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  1. 1

    Hevea leaf features extraction and recognition algorithm for hevea clones classification using image / Mohamad Faizal Ab Jabal, Suhardi Hamid, Salehuddin Shuib by Ab Jabal, Mohamad Faizal, Hamid, Suhardi, Shuib, Salehuddin

    Published 2013
    “…As a result Wu et al. [1] was successful developed an efficient algorithm for the plant classification. 32 kinds of plants have been classified by using the algorithm. …”
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    Research Reports
  2. 2

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

    Published 2018
    “…This reserarch adapted a methodology of computer vision and algorithms that exploit image segmentation, feature extraction and fuzzy classification to guide the research activities. …”
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    Thesis
  3. 3

    Review of Wheat Disease Classification and Severity Detection Models by Hongyan, Zang, Annie, Joseph, Shourong, Zhang, Rong, Liu, Wanzhen, Wang

    Published 2023
    “…In the current study, there are many researches for wheat disease classification, but less for wheat disease severity recognition or estimate. …”
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    Article
  4. 4

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

    Published 2019
    “…To realize these objectives, the research in this thesis follows three basic stages, succeeded by extensive evaluations.…”
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    Thesis
  5. 5

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

    Published 2005
    “…Therefore generating a good decision model or classification model is a major component in many data mining researches. …”
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    Thesis
  6. 6

    Recent Advances in Classification of Brain Tumor from MR Images – State of the Art Review from 2017 to 2021 by Ghazanfar, Latif, Faisal Yousif, Al Anezi, Dayang Nurfatimah, Awang Iskandar, Abul, Bashar, Jaafar, Alghazo

    Published 2022
    “…In addition, the review paper will facilitate researchers who are new to machine learning algorithms for brain tumor recognition to understand the basics of the field and pave the way for them to be able to contribute to this vital field of medical research. …”
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    Article
  7. 7

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

    Published 2025
    “…The evolution of spam filtering has progressed considerably, transitioning from basic rule-based filters to more sophisticated machine learning algorithms. …”
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    Article
  8. 8

    Model of Improved a Kernel Fast Learning Network Based on Intrusion Detection System by Ali, Mohammed Hasan, Mohamed Fadli, Zolkipli

    Published 2019
    “…The approach was tested on the KDD Cup99 intrusion detection dataset and the results proved the proposed PSO-RKFLN as an accurate, reliable, and effective classification algorithm.…”
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    Conference or Workshop Item
  9. 9
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    A Novel Method for Fashion Clothing Image Classification Based on Deep Learning by Yoon Shin, Seong, Jo, Gwanghyun, Wang, Guangxing

    Published 2023
    “…Image recognition and classification is a significant research topic in computational vision and widely used computer technology. …”
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    Article
  11. 11

    Review of deep convolution neural network in image classification by Al-Saffar, Ahmed Ali Mohammed, Tao, Hai, Mohammed, Ahmed Talab

    Published 2017
    “…Then, the research status and development trend of convolution neural network model based on deep learning in image classification are reviewed, which is mainly introduced from the aspects of typical network structure construction, training method and performance. …”
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    Article
  12. 12

    Modelling of clinical risk groups (CRGs) classification using FAM by Mohd. Asi, Salina, Saad, Puteh

    Published 2006
    “…FAM is a fast learning algorithm and used less epoch training [4]. Based on its performance in doing the classification, FAM is theoretically suitable to do the CRGs classification. …”
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    Conference or Workshop Item
  13. 13

    Making programmer effective for software development teams: An extended study by Gilal, A.R., Jaafar, J., Abro, A., Umrani, W.A., Basri, S., Omar, M.

    Published 2017
    “…Basically, two types of decision rules were formed: rules without gender classification of programmer but they only discussed the personality types of team-leader and programmer. …”
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    Article
  14. 14

    Al-Hams and Al-Jahr Sifaat evaluation using classification approach by Altalmas, Tareq, M., Ahmad, Salmiah, Sediono, Wahju, Nik Hashim, Nik Nur Wahidah, Embong, Abd Halim, Hassan, Surul Shahbudin

    Published 2021
    “…Features selection technique was then implemented to reduce the size of the features vector, where later, K-nearest Neighbor (KNN) algorithm was used as the classification technique. …”
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    Proceeding Paper
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    Improved normalization and standardization techniques for higher purity in K-means clustering by Dalatu, Paul Inuwa, Fitrianto, Anwar, Mustapha, Aida

    Published 2016
    “…Clustering is basically one of the major sources of primary data mining tools, which make researchers understand the natural grouping of attributes in datasets. …”
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    Article
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    Application Of Multi-Layer Perceptron Technique To Detect And Locate The Base Of A Young Corn Plant by Morshidi, Malik Arman

    Published 2007
    “…The recognition rate is presented and compared with another related research work, where the results show equal performance of both algorithms. …”
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    Thesis
  19. 19

    Rough Neural Networks Architecture For Improving Generalization In Pattern Recognition by Ali Adlan, Hanan Hassan

    Published 2004
    “…The basic design for the network is provided together with the learning rules. …”
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    Thesis
  20. 20

    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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    Article