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Abdelaziz A. Abdelhamid

Bio: Abdelaziz A. Abdelhamid is an academic researcher from Ain Shams University. The author has contributed to research in topics: Computer science & Artificial intelligence. The author has an hindex of 5, co-authored 21 publications receiving 83 citations. Previous affiliations of Abdelaziz A. Abdelhamid include Shaqra University & University of Auckland.

Papers published on a yearly basis

Papers
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Journal ArticleDOI
TL;DR: Two algorithms based on transfer learning for feature extraction and meta-heuristic optimization for feature selection and optimization of the parameters of a multi-layer neural network are proposed for improving the classification accuracy of monkeypox images.
Abstract: The world is still trying to recover from the devastation caused by the wide spread of COVID-19, and now the monkeypox virus threatens becoming a worldwide pandemic. Although the monkeypox virus is not as lethal or infectious as COVID-19, numerous countries report new cases daily. Thus, it is not surprising that necessary precautions have not been taken, and it will not be surprising if another worldwide pandemic occurs. Machine learning has recently shown tremendous promise in image-based diagnosis, including cancer detection, tumor cell identification, and COVID-19 patient detection. Therefore, a similar application may be implemented to diagnose monkeypox as it invades the human skin. An image can be acquired and utilized to further diagnose the condition. In this paper, two algorithms are proposed for improving the classification accuracy of monkeypox images. The proposed algorithms are based on transfer learning for feature extraction and meta-heuristic optimization for feature selection and optimization of the parameters of a multi-layer neural network. The GoogleNet deep network is adopted for feature extraction, and the utilized meta-heuristic optimization algorithms are the Al-Biruni Earth radius algorithm, the sine cosine algorithm, and the particle swarm optimization algorithm. Based on these algorithms, a new binary hybrid algorithm is proposed for feature selection, along with a new hybrid algorithm for optimizing the parameters of the neural network. To evaluate the proposed algorithms, a publicly available dataset is employed. The assessment of the proposed optimization of feature selection for monkeypox classification was performed in terms of ten evaluation criteria. In addition, a set of statistical tests was conducted to measure the effectiveness, significance, and robustness of the proposed algorithms. The results achieved confirm the superiority and effectiveness of the proposed methods compared to other optimization methods. The average classification accuracy was 98.8%.

42 citations

Journal ArticleDOI
TL;DR: This study offers an adaptive dynamic sine cosine fitness grey wolf optimizer (ADSCFGWO) for optimizing the parameters of two types of antennas and demonstrates the suggested method’s efficacy and superiority over numerous competing algorithms.
Abstract: This study offers an adaptive dynamic sine cosine fitness grey wolf optimizer (ADSCFGWO) for optimizing the parameters of two types of antennas. The two types of antennas are metamaterial and double T-shape monopoles. The ADSCFGWO algorithm is based on an adaptive dynamic technique and two recently developed and powerful optimization techniques: a modified grey wolf optimization (GWO) based on fitness value and a sine cosine algorithm (SCA). The suggested approach utilizes both algorithms’ capabilities to better balance the exploration and exploitation responsibilities of the optimization process while achieving rapid convergence. First, a new feature selection approach is proposed to choose the most significant features from the metamaterial dataset using the suggested ADSCFGWO-based ensemble model for optimal performance. The ADSCFGWO algorithm also optimizes a bidirectional recurrent neural network (BRNN) to estimate the double T-shape monopole antenna characteristics. Several experiments were undertaken to demonstrate the superiority of the suggested algorithms by comparing their results to those of existing optimization algorithms, feature selectors, and regression models. In addition, a statistical analysis is offered to evaluate the algorithm’s effectiveness and stability. The findings demonstrate the suggested method’s efficacy and superiority over numerous competing algorithms.

28 citations

Journal ArticleDOI
TL;DR: A novel meta-heuristic technique, based on Gravitational Search and Dipper Throated Optimization Algorithms (GSDTO), is used to enhance the transformer faults’ diagnostic accuracy, which was considered a novelty in this work to reduce the misinterpretation of the transformers' faults.
Abstract: Detecting transformer faults is critical to avoid the undesirable loss of transformers from service and ensure utility service continuity. Transformer faults diagnosis can be determined based on dissolved gas analysis (DGA). The DGA traditional techniques, such as Duval triangle, Key gas, Rogers’ ratio, Dornenburg, and IEC code 60599, suffer from poor transformer faults diagnosis. Therefore, recent research has been developed to diagnose transformer fault and the diagnostic accuracy using combined traditional methods of DGA with artificial intelligence and optimization methods. This paper used a novel meta-heuristic technique, based on Gravitational Search and Dipper Throated Optimization Algorithms (GSDTO), to enhance the transformer faults’ diagnostic accuracy, which was considered a novelty in this work to reduce the misinterpretation of the transformer faults. The robustness of the constructed GSDTO-based model was addressed by the statistical study using Wilcoxon’s rank-sum and ANOVA tests. The results revealed that the constructed model enhanced the diagnostic accuracy up to 98.26% for all test cases.

