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Energy saving is a significant research area in Saudi Arabia; however, significant problems have emerged related to its distribution and consumption. Use of an agent is assumed to combat these problems by forming efficient coalitions to control the energy consumption and energy distribution process. This study presents a novel algorithm for distributing the value calculation among the cooperative agents. This is likely to reduce the consumption of energy and extend the coalition lifetime used. The developed algorithm is compared with the basic modified coalition formation algorithm for evaluating its effectiveness. The results showed a reduction in cooling consumption by 20% after applying optimization algorithms. The amount of reduction in the cooling consumption reflects a 31% reduction in expected cooling costs, without affecting the household comfort. Therefore, the study concludes that DNsys provided better performance than the NNsys.
Areej Malibari; Daniyal Alghazzawi; Maha Lashin. Coalition Formation among the Cooperative Agents for Efficient Energy Consumption. Sustainability 2021, 13, 8662 .
AMA StyleAreej Malibari, Daniyal Alghazzawi, Maha Lashin. Coalition Formation among the Cooperative Agents for Efficient Energy Consumption. Sustainability. 2021; 13 (15):8662.
Chicago/Turabian StyleAreej Malibari; Daniyal Alghazzawi; Maha Lashin. 2021. "Coalition Formation among the Cooperative Agents for Efficient Energy Consumption." Sustainability 13, no. 15: 8662.
Rapid advancements in the internet of things (IoT) are driving massive transformations of health care, which is one of the largest and critical global industries. Recent pandemics, such as coronavirus 2019 (COVID-19), include increasing demands for ubiquitous, preventive, and personalized health care to be provided to the public at reduced risks and costs with rapid care. Mobile crowdsourcing could potentially meet the future massive health care IoT (mH-IoT) demands by enabling anytime, anywhere sense and analyses of health-related data to tackle such a pandemic situation. However, data reliability and availability are among the many challenges for the realization of next-generation mH-IoT, especially in COVID-19 epidemics. Therefore, more intelligent and robust health care frameworks are required to tackle such pandemics. Recently, reinforcement learning (RL) has proven its strengths to provide intelligent data reliability and availability. The action-state learning procedure of RL-based frameworks enables the learning system to enhance the optimal use of the information as the time passes and data increases. In this article, we propose an RL-based crowd-to-machine (RLC2M) framework for mH-IoT, which leverages crowdsourcing and an RL model (Q-learning) to address the health care information processing challenges. The simulation results show that the proposed framework rapidly converges with accumulated rewards to reveal the sensing environment situation.
Alaa Omran Almagrabi; Rashid Ali; Daniyal Alghazzawi; Abdullah AlBarakati; Tahir Khurshaid. A Reinforcement Learning-Based Framework for Crowdsourcing in Massive Health Care Internet of Things. Big Data 2021, 1 .
AMA StyleAlaa Omran Almagrabi, Rashid Ali, Daniyal Alghazzawi, Abdullah AlBarakati, Tahir Khurshaid. A Reinforcement Learning-Based Framework for Crowdsourcing in Massive Health Care Internet of Things. Big Data. 2021; ():1.
Chicago/Turabian StyleAlaa Omran Almagrabi; Rashid Ali; Daniyal Alghazzawi; Abdullah AlBarakati; Tahir Khurshaid. 2021. "A Reinforcement Learning-Based Framework for Crowdsourcing in Massive Health Care Internet of Things." Big Data , no. : 1.
The development of the Internet of Things (IoT) expands to an ultra-large-scale, which provides numerous services across different domains and environments. The use of middleware eases application development by providing the necessary functional capability. This paper presents a new form of middleware for controlling smart devices installed in an intelligent environment. This new form of middleware functioned seamlessly with any manufacturer API or bespoke controller program. It acts as an all-encompassing top layer of middleware in an intelligent environment control system capable of handling numerous different types of devices simultaneously. This protected de-synchronization of data stored in clone devices. It showed that in this middleware, the clone devices were regularly synchronized with their original master such as locally stored representations were continuously updated with the known true state values.
Daniyal Alghazzawi; Ghadah Aldabbagh; Abdullah Saad Al-Malaise Al-Ghamdi. ScaleUp: middleware for intelligent environments. PeerJ Computer Science 2021, 7, e545 .
AMA StyleDaniyal Alghazzawi, Ghadah Aldabbagh, Abdullah Saad Al-Malaise Al-Ghamdi. ScaleUp: middleware for intelligent environments. PeerJ Computer Science. 2021; 7 ():e545.
