Makó Csaba
5 Papers
Makó Csaba is an academic researcher. The author has contributed to research in topics: Computer science & Particle swarm optimization. The author has an hindex of 2, co-authored 5 publications.
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Papers
Hill Climbing Artificial Electric Field Algorithm for Maximum Power Point Tracking of Photovoltaics
TL;DR: In this paper , a hill climbing-artificial electric field algorithm (AEFA) was proposed to derive an MPP by tuning the converter duty cycle, considering the objective function for maximizing the PV system extracted power.
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Data driven models to predict pore pressure using drilling and petrophysical data
Farshad Jafarizadeh,Meysam Rajabi,Somayeh Tabasi,Reza Seyedkamali,Shadfar Davoodi,Mehdi Ahmadi Alvar,Ahmed E. Radwan,Makó Csaba +7 more
TL;DR: In this article , the most influential set of input features are developed to predict pore pressure, including rate of penetration (ROP), deep resistivity (ILD), density (RHOB), photoelectric index (PEF), corrected gamma ray (CGR), compression-wave velocity (Vp), weight on bit (WOB), shear-wave velocities (Vs) and pore compressibility (Cp).
When Smart Cities Get Smarter via Machine Learning: An In-depth Literature Review
Shahab S. Band,Sina Ardabili,Mehdi Sookhak,Anthony Theodore,Said Elnaffar,Massoud Moslehpour,Makó Csaba,Bernat Torok,Hao-Ting Pai,Amir Mosavi +9 more
TL;DR: The study concludes that the hybrid models and ensembles are the best performers since they exhibit both high accuracy and not-costly complexity and it can be concluded that using either of them is appropriate.
Modeling and optimization of the oyster mushroom growth using artificial neural network: Economic and environmental impacts.
TL;DR: In this paper , the authors investigated the growth of oyster mushrooms in two substrates, namely straw and wheat straw, and developed an ANN-based model for the prediction of dependent variables.
Prediction of fracture density in a gas reservoir using robust computational approaches
Guozhong Gao,Shadfar Davoodi,Somayeh Tabasi,Meysam Rajabi,H. Ghorbani,Ahmed E. Radwan,Makó Csaba,Amirhosein Mosavi +7 more
TL;DR: In this article , four hybrid machine learning models were used for predicting the fracture density (FVDC) of a gas reservoir in Southwest Asia, including least squares support vector machine (LSSVM), multi-layer perceptron (MLP) and genetic algorithm (GA).