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  1. 1
    A new hybrid deep neural networks (DNN) algorithm for Lorenz chaotic system parameter estimation in image encryption

    A new hybrid deep neural networks (DNN) algorithm for Lorenz chaotic system parameter estimation in image encryption by Nurnajmin Qasrina Ann, Ayop Azmi

    Published 2023
    “…The fitness function used is the correlation function in the SKF algorithm to optimize the cipher image produced using the Lorenz system. …”
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  2. 2
    Implementation of vision-assisted path planning system for bulk die sorting in semiconductor industry

    Implementation of vision-assisted path planning system for bulk die sorting in semiconductor industry by Yap, Hwa Jen, Ooi, C.H., Chang, Siow Wee

    Published 2018
    “…Firstly, the wafer image is acquired using the image mosaicing method. …”
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  3. 3
    Implementation of vision-assisted path planning system for bulk die sorting in semiconductor industry

    Implementation of vision-assisted path planning system for bulk die sorting in semiconductor industry by Yap, Hwa Jen, Ooi, C.H., Chang, Siow Wee

    Published 2018
    “…Firstly, the wafer image is acquired using the image mosaicing method. …”
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  4. 4
    Face emotion recognition using artificial intelligence techniques

    Face emotion recognition using artificial intelligence techniques by Kartigayan Muthukaruppan

    Published 2008
    “…FER is achieved in two parts, they are image processing part and classification part. The first part investigates a set of image processing methods suitable for recognizing the face emotion. …”
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  5. 5
    Improvement of land cover mapping using Sentinel 2 and Landsat 8 imageries via non-parametric classification

    Improvement of land cover mapping using Sentinel 2 and Landsat 8 imageries via non-parametric classification by Myaser, Jwan

    Published 2020
    “…Nevertheless, AC is not required for LCM if the original multi-spectral image is used. The last phase involves developing a new fusion algorithm using SVM and Fuzzy K-Means Clustering (FKM) algorithms for Sentinel 2 data to enhance LCM accuracy. …”
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  6. 6
    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
    “…In addition, the best band selected for image classification is not necessarily the best for classification.A Best Band Selection Index (BBSI) algorithm was developed which is capable of selecting the best band combination for image visualization and supervised classification. …”
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  7. 7
    Pattern Classification of Human Epithelial Images

    Pattern Classification of Human Epithelial Images by Mohd Isa, Mohd Fazlie

    Published 2016
    “…In this project, there are four stages will be used to analyze pattern classification in human epithelial (HEp-2) images. First of all, image enhancement will take part in order to boost efficiency of algorithm by implementing some of the adjustment and filtering technique to increase the visibility of image. …”
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  8. 8
    Efficient classifying and indexing for large iris database based on enhanced clustering method

    Efficient classifying and indexing for large iris database based on enhanced clustering method by Khalaf, Emad Taha, Mohammed, Muamer N., Kohbalan, Moorthy, Khalaf, Ahmad Taha

    Published 2018
    “…The proposed method can be used to perform global search and exhibits quick convergence rate while optimizing the initial clustering centers of the K-means algorithm. From the experimental results, the proposed method was indeed more effective for clustering and classification and outperformed the traditional k-mean algorithm. …”
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  9. 9
    Improved Fast Fuzzy C-Means Algorithm for Medical MR Images Segmentation

    Improved Fast Fuzzy C-Means Algorithm for Medical MR Images Segmentation by Li, Min, Huang, Tinglei, Zhu, Gangqiang

    Published 2008
    “…Fuzzy c-means (FCM) clustering algorithm has been widely used in automated image segmentation. …”
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  10. 10
    Detection of sweetness level for fruits (watermelon) with machine learning

    Detection of sweetness level for fruits (watermelon) with machine learning by Wan Nazulan, Wan Nurul Suraya, Asnawi, Ani Liza, Mohd Ramli, Huda Adibah, Jusoh, Ahmad Zamani, Ibrahim, Siti Noorjannah, Mohamed Azmin, Nor Fadhillah

    Published 2020
    “…The objective of this work is to investigate the sweetness parameter for the fruit’s detection and classification algorithm in machine learnings. This study applies image processing techniques to detect the color and shape of watermelon’s skin for grading based on the sweetness level using K-means clustering method via the Python platform. 13 samples of watermelon images are used to test the functionality of the proposed detection system in this study. …”
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  11. 11
    Extreme learning machine classification of file clusters for evaluating content-based feature vectors