Chicago/Turabian StyleDaniyal Alghazzawi; Ghadah Aldabbagh; Abdullah Saad Al-Malaise Al-Ghamdi. 2021. "ScaleUp: middleware for intelligent environments." PeerJ Computer Science 7, no. : e545.
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.
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 StyleMajda 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 StyleMajda 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.
The utilization of mobile learning continues to rise and has attracted many organizations, university environments and institutions of higher education all over the world. The cloud storage system consists of several defense issues since data security and privacy have become known as the foremost apprehension for the users. Uploading and storing specific data in the cloud is familiar and widespread, but securing the data is a complicated task. This paper proposes a cloud-based mobile learning system using a hybrid optimal elliptic curve cryptography (HOECC) algorithm comprising public and private keys for data encryption. The proposed approach utilizes an adaptive tunicate slime-mold (ATS) algorithm to generate optimal key value. Thus, the data uploaded in the cloud system are secured with high authentication, data integrity and confidentiality. The study investigation employed a survey consisting of 50 students and the questionnaire was sent to all fifty students. In addition to this, for obtaining secure data transmission in the cloud, various performance measures, namely the encryption time, decryption time and uploading/downloading time were evaluated. The results reveal that the time of both encryption and decryption is less in ATF approach when compared with other techniques.
Ghadah Aldabbagh; Daniyal Alghazzawi; Syed Hasan; Mohammed Alhaddad; Areej Malibari; Li Cheng. Secure Data Exchange in M-Learning Platform using Adaptive Tunicate Slime-Mold-Based Hybrid Optimal Elliptic Curve Cryptography. Applied Sciences 2021, 11, 5316 .
AMA StyleGhadah Aldabbagh, Daniyal Alghazzawi, Syed Hasan, Mohammed Alhaddad, Areej Malibari, Li Cheng. Secure Data Exchange in M-Learning Platform using Adaptive Tunicate Slime-Mold-Based Hybrid Optimal Elliptic Curve Cryptography. Applied Sciences. 2021; 11 (12):5316.
Chicago/Turabian StyleGhadah Aldabbagh; Daniyal Alghazzawi; Syed Hasan; Mohammed Alhaddad; Areej Malibari; Li Cheng. 2021. "Secure Data Exchange in M-Learning Platform using Adaptive Tunicate Slime-Mold-Based Hybrid Optimal Elliptic Curve Cryptography." Applied Sciences 11, no. 12: 5316.
The term “mobile learning” (or “m-learning”) refers to using handheld phones to learn and wireless computing as a learning tool and connectivity technology. This paper presents and explores the latest mobile platform for teaching and studying programming basics. The M-Learning tool was created using a platform-independent approach to target the largest available number of learners while reducing development and maintenance time and effort. Since the code is completely shared across mobile devices (iOS, Android, and Windows Phone), students can use any smartphone to access the app. To make the programme responsive, scalable, and dynamic, and to provide students with personalised guidance, the core application is based on an analysis design development implementation and assessment (ADDIE) model implemented in the Xamarin framework. The application’s key features are depicted in a prototype. An experiment is carried out on BS students at a university to evaluate the efficacy of the generated application. A usefulness questionnaire is administered to an experimental community in order to determine students’ expectations of the developed mobile application’s usability. The findings of the experiment show that the application is considerably more successful than conventional learning in developing students’ online knowledge assessment abilities, with an impact size of 1.96. The findings add to the existing mobile learning literature by defining usability assessment features and offering a basis for designing platform-independent m-learning applications. The current findings are explored in terms of their implications for study and teaching practice.
Daniyal Alghazzawi; Syed Hasan; Ghadah Aldabbagh; Mohammed Alhaddad; Areej Malibari; Muhammad Asghar; Hanan Aljuaid. Development of Platform Independent Mobile Learning Tool in Saudi Universities. Sustainability 2021, 13, 5691 .
AMA StyleDaniyal Alghazzawi, Syed Hasan, Ghadah Aldabbagh, Mohammed Alhaddad, Areej Malibari, Muhammad Asghar, Hanan Aljuaid. Development of Platform Independent Mobile Learning Tool in Saudi Universities. Sustainability. 2021; 13 (10):5691.
Chicago/Turabian StyleDaniyal Alghazzawi; Syed Hasan; Ghadah Aldabbagh; Mohammed Alhaddad; Areej Malibari; Muhammad Asghar; Hanan Aljuaid. 2021. "Development of Platform Independent Mobile Learning Tool in Saudi Universities." Sustainability 13, no. 10: 5691.
