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Rashid Mehmood (Senior Member, IEEE) is the Research Professor of Big Data Systems and the Director of Research, Training, and Consultancy at the High Performance Computing Centre, King Abdulaziz University, Saudi Arabia. He has gained qualifications and work experience from universities in the UK including Cambridge and Oxford Universities. Rashid has 25 years of academic and industrial experience in computational modelling, simulations, and design using computational intelligence, big data, high performance computing, and distributed systems. His broad research aim is to develop multi-disciplinary science and technology to enable a better quality of life and smart economy with a focus on real-time intelligence and dynamic (autonomic) system management. He has published nearly 200 research papers including 6 edited books. He has organised and chaired international conferences and workshops including EuropeComm 2009, Nets4Cars 2010-2013, SCE 2017-19, SCITA 2017, and HPC Saudi 2018-2020. He has led and contributed to academia-industry collaborative projects funded by EPSRC, EU, UK regional funds, and Technology Strategy Board UK with the value of over £50 million. He is a founding member of the Future Cities and Community Resilience (FCCR) Network, a member of ACM, OSA, Senior Member IEEE and former Vice-Chairman of IET Wales SW Network.
Smart cities and artificial intelligence (AI) are among the most popular discourses in urban policy circles. Most attempts at using AI to improve efficiencies in cities have nevertheless either struggled or failed to accomplish the smart city transformation. This is mainly due to short-sighted, technologically determined and reductionist AI approaches being applied to complex urbanization problems. Besides this, as smart cities are underpinned by our ability to engage with our environments, analyze them, and make efficient, sustainable and equitable decisions, the need for a green AI approach is intensified. This perspective paper, reflecting authors’ opinions and interpretations, concentrates on the “green AI” concept as an enabler of the smart city transformation, as it offers the opportunity to move away from purely technocentric efficiency solutions towards efficient, sustainable and equitable solutions capable of realizing the desired urban futures. The aim of this perspective paper is two-fold: first, to highlight the fundamental shortfalls in mainstream AI system conceptualization and practice, and second, to advocate the need for a consolidated AI approach—i.e., green AI—to further support smart city transformation. The methodological approach includes a thorough appraisal of the current AI and smart city literatures, practices, developments, trends and applications. The paper informs authorities and planners on the importance of the adoption and deployment of AI systems that address efficiency, sustainability and equity issues in cities.
Tan Yigitcanlar; Rashid Mehmood; Juan M. Corchado. Green Artificial Intelligence: Towards an Efficient, Sustainable and Equitable Technology for Smart Cities and Futures. Sustainability 2021, 13, 8952 .
AMA StyleTan Yigitcanlar, Rashid Mehmood, Juan M. Corchado. Green Artificial Intelligence: Towards an Efficient, Sustainable and Equitable Technology for Smart Cities and Futures. Sustainability. 2021; 13 (16):8952.
Chicago/Turabian StyleTan Yigitcanlar; Rashid Mehmood; Juan M. Corchado. 2021. "Green Artificial Intelligence: Towards an Efficient, Sustainable and Equitable Technology for Smart Cities and Futures." Sustainability 13, no. 16: 8952.
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.
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 StyleEbtesam 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 StyleEbtesam 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.
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.
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 StyleFurqan 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 StyleFurqan 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.
