Search Results - (( machine ((loading algorithm) OR (mining algorithm)) ) OR ( machines training algorithm ))

Machine Learning Regression Approach for Estimating Energy Consumption of Appliances in Smart Home by Husin N.S.I.M., Mostafa S.A., Jaber M.M., Gunasekaran S.S., Al-Shakarchi A.H., Abdulsattar N.F.

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Parallel execution of distributed SVM using MPI (CoDLib) by Salleh N.S.M., Suliman A., Ahmad A.R.

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An optimized variant of machine learning algorithm for datadriven electrical energy efficiency management (D2EEM) by Shamim, Akhtar

“…The scope of this study is tri folded, First, an exhaustive and parametric comparative study on a wide variety of machine learning algorithms is presented to evaluate the performance of machine learning algorithms in energy load prediction. …”
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Comparison of algorithm Support Vector Machine and C4.5 for identification of pests and diseases in chili plants by M, Irfan, N, Lukman, A. A, Alfauzi, J, Jumadi

“…In this study comparing the performance classification techniques of Support Vector Machine (SVM) and C4.5 algorithms. The attributes used consist of Leaves, Stems, and Fruits. …”
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Comparison of Electricity Load Prediction Errors Between Long Short-Term Memory Architecture and Artificial Neural Network on Smart Meter Consumer by Salleh N.S.M., Suliman A., J�rgensen B.N.

“…Brain; Errors; Forecasting; Learning algorithms; Mean square error; Memory architecture; Network architecture; Smart meters; Time series; Demand-side; Electricity load; Error values; Load predictions; Machine learning algorithms; Mean absolute error; Mean squared error; Prediction errors; Regression problem; Times series; Long short-term memory…”

Tracing the real power transfer of individual generators to loads using least squares support vector machine with continuous genetic algorithm by Mohd Wazir, Mustafa, Saifulnizam, Abd.Khalid, Mohd Herwan, Sulaiman, Siti Rafidah, Abd Rahim, Omar, Aliman, Shareef, Hussain

“…This paper attempts to trace the real power transfer of individual generators to loads in pool based power system by incorporating the hybridization of Least Squares Support Vector Machine (LS-SVM) with Continuous Genetic Algorithm (CGA)- CGA-LSSVM. …”
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Machine learning application in predicting anterior cruciate ligament injury among basketball players by Longfei, Guo

“…A one-year follow-up was conducted to monitor ACL injury, identifying n=11 injured players. Four machine learning algorithms—Random Forest (RF), Support Vector Machine (SVM), eXtreme Gradient Boosting (XGBoost), and Logistic Regression (LR)—were developed to predict ACL injury. …”
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Tracing the real power transfer of individual generators to loads using least squares support vector machine with continuous genetic algorithm by Mohd Wazir, Mustafa, Dr., Saifulnizam, Abd. Khalid, Dr., Mohd Herwan, Sulaiman, Siti Rafidah, Abd Rahim, Omar, Aliman, Hussain, Shareef, Dr.

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Training functional link neural network with ant lion optimizer by Mohmad Hassim, Yana Mazwin, Ghazali, Rozaida

“…This paper proposed the implementation of Ant Lion Algorithm as learning algorithm to train the FLNN for classification tasks. …”
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DATA CLASSIFICATION SYSTEM WITH FUZZY NEURAL BASED APPROACH by LUONG, TRUNG TUAN

“…The project's objective is identifying the available data mining algorithms in data classification and applying new data mining algorithm to perform classification tasks. …”
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Cyberbullying detection: a machine learning approach by Yeong, Su Yen

“…Bag of Words model was used to convert text into numerical inputs. The machine learning algorithm, Support Vector Machine was chosen after comparing it with other algorithms such as Multinomial Naïve Bayes, Decision Tree Classifier, and Random Forest Classifier. …”
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Semi-supervised learning for feature selection and classification of data / Ganesh Krishnasamy by Ganesh , Krishnasamy

“…Additionally, HSS-ELM requires remarkably less training time compared to semi-supervised SVMs/regularized least-squares algorithms. …”
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ANALYSIS OF STOCK PRICE PREDICTION USING DATA MINING APPROACH by Mukhariz bin Muhamad, Mukhariz

“…Using Data Mining approach in training the algorithms that will produce the best results based on Public Listed Companies‟ stock price data that dates back until 1998. …”
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CNC Cutting Tools` Life Prediction Using Data Mining Approach by Chan, Choon Kit, Wong, Marven, Zhen Siang

“…In this paper, classification is chosen to be the data mining approach with two algorithms to build the model for prediction, which are linear regression and multilayer perceptron. …”
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Cyberbullying detection and prevention: Data mining in social media by Sultan D., Suliman A., Toktarova A., Omarov B., Mamikov S., Beissenova G.

“…Cloud computing; Computer crime; Data Science; Deep learning; Learning algorithms; Social networking (online); Children and adolescents; Cyber bullying; England; European Countries; Real time; Social media; Training machines; Data mining…”

Reactive power tracing in pool-based power system utilising the hybrid genetic algorithm and least squares support vector machine by Mohd Wazir, Mustafa, Prof. Dr., Mohd Herwan, Sulaiman, Hussain, Shareef, Dr., Siti Nur Hidayah, Abd Khalid

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An application of genetic algorithm and least squares support vector machine for tracing the transmission loss in deregulated power system by M. W., Mustafa, H., Shareef, M. H., Sulaiman, S. N., Abd. Khalid, S. R., Abd. Rahim, Omar, Aliman

“…This paper proposes a new method to trace the transmission loss in deregulated power system by applying Genetic Algorithm (GA) and Least Squares Support Vector Machine (LS-SVM). …”
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Prediction of COVID-19 outbreak using Support Vector Machine / Muhammad Qayyum Mohd Azman by Mohd Azman, Muhammad Qayyum

“…Evaluation metrics, including a comprehensive analysis of the confusion matrix, highlight the consistent superiority of the SVM model with the sigmoid kernel across various training/testing split ratios. This research significantly contributes to the understanding of machine learning applications in the context of COVID-19 outbreak prediction, emphasizing the importance of algorithm and configuration selections for robust forecasting. …”
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Measuring GPU-accelerated parallel SVM performance using large datasets for multi-class machine learning problem by Sulaiman M.A.H.B., Suliman A., Ahmad A.R.

Training of interval type-2 fuzzy logic system using extreme learning machine for load forecasting by Hassan, S., Khosravi, A., Jaafar, J.

“…Hence the fuzzy logic systems can be trained using SLFN's learning algorithms. Considering the same equivalence, ELM is utilized here to train interval type-2 fuzzy logic systems (IT2FLSs). …”
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