Tarek Gaber

Tarek Gaber

University of Salford

H-index: 30

Europe-United Kingdom

About Tarek Gaber

Tarek Gaber, With an exceptional h-index of 30 and a recent h-index of 26 (since 2020), a distinguished researcher at University of Salford, specializes in the field of Cyber Security, Machine Learning, Artificial Intelligence, Secure Software Engineering.

His recent articles reflect a diverse array of research interests and contributions to the field:

Exploring the Metaverse: A Novel AI-Based Approach to Medical Training for Dental Students

ONE3A: one-against-all authentication model for smartphone using GAN network and optimization techniques

Integrating ChatGPT into Medical Education: A Combined SEM-ML Approach

An Efficient Hybrid Feature Selection Technique towards Prediction of Suspicious URLs in IoT Environment

FCA-VBN: Fog computing-based authentication scheme for 5G-assisted vehicular blockchain network

Smart Home Privacy: A Scoping Review

FC-LSR: Fog Computing-Based Lightweight Sybil Resistant Scheme in 5G-Enabled Vehicular Networks

RFID-Based Student Identification Card Attendance Monitoring System Check for updates

Tarek Gaber Information

University

University of Salford

Position

Associate Prof. @ Suez Canal University EG Lecturer in Software Engineering

Citations(all)

3210

Citations(since 2020)

2682

Cited By

1246

hIndex(all)

30

hIndex(since 2020)

26

i10Index(all)

54

i10Index(since 2020)

47

Email

University Profile Page

University of Salford

Tarek Gaber Skills & Research Interests

Cyber Security

Machine Learning

Artificial Intelligence

Secure Software Engineering

Top articles of Tarek Gaber

Exploring the Metaverse: A Novel AI-Based Approach to Medical Training for Dental Students

Authors

Said Salloum,Khaled Shaalan,Mohammad Alfaisal,Ayham Salloum,Tarek Gaber

Published Date

2024/1/27

The COVID-19 pandemic significantly disrupted dental education, highlighting the need for innovative remote learning solutions. This study, centered in the United Arab Emirates, explores dental students' perceptions of the Metaverse as an educational tool, contrasting it with traditional digital platforms like Zoom. Our research aims to fill the gap in understanding the effectiveness of Metaverse technology in dental education. Employing Partial Least Squares-Structural Equation Modeling (PLS-SEM) and an Artificial Neural Network (ANN) approach, we analyzed data from 833 students across various institutions. The findings reveal that user adoption decisions in the Metaverse are greatly influenced by User Mobility and Users' Accessibility. The ANN model showed superior accuracy in predicting outcomes compared to other methods. These results contribute to the broader discussion on artificial intelligence …

ONE3A: one-against-all authentication model for smartphone using GAN network and optimization techniques

Authors

Mohamed Meselhy Eltoukhy,Tarek Gaber,Abdulwahab Ali Almazroi,Marwa F Mohamed

Journal

PeerJ Computer Science

Published Date

2024/4/29

This study focuses on addressing computational limits in smartphones by proposing an efficient authentication model that enables implicit authentication without requiring additional hardware and incurring less computational cost. The research explores various wrapper feature selection strategies and classifiers to enhance authentication accuracy while considering smartphone limitations such as hardware constraints, battery life, and memory size. However, the available dataset is small; thus, it cannot support a general conclusion. In this article, a novel implicit authentication model for smartphone users is proposed to address the one-against-all classification problem in smartphone authentication. This model depends on the integration of the conditional tabular generative adversarial network (CTGAN) to generate synthetic data to address the imbalanced dataset and a new proposed feature selection technique based on the Whale Optimization Algorithm (WOA). The model was evaluated using a public dataset (RHU touch mobile keystroke dataset), and the results showed that the WOA with the random forest (RF) classifier achieved the best reduction rate compared to the Harris Hawks Optimization (HHO) algorithm. Additionally, its classification accuracy was found to be the best in mobile user authentication from their touch behavior data. WOA-RF achieved an average accuracy of 99.62±0.40% with a reduction rate averaging 87.85% across ten users, demonstrating its effectiveness in smartphone authentication.

