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  1. 1
    Integrated combined layer algorithm of jamming detection and classification in manet / Ahmad Yusri Dak

    Integrated combined layer algorithm of jamming detection and classification in manet / Ahmad Yusri Dak by Dak, Ahmad Yusri

    Published 2019
    “…The fourth stage is to design evaluation methodology of Max-Min Rule-Based Classification Algorithm using classifier model. …”
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  2. 2
    Identification of vocal and voice disorders

    Identification of vocal and voice disorders by Murugesa Pandiyan, Paulraj, Prof. Madya Dr., Sazali, Yaacob, Prof. Dr., Mohd Rizon, Mohammed Juhari, Prof. Dr., Sivanandam, S. N., Muthusamy, Hariharan, Dr.

    Published 2011
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  3. 3
    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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  4. 4
    Symmetric Key Size for Different Level of Information Classification

    Symmetric Key Size for Different Level of Information Classification by Ibrahim, Subariah, Maarof, Mohd. Aizaini

    Published 2006
    “…Therefore confidential information is normally protected by using cryptographic algorithms. …”
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  5. 5
    Whale optimization algorithm based on tent chaotic map for feature selection in soft sensors

    Whale optimization algorithm based on tent chaotic map for feature selection in soft sensors by AlRijeb, Mothena Fakhri Shaker, Othman, Mohammad Lutfi, Ishak, Aris, Hassan, Mohd Khair, Albaker, Baraa Munqith

    Published 2025
    “…The proposed TWOA achieved a higher fault classification result of 99.98% compared to other algorithms.…”
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  6. 6
    Pathological infant cry analysis using wavelet packet transform and probabilistic neural network

    Pathological infant cry analysis using wavelet packet transform and probabilistic neural network by Muthusamy, Hariharan, Sazali, Yaacob, Prof. Dr., Saidatul Ardeenaawatie, Awang

    Published 2011
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  7. 7
    Spectral discrimination and index development of roofing materials and conditions using field spectroscopy and worldview-3 satellite image

    Spectral discrimination and index development of roofing materials and conditions using field spectroscopy and worldview-3 satellite image by Samsudin, Sarah Hanim

    Published 2016
    “…The classification accuracy using spectral indices were compared with the normal supervised pixel-based classification of SVM. …”
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  8. 8
    Supervised neural network classifier for voice pathology

    Supervised neural network classifier for voice pathology by Murugesa Pandiyan, Paulraj, Prof. Madya Dr, Sazali, Yaacob, Prof. Dr., Sivanandam, S. N., Hariharan, Muthusamy

    Published 2011
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  9. 9
    Optimal selection of long time acoustic features using GA for the assessment of vocal fold disorders

    Optimal selection of long time acoustic features using GA for the assessment of vocal fold disorders by Sindhu, Ravindran, Neoh, Siew Chin, Dr., Muthusamy, Hariharan, Dr.

    Published 2013
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  10. 10
    Heart sound diagnosis using nonlinear ARX model / Noraishah Shamsuddin

    Heart sound diagnosis using nonlinear ARX model / Noraishah Shamsuddin by Shamsuddin, Noraishah

    Published 2011
    “…The Resilient Backpropagation (RPROP) algorithm is used to train the network. The optimized learning parameter used is 0.07 and the network has best performance when hidden neurons equal to 220. …”
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  11. 11
    Handgrip strength evaluation using neuro fuzzy approach

    Handgrip strength evaluation using neuro fuzzy approach by Seng, W.C., Chitsaz, M.

    Published 2010
    “…The results indicate that the classification accuracy of normal and pathological patients are 90 and 75 respectively. …”
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  12. 12
    Detection of proliferative diabetic retinopathy in fundus images using convolution neural network

    Detection of proliferative diabetic retinopathy in fundus images using convolution neural network by Hasliza, Abu Hassan, Marzuqi, Yaakob, Sasni, Ismail, Juwairiyyah, Abd Rahman, Izyani, Mat Rusni, Azlee, Zabidi, Ihsan, Mohd Yassin, Nooritawati, Md Tahir, Suraiya, Mohamad Shafie

    Published 2020
    “…Convolution Neural Network (CNN) is one of the techniques under Artificial Neural Network (ANN) used to develop a Deep Learning Neural Network (DLNN) algorithm for detection of Proliferative Diabetic Retinopathy (PDR) on the fundus images. …”
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  13. 13
    Classification of transient disturbance using Wavelet based support vector machine / Fahteem Hamamy Anuwar

    Classification of transient disturbance using Wavelet based support vector machine / Fahteem Hamamy Anuwar by Anuwar, Fahteem Hamamy

    Published 2012
    “…Cross validation is used to find the best parameters related to kernels used followed by training and testing of the data sets. …”
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  14. 14
    The effect of pre-processing techniques and optimal parameters on BPNN for data classification

    The effect of pre-processing techniques and optimal parameters on BPNN for data classification by HUSSEIN, AMEER SALEH

    Published 2015
    “…In this research, a performance analysis based on different activation functions; gradient descent and gradient descent with momentum, for training the BP algorithm with pre-processing techniques was executed. …”
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  15. 15
    An enhanced gated recurrent unit with auto-encoder for solving text classification problems

    An enhanced gated recurrent unit with auto-encoder for solving text classification problems by Zulqarnain, Muhammad

    Published 2020
    “…Classification has become an important task for categorizing documents automatically based on their respective groups. …”
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  16. 16
    Machine-learning-based adaptive distance protection relay to eliminate zone-3 protection under-reach problem on statcom-compensated transmission lines

    Machine-learning-based adaptive distance protection relay to eliminate zone-3 protection under-reach problem on statcom-compensated transmission lines by Aker, Elhadi Emhemed Alhaaj Ammar

    Published 2020
    “…These parameters are used to develop a standalone intelligently machine learning adaptive distance relay (ML-ADR) modification. …”
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  17. 17
    Development of computer aided design system based on artificial neural network for macular hole detection

    Development of computer aided design system based on artificial neural network for macular hole detection by Jayapalan, Mohana Phriya

    Published 2021
    “…There are browse image, pre-processing, segmentation, feature extraction and lastly classification steps. This study successfully classified the Macular Hole and normal retinal images correctly using Artificial Neural Network (ANN) classification. …”
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  18. 18
    Hybrid ACO and SVM algorithm for pattern classification

    Hybrid ACO and SVM algorithm for pattern classification by Alwan, Hiba Basim

    Published 2013
    “…This study presents four algorithms for tuning the SVM parameters and selecting feature subset which improved SVM classification accuracy with smaller size of feature subset. …”
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  19. 19
    Intelligent classification algorithms in enhancing the performance of support vector machine

    Intelligent classification algorithms in enhancing the performance of support vector machine by Alwan, Hiba Basim, Ku-Mahamud, Ku Ruhana

    Published 2019
    “…Common methods associated in tuning SVM parameters will discretize the continuous value of these parameters which will result in low classification performance. …”
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  20. 20
    Incremental continuous ant colony optimization for tuning support vector machine’s parameters

    Incremental continuous ant colony optimization for tuning support vector machine’s parameters by Alwan, Hiba Basim, Ku-Mahamud, Ku Ruhana

    Published 2013
    “…Support Vector Machines are considered to be excellent patterns classification techniques. The process of classifying a pattern with high classification accuracy counts mainly on tuning Support Vector Machine parameters which are the generalization error parameter and the kernel function parameter.Tuning these parameters is a complex process and Ant Colony Optimization can be used to overcome the difficulty. …”
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