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  1. 1
    Automatic Segmentation and Classification of Skin Lesions in Dermoscopic Images

    Automatic Segmentation and Classification of Skin Lesions in Dermoscopic Images by Adil Humayun, Khan

    Published 2024
    “…This proposed classifier achieved 97.9% classification accuracy on the ISIC dataset. In the third classification algorithm, hybrid features are extracted using AlexNet and VGG-16 through a transfer learning approach where parameter manipulation is implemented to simplify the network. …”
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  2. 2
    Extremal region selection for MSER detection in food recognition

    Extremal region selection for MSER detection in food recognition by Razali, Mohd Norhisham, Manshor, Noridayu, Abdul Halin, Alfian, Mustapha, Norwati, Yaakob, Razali

    Published 2021
    “…Therefore, this research proposes an Extremal Region Selection (ERS) algorithm to improve MSER detection by reducing the number of irrelevant extremal regions by using unsupervised learning based on the k-means algorithm. …”
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  3. 3
    Building detection using object-based Image analysis (OBIA) and machine learning (ML) algorithms / Hanani Mohd Shahar

    Building detection using object-based Image analysis (OBIA) and machine learning (ML) algorithms / Hanani Mohd Shahar by Mohd Shahar, Hanani

    Published 2020
    “…Thus, by enhancing the classification techniques in OBIA, building extraction accuracy using ML algorithms for medium resolution images can be improved and the expenses also can be reduced indirectly.…”
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  4. 4
    Detection of corneal arcus using rubber sheet and machine learning methods

    Detection of corneal arcus using rubber sheet and machine learning methods by Ramlee, Ridza Azri

    Published 2019
    “…The elements extracted from the confusion matrix parameters (i.e. accuracy, specificity, sensitivity, AUC, precision and f-score) are used in benchmarking the optimal performance of classification algorithms. …”
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  5. 5
    Extremal Region Selection for MSER Detection in Food Recognition

    Extremal Region Selection for MSER Detection in Food Recognition by Mohd Norhisham Razali @ Ghazali, Noridayu Manshor, Alfian Abdul Halin, Norwati Mustapha, Razali Yaakob

    Published 2021
    “…Therefore, this research proposes an Extremal Region Selection (ERS) algorithm to improve MSER detection by reducing the number of irrelevant extremal regions by using unsupervised learning based on the k-means algorithm. …”
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  6. 6
    Extremal region detection and selection with fuzzy encoding for food recognition

    Extremal region detection and selection with fuzzy encoding for food recognition by Razali @ Ghazali, Mohd Norhisham

    Published 2019
    “…The performance of algorithms was measured based on classification accuracy, error rate, and precision and recall. …”
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  7. 7
    A joint Bayesian optimization for the classification of fine spatial resolution remotely sensed imagery using object-based convolutional neural networks

    A joint Bayesian optimization for the classification of fine spatial resolution remotely sensed imagery using object-based convolutional neural networks by Azeez, Omer Saud, M. Shafri, Helmi Z., Alias, Aidi Hizami, Haron, Nuzul Azam

    Published 2022
    “…A Bayesian technique was used to find the best parameters for the multiresolution segmentation (MRS) algorithm while the CNN model learns the image features at different layers, achieving joint optimization. …”
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  8. 8
    Optimized techniques for landslide detection and characteristics using LiDAR data

    Optimized techniques for landslide detection and characteristics using LiDAR data by Mezaal, Mustafa Ridha

    Published 2018
    “…In this task, two neural network algorithms, Recurrent Neural Networks (RNN) and Multi-Layer Perceptron Neural Networks (MLP-NN) were used and the hyper-parameters of the network architecture was optimized based on a systematic grid search. …”
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  9. 9
    Hybridization of SLIC and extra tree for object based image analysis in extracting shoreline from medium resolution satellite images

    Hybridization of SLIC and extra tree for object based image analysis in extracting shoreline from medium resolution satellite images by Abd Manaf, Syaifulnizam, Mustapha, Norwati, Sulaiman, Md. Nasir, Husin, Nor Azura, Mohd Shafri, Helmi Zulhaidi, Razali, Mohd Norhisham

    Published 2018
    “…Thus, the object-based approach is proposed using a combination of segmentation algorithms, namely Felzenswalb, Quickshift, and SLIC, together with 15 machine learning classifiers, to classify segmented images of Langkawi Island. …”
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  10. 10
    Modeling of road geometry and traffic accidents by hierarchical object-based and deep learning methods using laser scanning data

    Modeling of road geometry and traffic accidents by hierarchical object-based and deep learning methods using laser scanning data by Sameen, Maher Ibrahim

    Published 2018
    “…There was a need for efficient segmentation algorithm, optimization strategy, feature extraction and classification, and robust statistical and computational intelligence models to accomplish the set aims. …”
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  11. 11
    Analytical framework for predicting online purchasing behavior in Malaysia using a machine learning approach

