Abdelhamid Abdesselam

Abdelhamid Abdesselam

Sultan Qaboos University

H-index: 9

Asia-Oman

Abdelhamid Abdesselam Information

University

Sultan Qaboos University

Position

Associate Professor of Computer Science

Citations(all)

230

Citations(since 2020)

137

Cited By

115

hIndex(all)

9

hIndex(since 2020)

8

i10Index(all)

9

i10Index(since 2020)

7

Email

University Profile Page

Sultan Qaboos University

Abdelhamid Abdesselam Skills & Research Interests

Image Processing

Computer Vision

Pattern Recognition

Machine Learning

Deep Learning

Top articles of Abdelhamid Abdesselam

Feed-forward networks using logistic regression and support vector machine for whole-slide breast cancer histopathology image classification

The performance of an image classification depends on the efficiency of the feature learning process. This process is a challenging task that traditionally requires prior knowledge from domain experts. Recently, representation learning was introduced to extract features directly from the raw images without any prior knowledge. Deep learning using a Convolutional Neural Network (CNN) has gained massive attention for performing image classification, as it achieves remarkable accuracy that sometimes exceeds human performance. But this type of network learns features by using a back-propagation approach. This approach requires a huge amount of training data and suffers from the vanishing gradient problem that deteriorates the feature learning. The forward-propagation approach uses predefined filters or filters learned outside the model and applied in a feed-forward manner. This approach is proven to …

Authors

ArunaDevi Karuppasamy,Abdelhamid Abdesselam,Rachid Hedjam,Maiya Al-Bahri

Journal

Intelligence-Based Medicine

Published Date

2024/1/1

Performance Evaluation of Deep, Shallow and Ensemble Machine Learning Methods for the Automated Classification of Alzheimer's Disease

Artificial Intelligence (AI)-based approaches are crucial in Computer-Aided Diagnosis (CAD) for various medical applications. Their ability to quickly and accurately learn from complex data is remarkable. Deep Learning (DL) models have shown promising results in accurately classifying Alzheimer’s Disease (AD) and its related cognitive states, Early Mild Cognitive Impairment (EMCI) and Late Mild Cognitive Impairment (LMCI), along with healthy conditions known as Cognitively Normal (CN). This offers valuable insights into disease progression and diagnosis. However, certain traditional machine learning (ML) classifiers perform equally well or even better than DL models, requiring less training data. This is particularly valuable in CAD in situations with limited labelled datasets. In this paper, we propose an ensemble classifier based on ML models for Magnetic Resonance Imaging data, which achieved an impressive accuracy of 96.52%. This represents a 3-5% improvement over the best individual classifier. We evaluated popular ML classifiers for AD classification under both data-scarce and data-rich conditions using the Alzheimer’s Disease Neuroimaging Initiative and Open Access Series of Imaging Studies datasets. By comparing the results to state-of-the-art CNN-centric DL algorithms, we gain insights into the strengths and weaknesses of each approach. This work will help users to select the most suitable algorithm for AD classification based on data availability.

Authors

Noushath Shaffi,Karthikeyan Subramanian,Viswan Vimbi,Faizal Hajamohideen,Abdelhamid Abdesselam,Mufti Mahmud

Journal

International Journal of Neural Systems

Published Date

2024/4/5

Entropy in Fuzzy k-Means Algorithm for Multi-view Data

Multi-view data clustering plays a crucial role in various real-world applications. This kind of data from various domains can exhibit a range of distributions, making it challenging for algorithms to uncover robust patterns. This paper extends the fuzzy k-means clustering algorithm to cluster multi-view data. The objective function includes two additional matrixes to measure the compactness of each view and the importance of individual features. The objective function also includes entropy weights. Experiments on real-life data indicate that the proposed algorithm outperforms current state-of-the-art algorithms. These set of algorithms comprises of clustering techniques that incorporate variable weighting, such as W-k-means , LAC , and EWKM , along with a multiview clustering algorithm called TW-k-means . The evaluation of the algorithms involves measuring their accuracy, as well as comparing their respective …

Authors

Imran Khan,Maya Al-Ghafri,Abdelhamid Abdesselam

Published Date

2023/5/27

Four-way classification of Alzheimer’s disease using deep Siamese convolutional neural network with triplet-loss function

