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27 articles for “Gradient Boosting”
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Research ArticleOpen accessPhrase in the titleRandom Forest and Extreme Gradient Boosting with Bayesian Hyperparameter Optimization for Landslide Susceptibility Mapping in Penang Island, MalaysiaDorothy Anak Martin Atok, Soo See Chai, Kok Luong Goh et al.Landslide susceptibility models often face challenges of overfitting and overestimation. This research focuses on improving the predictive capabilities of the Extreme Gradient Boosting (XGBoost) and Random Forest (RF) algorithms by applying Bayesian Hyperparameter Optimization (BayesOpt). Penang Island, a region in Malaysia prone to frequent landslides, was chosen as the study area. Ten Landslide Conditioning Factors (LCFs), including elevation, slope angle, … …Journal of Computer Science, Vol. 21, Issue 10 · 16 Dec 2025 · 3,103 accessesView PDFCite
Research ArticleOpen accessPhrase in the titleGradient Boosting for Heart Stroke Prediction: Investigating Unexpected Risk FactorsAniket Kailas Shahade, Priyanka V. DeshmukhHeart stroke prediction is a critical area in healthcare, aiming to identify individuals at risk and provide timely intervention. This research leverages machine learning algorithms, including Decision Tree, Random Forest, AdaBoost, and Gradient Boost, to predict the likelihood of stroke, with Gradient Boosting delivering the most accurate results. Our analysis uncovers intriguing and unexpected relationships between stroke risk and various … …Journal of Computer Science, Vol. 21, Issue 1 · 16 Dec 2024 · 4,939 accessesView PDFCite
Research ArticleOpen accessPhrase in the titleSentiment Analysis Using Light Weight - Gradient Boosting Machine based Feature SelectionBikku Ramavath, Srikanth Kadainti, Nemani SubashSentiment analysis is a significant task in Natural Language Processing (NLP) that differentiates the emotions and opinions expressed in text or reviews. The sentiment analysis is challenging due to the complex language patterns and inappropriate or redundant features used for classification. In this research, the Light Weight - Gradient Boosting Machine (LWGBM) based feature selection is proposed to choose relevant … …Journal of Computer Science, Vol. 21, Issue 5 · 26 May 2025 · 4,695 accessesView PDFCite
Research ArticleOpen accessPhrase in the titleModel Classification for Predicting the Post-Translational Modification (PTM) Glycosylation in Sequence O Using an Extreme Gradient Boosting AlgorithmDamayanti, Sutyarso et al.Post Translational Modification (PTM) is an important mechanism involved in regulating protein function. Post-translational modification refers to the addition of covalent and enzymatic modifications of proteins in protein biosynthesis, which has an important role in modifying protein function and regulating gene expression. One of the post-translational modifications is glycosylation. Glycosylation is the addition of a sugar group to a protein … …Journal of Computer Science, Vol. 20, Issue 7 · 29 Apr 2024 · 5,554 accessesView PDFCite
Research ArticleOpen accessEvery word in the titleParameter Optimization of Gradient Tree Boosting Using Dragonfly Algorithm in Crime Forecasting and AnalysisAlif Ridzuan Khairuddin, Nor Azizah Ali, Razana Alwee et al.Crime forecasting and analysis are very important in predicting future crime patterns and beneficial to the authorities in planning effective crime prevention measures. One of the challenges found in crime analysis is the crime data itself as its form, representation and distribution are varied and unpredictable. To handle such data, most researchers have been focusing on applying various Artificial Intelligence … …Journal of Computer Science, Vol. 15, Issue 8 · 7 Aug 2019 · 7,918 accessesView PDFCite
Research ArticleOpen accessIn the keywordsComparative 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
Research ArticleOpen accessIn the keywordsEvaluation of Machine Learning Models to Predict Student Academic Performance Using Structured Educational DataHardik Ishwarbhai Patel, Dharmendra PatelThis study analyses the use of machine learning for predicting the academic performance of students using their academic information from the institution, combined with socio-economic information that comes from outside sources. The collection of information is done using structured questionnaires as well as through data extraction from the Student Information System (SIS). To increase the reliability of models built, a … …Journal of Computer Science, Vol. 22, Issue 5 · 26 May 2026 · 1,419 accessesView PDFCite
Research ArticleOpen accessIn the keywordsEnsemble 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 keywordsDeveloping an Effective Churn Prediction Model for Telecommunications: Enhancing Customer Retention through Advanced Machine Learning TechniquesAshu Goyal, Anuj Gupta, Sharad Kumar et al.Customer churn poses a significant challenge for the telecommunications sector, resulting in substantial revenue losses and increased customer acquisition costs. This research creates an efficient churn prediction model that combines state-of-the-art machine learning with ensemble learning to maximize customer retention. With the IBM Telco Customer Churn dataset, several baseline models, including Gradient Boosting, AdaBoost, Logistic Regression, Random Forest, and Support … …Journal of Computer Science, Vol. 22, Issue 1 · 3 Feb 2026 · 3,164 accessesView PDFCite
Research ArticleOpen accessIn the keywordsComputational Analysis of Genre Effects on Movie Ratings Using MLP AlgorithmsSubir Gupta, Upasana Adhikari, Shefali Varshney, Tanupriya ChoudhuryPredictive analytics are what the entertainment industry depends on so much in prognosticating movie ratings, which is why they inform filmmakers, distributors and stream platforms strategic moves. Traditional prediction models most times only succeed in missing out on the intricate dynamics that are genre-specific to movie ratings thereby leading to inaccuracies and suboptimal decision making. The paper proposes this research … …Journal of Computer Science, Vol. 21, Issue 4 · 19 Mar 2025 · 5,534 accessesView PDFCite