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A comprehensive survey on security issues in vehicle-to-grid networks

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Journal of Control and Decision 2023年第2期10卷 150-159页

作者： Arun Sekar Rajasekaran Maria Azees fadi al-turjman Department of Electronics and Communication Engineering GMR Institute of TechnologyRajamIndia Artificial Intelligence Engineering Department. Research Center for AI and IoTAI and Robotics InstituteNear East UniversityMersin 10Turkey

Vehicle to grid(V2G)is the most hopeful approach to transfer energy as well as information in the bidirectional way.V2G network is formed by electric vehicles which connect with smart metres for information and energy transfer in a wireless *** though many security preserving schemes developed in V2G networks,they were prone to enormous number of security breaches.A countless deal of works has been done towards it,but security mechanisms in V2G networks are not *** survey work provides a summary about the V2G network characteristics,significance,security services and the security ***,this work offers a summary of some foremost security attacks on various security services such as accessibility,confidentiality,authentication,integrity and non-repudiation and the related countermeasures to make the V2G communications more protected.

关键词： Authentication confidentiality integrity privacy security

Intelligent Framework for Secure Transportation Systems Using Software-Defined-Internet of Vehicles

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Computers, Materials & Continua 2021年第9期68卷 3947-3966页

作者： Mohana Priya Pitchai Manikandan Ramachandran fadi al-turjman Leonardo Mostarda School of Computing SASTRA Deemed UniversityThanjavur613401India Research Centre for A.I.and IoT Near East UniversityNicosiaMersin10Turkey Department of Software Engineering Camareno UniversityItaly

The Internet of Things plays a predominant role in automating all real-time *** such application is the Internet of Vehicles which monitors the roadside traffic for automating traffic *** vehicles are connected to the internet through wireless communication technologies,the Internet of Vehicles network infrastructure is susceptible to flooding *** the network infrastructure is difficult as network customization is not *** Software Defined Network provide a flexible programming environment for network customization,detecting flooding attacks on the Internet of Vehicles is integrated on top of *** basic methodology used is crypto-fuzzy rules,in which cryptographic standard is incorporated in the traditional fuzzy *** this research work,an intelligent framework for secure transportation is proposed with the basic ideas of security attacks on the Internet of Vehicles integrated with software-defined *** intelligent framework is proposed to apply for the smart city *** proposed cognitive framework is integrated with traditional fuzzy,cryptofuzzy and Restricted Boltzmann Machine algorithm to detect malicious traffic flows in Software-Defined-Internet of *** is inferred from the result interpretations that an intelligent framework for secure transportation system achieves better attack detection accuracy with less delay and also prevents buffer overflow *** proposed intelligent framework for secure transportation system is not compared with existing methods;instead,it is tested with crypto and machine learning algorithms.

关键词： Internet of things smart cities software-defined network intelligent transportation system fuzzy inference system

Computer-aided Detection of Tuberculosis from Microbiological and Radiographic Images

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Data Intelligence 2023年第4期5卷 1008-1032页

作者： Abdullahi Umar Ibrahim Ayse Gunnay Kibarer fadi al-turjman Department of Biomedical Engineering Near East UniversityNicosiaMersin 10Turkey Research Center for al and loT Faculty of EngineeringUniversity of KyreniaMersin 10Turkey Artificial Intelligence Engineering Dept. Al and Robotics InstituteNear East UniversityMersin 1Turkey

Tuberculosis caused by Mycobacterium tuberculosis have been a major challenge for medical and healthcare sectors in many underdeveloped countries with limited diagnosis *** can be detected from microscopic slides and chest X-ray but as a result of the high cases of tuberculosis,this method can be tedious for both Microbiologists and Radiologists and can lead to *** challenges can be solved by employing Computer-Aided Detection(CAD)via al-driven models which learn features based on convolution and result in an output with high *** this paper,we described automated discrimination of X-ray and microscope slide images into tuberculosis and non-tuberculosis cases using pretrained alexNet *** study employed Chest X-ray dataset made available on Kaggle repository and microscopic slide images from both Near East University Hospital and Kaggle *** classification of tuberculosis using microscopic slide images,the model achieved 90.56%accuracy,97.78%sensitivity and 83.33%specificity for 70:30 *** classification of tuberculosis using X-ray images,the model achieved 93.89%accuracy,96.67%sensitivity and 91.11%specificity for 70:30 *** result is in line with the notion that CNN models can be used for classifying medical images with higher accuracy and precision.

