Search Results - regression ((((ant algorithm) OR (path algorithm))) OR (_ algorithm))

Modeling of cardiovascular diseases (CVDs) and development of predictive heart risk score by Mirza Rizwan, Sajid

“…In developing risk prediction models, two ML algorithms, linear support vector machine and artificial neural network outperformed the existing conventional logistic regression analysis (LRA) model. …”
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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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Developing and validating a simulation model for counting and classification of vehicles by Ali Emad Jehad, Riza Atiq O.K. Rahmat

“…An algorithm system approach has been presented for extracting traffic data using video image processing. …”
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Ant colony optimization and genetic algorithm models for suspended sediment discharge estimation for gorgan-river, Iran by Mohammad Reza Pour, Omolbani

“…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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Heart Attack Prediction with Deep Learning by Syasya Putri binti Mohamad Razauddin

A survey on improvement of Mahalanobis Taguchi system and its application by Tan L.M., Wan Muhamad W.Z.A., Yahya Z.R., Junoh A.K., Azziz N.H.A., Ramlie F., Harudin N., Abu M.Y., Tan X.J.

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Associated Factors of Cardiovascular Diseases in Pakistan: Assessment of Path Analyses Using Warp Partial Least Squares Estimation by Mirza Rizwan, Sajid, Noryanti, Muhammad, Roslinazairimah, Zakaria, Shahbaz, Ahmad, Nauman, Ahmad

“…Warp partial least squares (PLS) based on warp 3 algorithm was used to estimate the simultaneous linear and non-linear path coefficients of the proposed model of study. …”
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A novel prediction of the PV system output current based on integration of optimized hyperparameters of multi-layer neural networks and polynomial regression models by Ridha, Hussein Mohammed, Hizam, Hashim, Mirjalili, Seyedali, Othman, Mohammad Lutfi, Ya’acob, Mohammad Effendy, Abdul Wahab, Noor Izzri, Ahmadipour, Masoud

“…The experimental results demonstrated that the proposed IMGO algorithm is very effective in addressing complex problems with high accuracy, capability, and speedy convergence. …”
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Investigating the reliability of machine learning algorithms as an advanced tool for ozone concentration prediction by Ayman Mohammed Shaher Yafouz, Mr.

“…The hybrid technique has been developed by using deep learning algorithms with the structure of multiple layers (with several neurons) of CNN and LSTM. …”

Evolutionary cost-cognizant regression test case prioritization for object-oriented programs by Bello, AbdulKarim

“…Afterward evolutionary algorithm (EA) was employed to prioritize test cases based on the rate severity of fault detection per unit test cost. …”
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Development of a precision measuring device on a loat cell of a pin-on-disc tribometer by Teh Eu Sam

Identification and analysis of hoisting safety risk factors for IBS construction based on the AcciMap and cases study by Junjia, Yin, Alias, Aidi Hizami, Haron, Nuzul Azam, Abu Bakar, Nabilah

“…This study used three algorithms (Rindge regression, Lasson regression, and partial least squares regression) to explore the impact of deaths and injuries on direct economic losses. …”
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Classification and quantification of palm oil adulteration via portable NIR spectroscopy by Basri, Katrul Nadia, Hussain, Mutia Nurulhusna, Bakar, Jamilah, Sharif, Zaiton, Abdul Khir, Mohd Fared, Zoolfakar, Ahmad Sabirin

“…Pure and adulterated palm oil sample were classified using soft independent modeling class analogy (SIMCA) algorithm with model accuracy more than 0.95 reported for both transflectance and transmission modes. …”
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Classification and quantification of palm oil adulteration via portable NIR spectroscopy by Basri, Katrul Nadia, Hussain, Mutia Nurulhusna, Bakar, Jamilah, Sharif, Zaiton and, Zoolfakar, Ahmad Sabirin, Mohd Fared Abdul Khir

“…Pure and adulterated palm oil sample were classified using soft independent modeling class analogy (SIMCA) algorithm with model accuracy more than 0.95 reported for both transflectance and transmission modes. …”

Peramalan Isyarat Satelit Menggunakan Kaedah Rangkaian Neural by Kahar, Nur Farhan

“…Regression analyses are also done to all the data that has been used. …”
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JMASM 46: Algorithm for comparison of robust regression methods in multiple linear regression by weighting least Square Regression by Mohd Ibrahim, Mohamad Shafiq, Wan Ahmad, Wan Muhamad Amir, Zafakali, Nur Syabiha

“…An algorithm for weighting multiple linear regression by standard deviation and variance for combining different robust method is given in SAS along with an application.…”
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LED based NIR spectroscopy for detection of lard adulteration in palm oil via chemometrics / Katrul Nadia Basri by Basri, Katrul Nadia

“…Pure and adulterated palm oil sample were classified using soft independent modeling class analogy (SIMCA) algorithm with model accuracy more than 0.95 reported for both transflectance and transmission modes. …”
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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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Real-Time State of Charge Estimation of Lithium-Ion Batteries Using Optimized Random Forest Regression Algorithm by Hossain Lipu M.S., Hannan M.A., Hussain A., Ansari S., Rahman S.A., Saad M.H.M., Muttaqi K.M.

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Elucidating the effect of process parameters on the production of hydrogen-rich syngas by biomass and coal Co-gasification techniques: A multi-criteria modeling approach by Bahadar A., Kanthasamy R., Sait H.H., Zwawi M., Algarni M., Ayodele B.V., Cheng C.K., Wei L.J.

“…Biomass; Coal; Complex networks; Errors; Forecasting; Gasification; Hydrogen production; Learning algorithms; Mean square error; Neural networks; Regression analysis; Sensitivity analysis; Support vector machines; Co-gasification; Gaussian process regression; Hydrogen-rich syngas; Machine learning algorithms; Machine-learning; Neural-networks; Process parameters; Regression model; Support vectors machine; Syn gas; Synthesis gas; coal; hydrogen; synfuel; biomass; chemical reaction; detection method; hydrogen; machine learning; multicriteria analysis; algorithm; Article; artificial neural network; biomass; controlled study; gasification; Gaussian processing regression; linear regression analysis; machine learning; mean absolute error; mean square error; parameters; prediction; root mean square error; sensitivity analysis; support vector machine; temperature; Bayes theorem; biomass; Bayes Theorem; Biomass; Coal; Hydrogen; Temperature…”