Integrating ChatGPT into Medical Education: A Combined SEM-ML Approach

Authors

Said Salloum,Khaled Shaalan,Raghad Alfaisal,Ayham Salloum,Tarek Gaber

Published Date

2024/1/27

The educational realm has undergone profound transformations due to technological advancements, evolving from conventional classrooms to digital platforms, such as online learning modules and virtual simulations. Within this context, ChatGPT, an innovation in artificial intelligence, stands out for its capacity to enhance and personalize learning experiences in medical education. While the potential of tools like ChatGPT is acknowledged, there is a need to understand the determinants of their acceptance, especially in the backdrop of dynamic medical curricula. Drawing on The Technology Acceptance Model (TAM) and focusing on perceived value, we collected 563 questionnaires across multiple academic institutions. We then analyzed this data using partial least squares-structural equation modeling (PLS-SEM), and machine learning (ML) algorithms. Findings highlighted ChatGPT's pronounced influence on …

An Efficient Hybrid Feature Selection Technique towards Prediction of Suspicious URLs in IoT Environment

Authors

Sanjukta Mohanty,Arup Abhinna Acharya,Tarek Gaber,Namita Panda,Esraa Eldesouky,Ibrahim A Hameed

Journal

IEEE Access

Published Date

2024/4/3

With the growth of IoT, a vast number of devices are connected to the web. Consequently, both users and devices are susceptible to deception by intruders through malicious links leading to the disclosure of personal information. Hence, it is essential to identify suspicious URLs before accessing them. While numerous researchers have proposed several URL detection approaches, the machine learning-based technique stands out as particularly effective because of its ability to detect zero-day attacks; however, its success depends on the type and dimension of features utilized. In earlier research, only the lexical features of URLs were employed for classification to attain high detection speeds. However, this approach does not allow for the retrieval of comprehensive information about a website. Hence, to enhance the security of IoT devices, both lexical and page content-based features of URLs must be considered …

FCA-VBN: Fog computing-based authentication scheme for 5G-assisted vehicular blockchain network

Authors

Abdulwahab Ali Almazroi,Mohammed A Alqarni,Mahmood A Al-Shareeda,Monagi H Alkinani,Alaa Atallah Almazroey,Tarek Gaber

Journal

Internet of Things

Published Date

2024/4/1

Emerging technology known as intelligent transportation systems allows for seamless two-way communication between moving vehicles and stationary infrastructure. Therefore, core security services in a vehicular network consist of encrypting and trusting information packets for intra- and inter-vehicle systems. However, conventional reconciliation methods incur high communication costs and security risks, and channel imperfection causes important extraction disparities. For a 5G-enabled vehicular blockchain network, we suggested a novel fog computing-based authentication approach for the 5G-assisted vehicular blockchain network (FCA-VBN) scheme, which incorporated fog computing and secure authentication to address these issues. The FCA-VBN scheme employs a channel stage response instituted private key extraction technique for terminal key agreement. Using the blockchain’s immutability and …

Smart Home Privacy: A Scoping Review

Authors

Ali Ahmed,Victor Ungureanu,Tarek Gaber,Craig Watterson,Fatma Masmoudi

Published Date

2024/3/22

Privacy concerns in smart home technologies have surged as their adoption becomes ubiquitous. This scoping review paper undertakes an exhaustive examination of the current literature to elucidate the state of privacy within this burgeoning context. Employing a scoping review methodology, we have analysed about 78 peerreviewed articles. Key emergent themes include privacy concerns, trust, user perception, and a range of technical risks and mitigation. Our findings reveal significant gaps in privacy design and protection, establishing this paper as a novel contribution that sets the groundwork for future research. Additionally, it provides practitioners and policymakers with actionable insights for enhancing privacy measures in smart homes. Supplemental material, including a curated database of the reviewed literature and previously published papers, will be available to reviewers to enrich the understanding of …

FC-LSR: Fog Computing-Based Lightweight Sybil Resistant Scheme in 5G-Enabled Vehicular Networks

Authors

Abdulwahab Ali Almazroi,Monagi H Alkinani,Mahmood A Al-Shareeda,Mohammed A Alqarni,Alaa Atallah Almazroey,Tarek Gaber

