Research ArticleOpen accessIn the keywordsEfficient Web Usage Miner Using Decisive Induction RulesK. Poongothai, S. SathiyabamaProblem statement: Web usage mining is the technique of extracting useful information from server logs (user’s history) and finding out what users are looking for on the Internet. This type of web mining allows for the collection of Web access data for Web pages. Scope: The web usage data provides the paths leading to accessed Web pages with prefrences and … …Journal of Computer Science, Vol. 8, Issue 6 · 14 Mar 2012 · 6,899 accessesView PDFCite
Research ArticleOpen accessIn the keywordsAn Efficient Age Estimation System based on Multi Linear Principal Component AnalysisV. Tamil Selvi, K. VaniProblem statement: Human age estimation is an active research topic in computer vision. A vital characteristic in establishing identity of the person is the age. Age estimation from face images continues to be an extremely challenging task compared to other cognition problems. Age is a crucial factor in ascertaining the identity of a person. Age is estimated from the human … …Journal of Computer Science, Vol. 7, Issue 10 · 4 Aug 2011 · 6,480 accessesView PDFCite
Research ArticleOpen accessIn the keywordsRanking of Simultaneous Equation Techniques to Small Sample Properties and Correlated Random DeviatesA. A. Adepoju, J. O. OlaomiProblem statement: All simultaneous equation estimation methods have some desirable asymptotic properties and these properties become effective in large samples. This study is relevant since samples available to researchers are mostly small in practice and are often plagued with the problem of mutual correlation between pairs of random deviates which is a violation of the assumption of mutual independence between … …Journal of Mathematics and Statistics, Vol. 5, Issue 4 · 31 Dec 2009 · 6,594 accessesView PDFCite
Research ArticleOpen accessIn the keywordsFuel Prediction Model for Driving Patterns Using Machine Learning TechniquesManjunath TK, Ashok Kumar PSIn recent years, the demand for fuel efficiency has become a crucial aspect of the automotive industry. Predicting fuel economy accurately is essential for optimizing vehicle performance and reducing environmental impact. This proposed model presents a machine learning-based approach for developing a fuel prediction model using the real dataset. The model aims to predict fuel efficiency for different drivers by … …Journal of Computer Science, Vol. 20, Issue 3 · 29 Jan 2024 · 5,480 accessesView PDFCite
Research ArticleOpen accessIn the keywordsModeling Positive Time Series Data: A Neglected Aspect in Time Series CoursesMaman A. Djauhari, Lee Siaw Li, Rohayu Mohd SallehSomething has been forgotten in time series courses, in particular, when dealing with positive datasets. To describe the pattern hidden in this type of datasets, before we use a sophisticated method of modeling such as Autoregressive Integrated Moving Average (ARIMA), we propose to check first whether the data represent a Geometric Brownian Motion (GBM) process. If it is affirmative, unlike … …American Journal of Applied Sciences, Vol. 13, Issue 7 · 1 Jul 2016 · 7,533 accessesView PDFCite
Research ArticleOpen accessIn the abstractTAGN-IDC: Implicit Discrete Constraints for Tsunami Aware Graph Neural Networks in Spatial Evacuation RoutingSularno, Wendi Boy, Putri Anggraini et al.Tsunami evacuation route requires rapid identification of safe evacuation paths under dynamically changing hazard conditions. A major challenge is incorporating hard binary road-blockage constraints caused by tsunami inundation while maintaining end-to-end differentiability for deep learning models. Existing approaches commonly rely on continuous relaxations or stochastic sampling techniques, which either compromise physical feasibility or increase computational complexity. This study proposes the … …Journal of Computer Science, Vol. 22, Issue 9 · 24 Sep 2026 · 704 accessesView PDFCite
Research ArticleOpen accessIn the abstractAbsolute Measures of Interindividual Variation: A Simulation StudyJosé Moral-de la RubiaFour absolute measures and one relative measure of interindividual variability have been defined, together with their point and interval estimation (bootstrap and asymptotic), and an R script has been developed for their computation. However, further simulation studies are required to strengthen their methodological evaluation. This study focused on the three absolute measures with potentially better regularity conditions, Mean Interindividual Differences … …Journal of Mathematics and Statistics, Vol. 22, Issue 1 · 4 Aug 2026 · 957 accessesView PDFCite
Research ArticleOpen accessIn the abstractAnchoring Public Health Forecasts: High-Precision Obesity Prevalence Estimation Via Hybrid Geodemographic Ensemble RegressionTrenton Ward, Isaac OsunmakindeObesity remains a major driver of chronic disease and rising healthcare costs in the United States. Yet, most existing predictive models rely on single-model classifiers and rarely incorporate spatial structure or ensemble-based regression, thereby limiting their ability to capture geodemographic variation. This study introduces a hybrid unsupervised–supervised ensemble framework that integrates K-Means spatial profiling with a Stacking Regressor to address … …Journal of Computer Science, Vol. 22, Issue 7 · 31 Jul 2026 · 632 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