Search Results - (( site selection model algorithm ) OR ( whale optimization max algorithm ))*

Artificial neural network-salp-swarm algorithm for stock price prediction by Zuriani, Mustaffa, Mohd Herwan, Sulaiman, Azlan, Abdul Aziz

“…Additionally, the SSA-ANN model is compared with other two hybrid models: the ANN optimized by the Whale Optimization Algorithm (WOA-ANN) and Moth-Flame Optimizer (MOA-ANN), as well as a single model, namely the Autoregressive Integrated Moving Average (ARIMA). …”
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Electric vehicle battery state of charge estimation using metaheuristic-optimized CatBoost algorithms by Mohd Herwan, Sulaiman, Zuriani, Mustaffa, Ahmad Salihin, Samsudin, Amir Izzani, Mohamed, Mohd Mawardi, Saari

“…A comprehensive data preprocessing pipeline was implemented, including missing value treatment, outlier removal, and feature normalization using Min-Max scaling. Three distinct metaheuristic algorithms were investigated: Barnacles Mating Optimizer (BMO), Particle Swarm Optimization (PSO), Genetic Algorithm (GA), and Whale Optimization Algorithm (WOA), each integrated with CatBoost to optimize critical parameters including learning rate, tree depth, regularization, and bagging temperature. …”
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Design Optimization of a Gas Turbine Engine for Marine Applications: Off-Design Performance and Control System Considerations by Machmudah, A., Lemma, T.A., Solihin, M.I., Feriadi, Y., Rajabi, A., Afandi, M.I., Abbasi, A.

“…Meta-heuristic optimizations, namely a genetic algorithm (GA) and a whale optimization algorithm (WOA), are applied to optimize the designed control system. …”
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A greedy heuristics multiple criteria model for solving multi-landfill site selection and plant propagation algorithm for improving waste collection vehicle routing solutions by Nur Azriati, Mat

“…However, there are two main issues with the existing LSSP and WCVRP models. First, previous models focused only on a single landfill site based on the highest score without considering the operational costs criterion in solving LSSP. …”
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Spatial Data Mining Model For Landfill Sites Suitability Mapping Based On Neural Networks And Multivariate Analysis by Abujayyab, Sohaib K. M.

“…A case study on landfill site selection in four northern states of Malaysia was conducted to demonstrate the validity of the new SDM model. …”
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Assessment of suitable hospital location using GIS and machine learning by Almansi, Khaled Y. M.

“…Large data availability with challenging features and the proliferation of different methodologies have made it extremely difficult to select the best models that perform for a particular site selection problem. …”
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Autonomous flight algorithm of a quadcopter sensing system for methane gas concentration measurements at landfill site by Bashi, Omar Ibrahim Dallal

“…This quadcopter uses an algorithm to remotely and autonomously measure the methane gas concentrations in user defined areas at landfill sites. …”
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Integration Of Travel Time Zone For Optimal Siting Of Emergency Facilities by Indriasari, Vini

“…The MSAP is a discrete model where a specified number of facilities that achieve the best objective function value of the model are selected out of a finite set of candidate sites. …”
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Modelling the yield loss of oil palm due to Ganoderma Basal Stem Rot disease by Assis Kamu

“…For estimation-post-selection approach, there were two subset selection algorithms were applied, namely backward stepwise subset selection and best-subset selection. …”
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Modelling the yield loss of oil palm due to ganoderma basal stem rot disease by Assis bin Kamu

“…For estimation-post-selection approach, there were two subset selection algorithms were applied, namely backward stepwise subset selection and best-subset selection. …”
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A comparative study for parameter selection in online auctions by Gan, Kim Soon

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The selection of the Bayesian coincident-index models using model comparison criterion with application in Langat river water quality data by Mohd Ali, Zalina, Ibrahim, Noor Akma, Mengersen, Kerrie, Shitan, Mahendran, Juahir, Hafizan

“…The results showed that the model with σ prior 2 was the most appropriate for the Langat river water quality data in the selected sampling sites. …”
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Performance evaluation of hospital site suitability using multilayer perceptron MLP and analytical hierarchy process AHP models in Malacca, Malaysia by Almansi, Khaled Yousef, Mohamed Shariff, Abdul Rashid, Kalantar, Bahareh, Abdullah, Ahmad Fikri, Syed Ismail, Sharifah Norkhadijah, Ueda, Naonori

“…To model the potential hospital site map, we utilized multilayer perceptron (MLP) and analytical hierarchy process (AHP) models. …”
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Modeling flood occurences using soft computing technique in southern strip of Caspian Sea Watershed by Borujeni, Sattar Chavoshi

“…After defining homogeneous region in the study area, flood models were developed. A total of 24 sites which were eligible in terms of adequate rainfall and runoff observed data were selected in this region. …”
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Stochastic And Modified Sequent Peak Algorithm For Reservoir Planning Analysis Considering Performance Indices by Oskoui, Issa Saket

“…Three sites in the Southern part of Peninsular Malaysia are selected as conceptual reservoirs to be the case studies: Johor at Rantau Panjang; Melaka at Pantai Belimbing and Muar at Buluh Kasap gauging stations. …”
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Comparative Analysis Using Bayesian Approach To Neural Network Of Translational Initiation Sites In Alternative Polymorphic Contex by Herman, Nanna Suryana, Husin, Nurul Arneida, Hussin, Burairah

“…The objectives of this paper are to develop useful algorithms and to build a new classification model for the case study.The first approach of neural network includes training on algorithms of Resilient Backpropagation,Scaled Conjugate Gradient Backpropagation and Levenberg-Marquardt.The outputs are used in comparison with Bayesian Neural Network for efficiency comparison.The results showed that Resilient Backpropagation have the consistency in all measurement but performs less in accuracy.In second approach,the Bayesian Classifier_01 outperforms the Resilient Backpropagation by successfully increasing the overall prediction accuracy by 16.0%.The Bayesian Classifier_02 is built to improve the accuracy by adding new features of chemical properties as selected by the Information Gain Ratio method,and increasing the length of the window sequence to 201.The result shows that the built model successfully increases the accuracy by 96.0%.In comparison,the Bayesian model outperforms Tikole and Sankararamakrishnan (2008) by increasing the sensitivity by 10% and specificity by 26%. …”
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Dynamic feature selection model for adaptive cross site scripting attack detection using developed multi-agent deep Q learning model by Kareem Thajeel, Isam, Samsudin, Khairulmizam, Hashim, Shaiful Jahari, Hashim, Fazirulhisyam

“…Thus, this study attempts to fill the gap by proposing a feature drift-aware algorithm for detecting the evolved XSS attacks. The proposed approach is a dynamic feature selection based on a deep Q-network multi-agent feature selection (DQN-MAFS) framework. …”
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An Optimized ANN Measure-Correlate-Predict Method for Long-term Wind Prediction in Malaysia by Hwang Y.K., Ibrahim M.Z., Ahmed A.N., Albani A.

Covid-19 Fake News Detection Model On Social Media Data Using Machine Learning Techniques by Liew, Kelvin Kai Xuan

“…Support Vector Machine (SVM), Naïve Bayes (NB), and Decision Tree (DT) are the machine learning models presented. Finally, numerous measures are used to evaluate these algorithms.…”
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Road condition assessment by OBIA and feature selection techniques using very high-resolution WorldView-2 imagery by Shahi, Kaveh, Mohd Shafri, Helmi Zulhaidi, Hamedianfar, Alireza

“…The chi-square algorithm outperformed SVM and RF techniques. The classification result based on CHI algorithm achieved an overall accuracy of 83.19% for the training image (first site). …”
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