27 citations

Journal ArticleDOI
TL;DR: To fine-tune the hyper-parameters of the LSTM-based deep network, the Al-Biruni Earth Radius (BER) optimization algorithm was employed; thus, the proposed approach is denoted by BER-LSTM.
Abstract: Recent technologies such as artificial intelligence, machine learning, and big data are essential for supporting healthcare monitoring systems, particularly for monitoring Monkeypox confirmed cases. Infected and uninfected cases around the world have contributed to a growing dataset, which is publicly available and can be used by artificial intelligence and machine learning to predict the confirmed cases of Monkeypox at an early stage. Motivated by this, we propose in this paper a new approach for accurate prediction of the Monkeypox confirmed cases based on an optimized Long Short-Term Memory (LSTM) deep network. To fine-tune the hyper-parameters of the LSTM-based deep network, we employed the Al-Biruni Earth Radius (BER) optimization algorithm; thus, the proposed approach is denoted by BER-LSTM. Experimental results show the effectiveness of the proposed approach when assessed using various evaluation criteria, such as Mean Bias Error, which is recorded as (0.06) using BER-LSTM. To prove the superiority of the proposed approach, six different machine learning models are included in the conducted experiments. In addition, four different optimization algorithms are considered for comparison purposes. The results of this comparison confirmed the superiority of the proposed approach. On the other hand, several statistical tests are applied to analyze the stability and significance of the proposed approach. These tests include one-way Analysis of Variance (ANOVA), Wilcoxon, and regression tests. The results of these tests emphasize the robustness, significance, and efficiency of the proposed approach.

26 citations

Journal ArticleDOI
TL;DR: An optimized deep learning model is proposed in which the hyperparameters are optimized to find their best settings and thus achieve more recognition results and a statistical analysis of the achieved results is provided to emphasize the stability of the proposed approach.
Abstract: One of the main challenges facing the current approaches of speech emotion recognition is the lack of a dataset large enough to train the currently available deep learning models properly. Therefore, this paper proposes a new data augmentation algorithm to enrich the speech emotions dataset with more samples through a careful addition of noise fractions. In addition, the hyperparameters of the currently available deep learning models are either handcrafted or adjusted during the training process. However, this approach does not guarantee finding the best settings for these parameters. Therefore, we propose an optimized deep learning model in which the hyperparameters are optimized to find their best settings and thus achieve more recognition results. This deep learning model consists of a convolutional neural network (CNN) composed of four local feature-learning blocks and a long short-term memory (LSTM) layer for learning local and long-term correlations in the log Mel-spectrogram of the input speech samples. To improve the performance of this deep network, the learning rate and label smoothing regularization factor are optimized using the recently emerged stochastic fractal search (SFS)-guided whale optimization algorithm (WOA). The strength of this algorithm is the ability to balance between the exploration and exploitation of the search agents’ positions to guarantee to reach the optimal global solution. To prove the effectiveness of the proposed approach, four speech emotion datasets, namely, IEMOCAP, Emo-DB, RAVDESS, and SAVEE, are incorporated in the conducted experiments. Experimental results confirmed the superiority of the proposed approach when compared with state-of-the-art approaches. Based on the four datasets, the achieved recognition accuracies are 98.13%, 99.76%, 99.47%, and 99.50%, respectively. Moreover, a statistical analysis of the achieved results is provided to emphasize the stability of the proposed approach.

