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  1. 1
    Edge detection and contour segmentation for fruit classification in natural environment / Khairul Adilah Ahmad

    Edge detection and contour segmentation for fruit classification in natural environment / Khairul Adilah Ahmad by Ahmad, Khairul Adilah

    Published 2018
    “…Therefore, this research has designed fuzzy learning algorithm that is able to classify fruits based on their shape and size features using Harumanis dataset. …”
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  2. 2
    Email spam classification based on deep learning methods: A review

    Email spam classification based on deep learning methods: A review by Tusher, Ekramul Haque, Mohd Arfian, Ismail, Anis Farihan, Mat Raffei

    Published 2025
    “…Deep learning has become a potent collection of techniques for addressing intricate issues such as spam classification in recent times. …”
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  3. 3
    Metaheuristic-Based Neural Network Training And Feature Selector For Intrusion Detection

    Metaheuristic-Based Neural Network Training And Feature Selector For Intrusion Detection by Ghanem, Waheed Ali Hussein Mohammed

    Published 2019
    “…However, the training datasets usually compose feature sets of irrelevant or redundant information, which impacts the performance of classification, and traditional learning algorithms such as backpropagation suffer from known issues, including slow convergence and the trap of local minimum. …”
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  4. 4
    BONE AGE ANALYSIS FROM BONE X-RAY

    BONE AGE ANALYSIS FROM BONE X-RAY by ABD GHANI, NOOR SYAZANA

    Published 2018
    “…Manual bone age assessment basically take time f task in for radiologist and there are always issue related to intra observer and inter observer differences. …”
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  5. 5
    Comprehensive Review of Machine Learning (ML) in Image Defogging: Taxonomy of Concepts, Scenes, Feature Extraction, and Classification techniques

    Comprehensive Review of Machine Learning (ML) in Image Defogging: Taxonomy of Concepts, Scenes, Feature Extraction, and Classification techniques by Arif Z.H., Mahmoud M.A., Abdulkareem K.H., Mohammed M.A., Al-Mhiqani M.N., Mutlag A.A., Dama�evi?ius R.

    Published 2023
    Review
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  6. 6
    Twofold Integer Programming Model for Improving Rough Set Classification Accuracy in Data Mining.

    Twofold Integer Programming Model for Improving Rough Set Classification Accuracy in Data Mining. by Saeed, Walid

    Published 2005
    “…The accuracy for rules and classification resulted from the TIP method are compared with other methods such as Standard Integer Programming (SIP) and Decision Related Integer Programming (DRIP) from Rough Set, Genetic Algorithm (GA), Johnson reducer, HoltelR method, Multiple Regression (MR), Neural Network (NN), Induction of Decision Tree Algorithm (ID3) and Base Learning Algorithm (C4.5); all other classifiers that are mostly used in the classification tasks. …”
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  7. 7
    The Role of Machine Learning and Deep Learning Approaches for the Detection of Skin Cancer

    The Role of Machine Learning and Deep Learning Approaches for the Detection of Skin Cancer by Tehseen Mazhar, Inayatul Haq, Allah Ditta, Syed Agha Hassnain Mohsan, Faisal Rehman, Imran Zafar, Jualang Azlan Gansau, Lucky Poh Wah Goh

    Published 2023
    “…Moreover, this paper also defined the basic requirements for creating a skin cancer detection application, which revolves around two main issues: the full segmentation image and the tracking of the lesion on the skin using deep learning. …”
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  8. 8
    Satellite Image Segmentation Using Thresholding Technique

    Satellite Image Segmentation Using Thresholding Technique by Khalik, Mohd Haffez

    Published 2017
    “…Image segmentation is one of the basic techniques of image processing and computer vision. …”
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  9. 9
    Enhancement processing time and accuracy training via significant parameters in the batch BP algorithm

    Enhancement processing time and accuracy training via significant parameters in the batch BP algorithm by Fatma Susilawati, Mohamad, Mumtazimah, Mohamad, Sarhan, AlDuais

    Published 2020
    “…We created the dynamic learning rate and dynamic momentum factor for increasing the efficiency of the algorithm. …”
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  10. 10
    A Feature Ranking Algorithm in Pragmatic Quality Factor Model for Software Quality Assessment

    A Feature Ranking Algorithm in Pragmatic Quality Factor Model for Software Quality Assessment by Ruzita, Ahmad

    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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  11. 11
    Modeling time series data using Genetic Algorithm based on Backpropagation Neural network

    Modeling time series data using Genetic Algorithm based on Backpropagation Neural network by Haviluddin

    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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  12. 12
    Prediction models of heritage building based on machine learning / Nur Shahirah Ja'afar

    Prediction models of heritage building based on machine learning / Nur Shahirah Ja'afar by Ja'afar, Nur Shahirah

    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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  13. 13
    An ensemble feature selection method to detect web spam

    An ensemble feature selection method to detect web spam by Oskouei, Mahdieh Danandeh, Razavi, Seyed Naser

    Published 2018
    “…In addition, it improves classification metrics in comparison to basic feature selection methods.…”
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  14. 14
    Learning Algorithm effect on Multilayer Feed Forward Artificial Neural Network performance in image coding

    Learning Algorithm effect on Multilayer Feed Forward Artificial Neural Network performance in image coding by Mahmoud, Omer, Anwar, Farhat, Salami, Momoh Jimoh Emiyoka

    Published 2007
    “…One of the essential factors that affect the performance of Artificial Neural Networks is the learning algorithm. …”
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  15. 15
    Optimization of chest X-ray exposure factors using machine learning algorithm

    Optimization of chest X-ray exposure factors using machine learning algorithm by Hamd, Zuhal Y., Alrebdi, H.I., Osman, Eyas G., Awwad, Areej, Alnawwaf, Layan, Nashri, Nawal, Alfnekh, Rema, Khandaker, Mayeen Uddin *

    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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  16. 16
    A Machine Learning Classification Approach to Detect TLS-based Malware using Entropy-based Flow Set Features

    A Machine Learning Classification Approach to Detect TLS-based Malware using Entropy-based Flow Set Features by Keshkeh, Kinan, Jantan, Aman, Alieyan, Kamal

    Published 2022
    “…Furthermore, using the basic features, TLSMalDetect achieved the highest accuracy of 93.69% by Naïve Bayes (NB) among the ML algorithms applied. …”
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  17. 17
    A bayesian network approach to identify factors affecting learning of Additional Mathematics

    A bayesian network approach to identify factors affecting learning of Additional Mathematics by Ong, Hong Choon, Kumarenthiran A/L Chandrasekaran

    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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  18. 18
    Three-term backpropagation algorithm for classification problem

    Three-term backpropagation algorithm for classification problem by Saman, Fadhlina Izzah

    Published 2006
    “…This algorithm utilizes two term parameters which are Learning Rate, α and Momentum Factor,β. …”
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  19. 19
    Reverse migration prediction model based on machine learning / Azreen Anuar

    Reverse migration prediction model based on machine learning / Azreen Anuar by Anuar, Azreen

    Published 2024
    “…A significant way to minimize the errors is by using a machine learning approach that can predict reverse migration intelligently depending on the tested dataset. …”
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  20. 20
    A comparative study of deep learning algorithms in univariate and multivariate forecasting of the Malaysian stock market

    A comparative study of deep learning algorithms in univariate and multivariate forecasting of the Malaysian stock market by Mohd. Ridzuan Ab. Khalil, Azuraliza Abu Bakar

    Published 2023
    “…This study aims to develop a univariate and multivariate stock market forecasting model using three deep learning algorithms and compare the performance of those models. …”
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