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244 results for “Predicting”
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Research ArticleOpen accessEvery word in the titlePredicting Missing Attribute Values Using k-Means ClusteringNambiraj Suguna, Keppana Gowder ThanushkodiProblem statement: Predicting the value for missing attributes is an important data preprocessing problem in data mining and knowledge discovery tasks. Several methods have been proposed to treat missing data and the one used more frequently is deleting instances containing at least one missing value of a feature. When the dataset has minimum number of missing attribute values then we … …Journal of Computer Science, Vol. 7, Issue 2 · 25 Feb 2011 · 7,873 accessesView PDFCite
Research ArticleOpen accessEvery word in the titleModification Approach of Fuzzy Logic Model for Predicting of Cutting Force When Machining Nickel Based Hastelloy C-276Basim A. Khidhir, Bashir Mohamed, Mahmoud A.A. YounisProblem statement: Most Nickel based Hastelloy C-276 is a difficult-to-machine material because of its low thermal diffusive property and high strength at high temperature. Machinability consideration of nickel based Hastelloy C-276 in turning operations has been carried out using ceramic inserts under dry conditions. Approach: This study described a modification approach applied to a fuzzy logic based model for predicting … …American Journal of Engineering and Applied Sciences, Vol. 3, Issue 1 · 31 Mar 2010 · 8,456 accessesView PDFCite
Research ArticleOpen accessEvery word in the titlePredicting Packet Transmission Data over IP Networks Using Adaptive Neuro-Fuzzy Inference SystemsSamira Chabaa, Abdelouhab ZeroualProblem statement: The statistical modeling for predicting network traffic has now become a major tool used for network and is of significant interest in many domains: Adaptive application, congestion and admission control, wireless, network management and network anomalies. To comprehend the properties of IP-network traffic and system conditions, many kinds of reports based on measured network traffic data have been … …Journal of Computer Science, Vol. 5, Issue 2 · 28 Feb 2009 · 7,533 accessesView PDFCite
Research ArticleOpen accessEvery word in the titleEvaluation of Slope Assessment Systems for Predicting Landslides of Cut Slopes in Granitic and Meta-sediment FormationsSuhaimi Jamaludin, Bujang B.K. Huat, Husaini OmarIn Malaysia, slope assessment systems (SAS) are widely used in assessing the instability of slope or the probability of occurrence and the likely severity of landslides. These SAS can be derived based on either one particular approach or combination of several approaches of landslide assessments and prediction. This study overviews four slope assessment systems (SAS) developed in Malaysia for predicting … …American Journal of Environmental Sciences, Vol. 2, Issue 4 · 31 Dec 2006 · 6,909 accessesView PDFCite
Research ArticleOpen accessEvery word in the titleUsing Neutral Network in Predicting Corporate FailureHuong G. NguyenThis study investigates the predictive power of three neutral network models: Multi-layer neural network, probabilistic neural network, and logistic regression model in predicting corporate failure. Basing on the database provided by The Corporate Scorecard Group (CSG), we combine financial ratios which deem to be significant predictors of corporate bankruptcy in many previous empirical studies to build our predictive models and … …Journal of Social Sciences, Vol. 1, Issue 4 · 31 Dec 2005 · 7,324 accessesView PDFCite
Research ArticleOpen accessEvery word in the titleEvaluation of Slope Assessment System in Predicting Landslides along Roads Underlain by Granitic FormationBujang B.K. Huat, S. JamaludinA slope assessment is to estimate the probability of occurrence and likely severity of landslides in a given area. This study evaluates two existing Slope Assessment Systems (SAS) for predicting landslide at the micro level of assessment developed by the Public Works Department of Malaysia, namely the Slope Information Management System (SIMS) and the Slope Management and Risk Tracking System … …American Journal of Environmental Sciences, Vol. 1, Issue 2 · 30 Jun 2005 · 6,574 accessesView PDFCite
Research ArticleOpen accessEvery word in the titleAssessing the Comparative Effectiveness of Ensemble CatBoost Versus XGBoost Models in Predicting Heart Disease OutcomesManivannan D., G. Gifta Jerith, G. Chandra Sekhar et al.Cardiovascular Diseases (CVDs) are a common form of heart disease that remains a significant global cause of mortality, responsible for more than 30% of all fatalities. Without intervention, the global fatality count is projected to reach 22 million by 2030. Arterial plaque buildup may impede blood flow, potentially causing heart attacks or strokes. A combination of risk causes, including lack … …Journal of Computer Science, Vol. 22, Issue 4 · 4 May 2026 · 1,544 accessesView PDFCite
Research ArticleOpen accessEvery word in the titleDeep 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 accessEvery word in the titleCRISP-DM-Based Mobile Application for Predicting High-Crime Areas in Metropolitan LimaHugo Vega Huerta, Javier Vilca Velasquez, Nicolas Anicama Espinoza et al.The city of Lima, Peru, has been facing a serious climate of citizen security that has risen extremely high in recent years. The objective of this work is to identify and predict areas of high crime incidence through a mobile application based on historical data on criminal incidents recorded by users. The mobile application has been implemented using the CRISP-DM … …Journal of Computer Science, Vol. 22, Issue 2 · 27 Feb 2026 · 1,972 accessesView PDFCite
Research ArticleOpen accessEvery word 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