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  1. 1
    A Hybrid of Ant Colony Optimization Algorithm and Simulated Annealing for Classification Rules

    A Hybrid of Ant Colony Optimization Algorithm and Simulated Annealing for Classification Rules by Rizauddin, Saian

    Published 2013
    “…Benchmark data sets from various fields were used to test the proposed algorithms. …”
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  2. 2
    Improved cuckoo search based neural network learning algorithms for data classification

    Improved cuckoo search based neural network learning algorithms for data classification by Abdullah, Abdullah

    Published 2014
    “…Specifically, 6 benchmark classification datasets are used for training the hybrid Artificial Neural Network algorithms. …”
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  3. 3
    Application of Optimization Methods for Solving Clustering and Classification Problems

    Application of Optimization Methods for Solving Clustering and Classification Problems by Shabanzadeh, Parvaneh

    Published 2011
    “…Also a new algorithm for finding the initial point is proposed. …”
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  4. 4
    Hybrid Genetic Algorithm On Predicting Next Rainfall Distribution

    Hybrid Genetic Algorithm On Predicting Next Rainfall Distribution by Mohd Zain, Rasuan

    Published 2017
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  5. 5
    Machine Learning Algorithms for Diabetes Prediction: A Review Paper

    Machine Learning Algorithms for Diabetes Prediction: A Review Paper by Al-Sideiri A., Cob Z.B.C., Drus S.B.M.

    Published 2023
    Conference Paper
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  6. 6
    Balancing Exploitation And Exploration Search Behavior On Nature-Inspired Clustering Algorithms

    Balancing Exploitation And Exploration Search Behavior On Nature-Inspired Clustering Algorithms by Alswaitti, Mohammed Y. T.

    Published 2018
    “…In the OGC framework, the exhibited explorative search behavior of the Gravitational Clustering (GC) algorithm has been addressed by (i) eliminating the agent velocity accumulation, and (ii) integrating an initialization method of agents using variance and median to subrogate the exploration process. …”
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  7. 7
    An improved genetic-fuzzy system for classification and data analysis

    An improved genetic-fuzzy system for classification and data analysis by Lahsasna, A., Seng, W.C.

    Published 2017
    “…In the second variant classifier, we further improve the first variant classifier by enhancing the selection method of the antecedent conditions of the rules generated in the initial population of genetic algorithm. Unlike the method applied in the original classifier and the first variant classifier, which uses a random selection of the antecedent conditions, we proposed a feature-based selection method to favor the antecedent conditions associated with the most relevant features. …”
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  8. 8
    Fuzzification of epileptic data: an application for prediction and identification of partial seizure

    Fuzzification of epileptic data: an application for prediction and identification of partial seizure by Malik, Aamir Saeed, Nasif, Mohammad Shakir, Kamel , Nidal, Qidwai, U.

    Published 2013
    “…This paper presents a classification technique by using Fuzzy Logic System to identify and predict the partial seizure from epileptic data. …”
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  9. 9
    Improved Genetic Algorithm Multilayer Perceptron Network For Data Classification

    Improved Genetic Algorithm Multilayer Perceptron Network For Data Classification by Ahmad, Fadzil

    Published 2017
    “…Meanwhile, the improved GA-MLP classification performance has been evaluated using datasets that vary in input features and output sizes. …”
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  10. 10
    Malicious URL classification using artificial fish swarm optimization and deep learning

    Malicious URL classification using artificial fish swarm optimization and deep learning by Mustafa Hilal, Anwer, Hassan Abdalla Hashim, Aisha, G. Mohamed, Heba, K. Nour, Mohamed, M. Asiri, Mashael, M. Al-Sharafi, Ali, Othman, Mahmoud, Motwakel, Abdelwahed

    Published 2023
    “…To attain this, AFSADL-MURLC model initially carries out data preprocessing and makes use of glove-based word embedding technique. …”
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  11. 11
    Enhancement of feature sets for subjectivity analysis on Malay-English code-switching text

    Enhancement of feature sets for subjectivity analysis on Malay-English code-switching text by Kasmuri, Emaliana

    Published 2023
    “…In the unified code-switching feature set, the extracted Malay and English features were unified using an adapted algorithm known as the Malay-English Unified POS. …”
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  12. 12
    Digital economy tax compliance model in Malaysia using machine learning approach

    Digital economy tax compliance model in Malaysia using machine learning approach by Raja Azhan Syah Raja Wahab, Azuraliza Abu Bakar

    Published 2021
    “…We conduct descriptive analytics to explore and extract a summary of data for initial understanding. Through a brief description of the descriptive model, the data distribution in a histogram shows that the information extracted can give a clear picture in influencing the results to classify digital economic tax compliance. …”
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  13. 13
    Small Dataset Learning In Prediction Model Using Box-Whisker Data Transformation

    Small Dataset Learning In Prediction Model Using Box-Whisker Data Transformation by Lateh, Masitah bdul

    Published 2020
    “…From the previous studies, there are solutions to improve learning accuracy and predictive capability where some artificial data will be added to the system using artificial data generation approach. …”
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  14. 14
    Intelligent image noise types recognition and denoising system using deep learning / Khaw Hui Ying

    Intelligent image noise types recognition and denoising system using deep learning / Khaw Hui Ying by Khaw , Hui Ying

    Published 2019
    “…An ensemble of these algorithms is an intelligent and adaptive solution, producing a clean output, while preserving significant pixel information. …”
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  15. 15
    Problem restructuring in interger programming for reduct searching

    Problem restructuring in interger programming for reduct searching by Ungku Chulan, Ungku Azmi Iskandar

    Published 2003
    “…In effect, they are very useful in generating rules when solving the classification problem that is inherent in data mining. …”
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  16. 16
    Evaluation tool of land suitability for medicinal plants

    Evaluation tool of land suitability for medicinal plants by Rahim, Supli Effendi, Supli, Ahmad Affandi, Damiri, Nurhayati, Zaman, Chairil, Husin, Husin

    Published 2019
    “…A usability model was studied from 40 respondents using the app. The usability of the app was in “very good” classification.…”
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  17. 17
    Design and statistical analysis of initial solution construction approach in curriculum based course timetabling problem

    Design and statistical analysis of initial solution construction approach in curriculum based course timetabling problem by Wahid, Juliana, Mohd Hussin, Naimah

    Published 2017
    “…To produce a population of initial solution require algorithm that can produce multiple feasible solutions and these solutions must be diverse. …”
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  18. 18
    Machine-learning approach using thermal and synthetic aperture radar data for classification of oil palm trees with basal stem rot disease

    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

    Published 2021
    “…The sampling-based method, also known as the data level method, is used to deal with this problem. …”
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  19. 19
    Dengue classification system using clonal selection algorithm / Karimah Mohd

    Dengue classification system using clonal selection algorithm / Karimah Mohd by Mohd, Karimah

    Published 2012
    “…This project can be improved by making a comparative study on Artificial Immune System and other techniques or algorithms used to solve dengue classification problems.…”
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  20. 20
    Improving Classification of Remotely Sensed Data Using Best Band Selection Index and Cluster Labelling Algorithms

    Improving Classification of Remotely Sensed Data Using Best Band Selection Index and Cluster Labelling Algorithms by Teoh, Chin Chuang

    Published 2005
    “…The comparison showed that, the accuracy of the unsupervised classification map with value of 88.4% that was generated by using the cluster labelling algorithm was slightly more than the maximum-likelihood supervised classification map with value of 87.5%. …”
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