Alzheimer’s disease (AD) is a neurodegenerative disease that causes irreversible damage to several brain regions, including the hippocampus causing impairment in cognition, function, and behaviour. Early diagnosis of the disease will reduce the suffering of the patients and their family members. Towards this aim, in this paper, we propose a Siamese Convolutional Neural Network (SCNN) architecture that employs the triplet-loss function for the representation of input MRI images as k-dimensional embeddings. We used both pre-trained and non-pretrained CNNs to transform images into the embedding space. These embeddings are subsequently used for the 4-way classification of Alzheimer’s disease. The model efficacy was tested using the ADNI and OASIS datasets which produced an accuracy of 91.83% and 93.85%, respectively. Furthermore, obtained results are compared with similar methods proposed in …

Authors

Faizal Hajamohideen,Noushath Shaffi,Mufti Mahmud,Karthikeyan Subramanian,Arwa Al Sariri,Viswan Vimbi,Abdelhamid Abdesselam

Journal

Brain Informatics

Published Date

2023/12

Non-Negative Matrix Factorization with Scale Data Structure Preservation

The model described in this paper belongs to the family of non-negative matrix factorization methods designed for data representation and dimension reduction. In addition to preserving the data positivity property, it aims also to preserve the structure of data during matrix factorization. The idea is to add, to the NMF cost function, a penalty term to impose a scale relationship between the pairwise similarity matrices of the original and transformed data points. The solution of the new model involves deriving a new parametrized update scheme for the coefficient matrix, which makes it possible to improve the quality of reduced data when used for clustering and classification. The proposed clustering algorithm is compared to some existing NMF-based algorithms and to some manifold learning-based algorithms when applied to some real-life datasets. The obtained results show the effectiveness of the proposed algorithm.

Authors

Rachid Hedjam,Abdelhamid Abdesselam,Abderrahmane Rahiche,Mohamed Cheriet

Journal

arXiv preprint arXiv:2209.10881

Published Date

2022/9/22

Ensemble classifiers for a 4-way classification of Alzheimer’s disease

Machine Learning (ML) techniques remain a massively influential tool in the Computer-Aided Diagnosis (CAD) of several health applications. Mainly due to its ability to rapid learning of end-to-end models accurately using compound data. Recent years have seen an extensive application of Deep Learning (DL) models in solving the 4-way classification of Alzheimer’s Disease (AD) and achieved good results too. However, traditional machine learning classifiers such as KNN, XGBoost, SVM, etc perform either the same or better than the DL models and usually require less data for training. This property is very useful when it comes to medical applications which is characterized by unavailability of large labelled datasets. In this paper, we demonstrate the application of state-of-the-art ML classifiers in the 4-way classification of AD using the OASIS dataset. Furthermore, an ensemble classifier model is proposed based …

Authors

Noushath Shaffi,Faizal Hajamohideen,Abdelhamid Abdesselam,Mufti Mahmud,Karthikeyan Subramanian

Published Date

2022/9/1

Triplet-loss based Siamese convolutional neural network for 4-way classification of Alzheimer’s disease

Alzheimer’s disease (AD) is a neurodegenerative disease that causes irreversible damage to several brain regions including the hippocampus causing impairment in cognition, function and behaviour. Earlier diagnosis of the disease will reduce the suffering of the patients and their family members. Towards that aim, this paper presents a Siamese Convolutional Neural Network (CNN) based model using the Triplet-loss function for the 4-way classification of AD. We evaluated our models using both pre-trained and non-pre-trained CNNs. The models’ efficacy was tested on the OASIS dataset and obtained satisfactory results under a data-scarce real-time environment.

Authors

Noushath Shaffi,Faizal Hajamohideen,Mufti Mahmud,Abdelhamid Abdesselam,Karthikeyan Subramanian,Arwa Al Sariri

Published Date

2022/7/15

Recent CNN-based techniques for breast cancer histology image classification

Histology images are extensively used by pathologists to assess abnormalities and detect malignancy in breast tissues. On the other hand, Convolutional Neural Networks (CNN) are by far, the privileged models for image classification and interpretation. Based on these two facts, we surveyed the recent CNN-based methods for breast cancer histology image analysis. The survey focuses on two major issues usually faced by CNN-based methods namely the design of an appropriate CNN architecture and the lack of a sufficient labelled dataset for training the model. Regarding the design of the CNN architecture, methods examining breast histology images adopt three main approaches: Designing manually from scratch the CNN architecture, using pre-trained models and adopting an automatic architecture design. Methods addressing the lack of labelled datasets are grouped into four categories: methods using pre-trained models, methods using data augmentation, methods adopting weakly supervised learning and those adopting feedforward filter learning. Research works from each category and reported performance are presented in this paper. We conclude the paper by indicating some future research directions related to the analysis of histology images.

