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An Efficient Three-Factor Authenticated Key Agreement Technique Using FCM Under HC-IoT Architectures

Computers, Materials & Continua 2022年第7期72卷 1373-1389页

作者： Chandrashekhar Meshram Agbotiname Lucky Imoize Sajjad Shaukat Jamal Parkash Tambare Adel R.Alharbi Iqtadar Hussain Department of Post Graduate Studies and Research in Mathematics Jayawanti Haksar Government Post-Graduation CollegeCollege of Chhindwara UniversityBetul460001M.P.India Department of Electrical and Electronics Engineering Faculty of EngineeringUniversity of LagosAkokaLagos100213Nigeria Department of Electrical Engineering and Information Technology Institute of Digital CommunicationRuhr University44801BochumGermany Department of Mathematics College of ScienceKing Khalid UniversityAbhaSaudi Arabia Water Resources&Applied Mathematics Research Lab Nagpur440027India College of Computing and Information Technology University of TabukTabuk71491Saudi Arabia Mathematics Program Department of MathematicsStatistics and PhysicsCollege of Arts and SciencesQatar University2713DohaQatar

The Human-Centered Internet of Things(HC-IoT)is fast becoming a hotbed of security and privacy *** users can establish a common session key through a trusted server over an open communication channel using a three-party authenticated key *** of the early authenticated key agreement systems relied on pairing,hashing,or modular exponentiation processes that are computationally intensive and *** order to address this problem,this paper offers a new three-party authenticated key agreement technique based on fractional chaotic *** new scheme uses fractional chaotic maps and supports the dynamic sensing of HC-IoT devices in the network architecture without a password *** projected security scheme utilized a hash function,which works well for the resource-limited HC-IoT *** results show that our new technique is resistant to password guessing attacks since it does not use a ***,our approach provides users with comprehensive privacy protection,ensuring that a user forgery attack causes no ***,our new technique offers better security features than the techniques currently available in the literature.

关键词： Three-party authenticated key agreement anonymity fractional chaotic maps Chebyshev polynomial password table human-centered internet of things(HC-IoT)

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Impact on mental health due to COVID-19 pandemic: A cross-sectional study in Bangladesh

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Clinical eHealth 2023年第1期6卷 42-52页

作者： Pathan, Refat Khan Biswas, Munmun Yasmin, Suraiya Uddin, Mohammad Amaz Das, Anindya Khandaker, Mayeen Uddin Faruque, M.R.I. Sarker, Moniruzzaman Dept. of Computing and Information Systems School of Engineering and Technology Sunway University Selangor Bandar Sunway 47500 Malaysia Dept. of Computer Science and Engineering BGC Trust University Bangladesh Chittagong 4381 Bangladesh Department of Computer and Information Science Tokyo University of Agriculture and Technology Koganei - 184-0012 Tokyo Japan Dept. of Computer Science and Engineering Chittagong University of Engineering and Technology Chittagong 4349 Bangladesh Dept. of Electrical and Electronic Engineering Chittagong University of Engineering and Technology Chittagong 4349 Bangladesh Center for Applied Physics and Radiation Technologies School of Engineering and Technology Sunway University Selangor Bandar Sunway 47500 Malaysia Space Science Centre Universiti Kebangsaan Malaysia 43600 UKM Selangor Bangi Malaysia Department of Marketing Sunway University Business School Sunway University Selangor Bandar Sunway 47500 Malaysia Faculty of Graduate Studies Daffodil International University Daffodil Smart City Birulia Savar Dhaka – 1216 Bangladesh

