Search Results - (( peer optimization method algorithm ) OR ( variable machine learning algorithm ))

Applying machine learning and particle swarm optimization for predictive modeling and cost optimization in construction project management by almahameed, Bader aldeen, Bisharah, Majdi

“…This study examines the utilization of different Machine Learning algorithms, such as Linear Regression, Decision Trees, Support Vector Machines (SVM), Gradient Boosting, Random Forest, K-Nearest Neighbors (KNN), Convolutional Neural Network (CNN) Regression, and Particle Swarm Optimization (PSO), in the domain of predictive modeling and cost optimization in the field of construction project management. …”
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Short-Term Electricity Price Forecasting via Hybrid Backtracking Search Algorithm and ANFIS Approach by Pourdaryaei, Alireza, Mokhlis, Hazlie, Illias, Hazlee Azil, Kaboli, S. Hr. Aghay, Ahmad, Shameem

“…Through the combination of backtracking search algorithm (BSA) in learning process of ANFIS approach, a hybrid machine learning algorithm has been developed to forecast the electricity price more accurately. …”
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Information Theoretic-based Feature Selection for Machine Learning by Muhammad Aliyu, Sulaiman

“…Three major factors that determine the performance of a machine learning are the choice of a representative set of features, choosing a suitable machine learning algorithm and the right selection of the training parameters for a specified machine learning algorithm. …”
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Synchronizing Artificial Intelligence Models for Operating the Dam and Reservoir System by Allawi, Mohammed Falah, Jaafar, Othman, Ehteram, Mohammad, Mohamad Hamzah, Firdaus, El-Shafie, Ahmed

“…The present study developed artificial intelligence model, called Shark Machine Learning Algorithm (SMLA) to provide optimal operational rules. …”
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Prediction of lattice constant of pyrochlore compounds using optimized machine learning model by Mohamad Zamri, Isma Uzayr, Abd Rahman, Mohd Amiruddin, Bundak, Caceja Elyca

“…In this study, we used an optimized machine learning technique based on Particle Swarm Optimization-Support Vector Regression (PSO-SVR) to learn the relationship between its structural properties with its lattice constants. …”
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Modeling time series data using Genetic Algorithm based on Backpropagation Neural network by Haviluddin

“…The first objective is to study the effects of varying the architecture designs and parameter values of the backpropagation neural network (BPNN) learning algorithm. The second objective is to compare the performances of machine learning (ML) techniques (e.g., BPNN and GA) with the statistical techniques (e.g., autoregressive integrated moving average (ARIMA)) in learning time series data. …”
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A decomposed streamflow non-gradientbased artificial intelligence forecasting algorithm with factoring in aleatoric and epistemic variables / Wei Yaxing by Wei , Yaxing

“…The firefly algorithm remains a feasible alternative for shallow architectural network models, while metaheuristic algorithms such as the Particle swarm algorithm and Bat algorithm are better options for deeper architectural network models. …”
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Multi-Objective Multi-Exemplar Particle Swarm Optimization Algorithm with Local Awareness by Noori, Mustafa Sabah, Sahbudin, Ratna K.Z., Sali, Aduwati, Hashim, Fazirulhisyam

“…Many machine learning algorithms excel at handling problems with conflicting objectives. …”
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A supervised machine-learning method for optimizing the automatic transmission system of wind turbines by Aladwani, Habeeb A. H. R., Ariffin, Mohd Khairol Anuar, Mustapha, Faizal

“…In this research, an unsupervised machine-learning algorithm is proposed to address the energy efficiency of the automatic transmission system in vertical axis wind turbines (VAWT), to increase its efficiency in harvesting energy. …”
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Application of Decision Trees in Athlete Selection: A Cart Algorithm Approach by Riska Wahyu, Romadhonia, A'yunin, Sofro, Danang, Ariyanto, Dimas Avian, Maulana, Junaidi Budi, Prihanto

