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158 results for “Predictive Model”
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Research ArticleOpen accessIn the abstractComputational 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
Research ArticleOpen accessIn the abstractEcological Function Evaluation of Soil and Water Conservation Measures for Power Transmission and Transformation Project Construction Based on Remote Sensing and it’s Improvement PathYang Han, Songsong Wang, Yanhui Yang et al.Assessing ecological functions is critical for determining the efficacy of soil and water conservation measures, especially during the development of power transmission and transformation projects. Efficient conservation practices are essential for reducing ecological effects and guaranteeing sustainable development. This research aims to create a resilient model, Ecological Function Prediction for Conservation Effectiveness (EFP-CE) that will classify conservation effectiveness as either … …American Journal of Engineering and Applied Sciences, Vol. 18, Issue 1 · 17 Mar 2025 · 7,725 accessesView PDFCite
Research ArticleOpen accessIn the abstractEnsemble Feature Selection for Elderly Health Condition ClassificationRattanawadee PanthongFeature selection is a pivotal technique for constructing classification models with high-dimensional data. It facilitates the identification and elimination of redundant or irrelevant features, thereby reducing model complexity and enhancing predictive accuracy. The success of a classification model largely depends on the quality of the selected features. Accordingly, feature selection is performed during data preparation, prior to data processing, to … …Journal of Computer Science, Vol. 22, Issue 8 · 31 Aug 2026 · 774 accessesView PDFCite
Research ArticleOpen accessIn the abstractSocial Protection Cash Transfers and Gendered Equality: An Analysis of Gendered Differences in Poverty Reduction and Income DistributionJulius OkelloThis research investigates key determinants of poverty reduction and income distribution in eastern Uganda's Teso sub-region. Regression analysis reveals significant negative coefficients for access to healthcare (β = -0.414, p = 0.029) and road infrastructure (β = -0.363, p = 0.014), indicating lower poverty levels in communities with better access. Employment (β = -2.8, p = 0.000) and income (β … …American Journal of Economics and Business Administration, Vol. 17, Issue 1 · 5 Aug 2026 · 942 accessesView PDFCite
Research ArticleOpen accessIn the abstractDeep Learning Models for Predicting Stock Closing Prices in the Saudi Stock MarketAreej Alhumaidi, Hoda A. AbdelhafezFinancial time series data are inherently volatile and nonlinear, presenting considerable challenges in accurately forecasting stock market trends. This study aims to predict the closing prices of companies listed on the Saudi stock market by analyzing historical data spanning from 2013 to 2023. To this end, four deep learning models were employed-Long Short-Term Memory (LSTM), Bidirectional LSTM (Bi-LSTM), Gated Recurrent … …Journal of Computer Science, Vol. 22, Issue 4 · 22 Apr 2026 · 1,355 accessesView PDFCite
Research ArticleOpen accessIn the abstractDengue Fever Prediction Empowered by Radial Basis Function Networks, Dynamic Mode Decomposition, and Learning-Based Foraging AlgorithmArchana T, Faritha Banu JDengue fever is presently considered a major health threat that must be addressed. External and internal factors that induce nonlinear oscillations in the occurrence of dengue disease have made optimal resource allocation stimulating. Public health initiatives aimed at eradicating this vector use accurate and timely data through field processes. In dengue-endemic nations, early dengue prediction remains a main concern for … …Journal of Computer Science, Vol. 22, Issue 4 · 16 Apr 2026 · 1,496 accessesView PDFCite
Research ArticleOpen accessIn the abstractComparative Analysis of Neural Network Models for Predicting EUR/USD Direction: An Empirical StudyEl Badaoui Mohamed, Raouyane Brahim, El Moumen Samira, Bellafkih MostafaThis paper presents a rigorous comparative analysis of six feedforward neural network models for predicting the directional movement of the EUR/USD currency pair. The evaluated models include the Learning Vector Quantization, Cascade Neural Network, Feedforward Neural Network, Single Layer Perceptron, Multi-Layer Perceptron, and Radial Basis Function network. Utilizing daily historical data from April 2009 to May 2024, each model was … …Journal of Computer Science, Vol. 22, Issue 1 · 3 Feb 2026 · 2,330 accessesView PDFCite
Research ArticleOpen accessIn the abstractStock Closing Price Forecasting Using LSTM, Sentiment Analysis, Kalman FilterAssel Abdildayeva, Galymzhan Nurtugan, Guldana Taganova, Ardak AkhmetovaThe article explores the problem of predicting the closing prices of stocks using an innovative approach that combines methods of machine learning, sentiment analysis and noise filtering. The focus is on the development and testing of a comprehensive model that integrates recurrent neural networks (LSTM) for time series analysis, the GPT-3 language model for processing and analyzing textual data from … …Journal of Computer Science, Vol. 20, Issue 11 · 4 Sep 2024 · 6,262 accessesView PDFCite
Research ArticleOpen accessIn the abstractA Multi-Split Cross-Strategy for Enhancing Machine Learning Algorithms Prediction Results with Data Generated by Conditional Generative Adversarial NetworkAbdelfattah Abassi, Brahim Bakkas et al.In this study, we present a Multi-Split Cross-Strategy (MSC-Strategy) designed to leverage synthetic tabular data generated by a Conditional Generative Adversarial Network (CGAN). Our study aims to investigate the potential of synthetic data in comparison to real-world data for improving machine learning predictive results. Firstly, we develop a CGAN architecture tailored to generate synthetic tabular data, trained on a comprehensive … …Journal of Computer Science, Vol. 20, Issue 7 · 9 Apr 2024 · 6,695 accessesView PDFCite
Research ArticleOpen accessIn the abstractMachine Learning Approaches for the Prediction of Gas Turbine TransientsArnaud Nguembang Fadja, Giuseppe Cota, Francesco Bertasi et al.Gas Turbine (GT) emergency shutdowns can lead to energy production interruption and may also reduce the lifespan of a turbine. In order to remain competitive in the market, it is necessary to improve the reliability and availability of GTs by developing predictive maintenance systems that are able to predict future conditions of GTs within a certain time. Predicting such situations … …Journal of Computer Science, Vol. 20, Issue 5 · 28 Feb 2024 · 7,660 accessesView PDFCite