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  1. 1
    Deep learning object detector using a combination of Convolutional Neural Network (CNN) architecture (MiniVGGNet) and classic object detection algorithm

    Deep learning object detector using a combination of Convolutional Neural Network (CNN) architecture (MiniVGGNet) and classic object detection algorithm by Ismail, Asmida, Ahmad, Siti Anom, Che Soh, Azura, Hassan, Mohd Khair, Harith, Hazreen Haizi

    Published 2020
    “…This paper presented an analysis performance of deep learning object detector by combining a deep learning Convolutional Neural Network (CNN) for object classification and applies classic object detection algorithms to devise our own deep learning object detector. …”
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  2. 2
    Automated underwater vision system for detection and classification of marine life using CNN YOLO-based model / Mohamed Syazwan Asyraf Rosli

    Automated underwater vision system for detection and classification of marine life using CNN YOLO-based model / Mohamed Syazwan Asyraf Rosli by Rosli, Mohamed Syazwan Asyraf

    Published 2022
    “…Recently, the integration of computer vision and machine learning has given solutions to improve the underwater detection system by using intelligent classifier algorithm in real-time computer vision to detect underwater animals with challenging environments. …”
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  3. 3
    Depth frame loss concealment for wireless transmission utilising motion detection information

    Depth frame loss concealment for wireless transmission utilising motion detection information by Ranjbari, Mohamadreza

    Published 2014
    “…The methods are able to facilitate a frame copy algorithm and a high correlation of colour and depth frames to conceal depth frame loss. …”
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  4. 4
    Case Slicing Technique for Feature Selection

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

    Published 2004
    “…One of the problems addressed by machine learning is data classification. Finding a good classification algorithm is an important component of many data mining projects. …”
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  5. 5
    Integration Of Unsupervised Clustering Algorithm And Supervised Classifier For Pattern Recognition

    Integration Of Unsupervised Clustering Algorithm And Supervised Classifier For Pattern Recognition by Leong, Shi Xiang

    Published 2017
    “…Whereas for supervised learning method, it requires teacher or prior data (i.e. large, prohibitive and labelled training data) during classification process which in real life, the cost of obtaining sufficient labelled training data is high. …”
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  6. 6
    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
    “…In data mining, classification learning is broadly categorized into two categories; supervised and unsupervised. …”
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  7. 7
    A new multi-frames watermarking technique for tamper detection and recovery

    A new multi-frames watermarking technique for tamper detection and recovery by Khor, Hui Liang

    Published 2017
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  8. 8
    Semi-supervised learning for feature selection and classification of data / Ganesh Krishnasamy

    Semi-supervised learning for feature selection and classification of data / Ganesh Krishnasamy by Ganesh , Krishnasamy

    Published 2019
    “…Feature selection and classification are widely utilized for data analysis. …”
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  9. 9
    A New Approach of Optimal Search Solution in Particle Swarm Optimization (PSO) Algorithm for Object Detection Method

    A New Approach of Optimal Search Solution in Particle Swarm Optimization (PSO) Algorithm for Object Detection Method by Zalili, Musa, Mohd Hafiz, Mohd Hassin, Nurul Izzatie Husna, Fauzi, Rohani, Abu Bakar, Watada, Junzo

    Published 2018
    “…Therefore, to overcome the several problems associated with the object detection method, a new approach in Particle Swarm Optimization (PSO) algorithm for optimal search solution as an alternative method to detect of object tracking quickly, precisely and accurately. …”
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  10. 10
    Automated threshold detection for object segmentation in colour image

    Automated threshold detection for object segmentation in colour image by Akhtaruzzaman, Md., Shafie, Amir Akramin, Khan, Md. Raisuddin

    Published 2016
    “…In solving this issue, this paper presents an Automated Threshold Detection Algorithm, H(•). The algorithm is applied in segmenting human lower limbs from a series of image frames of human walking. …”
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  11. 11
    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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  12. 12
    Ulcer detection and classification of wireless capsule endoscopy images using RGB masking

    Ulcer detection and classification of wireless capsule endoscopy images using RGB masking by Suman, S., Hussi, F.A., Nicolas, W., Malik, A.S.