26 citations


Cited by
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Journal ArticleDOI
TL;DR: This article reviews the available trust management techniques in the IoT in a systematic way, including recommendation-based, prediction- based, policy-Based, and reputation-based techniques, which are compared based on some trust metrics such as accuracy, adaptability, availability, heterogeneity, integrity, privacy, reliability, and scalability.
Abstract: Internet of Things (IoT) has been developed as one of the most significant technology in the future of the Internet, in which the physical objects are transformed into smart objects that can be handled and monitored via the Internet. The trust management takes a significant role in the IoT for enabling trustworthy data collection, context-awareness, and enhanced user privacy. Despite the critical significance of trust management techniques in the IoT, there is not any organized and comprehensive study in this field. Therefore, the aim of this article is to review the available methods in this field in a systematic way. In this regard, the selected techniques are categorized into four main classes, including recommendation-based, prediction-based, policy-based, and reputation-based. Then they are discussed and also compared based on some trust metrics, such as accuracy, adaptability, availability, heterogeneity, integrity, privacy, reliability, and scalability. Furthermore, some hints and challenges for further studies are outlined.

93 citations

Journal ArticleDOI
TL;DR: Comparison depicts that the two-way trust management proposals have lower overhead, allow a balanced load distribution, are effective in selecting the right service providers and are more accurate than the conventional one- way trust management systems.

54 citations

Journal ArticleDOI
TL;DR: A novel lip descriptor is presented involving both geometry-based and appearance-based features in this paper and a parallel two-step keyword spotting strategy based on decision fusion is proposed in order to make the best use of audio-visual speech and adapt to diverse noise conditions.
Abstract: Keyword spotting remains a challenge when applied to real-world environments with dramatically changing noise. In recent studies, audio-visual integration methods have demonstrated superiorities since visual speech is not influenced by acoustic noise. However, for visual speech recognition, individual utterance mannerisms can lead to confusion and false recognition. To solve this problem, a novel lip descriptor is presented involving both geometry-based and appearance-based features in this paper. Specifically, a set of geometry-based features is proposed based on an advanced facial landmark localization method. In order to obtain robust and discriminative representation, a spatiotemporal lip feature is put forward concerning similarities among textons and mapping the feature to intra-class subspace. Moreover, a parallel two-step keyword spotting strategy based on decision fusion is proposed in order to make the best use of audio-visual speech and adapt to diverse noise conditions. Weights generated using a neural network combine acoustic and visual contributions. Experimental results on the OuluVS dataset and PKU-AV dataset demonstrate that the proposed lip descriptor shows competitive performance compared to the state of the art. Additionally, the proposed audio-visual keyword spotting (AV-KWS) method based on decision-level fusion significantly improves the noise robustness and attains better performance than feature-level fusion, which is also capable of adapting to various noisy conditions.

47 citations

Journal ArticleDOI
TL;DR: A comprehensive review of the existing literature for IoMT is presented, and the convergence of emerging technologies with security, blockchain and IoMT to visualize the future of tomorrow's applications is discussed.

45 citations

Journal ArticleDOI
TL;DR: Two algorithms based on transfer learning for feature extraction and meta-heuristic optimization for feature selection and optimization of the parameters of a multi-layer neural network are proposed for improving the classification accuracy of monkeypox images.
Abstract: The world is still trying to recover from the devastation caused by the wide spread of COVID-19, and now the monkeypox virus threatens becoming a worldwide pandemic. Although the monkeypox virus is not as lethal or infectious as COVID-19, numerous countries report new cases daily. Thus, it is not surprising that necessary precautions have not been taken, and it will not be surprising if another worldwide pandemic occurs. Machine learning has recently shown tremendous promise in image-based diagnosis, including cancer detection, tumor cell identification, and COVID-19 patient detection. Therefore, a similar application may be implemented to diagnose monkeypox as it invades the human skin. An image can be acquired and utilized to further diagnose the condition. In this paper, two algorithms are proposed for improving the classification accuracy of monkeypox images. The proposed algorithms are based on transfer learning for feature extraction and meta-heuristic optimization for feature selection and optimization of the parameters of a multi-layer neural network. The GoogleNet deep network is adopted for feature extraction, and the utilized meta-heuristic optimization algorithms are the Al-Biruni Earth radius algorithm, the sine cosine algorithm, and the particle swarm optimization algorithm. Based on these algorithms, a new binary hybrid algorithm is proposed for feature selection, along with a new hybrid algorithm for optimizing the parameters of the neural network. To evaluate the proposed algorithms, a publicly available dataset is employed. The assessment of the proposed optimization of feature selection for monkeypox classification was performed in terms of ten evaluation criteria. In addition, a set of statistical tests was conducted to measure the effectiveness, significance, and robustness of the proposed algorithms. The results achieved confirm the superiority and effectiveness of the proposed methods compared to other optimization methods. The average classification accuracy was 98.8%.

42 citations