Journal

IEEE Access

Published Date

2024/2/20

Vehicular networks with Fifth-Generation (5G) are a new form of wireless communication that could greatly benefit society by lowering the number of preventable car accidents and entertaining passengers in a variety of ways. Security threats can compromise the communications transmitted by a vehicle in a vehicular network because of the open nature of these networks. This means that there are potential security and privacy concerns with VANET. Many methods for fixing VANET’s issues have been offered recently. Unfortunately, most of them suffer from significant overhead and security concerns such as Sybil attacks. Therefor, this paper proposes a novel fog computing-based lightweight Sybil resistant attacks, called FC-LSR in 5G-enabled vehicular networks. The proposed FC-LSR scheme makes use of Modified Merkle Patricia Trie (MMPT) in conjunction with Merkle Hash Tree (MHT) to securely store the …

RFID-Based Student Identification Card Attendance Monitoring System Check for updates

Authors

Joseph Bamidele Awotunde,Samarendra Nath Sur,Muqeetat Tolulope Aderinto,Tarek Gaber

Journal

Advances in Communication, Devices and Networking: Proceedings of ICCDN 2022

Published Date

2023/7/7

Abstract Passive (low-frequency) radio-frequency identification (RFID) technology is being used in an increasing number of contactless systems. This project presents contactless attendance system passive radio-frequency identification (RFID)-based approach. RFID is a technology that uses radio waves to send information from an RFID tag attached to an object to a reader for the purpose of monitoring and identifying the object. This technology is one of the most widely used in the world; almost every organization uses RFID-based tracking, identification, and other functions on some level. A contactless RFID-based attendance monitoring system is a less cost-effective and efficient automated solution. The proposed system focuses on an RFID-based attendance monitoring system that uses RFID technology in connection with a programmable logic circuit (such as an Arduino) to address the problems that traditional paper and contact-based attendance systems experience. Each user (student or lecturer) will be given an RFID card. When the user puts the RFID card near the RFID reader, the RFID reader scans the user's data stored on the card, recording the current time using the real-time clock (RTC) module and the RFID tag number (UID) on the SD card. The Arduino uses the SD card module to communicate with the SD card. The buzzer beeps when a tag is scanned, and the system employs a 16× 2 LCD module for display purposes. This

Privacy and security enhancement of smart cities using hybrid deep learning-enabled blockchain

Authors

Joseph Bamidele Awotunde,Tarek Gaber,LV Narasimha Prasad,Sakinat Oluwabukonla Folorunso,Vuyyuru Lakshmi Lalitha

Journal

Scalable Computing: Practice and Experience

Published Date

2023/9/10

The emergence of the Internet of Things (IoT) accelerated the implementation of various smart city applications and initiatives. The rapid adoption of IoT-powered smart cities is faced by a number of security and privacy challenges that hindered their application in areas such as critical infrastructure. One of the most crucial elements of any smart city is safety. Without the right safeguards, bad actors can quickly exploit weak systems to access networks or sensitive data. Security issues are a big worry for smart cities in addition to safety issues. Smart cities become easy targets for attackers attempting to steal data or disrupt services if they are not adequately protected against cyberthreats like malware or distributed denial-of-service (DDoS) attacks. Therefore, in order to safeguard their systems from potential threats, businesses must employ strong security protocols including encryption, authentication, and access control measures. In order to ensure that their network traffic remains secure, organizations should implement powerful network firewalls and intrusion detection systems (IDS). This article proposes a blockchain-supported hybrid Convolutional Neural Network (CNN) with Kernel Principal Component Analysis (KPCA) to provide privacy and security for smart city users and systems. Blockchain is used to provide trust, and CNN enabled with KPCA is used for classifying threats. The proposed solution comprises three steps, preprocessing, feature selection, and classification. The standard features of the datasets used are converted to a numeric format during the preprocessing stage, and the result is sent to KPCA for feature extraction. Feature …

Deep churn prediction method for telecommunication industry

Authors

Lewlisa Saha,Hrudaya Kumar Tripathy,Tarek Gaber,Hatem El-Gohary,El-Sayed M El-kenawy

