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Dr. Aiiad Albeshri
Associate Professor

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0 Cloud Computing
0 Cloud Security
0 Information Security
0 Big Data Security

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Journal article
Published: 19 August 2021 in Algorithms
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Many smart city and society applications such as smart health (elderly care, medical applications), smart surveillance, sports, and robotics require the recognition of user activities, an important class of problems known as human activity recognition (HAR). Several issues have hindered progress in HAR research, particularly due to the emergence of fog and edge computing, which brings many new opportunities (a low latency, dynamic and real-time decision making, etc.) but comes with its challenges. This paper focuses on addressing two important research gaps in HAR research: (i) improving the HAR prediction accuracy and (ii) managing the frequent changes in the environment and data related to user activities. To address this, we propose an HAR method based on Soft-Voting and Self-Learning (SVSL). SVSL uses two strategies. First, to enhance accuracy, it combines the capabilities of Deep Learning (DL), Generalized Linear Model (GLM), Random Forest (RF), and AdaBoost classifiers using soft-voting. Second, to classify the most challenging data instances, the SVSL method is equipped with a self-training mechanism that generates training data and retrains itself. We investigate the performance of our proposed SVSL method using two publicly available datasets on six human activities related to lying, sitting, and walking positions. The first dataset consists of 562 features and the second dataset consists of five features. The data are collected using the accelerometer and gyroscope smartphone sensors. The results show that the proposed method provides 6.26%, 1.75%, 1.51%, and 4.40% better prediction accuracy (average over the two datasets) compared to GLM, DL, RF, and AdaBoost, respectively. We also analyze and compare the class-wise performance of the SVSL methods with that of DL, GLM, RF, and AdaBoost.

ACS Style

Aiiad Albeshri. SVSL: A Human Activity Recognition Method Using Soft-Voting and Self-Learning. Algorithms 2021, 14, 245 .

AMA Style

Aiiad Albeshri. SVSL: A Human Activity Recognition Method Using Soft-Voting and Self-Learning. Algorithms. 2021; 14 (8):245.

Chicago/Turabian Style

Aiiad Albeshri. 2021. "SVSL: A Human Activity Recognition Method Using Soft-Voting and Self-Learning." Algorithms 14, no. 8: 245.

Journal article
Published: 27 July 2021 in Applied Mathematics and Nonlinear Sciences
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As a special commodity with high-tech characteristics required by national development, aviation equipment has the characteristics of confidentiality, long cycle and high risk during the production and research and development. Therefore, the supply chain of its manufacturing and research and development is the focus of national construction and development. On the basis of understanding the current development status of aviation equipment application, this paper analyzes its evaluation system and feedback basic mode through tree vector multiplication and X-0-1 determinant feedback basic mode calculation method, and puts forward corresponding improvement strategies in combination with specific contents, so as to ensure each interest subject to obtain more benefits at the same time. A new management method is proposed for the development of aviation equipment in the future.

ACS Style

Pei Zhang; Aiiad A. Albeshri; Mohammad Salem Oudat. Study on Establishment and Improvement Strategy of Aviation Equipment. Applied Mathematics and Nonlinear Sciences 2021, {"content-, 1 .

AMA Style

Pei Zhang, Aiiad A. Albeshri, Mohammad Salem Oudat. Study on Establishment and Improvement Strategy of Aviation Equipment. Applied Mathematics and Nonlinear Sciences. 2021; {"content- ():1.

Chicago/Turabian Style

Pei Zhang; Aiiad A. Albeshri; Mohammad Salem Oudat. 2021. "Study on Establishment and Improvement Strategy of Aviation Equipment." Applied Mathematics and Nonlinear Sciences {"content-, no. : 1.

Journal article
Published: 27 June 2021 in Future Internet
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Mobile ad hoc networks (MANETs) play a highly significant role in the Internet of Things (IoT) for managing node mobility. MANET opens the pathway for different IoT-based communication systems with effective abilities for a variety of applications in several domains. In IoT-based systems, it provides the self-formation and self-connection of networks. A key advantage of MANETs is that any device or node can freely join or leave the network; however, this makes the networks and applications vulnerable to security attacks. Thus, authentication plays an essential role in protecting the network or system from several security attacks. Consequently, secure communication is an important prerequisite for nodes in MANETs. The main problem is that the node moving from one group to another may be attacked on the way by misleading the device to join the neighboring group. To address this, in this paper, we present an authentication mechanism based on image hashing where the network administrator allows the crosschecking of the identity image of a soldier (i.e., a node) in the joining group. We propose the node joining and node migration algorithms where authentication is involved to ensure secure identification. The simulation tool NS-2 is employed to conduct extensive simulations for extracting the results from the trace files. The results demonstrate the effectiveness of the proposed scheme based on the memory storage communication overhead and computational cost. In our scheme, the attack can be detected effectively and also provides a highly robust assurance.

ACS Style

Aiiad Albeshri. An Image Hashing-Based Authentication and Secure Group Communication Scheme for IoT-Enabled MANETs. Future Internet 2021, 13, 166 .

AMA Style

Aiiad Albeshri. An Image Hashing-Based Authentication and Secure Group Communication Scheme for IoT-Enabled MANETs. Future Internet. 2021; 13 (7):166.

