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78 results for “MAE”
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Research ArticleOpen accessIn the abstractMultiple Linear Regression to Predict Electrical Energy Consumption Based on Meteorological Data: Application to Some Sites Supplied by the CEB in TogoApaloo Bara Komla Kpomonè et al.The prediction model developed in this article is based on the use of meteorological variables to estimate the consumption of electrical energy at the substations of the Electric Community of Benin. The objective is to predict this consumption in order to adapt production to it. The posts (Lomé Aflao, Légbasito, and Lomé port) are the targets that were used in … …American Journal of Applied Sciences, Vol. 21, Issue 1 · 10 Jun 2024 · 5,785 accessesView PDFCite
Research ArticleOpen accessIn the abstractMachine Learning Oceanographic Data for Prediction of the Potential of Marine ResourcesDenny Arbahri, Oky Dwi Nurhayati, Imam MuditaMarine data and information are very important for human survival, therefore this data and information is attractive to investors because of the potential economic value. This data and information has been difficult to obtain, the solution to overcome this is by analyzing oceanographic data for 2009-2019 collected from the marine database belonging to the Agency for the Study and Application … …Journal of Computer Science, Vol. 20, Issue 2 · 8 Jan 2024 · 5,970 accessesView PDFCite
Research ArticleOpen accessIn the abstractResearch on the Link Between Economic Development Variables and the Rate of Access to Drinking Water in Rural Senegal Using Machine LearningAnabilaye Moussa Coly, Alioune Ly, Ndolane Diouf et al.After pre-processing a set of data collected from Senegalese state structures, official United Nations (UN), and world bank sites and after replacing missing values by interpolation using the mean between two values, we study the effect of macroeconomic development variables on the rate of access to drinking water in rural Senegal. To do this, we use Machine Learning (ML) techniques … …American Journal of Applied Sciences, Vol. 20, Issue 1 · 20 Dec 2023 · 6,204 accessesView PDFCite
Research ArticleOpen accessIn the abstractMeasuring Uncertainty to Extract Fuzzy Membership Functions in Recommender SystemsHeersh Azeez Khorsheed, Sadegh AminifarNowadays, due to the high volume of choices for customers which causes confusion, the use of recommender systems is strongly growing. Of course, existing systems have two problems, one is complexity and the other is failure to consider uncertainty. In this article, we have reduced the complexity of the system by using a fuzzy innovative system and solved the problem … …Journal of Computer Science, Vol. 19, Issue 11 · 15 Oct 2023 · 5,491 accessesView PDFCite
Research ArticleOpen accessIn the abstractSoftware Development Effort Estimation Using Relational Database and Optimized Learning MechanismRavi Kumar B. N., Yeresime SureshAccurately estimating the cost of software development is crucial for effective project planning and resource allocation. However, traditional cost estimation methods rely heavily on expert judgment and historical data, which can be time consuming and prone to errors. This study suggests a learning-based cost estimation model that leverages relational databases to improve accuracy. The proposed approach estimates project cost based … …Journal of Computer Science, Vol. 19, Issue 4 · 7 Apr 2023 · 6,313 accessesView PDFCite
Research ArticleOpen accessIn the abstractHigh Accurate Multicriteria Cluster-Based Collaborative Filtering Recommender SystemZhila Yaseen Taha, Sadegh Abdollah AminifarA Recommender System (RS) is one of the appropriate answers by researchers for alleviating the problem of customers' overload with information from an internet source. In RS, users' evaluation of the items assists in quickly determining the user's preferences and removing the choosing overhead for the user the next time they search. However, using user ratings for an item based … …Journal of Computer Science, Vol. 18, Issue 12 · 10 Dec 2022 · 5,914 accessesView PDFCite
Research ArticleOpen accessIn the abstractComparison of Multiple Linear Regression and Neural Network Models in Bank Performance Prediction in BotswanaHassan Kablay, Victor GumboBank performance is critical to the banking sector and the economy as a whole. In this study, Multiple Linear Regression (MLR) technique and feed forward Neural Network (NN) are used to predict the performance of 11 banks in Botswana. Return on Assets (RoA) is used as the dependent variable, while management quality, credit risk, liquidity, financial leverage and capital adequacy … …Journal of Mathematics and Statistics, Vol. 17, Issue 1 · 10 Jan 2022 · 6,465 accessesView PDFCite
Research ArticleOpen accessIn the abstractThe Exploration of Restaurant Recommender SystemTora Fahrudin, Nelsi WisnaThe exploitation of Recommender Systems (RS) isstill a challenge, hence it is important to explore the three correlatedattributes, such as restaurant, food, and service ratings. Therefore, thisstudy provides an in-depth review of these attribute ratings using theCollaborative Filtering (CF) technique. Experiments were performed with k-NN,SVD, Slope One, and Co-Clustering algorithms, while RMSE, MSE, MAE, and FCPwere used as evaluation metrics. … …Journal of Computer Science, Vol. 18, Issue 8 · 2 Sep 2022 · 6,172 accessesView PDFCite
Research ArticleOpen accessIn the abstractComparative Analysis of Deep Learning Models for Multi-Step Prediction of Financial Time SeriesSaugat Aryal, Dheynoshan Nadarajah, Prabath Lakmal Rupasinghe et al.Financial time series prediction has been a key topic of interest among researchers considering the complexity of the domain and also due to its significant impact on a wide range of applications. In contrast to one-step ahead prediction, multi-step forecasting is more desirable in the industry but the task is more challenging. In recent days, advancement in deep learning has … …Journal of Computer Science, Vol. 16, Issue 10 · 27 Oct 2020 · 6,967 accessesView PDFCite
Research ArticleOpen accessIn the abstractPrediction of Data Traffic in Telecom Networks based on Deep Neural NetworksQuang Hung Do, Thi Thanh Hang Doan, Thi Van Anh Nguyen et al.Accurate prediction of data traffic in telecom network is a challenging task for a better network management. It advances dynamic resource allocation and power management. This study employs deep neural networks including Long Short-Term Memory (LSTM) and Gated Recurrent Units (GRU) techniques to one-hour-ahead forecast the volume of expected traffic and compares this approach to other methods including Adaptive Neuro-Fuzzy … …Journal of Computer Science, Vol. 16, Issue 9 · 30 Sep 2020 · 8,059 accessesView PDFCite