Despite numerous efforts, developing an authentication scheme that offers strong security while offering memorability and usability remains a grand challenge. In this paper, we propose a textual-graphical hybrid authentication scheme that improves the security, memorability and usability inadequacies of existing authentication schemes. This has been achieved by combining a range of mechanisms together, in a novel manner, to address weaknesses of the existing security schemes. Firstly, two dynamically selectable modes of password entry (Easy Login, and Secure Login) provide a trade-off between usability and security, allowing the user to dynamically switch to any of these methods in real-time based on the security of the surrounding environment (e.g., secure home environment versus insecure public places) or the criticality of the user account (e.g., a bank account). The other mechanisms included a novel use of the drawmetric mechanism for setting the password to improve memorability, multistep authentication, a novel adaptation of one-time password (OTP) concept using a random selection of password elements, random placement of password elements in different steps, assigning random numbers to the password elements to increase security, and use of simple addition to improve security. We have implemented and analysed the proposed scheme for its security against brute-force attacks, dictionary, shoulder surfing, random guessing, phishing or forming, keystroke/mouse logger, and multiple recording attacks. We have also investigated its usability and memorability, reporting various trends of password elements used and the respective authentication times. Moreover, we have compared the proposed scheme with eight other well-known authentication schemes in terms of its resilience and authentication time. The results and analyses demonstrate the effectiveness of the proposed scheme.We believe that a range of novel methods introduced in this proposed scheme opens several doors for innovation in security techniques.
Shah Zaman Nizamani; Syed Raheel Hassan; Riaz Ahmed Shaikh; Ehab Atif Abozinadah; Rashid Mehmood. A Novel Hybrid Textual-Graphical Authentication Scheme with Better Security, Memorability, and Usability. IEEE Access 2021, PP, 1 -1.
AMA StyleShah Zaman Nizamani, Syed Raheel Hassan, Riaz Ahmed Shaikh, Ehab Atif Abozinadah, Rashid Mehmood. A Novel Hybrid Textual-Graphical Authentication Scheme with Better Security, Memorability, and Usability. IEEE Access. 2021; PP (99):1-1.
Chicago/Turabian StyleShah Zaman Nizamani; Syed Raheel Hassan; Riaz Ahmed Shaikh; Ehab Atif Abozinadah; Rashid Mehmood. 2021. "A Novel Hybrid Textual-Graphical Authentication Scheme with Better Security, Memorability, and Usability." IEEE Access PP, no. 99: 1-1.
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.
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 StyleAakash 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 StyleAakash 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.
Renewable energy is essential for planet sustainability. Renewable energy output forecasting has a significant impact on making decisions related to operating and managing power systems. Accurate prediction of renewable energy output is vital to ensure grid reliability and permanency and reduce the risk and cost of the energy market and systems. Deep learning's recent success in many applications has attracted researchers to this field and its promising potential is manifested in the richness of the proposed methods and the increasing number of publications. To facilitate further research and development in this area, this paper provides a review of deep learning-based solar and wind energy forecasting research published during the last five years discussing extensively the data and datasets used in the reviewed works, the data pre-processing methods, deterministic and probabilistic methods, and evaluation and comparison methods. The core characteristics of all the reviewed works are summarised in tabular forms to enable methodological comparisons. The current challenges in the field and future research directions are given. The trends show that hybrid forecasting models are the most used in this field followed by Recurrent Neural Network models including Long Short-Term Memory and Gated Recurrent Unit, and in the third place Convolutional Neural Networks. We also find that probabilistic and multistep ahead forecasting methods are gaining more attention. Moreover, we devise a broad taxonomy of the research using the key insights gained from this extensive review, the taxonomy we believe will be vital in understanding the cutting-edge and accelerating innovation in this field.
Ghadah Alkhayat; Rashid Mehmood. A review and taxonomy of wind and solar energy forecasting methods based on deep learning. Energy and AI 2021, 4, 100060 .
AMA StyleGhadah Alkhayat, Rashid Mehmood. A review and taxonomy of wind and solar energy forecasting methods based on deep learning. Energy and AI. 2021; 4 ():100060.
Chicago/Turabian StyleGhadah Alkhayat; Rashid Mehmood. 2021. "A review and taxonomy of wind and solar energy forecasting methods based on deep learning." Energy and AI 4, no. : 100060.