In Internet of Things (IoT) environments, privacy and security are among some of the significant challenges. Recently, several studies have attempted to apply blockchain technology to increase IoT network security. However, the lightweight feature of IoT devices commonly fails to meet computational intensive requirements for blockchain-based security models. In this work, we propose a mechanism to address this issue. We design an IoT blockchain architecture to store device identity information in a distributed ledger. We propose a Blockchain of Things (BCoT) Gateway to facilitate the recording of authentication transactions in a blockchain network without modifying existing device hardware or applications. Furthermore, we introduce a new device recognition model that is suitable for blockchain-based identity authentication, where we employ a novel feature selection method for device traffic flow. Finally, we develop the BCoT Sentry framework as a reference implementation of our proposed method. Experiment results verify the feasibility of our proposed framework.
Liangqin Gong; Daniyal Alghazzawi; Li Cheng. BCoT Sentry: A Blockchain-Based Identity Authentication Framework for IoT Devices. Information 2021, 12, 203 .
AMA StyleLiangqin Gong, Daniyal Alghazzawi, Li Cheng. BCoT Sentry: A Blockchain-Based Identity Authentication Framework for IoT Devices. Information. 2021; 12 (5):203.
Chicago/Turabian StyleLiangqin Gong; Daniyal Alghazzawi; Li Cheng. 2021. "BCoT Sentry: A Blockchain-Based Identity Authentication Framework for IoT Devices." Information 12, no. 5: 203.
Upon the working principles of the human neocortex, the Hierarchical Temporal Memory model has been developed which is a proposed theoretical framework for sequence learning. Both categorical and numerical types of data are handled by HTM. Semantic Folding Theory (SFT) is based on HTM to represent a data stream for processing in the form of sparse distributed representation (SDR). For natural language perception and production, SFT delivers a solid structural background for semantic evidence description to the fundamentals of the semantic foundation during the phase of language learning. Anomalies are the patterns from data streams that do not follow the expected behavior. Any stream of data patterns could have a number of anomaly types. In a data stream, a single pattern or combination of closely related patterns that diverges and deviates from standard, normal, or expected is called a static (spatial) anomaly. A temporal anomaly is a set of unexpected changes between patterns. When a change first appears, this is recorded as an anomaly. If this change looks a number of times, then it is set to a “new normal” and terminated as an anomaly. An HTM system detects the anomaly, and due to continuous learning nature, it quickly learns when they become the new normal. A robust anomalous behavior detection framework using HTM-based SFT for improving decision-making (SDR-ABDF/P2) is a proposed framework or model in this research. The researcher claims that the proposed model would be able to learn the order of several variables continuously in temporal sequences by using an unsupervised learning rule.
Hamid Masood Khan; Fazal Masud Khan; Aurangzeb Khan; Muhammad Zubair Asghar; Daniyal M. Alghazzawi. Anomalous Behavior Detection Framework Using HTM-Based Semantic Folding Technique. Computational and Mathematical Methods in Medicine 2021, 2021, 1 -14.
AMA StyleHamid Masood Khan, Fazal Masud Khan, Aurangzeb Khan, Muhammad Zubair Asghar, Daniyal M. Alghazzawi. Anomalous Behavior Detection Framework Using HTM-Based Semantic Folding Technique. Computational and Mathematical Methods in Medicine. 2021; 2021 ():1-14.
Chicago/Turabian StyleHamid Masood Khan; Fazal Masud Khan; Aurangzeb Khan; Muhammad Zubair Asghar; Daniyal M. Alghazzawi. 2021. "Anomalous Behavior Detection Framework Using HTM-Based Semantic Folding Technique." Computational and Mathematical Methods in Medicine 2021, no. : 1-14.
The 5th generation (5G) wireless networks propose to address a variety of usage scenarios, such as enhanced mobile broadband (eMBB), massive machine-type communications (mMTC), and ultra-reliable low-latency communications (URLLC). Due to the exponential increase in the user equipment (UE) devices of wireless communication technologies, 5G and beyond networks (B5G) expect to support far higher user density and far lower latency than currently deployed cellular technologies, like long-term evolution-Advanced (LTE-A). However, one of the critical challenges for B5G is finding a clever way for various channel access mechanisms to maintain dense UE deployments. Random access channel (RACH) is a mandatory procedure for the UEs to connect with the evolved node B (eNB). The performance of the RACH directly affects the performance of the entire network. Currently, RACH uses a uniform distribution-based (UD) random access to prevent a possible network collision among multiple UEs attempting to access channel resources. However, in a UD-based channel access, every UE has an equal chance to choose a similar contention preamble close to the expected value, which causes an increase in the collision among the UEs. Therefore, in this paper, we propose a Poisson process-based RACH (2PRACH) alternative to a UD-based RACH. A Poisson process-based distribution, such as exponential distribution, disperses the random preambles between two bounds in a Poisson point method, where random variables occur continuously and independently with a constant parametric rate. In this way, our proposed 2PRACH approach distributes the UEs in a probability distribution of a parametric collection. Simulation results show that the shift of RACH from UD-based channel access to a Poisson process-based distribution enhances the reliability and lowers the network’s latency.
