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Optimization of attribute selection model using bio-inspired algorithms
Published 2019“…This is achieved in two stages; 1) create attribute selection models by combining search method and feature selection algorithms, and 2) determine an optimized attribute set by employing bio-inspired algorithms.Classification performance of the produced attribute set is analyzed based on accuracy and number of selected attributes. …”
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Heart disease prediction using artificial neural network with ADAM optimization and harmony search algorithm
Published 2025“…Complementing this, the Harmony Search Algorithm (HSA) is incorporated to augment data features, facilitating better pattern recognition and enhancing overall classification accuracy through optimized feature engineering. …”
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An ensemble learning method for spam email detection system based on metaheuristic algorithms
Published 2015“…In the second phase, a classifier ensemble learning model is proposed consisting of separate outputs: (i) To select a relevant subset of original features based on Binary Quantum Gravitational Search Algorithm (QBGSA), (ii) To mine data streams using various data chunks and overcome a failure of single classifiers based on SVM, MLP and K-NN algorithms. …”
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Data mining techniques for disease risk prediction model: A systematic literature review
Published 2023Conference Paper -
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A Novel Wrapper-Based Optimization Algorithm for the Feature Selection and Classification
Published 2023“…These factors indirectly upset the disease prediction and classification accuracy of any ML model. …”
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Comparison between fuzzy and non-fuzzy classification methods in the prediction of residential household water leakage / Nor Aishah Md Noh, Dr. Khairul Anwar Rasmani and Nur Rasyid...
Published 2013“…The aim of this research is to predict residential households water leakage using models created based on training data with fuzzy rule-based and non-fuzzy rule-based algorithms available in WEKA Machine Learning Software (Witten and Frank, 2005). …”
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Research Reports -
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Recommendation System Model For Decision Making in the E-Commerce Application
Published 2024thesis::doctoral thesis -
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Hybridization of metaheuristic algorithm in training radial basis function with dynamic decay adjustment for condition monitoring / Chong Hue Yee
Published 2023“…Hence, a wide range of studies has been focused on determining the optimal weight values of ANN models and the number of hidden neurons. In this research work, the motivation is to develop an autonomous learning model based on the hybridization of an adaptive ANN and a metaheuristic algorithm for optimizing ANN parameters so that the network could perform learning and adaptation in a more flexible way and handle condition classification tasks more accurately in industries, such as in power systems. …”
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Improved Ozone Level Detection through Feature Selection with Modified Whale Optimization Algorithm
Published 2024“…This study presents a new approach for ozone level detection through feature selection by the modified Whale Optimization Algorithm (mWOA). This study aims to enhance the accuracy and efficiency of ozone level prediction models by selecting the most informative features from the dataset. …”
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An ensemble of neural network and modified grey wolf optimizer for stock prediction
Published 2019“…The proposed approachoutperforms existing available meta-heuristic algorithms. Specifically, the proposed model achieved 97% classification rate, 95% precise prediction and less than 2.0 error rate. …”
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Improved Ozone Level Detection through Feature Selection with Modified Whale Optimization Algorithm
Published 2024“…This study presents a new approach for ozone level detection through feature selection by the modified Whale Optimization Algorithm (mWOA). This study aims to enhance the accuracy and efficiency of ozone level prediction models by selecting the most informative features from the dataset. …”
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Optimized feature construction methods for data summarizations of relational data
Published 2014“…DARA transforms the data relational representation into a vector space representation and a clustering process is applied to group the data based on their characteristics similarity. The summarized data will then be fed to any classification algorithm to perform the classification task. …”
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Automatic detection and indication of pallet-level tagging from rfid readings using machine learning algorithms
Published 2020“…Furthermore, the efficacy of different models based on heuristic hyperparameter tuning is evaluated in which the different kernel function for Support Vector Machine, various distance metrics of k-Nearest Neighbors. …”
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Digital economy tax compliance model in Malaysia using machine learning approach
Published 2021“…Based on the validation of training data with the presence of seven single classifier algorithms, three performance improvements have been established through ensemble classification, namely wrapper, boosting, and voting methods, and two techniques involving grid search and evolution parameters. …”
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A Stacked Ensemble Deep Learning Approach For Imbalanced Multi-class Water Quality Index Prediction
Published 2024journal::journal article -
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Predictive analytics for the sentiment of malaysian place of interest using machine learning models
Published 2023“…The focus of this study is to conduct Natural Language Processing (NLP) on tweets and make a better classification of sentiment using Malaya. Furthermore, this study also trains three machine learning algorithms to predict the sentiment of textual data. …”
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Undergraduates Project Papers -
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Feature and Instance selection via cooperative PSO
Published 2011“…The proposed method is applied to three real-world datasets from the machine learning repository. The algorithm's performance is illustrated by the corresponding plots of the prediction error for the different amounts of data being selected.…”
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Conference or Workshop Item -
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An adaptive ant colony optimization algorithm for rule-based classification
Published 2020“…The algorithm’s performance was compared with other variants of Ant-Miner and state-of-the-art rules-based classification algorithms based on classification accuracy and model complexity. …”
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