Journal

Sustainability

Published Date

2023/3/3

Being able to predict the churn rate is the key to success for the telecommunication industry. It is also important for the telecommunication industry to obtain a high profit. Thus, the challenge is to predict the churn percentage of customers with higher accuracy without comprising the profit. In this study, various types of learning strategies are investigated to address this challenge and build a churn predication model. Ensemble learning techniques (Adaboost, random forest (RF), extreme randomized tree (ERT), xgboost (XGB), gradient boosting (GBM), and bagging and stacking), traditional classification techniques (logistic regression (LR), decision tree (DT), and k-nearest neighbor (kNN), and artificial neural network (ANN)), and the deep learning convolutional neural network (CNN) technique have been tested to select the best model for building a customer churn prediction model. The evaluation of the proposed models was conducted using two pubic datasets: Southeast Asian telecom industry, and American telecom market. On both of the datasets, CNN and ANN returned better results than the other techniques. The accuracy obtained on the first dataset using CNN was 99% and using ANN was 98%, and on the second dataset it was 98% and 99%, respectively.

A novel CNN gap layer for growth prediction of palm tree plantlings

Authors

T Ananth Kumar,R Rajmohan,Sunday Adeola Ajagbe,Tarek Gaber,Xiao-Jun Zeng,Fatma Masmoudi

Journal

Plos one

Published Date

2023/8/11

Monitoring palm tree seedlings and plantlings presents a formidable challenge because of the microscopic size of these organisms and the absence of distinguishing morphological characteristics. There is a demand for technical approaches that can provide restoration specialists with palm tree seedling monitoring systems that are high-resolution, quick, and environmentally friendly. It is possible that counting plantlings and identifying them down to the genus level will be an extremely time-consuming and challenging task. It has been demonstrated that convolutional neural networks, or CNNs, are effective in many aspects of image recognition; however, the performance of CNNs differs depending on the application. The performance of the existing CNN-based models for monitoring and predicting plantlings growth could be further improved. To achieve this, a novel Gap Layer modified CNN architecture (GL-CNN) has been proposed with an IoT effective monitoring system and UAV technology. The UAV is employed for capturing plantlings images and the IoT model is utilized for obtaining the ground truth information of the plantlings health. The proposed model is trained to predict the successful and poor seedling growth for a given set of palm tree plantling images. The proposed GL-CNN architecture is novel in terms of defined convolution layers and the gap layer designed for output classification. There are two 64×3 conv layers, two 128×3 conv layers, two 256×3 conv layers and one 512×3 conv layer for processing of input image. The output obtained from the gap layer is modulated using the ReLU classifier for determining the seedling …

Efficient thermal face recognition method using optimized curvelet features for biometric authentication

Authors

Mona AS Ali,Mohamed Meselhy Eltoukhy,Fathimathul Rajeena PP,Tarek Gaber

Journal

PloS one

Published Date

2023/6/26

Biometric technology is becoming increasingly prevalent in several vital applications that substitute traditional password and token authentication mechanisms. Recognition accuracy and computational cost are two important aspects that are to be considered while designing biometric authentication systems. Thermal imaging is proven to capture a unique thermal signature for a person and thus has been used in thermal face recognition. However, the literature did not thoroughly analyse the impact of feature selection on the accuracy and computational cost of face recognition which is an important aspect for limited resources applications like IoT ones. Also, the literature did not thoroughly evaluate the performance metrics of the proposed methods/solutions which are needed for the optimal configuration of the biometric authentication systems. This paper proposes a thermal face-based biometric authentication system. The proposed system comprises five phases: a) capturing the user’s face with a thermal camera, b) segmenting the face region and excluding the background by optimized superpixel-based segmentation technique to extract the region of interest (ROI) of the face, c) feature extraction using wavelet and curvelet transform, d) feature selection by employing bio-inspired optimization algorithms: grey wolf optimizer (GWO), particle swarm optimization (PSO) and genetic algorithm (GA), e) the classification (user identification) performed using classifiers: random forest (RF), k-nearest neighbour (KNN), and naive bayes (NB). Upon the public dataset, Terravic Facial IR, the proposed system was evaluated using the metrics: accuracy …