Alaa Almagrabi; Rashid Ali; Daniyal Alghazzawi; Abdullah AlBarakati; Tahir Khurshaid. A Poisson Process-Based Random Access Channel for 5G and Beyond Networks. Mathematics 2021, 9, 508 .
AMA StyleAlaa Almagrabi, Rashid Ali, Daniyal Alghazzawi, Abdullah AlBarakati, Tahir Khurshaid. A Poisson Process-Based Random Access Channel for 5G and Beyond Networks. Mathematics. 2021; 9 (5):508.
Chicago/Turabian StyleAlaa Almagrabi; Rashid Ali; Daniyal Alghazzawi; Abdullah AlBarakati; Tahir Khurshaid. 2021. "A Poisson Process-Based Random Access Channel for 5G and Beyond Networks." Mathematics 9, no. 5: 508.
Intelligent Crowd Monitoring and Management Systems (ICMMSs) have become effective resources for strengthening safety and security along with enhancing early-warning capabilities to manage emergencies in crowded situations of smart cities and massive gatherings events. The main advantage of such systems is their ability to detect multiple features associated with the crowd gathering, as they enable multi-source sensors, multi-modal data, and powerful intelligent and analytical methods. Unlike traditional crowd monitoring systems, which make use of simplex forms of different data types, data and information associated with crowded scenarios can be collected, fused, processed and analyzed in large quantities for accurate global assessment and enhanced decision making processes in an ICMMS. Therefore, data fusion is introduced as an enabler to decrease data quantity, reduce data dimensions, and improve data quality. In this paper, we first survey the literature on data fusion application in crowd monitoring systems as we are developing a state-of-the-art ICMMS with data fusion as a major platform enabler. Next, we discuss some popular data fusion architectures and classifications from different perspectives. Based on this, we propose a multi-sensor, multi-modal, and dimensional ICMMS architecture based on data fusion. Then, we identify the data fusion processes in the ICMMS and classify them into sensor fusion, feature-based data fusion, and decision fusion. Relevant algorithms, applications and examples of three classes are elaborated. Finally, future data fusion research directions are discussed.
Xianzhi Li; Qiao Yu; Bander Alzahrani; Ahmed Barnawi; Ahmed Alhindi; Daniyal Alghazzawi; Yiming Miao. Data Fusion for Intelligent Crowd Monitoring and Management Systems: A Survey. IEEE Access 2021, PP, 1 -1.
AMA StyleXianzhi Li, Qiao Yu, Bander Alzahrani, Ahmed Barnawi, Ahmed Alhindi, Daniyal Alghazzawi, Yiming Miao. Data Fusion for Intelligent Crowd Monitoring and Management Systems: A Survey. IEEE Access. 2021; PP (99):1-1.
Chicago/Turabian StyleXianzhi Li; Qiao Yu; Bander Alzahrani; Ahmed Barnawi; Ahmed Alhindi; Daniyal Alghazzawi; Yiming Miao. 2021. "Data Fusion for Intelligent Crowd Monitoring and Management Systems: A Survey." IEEE Access PP, no. 99: 1-1.
Alaa Omran Almagrabi; Rashid Ali; Daniyal Alghazzawi; Abdullah AlBarakati; Tahir Khurshaid. Blockchain-as-a-Utility for Next-Generation Healthcare Internet of Things. Computers, Materials & Continua 2021, 68, 359 -376.
AMA StyleAlaa Omran Almagrabi, Rashid Ali, Daniyal Alghazzawi, Abdullah AlBarakati, Tahir Khurshaid. Blockchain-as-a-Utility for Next-Generation Healthcare Internet of Things. Computers, Materials & Continua. 2021; 68 (1):359-376.
Chicago/Turabian StyleAlaa Omran Almagrabi; Rashid Ali; Daniyal Alghazzawi; Abdullah AlBarakati; Tahir Khurshaid. 2021. "Blockchain-as-a-Utility for Next-Generation Healthcare Internet of Things." Computers, Materials & Continua 68, no. 1: 359-376.
