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Improved Whale Optimization Algorithm for Optimal Network Coverage in Industrial Wireless Sensor Networks by Devan, P Arun Mozhi, Ibrahim, Rosdiazli, Omar, Madiah Binti, Bingi, Kishore, Abdulrab, Hakim, Hussin, Fawnizu Azmadi

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Whale optimization algorithm strategies for higher interaction strength t-way testing by Ali Abdullah, Hassan, Salwani, Abdullah, Kamal Z., Zamli, Rozilawati, Razali

“…To ensure that WOA conquers premature convergence and avoids local optima for large search spaces (owing to high-order interaction), three variants of WOA have been developed, namely Structurally Modified Whale Optimization Algorithm (SWOA), Tolerance Whale Optimization Algorithm (TWOA), and Tolerance Structurally Modified Whale Optimization Algorithm (TSWOA). …”
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Whale optimization algorithm based on tent chaotic map for feature selection in soft sensors by AlRijeb, Mothena Fakhri Shaker, Othman, Mohammad Lutfi, Ishak, Aris, Hassan, Mohd Khair, Albaker, Baraa Munqith

“…One of the powerful optimization algorithms that is used for feature selection is the Whale Optimization Algorithm (WOA), which is a nature-inspired metaheuristic optimization algorithm that mimics the social behavior of humpback whales. …”
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Using the evolutionary mating algorithm for optimizing the user comfort and energy consumption in smart building by Mohd Herwan, Sulaiman, Zuriani, Mustaffa

“…EMA belongs to the evolutionary computation group of nature-inspired metaheuristic algorithms and offers a promising solution. A comparative analysis is conducted with other well-known algorithms such as Particle Swarm Optimization (PSO), Differential Evolution (DE), Ant Colony Optimization (ACO), Biogeography-Based Optimization (BBO), Teaching-Learning Based Optimization (TLBO), and Beluga Whale Optimization (BWO). …”
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Modeling flood occurences using soft computing technique in southern strip of Caspian Sea Watershed by Borujeni, Sattar Chavoshi

“…Three algorithms of Linear (PURELIN), hyperbolic tangent sigmoid (TANSIG) and logistic sigmoid (LOGSIG) activation functions were selected for output layer. …”
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Modelling the yield loss of oil palm due to Ganoderma Basal Stem Rot disease by Assis Kamu

“…The predictive performance of the three best models which represent three different model building algorithms were assessed and compared. Based on mean square error (MSE), root mean square error (RMSE), and mean absolute error (MAE), BMA model has the lowest values, thus selected as the best model for oil palm yield loss. …”
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Modelling the yield loss of oil palm due to ganoderma basal stem rot disease by Assis bin Kamu

“…The predictive performance of the three best models which represent three different model building algorithms were assessed and compared. Based on mean square error (MSE), root mean square error (RMSE), and mean absolute error (MAE), BMA model has the lowest values, thus selected as the best model for oil palm yield loss. …”
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Assessment of suitable hospital location using GIS and machine learning by Almansi, Khaled Y. M.

“…First, the conditioning factors were optimized and ranked to identify and select the most correlated factors to predict the suitability of a hospital site by applying the correlation feature selection (CFS) algorithm and the greedy-stepwise search method. …”
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Reliability of voxel gray values in cone beam computed tomography for preoperative implant planning assessment by Parsa, A., Ibrahim, N., Hassan, B., Motroni, A., Van der Stelt, P., Wismeijer, D.

“…In all the selected ROIs, CBCT showed higher mean values than MSCT. …”
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Molecular docking studies of Pyranose-2-oxidase from trametes multicolor by Noorbatcha, Ibrahim Ali, Nuge, Tamrin, Yusof, Amirulakmal, Othman, Raihan, Mohd. Salleh, Hamzah

“…Lamarckian genetic algorithm (LGA) is used to search for active sites and potential mean force (PMF) scoring method is used to evaluate different binding sites. …”
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Multidimensional Minimization Training Algorithms for Steam Boiler Drum Level Trip Using Artificial Intelligence Monitoring System by Ismail, F. B., Al-Kayiem, Hussain H.

