Research ArticleOpen accessEvery word in the titlePROBABILISTIC PREDICTING IN SPEECH IN DISTRESS AND AT THE NEUROTIC DISORDERSMarina M. SolobutinaThe present article provides the results obtained in course of the empirical investigation on the probabilistic predicting in the speech activity. The aim of the research was to show the nature of interrelation between the predicting abilities in the speech activity and severity of distress at the neurotic disorders. In course of the research the following hypotheses were developed: (1) … …OnLine Journal of Biological Sciences, Vol. 14, Issue 2 · 25 Jun 2014 · 7,161 accessesView PDFCite
Research ArticleOpen accessEvery word in the titleCreep predicting model in masonry structure utilizing dynamic neural networkMustafa M. Abed, A. El-Shafie, Siti Aminah Bt. OsmanProblem statement: When the loads are applied to a brickwork structure, visco-elastic behavior upon their stress-strain relationships is exhibited, where the response can be classified into two separate parts: an instantaneous elastic strains and time-dependent creep strains. The creep strain represents the non- instantaneous strain that happens with time when the stress is sustained. Through the previous century, along with … …Journal of Computer Science, Vol. 6, Issue 5 · 31 May 2010 · 7,627 accessesView PDFCite
Research ArticleOpen accessEvery word in the titleDeep Learning Analysis of Drug Reviews: Identifying Patient Conditions, Predicting Drug Ratings, and Sentiment AnalysisRafat Hammad, Mohammad Ashraf Ottom, Ayah Bani BakerUnderstanding patients' experiences with medications is essential for improving drug effectiveness, detecting potential side effects, and enhancing overall patient satisfaction. This research investigates applying machine learning to patient drug reviews across three main tasks: Identifying patient conditions, predicting drug ratings, and performing sentiment analysis. We implemented and evaluated Convolutional Neural Networks (CNNs), Artificial Neural Networks (ANNs), and Recurrent Neural Networks … …Journal of Computer Science, Vol. 22, Issue 9 · 7 Sep 2026 · 736 accessesView PDFCite
Research ArticleOpen accessEvery word in the titleTime Series Analysis for Predicting Tea Harvest Yield: A SARIMAX-Based ApproachPallavi Nagpal, Deepika Chaudhary, Jaiteg SinghPrecise prediction of tea yield is crucial for both agricultural planning and economic forecasting. Projection of the future trends, which rely on present and historical data, is the process termed 'forecasting'. Yield prediction is fundamental for research and development. By gathering the yield data over historic times, the researchers can make their work more valuable by predicting the yield patterns. … …Journal of Computer Science, Vol. 22, Issue 2 · 19 Feb 2026 · 1,891 accessesView PDFCite
Research ArticleOpen accessEvery word in the titleComparative 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 accessEvery word in the titlePredicting CO2 Emissions Using Multivariate Regression: Assessing the Impact of Pandemics and Industrial RevolutionsYoussef Mekki, Chouaib Moujahdi, Noureddine Assad, Aziz DahbiThis paper explores the prediction of CO2 emissions usingMultivariate Regression models, incorporating the influence of significanthistorical events such as pandemics and industrial revolutions. Whileexisting research primarily focuses on carbon emissions alone in forecastingmodels, this study emphasizes the importance of incorporating multiplefactors for a comprehensive understanding of CO2 dynamics. Beyondemissions, factors such as socioeconomic indicators, industrial activities,environmental policies, and historical events … …Journal of Computer Science, Vol. 21, Issue 7 · 23 Jul 2025 · 3,426 accessesView PDFCite
Research ArticleOpen accessEvery word in the titlePredicting Antigenic Peptides Using a Multi-Level Transformer Model with Enhanced Feature SelectionAshwini S., Minu R. I., Jeevan KumarThis Antigenic Peptide (APs) prediction is one of the most important roles to improve vaccine design and interpret immune responses. This paper develops a Multi-Level Pooling-based Transformer model, which improves the accuracy and efficiency of predicting T-cell epitopes. The model utilizes peptide sequences from the Immune Epitope Database, employing a refined Kolaskar and Tongaonkar algorithm for feature extraction and a … …Journal of Computer Science, Vol. 21, Issue 7 · 17 Jul 2025 · 3,443 accessesView PDFCite
Research ArticleOpen accessEvery word in the titleBig Data Framework for Predicting Infectious Diseases to Improve Healthcare by Discovering New Symptom PatternsAmal Mohamed Mounir, Mohamed Ibrahim Marie, Laila Abd-ElhamidThe utilization of big data in infectious disease control represents a captivating opportunity, as these novel data streams offer the potential to enhance the timeliness of preventive measures. Various healthcare providers in both the public and private sectors generate, store, and analyse extensive datasets to enhance the quality of services they deliver. Recently, the outbreak of the new coronavirus, COVID-19, … …Journal of Computer Science, Vol. 20, Issue 10 · 2 Aug 2024 · 4,915 accessesView PDFCite
Research ArticleOpen accessEvery word in the titlePredicting Smartphone Addiction in Teenagers: An Integrative Model Incorporating Machine Learning and Big Five Personality TraitsJacobo Osorio, Marko Figueroa, Lenis WongSmartphone addiction has emerged as a growing concern in society, particularly among teenagers, due to its potential negative impact on physical, emotional social well-being. The excessive use of smartphones has consistently shown associations with negative outcomes, highlighting a strong dependence on these devices, which often leads to detrimental effects on mental health, including heightened levels of anxiety, distress, stress depression. … …Journal of Computer Science, Vol. 20, Issue 2 · 29 Dec 2023 · 8,216 accessesView PDFCite