    Analytical framework for predicting online purchasing behavior in Malaysia using a machine learning approach by Mustakim, Nurul Ain

    Published 2025
    “…The descriptive analysis examines purchasing behavior through correlation and regression analyses, while the predictive model uses decision trees (J48, Random Tree, REPTree), rule-based algorithms (JRip, OneR, PART), and clustering (K-Means) to identify patterns and predict trends. …”
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  12. 12
    Case Slicing Technique for Feature Selection

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

    Published 2004
    “…CST was compared to other selected classification methods based on feature subset selection such as Induction of Decision Tree Algorithm (ID3), Base Learning Algorithm K-Nearest Nighbour Algorithm (k-NN) and NaYve Bay~sA lgorithm (NB). …”
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  13. 13
    Enhanced segment particle swarm optimization for large-scale kinetic parameter estimation of escherichia coli network model

    Enhanced segment particle swarm optimization for large-scale kinetic parameter estimation of escherichia coli network model by Mohammed Adam, Kunna Azrag

    Published 2021
    “…In this regard, a Local Sensitivity Analysis, Segment Particle Swarm Optimization (Se-PSO) algorithm, and the Enhanced Segment Particle Swarm Optimization (ESe-PSO) algorithm was adapted and proposed to estimate the parameters. …”
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  14. 14
    Grid-Based Classifier as a Replacement for Multiclass Classifier in a Supervised Non-Parametric Approach

    Grid-Based Classifier as a Replacement for Multiclass Classifier in a Supervised Non-Parametric Approach by Moheb Pour, Majid Reza

    Published 2009
    “…The two algorithms were compared, and the proposed algorithm was found to be able to both learn and classify quickly. …”
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  15. 15
    Grid base classifier in comparison to nonparametric methods in multiclass classification

    Grid base classifier in comparison to nonparametric methods in multiclass classification by Moheb Pour, Majid Reza, Jantan, Adznan, Saripan, M. Iqbal

    Published 2010
    “…The two algorithms were compared, and the proposed algorithm was found to be able to both learn and classify quickly. …”
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  16. 16
    Comparison of expectation maximization and K-means clustering algorithms with ensemble classifier model

    Comparison of expectation maximization and K-means clustering algorithms with ensemble classifier model by Sulaiman, Md. Nasir, Mohamed, Raihani, Mustapha, Norwati, Zainudin, Muhammad Noorazlan Shah

    Published 2018
    “…Hence, this situation is believed in yielding of decreasing the classification accuracy. In this article, we present the exploration on the combination of the clustering based algorithm with an ensemble classification learning. …”
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  17. 17
    An automated strabismus classification using machine learning algorithm for binocular vision management system

    An automated strabismus classification using machine learning algorithm for binocular vision management system by Rohismadi, Muhammad Amirul Isyraf, Mat Raffei, Anis Farihan, Zulkifli, Nor Saradatul Akmar, Ithnin, Mohd. Hafidz, Othman, Shah Farez

    Published 2023
    “…To overcome these limitations, a machine learning algorithm, which is a case-based reasoning, is developed to automate the strabismus classification. …”
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  18. 18
    Lexicon-based and immune system based learning methods in Twitter sentiment analysis

    Lexicon-based and immune system based learning methods in Twitter sentiment analysis by Jantan, Hamidah, Drahman, Fatimatul Zahrah, Alhadi, Nazirah, Mamat, Fatimah

    Published 2016
    “…The aim of this article attempts to study the potential of this method in text classification for sentiment analysis.This study consists of three phases; data preparation; classification model development using three selected Immune System based algorithms i.e. …”
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  19. 19
    Propose a New Machine Learning Algorithm based on Cancer Diagnosis

    Propose a New Machine Learning Algorithm based on Cancer Diagnosis by Ali, Mohammed Hasan, Kohbalan, Moorthy, Anad, Mohammed Morad, Mohammed, Mohammed Abdulameer

    Published 2018
    “…However, the traditional machine learning algorithms are facing several limitations, which may be impacted on the accuracy of classification based cancer diagnosis area. …”
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  20. 20
    Estimation of small-scale kinetic parameters of escherichia coli (E. coli) model by enhanced segment particle swarm optimization algorithm ese-pso

    Estimation of small-scale kinetic parameters of escherichia coli (E. coli) model by enhanced segment particle swarm optimization algorithm ese-pso by Mohammed Adam Kunna, Azrag, Jasni Mohamad, Zain, Tuty Asmawaty, Abdul Kadir, Marina, Yusoff, Jaber, Aqeel Sakhy, Abdlrhman, Hybat Salih Mohamed, Ahmed, Yasmeen Hafiz Zaki, Husain, Mohamed Saad Bala

    Published 2023
    “…In this study, an Enhanced Segment Particle Swarm Optimization (ESe-PSO) algorithm that can estimate the values of small-scale kinetic parameters is described and applied to E. coli’s main metabolic network as a model system. …”
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