Widespread development of system software, the process of learning, and the excellence in profession of teaching are the formidable challenges faced by the learning behavior prediction system. The learning styles of teachers have different kinds of content designs to enhance their learning. In this learning environment, teachers can work together with the students, but the learning materials are designed by the teachers. The cognitive style deals with mental activities such as learning, remembering, thinking, and the usage of language. Therefore, being motivated by the problems mentioned above, this paper proposes the concept of adaptive optimization-based neural network (AONN). The learning behavior and browsing behavior features are extracted and incorporated into the input of artificial neural network (ANN). Hence, in this paper, the neural network weights are optimized with the use of grey wolf optimizer (GWO) algorithm. The output operation of e-learning with teaching equipment is chosen based on the cognitive style predicted by AONN. In experimental section, the measures of accuracy, sensitivity, specificity, time (sec), and memory (bytes) are carried out. Each of the measure is compared with the proposed AONN and existing fuzzy logic methodologies. Ultimately, the proposed AONN method produces higher accuracy, specificity, and sensitivity results. The results demonstrate that the algorithm proposed in this study can automatically learn network structures competitively, unlike those achieved for neural networks through standard approaches.
Ghada Aldabbagh; Daniyal M. AlGhazzawi; Syed Hamid Hasan; Mohammed Alhaddad; Areej Malibari; Li Cheng. Optimal Learning Behavior Prediction System Based on Cognitive Style Using Adaptive Optimization-Based Neural Network. Complexity 2020, 2020, 1 -13.
AMA StyleGhada Aldabbagh, Daniyal M. AlGhazzawi, Syed Hamid Hasan, Mohammed Alhaddad, Areej Malibari, Li Cheng. Optimal Learning Behavior Prediction System Based on Cognitive Style Using Adaptive Optimization-Based Neural Network. Complexity. 2020; 2020 ():1-13.
Chicago/Turabian StyleGhada Aldabbagh; Daniyal M. AlGhazzawi; Syed Hamid Hasan; Mohammed Alhaddad; Areej Malibari; Li Cheng. 2020. "Optimal Learning Behavior Prediction System Based on Cognitive Style Using Adaptive Optimization-Based Neural Network." Complexity 2020, no. : 1-13.
This article seeks to present the previous experience made and the definition of inclusive application design criteria taken into account within a heuristic assessment process applied to validate the relevance of these tools in the treatment of Autism Spectrum Disorder -ASD. The article presents the procedure performed, as well as the formation of the team of experts who participated in this study and finally the results obtained. What is expressed in this letter is part of an investigation that, from the systematic review of literature and the conduct of a case study, obtains the identification of the most relevant - functional and non-functional - characteristics that the software used in the treatment of Autism Spectrum Disorder -TEA should have. The study is based on the use of computer applications focused on strengthening social and motivation skills, as well as the characteristics linked to the training processes of autistic children. The project includes the exploration of state of the art and technique on Emotional Intelligence, children with disabilities such as autism and architecture models for the design of inclusive software applications. All this is validated by qualitative and quantitative metrics and analyses with indicators of assessment on appropriation or strengthening of emotional skills in autistic children. The tools considered are tools for collecting non-invasive information, filming activities and analyzing emotions through recognition of facial expressions.
Gustavo Eduardo Constain Moreno; César A. Collazos; Habib M. Fardoun; Daniyal M. AlGhazzawi. Heuristic Evaluation for the Assessment of Inclusive Tools in the Autism Treatment. Transactions on Petri Nets and Other Models of Concurrency XV 2020, 34 -51.
AMA StyleGustavo Eduardo Constain Moreno, César A. Collazos, Habib M. Fardoun, Daniyal M. AlGhazzawi. Heuristic Evaluation for the Assessment of Inclusive Tools in the Autism Treatment. Transactions on Petri Nets and Other Models of Concurrency XV. 2020; ():34-51.
Chicago/Turabian StyleGustavo Eduardo Constain Moreno; César A. Collazos; Habib M. Fardoun; Daniyal M. AlGhazzawi. 2020. "Heuristic Evaluation for the Assessment of Inclusive Tools in the Autism Treatment." Transactions on Petri Nets and Other Models of Concurrency XV , no. : 34-51.
