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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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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. …”

Development of electromyography-controlled 3D printed robot hand and supervised machine learning for signal classification by Abdul Wahit, Mohamad Aizat

“…Furthermore, the Support vector machine (SVM) and Linear discriminant analysis (LDA) machine learning for the hand posture classification based on the EMG signal pattern were investigated and compared in term of classification performance. …”
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Assessment of cognitive load using multimedia learning and resting states with deep learning perspective by Qayyum, A., Faye, I., Malik, A.S., Mazher, M.

“…It is a well-understood fact that the brain activity increases with the increased demand of cognition. The deep learning algorithm based on Pre-trained convolutional neural network (CNN) networks have been used as a transfer learning for the classification of rest and cognitive states and also assessed the cognitive load using brain waves particularly alpha wave. …”
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Detection and classification of conflict flows in SDN using machine learning algorithms by Mutaz Hamed Hussien Khairi, Sharifah Hafizah Syed Ariffin, Nurul Mu'azzah Abdul Latiff, Kamaludin Mohamad Yusof, Mohamed Khalafalla Hassan, Fahad Taha Al-Dhief, Mosab Hamda, Suleman Khan, Muzaffar Hamzah

“…Moreover, applying machine learning algorithms in the identification and classification of conflicting flows has limitations. …”
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Daily maximum load forecasting of consecutive national holidays using OSELM-based multi-agents system with weighted average strategy by Yap K.S., Yap H.J.

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Cyberbullying detection: a machine learning approach by Yeong, Su Yen

“…Those algorithms are used in the classification or regression model to predict an input. …”
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Ganoderma boninense classification based on near-infrared spectral data using machine learning techniques by Mas Ira Syafila, Mohd Hilmi Tan, Mohd Faizal, Jamlos, Ahmad Fairuz, Omar, Kamarulzaman, Kamarudin, Mohd Aminudin, Jamlos

“…However, a few overlapped NIRs’ spectral data between healthy and infected samples require for further validation which chemometric and machine learning (ML) classification technique are chosen. …”
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Gender Classification: A Convolutional Neural Network Approach by Shan, Sung Liew, Mohamed, Khalil-Hani, Syafeeza, Ahmad Radzi, Rabia, Bakhteri

“…Unlike in conventional CNNs, we replace the convolution operation with cross-correlation, hence reducing the computational load. The network is trained using a second-order backpropagation learning algorithm with annealed global learning rates. …”
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A Reference Based Surface Defect Segmentation Algorithm For Automatic Optical Inspection System by Wong, Ze-Hao

“…However, present complex algorithms which are accurate require high processing power using a large size of learning dataset without labelling error. …”
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Driver behaviour classification: a research using OBD-II data and machine learning by Muhamad Fadzil, Nur Farisya Aqilah, Mohd Fadzir, Hilda, Mansor, Hafizah, Rahardja, Untung

“…The relationship between all features and engine speed is analysed to select the optimal features, which include engine speed, vehicle speed, throttle position, and calculated engine load. Then, the proposed model makes use of the K-Means algorithm to create driving behaviour labels whether belong to safe or aggressive - validated by the safety score criteria. …”
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Training functional link neural network with ant lion optimizer by Mohmad Hassim, Yana Mazwin, Ghazali, Rozaida

“…Functional Link Neural Network (FLNN) has becoming as an important tool used in machine learning due to its modest architecture. …”
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Short term load forecasting using a hybrid neural network by Yap K.S., Abidin I.Z., Lim C.P., Shah M.S.

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Functional link neural network with modified bee-firefly learning algorithm for classification task by Mohmad Hassim, Yana Mazwin

“…The single layer property of FLNN also make the learning algorithm used less complicated compared to MLP network. …”
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A modified generalized RBF model with EM-based learning algorithm for medical applications by Ma, Li Ya, Abdul Rahman, Abdul Wahab, Quek, Chai

“…Radial Basis Function (RBF) has been widely used in different fields, due to its fast learning and interpretability of its solution. …”
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Development of vehicle detection and counting system for traffic analysis using computer vision by Saw, Kenny Wei Wen

“…Once the trained weights are ready a deep learning algorithm detection model is applied to detect and draw a bounding box on the vehicles. …”
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An improved artificial bee colony algorithm for training multilayer perceptron in time series prediction by Shah, Habib

“…Furthermore, here these algorithms used to train the MLP on two tasks; the seismic event's prediction and Boolean function classification. …”
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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 Pr... by Sammen S.S., Ehteram M., Sheikh Khozani Z., Sidek L.M.

“…For water level prediction, lagged rainfall and water level are used. In this study, we used extreme learning machine (ELM)-multi-kernel least square support vector machine (ELM-MKLSSVM), extreme learning machine (ELM)-LSSVM-polynomial kernel function (PKF) (ELM-LSSVM-PKF), ELM-LSSVM-radial basis kernel function (RBF) (ELM-LSSVM-RBF), ELM-LSSVM-Linear Kernel function (LKF), ELM, and MKLSSVM models to predict water level. …”

Differential evolution for neural networks learning enhancement by Ismail Wdaa, Abdul Sttar

“…These algorithms can be used successfully in many applications requiring the optimization of a certain multi-dimensional function. …”
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Dynamic training rate for backpropagation learning algorithm by Al-Duais, M. S., Yaakub, Abdul Razak, Yusoff, Nooraini

“…In this paper, we created a dynamic function training rate for the Back propagation learning algorithm to avoid the local minimum and to speed up training.The Back propagation with dynamic training rate (BPDR) algorithm uses the sigmoid function.The 2-dimensional XOR problem and iris data were used as benchmarks to test the effects of the dynamic training rate formulated in this paper.The results of these experiments demonstrate that the BPDR algorithm is advantageous with regards to both generalization performance and training speed. …”
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