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Research ArticleOpen accessEvery word in the titleDetermination of Crop Yield Detection and Prediction With Multimodal Data Using Fusion TechniquesKutlwano Sedimo, Rajalakshmi Selveraj, Venumadhav Kuthadi, Thulaganyo DimakatsoMost often, soil, environmental, and agronomic data are used in pieces, hence limiting the performance of crop selection and yield forecasting. This study develops an integrated, fusion-based framework that combines soil image classification, environmental feature analysis, and input-driven yield prediction to support precise agricultural decision-making. The system consists of three major modules: (i) a CNN for the classification of soil … …Journal of Computer Science, Vol. 22, Issue 10 · 5 Oct 2026 · 481 accessesView PDFCite
Research ArticleOpen accessEvery word in the titleAn Active Learning Ensemble Framework for Tropical Crop Yield PredictionAmol Bhilare, Debabrata Swain, Megh Patel et al.Agriculture always has great importance among all different sectors, not only in the world but also in India. It has a significant impact on food security and largely controls the economy. At present, technological advancements have significantly enhanced agricultural productivity, but it can still be further improved by the integration of new cutting-edge techniques like Artificial intelligence. Among the factors … …Journal of Computer Science, Vol. 22, Issue 9 · 11 Sep 2026 · 819 accessesView PDFCite
Research ArticleOpen accessEvery word in the titleHybrid 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 · 1,390 accessesView PDFCite
Research ArticleOpen accessEvery word in the titleA Metaheuristic-Optimized Feature Selection for Early-Stage Diabetes Prediction With SHAP-Guided Insight into Influential AttributesEsmay Azam Rochy, Jannatul Ferdaus, Uzzal Biswas et al.Diabetes is a metabolic disorder that causes elevated blood glucose. This long-term health condition can lead to cardiovascular diseases, stroke, kidney failure, visual impairment, neuropathy, and even death in critical cases. So, a Computer-Aided Diagnostic (CAD) system is necessary to diagnose diabetes automatically. A clinician can utilize a machine learning-based CAD system that automatically diagnoses many people. This paper will … …Journal of Computer Science, Vol. 22, Issue 2 · 24 Feb 2026 · 1,736 accessesView PDFCite
Research ArticleOpen accessEvery word in the titleA Dynamic Lifelong Learning Network for Real-Time Stock Prediction With Reinforcement TuningTalent Mawere, Selvaraj Rajalakshmi, Venu Madhav Kuthadi, Otlhapile DinakanyaneCompanies and investors require accurate market forecasting to make more informed decisions. Traditional methods for predicting stock market performance have become less useful considering the dynamic and volatile nature that characterizes stock markets today. In this paper, we propose an innovative Dynamic Lifelong Learning Network for stock market prediction. The model incorporates a hybrid convolutional long short-term memory with an … …Journal of Computer Science, Vol. 22, Issue 2 · 20 Feb 2026 · 1,822 accessesView PDFCite
Research ArticleOpen accessEvery word in the titleImproving Diabetes Risk Prediction Using Ensemble Boosting and SMOTE-Based Class BalancingKittipol Wisaeng, Pankom Sriboonlue, Benchalak MuangmeesriAccurate diabetes prediction is vital for early intervention, optimized resource allocation, and minimizing long-term complications. This study presents a comparative evaluation of traditional and advanced machine learning models for diabetes classification using a structured clinical dataset. Seven baseline algorithms were assessed against five advanced ensemble methods: CatBoost, LightGBM, XGBoost, Voting Ensemble, and Stacking Ensemble. To improve algorithm learning, the Synthetic … …Journal of Computer Science, Vol. 22, Issue 1 · 1 Feb 2026 · 1,976 accessesView PDFCite
Research ArticleOpen accessEvery word in the titleInternet of Things and Human Neocortex Inspired Algorithms for the Plant Disease Prediction-Sheath Blight for Paddy PlantThangaraj Ethilu, K. Parthiban, Arockia Jayadhas S et al.Sheath blight disease (Rhizoctonia solani) poses a critical threat to paddy (Oryza sativa) production, causing up to 50% yield losses under conducive environmental conditions. Existing computer vision-based disease detection systems identify infections only after symptom manifestation, while conventional machine learning prediction models suffer from high computational demands, data preprocessing requirements, and noise sensitivity. This research proposes a novel Hierarchical Temporal … …Journal of Computer Science, Vol. 21, Issue 11 · 18 Dec 2025 · 1,772 accessesView PDFCite
Research ArticleOpen accessEvery word in the titleIntegrated ARIMA and Multi-Scale GRU for Crop Recommendation and Yield Prediction in Precision AgricultureVani Vijay Kumar Ballapuram, Gurupraksh Chirathahalli DyamannaAgriculture is the fundamental source of food, income, and livelihood for rural communities in India. Numerous crops are affected due to the lack of technical decision-making support and variations in weather patterns, temperature, rainfall, and atmosphere factors, which play a critical role in defining crop yield. Hence, choosing the appropriate crop to maximize yield is key to enhancing real-time farming … …Journal of Computer Science, Vol. 21, Issue 10 · 26 Nov 2025 · 2,022 accessesView PDFCite
Research ArticleOpen accessEvery word in the titleCrop Yield Prediction in Niger Using Machine Learning TechniquesMahaman Lawali Inoussa Garba, Naroua Harouna, Chaibou Kadri et al.Agriculture is the most impactful sector on Niger's economic growth. Stresses related to climate change and the environment are leading to a continuous decline in crop yield. Accurate crop yield forecasting is of paramount importance for farmers, government, policy makers, and other stakeholders to make most appropriate decisions regarding resource allocation and food security. This study examines the power of … …Journal of Computer Science, Vol. 21, Issue 8 · 16 Sep 2025 · 5,792 accessesView PDFCite
Research ArticleOpen accessEvery word in the titleComparative Study of Garuda Indonesia Stock Price Prediction Using SVM, LSTM and Multiple Linear RegressionMuhammad Naufal Luthfi, Evaristus Didik MadyatmadjaStock shares are one of the investment products or tools that have been used by many people. Shares have interesting options for saving or investment and are able to provide attractive returns based on corporate or company growth. Many factors can affect the share prices, whether internal or external companies. This research was conducted on Garuda Indonesia's stock price with … …Journal of Computer Science, Vol. 21, Issue 4 · 25 Mar 2025 · 5,032 accessesView PDFCite