Unmanned Aerial Vehicles (UAVs) are an emerging technology with the potential to be used in industries and various sectors of human life to provide a wide range of applications and services. During the last decade, there has been a growing focus of research in the UAV's assistance paradigm as a fundamental concept resulting in the constant improvement between different kinds of ground networks and the hovering UAVs in the sky. Recently, the wide availability of embedded wireless interfaces in the communicating entities has allowed the deployment of such a paradigm simpler and easiest. Moreover, due to UAVs' controlled mobility and adjustable altitudes, they can be considered as the most appropriate candidate to enhance the performance and overcome the restrictions of ground networks. This comprehensive survey both studies and summarizes the existing UAV-assisted research, such as routing, data gathering, cellular communications, Internet of Things (IoT) networks, and disaster management that supports existing enabling technologies. Descriptions, classifications, and comparative studies related to different UAV-assisted proposals are presented throughout the paper. By pointing out numerous future challenges, it is expected to simulate research in this emerging and hot research area. To the best of our knowledge, there are many survey papers on the topic from a technology perspective. Nevertheless, this survey can be considered as the first attempt at a comprehensive analysis of different types of existing UAV-assisted networks and describes the state-of-the-art in UAV-assisted research.
Bander Alzahrani; Omar Sami Oubbati; Ahmed Barnawi; Mohammed Atiquzzaman; Daniyal Alghazzawi. UAV assistance paradigm: State-of-the-art in applications and challenges. Journal of Network and Computer Applications 2020, 166, 102706 .
AMA StyleBander Alzahrani, Omar Sami Oubbati, Ahmed Barnawi, Mohammed Atiquzzaman, Daniyal Alghazzawi. UAV assistance paradigm: State-of-the-art in applications and challenges. Journal of Network and Computer Applications. 2020; 166 ():102706.
Chicago/Turabian StyleBander Alzahrani; Omar Sami Oubbati; Ahmed Barnawi; Mohammed Atiquzzaman; Daniyal Alghazzawi. 2020. "UAV assistance paradigm: State-of-the-art in applications and challenges." Journal of Network and Computer Applications 166, no. : 102706.
Physical rehabilitation is generally perceived as a face-to-face interaction between therapist and patient. However, thanks to technology developments, this picture has been changed. The massive innovation of information and communication technologies (ICTs) has brought a revolution to the view of health, people, and work.[1] Especially, the application of virtual reality (VR) and augmented reality (AR) has given an important contribution to health. Lastly, a growing number of studies have shown important implications of the use of ICTs, VR, and AR for treating several disorders and promotion of healthy lifestyles or well-being. Initially, most of these studies have focused on treating anxiety disorders,[1] phobias (e.g., specific phobias, social phobia, and agoraphobia),[2] posttraumatic stress disorder (PTSD),[3] attention deficit disorder,[4] eating disorder,[5] the reduction in stress,[6] and posttraumatic stress disorders in patients with limbs amputation[7] among others. In all these studies, the use of these ICTs has supported doctors and researchers to reach the best results for patients. Thanks to the technological advances, it is possible to reproduce virtual environment where people can move as they are in the real world or having some mobile applications which can enlarge the world around us and facing specific phobia. But for professionals, it is not always an easy work because the use of ICTs usually implies that psychologists have to open their mind and cowork with engineers and other professionals who have different backgrounds. Publication Date:07 September 2020 (online) © . Georg Thieme Verlag KGStuttgart · New York
Habib M. Fardoun; Daniyal Alghazzawi; M. Elena De La Guia. Using Information and Communication Technologies to Enhance Patient Rehabilitation Research Techniques. Methods of Information in Medicine 2020, 59, 059 -060.
AMA StyleHabib M. Fardoun, Daniyal Alghazzawi, M. Elena De La Guia. Using Information and Communication Technologies to Enhance Patient Rehabilitation Research Techniques. Methods of Information in Medicine. 2020; 59 (02/03):059-060.
Chicago/Turabian StyleHabib M. Fardoun; Daniyal Alghazzawi; M. Elena De La Guia. 2020. "Using Information and Communication Technologies to Enhance Patient Rehabilitation Research Techniques." Methods of Information in Medicine 59, no. 02/03: 059-060.
