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Study and Implementation of Data Mining in Urban Gardening by Mohana, Muniandy, Lee, Eu Vern

“…The system is essentially a three-part development, utilising Android, Java Servlets, and Arduino platforms to create an optimised and automated urban-gardening system. …”
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Neutralisation state driven single-agent search strategy for solving constraint satisfaction problem / Saajid Akram Ahmed Abuluaih by Ahmed Abuluaih, Saajid Akram

“…For example, the framework of solving CSP imposes a complete permutation of assignments to all remaining variables in order to derive a valid model. The author argues in this study that the problem can be neutralised and it is not necessary to perform brute-force searching all the time if a search strategy could have guided the process to the level where the values of the remaining variables can be determined implicitly, creating what the author calls Solo-Path of assignments in the problem search tree.…”
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Data Analysis and Rating Prediction on Google Play Store Using Data-Mining Techniques by Kayalvily, Tabianan, Denis, Arputharaj, Mohd Norshahriel, Abd Rani, Sarasvathi, Nahalingham

“…The goal of using a Decision Tree is to create a training model that can use to predict the class or value of the target variable by learning simple decision rules inferred from prior data. …”
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Web-based expert system for material selection of natural fiber- reinforced polymer composites by Ahmed Ali, Basheer Ahmed

“…Finally, the developed expert system was deployed over the internet with central interactive interface from the server as a web-based application. As Java is platform independent and easy to be deployed in web based application and accessible through the World Wide Web (www), this expert system can be one stop application for materials selection.…”
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Bayesian random forests for high-dimensional classification and regression with complete and incomplete microarray data by Oyebayo, Olaniran Ridwan

“…Random Forests (RF) are ensemble of trees methods widely used for data prediction, interpretation and variable selection purposes. …”
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Optimized Model Inputs Selections for Enhancing River Streamflow Forecasting Accuracy Using Different Artificial Intelligence Techniques by Tofiq Y.M., Latif S.D., Ahmed A.N., Kumar P., El-Shafie A.

Using predictive analytics to solve a newsvendor problem / S. Sarifah Radiah Shariff and Hady Hud by Shariff, S. Sarifah Radiah, Hud, Hady

“…Secondly, in solving every Machine Learning problem, there is no one algorithm superior to other algorithms. Every algorithm makes its own respective prior assumptions about the relationships between the features and target variables, which create different types and levels of bias. …”
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Pattern Recognition for Human Diseases Classification in Spectral Analysis by Nur Hasshima Hasbi, Abdullah Bade, Fuei, Pien Chee, Muhammad Izzuddin Rumaling

“…To conclude, many pattern recognition algorithms have the potential to overcome each of their distinct limits, and there is also the option of combining all of these algorithms to create an ensemble of methods.…”
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Machine-learning approach using thermal and synthetic aperture radar data for classification of oil palm trees with basal stem rot disease by Che Hashim, Izrahayu

“…The sample size was comprised of 55 non-infected trees and 37 infected trees. During the field experiments, oil palm tree samples of non-infected (T0), mild infected (T1), moderate infected (T2), and severe infected (T3) were measured using the FLIR T620 IR infrared thermal imaging camera to obtain the temperature of the oil palm trees. …”
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Water Quality Index Classification of Southeast, South and West Asia Rivers using Machine Learning Algorithms by Shaheed H., Zawawi M.H., Hayder G.

Automated model selection for corporation credit risk assessment using machine learning / Zulkifli Halim by Halim, Zulkifli

“…Through the automated model selection, 176 models are created across the experiment settings. The models are based on the four machine learning algorithms: logistic regression, support vector machine, decision tree, and neural network; two ensemble techniques: adaptive boost and bootstrap aggregation; three deep learning algorithms: recurrent neural network, long short-term memory(LSTM), and gated recurrent unit (GRU). …”
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Prediction on the mechanical strength of coal ash concrete using artificial neural network by Muhammad Nor Syahrul, Zaimi, Nur Farhayu, Ariffin, Sharifah Maszura, Syed Mohsin, Abdul Muiz, Hasim, Nurul Natasha, Nasrudin

“…This type of analysis involves one or more independent variables that may most accurately predict the value of the dependent variable and calculates the coefficients of the linear equation. …”
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An application of predicting student performance using kernel k-means and smooth support vector machine by Sajadin, Sembiring

“…In this study, psychometric factors used as predictor variables, thereare Interest, Study Behavior, Engaged Time, Believe, and Family Support.The rulemodel developed using Kernel K-means Clustering and Smooth Support Vector MachineClassification.Both of these techniquesbased on kernel methodsand relativelynew algorithms of data mining techniques, recently received increasingly popularity in machine learning community. …”
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Improved robust estimator and clustering procedures for multivariate outliers detection by Sharifah Sakinah, Syed Abd Mutalib

“…Furthermore, the improved single linkage robust clustering procedure in this study can be incorporated with Minimum Spanning Tree (MST).…”
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