Optimized and efficient image-based IoT malware detection method

Authors

Amir El-Ghamry,Tarek Gaber,Kamel K Mohammed,Aboul Ella Hassanien

Journal

Electronics

Published Date

2023/1/31

With the widespread use of IoT applications, malware has become a difficult and sophisticated threat. Without robust security measures, a massive volume of confidential and classified data could be exposed to vulnerabilities through which hackers could do various illicit acts. As a result, improved network security mechanisms that can analyse network traffic and detect malicious traffic in real-time are required. In this paper, a novel optimized machine learning image-based IoT malware detection method is proposed using visual representation (i.e., images) of the network traffic. In this method, the ant colony optimizer (ACO)-based feature selection method was proposed to get a minimum number of features while improving the support vector machines (SVMs) classifier’s results (i.e., the malware detection results). Further, the PSO algorithm tuned the SVM parameters of the different kernel functions. Using a public dataset, the experimental results showed that the SVM linear function kernel is the best with an accuracy of 95.56%, recall of 96.43%, precision of 94.12%, and F1_score of 95.26%. Comparing with the literature, it was concluded that bio-inspired techniques, i.e., ACO and PSO, could be used to build an effective and lightweight machine-learning-based malware detection system for the IoT environment.

An improved long short term memory network for intrusion detection

Authors

Asmaa Ahmed Awad,Ahmed Fouad Ali,Tarek Gaber

Journal

Plos one

Published Date

2023/8/1

Over the years, intrusion detection system has played a crucial role in network security by discovering attacks from network traffics and generating an alarm signal to be sent to the security team. Machine learning methods, e.g., Support Vector Machine, K Nearest Neighbour, have been used in building intrusion detection systems but such systems still suffer from low accuracy and high false alarm rate. Deep learning models (e.g., Long Short-Term Memory, LSTM) have been employed in designing intrusion detection systems to address this issue. However, LSTM needs a high number of iterations to achieve high performance. In this paper, a novel, and improved version of the Long Short-Term Memory (ILSTM) algorithm was proposed. The ILSTM is based on the novel integration of the chaotic butterfly optimization algorithm (CBOA) and particle swarm optimization (PSO) to improve the accuracy of the LSTM algorithm. The ILSTM was then used to build an efficient intrusion detection system for binary and multi-class classification cases. The proposed algorithm has two phases: phase one involves training a conventional LSTM network to get initial weights, and phase two involves using the hybrid swarm algorithms, CBOA and PSO, to optimize the weights of LSTM to improve the accuracy. The performance of ILSTM and the intrusion detection system were evaluated using two public datasets (NSL-KDD dataset and LITNET-2020) under nine performance metrics. The results showed that the proposed ILSTM algorithm outperformed the original LSTM and other related deep-learning algorithms regarding accuracy and precision. The ILSTM achieved …

Metaverse-IDS: Deep learning-based intrusion detection system for Metaverse-IoT networks

Authors

Tarek Gaber,Joseph Bamidele Awotunde,Mohamed Torky,Sunday A Ajagbe,Mohammad Hammoudeh,Wei Li

Journal

Internet of Things

Published Date

2023/12/1

Combining the metaverse and the Internet of Things (IoT) will lead to the development of diverse, virtual, and more advanced networks in the future. The integration of IoT networks with the metaverse will enable more meaningful connections between the'real'and'virtual'worlds, allowing for real-time data analysis, access, and processing. However, these metaverse-IoT networks will face numerous security and privacy threats. Intrusion Detection Systems (IDS) offer an effective means of early detection for such attacks. Nevertheless, the metaverse generates substantial volumes of data due to its interactive nature and the multitude of user interactions within virtual environments, posing a computational challenge for building an intrusion detection system. To address this challenge, this paper introduces an innovative intrusion detection system model based on deep learning. This model aims to detect most attacks …

Comparing Object Recognition Models and Studying Hyperparameter Selection for the Detection of Bolts