Authors

ArunaDevi Karuppasamy,Abdelhamid Abdesselam,Rachid Hedjam,Hamza Zidoum,Maiya Al-Bahri

Journal

The Journal of Engineering Research [TJER]

Published Date

2022/3/31

Robust vegetation segmentation under field conditions using new adaptive weights for hybrid multichannel images based on the Chan-Vese model

This paper proposes a method for detecting vegetation in agricultural images under real field conditions. It includes two modules: The first module constructs a multichannel image by combining four color indices and the L∗ a∗ b∗ color space using Principal Component Analysis (PCA). The second module detects the vegetation by applying an improved Chan-Vese method. In this method, the energy weights are automatically estimated based on the contrast between foreground regions and the background. To speed up the segmentation process a sweeping algorithm is applied. Experimental results demonstrate that our algorithm outperforms ten state-of-the-art methods, yielding higher accuracy, precision, and achieving better recall and F-score rates. The main advantage of the proposed method is that it performs well under different field conditions. On the seven datasets considered in this work, the proposed …

Authors

Yamina Boutiche,Abdelhamid Abdesselam,Nabil Chetih,Mohammed Khorchef,Naim Ramou

Journal

Ecological Informatics

Published Date

2022/12/1

Supervised Class-pairwise NMF for Data Representation and Classification

Various Non-negative Matrix factorization (NMF) based methods add new terms to the cost function to adapt the model to specific tasks, such as clustering, or to preserve some structural properties in the reduced space (e.g., local invariance). The added term is mainly weighted by a hyper-parameter to control the balance of the overall formula to guide the optimization process towards the objective. The result is a parameterized NMF method. However, NMF method adopts unsupervised approaches to estimate the factorizing matrices. Thus, the ability to perform prediction (e.g. classification) using the new obtained features is not guaranteed. The objective of this work is to design an evolutionary framework to learn the hyper-parameter of the parameterized NMF and estimate the factorizing matrices in a supervised way to be more suitable for classification problems. Moreover, we claim that applying NMF-based algorithms separately to different class-pairs instead of applying it once to the whole dataset improves the effectiveness of the matrix factorization process. This results in training multiple parameterized NMF algorithms with different balancing parameter values. A cross-validation combination learning framework is adopted and a Genetic Algorithm is used to identify the optimal set of hyper-parameter values. The experiments we conducted on both real and synthetic datasets demonstrated the effectiveness of the proposed approach.

Authors

Rachid Hedjam,Abdelhamid Abdesselam,Seyed Mohammad Jafar Jalali,Imran Khan,Samir Brahim Belhaouari

Journal

arXiv preprint arXiv:2209.13831

Published Date

2022/9/28

Estimate of the HOMA-IR cut-off value for identifying subjects at risk of insulin resistance using a machine learning approach

ObjectivesThis study describes an unsupervised machine learning approach used to estimate the homeostatic model assessment-insulin resistance (HOMA-IR) cut-off for identifying subjects at risk of IR in a given ethnic group based on the clinical data of a representative sample.MethodsThe approach was applied to analyse the clinical data of individuals with Arab ancestors, which was obtained from a family study conducted in Nizwa, Oman, between January 2000 and December 2004. First, HOMA-IR-correlated variables were identified to which a clustering algorithm was applied. Two clusters having the smallest overlap in their HOMA-IR values were retrieved. These clusters represented the samples of two populations, which are insulin-sensitive subjects and individuals at risk of IR. The cut-off value was estimated from intersections of the Gaussian functions, thereby modelling the HOMA-IR distributions of these …

Authors

Abdelhamid Abdesselam,Hamza Zidoum,Fahd Zadjali,Rachid Hedjam,Aliya Al-Ansari,Riad Bayoumi,Said Al-Yahyaee,Mohammed Hassan,Sulayma Albarwani

Journal

Sultan Qaboos University Medical Journal

Published Date

2021/11

NMF with feature relationship preservation penalty term for clustering problems

The method proposed in this paper belongs to the family of orthogonal non-negative matrix factorization (ONMF) methods designed to solve clustering problems. Unlike some existing ONMF methods that explicitly constrain the orthogonality of the coefficient matrix in the cost function to derive their clustering models, the proposed method integrates it implicitly, so that it results in a new optimization model with a penalty term. The latter is added to impose a scale relationship between the scatter of the cluster centroids and that of the data points. The solution of the new model involves deriving a new parametrized update scheme for the basis matrix, which makes it possible to improve the performance of the clustering by adjusting a parameter. The proposed clustering algorithm, which we call “pairwise Feature Relationship preservation-based NMF” (FR-NMF), is evaluated on several real-life and synthetic datasets and …