“…This study investigates the application of Decision Trees (DTs), a non-parametric supervised learning method, renowned for its simplicity, interpretability, and wide applicability in various domains, including machine learning for classification and regression tasks. …”
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Integration of machine learning and remote sensing for above ground biomass estimation through Landsat-9 and field data in temperate forests of the Himalayan region by Anees, Shoaib Ahmad, Mehmood, Kaleem, Khan, Waseem Razzaq, Sajjad, Muhammad, Alahmadi, Tahani Awad, Alharbi, Sulaiman Ali, Luo, Mi

“…While Landsat-9 provides reliable data crucial for long-term monitoring, it is part of a broader suite of available remote sensing technologies. We employ machine learning algorithms such as Extreme Gradient Boosting (XGBoost), Support Vector Regression (SVR), and Random Forest (RF), alongside linear regression techniques like Multiple Linear Regression (MLR). …”
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Hypertension Prediction in Adolescents Using Anthropometric Measurements: Do Machine Learning Models Perform Equally Well? by Chai, Soo See, Goh, Kok Luong, Cheah, Whye Lian, Chang, Robin Yee Hui, Ng, Giap Weng

“…However, different machine learning algorithms were utilized in conjunction with various anthropometric data, either alone or in combination with other biophysical and lifestyle variables. …”
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A Novel Wrapper-Based Optimization Algorithm for the Feature Selection and Classification by Talpur, N., Abdulkadir, S.J., Hasan, M.H., Alhussian, H., Alwadain, A.

“…Machine learning (ML) practices such as classification have played a very important role in classifying diseases in medical science. …”
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Optimized conditioning factors using machine learning techniques for groundwater potential mapping by Kalantar, Bahareh, Al-Najjar, Husam A. H., Pradhan, Biswajeet, Saeidi, Vahideh, Abdul Halin, Alfian, Ueda, Naonori, Naghibi, Seyed Amir

“…In addition, 917 spring locations were identified and used to train and test three machine learning algorithms, namely Mixture Discriminant Analysis (MDA), Linear Discriminant Analysis (LDA) and Random Forest (RF). …”
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Hypertension prediction in adolescents using anthropometric measurements: Do machine learning models perform equally well? by Chai, Soo See, Goh, Kok Luong, Cheah, Whye Lian, Chang, Yee Hui Robin, Ng, Giap Weng

“…However, different machine learning algorithms were utilized in conjunction with various anthropometric data, either alone or in combination with other biophysical and lifestyle variables. …”
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Development of an artificial neural network algorithm for predicting the cutting force in end milling of Inconel 718 alloy by Hossain, Ishtiaq, Amin, A. K. M. Nurul, Patwari, Muhammed Anayet Ullah

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Fuzzy subtractive clustering (FSC) with exponential membership function for heart failure disease clustering by Annisa Eka Haryati, ., Sugiyarto, Surono, Tommy Tanu, Wijaya, Goh, Khang Wen, Aris, Thobirin

“…Methods: The data set which is heart failure patient data is 150 data obtained from UCI Machine Learning. The data consists of 11 variables, including age , anemia , creatinine phosphokinase , diabetes ejection fraction , high blood pressure , platelets , serum creatinine , serum sodium , gender , and smoke . …”
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Multivariate Based Analysis of Methane Adsorption Correlated to Toc and Mineralogy Impact from Different Shale Fabrics by Irfan, S.A., Azli, N.M., Abdulkareem, F.A., Padmanabhan, E.

“…The statistical analysis presented in this study incorporated one of the best regression models algorithms based on machine learning approach to study the adsorption variation with shale fabric this study. …”
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Hospital site suitability assessment using three machine learning approaches: evidence from the Gaza strip in Palestine by Almansi, Khaled Yousef, Mohamed Shariff, Abdul Rashid, Abdullah, Ahmad Fikri, Syed Ismail, Sharifah Norkhadijah

“…The study aims to find an optimal solution for a suitable hospital location through suitability mapping using relevant environmental, topographic, and geodemographic parameters and their variable criteria. To find the most significant parameters that reduce the error rate and increase the efficiency for the suitability analysis, this study utilized machine learning methods. …”
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Prediction of Machine Failure by Using Machine Learning Algorithm by Fakhrurazi, Nur Amalina

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