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  1. 1

    The classification of wink-based eeg signals by means of transfer learning models by Jothi Letchumy, Mahendra Kumar

    Published 2021
    “…It was demonstrated from the simulation investigation that the CWT model could yield a better signal transformation amongst the preprocessing algorithms. In addition, amongst the eighteen TL models evaluated based on the CWT transformation, fourteen was f ound to be able to extract the features reasonable, i.e., VGG16, VGG19, ResNet101, ResNet101 V2, ResNet152, ResNet152 V2, Inception V3, Inception ResNet V2, Xception, MobileNetV2, DenseNet 121, DenseNet 169, NasNetMobile and NasNetLarge. …”
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  2. 2

    Automatic Segmentation and Classification of Skin Lesions in Dermoscopic Images by Adil Humayun, Khan

    Published 2024
    “…This process consists of four steps: pre-processing, segmentation, feature extraction, and classification. …”
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  3. 3

    Diabetic Retinopathy Detection Model using Hybrid of U-Net and Vision Transformer Algorithms by Mudit, Khater

    Published 2024
    “…We leverage the use of U-Net algorithm in image segmentation process to delineate the regions of interest in retinal images. …”
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    Leveraging CQT-VMD and pre-trained AlexNet architecture for accurate pulmonary disease classification from lung sound signals by Neili, Zakaria, Sundaraj, Kenneth

    Published 2025
    “…These images are then processed by the AlexNet model, achieving an impressive classification accuracy of 93.30%. …”
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  7. 7

    Application of Fuzzy C-Means with YCbCr and DenseNet-201 for Automated Corn Leaf Disease Detection by Chyntia Jaby, Entuni

    Published 2021
    “…This is due to instability and complexity of the network. Hence, algorithm that performed better is required. Thus, in this study, image segmentation method of Fuzzy C-Means with YCbCr and image classification method of DenseNet-201 to detect plant leaf diseases is proposed. …”
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    An efficient AdaBoost algorithm for enhancing skin cancer detection and classification by Gamil, Seham, Zeng, Feng, Alrifaey, Moath, Asim, Muhammad, Ahmad, Naveed

    Published 2024
    “…To tackle these issues, researchers have explored the application of artificial intelligence (AI) tools, particularly machine learning techniques such as shallow and deep learning, to enhance the diagnostic process for skin cancer. These tools employ computer algorithms and deep neural networks to identify and categorize skin cancer. …”
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  12. 12

    Semantic focus fusion based on deep learning for deblurring effect by Ismail, .

    Published 2024
    “…In a rapid development of intelligent computation, such as deep learning algorithm, multi-focus image fusion methods indirectly being involved, such as CNN and PCA Net architectures. …”
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    Driver drowsiness detection system through facial expression using Convolutional Neural Networks (CNN) / Nipa Das Gupta, Rajesvary Rajoo and Patricia Jayshree Jacob by Gupta, Nipa Das, Rajoo, Rajesvary, Jacob, Patricia Jayshree

    Published 2023
    “…We gathered a dataset from public stock image websites, and manual image captures to develop the system. After processing the dataset, we extracted a wide range of features, which we fed into a deep convolutional neural network (CNN) algorithm. …”
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    Software Classification Using Structure-Based Descriptors by Ramadan, Qusai Hussein

    Published 2009
    “…Three software metrics namely; LOC, MVG and WMCl have been extracted from programs of category board and puzzle obtained from SourceForge.net. …”
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  17. 17

    Classification Of Gender Using Global Level Features In Fingerprint For Malaysian Population by Abdullah, Siti Fairuz

    Published 2016
    “…A new approach of algorithm based on the Mark Acree’s theory, focusing on fingerprint global extracted features is proposed and implemented for enhancing gender classification method. …”
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    Bayesian Network Classifiers for Damage Detection in Engineering Material by Mohamed Addin, Addin Osman

    Published 2007
    “…The methodology used in the thesis to implement the Bayesian network for the damage detection provides a preliminary analysis used in proposing a novel fea- ture extraction algorithm (f-FFE: the f-folds feature extraction algorithm). …”
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