Chicago/Turabian Style

Aiiad Albeshri. 2021. "An Image Hashing-Based Authentication and Secure Group Communication Scheme for IoT-Enabled MANETs." Future Internet 13, no. 7: 166.

Systematic review
Published: 20 June 2021 in Applied Sciences
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Internet of Things (IoT) is promising technology that brings tremendous benefits if used optimally. At the same time, it has resulted in an increase in cybersecurity risks due to the lack of security for IoT devices. IoT botnets, for instance, have become a critical threat; however, systematic and comprehensive studies analyzing the importance of botnet detection methods are limited in the IoT environment. Thus, this study aimed to identify, assess and provide a thoroughly review of experimental works on the research relevant to the detection of IoT botnets. To accomplish this goal, a systematic literature review (SLR), an effective method, was applied for gathering and critically reviewing research papers. This work employed three research questions on the detection methods used to detect IoT botnets, the botnet phases and the different malicious activity scenarios. The authors analyzed the nominated research and the key methods related to them. The detection methods have been classified based on the techniques used, and the authors investigated the botnet phases during which detection is accomplished. This research procedure was used to create a source of foundational knowledge of IoT botnet detection methods. As a result of this study, the authors analyzed the current research gaps and suggest future research directions.

ACS Style

Majda Wazzan; Daniyal Algazzawi; Omaima Bamasaq; Aiiad Albeshri; Li Cheng. Internet of Things Botnet Detection Approaches: Analysis and Recommendations for Future Research. Applied Sciences 2021, 11, 5713 .

AMA Style

Majda Wazzan, Daniyal Algazzawi, Omaima Bamasaq, Aiiad Albeshri, Li Cheng. Internet of Things Botnet Detection Approaches: Analysis and Recommendations for Future Research. Applied Sciences. 2021; 11 (12):5713.

Chicago/Turabian Style

Majda Wazzan; Daniyal Algazzawi; Omaima Bamasaq; Aiiad Albeshri; Li Cheng. 2021. "Internet of Things Botnet Detection Approaches: Analysis and Recommendations for Future Research." Applied Sciences 11, no. 12: 5713.

Journal article
Published: 01 June 2021 in Egyptian Informatics Journal
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The safety of cloud services within a Trust Model System (TMS) is certainly compromised by a lack of defense against security threats as well as by inaccuracy of the trust results. Our proposed model addresses well-known security threats to the reputation trust model system, and is shown to deal with all possible potential attack threats, such as Sybil, on–off, and collusion attacks, by specifying the identity of users and tracking activities undertaken by them in order to easily track unauthorized consumers or attackers and to provide proof of any kind of data leakage. The TMS can also oversee the authorization of whoever uploads feedback into the system. It can also identify invalid feedback and discard it from the system. The algorithms of the TMS first establish a variety of trust criteria in which trustworthiness is calculated. Then, feedback from the cloud service provider nodes is accepted only according to the rules of the TMS. A consumer’s trust value is finally computed using a flexible system capable of guaranteeing a good balance of consumer trust and owners' feedback. Furthermore, a majority of the existing TMS models do not take full account of interaction importance, thus impeding the accuracy of the trust values, a shortcoming that has been rectified in our proposed model.

ACS Style

Salah T. Alshammari; Aiiad Albeshri; Khalid Alsubhi. Building a trust model system to avoid cloud services reputation attacks. Egyptian Informatics Journal 2021, 1 .

AMA Style

Salah T. Alshammari, Aiiad Albeshri, Khalid Alsubhi. Building a trust model system to avoid cloud services reputation attacks. Egyptian Informatics Journal. 2021; ():1.

Chicago/Turabian Style

Salah T. Alshammari; Aiiad Albeshri; Khalid Alsubhi. 2021. "Building a trust model system to avoid cloud services reputation attacks." Egyptian Informatics Journal , no. : 1.

Journal article
Published: 24 April 2021 in Sensors
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Digital societies could be characterized by their increasing desire to express themselves and interact with others. This is being realized through digital platforms such as social media that have increasingly become convenient and inexpensive sensors compared to physical sensors in many sectors of smart societies. One such major sector is road transportation, which is the backbone of modern economies and costs globally 1.25 million deaths and 50 million human injuries annually. The cutting-edge on big data-enabled social media analytics for transportation-related studies is limited. This paper brings a range of technologies together to detect road traffic-related events using big data and distributed machine learning. The most specific contribution of this research is an automatic labelling method for machine learning-based traffic-related event detection from Twitter data in the Arabic language. The proposed method has been implemented in a software tool called Iktishaf+ (an Arabic word meaning discovery) that is able to detect traffic events automatically from tweets in the Arabic language using distributed machine learning over Apache Spark. The tool is built using nine components and a range of technologies including Apache Spark, Parquet, and MongoDB. Iktishaf+ uses a light stemmer for the Arabic language developed by us. We also use in this work a location extractor developed by us that allows us to extract and visualize spatio-temporal information about the detected events. The specific data used in this work comprises 33.5 million tweets collected from Saudi Arabia using the Twitter API. Using support vector machines, naïve Bayes, and logistic regression-based classifiers, we are able to detect and validate several real events in Saudi Arabia without prior knowledge, including a fire in Jeddah, rains in Makkah, and an accident in Riyadh. The findings show the effectiveness of Twitter media in detecting important events with no prior knowledge about them.

