Search Results - (( image classification learning algorithm ) OR ( pattern extraction method algorithm ))

Refine Results
  1. 1

    Rough Neural Networks Architecture For Improving Generalization In Pattern Recognition by Ali Adlan, Hanan Hassan

    Published 2004
    “…The RNN was used to detect patterns present in satellite image. A novel feature extraction algorithm was developed to extract the feature vectors. …”
    Get full text
    Get full text
    Thesis
  2. 2

    Identifying melanoma characteristics using directional imaging algorithm and convolutional neural network on dermoscopic images / Mohammad Asaduzzaman Rasel by Mohammad Asaduzzaman , Rasel

    Published 2024
    “…Multiple deep-learning models are proposed for segmentation. Several imaging, computer vision, and pattern recognition algorithms are employed to describe five dermoscopic features. …”
    Get full text
    Get full text
    Get full text
    Thesis
  3. 3

    Plant recognition based on identification of leaf image using image processing / Nor Silawati Sha’ari by Sha’ari, Nor Silawati

    Published 2018
    “…The main objective is to developing a classification system for agriculture plant by image pre-processing, feature extraction, network training and classification. …”
    Get full text
    Get full text
    Student Project
  4. 4

    Image classification based on sparse-coded features using sparse coding technique for aerial imagery: a hybrid dictionary approach by Qayyum A., Saeed Malik A., Saad N.M., Iqbal M., Abdullah M.F., Rasheed W., Abdullah T.A.B.R., Bin Jafaar M.Y.

    Published 2023
    “…Aerial photography; Aircraft detection; Antennas; Codes (symbols); Discrete cosine transforms; Discrete wavelet transforms; Glossaries; Image classification; Image coding; Image enhancement; Learning algorithms; Learning systems; Object recognition; Remote sensing; Satellite imagery; Satellites; Unmanned aerial vehicles (UAV); Discrete tchebichef transforms; Discriminative features; Finite Ridgelet Transform; Histogram of oriented gradients; Image processing and computer vision; Scale invariant feature transforms; SIFT; Sparse coding; Classification (of information)…”
    Article
  5. 5

    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. …”
    Get full text
    Get full text
    Article
  6. 6

    Deep plant: A deep learning approach for plant classification / Lee Sue Han by Lee , Sue Han

    Published 2018
    “…The existing CNN based approaches can only capture the similar region-wise patterns within an image but not the structural patterns of a plant composed of varying number of plant views images composed of one or more organs. …”
    Get full text
    Get full text
    Get full text
    Thesis
  7. 7

    STATISTICAL FEATURE LEARNING THROUGH ENHANCED DELAUNAY CLUSTERING AND ENSEMBLE CLASSIFIERS FOR SKIN LESION SEGMENTATION AND CLASSIFICATION by Adil H., Khan, Dayang Nurfatimah, Awang Iskandar, Jawad F., Al-Asad, SAMIR, EL-NAKLA, SADIQ A., ALHUWAIDI

    Published 2021
    “…A boost ensemble learning algorithm using Support Vector Machines (SVM) as initial classifiers and Artificial Neural Networks (ANN) as a final classifier is employed to learn the patterns of different skin lesion class features. …”
    Get full text
    Get full text
    Get full text
    Article
  8. 8

    Diabetic retinopathy detection using fusion of textural and optimized convolutional neural network features / Uzair Ishtiaq by Uzair , Ishtiaq

    Published 2024
    “…The hybrid approach based on image preprocessing and fusion of features is the foundation of the proposed classification method. …”
    Get full text
    Get full text
    Get full text
    Thesis
  9. 9

    VGG16-based deep learning architectures for classification of lung sounds into normal, crackles, and wheezes using Gammatonegrams by Zakaria, Neili, Sundaraj, Kenneth

    Published 2023
    “…To implement these systems, researchers traditionally follow two main steps: feature extraction and pattern classification. In recent years, deep neural networks have gained attention in the field of breathing sound classification as they have proven effective for training large datasets. …”
    Get full text
    Get full text
    Get full text
    Conference or Workshop Item
  10. 10
  11. 11

    Enhancing land cover classification in remote sensing imagery using an optimal deep learning model by Motwake, Abdelwahed, Hassan Abdalla Hashim, Aisha, Obayya, Marwa, Eltahir, Majdy M.

    Published 2023
    “…The ISCSODL-LCC technique utilizes advanced machine learning methods by employing the Squeeze-Excitation ResNet (SE-ResNet) model for feature extraction and the Stacked Gated Recurrent Unit (SGRU) mechanism for land cover classification. …”
    Get full text
    Get full text
    Get full text
    Get full text
    Get full text
    Article
  12. 12

    Ultrasound-based tissue characterization and classification of fatty liver disease: A screening and diagnostic paradigm by Acharya, U.R., Faust, O., Molinari, F., Sree, S.V., Junnarkar, S.P., Sudarshan, V.

    Published 2015
    “…These classification algorithms are trained using the features extracted from the patient data in order for them to learn the relationship between the features and the end-result (FLD present or absent). …”
    Get full text
    Get full text
    Get full text
    Article
  13. 13

    Phylogenetic tree classification system using machine learning algorithm by Tan, Jia Kae

    Published 2015
    “…A study is conducted to develop an automated phylogenetic tree image classification system by using machine learning algorithm. …”
    Get full text
    Get full text
    Get full text
    Final Year Project Report / IMRAD
  14. 14
  15. 15

    Evaluation of the Transfer Learning Models in Wafer Defects Classification by Jessnor Arif, Mat Jizat, Anwar, P. P. Abdul Majeed, Ahmad Fakhri, Ab. Nasir, Zahari, Taha, Yuen, Edmund, Lim, Shi Xuen

    Published 2022
    “…The key metrics for the evaluation are classification accuracy, classification precision and classification recall. 855 images were used to train and test the algorithms. …”
    Get full text
    Get full text
    Get full text
    Conference or Workshop Item
  16. 16

    The use of SOM for fingerprint classification by Turky A.M., Ahmad M.S.

    Published 2023
    Subjects:
    Conference paper
  17. 17

    Classification of brain tumors: using deep transfer learning by Husin, Nor Azura, Husam, Mohamed, Hussin, Masnida

    Published 2023
    “…Experimenters employed data augmentation and learning algorithms.…”
    Get full text
    Get full text
    Article
  18. 18

    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
    “…In general, this thesis introduces an automated machine learning algorithm for detecting diabetic retinopathy (DR) in fundus images. …”
    Get full text
    Get full text
    Thesis
  19. 19

    Transfer Learning for Lung Nodules Classification with CNN and Random Forest by Abdulrazak, Saleh, Chee, Ka Chin, Ros Ameera, Rosdi

    Published 2023
    “…This research demonstrates the potential of using machine learning algorithms in the healthcare industry, especially in disease detection and classification.…”
    Get full text
    Get full text
    Get full text
    Article
  20. 20

    Gender classification based on asian faces using deep learning by Janahiraman T.V., Subramaniam P.

    Published 2023
    “…Face recognition; Human computer interaction; Image classification; Learning algorithms; Natural language processing systems; Neural networks; Speech recognition; Systems engineering; Convolutional neural network; Gender classification; Learning architectures; Learning frameworks; NAtural language processing; Recognition accuracy; Recognition process; TensorFlow; Deep learning…”
    Conference Paper