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  1. 1

    An intra-severity classification and adaptation technique to improve dysarthric speech recognition accuracy / Bassam Ali Qasem Al-Qatab by Bassam Ali Qasem, Al-Qatab

    Published 2020
    “…Our proposed method introduces the intra-severity classification and adaptation techniques which are applied sequentially in two stages of system development. …”
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    Thesis
  2. 2

    Performance analysis of machine learning algorithms for classification of infection severity levels on rubber leaves by Mat Lazim, Siti Saripa Rabiah, Sulaiman, Zulkefly, Mat Nawi, Nazmi, Mohd Mustafah, Anas

    Published 2023
    “…This work shows that the spectroscopic measurement combined with classification techniques are promising strategy to classify severity level of WRD based on the spectral data of the rubber leaves.…”
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    Book Section
  3. 3

    Non-invasive gliomas grading using swarm intelligence algorithm / Muhammad Harith Ramli by Ramli, Muhammad Harith

    Published 2017
    “…Bat algorithm is chosen for the development of the prototype for segmentation and classification purpose. …”
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    Thesis
  4. 4

    Mid-infrared spectroscopy for early detection of basal stem rot disease in oil palm by Liaghat, Shohreh, Mansor, Shattri, Ehsani, Reza, Mohd Shafri, Helmi Zulhaidi, Meon, Sariah, Sankaran, Sindhuja

    Published 2014
    “…The selected principal component scores were used in classification using linear discriminant analysis (LDA), quadratic discriminant analysis (QDA), k-nearest neighbor (kNN) and Naive-Bayes (NB) multivariate classification algorithms. …”
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    Article
  5. 5

    Classification of Mental Health Level of Students Using SMOTE and Soft Voting Ensemble Classifier and the DASS-21 Profile by Muhammad Imron, Rosadi, Khoirun, Nisa, Nanik, Kholifah

    “…It leverages the Synthetic Minority Over-sampling Technique (SMOTE) to address the class imbalance in the dataset and employs a Voting Ensemble with soft voting to combine several base algorithms (Logistic Regression, Random Forest, Gradient Boosting, and XGBoost/SVM) for accurate prediction of mental health levels (normal, mild, moderate, severe, very severe). …”
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    Article
  6. 6
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    Modified symbiotic organisms search optimization for automatic construction of convolutional neural network architectures by Jauro, Fatsuma, Abdullahi, Usman Ali, Abdulsalami, Aminu Onimisi, Ibrahim, Adamu Abubakar, Abdullahi, Mohammed, Chiroma, Haruna

    Published 2024
    “…This research introduces an innovative approach to Convolutional Neural Network (ConvNet) architecture generation through the utilization of the Symbiotic Organism Search ConvNet (SOS_ConvNet) algorithm. Leveraging the Symbiotic Organism Search optimization technique, SOS_ConvNet evolves ConvNet architectures tailored for diverse image classification tasks. …”
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    Article
  8. 8

    An improved hybrid learning approach for better anomaly detection by Mohamed Yassin, Warusia

    Published 2011
    “…In this thesis, an improved hybrid mining approach is proposed through combination of K-Means clustering and classification techniques. K-Means clustering is an anomaly detection technique that is naturally capable for dealing with huge data in high speed network. …”
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    Thesis
  9. 9

    Assessment of near-infrared and mid-infrared spectroscopy for early detection of basal stem rot disease in oil palm plantation by Liaghat, Shohreh

    Published 2013
    “…Linear discriminant analysis (LDA), quadratic discriminant analysis (QDA), k-nearest neighbor (kNN), Naïve-Bayes (NB), artificial neural networks (ANNs) and support vector machines (SVMs) classification techniques, were tested to classify the leaf and trunk samples into four levels of disease severity. …”
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    Thesis
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    FT-IR absorbance data for early detection of oil palm fungal disease infestation by Liaghat, Shohreh, Mansor, Shattri, Mohd Shafri, Helmi Zulhaidi, Meon, Sariah, Ehsani, Reza, Md Nor Azam, Siti Hajar

    Published 2012
    “…The selected principal component (PC) scores were used as input features in quadratic discriminant analysis (QDA) as a pattern recognition algorithm. The results indicated that QDA-based algorithm can distinguish between healthy and infected leaves at three stages of infection with high classification accuracies (>80%) when leaves demonstrated that the proposed technique has the potential for early detection of the Ganoderma disease.…”
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    Conference or Workshop Item
  13. 13
  14. 14

    Computer aided diagnoses for detecting the severity of Keratoconus by Abdullah, Osamah Qays, Boughariou, Aicha, Al-Azawi, Fadia W., Al-Araji, Ahmed Mohammed Khadum Abdulamer, Mehdy, Mehdy Mwaffeq

    Published 2024
    “…Disease severity was categorized into three stages, namely, mild, moderate, and severe, according to the topographic KC classification by a senior ophthalmologist. …”
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    Article
  15. 15

    Machine-learning approach using thermal and synthetic aperture radar data for classification of oil palm trees with basal stem rot disease by Che Hashim, Izrahayu

    Published 2021
    “…The machine learning algorithm consistently performs well when presented with a well-balanced dataset. …”
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    Thesis
  16. 16

    Hybrid neural network in medicolegal degree of injury determination based on Visum et Repertum by Wardhana, Mohammad Hadyan

    Published 2023
    “…Then, the selection of the critical features is chosen via Neural Network (NN) as classification algorithm and Genetic Algorithm (GA) as an optimization technique. …”
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    Thesis
  17. 17

    Evaluation of multiple In Situ and remote sensing system for early detection of Ganoderma boninense infected oil palm by Ahmadi, Seyedeh Parisa

    Published 2018
    “…Even though at first glance the classification accuracy was moderate, the level of details provided by the imageries suggests that the accuracies were acceptable. …”
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    Thesis
  18. 18

    A New Thermographic NDT for Condition Monitoring of Electrical Components Using ANN with Confidence Level Analysis by A. S. N., Huda, S., Taib, Kamarul Hawari, Ghazali, M. S., Jadin

    Published 2014
    “…Infrared thermography technology is one of the most effective non-destructive testing techniques for predictive faults diagnosis of electrical components. …”
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    Article
  19. 19

    Relationship between deforestation and land surface temperature across an elevation gradient using satellite imagery in Cameron Highlands, Malaysia by How, Darren Jin Aik

    Published 2021
    “…First, land cover classes and detection were identified using an Object-based Image Analysis (OBIA) classification technique on both Landsat 7 and 8 sensors, using a combination of nearest neighbour and multiresolution segmentation algorithm (MSA). …”
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    Thesis
  20. 20

    Development of an automated detector and counter for bagworm census by Ahmad, Mohd Najib

    Published 2020
    “…The economic impact from a moderate bagworm attack of 10%-50% leaf damage may cause 43% yield loss. …”
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    Thesis