Search Results - (( model evaluation from algorithm ) OR ( image classification system algorithm ))

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  1. 1

    Intelligent Color Vision System For Ripeness Classification Of Oil Palm Fresh Fruit Bunch by Fadilah, Norasyikin

    Published 2015
    “…The system consists of an image acquisition system, image processing component and oil palm FFB classification system. …”
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    Thesis
  2. 2

    Real-time intelligent recycle waste detection and classification using you only look once version 5 / Aiman Syafwan Amran by Amran, Aiman Syafwan

    Published 2023
    “…It uses YOLOv5 object detection and classification algorithm. This project uses the images of paper, plastic and metal gathered from Kaggle and GitHub dataset. …”
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    Thesis
  3. 3

    Classification of diabetic retinopathy clinical features using image enhancement technique and convolutional neural network / Abdul Hafiz Abu Samah by Abu Samah, Abdul Hafiz

    Published 2021
    “…To improve the performance from current systems, this work has investigation on different of image pre-processing enhancement technique to support accuracy on deep learning for DR classification. …”
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    Thesis
  4. 4

    A new hybrid deep neural networks (DNN) algorithm for Lorenz chaotic system parameter estimation in image encryption by Nurnajmin Qasrina Ann, Ayop Azmi

    Published 2023
    “…Then, this study aims to optimize the hyperparameters of the developed DNN model using the Arithmetic Optimization Algorithm (AOA) and, lastly, to evaluate the performance of the newly proposed deep learning model with Simulated Kalman Filter (SKF) algorithm in solving image encryption application. …”
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    Thesis
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    Identifying melanoma characteristics using directional imaging algorithm and convolutional neural network on dermoscopic images / Mohammad Asaduzzaman Rasel by Mohammad Asaduzzaman , Rasel

    Published 2024
    “…Several imaging, computer vision, and pattern recognition algorithms are employed to describe five dermoscopic features. …”
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    Thesis
  7. 7

    Residual Attention Network for Brain Tumour Classification by Sashwini, A/P S. Thiagaraju

    Published 2019
    “…The main aim of this study is to design and produce an automated algorithm system using Residual Attention Network (RAN) model, which will classify brain tumour. …”
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    Final Year Project Report / IMRAD
  8. 8

    FRUIT RECOGNITION APPLICATION USING MACHINE LEARNING by LOH, WENG KEONG

    Published 2023
    “…In this study, we present a fruit recognition system using machine learning techniques. The system consists of several distinct phases, including image acquisition, image pre-processing, feature extraction, classification, and results and evaluation. …”
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    Final Year Project Report / IMRAD
  9. 9

    Novel techniques for enhancement and segmentation of acne vulgaris lesions by Malik, A. S., Humayun, J., Kamel, N., Yap, F. B.-B.

    Published 2013
    “…The proposed algorithm uses local rank transform to generate the HDR images from a single acne image followed by the log transformation. …”
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    Citation Index Journal
  10. 10

    Malaysian license plate recognition system using Convolutional Neural Network (CNN) on web application / Nur Farahana Mahmud by Mahmud, Nur Farahana

    Published 2022
    “…The binary image of the extracted characters was fed to the CNN model for classification. …”
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    Student Project
  11. 11

    Leaf condition analysis using convolutional neural network and vision transformer by Yong, Wai Chun, Ng, Kok Why, Haw, Su Cheng, Naveen, Palanichamy, Ng, Seng Beng

    Published 2024
    “…The model performance is evaluated and compared with the other methods to ensure quality and usage compatibility in the plantation domain. …”
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    Article
  12. 12

    Radiomics analysis and supervised machine learning model for classification of cervical cancer images using diffusion weighted imaging-MRI by Ramli, Zarina

    Published 2024
    “…Additionally, the SVM algorithm was evaluated based on its performance across different DWI bvalues, aiming to optimize scanning time. …”
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    Thesis
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    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
    “…The SVGPM performance is compared against current methods in developing a prediction model for IDS. In the third experiment, SVGPM is evaluated on wilt disease data set from remote sensing study, to identify wilt diseased trees in high-resolution image. …”
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    Thesis
  15. 15

    MEDCnet : A Memory Efficient Approach for Processing High-Resolution Fundus Images for Diabetic Retinopathy Classification Using CNN by Mohsin, Butt, Dayang Nurfatimah, Awang Iskandar, Majid, Ali Khan, Ghazanfar, Latif, Abul, Bashar

    Published 2025
    “…The proposed approach initially uses the traditional transfer learning technique to train the deep learning model with reduced-sized images. This trained model is used to extract detailed low-level features from fixed-size patches of higher-resolution fundus images. …”
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    Article
  16. 16

    Intelligent image noise types recognition and denoising system using deep learning / Khaw Hui Ying by Khaw , Hui Ying

    Published 2019
    “…Based on the final denoised images, the model has proven its reliability, in terms of both visual quality and quantitative evaluation. …”
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    Thesis
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    VGG16-based deep learning architectures for classification of lung sounds into normal, crackles, and wheezes using Gammatonegrams by Zakaria, Neili, Sundaraj, Kenneth

    Published 2023
    “…Breathing sounds are a rich source of information that can assist doctors in diagnosing pulmonary diseases in a non-invasive manner. Several algorithms can be developed based on these sounds to create an automatic classification system for lung diseases. …”
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    Conference or Workshop Item
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    A web-based image recognition system for detecting harumanis mangoes / Mohamad Shahmil Saari, Romiza Md Nor and Huzaifah A Hamid by Saari, Mohamad Shahmil, Md Nor, Romiza, Huzaifah, A Hamid

    Published 2020
    “…Furthermore, the accuracy of the image classification can be improved by increasing the number of datasets, the distance of images from the camera, and the labelling process. …”
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    Article