Authors

Tom Bolton,Julian Bass,Tarek Gaber,Taha Mansouri

Published Date

2023/6/14

The commonly-used method of bolting, used to secure parts of apparatus together, relies on the bolts having a sufficient preload force in order to the ensure mechanical strength. Failing to secure bolted connections to a suitable torque rating can have dangerous consequences. As part of a wider system that might monitor the integrity of bolted connections using artificial intelligence techniques such as machine learning, it is necessary to first identify and isolate the location of the bolt. In this study, we make use of several contemporary machine learning-based object detection algorithms to address the problem of bolt recognition. We use the latest version of You Only Look Once (YOLO) and compare it with algorithms RetinaNet and Faster R-CNN. In doing so, we determine the optimum learning rate for use with a given dataset and make a comparison showing how this particular hyperparameter has a considerable …

KryptosChain—a blockchain-inspired, AI-combined, DNA-encrypted secure information exchange scheme

Authors

Pratyusa Mukherjee,Chittaranjan Pradhan,Hrudaya Kumar Tripathy,Tarek Gaber

Journal

Electronics

Published Date

2023/1/17

Today’s digital world necessitates the adoption of encryption techniques to ensure secure peer-to-peer communication. The sole purpose of this paper is to conglomerate the fundamentals of Blockchain, AI (Artificial Intelligence) and DNA (Deoxyribonucleic Acid) encryption into one proposed scheme, KryptosChain, which is capable of providing a secure information exchange between a sender and his intended receiver. The scheme firstly suggests a DNA-based Huffman coding scheme, which alternatively allocates purines—Adenine (A) and Guanine (G), and pyrimidines—Thymine (T) and Cytosine (C) values, while following the complementary rule to higher and lower branches of the resultant Huffman tree. Inculcation of DNA concepts makes the Huffman coding scheme eight times stronger than the traditional counterpart based on binary—0 and 1 values. After the ciphertext is obtained, the proposed methodology next provides a Blockchain-inspired message exchange scheme that achieves all the principles of security and proves to be immune to common cryptographic attacks even without the deployment of any smart contract, or possessing any cryptocurrency or arriving at any consensus. Lastly, different classifiers were engaged to check the intrusion detection capability of KryptosChain on the NSL-KDD dataset and AI fundamentals. The detailed analysis of the proposed KryptosChain validates its capacity to fulfill its security goals and stands immune to cryptographic attacks. The intrusion possibility curbing concludes that the J84 classifier provides the highest accuracy of 95.84% among several others as discussed in the paper.

Implications of regulatory policy for building secure agile software in Nigeria: A grounded theory

Authors

Abdulhamid A Ardo,Julian M Bass,Tarek Gaber

Journal

The Electronic Journal of Information Systems in Developing Countries

Published Date

2023/11

Nigeria is ranked second worldwide, after India, in reported incidences of cyberattacks. Attackers usually exploit vulnerabilities in software which may not have adequately considered security features during the development process. Agile methods have the potential to increase productivity and ensure faster delivery of software, although they tend to neglect non‐functional requirements such as security. The implementation of government policies, such as the Nigeria Data Protection Regulation (NDPR) Act 2019, impacts the security activities carried out by agile teams. Despite its significance, there is a paucity of research on security issues especially in the Agile Software Development (ASD) domain. To address this gap, a grounded theory study was conducted with 15 agile software practitioners in Nigeria. Based on our analysis of the interview transcripts, we developed a grounded theory of the security …

A novel drone-station matching model in smart cities based on strict preferences

Authors

Debolina Nath,Anjan Bandyopadhyay,Ankit Rana,Tarek Gaber,Aboul Ella Hassanien

Journal

Unmanned Systems

Published Date

2023/7/29

There has been a considerable increase in the use of drones, or unmanned aerial vehicles (UAVs), in recent times, for a wide variety of purposes such as security, surveillance, delivery, search and rescue operations, penetration of inaccessible or unsafe areas, etc. The increasing number of drones working in an area poses a challenge to finding a suitable charging or resting station for each drone after completing its task or when it goes low on its charge. The classical methodology followed by drones is to return to their pre-assigned charging station every time it requires a station. This approach is found to be inefficient as it leads to an unnecessary waste of time as well as power, which could be easily saved if the drone is allotted a nearby charging station that is free. Therefore, we propose a drone-allocation model based on a preference matching algorithm where the drones will be allotted the nearest available …