Authors

Rahid Hedjam,Abdelhamid Abdesselam,Farid Melgani

Journal

Pattern Recognition

Published Date

2021/4

Change detection in unlabeled optical remote sensing data using Siamese CNN

In this article, we propose a new semisupervised method to detect the changes occurring in a geographical area after a major damage. We detect the changes by processing a pair of optical remote sensing images. The proposed method adopts a patch-based approach, whereby we use a Siamese convolutional neural network (S-CNN), trained with augmented data, to compare successive pairs of patches obtained from the input images. The main contribution of this work lies in developing an S-CNN training phase without resorting to class labels that are actually not available from the input images. We train the S-CNN using genuine and impostor patch-pairs defined in a semisupervised way from the input images. We tested the proposed change detection model on four real datasets and compared its performance to those of two existing models. The obtained results were very promising.

Authors

Rachid Hedjam,Abdelhamid Abdesselam,Farid Melgani

Journal

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing

Published Date

2020/7/14

Fast binary network intrusion detection based on matched filter optimization

Securing networks has become very critical task because of the continued appearance of attacks and the growing number of Internet users. The detection, classification and prevention of attacks are provided by the so-called Intrusion Detection System (IDS). In this article, we have proposed and evaluated a new model of network intrusion detection based on matched filter optimization called NIDeMFO for Network Intrusion Detection based on Matched Filter Optimization. Similar to Linear Discriminant Analysis (LDA), the goal is to design a linear filter that projects data into a space where both classes, normal and attack, are well separated. The difference with LDA is that the margin between the averages of the two classes in the projected space is controlled by a parameter. The proposed detection model is evaluated on the NSL-KDD benchmark. The results show its competitiveness and effectiveness compared to …

Authors

Hajar Saif Alsaadi,Rachid Hedjam,Abderezak Touzene,Abdelhamid Abdessalem

Published Date

2020/2/2

See List of Professors in Abdelhamid Abdesselam University(Sultan Qaboos University)

Abdelhamid Abdesselam FAQs

What is Abdelhamid Abdesselam's h-index at Sultan Qaboos University?

The h-index of Abdelhamid Abdesselam has been 8 since 2020 and 9 in total.

What are Abdelhamid Abdesselam's top articles?

The articles with the titles of

Feed-forward networks using logistic regression and support vector machine for whole-slide breast cancer histopathology image classification

Performance Evaluation of Deep, Shallow and Ensemble Machine Learning Methods for the Automated Classification of Alzheimer's Disease

Entropy in Fuzzy k-Means Algorithm for Multi-view Data

Four-way classification of Alzheimer’s disease using deep Siamese convolutional neural network with triplet-loss function

Non-Negative Matrix Factorization with Scale Data Structure Preservation

Ensemble classifiers for a 4-way classification of Alzheimer’s disease

Triplet-loss based Siamese convolutional neural network for 4-way classification of Alzheimer’s disease

Recent CNN-based techniques for breast cancer histology image classification

...

are the top articles of Abdelhamid Abdesselam at Sultan Qaboos University.

What are Abdelhamid Abdesselam's research interests?

The research interests of Abdelhamid Abdesselam are: Image Processing, Computer Vision, Pattern Recognition, Machine Learning, Deep Learning

What is Abdelhamid Abdesselam's total number of citations?

Abdelhamid Abdesselam has 230 citations in total.

What are the co-authors of Abdelhamid Abdesselam?

The co-authors of Abdelhamid Abdesselam are Mohamed Cheriet, Mufti Mahmud, PhD, Narayanan Kulathuramaiyer, Rachid Hedjam, Kang Leng Chiew, D.N.F Awang Iskandar.

    Co-Authors

    H-index: 56
    Mohamed Cheriet

    Mohamed Cheriet

    École de Technologie Supérieure

    H-index: 47
    Mufti Mahmud, PhD

    Mufti Mahmud, PhD

    Nottingham Trent University

    H-index: 18
    Narayanan Kulathuramaiyer

    Narayanan Kulathuramaiyer

    Universiti Malaysia Sarawak

    H-index: 18
    Rachid Hedjam

    Rachid Hedjam

    Sultan Qaboos University

    H-index: 15
    Kang Leng Chiew

    Kang Leng Chiew

    Universiti Malaysia Sarawak

    H-index: 14
    D.N.F Awang Iskandar

    D.N.F Awang Iskandar

    Universiti Malaysia Sarawak

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