In recent years, accumulating evidences have shown that microRNA (miRNA) plays an important role in the exploration and treatment of diseases, so detection of the associations between miRNA and disease has been drawn more and more attentions. However, traditional experimental methods have the limitations of high cost and time- consuming, a computational method can help us more systematically and effectively predict the potential miRNA-disease associations. In this work, we proposed a novel network embedding-based heterogeneous information integration method to predict miRNA-disease associations. More specifically, a heterogeneous information network is constructed by combining the known associations among lncRNA, drug, protein, disease, and miRNA. After that, the network embedding method Learning Graph Representations with Global Structural Information (GraRep) is employed to learn embeddings of nodes in heterogeneous information network. In this way, the embedding representations of miRNA and disease are integrated with the attribute information of miRNA and disease (e.g. miRNA sequence information and disease semantic similarity) to represent miRNA-disease association pairs. Finally, the Random Forest (RF) classifier is used for predicting potential miRNA-disease associations. Under the 5-fold cross validation, our method obtained 85.11% prediction accuracy with 80.41% sensitivity at the AUC of 91.25%. In addition, in case studies of three major Human diseases, 45 (Colon Neoplasms), 42 (Breast Neoplasms) and 44 (Esophageal Neoplasms) of top-50 predicted miRNAs are respectively verified by other miRNA-disease association databases. In conclusion, the experimental results suggest that our method can be a powerful and useful tool for predicting potential miRNA-disease associations.
Bo-Ya Ji; Zhu-Hong You; Li Cheng; Ji-Ren Zhou; Daniyal AlGhazzawi; Li-Ping Li. Predicting miRNA-disease association from heterogeneous information network with GraRep embedding model. Scientific Reports 2020, 10, 1 -12.
AMA StyleBo-Ya Ji, Zhu-Hong You, Li Cheng, Ji-Ren Zhou, Daniyal AlGhazzawi, Li-Ping Li. Predicting miRNA-disease association from heterogeneous information network with GraRep embedding model. Scientific Reports. 2020; 10 (1):1-12.
Chicago/Turabian StyleBo-Ya Ji; Zhu-Hong You; Li Cheng; Ji-Ren Zhou; Daniyal AlGhazzawi; Li-Ping Li. 2020. "Predicting miRNA-disease association from heterogeneous information network with GraRep embedding model." Scientific Reports 10, no. 1: 1-12.
Recently Internet of Things (IoT) attains tremendous popularity, although this promising technology leads to a variety of security obstacles. The conventional solutions do not suit the new dilemmas brought by the IoT ecosystem. Conversely, Artificial Immune Systems (AIS) is intelligent and adaptive systems mimic the human immune system which holds desirable properties for such a dynamic environment and provides an opportunity to improve IoT security. In this work, we develop a novel hybrid Deep Learning and Dendritic Cell Algorithm (DeepDCA) in the context of an Intrusion Detection System (IDS). The framework adopts Dendritic Cell Algorithm (DCA) and Self Normalizing Neural Network (SNN). The aim of this research is to classify IoT intrusion and minimize the false alarm generation. Also, automate and smooth the signal extraction phase which improves the classification performance. The proposed IDS selects the convenient set of features from the IoT-Bot dataset, performs signal categorization using the SNN then use the DCA for classification. The experimentation results show that DeepDCA performed well in detecting the IoT attacks with a high detection rate demonstrating over 98.73% accuracy and low false-positive rate. Also, we compared these results with State-of-the-art techniques, which showed that our model is capable of performing better classification tasks than SVM, NB, KNN, and MLP. We plan to carry out further experiments to verify the framework using a more challenging dataset and make further comparisons with other signal extraction approaches. Also, involve in real-time (online) attack detection.
Sahar Aldhaheri; Daniyal AlGhazzawi; Li Cheng; Bander Alzahrani; Abdullah Al-Barakati. DeepDCA: Novel Network-Based Detection of IoT Attacks Using Artificial Immune System. Applied Sciences 2020, 10, 1909 .
AMA StyleSahar Aldhaheri, Daniyal AlGhazzawi, Li Cheng, Bander Alzahrani, Abdullah Al-Barakati. DeepDCA: Novel Network-Based Detection of IoT Attacks Using Artificial Immune System. Applied Sciences. 2020; 10 (6):1909.
Chicago/Turabian StyleSahar Aldhaheri; Daniyal AlGhazzawi; Li Cheng; Bander Alzahrani; Abdullah Al-Barakati. 2020. "DeepDCA: Novel Network-Based Detection of IoT Attacks Using Artificial Immune System." Applied Sciences 10, no. 6: 1909.
As the Internet of Things (IoT) recently attains tremendous popularity, this promising technology leads to a variety of security challenges. The traditional solutions do not fit the new challenges brought by the IoT ecosystem. Although the development's area of Artificial Immune Systems (AIS) provides an opportunity to improve security issues and create a fertile and exciting environment for further research and experiments, there is not any systematic and comprehensive study about analyzing its importance for IoT environment. Therefore, this work aims to identify, evaluate, and perform a comprehensive study of empirical research on the studies of AIS approaches to secure the IoT environment. The relevant and high-quality studies are addressing using three research questions about the main research motivations, existing solutions, and future gaps and directions. The AIS approaches have been divided into three main categories based on IoT layers, and detailed classifications have also been included based on different parameters. To achieve this aim, the authors use a systematic literature review (SLR) as a powerful method to collect and critically analyze the research papers. Also, the authors discuss the selected studies and their main techniques, as well as their benefits and drawbacks in general. This research process strives to build a knowledge base for AIS solutions under the umbrella of IoT security and suggest directions for future research.
