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79 articles for “Empirical Models”
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Research ArticleOpen accessEvery word in the titleRandom Walk Models Classifications: An Empirical Study for Malaysian Stock IndicesChin Wen CheongThis article studied the Random Walk models introduced by Campbell et al.[2] for Malaysian stock market. The analysis is implemented under the possible drastic economics structural change using an iterative structural change test. After the break-date identification, the random walk hypothesis is tested by multiple variance ratios test in two separate periods. We further examined the serial correlations of return’s … …American Journal of Applied Sciences, Vol. 5, Issue 4 · 30 Apr 2008 · 6,850 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 titleAn Empirical Study of Robust Modified Recursive Fits of Moving Average ModelsMohamed Ali Ismail, Hend A. Auda, Joe W. McKean, Mahmoud Mohamed SadekThe time-series Moving Average (MA) model is a nonlinear model; see, for example. For traditional Least Squares (LS) fits, there are several algorithms to use for computing its fit. Since the model is nonlinear, Fuller discusses a Newton-type step algorithm. proposed a recursive algorithm based on a sequence of three linear LS regressions. In this study, we robustify Koreisha and … …Journal of Mathematics and Statistics, Vol. 18, Issue 1 · 28 Jul 2022 · 5,859 accessesView PDFCite
Research ArticleOpen accessEvery word in the titleFactors Affecting the Choice of Software Life Cycle Models in the Software Industry-An Empirical StudyRupa Mahanti, M. S. Neogi, Vandana BhattacherjeeProblem statement: The aim of this study was to present the results of the survey conducted with software professionals in a few Indian software companies. Approach: The study initially presents an overview of the common software life cycle models used in the software development. Results and Conclusion: The survey results revealed that the level of understanding of the user requirements … …Journal of Computer Science, Vol. 8, Issue 8 · 13 Jul 2012 · 8,051 accessesView PDFCite
Research ArticleOpen accessIn the keywordsForecasting the Stock Exchange of Thailand uses Day of the Week Effect and Markov Regime Switching GARCHP. Sattayatham, N. Sopipan, B. PremanodeProblem statement: We forecast return and volatility of the Stock Exchange of Thailand (SET) Index. Approach: In this study, we modeled the SET Index returns using mean equation with day of the week effect and autoregressive moving-average. Next we forecast the volatility of the SET Index by using the GARCH-type model and the Markov Regime Switching GARCH (MRS-GARCH) model. Results: … …American Journal of Economics and Business Administration, Vol. 4, Issue 1 · 15 Mar 2012 · 7,865 accessesView PDFCite
Research ArticleOpen accessIn the keywordsEMPIRICAL DETERMINATION OF THE AVERAGE ANNUAL RUNOFF COEFFICIENT IN THE MEDITERRANEAN AREAGiovanni Grillone, Giorgio Baiamonte, Francesco D’AsaroRunoff estimation in ungauged basin is a challenge for the hydrological engineers and planners. For any hydrological study on an ungauged basin, a methodology has to be appropriately selected for the determination of runoff at its outlet. Several methods have been used to estimate the basin runoff production. In this study the empirical Kennessey method to determine average annual runoff … …American Journal of Applied Sciences, Vol. 11, Issue 1 · 5 Dec 2013 · 8,218 accessesView PDFCite
Research ArticleOpen accessIn the abstractImplementing a Hybrid VMD–BiTCN–BiGRU Architecture With Intelligent Optimization for Agricultural Commodity Price Prognostication in Gujarat MarketsJignesh Hirapara, Ratnesh Kumar Namdeo, Ankit Kumar Dubey et al.In the agricultural sector, the prices of pulses require adequate forecasting to plan farming activities, store and market, and guide policy-making. However, the pulse price series is highly volatile, seasonal, nonstationary, and nonlinear, and thus conventional linear time series models are not necessarily very effective. In the suggested framework, the raw price data series is decomposed using Variational Mode Decomposition … …Journal of Computer Science, Vol. 22, Issue 7 · 1 Aug 2026 · 1,021 accessesView PDFCite
Research ArticleOpen accessIn the abstractAI-Integrated Probabilistic Optimization for Inventory Control Under Stochastic DemandJanardan BeheraThe identification and operationalization of increasingly nonstationary and volatile demand patterns challenge the conduct of inventory management in contemporary supply chains. The classical models, especially EOQ and static policies, rest on the stability of demand assumptions, either deterministically or distributionally, and are hence less apt under real-world uncertainty. In view of these limitations, the present study puts forward an integrated … …Journal of Mathematics and Statistics, Vol. 22, Issue 1 · 6 May 2026 · 2,217 accessesView PDFCite
Research ArticleOpen accessIn the abstractEmpirical Study on Modified Pre-Trained CNN Architectures for Fitzpatrick17k Skin Diseases Prediction ModellingSteven Matthew, Nur Afny Catur Andryani, Rojali et al.Preventing missed diagnosis of skin diseases is critical for enhancing patient outcomes. Technology support empowered by AI based algorithms have been recently developed to minimize the burden. Convolutional Neural Networks (CNNs) are increasingly utilized for image classification tasks including skin's diseases prediction modelling. Its advancement architectures such as pre-trained models, VGG16 andResNet50 provide a strong foundation to up-skill the prediction … …Journal of Computer Science, Vol. 21, Issue 7 · 8 Jul 2025 · 3,934 accessesView PDFCite
Research ArticleOpen accessIn the abstractPerson Re-Identification From Video Surveillance Systems Using Artificial Intelligence MethodsRevathi Lavanya Baggam, Vatsavayi Valli KumariThe study explores use of deep learning models in person re identification, leveraging the advancements made in face recognition however the abundance of model choices presents a challenge in selecting the optimal architecture. The study proposes a comprehensive framework for evaluating deep learning models on person re-identification tasks by considering various performance metrics, dataset preprocessing methods, model architectures, and evaluation … …Journal of Computer Science, Vol. 21, Issue 8 · 19 Sep 2025 · 4,836 accessesView PDFCite