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Interaction effect of process parameters and Pd-electrocatalyst in formic acid electro-oxidation for fuel cell applications: Implementing supervised machine learning algorithms by Hossain S.K.S., Ali S.S., Rushd S., Ayodele B.V., Cheng C.K.

“…Carbon nanotubes; Electrocatalysts; Electrooxidation; Forestry; Formic acid; Gaussian distribution; Learning algorithms; Palladium; Parameter estimation; Regression analysis; Support vector machines; Formic acid electrooxidation; Fuel cell application; Gaussian kernel functions; Gaussian process regression; Interaction effect; Machine learning algorithms; Performance; Process parameters; Regression trees; Support vector machine regressions; Sensitivity analysis…”

A new mobile botnet classification based on permission and API calls by Yusof M., Madihah Mohd Saudi, Farida Hazwani Mohd Ridzuan

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Implementation of machine learning algorithms for streamflow prediction of Dokan dam by Sarmad Dashti Latif, Mr.

“…This study aims at comparing the application of deep learning algorithms and conventional machine learning algorithms for predicting reservoir inflow. …”

Data Analysis and Rating Prediction on Google Play Store Using Data-Mining Techniques by Kayalvily, Tabianan, Denis, Arputharaj, Mohd Norshahriel, Abd Rani, Sarasvathi, Nahalingham

“…This study aims to predict the ratings of Google Play Store apps using decision trees for classification in machine learning algorithms. …”
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Identification Of Flow Blockage Levels In Centrifugal Pump By Machine Learning by Ng, Woon Li

“…The purpose of this research is to develop an effective machine learning model for the classification of flow blockage levels in the centrifugal pump by using the statistically significant features from vibration and acoustic analysis. …”
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The predictive machine learning model of a hydrated inverse vulcanized copolymer for effective mercury sequestration from wastewater by Ghumman, A.S.M., Shamsuddin, R., Abbasi, A., Ahmad, M., Yoshida, Y., Sami, A., Almohamadi, H.

“…A predictive machine learning model was also developed to predict the amount of mercury removed () using GPR, ANN, Decision Tree, and SVM algorithms. …”
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Car dealership web application by Yap, Jheng Khin

“…The transfer learning algorithm pre-trained the River adaptive random forest regressor and classifier by transferring the tree structures and weights from the Scikit-learn fitted random forest regressor and classifier, respectively. …”
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Power line corridor vegetation encroachment detection from satellite images using retinanet and support vector machine by Fathi Mahdi Elsiddig Haroun, Mr.

“…Also, a corridor extraction algorithm has been developed to extract the region of interest (ROI) around the transmission towers. …”

Sales prediction for Adha Station by using predictive analytics by Mohd Mokhid, Muhammad Amier Latieff

“…Additionally, pre-processing is conducted using the RapidMiner application prior to mapping the cleaned data with three distinct algorithms for predictive analysis: Decision Tree, Random Forest, and Multiple Linear Regression techniques. …”
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Data Mining Analysis Of Chronic Kidney Disease (CKD) Level by Mohd Harizi, Muhammad Hafizam Afiq

“…Adding the uncertain class the best accuracy obtained was 98.5% using the SMO algorithm. A predictive classification model that determines the accuracy for three classification classes was developed accordingly using the SMO algorithm.…”
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Reinforcement Learning Algorithm for Optimising Durian Irrigation Systems: Maximising Growth and Water Efficiency by Ramli, Muhammad Shahrul Azwan, Zainal Abidin, Mohamad Shukri, Hasan, Nor Shahida, Md Reba, Mohd Nadzri, Kolawole, Keshinro Kazeem, Ardiansyah, Rizqi Andry, Mpuhus, Sikudhan Lucas

“…This study presents a Reinforcement Learning-based algorithm designed to optimise irrigation for Durio Zibethinus (i.e., durian) trees, aiming to maximise tree growth and reduce water usage. …”
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An Intelligent Data-Driven Approach for Electrical Energy Load Management Using Machine Learning Algorithms by Akhtar, Shamim, Muhamad Zahim, Sujod, Rizvi, Syed Sajjad Hussain

