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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.

“…The one hidden layer with one neuron using BFG training algorithm provides the best optimum neural network structure. …”
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Study and Implementation of Data Mining in Urban Gardening by Mohana, Muniandy, Lee, Eu Vern

“…The process begins through the monitoring of plants using sensors connected to the Arduino device. Attached sensors generate data and send these data to the Java Servlet application through a WIFI module. …”
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Machine learning classifications of multiple organ failures in a malaysian intensive care unit by Norliyana, Nor Hisham Shah, Normy Norfiza, Abdul Razak, Athirah, Abdul Razak, Asma’, Abu-Samah, Fatanah, M. Suhaimi, Ummu Kulthum, Jamaludin

“…The random forest algorithm was able to achieve 99.8% accuracy and 99.9% sensitivity in the training dataset. …”
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Machine Learning Classifications of Multiple Organ Failures in a Malaysian Intensive Care Unit by Shah N.N.H., Razak N.N.A., Razak A.A., Abu-Samah A., Suhaimi F.M., Jamaluddin U.

“…The random forest algorithm was able to achieve 99.8% accuracy and 99.9% sensitivity in the training dataset. …”

Multidimensional Minimization Training Algorithms for Steam Boiler High Temperature Superheater Trip using Artificial Intelligence Monitoring System by Alnaimi, F. B. I., Al-Kayiem, Hussain H.

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Predictions of isolate and normal pentene of debutanizer catalytic reforming unit by using artificial neural network by S., Sulaiman, O.A., Abdalla, M.N., Zakaria, W.F.W., Ahmad

“…The developed ANN model is obtained by dividing the collected data set into three different group; training, validation, and testing group. Back-propagation algorithm was used to train the network. …”
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Design Of Robot Motion Planning Algorithm For Wall Following Robot by Ali Hassan, Muhamad Khairul

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Malay continuous speech recognition using continuous density hidden Markov model by Ting, Chee Ming

“…With their efficient training algorithm (Baum-Welch and Viterbi/Segmental K-mean) and recognition algorithm (Viterbi), as well as it’s modeling flexibility in model topology, observation probability distribution, representation of speech unit and other knowledge sources, HMM has been successfully applied in solving various tasks in this thesis. …”
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Human activity recognition via accelerometer and gyro sensors by Tee, Jia Lin

“…To implement the data engineering system proposed, two mobile applications, SensorData and SensorDataLogger with user-friendly interfaces and intuitive functionalities are developed using Java programming language and Android Studio. …”
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Sediment model for natural and man-made channels using general regression neural network by Junaidah, Ariffin, Nurashikin, Ahmad Kamal, Muhamad Syahreen, Sa’adon, Mohd Nasir, Taib, Suhaimi, Abdul-Talib, Aminuddin, Abd-Ghani, Nor Azazi, Zakaria, Ahmad Shukri, Yahaya

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What, how and when to use knowledge in neural network application by Wan Ishak, Wan Hussain, Abdul Rahman, Shuzlina

“…The methodology comprises five steps namely variable selection, data collection, data preprocessing, training &validation, and testing.…”
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Modeling time series data using Genetic Algorithm based on Backpropagation Neural network by Haviluddin

“…The performance of ANNs depend on many factors, including the network structure, the selection of activation function, the learning rate of the training algorithm, and initial synaptic weight values, the number of input variables, and the number of units in the hidden layer. …”
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Robust Data Fusion Techniques Integrated Machine Learning Models For Estimating Reference Evapotranspiration by Chia, Min Yan

“…Nonetheless, based on the literature review performed, machine learning models are data-hungry in nature, which increases the difficulty of training a model from scratch. The data hunger of machine learning models can be classified into two categories, namely the qualitative hunger (where machine learning models need for various features for training) and quantitative hunger (need for a vast amount of historical data for training). …”
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Price prediction model of green building based on machine learning algorithms / Nur Syafiqah Jamil by Jamil, Nur Syafiqah

“…Meanwhile, experiments using five common algorithms, Random Forest Regressor Model outperforms four (4) other algorithms in predicting the price of green building condominium, by training and validating the data-set using Split approach. …”
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Design & Development of a Robotic System Using LEGO Mindstorm by Abd Manap, Nurulfajar, Md Salim, Sani Irwan, Haron, Nor Zaidi

“…Since the model is built using LEGO bricks, the model is fully customized, in term of its applications, to perform any relevant tasks. Ultimately, the algorithm development program designed earlier is linked up directly to the robotic model for program implementation and verification. …”
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Development of ground truth data for automatic lumbar spine MRI image segmentation by Natalia, Friska, Meidia, Hira, Afriliana, Nunik, Al-Kafri, Ala S., Sudirman, Sud, Simpson, Andrew, Sophian, Ali, Al-Jumaily, Mohammed, Al-Rashdan, Wasfi, Bashtawi, Mohammad

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A review on artificial intelligence based load demand forecasting techniques for smart grid and buildings by Raza, M.Q., Khosravi, A.

“…The accuracy of ANN based forecast model is found to be dependent on number of parameters such as forecast model architecture, input combination, activation functions and training algorithm of the network and other exogenous variables affecting on forecast model inputs. …”
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A comparative study on aviation arrival delay prediction using machine learning methods by Chew, Pui Ting

“…Dataset from 2016 to 2020 with 35 variables for Southwest Airlines Co. carrier are sourced from the Bureau of Transportation Statistics (BTS) to be trained and validated as Southwest Airlines Co. holds the biggest share of number of flights as compared to other airlines across the years. …”
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An improved recommender system based on normalization of matrix factorization and collaborative filtering algorithms by Zahid, Aafaq

“…It is concluded that the resultant hybrid techniques can perform well if the variables provided to normalization by neighborhood model (MF and CF) do not have big differences in order for the hybrid normalization model to outperform every algorithm in comparison.…”
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Evaluation of machine learning classifiers in faulty die prediction to maximize cost scrapping avoidance and assembly test capacity savings in semiconductor integrated circuit (IC)... by Mohd Fazil, Azlan Faizal, Mohd Shaharanee, Izwan Nizal, Mohd Jamil, Jastini

“…The model training flow will have 2 classifier groupings which are control group and auto machine learning (ML) where feature selection with redundancy elimination method to be applied on input data to reduce the number of variables to minimum prior modeling flow. …”
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