Search Results - (( developing function using algorithm ) OR ( learning machine learning algorithm ))

Binary Coati Optimization Algorithm- Multi- Kernel Least Square Support Vector Machine-Extreme Learning Machine Model (BCOA-MKLSSVM-ELM): A New Hybrid Machine Learning Model for Predicting Reservoir Water Level by Sammen S.S., Ehteram M., Sheikh Khozani Z., Sidek L.M.

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Development of Machine Learning Algorithm for Acquiring Machining Data in Turning Process by Ali Al-Assadi, Hayder M. A.

“…Artificial Neural Network (ANN) was selected from Machine Learning Algorithms to be the learning algorithm. …”
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Support directional shifting vector: A direction based machine learning classifier by Kowsher, Md., Hossen, Imran, Tahabilder, Anik, Prottasha, Nusrat Jahan, Habib, Kaiser, Zafril Rizal, M Azmi

“…The positional error of the linear function has been modelled as a loss function which is iteratively optimized using the gradient descent algorithm. …”
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Extreme learning machine model for state-of-charge estimation of lithium-ion battery using gravitational search algorithm by Hossain Lipu M.S., Hannan M.A., Hussain A., Saad M.H., Ayob A., Uddin M.N.

“…Backpropagation; Charging (batteries); Electric vehicles; Estimation; Ions; Knowledge acquisition; Learning algorithms; Lithium-ion batteries; Machine learning; Radial basis function networks; Back-propagation neural networks; Electric vehicle drive cycles; Extreme learning machine; Gravitational search algorithm (GSA); Gravitational search algorithms; Lithium ions; Radial basis function neural networks; State of charge; Battery management systems…”

A comparative analysis of machine learning algorithms for diabetes prediction by Alansari, Waseem Abdulmahdi, Masnizah Mohd

“…This study focuses on comparing the performance of three machine learning algorithms, namely Naive Bayes (NB), Support Vector Machines (SVM), and Random Forest (RF), in predicting diabetes using two datasets: Pima Indians Diabetes Dataset (PIDD) and the Diabetes 2019 Dataset (DD2019), and the need to identify the most accurate and effective algorithm for diabetes prediction. …”
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Development of machine learning-based algorithm to determine the condition in transformer oil by Mohsen Al-Katheri, Hussein Hasan

“…Machine Learning (ML) algorithm have been utilized to detect the fault more accurate. …”
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Semi-supervised learning for feature selection and classification of data / Ganesh Krishnasamy by Ganesh , Krishnasamy

“…An efficient iterative algorithm is developed to optimize the objective function of the proposed algorithm since it is non-smooth and difficult to solve. …”
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Sentiment Analysis on COVID-19 Vaccine Tweets using Machine Learning and Deep Learning Algorithms by Tarun Jain, Tarun Jain, Vivek Kumar Verma, Vivek Kumar Verma, Akhilesh Kumar Sharma, Akhilesh Kumar Sharma, Bhavna Saini, Bhavna Saini, Nishant Purohit, Nishant Purohit, Bhavika, Bhavika, Hairulnizam Mahdin, Hairulnizam Mahdin, Masitah Ahmad, Masitah Ahmad, Rozanawati Darman, Rozanawati Darman, Su-Cheng Haw, Su-Cheng Haw, Shazlyn Milleana Shaharudin, Shazlyn Milleana Shaharudin, Mohammad Syafwan Arshad, Mohammad Syafwan Arshad

“…The reviews are translated into vector representations using various techniques, including BagOf-Words and TF-IDF to ensure the best result. Machine learning algorithms like Logistic Regression, Naïve Bayes, Support Vector Machine (SVM) and others, and Deep Learning algorithms like LSTM and Bert were used to train the predictive models. …”
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A New Mobile Botnet Classification based on Permission and API Calls by Yusof, M, Saudi, MM, Ridzuan, F

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Interaction effect of process parameters and Pd-electrocatalyst in formic acid electro-oxidation for fuel cell applications: Implementing supervised machine learning algorithms by Hossain S.K.S., Ali S.S., Rushd S., Ayodele B.V., Cheng C.K.

