOpen accessIn the keywordsComparison Between Selective Sampling and Random Undersampling for Classification of Customer Defection Using Support Vector MachineHeri Kuswanto, Yogi Sarumaha, Hayato OhwadaQuality of a product determines the customer loyalty and it can be measured by conducting a survey. Company ‘X’ that sells three kinds of product (low, medium and high price) collected very large dataset through an online survey and recorded customer defection and their characteristic. The measured variables are Update Accumulation, Product Price, Customer Type, Delivery Status and Customer Defection. … …Journal of Computer Science, Vol. 13, Issue 8 · 16 Aug 2017 · 7,272 accessesView PDFCite
Research ArticleOpen accessIn the keywordsGreen Tea Treatment Attenuates Oxidative Damage and Neuromotor Deficit Induced by an Experimental Model of Intracerebral Hemorrhage in RatsMauren Assis Souza, Caroline Dalla Colletta Altermann, Alexandre dos Santos Martins et al.The present study shows the neuroprotective effect of green tea supplementation (GT; Camellia sinensis) in a model of Intracerebral Hemorrhage (ICH) in rats. ICH was induced by intra-striatum collagenase infusion in male Wistar rats. GT (400 mg Kg-1) was administered via gavage for 10 days after ICH. We assessed the effect of GT on neuromotor recovery and oxidative damage. Our … …Neuroscience International, Vol. 7, Issue 2 · 19 Mar 2017 · 8,603 accessesView PDFCite
Case ReportOpen accessIn the keywordsAlcohol-Induced Rhabdomyolysis Complicated by Hyponatremia and Acute Kidney Injury Following Minor Trauma: A Case ReportVinay Tulsian, Drupad Das, M Sukumar, Prasan Kumar PandaRhabdomyolysis from alcohol is established, but combined binge intake, minor trauma, and vomiting-induced hyponatremia is a complex clinical scenario. Case study: This case report describes a young male with a three-year history of alcohol consumption who presented with rhabdomyolysis following binge drinking and a mild traumatic fall. He experienced 10 to 15 episodes of vomiting post-alcohol intake, subsequently developing anasarca. … …American Journal of Pharmacology and Toxicology, Vol. 20, Issue 1 · 23 May 2026 · 1,707 accessesView PDFCite
Research ArticleOpen accessIn the abstractHybrid Soft Voting Ensemble of XGBoost and DNN for At-Risk Student Performance PredictionEugene Wan, Po Chan Chiu, Mohammad bin Hossin et al.Early identification of at-risk students in higher education is important for timely academic intervention, yet conventional prediction methods often struggle with data imbalance and limited model precision. This study proposes a hybrid soft voting ensemble model that integrates Extreme Gradient Boosting (XGBoost) and Deep Neural Network (DNN) to enhance multi-class student grade prediction (A-F classification) and at-risk student identification. This … …Journal of Computer Science, Vol. 22, Issue 5 · 28 May 2026 · 1,404 accessesView PDFCite
Research ArticleOpen accessIn the abstractAn Integrated Disease Progression Model to Analyze Electronic Health Records With Multimodal Datasets Using Deep Learning TechniquesThulaganyo Dimakatso, Venumadhav Kuthadi, Rajalakshmi Selvaraj, Othapile DinakenyaneIn contemporary clinical environments, precisely simulating the emergence of disease is an intricate task owing to heterogeneously derived data and variability in the record of diagnoses. The research presents a stable classification model that integrates image data with formalized patient data to track patterns in disease through various stages. The suggested approach combines a multi-stage analytical methodology that includes systematic … …Journal of Computer Science, Vol. 22, Issue 2 · 19 Feb 2026 · 1,686 accessesView PDFCite
Research ArticleOpen accessIn the abstractPrompt-Based Data Augmentation with Large Language Models for Indonesian Gender-Based Hate Speech DetectionMuhammad Amien Ibrahim, Faisal, Zefanya Delvin Sulistiya, Tora Sangputra Yopie WinartoThe increasing amount of content on social media content makes the use of automatic moderation crucial for preserving a healthy online community and reducing the spread of offensive and abusive content, such as hate speech based on gender. Developing automated social media moderation using machine learning demands a large and balanced dataset. However, difficulties such as data scarcity and class … …Journal of Computer Science, Vol. 20, Issue 8 · 27 May 2024 · 5,886 accessesView PDFCite
Review ArticleOpen accessIn the abstractA Systematic Review of Artificial Intelligence and Deep Learning Approaches for Cocoa Pod Disease DetectionHenry Techie-Menson, Michael Asante, Yaw Marfo Missah et al.Cocoa pod diseases destroy an estimated 700,000 metric tons of cocoa annually, threatening 40–50 million smallholder farmers across tropical regions. Accurate field diagnosis remains constrained by symptom similarity, labour-intensive scouting, and limited specialist access in remote communities. This paper presents a PRISMA-guided systematic review of 25 peer-reviewed studies on automated cocoa pod disease detection and classification (2016–2026), addressing architectural approaches, … …Journal of Computer Science, Vol. 22, Issue 8 · 8 Sep 2026 · 743 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 · 1,284 accessesView PDFCite
Research ArticleOpen accessIn the abstractHybrid Deep Learning Model for Evaluating Subjective Answers Based on Semantic Textual SimilaritySiddhesh Kudtarkar, Kavita ShirsatSubjective answer evaluation requires accurately identifying semantic similarities between student and reference responses. This study introduces a Hybrid Deep Learning Model (HDLM) that integrates CNN, GRU, and LSTM architectures to assess Semantic Textual Similarity (STS) more effectively. The HDLM employs two parallel branches-CNN-GRU and CNN-LSTM to capture both local syntactic features and long-range contextual dependencies, followed by Manhattan distance for … …Journal of Computer Science, Vol. 22, Issue 4 · 7 Apr 2026 · 1,695 accessesView PDFCite