Search Results - ((regression algorithm) OR (conversion algorithm))
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Carbon dioxide reforming of methane over Ni-based catalysts: Modeling the effect of process parameters on greenhouse gasses conversion using supervised machine learning algorithms
Published 2023“…Catalysts; Conjugate gradient method; Learning algorithms; Methane; Multilayer neural networks; Multilayers; Sensitivity analysis; Supervised learning; Auto-regressive; Bayesian regularization; CH$-4$; Greenhouse gasse; Multilayers perceptrons; Neural-networks; Nonlinear autoregressive exogenous; Performance; Process parameters; Supervised machine learning; Carbon dioxide…”
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Modeling of cardiovascular diseases (CVDs) and development of predictive heart risk score
Published 2021“…Further, it focuses on the development of various forms of local risk prediction models and simple heart risk scores using non-laboratory features and machine learning (ML) algorithms. However, the conversion of a complex form of ML algorithms into a simple statistical model is the prime concern. …”
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Metaheuristic optimization of perovskite solar cell using hybrid L₃₂ Taguchi DoE-based genetic algorithm
Published 2024“…The proposed approach is realized using Solar Cell Capacitance Simulator (SCAPS-1D) software incorporated with a hybrid L32 Taguchi DoE-based Genetic Algorithm. Based on Multiple Linear Regression (MLR) analysis, the thickness of mix halide perovskite (CH3NH3PbI3-XClX) was discovered to be the most crucial input parameter affecting the Power Conversion Efficiency (PCE) variations. …”
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Analysis and evaluation of various aspects of solar radiation in the Palestinian territories
Published 2013“…These coefficients were calculated using both MATLAB's fitting tool and genetic algorithm. Linear, quadratic and linear-algorithmic regression models displayed almost identical results. …”
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Metaheuristic Optimization of Perovskite Solar Cell Using Hybrid L32 Taguchi Doe-Based Genetic Algorithm
Published 2025“…The proposed approach is realized using Solar Cell Capacitance Simulator (SCAPS-1D) software incorporated with a hybrid L32 Taguchi DoE-based Genetic Algorithm. Based on Multiple Linear Regression (MLR) analysis, the thickness of mix halide perovskite (CH3NH3PbI3-XClX) was discovered to be the most crucial input parameter affecting the Power Conversion Efficiency (PCE) variations. …”
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Forecasting and Trading of the Stable Cryptocurrencies With Machine Learning and Deep Learning Algorithms for Market Conditions
Published 2023“…Thus, this proposed system employs a data science-based framework and six highly advanced data-driven Machine learning and Deep learning algorithms: Support Vector Regressor, Auto-Regressive Integrated Moving Average (ARIMA), Facebook Prophet, Unidirectional LSTM, Bidirectional LSTM, Stacked LSTM. …”
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Optimization of perovskite solar cell with MoS2-based HTM layer using hybrid L27 Taguchi-GRA based genetic algorithm
Published 2025“…This article proposes an optimization method to predictively model the perovskite solar cell with molybdenum disulfide (MoS2) based inorganic hole transport material (HTM) for improved fill factor (FF) and power conversion efficiency (PCE) by finding the most optimum thickness and donor/acceptor concentration for each layer via a hybrid L27 Taguchi grey relational analysis (GRA) based genetic algorithm (GA). …”
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Wind power prediction using Artificial Neural Network
Published 2010“…As a result, an ANN with the regression value of 0.81881 was developed, which has the ability to predict the wind power for the next hour with 81.881 % accuracy.…”
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Load forecasting for air conditioning systems using linear regression and artificial neural networks
Published 2024“…This study aims to develop a precise load forecasting model by integrating Linear Regression (LR) and Artificial Neural Networks (ANN). …”
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Proceeding Paper -
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Experimental and AI-driven enhancements in gas-phase photocatalytic CO2 conversion over synthesized highly ordered anodic TiO2 nanotubes
Published 2025“…Six popular ML algorithms of regression, kernel and neural network-based models were applied to predict the gas-phase CO2 photoconversion rate. …”
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Machine learning models for predicting the compressive strength of concrete with shredded pet bottles and m sand as fine aggregate
Published 2025“…Machine learning is a critical subset of AI that deliberates the development of self-trained algorithms that use previous databases and analysis for result predictions. …”
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Classification of Distracted Male Driver Based on Driving Performance Indicator (DPI)
Published 2024“…Weka is a strong data mining and machine learning program including algorithms for data preparation, classification, regression, clustering, and visualization. …”
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Ant colony optimization and genetic algorithm models for suspended sediment discharge estimation for gorgan-river, Iran
Published 2011“…New models based on artificial intelligence models, namely; Ant Colony Optimization (ACO) and Genetic Algorithm (GA) are now being used more frequently to solve optimization problems. …”
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Feature extraction and supervised learning for volatile organic compounds gas recognition
Published 2023“…This research project aims to investigate effective feature extraction techniques that can be employed as discriminative features for machine learning algorithms. A preliminary dataset was used to predict VOC classification through the application of five supervised machine learning algorithms: k-Nearest Neighbors (kNN), Random Forest (RF), Support Vector Machines (SVM), Logistic Regression (LR), and Artificial Neural Networks (ANN). …”
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Prediction of device performance in SnO2 based inverted organic solar cells using machine learning framework
Published 2024“…In this project, the machine learning (ML) framework with different algorithm models and kernel functions was employed to predict the device performance of solution-processed SnO2-based organic solar cells. …”
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Competitive algorithms for online conversion problems with interrelated prices
Published 2019“…The classical uni-directional conversion algorithms are based on the assumption that prices are arbitrarily chosen from the fixed price interval[m, M] where m and M represent the estimated lower and upper bounds of possible prices 0<m<M. …”
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Comparison of ANN performance towards agarwood oil compounds pre-processing based on principal component analysis (PCA) and stepwise regression selection method / Noratikah Zawani...
Published 2021“…The Resilient Back propagation as classifier algorithm was used and 1 to 10 hidden neuron in the hidden layer were varied. …”
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Kinetic modelling of hydrogen transfer deoxygenation of a prototypical fatty acid over a bimetallic Pd60Cu40catalyst: An investigation of the surface reaction mechanism and rate li...
Published 2020“…Kinetic expressions derived from the three kinetic models were investigated in rate data fitting through nonlinear regression using a Levenberg-Marquardt algorithm. Based on the statistical discrimination criteria, the experimental data of the dehydrogenation reaction of tetralin were best fitted by an L-H rate equation assuming the surface reaction as the rate controlling step. …”
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