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  1. 1
    Classification of Cardiac Disorders Based on Electrocardiogram Data with Fuzzy Cognitive Map (FCM) Algorithm Approach

    Classification of Cardiac Disorders Based on Electrocardiogram Data with Fuzzy Cognitive Map (FCM) Algorithm Approach by Sumiati, ., Hoga, Saragih, T.K.A, Rahman, Viktor Vekky, Ronald Repi, Agung, Triayudi

    Published 2021
    “…In this article, the classification of cardiac abnormalities from electrocardio�gram medical data has been carried out using the Fuzzy Cognitive Map (FCM) approach. …”
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  2. 2
    A hybrid spiking neural network model for multivariate data classification and visualization.

    A hybrid spiking neural network model for multivariate data classification and visualization. by Ming, Leong Yii, Teh, Chee Siong, Chen, Chwen Jen

    Published 2011
    “…Therefore, this hybrid learning model is proposed to harness the advantages of both SOM-AC and SNN to produce intuitive multivariate data classification and visualization. …”
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  3. 3
    Classification and visualization on eligibility rate of applicant’s LinkedIn account using Naïve Bayes / Nurul Atirah Ahmad

    Classification and visualization on eligibility rate of applicant’s LinkedIn account using Naïve Bayes / Nurul Atirah Ahmad by Ahmad, Nurul Atirah

    Published 2023
    “…This project implements the Naive Bayes algorithm as the classification algorithm. The collected data from LinkedIn profiles then undergoes data preprocessing. …”
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  4. 4
    An improved plant identification system by Fuzzy c-means bag of visual words model and sparse coding

    An improved plant identification system by Fuzzy c-means bag of visual words model and sparse coding by Safa, Soodabeh, Khalid, Fatimah

    Published 2020
    “…This demonstrate the intensity of the correlation between that aspect of data and a specific cluster. In the classic Bag of visual words model, the Fuzzy c-means algorithm is replaced with K-means and the accuracy of SIFT matching is increased. …”
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  5. 5
    An improved algorithm for iris classification by using support vector machine and binary random machine learning

    An improved algorithm for iris classification by using support vector machine and binary random machine learning by Kamarulzalis, Ahmad Haadzal

    Published 2018
    “…In machine learning, there are three type of learning branch that can used in classification procedures for data mining. …”
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  6. 6
    Analysis of hyperspectral reflectance for disease classification of soybean frogeye leaf spot using Knime analytics

    Analysis of hyperspectral reflectance for disease classification of soybean frogeye leaf spot using Knime analytics by Ang, Yuhao, Mohd Shafri, Helmi Zulhaidi

    Published 2023
    “…Hyperspectral reflectance data of healthy and FLS disease soybeans were used. …”
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  7. 7
    Imaging spectroscopy and light detection and ranging data fusion for urban features extraction

    Imaging spectroscopy and light detection and ranging data fusion for urban features extraction by Idrees, Mohammed, Mohd Shafri, Helmi Zulhaidi, Saeidi, Vahideh

    Published 2013
    “…The composite image was classified using unsupervised classification (k-mean algorithm) and the accuracy of the classification assessed. …”
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  8. 8
    Sentiment analysis on national cultural tourism using Linear Support Vector Machine (LSVM) / Nur Haida Hanna Samsuddin

    Sentiment analysis on national cultural tourism using Linear Support Vector Machine (LSVM) / Nur Haida Hanna Samsuddin by Samsuddin, Nur Haida Hanna

    Published 2020
    “…In future, it is recommended to experiment with different algorithm or kernel of Support Vector Machine. The volume of data should be large as it can generate better result of classification method.…”
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  9. 9
    The formulation of a transfer learning pipeline for the classification of the wafer defects

    The formulation of a transfer learning pipeline for the classification of the wafer defects by Lim, Shi Xuen

    Published 2023
    “…Automated processes have been used commonly in recent years, with the judgement done by using conventional image processing algorithm. …”
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  10. 10
    A novel framework for potato leaf disease detection using an efficient deep learning model

    A novel framework for potato leaf disease detection using an efficient deep learning model by Mahum, R., Munir, H., Mughal, Z.-U.-N., Awais, M., Sher Khan, F., Saqlain, M., Mahamad, S., Tlili, I.

    Published 2022
    “…Moreover, the usage of the reweighted cross-entropy loss function makes our proposed algorithm more robust as the training data is highly imbalanced. …”
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  11. 11
    A novel framework for potato leaf disease detection using an efficient deep learning model

    A novel framework for potato leaf disease detection using an efficient deep learning model by Mahum, R., Munir, H., Mughal, Z.-U.-N., Awais, M., Sher Khan, F., Saqlain, M., Mahamad, S., Tlili, I.