“…Results are obtained from one hidden layer neural network and two hidden layers neural network structures, for both adopted algorithms. Comparisons have been made between the results from the two algorithms based on the Root Mean Square Error. …”
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Dynamic feature selection model for adaptive cross site scripting attack detection using developed multi-agent deep Q learning model by Kareem Thajeel, Isam, Samsudin, Khairulmizam, Hashim, Shaiful Jahari, Hashim, Fazirulhisyam

“…Thus, this study attempts to fill the gap by proposing a feature drift-aware algorithm for detecting the evolved XSS attacks. The proposed approach is a dynamic feature selection based on a deep Q-network multi-agent feature selection (DQN-MAFS) framework. …”
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Development Of Construction Noise Prediction Method Using Deep Learning Model by Siew, Jun Teng

“…The mean absolute errors (0.2 to 0.25), root mean square errors (0.3 to 0.42), and explained variance scores (0.9975 to 0.9988) of the selected best deep learning models indicate an incredibly accurate, fewer outliers, and extraordinarily reliable noise prediction model. …”
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Modification and characterization of phytase for animal feed production by Noorbatcha, Ibrahim Ali, Samsudin, Nurhusna, Mohd. Salleh, Hamzah

“…Automated docking based on genetic algorithm is used to dock the phytate in the active site and Partial Mean Force (PMF) scoring is used to calculate the strength of the binding before and after mutation.…”
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Influence of object location in cone beam computed tomography (NewTom 5G and 3D Accuitomo 170) on gray value measurements at an implant site by Parsa, A., Ibrahim, N., Hassan, B., van der Stelt, P., Wismeijer, D.

“…On one of the scans, a probe designating the site for pre-operative implant placement was selected. …”
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Prediction of Oil Palm Yield Using Machine Learning in the Perspective of Fluctuating Weather and Soil Moisture Conditions: Evaluation of a Generic Workflow by Khan N., Kamaruddin M.A., Ullah Sheikh U., Zawawi M.H., Yusup Y., Bakht M.P., Mohamed Noor N.

“…The prediction was followed by data preprocessing and feature selection. Selected regression models were compared with Random Forest, Gradient Boosting, Decision Tree, and other non-tree algorithms to prove the R2 driven performance superiority of tree-based ensemble models. …”

Extreme air pollutant data analysis using classical and Bayesian approaches by Mohd Amin, Nor Azrita

“…Using threshold exceedances technique,the selection of threshold value was made using mean residual life plot and threshold choice plot. …”
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The assessment of ambient carbon monoxide concentration levels / Hasmawati Mat Hassan by Mat Hassan, Hasmawati

“…This study aims to assess the influence on the ambient carbon monoxide pollutant at three different land-use sites in Peninsular Malaysia as a case study by using five years database (from 2008 to 2012) within three selected Continuous Air Quality Monitoring stations. …”
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Termite mounds morphometry in predicting groundwater potentiality using geospatial technology by Bala, Ahmed Jamilu

“…The effects of five (5) environmental factors (elevation, land use/land cover, geology, drainage and static water level) on the distribution of termite mounds as well as their structure, mortality rate and species diversity formed a knowledge-base for multi-criteria evaluation of suitable sites for termite nesting. Thereafter, twelve (12) groundwater conditioning factors (GCFs) (geology, drainage density, lineament density, lineament intersection density, land use/land cover, topographic wetness index (TWI), normalized difference vegetation index (NDVI), slope, elevation, plan curvature, static water level and groundwater level fluctuation) were passed through a feature selection filter (Correlation-based feature selection using the best first algorithm) to select the optimum groundwater control factors (GCFs) for groundwater prediction in the study area. …”
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Real-time personal protective equipment compliance detection using you only look once by Nurhanisah, Aminuddin, Nor Azuana, Ramli, Pratondo, Agus

“…Developing automated systems to detect and identify protective equipment in working sites is not just a technical accomplishment but also a significant step towards improving the workplace, especially in high-risk areas like construction and manufacturing sites. …”
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