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  1. 1
    Image classification using two dimensional wavelet coefficients with parallel computing

    Image classification using two dimensional wavelet coefficients with parallel computing by Ong, Yew Fai

    Published 2020
    “…This research algorithm demonstrated a very promising result with Support Vector Machines, this algorithm produces a 90% of accuracies whereas the decision tree algorithm gets 100% accuracies. …”
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  2. 2
    Support vector classification of remote sensing images using improved spectral Kernels

    Support vector classification of remote sensing images using improved spectral Kernels by Md. Sap, Mohd. Noor, Kohram, Mojtaba

    Published 2008
    “…A very important task in pattern recognition is the incorporation of prior information into the learning algorithm. …”
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  3. 3
    Optimal input features selection of wavelet-based EEG signals using GA

    Optimal input features selection of wavelet-based EEG signals using GA by Mohd. Daud, Salwani, Yunus, Jasmy

    Published 2004
    “…A combination of genetic algorithm (GA) and artificial neural network (ANN) are used to select the relevant features. …”
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  4. 4
    A new classifier based on combination of genetic programming and support vector machine in solving imbalanced classification problem

    A new classifier based on combination of genetic programming and support vector machine in solving imbalanced classification problem by Mohd Pozi, Muhammad Syafiq

    Published 2016
    “…In addition, the classifier is also optimized such that it has a good generalization property. The main keys of the new classifier are based on the new kernel method, new learning metric and a new optimization algorithm in order to optimize the SVM decision function. …”
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  5. 5
    Development of sorting system for oil palm in vitro shoots using machine vision approach

    Development of sorting system for oil palm in vitro shoots using machine vision approach by Al-Ruhaimi, Hamdan Yahya Ahmed

    Published 2014
    “…Based on SOTA and classification task decision, a sorting algorithm that can acquire, recognize, and eject a shoot has been improved and tested in an offline mode. …”
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  6. 6
    Modelling semantic context for novelty detection in wildlife scenes

    Modelling semantic context for novelty detection in wildlife scenes by Yong, SP, Deng, JD, Purvis, MP

    Published 2010
    “…Working with wildlife image data, the framework starts with image segmentation, followed by feature extraction and classification of the image blocks extracted from image segments. …”
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  7. 7
    Task-state EEG signal classification for spatial cognitive evaluation based on multiscale high-density convolutional neural network

    Task-state EEG signal classification for spatial cognitive evaluation based on multiscale high-density convolutional neural network by Wen, Dong, Li, Rou, Tang, Hao, Liu, Yijun, Wan, Xianglong, Dong, Xianling, Saripan, M. Iqbal, Lan, Xifa, Song, Haiqing, Zhou, Yanhong

    Published 2022
    “…In this study, a multi-scale high-density convolutional neural network (MHCNN) classification method for spatial cognitive ability assessment was proposed, aiming at achieving the binary classification of task-state EEG signals before and after spatial cognitive training. …”
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  8. 8
    Enhanced Reinforcement Learning Model for Extraction of Objects in Complex Imaging

    Enhanced Reinforcement Learning Model for Extraction of Objects in Complex Imaging by Usmani, U.A., Roy, A., Watada, J., Jaafar, J., Aziz, I.A.

    Published 2022
    “…We examine a variety of image segmentation algorithms and give our reinforcement learning algorithm that uses Deep Convolutional Neural Networks for the detection of irregular objects, which has been tested on four datasets. …”
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  9. 9
    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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  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
    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
    “…In this study, an algorithm is developed using Multivariate pattern analysis (MVPA) and modified CNN to decode the behavior of brain for different images with limited data set. …”
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  12. 12
    Using genetic algorithms to optimise land use suitability

    Using genetic algorithms to optimise land use suitability by Pormanafi, Saeid

    Published 2012
    “…Second task is to determine the fitness function for the genetic algorithms. …”
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  13. 13
    Vehicle logo recognition using whitening transformation and deep learning

    Vehicle logo recognition using whitening transformation and deep learning by Soon, Foo Chong, Khaw, Hui Ying, Chuah, Joon Huang, Kanesan, Jeevan

    Published 2019
    “…This paper presents a vehicle logo recognition using a deep convolutional neural network (CNN) method and whitening transformation technique to remove redundancy of adjacent image pixels. …”
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  14. 14
    Identifying the mechanism to forecast the progression of Alzheimer’s disease from mild cognitive impairment using deep learning

    Identifying the mechanism to forecast the progression of Alzheimer’s disease from mild cognitive impairment using deep learning by Hiu, Theresa Wei Xin

    Published 2022
    “…This study also implemented a CNN algorithm based on 3D ResNet-18 model using weights from ImageNet for the classification task of CN vs AD and sMCI vs pMCI. …”
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  15. 15
    Automatic identification of epileptic seizures from EEG signals using sparse representation-based classification

    Automatic identification of epileptic seizures from EEG signals using sparse representation-based classification by Sobhan Sheykhivand, Tohid Yousefi Rezaii, Zohreh Mousavi, Azra Delpak, Ali Farzamnia

    Published 2020
    “…This study is based on sparse representation-based classification (SRC) theory and the proposed dictionary learning using electroencephalogram (EEG) signals. …”
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  16. 16
    Postal address handwritten recognition using convolutional neural network / Nur Hasyimah Abd Aziz

    Postal address handwritten recognition using convolutional neural network / Nur Hasyimah Abd Aziz by Abd Aziz, Nur Hasyimah

    Published 2020
    “…CNN was chosen as an algorithm for classification task because various studies had concluded that it is able to produce highly accurate result. …”
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  17. 17
    Classification Of Cervical Cancer Stage From Pap Smear Tests

    Classification Of Cervical Cancer Stage From Pap Smear Tests by Sendal, Ken Irok

    Published 2019
    “…The performance of the proposed classification algorithm gave satisfactory results of accuracy, 91.9% for KNN classification and 95.0% for SVM classification.…”
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  18. 18
    Autism Spectrum Disorder Classification Using Deep Learning

    Autism Spectrum Disorder Classification Using Deep Learning by Abdulrazak Yahya, Saleh, Lim Huey, Chern

    Published 2021
    “…However, there is a need to explore more algorithms that can yield better classification performance. …”
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  19. 19
    Contrastive Self-Supervised Learning for Image Classification

    Contrastive Self-Supervised Learning for Image Classification by Tan, Yong Le

    Published 2021
    “…The model will pretrain on a pretext task first and the pretext task will ensure the model learn some useful representation for the downstream tasks (e.g., classification, object localization and so on). …”
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  20. 20
    Evaluation and Comparative Analysis of Feature Extraction Methods on Image Data to increase the Accuracy of Classification Algorithms

    Evaluation and Comparative Analysis of Feature Extraction Methods on Image Data to increase the Accuracy of Classification Algorithms by Rachmad, Iqbal, Tri Basuki, Kurniawan, Misinem, ., Edi Surya, Negara, Tata, Sutabri

    Published 2024
    “…CNNs are well-suited for image classification tasks due to their ability to learn hierarchical feature representations from the input images automatically. …”
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