ACS Style

Ebtesam Alomari; Iyad Katib; Aiiad Albeshri; Tan Yigitcanlar; Rashid Mehmood. Iktishaf+: A Big Data Tool with Automatic Labeling for Road Traffic Social Sensing and Event Detection Using Distributed Machine Learning. Sensors 2021, 21, 2993 .

AMA Style

Ebtesam Alomari, Iyad Katib, Aiiad Albeshri, Tan Yigitcanlar, Rashid Mehmood. Iktishaf+: A Big Data Tool with Automatic Labeling for Road Traffic Social Sensing and Event Detection Using Distributed Machine Learning. Sensors. 2021; 21 (9):2993.

Chicago/Turabian Style

Ebtesam Alomari; Iyad Katib; Aiiad Albeshri; Tan Yigitcanlar; Rashid Mehmood. 2021. "Iktishaf+: A Big Data Tool with Automatic Labeling for Road Traffic Social Sensing and Event Detection Using Distributed Machine Learning." Sensors 21, no. 9: 2993.

Journal article
Published: 30 March 2021 in Sustainability
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SARS-CoV-2, a tiny virus, is severely affecting the social, economic, and environmental sustainability of our planet, causing infections and deaths (2,674,151 deaths, as of 17 March 2021), relationship breakdowns, depression, economic downturn, riots, and much more. The lessons that have been learned from good practices by various countries include containing the virus rapidly; enforcing containment measures; growing COVID-19 testing capability; discovering cures; providing stimulus packages to the affected; easing monetary policies; developing new pandemic-related industries; support plans for controlling unemployment; and overcoming inequalities. Coordination and multi-term planning have been found to be the key among the successful national and global endeavors to fight the pandemic. The current research and practice have mainly focused on specific aspects of COVID-19 response. There is a need to automate the learning process such that we can learn from good and bad practices during pandemics and normal times. To this end, this paper proposes a technology-driven framework, iResponse, for coordinated and autonomous pandemic management, allowing pandemic-related monitoring and policy enforcement, resource planning and provisioning, and data-driven planning and decision-making. The framework consists of five modules: Monitoring and Break-the-Chain, Cure Development and Treatment, Resource Planner, Data Analytics and Decision Making, and Data Storage and Management. All modules collaborate dynamically to make coordinated and informed decisions. We provide the technical system architecture of a system based on the proposed iResponse framework along with the design details of each of its five components. The challenges related to the design of the individual modules and the whole system are discussed. We provide six case studies in the paper to elaborate on the different functionalities of the iResponse framework and how the framework can be implemented. These include a sentiment analysis case study, a case study on the recognition of human activities, and four case studies using deep learning and other data-driven methods to show how to develop sustainability-related optimal strategies for pandemic management using seven real-world datasets. A number of important findings are extracted from these case studies.

ACS Style

Furqan Alam; Ahmed Almaghthawi; Iyad Katib; Aiiad Albeshri; Rashid Mehmood. iResponse: An AI and IoT-Enabled Framework for Autonomous COVID-19 Pandemic Management. Sustainability 2021, 13, 3797 .

AMA Style

Furqan Alam, Ahmed Almaghthawi, Iyad Katib, Aiiad Albeshri, Rashid Mehmood. iResponse: An AI and IoT-Enabled Framework for Autonomous COVID-19 Pandemic Management. Sustainability. 2021; 13 (7):3797.

Chicago/Turabian Style

Furqan Alam; Ahmed Almaghthawi; Iyad Katib; Aiiad Albeshri; Rashid Mehmood. 2021. "iResponse: An AI and IoT-Enabled Framework for Autonomous COVID-19 Pandemic Management." Sustainability 13, no. 7: 3797.

Journal article
Published: 17 March 2021 in Symmetry
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Cloud data storage is revolutionary because it eliminates the need for additional hardware, which is often costly, inconvenient, and requires additional space. Cloud data storage allows data owners to store large amounts of data in a flexible way and at low cost. The number of online cloud storage services and their consumers has therefore increased dramatically. However, ensuring the privacy and security of data on a digital platform is often a challenge. A cryptographic task-role-based access control (T-RBAC) approach can be used to protect data privacy. This approach ensures the accessibility of data for authorized consumers and keeps it safe from unauthorized consumers. However, this type of cryptographic approach does not address the issue of trust. In this paper, we propose a comprehensive trust model integrated with a cryptographic T-RBAC to enhance the privacy and security of data stored in cloud storage systems, and suggests that trust models involve inheritance and hierarchy in the roles and tasks of trustworthiness evaluation, where this study aims to identify the most feasible solution for the trust issue in T-RBAC approaches. Risk evaluations regarding other possible flaws of the design are also performed. The proposed design can decrease risk by providing high security for cloud storage systems and improve the quality of decisions of cloud operators and data owners.

ACS Style

Salah Alshammari; Aiiad Albeshri; Khalid Alsubhi. Integrating a High-Reliability Multicriteria Trust Evaluation Model with Task Role-Based Access Control for Cloud Services. Symmetry 2021, 13, 492 .

