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Wavelet neural networks based solutions for elliptic partial differential equations with improved butterfly optimization algorithm training by Lee, Sen Tan, Zainuddin, Zarita, Ong, Pauline

“…To date, on account of the derivative free characteristic and adaptability to respond to the complex dynamic changes of the interdependencies, numerous studies explored the potential benefit of integrating a meta-heuristic algorithm as the training algorithm of WNNs, where encouraging results are achieved. …”
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Change vulnerability forecasting using deep learning algorithm for Southeast Asia by Amelia Ritahani, Ismail, Nur ‘Atikah, Mohd Ali, Junaida, Sulaiman

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Advanced machine learning algorithm to predict the implication of climate change on groundwater level for protecting aquifer from depletion by Ahmed Osman A.I., Latif S.D., Wee Boo K.B., Ahmed A.N., Huang Y.F., El-Shafie A.

“…Ultimately, the results obtained in this study serve as a great benchmark for future GWL prediction using LSTM and XGBoost algorithm and give an insight into the influence of climate change on future GWL. …”

Enhancing professional development and training through AI for personalized learning: a framework to engaging learners / Zoel-Fazlee Omar ... [et al.] by Omar, Zoel-Fazlee, Mior Harun, Mior Harris, Mohd Ishar, Nor Irvoni, Mustapha, Nur Arfah, Ismail, Zurina

“…As the workforce landscape rapidly changes, the demand for tailored and engaging learning experiences has become increasingly evident. …”
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Comparison of PPO and SAC Algorithms towards decision making strategies for collision avoidance among multiple autonomous vehicles by Abu Jafar, Md Muzahid, Syafiq Fauzi, Kamarulzaman, Md Arafatur, Rahman

“…In terms of success rate, performance, training speed and stability two algorithms are comparable. …”
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An ensemble learning method for spam email detection system based on metaheuristic algorithms by Behjat, Amir Rajabi

“…In the second phase, a classifier ensemble learning model is proposed consisting of separate outputs: (i) To select a relevant subset of original features based on Binary Quantum Gravitational Search Algorithm (QBGSA), (ii) To mine data streams using various data chunks and overcome a failure of single classifiers based on SVM, MLP and K-NN algorithms. …”
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Hybridization of metaheuristic algorithm in training radial basis function with dynamic decay adjustment for condition monitoring / Chong Hue Yee by Chong , Hue Yee

“…In this research work, the motivation is to develop an autonomous learning model based on the hybridization of an adaptive ANN and a metaheuristic algorithm for optimizing ANN parameters so that the network could perform learning and adaptation in a more flexible way and handle condition classification tasks more accurately in industries, such as in power systems. …”
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Applying learning to filter text by Sainin, Mohd Shamrie

“…The use of probabilistic approaches such as naïve Bayes algorithm is the effective algorithms currently known for learning to filter or classify text document.Naïve Bayes algorithm is one of the algorithms in Machine Learning that manipulates probability estimation or reasoning about the observed data.The growing of bulk e-mail or known as spam e-mail becomes a threat to users’ privacy and network load and in the case of e -mail filtering,naïve Bayes classifier can be trained to automatically detect spam messages.Similar to the e-mail, forum application may be misused by the user to send bad messages and in some extent may offence other readers.Forum filtering may be less important compared to e-mail spam filtering; however there is a possibility of using naïve Bayes to learn the messages and automatically detect bad messages.Most of the forum application found in the web is applying keyword based text filtering which scan the words and change the detected words into certain representation.Instead of defining a set of keywords to filter the forum messages, this paper will explains the experiment in applying a learning to filter text especially in the educational and anonymous forum message, where there is no user registration required to submit messages.…”
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Analysis Of Personal Protective Equipment Classification Method Using Deep Learning by Siti Zahrah Nur Ain, Silopung

“…To avoid a tedious work in manually checking whether workers wear PPE or not, an automatic PPE classifier is constructed by utilizing a deep learning algorithm called Convolutional Neural Network (CNN). …”
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Hypertension Prediction in Adolescents Using Anthropometric Measurements: Do Machine Learning Models Perform Equally Well? by Chai, Soo See, Goh, Kok Luong, Cheah, Whye Lian, Chang, Robin Yee Hui, Ng, Giap Weng

