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Class binarization with self-adaptive algorithm to improve human activity recognition
Published 2018“…However, the learning complexity of classification is increased due to the expansion number of learning model. …”
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Thesis -
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Deep learning detector for pests and plant disease recognition
Published 2020“…However, in this project, deep learning techniques are used in developing a model for diseases and pest detection in plants, and then train and test the model before eventually integrating the model into a mobile application. …”
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Final Year Project / Dissertation / Thesis -
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Stock market turning points rule-based prediction / Lersak Photong … [et al.]
Published 2021“…News classification into factors affecting stock market turning point was done using Naïve Bayes, Deep Learning, Generalized Linear Model (GLM) and Support Vector Machine (SVM). …”
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Book Section -
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Digital economy tax compliance model in Malaysia using machine learning approach
Published 2021“…The experimental results show that the ensemble method can improve the single classification model’s accuracy with the highest classification accuracy of 87.94% compared to the best single classification model. …”
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Classification of Immunosignature Using Random Forests for Cancer Diagnosis
Published 2015“…In this work, we will develop a robust classification model that can be utilized in cancer diagnosis using immunofingerprint data. …”
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Proceeding Paper -
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Y-type Random 2-satisfiability In Discrete Hopfield Neural Network
Published 2024“…Finally, a new logic mining model namely Y-Type Random 2-Satisfiability Reverse Analysis was proposed, which showed optimal performances in terms of several metrics as compared to the existing classification models. …”
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Classification with degree of importance of attributes for stock market data mining
Published 2004“…The SVM is a training algorithm for learning classification and regression rules from data [7]. …”
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ios Mobile Malware Deep Analysis And Classification For Social Media And Online Banking Exploitation
Published 2024thesis::master thesis -
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Enhancement processing time and accuracy training via significant parameters in the batch BP algorithm
Published 2020“…We created the dynamic learning rate and dynamic momentum factor for increasing the efficiency of the algorithm. …”
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13
A Feature Ranking Algorithm in Pragmatic Quality Factor Model for Software Quality Assessment
Published 2013“…The assessment of quality attributes has been improved using FRA algorithm enriched with a formula to calculate the priority of attributes and followed by learning adaptation through Java Library for Multi Label Learning (MULAN) application. …”
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Thesis -
14
Modeling time series data using Genetic Algorithm based on Backpropagation Neural network
Published 2018“…This study showed the task of optimizing the topology structure and the parameter values (e.g., weights) used in the BPNN learning algorithm by using the GA. …”
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Thesis -
15
Prediction models of heritage building based on machine learning / Nur Shahirah Ja'afar
Published 2021“…These algorithms were developed by using prewar shophouses dataset from 2004 until 2018 based on factors of heritage properties. …”
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Thesis -
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Diagnosis of voice disorders using band energy spectrum in wavelet domain
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Working Paper -
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Learning Algorithm effect on Multilayer Feed Forward Artificial Neural Network performance in image coding
Published 2007“…One of the essential factors that affect the performance of Artificial Neural Networks is the learning algorithm. …”
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Optimization of chest X-ray exposure factors using machine learning algorithm
Published 2023“…In this study, the chest X-ray exposure factors for 178 patients with different body mass index (BMI) values have been analyzed using the Python Machine Learning algorithm. …”
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A bayesian network approach to identify factors affecting learning of Additional Mathematics
Published 2015“…Constraint-based algorithms and score-based algorithms are used to generate the networks into several categories to compare and identify the strong relationships among the factors that affect the students’ learning of the subject. …”
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