The urbanization problems we face may be alleviated using innovative digital technology. However, employing these technologies entails the risk of creating new urban problems and/or intensifying the old ones instead of alleviating them. Hence, in a world with immense technological opportunities and at the same time enormous urbanization challenges, it is critical to adopt the principles of responsible urban innovation. These principles assure the delivery of the desired urban outcomes and futures. We contribute to the existing responsible urban innovation discourse by focusing on local government artificial intelligence (AI) systems, providing a literature and practice overview, and a conceptual framework. In this perspective paper, we advocate for the need for balancing the costs, benefits, risks and impacts of developing, adopting, deploying and managing local government AI systems in order to achieve responsible urban innovation. The statements made in this perspective paper are based on a thorough review of the literature, research, developments, trends and applications carefully selected and analyzed by an expert team of investigators. This study provides new insights, develops a conceptual framework and identifies prospective research questions by placing local government AI systems under the microscope through the lens of responsible urban innovation. The presented overview and framework, along with the identified issues and research agenda, offer scholars prospective lines of research and development; where the outcomes of these future studies will help urban policymakers, managers and planners to better understand the crucial role played by local government AI systems in ensuring the achievement of responsible outcomes.
Tan Yigitcanlar; Juan Corchado; Rashid Mehmood; Rita Li; Karen Mossberger; Kevin Desouza. Responsible Urban Innovation with Local Government Artificial Intelligence (AI): A Conceptual Framework and Research Agenda. Journal of Open Innovation: Technology, Market, and Complexity 2021, 7, 71 .
AMA StyleTan Yigitcanlar, Juan Corchado, Rashid Mehmood, Rita Li, Karen Mossberger, Kevin Desouza. Responsible Urban Innovation with Local Government Artificial Intelligence (AI): A Conceptual Framework and Research Agenda. Journal of Open Innovation: Technology, Market, and Complexity. 2021; 7 (1):71.
Chicago/Turabian StyleTan Yigitcanlar; Juan Corchado; Rashid Mehmood; Rita Li; Karen Mossberger; Kevin Desouza. 2021. "Responsible Urban Innovation with Local Government Artificial Intelligence (AI): A Conceptual Framework and Research Agenda." Journal of Open Innovation: Technology, Market, and Complexity 7, no. 1: 71.
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.
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 StyleShumayla 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 StyleShumayla 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.
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.
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 StyleEbtesam 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 StyleEbtesam 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.
Artificial intelligence (AI) is a powerful technology with an increasing popularity and applications in areas ranging from marketing to banking and finance, from agriculture to healthcare and security, from space exploration to robotics and transport, and from chatbots to artificial creativity and manufacturing. Although many of these areas closely relate to the urban context, there is limited understanding of the trending AI technologies and their application areas—or concepts—in the urban planning and development fields. Similarly, there is a knowledge gap in how the public perceives AI technologies, their application areas, and the AI-related policies and practices of our cities. This study aims to advance our understanding of the relationship between the key AI technologies (n = 15) and their key application areas (n = 16) in urban planning and development. To this end, this study examines public perceptions of how AI technologies and their application areas in urban planning and development are perceived and utilized in the testbed case study of Australian states and territories. The methodological approach of this study employs the social media analytics method, and conducts sentiment and content analyses of location-based Twitter messages (n = 11,236) from Australia. The results disclose that: (a) digital transformation, innovation, and sustainability are the most popular AI application areas in urban planning and development; (b) drones, automation, robotics, and big data are the most popular AI technologies utilized in urban planning and development, and; (c) achieving the digital transformation and sustainability of cities through the use of AI technologies—such as big data, automation and robotics—is the central community discussion topic.
Tan Yigitcanlar; Nayomi Kankanamge; Massimo Regona; Andres Maldonado; Bridget Rowan; Alex Ryu; Kevin C. DeSouza; Juan M. Corchado; Rashid Mehmood; Rita Yi Man Li. Artificial Intelligence Technologies and Related Urban Planning and Development Concepts: How Are They Perceived and Utilized in Australia? Journal of Open Innovation: Technology, Market, and Complexity 2020, 6, 187 .