关键词： Tuberculosis Deep Learning Pretrained alexNet Chest X-ray Microscopic slide

Comparative Evaluation and Comprehensive Analysis of Machine Learning Models for Regression Problems

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Data Intelligence 2022年第3期4卷 620-652页

作者： Boran Sekeroglu Yoney Kirsal Ever Kamil Dimililer fadi al-turjman Information Systems Engineering Department Near East UniversityNicosiaCyprusMersin 10Turkey Software Engineering Department Near East UniversityNicosiaCyprusMersin 10Turkey Electrical and Electronic Engineering Department Near East UniversityNicosiaCyprusMersin 10Turkey Artificial Intelligence Engineering Department Near East UniversityNicosiaCyprusMersin 10Turkey Research Centre for AI and IoT Near East UniversityNicosiaCyprusMersin 10Turkey

Artificial intelligence and machine learning applications are of significant importance almost in every field of human life to solve problems or support human experts. However, the determination of the machine learning model to achieve a superior result for a particular problem within the wide real-life application areas is still a challenging task for researchers. The success of a model could be affected by several factors such as dataset characteristics, training strategy and model responses. Therefore, a comprehensive analysis is required to determine model ability and the efficiency of the considered strategies. This study implemented ten benchmark machine learning models on seventeen varied datasets. Experiments are performed using four different training strategies 60:40, 70:30, and 80:20 hold-out and five-fold cross-validation techniques. We used three evaluation metrics to evaluate the experimental results: mean squared error, mean absolute error, and coefficient of determination(R2score). The considered models are analyzed, and each model’s advantages, disadvantages, and data dependencies are indicated. As a result of performed excess number of experiments, the deep Long-Short Term Memory(LSTM) neural network outperformed other considered models, namely, decision tree, linear regression, support vector regression with a linear and radial basis function kernels, random forest, gradient boosting, extreme gradient boosting, shallow neural network, and deep neural network. It has also been shown that cross-validation has a tremendous impact on the results of the experiments and should be considered for the model evaluation in regression studies where data mining or selection is not performed.

关键词： Machine learning Regression Comparative evaluation Analysis Validation

Technology Landscape for Epidemiological Prediction and Diagnosis of COVID-19

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Computers, Materials & Continua 2021年第5期67卷 1679-1696页

作者： Siddhant Banyal Rinky Dwivedi Koyel Datta Gupta Deepak Kumar Sharma fadi al-turjman Leonardo Mostarda Department of Instrumentation and Control Netaji Subhas University of TechnologyNew Delhi110078India Department of Computer Science&Engineering Maharaja Surajmal Institute of TechnologyNew Delhi110058India Department of Information Technology Netaji Subhas University of TechnologyNew Delhi110078India Research Center for AI and IoT Near East UniversityNicosiaMersin10Turkey Computer Science Department Camerino UniversityCamerino62032Italy

The COVID-19 outbreak initiated from the Chinese city of Wuhanand eventually affected almost every nation around the globe. From China,the disease started spreading to the rest of the world. After China, Italybecame the next epicentre of the virus and witnessed a very high death *** nations like the USA became severely hit by SARS-CoV-2 virus. TheWorld Health Organisation, on 11th March 2020, declared COVID-19 a pandemic. To combat the epidemic, the nations from every corner of the worldhas instituted various policies like physical distancing, isolation of infectedpopulation and researching on the potential vaccine of SARS-CoV-2. Toidentify the impact of various policies implemented by the affected countrieson the pandemic spread, a myriad of AI-based models have been presented toanalyse and predict the epidemiological trends of COVID-19. In this work, theauthors present a detailed study of different articial intelligence frameworksapplied for predictive analysis of COVID-19 patient record. The forecastingmodels acquire information from records to detect the pandemic spreadingand thus enabling an opportunity to take immediate actions to reduce thespread of the virus. This paper addresses the research issues and correspondingsolutions associated with the prediction and detection of infectious diseaseslike COVID-19. It further focuses on the study of vaccinations to cope withthe pandemic. Finally, the research challenges in terms of data availability,reliability, the accuracy of the existing prediction models and other open issuesare discussed to outline the future course of this study.

关键词： COVID-19 diagnosis deep learning forecasting models machine learning metaheuristics prediction big data pandemic

Maximizing aged photovoltaic array power: a computer modelling study

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CLEAN ENERGY 2024年第1期8卷 171-187页

作者： Sibai, fadi N. Gulf Univ Sci & Technol Elect & Comp Engn Dept Mishref Kuwait

Photovoltaic (PV) modules age with time for various reasons such as corroded joints and terminals and glass coating defects, and their ageing degrades the PV array power. With the help of the PV array numerical model, this paper explores the effects of PV module ageing on the PV array power, and the power gains and costs of rearranging and recabling aged PV modules in a PV array. The numerical PV array model is first revised to account for module ageing, rearrangement and recabling, with the relevant equations presented herein. The updated numerical model is then used to obtain the array powers for seven different PV arrays. The power results are then analysed in view of the attributes of the seven PV array examples. A guiding method to recommend recabling after rearranging aged modules is then proposed, leading to further significant power gains, while eliminating intra-row mismatches. When certain conditions are met, it was shown that recabling PV modules after rearranging them may lead to further significant power gains, reaching 57% and 98% in two considered PV array examples. Higher gains are possible in other arrays. A cost-benefit analysis weighing annual power gains versus estimated recabling costs is also given for the seven considered PV array examples to guide recabling decisions based on technical and economic merits. In the considered examples, recabling costs can be recovered in <4 years. Compared with the powers of the aged arrays, power gains due to our proposed rearranging and recabling the PV arrays ranged between 73% and 131% in the considered examples-well over the gains reported in the literature. Moreover, the cost of our static module rearrangement and recabling method outshines the costs of dynamic reconfiguration methods recently published in the literature.