AMA Style

Salah Alshammari, Aiiad Albeshri, Khalid Alsubhi. Integrating a High-Reliability Multicriteria Trust Evaluation Model with Task Role-Based Access Control for Cloud Services. Symmetry. 2021; 13 (3):492.

Chicago/Turabian Style

Salah Alshammari; Aiiad Albeshri; Khalid Alsubhi. 2021. "Integrating a High-Reliability Multicriteria Trust Evaluation Model with Task Role-Based Access Control for Cloud Services." Symmetry 13, no. 3: 492.

Journal article
Published: 10 March 2021 in IEEE Access
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Internet of Things Driven Data Analytics (IoT-DA) has the potential to excel data-driven operationalisation of smart environments. However, limited research exists on how IoT-DA applications are designed, implemented, operationalised, and evolved in the context of software and system engineering life-cycle. This article empirically derives a framework that could be used to systematically investigate the role of software engineering (SE) processes and their underlying practices to engineer IoT-DA applications. First, using existing frameworks and taxonomies, we develop an evaluation framework to evaluate software processes, methods, and other artefacts of SE for IoT-DA. Secondly, we perform a systematic mapping study to qualitatively select 16 processes (from academic research and industrial solutions) of SE for IoT-DA. Thirdly, we apply our developed evaluation framework based on 17 distinct criterion (a.k.a. process activities) for fine-grained investigation of each of the 16 SE processes. Fourthly, we apply our proposed framework on a case study to demonstrate development of an IoT-DA healthcare application. Finally, we highlight key challenges, recommended practices, and the lessons learnt based on framework’s support for process-centric software engineering of IoT-DA. The results of this research can facilitate researchers and practitioners to engineer emerging and next-generation of IoT-DA software applications.

ACS Style

Aakash Ahmad; Mahdi Fahmideh; Ahmed B. Altamimi; Iyad Katib; Aiiad Albeshri; Abdulrahman Alreshidi; Adwan Alownie Alanazi; Rashid Mehmood. Software Engineering for IoT-Driven Data Analytics Applications. IEEE Access 2021, PP, 1 -1.

AMA Style

Aakash Ahmad, Mahdi Fahmideh, Ahmed B. Altamimi, Iyad Katib, Aiiad Albeshri, Abdulrahman Alreshidi, Adwan Alownie Alanazi, Rashid Mehmood. Software Engineering for IoT-Driven Data Analytics Applications. IEEE Access. 2021; PP (99):1-1.

Chicago/Turabian Style

Aakash Ahmad; Mahdi Fahmideh; Ahmed B. Altamimi; Iyad Katib; Aiiad Albeshri; Abdulrahman Alreshidi; Adwan Alownie Alanazi; Rashid Mehmood. 2021. "Software Engineering for IoT-Driven Data Analytics Applications." IEEE Access PP, no. 99: 1-1.

Article
Published: 22 February 2021 in Physical Review E
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Graph embedding methods are becoming increasingly popular in the machine learning community, where they are widely used for tasks such as node classification and link prediction. Embedding graphs in geometric spaces should aid the identification of network communities as well because nodes in the same community should be projected close to each other in the geometric space, where they can be detected via standard data clustering algorithms. In this paper, we test the ability of several graph embedding techniques to detect communities on benchmark graphs. We compare their performance against that of traditional community detection algorithms. We find that the performance is comparable, if the parameters of the embedding techniques are suitably chosen. However, the optimal parameter set varies with the specific features of the benchmark graphs, like their size, whereas popular community detection algorithms do not require any parameter. So, it is not possible to indicate beforehand good parameter sets for the analysis of real networks. This finding, along with the high computational cost of embedding a network and grouping the points, suggests that, for community detection, current embedding techniques do not represent an improvement over network clustering algorithms.

ACS Style

Aditya Tandon; Aiiad Albeshri; Vijey Thayananthan; Wadee Alhalabi; Filippo Radicchi; Santo Fortunato. Community detection in networks using graph embeddings. Physical Review E 2021, 103, 022316 .

AMA Style

Aditya Tandon, Aiiad Albeshri, Vijey Thayananthan, Wadee Alhalabi, Filippo Radicchi, Santo Fortunato. Community detection in networks using graph embeddings. Physical Review E. 2021; 103 (2):022316.

Chicago/Turabian Style

Aditya Tandon; Aiiad Albeshri; Vijey Thayananthan; Wadee Alhalabi; Filippo Radicchi; Santo Fortunato. 2021. "Community detection in networks using graph embeddings." Physical Review E 103, no. 2: 022316.