The government of Bangladesh has implemented the “Stay Home” policy following the WHO recommendation to resist the community transmission of Covid-19. As a result, the routine activities of all government, semi-government establishments, including educational institutions, are severely affected, and the country's economic growth becomes slowed down. To overcome such a situation, the relevant authorities have introduced the “Work from Home” policy for the employees and “Remote Education” for students. However, due to the persistence of multi-dimensional socio-economic problems, many employees and students face big challenges in performing their regular jobs while adopting such a policy. Consequently, enormous psychological anxiety has been developed for all people, including students, parents, employees, etc., and concurrently created severe changes in their behavior. This study aims to analyze the reasons for the behavioral changes of the employees, students, academic staff, and family members of different ages due to psychological anxiety, stress, or physical issues. A comprehensive online-based survey has been carried out on people working in various sectors in Bangladesh. A modified Apriori Algorithm has been used to sort out the associations between the causes and types of behavioral changes. Analyzed data revealed a massive human behavioral change in most participants. This finding indicates that the negligence of those significant human behavioral changes may cause a higher risk of creating psychological imbalance. Therefore, there is a need to have a solid understanding of the reasons for the behavioral changes and set up standard guidelines to maintain “Work from Home” in this Covid-19 situation to avoid psychological imbalance. Based on this study, some suggestions have been given for implementation by the government on an urgent basis. © 2023

关键词： Apriori algorithm Behavioral changes Covid-19 outbreak Family crisis Work from home

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Automated Segmentation of COVID-19 Lesion from Lung CT Images Using U-Net Architecture 6th

Automated Segmentation of COVID-19 Lesion from Lung CT Image...

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6th EAI International Conference on Science and Technologies for Smart Cities, SmartCity 2020

作者： Kadry, Seifedine Al-Turjman, Fadi Rajinikanth, V. Faculty of Applied Computing and Technology Noroff University College Kristiansand Norway Artificial Intelligence Department Research Center for AI and IoT Near East University Nicosia Mersin 10 Turkey Department of Electronics and Instrumentation Engineering St. Joseph’s College of Engineering Chennai India

ISBN: (纸本)9783030760625

Pneumonia caused by the novel Coronavirus Disease (COVID-19) is emerged as a global threat and considerably affected a large population globally irrespective of their age, race, and gender. Due to its rapidity and the infection rate, the World Health Organization (WHO) declared this disease as a pandemic. The proposed research work aims to develop an automated COVID-19 lesion segmentation system using the Convolutional Neural Network (CNN) architecture called the U-Net. The traditional U-Net scheme is employed to examine the COVID-19 infection present in the lung CT images. This scheme is implemented on the benchmark COVID-19 images existing in the literature (300 images) and the segmentation performance of the U-Net is confirmed by computing the essential performance measures using a relative assessment among the extracted lesion and the Ground-Truth (GT). The overall result attained with the proposed study confirms that, the U-Net scheme helps to get the better values for the performance values, such as Jaccard (>86%), Dice (>92%) and segmentation accuracy (>95%). © 2021, ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering.

关键词： COVID-19

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Leveraging K-Means Clustering for Analysis of Arabic Hate Speech Tweets

Leveraging K-Means Clustering for Analysis of Arabic Hate Sp...

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Emerging Technologies (GCET), Global Congress on

作者： Said Salloum Khalaf Tahat Ahmed Mansoori Raghad Alfaisal Dina Tahat School of Science Engineering and Environment University of Salford Manchester UK Media & Creative Industries United Arab Emirates University and Yarmouk University Jordan Media & Creative Industries Department United Arab Emirates University UAE Faculty of Computing and Meta-Technology Universiti Pendidikan Sultan Idris Tanjung Malim Malaysia Applied Sociology Department Al Ain University UAE

ISBN: (数字)9798331542603

ISBN: (纸本)9798331542610

As hate speech is becoming common on social media platforms, it is important to detect, and curb hate speech in order to provide a better and safe environment online. Given the heavy usage of manual methods of hate speech detection, researchers started putting efforts in the direction of machine-learning-based automated methods sooner or later. Many available datasets and models on hate speech detection are largely inadequate for Arab hate speech because of the complexity of language and cultural nuances. In this paper, the researchers eased these difficulties, as they, used the K-Means to apply compilation for Arab hate speech in the L-HSAB dataset on the tweets. Methodology consisted of Prospecting and Pre-processing, Term Frequency Inverse Document frequency (TF-IDF) dimension reductions, through Principal Component Analysis (PCA), and assembly via K-Means. They helped to identify different sets of hate speech tweets and understand the common topics and topics. It has led to new understandings of how Arab hate speech flows on the Internet and now offers the potential for tailored interventions. Automated hate speech analysis via machine learning would allow policymakers to formulate tailored modification strategies focused on making the Internet safer and the community more harmonious.