关键词： solar energy optimizing array power photovoltaic array modelling photovoltaic modules ageing photovoltaic module rearrangement module recabling and reconfiguration PV array mismatch POINT TRACKING MODULE RECONFIGURATION PERFORMANCE SIMULATION IMPACT CELL

The future of competitive advantage in Oman: Integrating green product innovation, al, and intellectual capital in business strategies

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International Journal of Innovation Studies 2024年第2期8卷 154-171页

作者： fadi Abdelfattah Mohammed Salah Khalid Dahleez Riyad Darwazeh Hussam al Halbusi Modern College of Business and Science(MCBS) MuscatOman Dean College of Business Administration A'Sharqiyah UniversityIbraOman Head of Accounting Department Business SchoolAl-Ahliya Amman UniversityAmmanJordan Management Department Ahmed Bin Mohammed Military College(ABMMC)DohaQatar

This study delves into the dynamics of green product innovation,artificial intelligence(al)adaption,and intellectual capital,investigating their impact on the competitiveness of firms in *** emphasizes the crucial role of government intervention and R&D investments in this *** on the responses of 214 top managers in Oman,the research employs structural equation modeling to analyze the intricate relationships between these *** findings underscore a significant positive correlation between green innovation,al implementation,and intellectual capital,with government involvement and R&D investments as vital *** study provides a novel perspective on the synergy of technology,innovation,and intellectual capital in developing *** offers essential insights for business leaders,policymakers,and scholars,highlighting the necessity of integrating advanced technologies and sustainable practices in business strategies to achieve competitive *** research adds to the existing body of knowledge on innovation and *** offers practical implications for enhancing firm performance in Oman and similar emerging markets.

关键词： Green product innovation R&D investments AI adoption Intellectual capital Government involvement Knowledge-driven culture Oman

Artificial intelligence in physiological characteristics recognition for internet of things authentication

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Digital Communications and Networks 2024年第3期10卷 740-755页

作者： Zhimin Zhang Huansheng Ning fadi Farha Jianguo Ding Kim-Kwang Raymond Choo School of Computer and Communication Engineering University of Science and Technology BeijingBeijing100083China Beijing Engineering Research Center for Cyberspace Data Analysis and Applications Beijing100083China Faculty of Informatics Engineering Aleppo UniversityAleppoSyria Department of Computer Science Blekinge Institute of Technology37179KarlskronaSweden Department of Information Systems and Cyber Security University of Texas at San AntonioSan AntonioTX78249-0631USA

Effective user authentication is key to ensuring equipment security,data privacy,and personalized services in Internet of Things(IoT)***,conventional mode-based authentication methods(e.g.,passwords and smart cards)may be vulnerable to a broad range of attacks(e.g.,eavesdropping and side-channel attacks).Hence,there have been attempts to design biometric-based authentication solutions,which rely on physiological and behavioral *** characteristics need continuous monitoring and specific environmental settings,which can be challenging to implement in ***,we can also leverage Artificial Intelligence(AI)in the extraction and classification of physiological characteristics from IoT devices processing to facilitate ***,we review the literature on the use of AI in physiological characteristics recognition pub-lished after *** use the three-layer architecture of the IoT(i.e.,sensing layer,feature layer,and algorithm layer)to guide the discussion of existing approaches and their *** also identify a number of future research opportunities,which will hopefully guide the design of next generation solutions.

关键词： Physiological characteristics recognition Artificial intelligence Internet of things Biological-driven authentication

Reinforcement Learning in Process Industries:Review and Perspective

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IEEE/CAA Journal of Automatica Sinica 2024年第2期11卷 283-300页

作者： Oguzhan Dogru Junyao Xie Om Prakash Ranjith Chiplunkar Jansen Soesanto Hongtian Chen Kirubakaran Velswamy fadi Ibrahim Biao Huang Department of Chemical and Materials Engineering University of AlbertaEdmontonT6G 1H9Canada IEEE

This survey paper provides a review and perspective on intermediate and advanced reinforcement learning(RL)techniques in process industries. It offers a holistic approach by covering all levels of the process control hierarchy. The survey paper presents a comprehensive overview of RL algorithms,including fundamental concepts like Markov decision processes and different approaches to RL, such as value-based, policy-based, and actor-critic methods, while also discussing the relationship between classical control and RL. It further reviews the wide-ranging applications of RL in process industries, such as soft sensors, low-level control, high-level control, distributed process control, fault detection and fault tolerant control, optimization,planning, scheduling, and supply chain. The survey paper discusses the limitations and advantages, trends and new applications, and opportunities and future prospects for RL in process industries. Moreover, it highlights the need for a holistic approach in complex systems due to the growing importance of digitalization in the process industries.

关键词： Process control process systems engineering reinforcement learning