Search Results - (( program implementation using algorithm ) OR ( learning implementation tree algorithm ))

Case Slicing Technique for Feature Selection by A. Shiba, Omar A.

“…The classification accuracy obtained from the CST method is compared to other selected classification methods such as Value Difference Metric (VDM), Pre-Category Feature Importance (PCF), Cross-Category Feature Importance (CCF), Instance-Based Algorithm (IB4), Decision Tree Algorithms such as Induction of Decision Tree Algorithm (ID3) and Base Learning Algorithm (C4.5), Rough Set methods such as Standard Integer Programming (SIP) and Decision Related Integer Programming (DRIP) and Neural Network methods such as the Multilayer method.…”
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Rapid software framework for the implementation of machine learning classification models by Rahman, A.S.A., Masrom, S., Rahman, R.A., Ibrahim, R.

“…However, to implement a complete machine learning model involves some technical hurdles such as the steep learning curve, the abundance of the programming skills, the complexities of hyper-parameters, and the lack of user friendly platform to be used for the implementation. …”
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Predicting Student Performance in Object Oriented Programming Using Decision Tree : A Case at Kolej Poly-Tech Mara, Kuantan by Mohd Hanis, Rani, Abdullah, Embong

“…The objective was to identify and implement the most accurate algorithm for the KPTM dataset and to come up with a good prediction model using decision tree technique. …”
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Implementation of Health Monitoring System for Patients using Machine Learning Algorithms by Hariprasad, U.S., UshaSree, R.

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Students’ attitude towards video-based learning: machine learning analysis with rapid software / Abdullah Sani Abd Rahman ... [et al.] by Abd Rahman, Abdullah Sani, Meutia, Rita, Hamid, Yusnaliza, Abdul Rahman, Rahayu

“…Data were collected from a university level accounting course (n=103), involving students who have different experienced or exposure on video-based online learning. Three machine learning algorithms (Support Vector Machine, Random Forest, and Decision Tree) have been tested on the dataset in a rapid software platform. …”
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Lightning Fault Classification for Transmission Line Using Support Vector Machine by Asman S.H., Aziz N.F.A., Kadir M.Z.A.A., Amirulddin U.A.U., Roslan N., Elsanabary A.

“…The proposed method was implemented in the MATLAB/SIMULINK programming platform. …”

Lightning fault classification for transmission line using support vector machine by Asman, Saidatul Habsah, Ab Aziz, Nur Fadilah, Ab Kadir, Mohd Zainal Abidin, Ungku Amirulddin, Ungku Anisa, Roslan, Nurzanariah, Elsanabary, Ahmed

“…The proposed method was implemented in the MATLAB/SIMULINK programming platform. …”
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Prediction of stroke disease using machine learning techniques / Syarifah Adilah Mohamed Yusoff ... [et al.] by Mohamed Yusoff, Syarifah Adilah, Warris, Saiful Nizam, Abu Bakar, Mohd Saifulnizam, Kadar, Rozita

“…This study has investigated five commonly used machine learning algorithm to be constructed as potential models for predicting stroke dataset. …”
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Exploring frogeye leaf spot disease severity in soybean through hyperspectral data analysis and machine learning with Orange Data Mining by Ang, Yuhao, Mohd Shafri, Helmi Zulhaidi, Al-Habshi, Mohammed Mustafa

“…Objectives: The main objective of the study is to classify the severity level of FLS disease in soybean using hyperspectral reflectance data and machine learning algorithms. …”
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Ensemble deep learning approach for apple fruitlet detection from digital images by Yusof, Mohamad Yusnisyahmi, Ishak, Iskandar, Sidi, Fatimah

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Attribute related methods for improvement of ID3 Algorithm in classification of data: A review by Nur Farahaina, Idris, Mohd Arfian, Ismail

“…There are several learning algorithms to implement the decision tree but the most commonly-used is ID3 algorithm. …”
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E4ML: Educational Tool for Machine Learning by Sainin, Mohd Shamrie, Siraj, Fadzilah

“…There are various types of machine learning algorithms with certain processes taken by the algorithm.In teaching of the machine learning algorithms, such processes need to be explained especially to the beginner in introductory level.This paper discusses the development the tool that addresses the process by certain algorithm to produce a hypothesis or output based on given data.This tool can also be used in teaching and learning purposes.The explanation of processes by the algorithms is demonstrated through simple simulation.The source of the algorithms was adapted from Mitchell book [1] that cover popular algorithms in machine learning for teaching and learning such as Concept Learning, Decision Tree, Bayesian Learning, Neural Networks, and Instance based Learning.The tool also used several classes of Weka (Waikato Environment for Knowledge Analysis) as a basis for the design and implementation of the new tool that focuses on explaining the processes taken by certain algorithm.…”
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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.

Edge assisted crime prediction and evaluation framework for machine learning algorithms by Adhikary, Apurba, Murad, Saydul Akbar, Munir, Md Shirajum, Choong Seon, Hong Seong

“…A maximum accuracy of 81% is obtained for Decision Tree algorithm during the prediction of crime. The findings demonstrate that employing Machine Learning techniques aids in the prediction of criminal events, which has aided in the enhancement of public security.…”
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Classification of breast cancer disease using bagging fuzzy-id3 algorithm based on fuzzydbd by Nur Farahaina, Idris

“…One of the most powerful machine learning methods to handle classification problems is the decision tree. …”
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Comparison of Gradient Boosting Decision Tree Algorithms for CPU Performance by Haithm Haithm, ALSHARI, Abdulrazak Yahya, Saleh, Alper, ODABAŞ

“…Gradient Boosting Decision Trees (GBDT) algorithms have been proven to be among the best algorithms in machine learning. …”
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Machine learning in predicting anti-money laundering compliance with protection motivation theory among professional accountants by Masrom, S., Tarmizi, M.A., Halid, S., Rahman, R.A., Abd Rahman, A.S., Ibrahim, R.

“…The research elaborates on the design and implementation of machine learning models based on three algorithms: Decision Tree, Gradient Boosted Tree, and Support Vector Machine. …”
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Propositional satisfiability method in rough classification modeling for data mining by Abu Bakar, Azuraliza

“…The improved search strategy implemented the non-chronological backtracking search that potentially prunes the large portion of search space. …”
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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.

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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