Journal article
Published: 28 January 2021 in Computer Communications
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Internet of Drones (IoDs) is getting growing interest of researchers due to its applicability in wide range of applications for transportation, weather monitoring, emergency monitoring for flood, earth quake, healthcare and road hazards. To update the data about emergency situation, a real-time data sharing is mandatory. However, regular message transmission by various drones may not only overwhelm a central server but it also causes congestion on the network. It is mandatory to reduce messaging cost and congestion. This paper presents a fog-assisted congestion avoidance approach for Smooth Message Dissemination (SMD). We present a message forwarding algorithm for congestion avoidance to select the appropriate next-hop node using layered model. This model is based on various layers having drones. In first phase, it looks for an appropriate drone in a layer near the fog server for message forwarding. In next step, the drone is identified in nearby layers to forward the emergency message to next-hop to further locate the group head as per priority. It is a drone that has less distance towards fog server and inform in its one-hop circle. It can stop forwarding message after delivering it to fog server. Finally, the fog server disseminates information timely towards upper layers for necessary actions for emergency situations. The performance of the proposed approach is validated through extensive simulations using NS 2.35. Results prove the dominance of SMD over counterparts in terms of messaging overhead, packet delivery ratio, throughput, energy consumption and average delay. Proposed SMD improves PDR by 85% and message overhead cost by 91% as compared to counterparts.

ACS Style

Shumayla Yaqoob; Ata Ullah; Muhammad Awais; Iyad Katib; Aiiad Albeshri; Rashid Mehmood; Mohsin Raza; Saif Ul Islam; Joel J.P.C. Rodrigues. Novel congestion avoidance scheme for Internet of Drones. Computer Communications 2021, 169, 202 -210.

AMA Style

Shumayla Yaqoob, Ata Ullah, Muhammad Awais, Iyad Katib, Aiiad Albeshri, Rashid Mehmood, Mohsin Raza, Saif Ul Islam, Joel J.P.C. Rodrigues. Novel congestion avoidance scheme for Internet of Drones. Computer Communications. 2021; 169 ():202-210.

Chicago/Turabian Style

Shumayla Yaqoob; Ata Ullah; Muhammad Awais; Iyad Katib; Aiiad Albeshri; Rashid Mehmood; Mohsin Raza; Saif Ul Islam; Joel J.P.C. Rodrigues. 2021. "Novel congestion avoidance scheme for Internet of Drones." Computer Communications 169, no. : 202-210.

Journal article
Published: 01 January 2021 in International Journal of Environmental Research and Public Health
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Today’s societies are connected to a level that has never been seen before. The COVID-19 pandemic has exposed the vulnerabilities of such an unprecedently connected world. As of 19 November 2020, over 56 million people have been infected with nearly 1.35 million deaths, and the numbers are growing. The state-of-the-art social media analytics for COVID-19-related studies to understand the various phenomena happening in our environment are limited and require many more studies. This paper proposes a software tool comprising a collection of unsupervised Latent Dirichlet Allocation (LDA) machine learning and other methods for the analysis of Twitter data in Arabic with the aim to detect government pandemic measures and public concerns during the COVID-19 pandemic. The tool is described in detail, including its architecture, five software components, and algorithms. Using the tool, we collect a dataset comprising 14 million tweets from the Kingdom of Saudi Arabia (KSA) for the period 1 February 2020 to 1 June 2020. We detect 15 government pandemic measures and public concerns and six macro-concerns (economic sustainability, social sustainability, etc.), and formulate their information-structural, temporal, and spatio-temporal relationships. For example, we are able to detect the timewise progression of events from the public discussions on COVID-19 cases in mid-March to the first curfew on 22 March, financial loan incentives on 22 March, the increased quarantine discussions during March–April, the discussions on the reduced mobility levels from 24 March onwards, the blood donation shortfall late March onwards, the government’s 9 billion SAR (Saudi Riyal) salary incentives on 3 April, lifting the ban on five daily prayers in mosques on 26 May, and finally the return to normal government measures on 29 May 2020. These findings show the effectiveness of the Twitter media in detecting important events, government measures, public concerns, and other information in both time and space with no earlier knowledge about them.

ACS Style

Ebtesam AlOmari; Iyad Katib; Aiiad Albeshri; Rashid Mehmood. COVID-19: Detecting Government Pandemic Measures and Public Concerns from Twitter Arabic Data Using Distributed Machine Learning. International Journal of Environmental Research and Public Health 2021, 18, 282 .

AMA Style

Ebtesam AlOmari, Iyad Katib, Aiiad Albeshri, Rashid Mehmood. COVID-19: Detecting Government Pandemic Measures and Public Concerns from Twitter Arabic Data Using Distributed Machine Learning. International Journal of Environmental Research and Public Health. 2021; 18 (1):282.

Chicago/Turabian Style

Ebtesam AlOmari; Iyad Katib; Aiiad Albeshri; Rashid Mehmood. 2021. "COVID-19: Detecting Government Pandemic Measures and Public Concerns from Twitter Arabic Data Using Distributed Machine Learning." International Journal of Environmental Research and Public Health 18, no. 1: 282.