关键词： Dimensionality reduction Social networking (online) Hate speech Manuals Internet Complexity theory Cultural differences Assembly Principal component analysis

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Cutting-Edge Predictive Methods for Enhancing Short-Term Load Prediction

Cutting-Edge Predictive Methods for Enhancing Short-Term Loa...

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Electrical and Computer Engineering Researches (ICECER), International Conference on

作者： Taher M. Ghazal Jamshaid Iqbal Janjua Walid Abushiba Munir Ahmad Tahir Abbas Karamath Ateeq College of Arts & Science Applied Science University Manama Kingdom of Bahrain Center for Cyber Security Faculty of Information Science and Technology Universiti Kebangsaan Malaysia Bangi Selangor Malaysia Al-Khawarizimi Institute of Computer Science (KICS) University of Engineering & Technology (UET) Lahore Pakistan College of Engineering Applied Science University Bahrain College of Informatics Korea University Seoul Republic of Korea Department of Computer Science TIMES Institute Multan Pakistan School of Computing Skyline University College University City Sharjah Sharjah UAE

ISBN: (数字)9798331539733

ISBN: (纸本)9798331539740

Efficient management and cost-related factors in the power sector call for accurate short-term load forecasting as it enables better planning with the electric grid and achieving stability within it. This paper looks into the state-of-the-art forecasting strategies that enhance load forecasts made in shorter time frames. Such techniques include but are not limited to machine learning applications, effective data models, and multi-dimensional modeling leveraging historical, meteorological, and economic data. Their review is carried out in detail with their advantages and disadvantages and their application in practice. Furthermore, this study strives to understand the barriers that need to be overcome in the quest to increase the accuracy of the forecasting models especially concerning renewable energy sources. We illustrate how new trends relate to future developments of predictive models about supporting the decision-making in operating power systems and improving the reliability and sustainability of energy systems.

关键词： Renewable energy sources Accuracy Load forecasting Machine learning Predictive models Power system stability Data models Forecasting Sustainable development Load modeling

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RETRACTED ARTICLE: Intrusion detection based on machine learning in the internet of things, attacks and counter measures

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The Journal of Supercomputing 2022年第6期78卷 8890-8924页

作者： Rehman, Eid Haseeb-ud-Din, Muhammad Malik, Arif Jamal Khan, Tehmina Karmat Abbasi, Aaqif Afzaal Kadry, Seifedine Khan, Muhammad Attique Rho, Seungmin Department of Software Engineering Foundation University Islamabad Islamabad Pakistan Faculty of Applied Computing and Technology Noroff University College Kristiansand Norway Department of Computer Science HITEC University Taxila Taxila Pakistan Chung-Ang University - Seoul Campus: Chung-Ang University Seoul Korea

Globally, data security and privacy over the Internet of Things (IoT) are necessary due to its emergence in daily life. As the IoT will soon invade each part of our lives, attention to IoT security is significant. The nature of attacks is dynamic, and addressing this requires designing dynamic methods and a self-adaptable scheme to discover security attacks from malicious use of IoT equipment. The best detection mechanism against attacks from compromised IoT devices includes machine learning techniques. This study emphasizes the latest literature on attack types and uses a scheme based on machine learning for network support in IoT and intrusion detection. Therefore, the current work includes a thorough analysis of multiple intelligence methods and their deployed architectures of network intrusion detection, focusing on IoT attacks and machine learning-based intrusion detection schemes. Moreover, it explores methods based on machine learning appropriate for identifying IoT devices associated with cyber attacks.

