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PREDICTIVE ANALYTICS FOR EQUIPMENT FAILURE BY USING GATED RECURRENT UNIT – GENETIC ALGORITHM (GRU – GA) by ZAINUDDIN, ZAHIRAH

“…Gated Recurrent Unit (GRU) algorithm is used to cater the predicting action of equipment state based on data from an oil and gas industry.…”
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Predicting machine failure using recurrent neural network-gated recurrent unit (RNN-GRU) through time series data by Zainuddin, Z., P. Akhir, E.A., Hasan, M.H.

“…It helps those sectors such as production to foresee the state of machine in line with saving the cost from sudden breakdown as unplanned machine failure can disrupt the operation and loss up to millions. Thus, this paper offers a deep learning algorithm named recurrent neural network-gated recurrent unit (RNN-GRU) to forecast the state of machines producing the time series data in an oil and gas sector. …”
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Optimal water supply reservoir operation by leveraging the meta-heuristic Harris Hawks algorithms and opposite based learning technique by Lai V., Huang Y.F., Koo C.H., Ahmed A.N., Sherif M., El-Shafie A.

“…In this study, an attempt via metaheuristic algorithms, namely the Harris Hawks Optimisation (HHO) Algorithm and the Opposite Based Learning of HHO (OBL-HHO) are made to minimise the water deficit as well as mitigate floods at downstream of the Klang Gate Dam (KGD). …”

Meta-heuristic approaches for reservoir optimisation operation and investigation of climate change impact at Klang gate dam by Lai, Vivien Mei Yen

“…The Whale Optimisation Algorithm (WOA), Harris Hawks Optimisation (HHO) Algorithm, Lévy Flight WOA (LFWOA) and the Opposition-Based Learning of HHO (OBL-HHO) were proposed to simulate the initial model’s response and optimise the Klang Gate Dam (KGD) release operation with observed inflow, water level (storage), release, and evaporation rate (loss). …”
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FPGA Implementation of Emergency Door Car Entry System by Zaini Sulaiman

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Reservoir water balance simulation model utilizing machine learning algorithm by Dashti Latif S., Najah Ahmed A., Sherif M., Sefelnasr A., El-Shafie A.

Gravitational Search Algorithm based Long Short-term Memory Deep Neural Network for Battery Capacity and Remaining Useful Life Prediction with Uncertainty by Reza M.S., Hannan M.A., Mansor M., Ker P.J., Tiong S.K., Hossain M.J.

SOC Estimation Using Deep Bidirectional Gated Recurrent Units with Tree Parzen Estimator Hyperparameter Optimization by How D.N.T., Hannan M.A., Lipu M.S.H., Ker P.J., Mansor M., Sahari K.S.M., Muttaqi K.M.

Simulated Kalman Filter with modified measurement, substitution mutation and hamming distance calculation for solving traveling salesman problem by Suhazri Amrin, Rahmad, Zuwairie, Ibrahim, Zulkifli, Md. Yusof

“…Researchers have worked on ideas to improve exploration capability to prevent premature convergence by trying prediction operators, opposition-based learning, and different iteration strategies. …”
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Malaysian vehicle license plate recognition using deep learning and computer vision by Pugalenthy, Kuken Raj, Mohd Zamri, Ibrahim, Ahmad Afif, Mohd Faudzi, Mohd Rizal, Othman

“…License plate recognition has become one of the popular topics under deep learning researches. There are many deep learning models and the suitable model for this project chose according to the ability to meet the system operation requirements such as speed, accuracy and precision of the outcome. …”
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The implications for ahybrid detection technique against malicious sqlattacks on web applications by Bahjat Arif, Sarajaldeen Akram, Wani, Sharyar

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A systematic review of recurrent neural network adoption in missing data imputation by Nur Aqilah, Fadzil Akbar, Mohd Izham, Mohd Jaya, Mohd Faizal, Ab Razak, Nurul Aqilah, Zamri

“…Over the past decade, deep learning methods, particularly Recurrent Neural Network (RNN), have been employed to tackle the problem. …”
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State-of-charge estimation for lithium-ion batteries with optimized self-supervised transformer deep learning model by Dickson Neoh Tze How, Dr.

“…In the first stage, the model is pre-trained using unlabeled data with unsupervised learning. In the second stage, the model is fine-tuned or re-trained using labeled data with supervised learning. …”

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

A systematic review of recurrent neural network adoption in missing data imputation by Nur Aqilah, Fadzil Akbar, Mohd Izham, Mohd Jaya, Mohd Faizal, Ab Razak, Nurul Aqilah, Zamri

“…Over the past decade, deep learning methods, particularly Recurrent Neural Network (RNN), have been employed to tackle the problem. …”
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