Article
Published: 30 November 2020 in The Journal of Supercomputing
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Sparse linear algebra is central to many areas of engineering, science, and business. The community has done considerable work on proposing new methods for sparse matrix-vector multiplication (SpMV) computations and iterative sparse solvers on graphical processing units (GPUs). Due to vast variations in matrix features, no single method performs well across all sparse matrices. A few tools on automatic prediction of best-performing SpMV kernels have emerged recently and require many more efforts to fully utilize their potential. The utilization of a GPU by the existing SpMV kernels is far from its full capacity. Moreover, the development and performance analysis of SpMV techniques on GPUs have not been studied in sufficient depth. This paper proposes DIESEL, a deep learning-based tool that predicts and executes the best performing SpMV kernel for a given matrix using a feature set carefully devised by us through rigorous empirical and mathematical instruments. The dataset comprises 1056 matrices from 26 different real-life application domains including computational fluid dynamics, materials, electromagnetics, economics, and more. We propose a range of new metrics and methods for performance analysis, visualization, and comparison of SpMV tools. DIESEL provides better performance with its accuracy \(88.2\%\), workload accuracy \(91.96\%\), and average relative loss \(4.4\%\), compared to \(85.9\%\), \(85.31\%\), and \(7.65\%\) by the next best performing artificial intelligence (AI)-based SpMV tool. The extensive results and analyses presented in this paper provide several key insights into the performance of the SpMV tools and how these relate to the matrix datasets and the performance metrics, allowing the community to further improve and compare basic and AI-based SpMV tools in the future.

ACS Style

Thaha Mohammed; Aiiad Albeshri; Iyad Katib; Rashid Mehmood. DIESEL: A novel deep learning-based tool for SpMV computations and solving sparse linear equation systems. The Journal of Supercomputing 2020, 77, 6313 -6355.

AMA Style

Thaha Mohammed, Aiiad Albeshri, Iyad Katib, Rashid Mehmood. DIESEL: A novel deep learning-based tool for SpMV computations and solving sparse linear equation systems. The Journal of Supercomputing. 2020; 77 (6):6313-6355.

Chicago/Turabian Style

Thaha Mohammed; Aiiad Albeshri; Iyad Katib; Rashid Mehmood. 2020. "DIESEL: A novel deep learning-based tool for SpMV computations and solving sparse linear equation systems." The Journal of Supercomputing 77, no. 6: 6313-6355.

Journal article
Published: 13 October 2020 in Electronics
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Graphics processing units (GPUs) have delivered a remarkable performance for a variety of high performance computing (HPC) applications through massive parallelism. One such application is sparse matrix-vector (SpMV) computations, which is central to many scientific, engineering, and other applications including machine learning. No single SpMV storage or computation scheme provides consistent and sufficiently high performance for all matrices due to their varying sparsity patterns. An extensive literature review reveals that the performance of SpMV techniques on GPUs has not been studied in sufficient detail. In this paper, we provide a detailed performance analysis of SpMV performance on GPUs using four notable sparse matrix storage schemes (compressed sparse row (CSR), ELLAPCK (ELL), hybrid ELL/COO (HYB), and compressed sparse row 5 (CSR5)), five performance metrics (execution time, giga floating point operations per second (GFLOPS), achieved occupancy, instructions per warp, and warp execution efficiency), five matrix sparsity features (nnz, anpr, nprvariance, maxnpr, and distavg), and 17 sparse matrices from 10 application domains (chemical simulations, computational fluid dynamics (CFD), electromagnetics, linear programming, economics, etc.). Subsequently, based on the deeper insights gained through the detailed performance analysis, we propose a technique called the heterogeneous CPU–GPU Hybrid (HCGHYB) scheme. It utilizes both the CPU and GPU in parallel and provides better performance over the HYB format by an average speedup of 1.7x. Heterogeneous computing is an important direction for SpMV and other application areas. Moreover, to the best of our knowledge, this is the first work where the SpMV performance on GPUs has been discussed in such depth. We believe that this work on SpMV performance analysis and the heterogeneous scheme will open up many new directions and improvements for the SpMV computing field in the future.

ACS Style

Sarah Alahmadi; Thaha Mohammed; Aiiad Albeshri; Iyad Katib; Rashid Mehmood. Performance Analysis of Sparse Matrix-Vector Multiplication (SpMV) on Graphics Processing Units (GPUs). Electronics 2020, 9, 1675 .

AMA Style

Sarah Alahmadi, Thaha Mohammed, Aiiad Albeshri, Iyad Katib, Rashid Mehmood. Performance Analysis of Sparse Matrix-Vector Multiplication (SpMV) on Graphics Processing Units (GPUs). Electronics. 2020; 9 (10):1675.

Chicago/Turabian Style

Sarah Alahmadi; Thaha Mohammed; Aiiad Albeshri; Iyad Katib; Rashid Mehmood. 2020. "Performance Analysis of Sparse Matrix-Vector Multiplication (SpMV) on Graphics Processing Units (GPUs)." Electronics 9, no. 10: 1675.

Journal article
Published: 13 October 2020 in Applied Sciences
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5G networks and Internet of Things (IoT) offer a powerful platform for ubiquitous environments with their ubiquitous sensing, high speeds and other benefits. The data, analytics, and other computations need to be optimally moved and placed in these environments, dynamically, such that energy-efficiency and QoS demands are best satisfied. A particular challenge in this context is to preserve privacy and security while delivering quality of service (QoS) and energy-efficiency. Many works have tried to address these challenges but without a focus on optimizing all of them and assuming fixed models of environments and security threats. This paper proposes the UbiPriSEQ framework that uses Deep Reinforcement Learning (DRL) to adaptively, dynamically, and holistically optimize QoS, energy-efficiency, security, and privacy. UbiPriSEQ is built on a three-layered model and comprises two modules. UbiPriSEQ devises policies and makes decisions related to important parameters including local processing and offloading rates for data and computations, radio channel states, transmit power, task priority, and selection of fog nodes for offloading, data migration, and so forth. UbiPriSEQ is implemented in Python over the TensorFlow platform and is evaluated using a real-life application in terms of SINR, privacy metric, latency, and utility function, manifesting great promise.