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Multi-objective RSM-based optimization of diesel-diethyl ether blends in diesel engine to achieve sustainable development goals

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Case Studies in Thermal Engineering 2024年 59卷

作者： Jamil, Muhammad Kashif Usman, Muhammad Hanif, Ahsan Nawaz, Haider Fouad, Yasser Saleem, Muhammad Wajid Abbas, Naseem Sajjad, Uzair Hamid, Khalid Mechanical Engineering Department RCET Campus University of Engineering and Technology Lahore Pakistan Pakistan Mechanical Engineering Department Main Campus University of Engineering and Technology Lahore Pakistan Pakistan Department of Applied Mechanical Engineering College of Applied Engineering Muzahimiyah Branch King Saud University P.O. Box 800 Riyadh11421 Saudi Arabia Department of Mechanical and Energy Engineering Faculty of Computing Engineering and Media School of Engineering and Sustainable Development De Montfort University Dubai United Arab Emirates Department of Mechanical Engineering Sejong University Seoul05006 Korea Republic of Department of Energy and Refrigerating Air-Conditioning Engineering National Taipei University of Technology Taipei10608 Taiwan Department of Energy and Process Engineering Norwegian University of Science and Technology Trondheim7491 Norway

With the plummeting reserves of fossil fuels at present, there has been a growing imperative for sustainable energy technologies over the last couple of decades. This imperative highlight the need for a shift towards renewable sources, not just to fulfill the current energy demands but also to lessen environmental degradation, fostering an eco-conscious and sustainable future. One way to accomplish such a goal is the utilization of diesel-diethyl ether (D-DEE) blends, instead of neat diesel in compression ignition (CI) engines. This study presents the multi-objective RSM-based analysis and optimization of D-DEE blends (0,5 and 10 %. vol) at manifold engine speeds (1200, 1400, and 1600 rpm). The responses that were recorded in this research included brake power (B.P), brake thermal efficiency (BTE), brake-specific fuel consumption, and CO2 emissions. The results demonstrated an increment in brake power as well as brake thermal efficiency, along with the enhanced CO2 emissions whereas the BSFC decreased while using the Di-ethyl ether enrichment in a 4-stroke CI engine. Upon implementing optimization parameters, the results illustrated the optimal blend ratio of 9.5786 %. vol at 1600 rpm, along with the associated values of B.P, BTE, BSFC, and CO2 emissions which were, 1.26726 kW, 20.861 %, 0.3354, and 7.837 % vol., respectively for this optimized fuel. This study addresses the lack of research on optimizing engine conditions for diesel-DEE blends in CI engines, specifically focusing on diethyl ether as a renewable fuel. Using response surface methodology (RSM), it aims to bridge this gap and contribute to the understanding of optimal blend utilization. The current research also aligns with the attainment of an important sustainable development goal (SDG) which includes the availability of responsible consumption (SDG 12) and improved power generation, contributing towards a greener and cleaner future. © 2024 The Authors

关键词： Sustainable development

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Analysis of Fraudulent Job Postings Using Machine Learning

Analysis of Fraudulent Job Postings Using Machine Learning

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Intelligent computing, Communication, Networking and Services (ICCNS), International Conference on

作者： Said Salloum Khalaf Tahat Ahmed Mansoori Raghad Alfaisal Dina Tahat School of Science Engineering and Environment University of Salford Manchester UK Media & Creative Industries United Arab Emirates University and Yarmouk University Jordan Media & Creative Industries department United Arab Emirates University UAE Faculty of Computing and Meta-Technology Universiti Pendidikan Sultan Idris Tanjung Malim Malaysia Applied sociology department Al Ain University UAE

ISBN: (数字)9798350354690

ISBN: (纸本)9798350354706

In the age of digital recruitment, the proliferation of fraudulent job postings poses significant challenges for job seekers and legitimate employers alike. These deceptive listings not only waste time and resources but also endanger personal data and propagate scams. Addressing this issue, we present a comprehensive machine learning methodology to accurately discern between genuine and counterfeit job opportunities. Leveraging a rich dataset procured from Kaggle, this paper details the deployment of a logistic regression classifier, judiciously trained on a fusion of textual and meta-features extracted from job advertisements. The classifier underwent rigorous evaluation, manifesting an impressive accuracy of 96.78% in segregating authentic posts from fraudulent ones. The implementation of Term Frequency-Inverse Document Frequency (TF-IDF) vectorization on textual data, alongside meta-features such as job description length, enabled the model to learn and predict with high precision. The implications of this research are substantial, offering a scalable and efficient tool for job platforms to safeguard their users and ensure the integrity of their listings.