    Published 2016
    “…This algorithm of capsule images can be clinically very useful as it can discriminate between normal and abnormal frame. …”
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  13. 13
    Ulcer detection and classification of wireless capsule endoscopy images using RGB masking

    Ulcer detection and classification of wireless capsule endoscopy images using RGB masking by Suman, S., Hussi, F.A., Nicolas, W., Malik, A.S.

    Published 2016
    “…This algorithm of capsule images can be clinically very useful as it can discriminate between normal and abnormal frame. …”
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  14. 14
    New Learning Models for Generating Classification Rules Based on Rough Set Approach

    New Learning Models for Generating Classification Rules Based on Rough Set Approach by Al Shalabi, Luai Abdel Lateef

    Published 2000
    “…Two different models for learning in data sets were proposed based on two different reduction algorithms. …”
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  15. 15
    Performances of machine learning algorithms for binary classification of network anomaly detection system

    Performances of machine learning algorithms for binary classification of network anomaly detection system by Nawir, M., Amir, A., Lynn, O.B., Yaakob, N., Ahmad, R.B.

    Published 2018
    “…Moreover, network anomaly detection using machine learning faced difficulty when dealing the involvement of dataset where the number of labelled network dataset is very few in public and this caused many researchers keep used the most commonly network dataset (KDDCup99) which is not relevant to employ the machine learning (ML) algorithms for a classification. …”
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  16. 16
    A hybrid method using haar-like and skin-color algorithm for hand posture detection, recognition and tracking

    A hybrid method using haar-like and skin-color algorithm for hand posture detection, recognition and tracking by Bilal, Sara Mohammed Osman Saleh, Akmeliawati, Rini, Salami, Momoh Jimoh Eyiomika, Shafie, Amir Akramin, Bouhabba, El Mehdi

    Published 2010
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  17. 17
    Comparative study of machine learning algorithms in data classification

    Comparative study of machine learning algorithms in data classification by Tan, Kai Jun

    Published 2025
    “…This research conducts a comparative study of various machine learning algorithms for dataset classification to identify the most accurate and reliable classifier. …”
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  18. 18
    An Improved Action Key Frames Extraction Algorithm for Complex Colour Video Shot Summarization

    An Improved Action Key Frames Extraction Algorithm for Complex Colour Video Shot Summarization by Mizher, Manar Abduljabbar Ahmad, Ang, Mei Choo, Sheikh Abdullah, Siti Norul Huda, Kok, Weng Ng

    Published 2019
    “…The objective of this work is to improve our previous action key frames extraction algorithm (AKF) by adapting a threshold which selects action key frames as final key frames. …”
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  19. 19
    Evaluation of the Transfer Learning Models in Wafer Defects Classification

    Evaluation of the Transfer Learning Models in Wafer Defects Classification by Jessnor Arif, Mat Jizat, Anwar, P. P. Abdul Majeed, Ahmad Fakhri, Ab. Nasir, Zahari, Taha, Yuen, Edmund, Lim, Shi Xuen

    Published 2022
    “…Transfer Learning is one of the common methods. Various algorithms under Transfer Learning had been developed for different applications. …”
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  20. 20
    Multi-Class Multi-Level Classification of Mental Health Disorders Based on Textual Data from Social Media

    Multi-Class Multi-Level Classification of Mental Health Disorders Based on Textual Data from Social Media by Sutranggono, Abi Nizar, Sarno, Riyanarto, Ghozali, Imam

    Published 2024
    “…The Multi-Class Multi-Level (MCML) classification algorithm was applied to perform detailed classification and address the limitations of the research scope using several approaches, including machine learning, deep learning, and transfer learning approaches. …”
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