Sahar Aldhaheri; Daniyal Alghazzawi; Li Cheng; Ahmed Barnawi; Bandar A. Alzahrani. Artificial Immune Systems approaches to secure the internet of things: A systematic review of the literature and recommendations for future research. Journal of Network and Computer Applications 2020, 157, 102537 .
AMA StyleSahar Aldhaheri, Daniyal Alghazzawi, Li Cheng, Ahmed Barnawi, Bandar A. Alzahrani. Artificial Immune Systems approaches to secure the internet of things: A systematic review of the literature and recommendations for future research. Journal of Network and Computer Applications. 2020; 157 ():102537.
Chicago/Turabian StyleSahar Aldhaheri; Daniyal Alghazzawi; Li Cheng; Ahmed Barnawi; Bandar A. Alzahrani. 2020. "Artificial Immune Systems approaches to secure the internet of things: A systematic review of the literature and recommendations for future research." Journal of Network and Computer Applications 157, no. : 102537.
We consider a demand response program in which a block of apartments receive a discount from their electricity supplier if they ensure that their aggregate load from air conditioning does not exceed a predetermined threshold. The goal of the participants is to obtain the discount, while ensuring that their individual temperature preferences are also satisfied. As such, the apartments need to collectively optimise their use of air conditioning so as to satisfy these constraints and minimise their costs. Given an optimal cooling profile that secures the discount, the problem that the apartments face then is to divide the total discounted cost in a fair way. To achieve this, we take a coalitional game approach and propose the use of the Shapley value from cooperative game theory, which is the normative payoff division mechanism that offers a unique set of desirable fairness properties. However, applying the Shapley value in this setting presents a novel computational challenge. This is because its calculation requires, as input, the cost of every subset of apartments, which means solving an exponential number of collective optimisations, each of which is a computationally intensive problem. To address this, we propose solving the optimisation problem of each subset suboptimally, to allow for acceptable solutions that require less computation. We show that, due to the linearity property of the Shapley value, if suboptimal costs are used rather than optimal ones, the division of the discount will be fair in the following sense: each apartment is fairly “rewarded” for its contribution to the optimal cost and, at the same time, is fairly “penalised” for its contribution to the discrepancy between the suboptimal and the optimal costs. Importantly, this is achieved without requiring the optimal solutions.
Sasan Maleki; Talal Rahwan; Siddhartha Ghosh; Areej Malibari; Daniyal AlGhazzawi; Alex Rogers; Hamid Beigy; Nicholas R. Jennings. The Shapley value for a fair division of group discounts for coordinating cooling loads. PLOS ONE 2020, 15, e0227049 .
AMA StyleSasan Maleki, Talal Rahwan, Siddhartha Ghosh, Areej Malibari, Daniyal AlGhazzawi, Alex Rogers, Hamid Beigy, Nicholas R. Jennings. The Shapley value for a fair division of group discounts for coordinating cooling loads. PLOS ONE. 2020; 15 (1):e0227049.
Chicago/Turabian StyleSasan Maleki; Talal Rahwan; Siddhartha Ghosh; Areej Malibari; Daniyal AlGhazzawi; Alex Rogers; Hamid Beigy; Nicholas R. Jennings. 2020. "The Shapley value for a fair division of group discounts for coordinating cooling loads." PLOS ONE 15, no. 1: e0227049.
Nisreen Alzahrani; Daniyal Alghazzawi. A Review on Android Ransomware Detection Using Deep Learning Techniques. Proceedings of the 11th International Conference on Management of Digital EcoSystems 2019, 1 .
AMA StyleNisreen Alzahrani, Daniyal Alghazzawi. A Review on Android Ransomware Detection Using Deep Learning Techniques. Proceedings of the 11th International Conference on Management of Digital EcoSystems. 2019; ():1.
Chicago/Turabian StyleNisreen Alzahrani; Daniyal Alghazzawi. 2019. "A Review on Android Ransomware Detection Using Deep Learning Techniques." Proceedings of the 11th International Conference on Management of Digital EcoSystems , no. : 1.