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Development of a scaled conjugate gradient algorithm for significant RF neural signal processing by Mohd Norden, Muhammad Farid Akmal, Mohd Isa, Roshakimah, Mohd Isa, Mohd Roshalizi, S. Abdul Kadir, Ros Shilawani, Md Azli, Muhammad Hariz, Muhammad Akram, Amir Syarif

“…Scale Conjugate Gradient (SCG) algorithm is an efficient training method for ANN that accelerates the learning process and improves output accuracy. …”
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Reinforcement learning-based target tracking for unmanned aerial vehicle with achievement rewarding and multistage traning by Ahmed Abo Mosali, Najm Addin Mohammed

“…The aim is to enable accurate target tracking by UAV with responding to the dynamic generated by the target such as sudden trajectory change using reinforcement learning which is proved to learn dynamic effectively. …”
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The effect of adaptive parameters on the performance of back propagation by Abdul Hamid, Norhamreeza

“…The results show that the proposed algorithm extensively improves the learning process of conventional Back Propagation algorithm.…”
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River flow prediction based on improved machine learning method: Cuckoo Search-Artificial Neural Network by Zanial W.N.C.W., Malek M.B.A., Reba M.N.M., Zaini N., Ahmed A.N., Sherif M., Elshafie A.

“…Therefore, it is necessary to precisely estimate how the river flow will alter as a result of changing rainfall patterns. Finding the best value for the hyper-parameters is one of the problems with machine learning algorithms, which have lately been adopted by many academics. …”

Investigating the Performance of Deep Reinforcement Learning-Based MPPT Algorithm under Partial Shading Condition by Yew W.H., Fat Chau C., Mahmood Zuhdi A.W., Syakirah Wan Abdullah W., Yew W.K., Amin N.

“…These DRL-based algorithms optimize the local and global maximum power point (MPP) using deep Q-learning and deep deterministic policy gradient (DDPG). …”

Development of an Adaptive Algorithm for Solving the Inverse Kinematics Problem for Serial Robot Manipulators by T. Hasan, Ali

“…The efficiency of the proposed algorithm is demonstrated through simulations of a general 6 D.O.F. serial robot manipulator…”
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Adaptive Non-Stationary Cardiac Signals Identification using an Augmented MLP Network by Asirvadam , Vijanth Sagayan, McLoone, Sean

“…In this paper hybrid form recursive training algorithms, which combines both linear and nonlinear orientation of weights, is being used to model or identify ElectroCardioGraphy (ECG) signals. …”
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Deep Learning Based image segmentation for expensive soil desiccation crack recognition and qualification by Ling, Hui Yean

“…Crack images obtained were processed and annotated to produce a dataset of 820 images for the training and testing of deep learning models. Deep learning models, including U-Net, Res-UNet, and DeepLabv3+ with pre-trained backbones such as MobileNetV2, ResNet-18, ResNet-50, and Xception, were trained and evaluated along side a traditional Otsu's thresholding method as the baseline for crack detection and segmentation. …”
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Deep reinforcement learning-based driving strategy for avoidance of chain collisions and its safety efficiency analysis in autonomous vehicles by Abu Jafar, Md Muzahid, Syafiq Fauzi, Kamarulzaman, Rahman, Md. Arafatur, Alenezi, Ali H.

“…Moreover, to demonstrate the accuracy of the safety efficiency analysis, multiple training runs of the neural networks in respect of training performance, speed of training, success rate, and stability of rewards with a trade-off between exploitation and exploration during training are presented. …”
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Deep reinforcement learning based driving strategy for avoidance of chain collisions and its safety efficiency analysis in autonomous vehicles by Abu Jafar, Md Muzahid, Syafiq Fauzi, Kamarulzaman, Rahman, Md. Arafatur, Alenezi, Ali H.

“…Moreover, to demonstrate the accuracy of the safety efficiency analysis, multiple training runs of the neural networks in respect of training performance, speed of training, success rate, and stability of rewards with a trade-off between exploitation and exploration during training are presented. …”
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