    Published 2022
    “…Moreover, the usage of the reweighted cross-entropy loss function makes our proposed algorithm more robust as the training data is highly imbalanced. …”
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  12. 12
    Classification of labour pain using electroencephalogram signal based on wavelet method / Sai Chong Yeh

    Classification of labour pain using electroencephalogram signal based on wavelet method / Sai Chong Yeh by Sai , Chong Yeh

    Published 2020
    “…These methods present a robust system that enables fully automated identification and removal of artifacts from EEG signals, without the need of visual inspection or arbitrary thresholding. The training and parameters selection of the machine learning algorithms are conducted using EEG data collected from ten subjects in the laboratory. …”
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  13. 13
    Decoding of visual activity patterns from fMRI responses using multivariate pattern analyses and convolutional neural network

    Decoding of visual activity patterns from fMRI responses using multivariate pattern analyses and convolutional neural network by Zafar, R., Kamel, N., Naufal, M., Malik, A.S., Dass, S.C., Ahmad, R.F., Abdullah, J.M., Reza, F.

    Published 2017
    “…General linear model (GLM) is used to find the unknown parameters of every individual voxel and the classification is done using multi-class support vector machine (SVM). …”
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  14. 14
    Machine learning application for concrete surface defects automatic damage classification

    Machine learning application for concrete surface defects automatic damage classification by Syahrul Fithry Senin, Khairullah Yusuf, Amer Yusuf, Rohamezan Rohim

    Published 2024
    “…This model is trained using 80% of the image data and tested using another 20% of the image data. …”
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  15. 15
    Classification and visualization of Malaysian fast food restaurant chain based on twitter sentiment analysis / Muhammad Hafeez Hakimi Muhd Zahidi Ridzuan

    Classification and visualization of Malaysian fast food restaurant chain based on twitter sentiment analysis / Muhammad Hafeez Hakimi Muhd Zahidi Ridzuan by Muhd Zahidi Ridzuan, Muhammad Hafeez Hakimi

    Published 2023
    “…This project uses an algorithm called Naïve Bayes and the visualization is aided by the Plotly library in Python. …”
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  16. 16
    Converged Classification Network For Matching Cost Computation

    Converged Classification Network For Matching Cost Computation by Hamid, Mohd Saad, Abd Manap, Nurulfajar, Hamzah, Rostam Affendi, Kadmin, Ahmad Fauzan

    Published 2020
    “…Stereoscopic vision lets us identify the world around us in 3D by incorporating data from depth signals into a clear visual model of the world. …”
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  17. 17
    Enhanced emotion recognition in videos: a convolutional neural network strategy for human facial expression detection and classification

    Enhanced emotion recognition in videos: a convolutional neural network strategy for human facial expression detection and classification by Ashraf, Arselan, Gunawan, Teddy Surya, Arifin, Fatchul, Kartiwi, Mira, Sophian, Ali, Habaebi, Mohamed Hadi

    Published 2023
    “…Despite extensive research employing machine learning algorithms like convolutional neural networks (CNN), challenges remain concerning input data processing, emotion classification scope, data size, optimal CNN configurations, and performance evaluation. …”
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  18. 18
    Unsupervised classification of multi-class chart images: A comparison of customized CNNs and transfer learning techniques

    Unsupervised classification of multi-class chart images: A comparison of customized CNNs and transfer learning techniques by Hassan Zaidi, Syed Muhammad, Jamil Alsayaydeh, Jamil Abedalrahim, Khan, Abdul Hafeez, Khan, Abdullah Ayub, AlZubi, Ahmad Ali, Ogunshola, Benny, Herawan, Safarudin Gazali

    Published 2025
    “…The k-means clustering algorithm is then applied to group visually similar chart images. …”
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  19. 19
    Predicting 30-day mortality after an acute coronary syndrome (ACS) using machine learning methods for feature selection, classification and visualization

    Predicting 30-day mortality after an acute coronary syndrome (ACS) using machine learning methods for feature selection, classification and visualization by Nanyonga Aziida, Sorayya Malek, Firdaus Aziz, Khairul Shafiq Ibrahim, Sazzli Kasim

    Published 2021
    “…Hybrid combinations of feature selection, classification and visualisation using machine learning (ML) methods have the potential for enhanced understanding and 30-day mortality prediction of patients with cardiovascular disease using population-specific data. …”
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  20. 20
    Pemetaan Pm10 Dan Aot Menggunakan Teknik Penderiaan Jauh Di Semenanjung Malaysia

    Pemetaan Pm10 Dan Aot Menggunakan Teknik Penderiaan Jauh Di Semenanjung Malaysia by San, Limhwee

    Published 2006
    “…Average filtering was applied to the PM10 map to mininise the noise effect. The proposed algorithms were also validated using the multidate data. …”
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