Search Results - ((regression algorithms) OR (((compression algorithms) OR (conversion algorithm))))

Machine learning models for predicting the compressive strength of concrete with shredded pet bottles and m sand as fine aggregate by Nadimalla, Altamashuddinkhan, Masjuki, Siti Aliyyah, Gubbi, Abdullah, Khan, Anjum, Mokashi, Imran

“…This study investigates the use of ML algorithms to predict the compressive strength of grade 30 concrete, incorporating shredded PET bottles and M-sand as fine aggregates. …”
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Evaluating enhanced predictive modeling of foam concrete compressive strength using artificial intelligence algorithms by Abdellatief M., Wong L.S., Din N.M., Mo K.H., Ahmed A.N., El-Shafie A.

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Feasibility analysis for predicting the compressive and tensile strength of concrete using machine learning algorithms by Ziyad Sami B.H., Ziyad Sami B.F., Kumar P., Ahmed A.N., Amieghemen G.E., Sherif M.M., El-Shafie A.

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Machine learning technique for the prediction of blended concrete compressive strength by Jubori, Dawood S. A., Abu B., Nabilah, Safiee, Nor A., Alias, Aidi H., Nasir, Noor A. M.

“…Generally, the BR algorithm gives a better overall performance, while underestimating the compressive strength compared to LM. …”
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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 by Ayodele B.V., Alsaffar M.A., Mustapa S.I., Kanthasamy R., Wongsakulphasatch S., Cheng C.K.

“…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…”

Investigation of machine learning models in predicting compressive strength for ultra-high-performance geopolymer concrete: A comparative study by Abdellatief M., Hassan Y.M., Elnabwy M.T., Wong L.S., Chin R.J., Mo K.H.

“…In the current study, random forest (RF), support vector regression (SVR), and extreme gradient boosting (XGB) are used to forecast the compressive strength (CS) of UHPGC. …”

Predication of entropy generation rate in a concentrating photovoltaic thermal system with twisted tube turbulator using Boosted regression tree algorithm by Wang G., Paw J.K.S., Pasupuleti J., Yaw C.T., Yusaf T., Abdalla A.N., Cai Y.

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Algorithm development for optimization of a refrigeration system by Izzat, Mohamad Adnan

“…By using the Statistica software the new algorithm was generate by using linear regression analysis and the algorithm defined as γ = 4.284109 - 0.057164 χR from the algorithm and the international domestic refrigerator using R-134a COP value, was showed that the optimum charge for the refrigerator system occur at 31.21psi.R…”
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Development Of A Robust Blind Digital Video Watermarking Algorithm Using Discrete Wavelet Transform by Al-Deen, Ahmed A. Baha'a

“…In order to be robust against format conversions, the watermark has to be inserted before compression. …”
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Compressing and improving fuzzy rules using genetic algorithm and its application to fault detection by Yap K.S., Wong S.Y., Tiong S.K.

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Video compression techniques: an overview by Abomhara, Mohamed, Khalifa, Othman Omran, Omar, Zakaria, Zaidan, Aos Alaa, Zaidan, Bilal Bahaa, Rame, A.

“…Therefore today, storing and transmitting uncompressed raw video requires large storage space and network bandwidth. Special algorithms which take these characteristics of the video into account can compress the video with high compression ratios. …”
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Response surface analysis, clustering, and random forest regression of pressure in suddenly expanded high-speed aerodynamic flows by Afzal, Asif, Aabid, Abdul, Khan, Ambareen, Khan, Sher Afghan, Rajak, Upendra, Verma, Tikendra Nath, Kumar, Rahul

“…Regression of both the pressures using a random forest classification algorithm is carried out. …”
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Modeling of cardiovascular diseases (CVDs) and development of predictive heart risk score by Mirza Rizwan, Sajid

“…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 by Salehuddin, Fauziyah, Ahmad Jalaludin, Nabilah, Kaharudin, Khairil Ezwan, Arith, Faiz, Mohd Zain, Anis Suhaila, Md Junos@Yunus, Siti Aisah, R Apte, Prakash

“…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 by Ismail, M.S., Moghavvemi, Mahmoud, Mahlia, T.M.I.

“…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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Apply and optimize machine learning algorithms for estimating battery health by Chin, Wai Yee

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Statistical analysis engine capacity, weight, and torque on MPV fuel consumption using regression and correlation algorithms by Hafidz Salafuddin, Nanang K. Pradipta, Farrah Anis Fazliatul Adnan, Jong Soo Rhee, Dianta Ginting

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Metaheuristic Optimization of Perovskite Solar Cell Using Hybrid L32 Taguchi Doe-Based Genetic Algorithm by Kaharudin K.E., Jalaludin N.A., Salehuddin F., Arith F., Mohd Zain A.S., Ahmad I., Mat Junos S.A., Apte P.R.

“…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. …”

Forecasting and Trading of the Stable Cryptocurrencies With Machine Learning and Deep Learning Algorithms for Market Conditions by Shamshad, H., Ullah, F., Ullah, A., Kebande, V.R., Ullah, S., Al-Dhaqm, A.

“…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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Digital Quran With Storage Optimization Through Duplication Handling And Compressed Sparse Matrix Method by Ashraf Saleh Mohammad Alomoush