ACS Style

Thaha Mohammed; Aiiad Albeshri; Iyad Katib; Rashid Mehmood. UbiPriSEQ—Deep Reinforcement Learning to Manage Privacy, Security, Energy, and QoS in 5G IoT HetNets. Applied Sciences 2020, 10, 7120 .

AMA Style

Thaha Mohammed, Aiiad Albeshri, Iyad Katib, Rashid Mehmood. UbiPriSEQ—Deep Reinforcement Learning to Manage Privacy, Security, Energy, and QoS in 5G IoT HetNets. Applied Sciences. 2020; 10 (20):7120.

Chicago/Turabian Style

Thaha Mohammed; Aiiad Albeshri; Iyad Katib; Rashid Mehmood. 2020. "UbiPriSEQ—Deep Reinforcement Learning to Manage Privacy, Security, Energy, and QoS in 5G IoT HetNets." Applied Sciences 10, no. 20: 7120.

Journal article
Published: 13 October 2020 in Sensors
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Artificial intelligence (AI) has taken us by storm, helping us to make decisions in everything we do, even in finding our “true love” and the “significant other”. While 5G promises us high-speed mobile internet, 6G pledges to support ubiquitous AI services through next-generation softwarization, heterogeneity, and configurability of networks. The work on 6G is in its infancy and requires the community to conceptualize and develop its design, implementation, deployment, and use cases. Towards this end, this paper proposes a framework for Distributed AI as a Service (DAIaaS) provisioning for Internet of Everything (IoE) and 6G environments. The AI service is “distributed” because the actual training and inference computations are divided into smaller, concurrent, computations suited to the level and capacity of resources available with cloud, fog, and edge layers. Multiple DAIaaS provisioning configurations for distributed training and inference are proposed to investigate the design choices and performance bottlenecks of DAIaaS. Specifically, we have developed three case studies (e.g., smart airport) with eight scenarios (e.g., federated learning) comprising nine applications and AI delivery models (smart surveillance, etc.) and 50 distinct sensor and software modules (e.g., object tracker). The evaluation of the case studies and the DAIaaS framework is reported in terms of end-to-end delay, network usage, energy consumption, and financial savings with recommendations to achieve higher performance. DAIaaS will facilitate standardization of distributed AI provisioning, allow developers to focus on the domain-specific details without worrying about distributed training and inference, and help systemize the mass-production of technologies for smarter environments.

ACS Style

Nourah Janbi; Iyad Katib; Aiiad Albeshri; Rashid Mehmood. Distributed Artificial Intelligence-as-a-Service (DAIaaS) for Smarter IoE and 6G Environments. Sensors 2020, 20, 5796 .

AMA Style

Nourah Janbi, Iyad Katib, Aiiad Albeshri, Rashid Mehmood. Distributed Artificial Intelligence-as-a-Service (DAIaaS) for Smarter IoE and 6G Environments. Sensors. 2020; 20 (20):5796.

Chicago/Turabian Style

Nourah Janbi; Iyad Katib; Aiiad Albeshri; Rashid Mehmood. 2020. "Distributed Artificial Intelligence-as-a-Service (DAIaaS) for Smarter IoE and 6G Environments." Sensors 20, no. 20: 5796.

Research article
Published: 15 April 2020 in International Journal of Communication Systems
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The ongoing Cloud‐IoT (Internet of Things)–based technological advancements have revolutionized the ways in which remote patients could be monitored and provided with health care facilities. The real‐time monitoring of patient's health leads to dispensing the right medical treatment at the right time. The health professionals need to access patients' sensitive data for such monitoring, and if treated with negligence, it could also be used for malevolent objectives by the adversary. Hence, the Cloud‐IoT–based technology gains could only be conferred to the patients and health professionals, if the latter authenticate one another properly. Many authentication protocols are proposed for remote patient health care monitoring, but with limitations. Lately, Sharma and Kalra (DOI: 10.1007/s40998‐018‐0146‐5) present a remote patient‐monitoring authentication scheme based on body sensors. However, we discover that the scheme still bears many drawbacks including stolen smart card attack, session key compromise, and user impersonation attacks. In view of those limitations, we have designed an efficient authentication protocol for remote patient health monitoring that counters all the above‐mentioned drawbacks. Moreover, we prove the security features of our protocol using BAN logic‐based formal security analysis and validate the results in ProVerif automated security tool.

ACS Style

Bander A. Alzahrani; Azeem Irshad; Khalid Alsubhi; Aiiad Albeshri. A secure and efficient remote patient-monitoring authentication protocol for cloud-IoT. International Journal of Communication Systems 2020, 33, e4423 .

AMA Style

Bander A. Alzahrani, Azeem Irshad, Khalid Alsubhi, Aiiad Albeshri. A secure and efficient remote patient-monitoring authentication protocol for cloud-IoT. International Journal of Communication Systems. 2020; 33 (11):e4423.