AMA StyleTan Yigitcanlar, Nayomi Kankanamge, Massimo Regona, Andres Maldonado, Bridget Rowan, Alex Ryu, Kevin C. DeSouza, Juan M. Corchado, Rashid Mehmood, Rita Yi Man Li. Artificial Intelligence Technologies and Related Urban Planning and Development Concepts: How Are They Perceived and Utilized in Australia? Journal of Open Innovation: Technology, Market, and Complexity. 2020; 6 (4):187.
Chicago/Turabian StyleTan Yigitcanlar; Nayomi Kankanamge; Massimo Regona; Andres Maldonado; Bridget Rowan; Alex Ryu; Kevin C. DeSouza; Juan M. Corchado; Rashid Mehmood; Rita Yi Man Li. 2020. "Artificial Intelligence Technologies and Related Urban Planning and Development Concepts: How Are They Perceived and Utilized in Australia?" Journal of Open Innovation: Technology, Market, and Complexity 6, no. 4: 187.
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.
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 StyleThaha 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 StyleThaha 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.
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.
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 StyleSarah 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 StyleSarah 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.
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.
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 StyleThaha 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 StyleThaha 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.
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.
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 StyleNourah 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 StyleNourah 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.
Road transportation is the backbone of modern economies despite costing annually millions of human deaths and injuries and trillions of dollars. Twitter is a powerful information source for transportation but major challenges in big data management and Twitter analytics need addressing. We propose Iktishaf, developed over Apache Spark, a big data tool for traffic-related event detection from Twitter data in Saudi Arabia. It uses three machine learning (ML) algorithms to build multiple classifiers to detect eight event types. The classifiers are validated using widely used criteria and against external sources. Iktishaf Stemmer improves text preprocessing, event detection and feature space. Using 2.5 million tweets, we detect events without prior knowledge including the KSA national day, a fire in Riyadh, rains in Makkah and Taif, and the inauguration of Al-Haramain train. We are not aware of any work, apart from ours, that uses big data technologies for event detection of road traffic events from tweets in Arabic. Iktishaf provides hybrid human-ML methods and is a prime example of bringing together AI theory, big data processing, and human cognition applied to a practical problem.
Ebtesam AlOmari; Iyad Katib; Rashid Mehmood. Iktishaf: a Big Data Road-Traffic Event Detection Tool Using Twitter and Spark Machine Learning. Mobile Networks and Applications 2020, 1 -16.
AMA StyleEbtesam AlOmari, Iyad Katib, Rashid Mehmood. Iktishaf: a Big Data Road-Traffic Event Detection Tool Using Twitter and Spark Machine Learning. Mobile Networks and Applications. 2020; ():1-16.
Chicago/Turabian StyleEbtesam AlOmari; Iyad Katib; Rashid Mehmood. 2020. "Iktishaf: a Big Data Road-Traffic Event Detection Tool Using Twitter and Spark Machine Learning." Mobile Networks and Applications , no. : 1-16.
In recent years, artificial intelligence (AI) has started to manifest itself at an unprecedented pace. With highly sophisticated capabilities, AI has the potential to dramatically change our cities and societies. Despite its growing importance, the urban and social implications of AI are still an understudied area. In order to contribute to the ongoing efforts to address this research gap, this paper introduces the notion of an artificially intelligent city as the potential successor of the popular smart city brand—where the smartness of a city has come to be strongly associated with the use of viable technological solutions, including AI. The study explores whether building artificially intelligent cities can safeguard humanity from natural disasters, pandemics, and other catastrophes. All of the statements in this viewpoint are based on a thorough review of the current status of AI literature, research, developments, trends, and applications. This paper generates insights and identifies prospective research questions by charting the evolution of AI and the potential impacts of the systematic adoption of AI in cities and societies. The generated insights inform urban policymakers, managers, and planners on how to ensure the correct uptake of AI in our cities, and the identified critical questions offer scholars directions for prospective research and development.