关键词： Logistic regression Accuracy Computational modeling Machine learning Predictive models Data models Recruitment

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A Systematic Literature Review on Task Allocation and Performance Management Techniques in Cloud Data Center

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Computer Systems Science & Engineering 2024年第3期48卷 571-608页

作者： Nidhika Chauhan Navneet Kaur Kamaljit Singh Saini Sahil Verma Abdulatif Alabdulatif Ruba Abu Khurma Maribel Garcia-Arenas Pedro A.Castillo University Institute of Computing Chandigarh UniversityPunjab143001India Department of Computer Science and Engineering Chandigarh UniversityPunjab143001India Department of Computer Science and Engineering Uttaranchal UniversityUttarakhand248007India Department of Computer Science College of Computer Qassim UniversityBuraydah52571Saudi Arabia MEU Research Unit Faculty of Information TechnologyMiddle East UniversityAmman11831Jordan Department of Computer Engineering Automatics and RoboticsUniversity of GranadaGranada18071Spain Applied Science Research Center Applied Science Private UniversityAmman11931Jordan

As cloud computing usage grows,cloud data centers play an increasingly important *** maximize resource utilization,ensure service quality,and enhance system performance,it is crucial to allocate tasks and manage performance *** purpose of this study is to provide an extensive analysis of task allocation and performance management techniques employed in cloud data *** aim is to systematically categorize and organize previous research by identifying the cloud computing methodologies,categories,and gaps.A literature review was conducted,which included the analysis of 463 task allocations and 480 performance management *** review revealed three task allocation research topics and seven performance management *** allocation research areas are resource allocation,load-Balancing,and *** management includes monitoring and control,power and energy management,resource utilization optimization,quality of service management,fault management,virtual machine management,and network *** study proposes new techniques to enhance cloud computing work allocation and performance ***-comings in each approach can guide future *** research’s findings on cloud data center task allocation and performance management can assist academics,practitioners,and cloud service providers in optimizing their systems for dependability,cost-effectiveness,and *** methodologies can steer future research to fill gaps in the literature.

关键词： Cloud computing data centre task allocation performance management resource utilization

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Patterns of Sociotechnical Design Preferences of Chatbots for Intergenerational Collaborative Innovation: A Q Methodology Study

arXiv

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arXiv 2022年

作者： Nurhas, Irawan Jahanbin, Pouyan Pawlowski, Jan Wingreen, Stephen Geisler, Stefan Institute of Positive Computing Ruhr West University of Applied Sciences Bottrop Germany Faculty of Information Technology University of Jyväskylä Jyväskylä Finland Department of Accounting and Information Systems University of Canterbury Christchurch New Zealand

Chatbot technology is increasingly emerging as a virtual assistant. Chatbots could allow individuals and organizations to accomplish objectives that are currently not fully optimized for collaboration across an intergenerational context. This paper explores the preferences of chatbots as a companion in intergenerational innovation. The Q methodology was used to investigate different types of collaborators and determine how different choices occur between collaborators that merge the problem and solution domains of chatbots’ design within intergenerational settings. The study’s findings reveal that various chatbot design priorities are more diverse among younger adults than senior adults. Additionally, our research further outlines the principles of chatbot design and how chatbots will support both generations. This research is the first step towards cultivating a deeper understanding of different age groups’ subjective design preferences for chatbots functioning as a companion in the workplace. Moreover, this study demonstrates how the Q methodology can guide technological development by shifting the approach from an age-focused design to a common goal-oriented design within a multigenerational context. © 2022, CC BY.

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