Innovative Artificial Intelligence-Based Internet of Things for Smart Cities and Smart Homes

Authors

Tien-Wen Sung,Chao-Yang Lee,Tarek Gaber,Hamed Nassar

Published Date

2023/6/8

The Internet of Things (IoT) consists of interconnected things with built-in and function-oriented sensors, essentially constituting a network of physical devices. These devices have the ability to gather measurement or observation data and then communicate or exchange data with each other by communication networks. IoT systems can be applied in various fields to improve human life, especially applications for smarter cities or smarter homes.However, basic IoT systems have been unable to meet the requirements of a modern smart city or smart home that features various and complex functionality with hybrid communication networks. Modern applications of IoT systems must be assisted by powerful artificial intelligence (AI) technology to process and analyze big data and deal with the problems of finding an optimal solution, making the best decision, detecting events, and identifying objects. Artificial intelligence simulates natural intelligence as exhibited by humans or animals and can make the system capable of performing tasks without the assistance of humans and even perform tasks better than humans can. Modern AI technology usually utilizes evolutionary computation, nature-inspired algorithms, machine learning, or deep learning to solve the problems of optimization, decision making, event detection, and object identification. The integration of IoT systems and AI technology is very suitable for interconnected things to enhance intelligence, thus the artificial intelligence of things (AIoT),

See List of Professors in Tarek Gaber University(University of Salford)

Tarek Gaber FAQs

What is Tarek Gaber's h-index at University of Salford?

The h-index of Tarek Gaber has been 26 since 2020 and 30 in total.

What are Tarek Gaber's top articles?

The articles with the titles of

Exploring the Metaverse: A Novel AI-Based Approach to Medical Training for Dental Students

ONE3A: one-against-all authentication model for smartphone using GAN network and optimization techniques

Integrating ChatGPT into Medical Education: A Combined SEM-ML Approach

An Efficient Hybrid Feature Selection Technique towards Prediction of Suspicious URLs in IoT Environment

FCA-VBN: Fog computing-based authentication scheme for 5G-assisted vehicular blockchain network

Smart Home Privacy: A Scoping Review

FC-LSR: Fog Computing-Based Lightweight Sybil Resistant Scheme in 5G-Enabled Vehicular Networks

RFID-Based Student Identification Card Attendance Monitoring System Check for updates

...

are the top articles of Tarek Gaber at University of Salford.

What are Tarek Gaber's research interests?

The research interests of Tarek Gaber are: Cyber Security, Machine Learning, Artificial Intelligence, Secure Software Engineering

What is Tarek Gaber's total number of citations?

Tarek Gaber has 3,210 citations in total.

What are the co-authors of Tarek Gaber?

The co-authors of Tarek Gaber are Aboul Ella Hassanien Ali, Dr. Muhammad Alshurideh, Prof. Ahmad Taher Azar, IEEE and IRSS Senior Member, ACM, ISA and IFAC member., Said A. Salloum, Abdelhameed Ibrahim, Ahmad Qasim Mohammad AlHamad.

    Co-Authors

    H-index: 87
    Aboul Ella Hassanien Ali

    Aboul Ella Hassanien Ali

    Cairo University

    H-index: 78
    Dr. Muhammad Alshurideh

    Dr. Muhammad Alshurideh

    University of Sharjah

    H-index: 70
    Prof. Ahmad Taher Azar, IEEE and IRSS Senior Member, ACM, ISA and IFAC member.

    Prof. Ahmad Taher Azar, IEEE and IRSS Senior Member, ACM, ISA and IFAC member.

    Prince Sultan University

    H-index: 67
    Said A. Salloum

    Said A. Salloum

    University of Sharjah

    H-index: 61
    Abdelhameed Ibrahim

    Abdelhameed Ibrahim

    Mansoura University

    H-index: 26
    Ahmad  Qasim Mohammad AlHamad

    Ahmad Qasim Mohammad AlHamad

    University of Sharjah

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