Tan Yigitcanlar; Luke Butler; Emily Windle; Kevin C. DeSouza; Rashid Mehmood; Juan M. Corchado. Can Building “Artificially Intelligent Cities” Safeguard Humanity from Natural Disasters, Pandemics, and Other Catastrophes? An Urban Scholar’s Perspective. Sensors 2020, 20, 2988 .
AMA StyleTan Yigitcanlar, Luke Butler, Emily Windle, Kevin C. DeSouza, Rashid Mehmood, Juan M. Corchado. Can Building “Artificially Intelligent Cities” Safeguard Humanity from Natural Disasters, Pandemics, and Other Catastrophes? An Urban Scholar’s Perspective. Sensors. 2020; 20 (10):2988.
Chicago/Turabian StyleTan Yigitcanlar; Luke Butler; Emily Windle; Kevin C. DeSouza; Rashid Mehmood; Juan M. Corchado. 2020. "Can Building “Artificially Intelligent Cities” Safeguard Humanity from Natural Disasters, Pandemics, and Other Catastrophes? An Urban Scholar’s Perspective." Sensors 20, no. 10: 2988.
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.
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 StyleShoayee 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 StyleShoayee 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.
Mobile cloud computing (MCC) has recently emerged as a state-of-the-art technology for mobile systems. MCC enables portable and context-aware computation via mobile devices by exploiting virtually unlimited hardware and software resources offered by cloud computing servers. Software architecture helps to abstract the complexities of system design, development, and evolution phases to implement MCC systems effectively and efficiently. This paper aims to identify, taxonomically classify, and systematically map the state of the art on architecting MCC-based software. We have used an evidence-based software engineering (EBSE) approach to conduct a systematic mapping study (SMS) based on 121 qualitatively selected research studies published from 2006 to 2019. The results of the SMS highlight that architectural solutions for MCC systems are mainly focused on supporting (i) software as a service for mobile computing, (ii) off-loading mobile device data to cloud-servers, (iii) internet of things, edge, and fog computing along with various aspects like (iv) security and privacy of mobile device data. The emerging research focuses on the existing and futuristic challenges that relate to MCC-based internet of things (IoTs), mobile-cloud edge systems, along with green and energy-efficient computing. The results of the SMS facilitate knowledge transfer that could benefit researchers and practitioners to understand the role of software architecture to develop the next generation of mobile-cloud systems to support internet-driven computing.
Abdulrahman Alreshidi; Aakash Ahmad; Ahmed B. Altamimi; Khalid Sultan; Rashid Mehmood; B. Altamimi. Software Architecture for Mobile Cloud Computing Systems. Future Internet 2019, 11, 238 .
AMA StyleAbdulrahman Alreshidi, Aakash Ahmad, Ahmed B. Altamimi, Khalid Sultan, Rashid Mehmood, B. Altamimi. Software Architecture for Mobile Cloud Computing Systems. Future Internet. 2019; 11 (11):238.
Chicago/Turabian StyleAbdulrahman Alreshidi; Aakash Ahmad; Ahmed B. Altamimi; Khalid Sultan; Rashid Mehmood; B. Altamimi. 2019. "Software Architecture for Mobile Cloud Computing Systems." Future Internet 11, no. 11: 238.