“…This is grounded in the fact that Bagged Trees is most effective algorithm for the said application and Medium Trees is the most efficient one. …”
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Tree-based machine learning in classifying reverse migration/ Azreen Anuar, Nur Huzeima Mohd Hussain and Hugh Byrd by Anuar, Azreen, Mohd Hussain, Nur Huzeima, Byrd, Hugh

“…However, there is a scarcity of studies that have applied machine learning algorithms to this problem. This paper aims to fill the gap in the literature by discussing the application of machine learning algorithms for predicting reverse migration. …”
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Binary Coati Optimization Algorithm- Multi- Kernel Least Square Support Vector Machine-Extreme Learning Machine Model (BCOA-MKLSSVM-ELM): A New Hybrid Machine Learning Model for Pr... by Sammen S.S., Ehteram M., Sheikh Khozani Z., Sidek L.M.

“…In this study, we used extreme learning machine (ELM)-multi-kernel least square support vector machine (ELM-MKLSSVM), extreme learning machine (ELM)-LSSVM-polynomial kernel function (PKF) (ELM-LSSVM-PKF), ELM-LSSVM-radial basis kernel function (RBF) (ELM-LSSVM-RBF), ELM-LSSVM-Linear Kernel function (LKF), ELM, and MKLSSVM models to predict water level. …”

Development Of Generative Computer-Aided Process Planning System For Lathe Machining by Zubair, Ahmad Faiz

“…To validate the generated tool-path, G-codes generated in media package file (MPF) file format and verified through CNC lathe machine. Indeed, the developed algorithm was able to determine the minimum unit production cost of lathe machining part model. …”
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An optimized variant of machine learning algorithm for datadriven electrical energy efficiency management (D2EEM) by Shamim, Akhtar

“…This study recommends a selection trade-off as the function of prediction efficiency and efficacy of the algorithm. Particularly, the proposed optimized Bagged Trees are the most effective algorithm for energy demand prediction applications, and the proposed optimized Medium Trees are the most efficient algorithm for real-time systems. …”
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Ensemble-based machine learning algorithms for classifying breast tissue based on electrical impedance spectroscopy by Rahman, Sam Matiur, Ali, Md. Asraf, Altwijri, Omar, Alqahtani, Mahdi, Ahmed, Nasim, Ahamed, Nizam Uddin

“…Therefore, we aimed to classify six classes of freshly excised tissues from a set of electrical impedance measurement variables using five ensemble-based machine learning (ML) algorithms, namely, the random forest (RF), extremely randomized trees (ERT), decision tree (DT), gradient boosting tree (GBT) and AdaBoost (Adaptive Boosting) (ADB) algorithms, which can be subcategorized as bagging and boosting methods. …”
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Detection and classification of conflict flows in SDN using machine learning algorithms by Mutaz Hamed Hussien Khairi, Sharifah Hafizah Syed Ariffin, Nurul Mu'azzah Abdul Latiff, Kamaludin Mohamad Yusof, Mohamed Khalafalla Hassan, Fahad Taha Al-Dhief, Mosab Hamda, Suleman Khan, Muzaffar Hamzah

“…As a result, this paper presents several machine learning algorithms that include Decision Tree (DT), Support Vector Machine (SVM), Extremely Fast Decision Tree (EFDT) and Hybrid (DT-SVM) for detecting and classifying conflicting flows in SDNs. …”
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Streamflow Prediction Utilizing Deep Learning and Machine Learning Algorithms for Sustainable Water Supply Management by Latif S.D., Ahmed A.N.

Application of Decision Trees in Athlete Selection: A Cart Algorithm Approach by Riska Wahyu, Romadhonia, A'yunin, Sofro, Danang, Ariyanto, Dimas Avian, Maulana, Junaidi Budi, Prihanto

“…This study investigates the application of Decision Trees (DTs), a non-parametric supervised learning method, renowned for its simplicity, interpretability, and wide applicability in various domains, including machine learning for classification and regression tasks. …”
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