    Extreme learning machine classification of file clusters for evaluating content-based feature vectors by Ali, Rabei Raad, Mohamad, Kamaruddin Malik, Jamel, Sapiee, Ahmad Khalid, Shamsul Kamal

    Published 2018
    “…The files are allocated in a continuous series of clusters. The ELM algorithm is applied to the DFRWS (2006) dataset and the results show that the combination of the three methods produces 93.46% classification accuracy.…”
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  12. 12
    Plant identification using combination of fuzzy c-means spatial pyramid matching, gist, multi-texton histogram and multiview dictionary learning

    Plant identification using combination of fuzzy c-means spatial pyramid matching, gist, multi-texton histogram and multiview dictionary learning by Safa, Soodabeh

    Published 2016
    “…For next, instead of k-means clustring, Fuzzy cmeans clustering is combined with Spatial Pyramid Matching image representation to improve the accuracy of classification results. …”
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  13. 13
    Classification of JPEG files by using extreme learning machine

    Classification of JPEG files by using extreme learning machine by Ali, Rabei Raad, Mohamad, Kamaruddin Malik, Jamel, Sapiee, Ahmad Khalid, Shamsul Kamal

    Published 2018
    “…This paper proposes an Extreme Learning Machine (ELM) algorithm to assign a class label of JPEG or Non-JPEG image for files in a continuous series of data clusters. …”
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  14. 14
    Hybrid 2D phase unwrapping methodology using spatial fuzzy clustering and neurofuzzy classification with application in sar images

    Hybrid 2D phase unwrapping methodology using spatial fuzzy clustering and neurofuzzy classification with application in sar images by Then, Patrick Hang Hui.

    Published 2001
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  15. 15
    Image Processing Of Digital Mammograms For Breast Cancer Detection And Classification

    Image Processing Of Digital Mammograms For Breast Cancer Detection And Classification by Mohd Nizom, Ahmad Nabil

    Published 2018
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    Monograph
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  16. 16
    Detecting lung cancer region from CT image using meta-heuristic optimized segmentation approach

    Detecting lung cancer region from CT image using meta-heuristic optimized segmentation approach by Shakeel, Pethuraj Mohamed, Mohd Aboobaider, Burhanuddin, Salahuddin, Lizawati

    Published 2022
    “…An optimal tumor detection requires noise reduced computed tomography (CT) images for pixel classification. In this paper, the butterfly optimization algorithm-based K-means clustering (BOAKMC) method is introduced for reducing CT image segmentation uncertainty. …”
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  17. 17
    Dense-cluster based voting approach for license plate identification

    Dense-cluster based voting approach for license plate identification by Asadzadehkaljahi, Maryam, Shivakumara, Palaiahnakote, Roy, Sangheeta, Olatunde, Mojeed Salmon, Anisi, Mohammad Hossein, Lu, Tong, Pal, Umapada

    Published 2018
    “…This process gives four clusters for the input image. The number of pixels in clusters (dense cluster) and the standard deviation are computed for deriving new hypotheses. …”
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  18. 18
    Blood cell image segmentation using unsupervised clustering techniques

    Blood cell image segmentation using unsupervised clustering techniques by Tuan Muda, Tuan Zalizam, Abdul Salam, Rosalina

    Published 2009
    “…The aim of our research is to develop an effective algorithm for segmentation of the blood cell images. …”
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  19. 19
    The classification of hunger behaviour of Lates Calcarifer through the integration of image processing technique and k-Nearest Neighbour learning algorithm

    The classification of hunger behaviour of Lates Calcarifer through the integration of image processing technique and k-Nearest Neighbour learning algorithm by Taha, Zahari, Razman, Mohd Azraan, Abdul Ghani, Ahmad Shahrizan, P.P. Abdul Majeed, Anwar, Musa, Rabiu Muazu, A. Adnan, Fatihah, Sallehudin, Muhammad Firdaus, Mukai, Yukinori

    Published 2018
    “…The clustered fish behaviour is then classified through k-Nearest Neighbour (k-NN) learning algorithm. …”
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  20. 20
    Intelligent Color Vision System For Ripeness Classification Of Oil Palm Fresh Fruit Bunch

    Intelligent Color Vision System For Ripeness Classification Of Oil Palm Fresh Fruit Bunch by Fadilah, Norasyikin

    Published 2015
    “…The images are collected and analyzed using digital image processing techniques. k-means clustering algorithm is used to segment the image into two separate regions which are fruit and spike regions. …”
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