Road transportation is among the global grand challenges affecting human lives, health, society, and economy, caused due to road accidents, traffic congestion, and other transportation deficiencies. Autonomous vehicles (AVs) are set to address major transportation challenges including safety, efficiency, reliability, sustainability, and personalization. The foremost challenge for AVs is to perceive their environments in real-time with the highest possible certainty. Relatedly, connected vehicles (CVs) have been another major driver of innovation in transportation. In this paper, we bring autonomous and connected vehicles together and propose TAAWUN, a novel approach based on the fusion of data from multiple vehicles. The aim herein is to share the information between multiple vehicles about their environments, enhance the information available to the vehicles, and make better decisions regarding the perception of their environments. TAWUN shares, among the vehicles, visual data acquired from cameras installed on individual vehicles, as well as the perceived information about the driving environments. The environment is perceived using deep learning, random forest (RF), and C5.0 classifiers. A key aspect of the TAAWUN approach is that it uses problem specific feature sets to enhance the prediction accuracy in challenging environments such as problematic shadows, extreme sunlight, and mirage. TAAWUN has been evaluated using multiple metrics, accuracy, sensitivity, specificity, and area-under-the-curve (AUC). It performs consistently better than the base schemes. Directions for future work to extend the tool are provided. This is the first work where visual information and decision fusion are used in CAVs to enhance environment perception for autonomous driving.
Furqan Alam; Rashid Mehmood; Iyad Katib; Saleh M. Altowaijri; Aiiad Albeshri. TAAWUN: a Decision Fusion and Feature Specific Road Detection Approach for Connected Autonomous Vehicles. Mobile Networks and Applications 2019, 1 -17.
AMA StyleFurqan Alam, Rashid Mehmood, Iyad Katib, Saleh M. Altowaijri, Aiiad Albeshri. TAAWUN: a Decision Fusion and Feature Specific Road Detection Approach for Connected Autonomous Vehicles. Mobile Networks and Applications. 2019; ():1-17.
Chicago/Turabian StyleFurqan Alam; Rashid Mehmood; Iyad Katib; Saleh M. Altowaijri; Aiiad Albeshri. 2019. "TAAWUN: a Decision Fusion and Feature Specific Road Detection Approach for Connected Autonomous Vehicles." Mobile Networks and Applications , no. : 1-17.
SpMV is a vital computing operation of many scientific, engineering, economic and social applications, increasingly being used to develop timely intelligence for the design and management of smart societies. Several factors affect the performance of SpMV computations, such as matrix characteristics, storage formats, software and hardware platforms. The complexity of the computer systems is on the rise with the increasing number of cores per processor, different levels of caches, processors per node and high speed interconnect. There is an ever-growing need for new optimization techniques and efficient ways of exploiting parallelism. In this paper, we propose ZAKI, a data-driven, machine-learning approach and tool, to predict the optimal number of processes for SpMV computations of an arbitrary sparse matrix on a distributed memory machine. The aim herein is to allow application scientists to automatically obtain the best configuration, and hence the best performance, for the execution of SpMV computations. We train and test the tool using nearly 2000 real world matrices obtained from 45 application domains including computational fluid dynamics (CFD), computer vision, and robotics. The tool uses three machine learning methods, decision trees, random forest, gradient boosting, and is evaluated in depth. A discussion on the applicability of our proposed tool to energy efficiency optimization of SpMV computations is given. This is the first work where the sparsity structure of matrices have been exploited to predict the optimal number of processes for a given matrix in distributed memory environments by using different base and ensemble machine learning methods.
Sardar Usman; Rashid Mehmood; Iyad Katib; Aiiad Albeshri; Saleh M. Altowaijri. ZAKI: A Smart Method and Tool for Automatic Performance Optimization of Parallel SpMV Computations on Distributed Memory Machines. Mobile Networks and Applications 2019, 1 -20.
AMA StyleSardar Usman, Rashid Mehmood, Iyad Katib, Aiiad Albeshri, Saleh M. Altowaijri. ZAKI: A Smart Method and Tool for Automatic Performance Optimization of Parallel SpMV Computations on Distributed Memory Machines. Mobile Networks and Applications. 2019; ():1-20.
Chicago/Turabian StyleSardar Usman; Rashid Mehmood; Iyad Katib; Aiiad Albeshri; Saleh M. Altowaijri. 2019. "ZAKI: A Smart Method and Tool for Automatic Performance Optimization of Parallel SpMV Computations on Distributed Memory Machines." Mobile Networks and Applications , no. : 1-20.