“…Carbon nanotubes; Electrocatalysts; Electrooxidation; Forestry; Formic acid; Gaussian distribution; Learning algorithms; Palladium; Parameter estimation; Regression analysis; Support vector machines; Formic acid electrooxidation; Fuel cell application; Gaussian kernel functions; Gaussian process regression; Interaction effect; Machine learning algorithms; Performance; Process parameters; Regression trees; Support vector machine regressions; Sensitivity analysis…”

Fault classification in smart distribution network using support vector machine by Chuan O.W., Ab Aziz N.F., Yasin Z.M., Salim N.A., Wahab N.A.

“…In this paper, a machine-learning algorithm known as Support Vector Machine (SVM) for fault type classification in distribution system has been developed. …”

A new mobile botnet classification based on permission and API calls by Yusof M., Madihah Mohd Saudi, Farida Hazwani Mohd Ridzuan

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AI recommendation penetration testing tool for cross-site scripting: support vector machine algorithm by Salim, Nur Saadah, Saad, Shahadan

“…This research introduces a new approach to enhancing cybersecurity by integrating Support Vector Machine (SVM) algorithms with penetration testing to develop a recommendation system focused on Cross-Site Scripting (XSS) attack detection. …”
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Adaptive model predictive control based on wavelet network and online sequential extreme learning machine for nonlinear systems by Salih, Dhiadeen Mohammed

“…Recently, an online sequential extreme learning machine (OSELM) algorithm has been introduced based on extreme learning machine (ELM) theories for single hidden layer feedforward neural networks (SLFN) and has been applied for different online applications. …”
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Neural network approach for estimating state of charge of lithium-ion battery using backtracking search algorithm by Hannan M.A., Lipu M.S.H., Hussain A., Saad M.H., Ayob A.

“…Backpropagation; Backpropagation algorithms; Charging (batteries); Electric batteries; Electric vehicles; Errors; Ions; Learning algorithms; Learning systems; Lithium; Lithium-ion batteries; Mean square error; Neural networks; Optimization; Radial basis function networks; Secondary batteries; Torsional stress; Back propagation neural networks; Backtracking search algorithms; Battery residual capacity; Extreme learning machine; Generalized Regression Neural Network(GRNN); Mean absolute percentage error; Radial basis function neural networks; State of charge; Battery management systems…”

Lithium-ion Battery State of Charge Estimation Method Using Optimized Deep Recurrent Neural Network Algorithm by Lipu M.S.H., Hannan M.A., Hussain A., Saad M.H.M., Ayob A., Muttaqi K.M.

“…Alumina; Aluminum oxide; Backpropagation; Battery management systems; Bioluminescence; Charging (batteries); Cobalt compounds; Deep neural networks; Genetic algorithms; Ions; Lithium compounds; Machine learning; Nickel oxide; Radial basis function networks; Recurrent neural networks; Back-propagation neural networks; Computation intelligences; Electrochemical batteries; Firefly algorithms; Manganese-cobalt oxides; Radial basis function neural networks; Self-learning capability; State of charge; Lithium-ion batteries…”

An Evaluation of Machine Learning Algorithms for Missing Values Imputation by Kohbalan, Moorthy, Ali, Mohammed Hasan, Mohd Arfian, Ismail, Chan, Weng Howe, Mohd Saberi, Mohamad, Safaai, Deris

“…The purpose of our review article is to focus on the developments of current techniques. For scientists rather applying different or newly develop algorithms with the identical functional goal. …”
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A deep reinforcement learning hybrid algorithm for the computational discovery and characterization of small proteins utilizing mycobacterium tuberculosis as a model by Ouwabunmi, Babalola AbdulHafeez

“…This study presents the development and evaluation of a novel hybrid machine learning algorithm that integrates the strengths of Random Forest and Gradient Boosting models to enhance the prediction of smORFs. …”
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Feedforward neural network for solving particular fractional differential equations by Admon, Mohd Rashid

“…The second approach relies on Chelyshkov basis functions for approximation and utilizes the extreme machine learning algorithm for weight determination, achieving high accuracy and low computational time. …”
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Implementation of machine learning algorithms for streamflow prediction of Dokan dam by Sarmad Dashti Latif, Mr.

Subjects: “…Implementation of machine learning algorithms for streamflow prediction of Dokan dam…”