Chicago/Turabian Style

Bander A. Alzahrani; Azeem Irshad; Khalid Alsubhi; Aiiad Albeshri. 2020. "A secure and efficient remote patient-monitoring authentication protocol for cloud-IoT." International Journal of Communication Systems 33, no. 11: e4423.

Article
Published: 29 March 2020 in Wireless Personal Communications
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Burgeoning wireless technology developments have positively affected nearly every aspect of human life, and remote patient-healthcare monitoring through the internet is no exception. By employing smart gadgets, wireless body area networks, and cloud-based server platforms, patients can submit their sensor-captured readings in real-time to e-health cloud servers and ultimately to medical professionals so that the latter may treat patients appropriately at any time and in any place. To make the system reliable, an authenticated key agreement is required for the participating entities in this system. Many remote patient-healthcare monitoring protocols have been seen so far; however, reliance on wireless technology brings many security challenges for existing protocols. Recently, Xu et al. presented a new patient healthcare monitoring protocol; however, we demonstrate that it is vulnerable to many attacks, including replay attacks and key compromise impersonation attacks, and also that it suffers from privacy issues. Thereafter, we have proposed an improved scheme and formally analyzed its security features by implementing BAN logic and an automated simulation tool.

ACS Style

Bander A. Alzahrani; Azeem Irshad; Aiiad Albeshri; Khalid Alsubhi. A Provably Secure and Lightweight Patient-Healthcare Authentication Protocol in Wireless Body Area Networks. Wireless Personal Communications 2020, 117, 47 -69.

AMA Style

Bander A. Alzahrani, Azeem Irshad, Aiiad Albeshri, Khalid Alsubhi. A Provably Secure and Lightweight Patient-Healthcare Authentication Protocol in Wireless Body Area Networks. Wireless Personal Communications. 2020; 117 (1):47-69.

Chicago/Turabian Style

Bander A. Alzahrani; Azeem Irshad; Aiiad Albeshri; Khalid Alsubhi. 2020. "A Provably Secure and Lightweight Patient-Healthcare Authentication Protocol in Wireless Body Area Networks." Wireless Personal Communications 117, no. 1: 47-69.

Journal article
Published: 19 February 2020 in Applied Sciences
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Smartness, which underpins smart cities and societies, is defined by our ability to engage with our environments, analyze them, and make decisions, all in a timely manner. Healthcare is the prime candidate needing the transformative capability of this smartness. Social media could enable a ubiquitous and continuous engagement between healthcare stakeholders, leading to better public health. Current works are limited in their scope, functionality, and scalability. This paper proposes Sehaa, a big data analytics tool for healthcare in the Kingdom of Saudi Arabia (KSA) using Twitter data in Arabic. Sehaa uses Naive Bayes, Logistic Regression, and multiple feature extraction methods to detect various diseases in the KSA. Sehaa found that the top five diseases in Saudi Arabia in terms of the actual afflicted cases are dermal diseases, heart diseases, hypertension, cancer, and diabetes. Riyadh and Jeddah need to do more in creating awareness about the top diseases. Taif is the healthiest city in the KSA in terms of the detected diseases and awareness activities. Sehaa is developed over Apache Spark allowing true scalability. The dataset used comprises 18.9 million tweets collected from November 2018 to September 2019. The results are evaluated using well-known numerical criteria (Accuracy and F1-Score) and are validated against externally available statistics.

ACS Style

Shoayee Alotaibi; Rashid Mehmood; Iyad Katib; Omer Rana; Aiiad Albeshri. Sehaa: A Big Data Analytics Tool for Healthcare Symptoms and Diseases Detection Using Twitter, Apache Spark, and Machine Learning. Applied Sciences 2020, 10, 1398 .

AMA Style

Shoayee Alotaibi, Rashid Mehmood, Iyad Katib, Omer Rana, Aiiad Albeshri. Sehaa: A Big Data Analytics Tool for Healthcare Symptoms and Diseases Detection Using Twitter, Apache Spark, and Machine Learning. Applied Sciences. 2020; 10 (4):1398.

Chicago/Turabian Style

Shoayee Alotaibi; Rashid Mehmood; Iyad Katib; Omer Rana; Aiiad Albeshri. 2020. "Sehaa: A Big Data Analytics Tool for Healthcare Symptoms and Diseases Detection Using Twitter, Apache Spark, and Machine Learning." Applied Sciences 10, no. 4: 1398.

Journal article
Published: 01 January 2020 in IEEE Access
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Bander A. Alzahrani; Azeem Irshad; Aiiad Albeshri; Khalid Alsubhi; Muhammad Shafiq. An Improved Lightweight Authentication Protocol for Wireless Body Area Networks. IEEE Access 2020, 8, 190855 -190872.

AMA Style

Bander A. Alzahrani, Azeem Irshad, Aiiad Albeshri, Khalid Alsubhi, Muhammad Shafiq. An Improved Lightweight Authentication Protocol for Wireless Body Area Networks. IEEE Access. 2020; 8 ():190855-190872.

Chicago/Turabian Style

Bander A. Alzahrani; Azeem Irshad; Aiiad Albeshri; Khalid Alsubhi; Muhammad Shafiq. 2020. "An Improved Lightweight Authentication Protocol for Wireless Body Area Networks." IEEE Access 8, no. : 190855-190872.