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2024 International Conference on Computing and Intelligent Reality Technologies, ICCIRT 2024

作者： Palagati, Anusha Balan, Santhosh Kumar Arun Joe Babulo, S. Raja, Laxmi Natarajan, K.K. Kalimuthu, Rajkumar Dept. of Computer Science and Engineering Guru Nanak Institute of Technology Ibrahimpatnam Hyderabad India Department of Computer Applications K.S.R College of Arts and Science for Women Tamilnadu Tiruchengode India Department of Cyber Security Karpagam Academy of Higher Education Tamilnadu Coimbatore India College of Engineering and Technology University of Technology and Applied Sciences Al Mussanah Oman Department of Cyber Security Guru Nanak Institute of Technology Ibrahimpatnam Hyderabad India

ISBN: (纸本)9798331510299

Cyberbullying is defined as sharing of undesirable material in the cyberspace, primarily- the use of the social sites that instigate hostility and create feelings of hatred among the individuals. Another increasing threat due to increase use of internet-based communication is cyberbullying especially on the site such as Twitter. Different ML and NLP approaches including support vector machine (SVM), Naïve bayes, Random Forest, and logistic regression is employed to detect cyberbul Bullivating. These methods for supervised learning mine patterns in text and image, allowing computers to promptly flag risky interactions. Some of these approaches also utilizes OCR for the identification of image-based bullying. Consequently, the objective of this research is to identify the correct algorithm and dataset for improving the identification of cyberbullying and solving the problem that manifests itself in the steady increase in cases of this type of bullying. © 2024 IEEE.

关键词： Adversarial machine learning

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The Reliability of Information Sources: Student Perceptions and Educational Opportunities 7

The Reliability of Information Sources: Student Perceptions ...

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7th IEEE International Conference and Workshop Obuda on Electrical and Power engineering, CANDO-EPE 2024

作者： Kollar, Anita Mihalovicsne Katona, Jozsef Obuda University Institute of Electronics and Communication Systems Kandó Kálmán Faculty of Electrical Engineering Budapest Hungary John Von Neumann University GAMF Faculty of Engineering and Computer Science Kecskemet Hungary Budapest Business University Faculty of Finance and Accountancy Department of Applied Quantitative Methods Budapest Hungary Department of Software Development and Application Institute of Computer Engineering University of Dunaujvaros Dunaujvaros Hungary

ISBN: (纸本)9798331531119

The modern digital age demands the skills to be able to assess information. In the current world, access to information and knowledge is abundant from different sources. Indeed, high-quality data are much needed in education and professional development, as the quality and accuracy of information directly influence the process of learning and decision-making. The study aims to investigate the perceptions of computer science students at the University of Dunaújváros related to the credibility of diverse information sources. Within a questionnaire, on a voluntary answer basis, 68 students took part, 59 males and nine females. This work is aimed at assessing the degree to which the surveyed students evaluate information sources based on a five-point Likert scale. The data shows that students find print books and textbooks as the most reliable sources;social media are the least reliable. The study also supports the importance of source credibility evaluation, for which lateral reading techniques and AI-based news source credibility identification systems can be helpful. These findings suggest the necessity of bettering the educational approaches to foster better source criticism skills in the students, especially for online content and social media. The study generally contributes to understanding the information society and helps prepare students to obtain and use reliable and accurate information in their future careers. Using AI-based tools and systems can then further support these educational efforts with robust mechanisms to evaluate information credibility in an ever more complex digital landscape. © 2024 IEEE.

关键词： Data reliability

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Review of on-board conductive charger topologies for electric transportation

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International Journal of Power Electronics 2023年第4期17卷 406-442页

作者： Vijaya Anand, N. Jaya Sai Praneeth, A.V. Yalla, Naveen Sood, Vijay K. Department of EEE P.V.P. Siddhartha Institute of Technology Kanuru Vijayawada520007 India Advance Engineering Group Borg Warner Technical Center Bascharage Luxembourg Varanasi221005 India Department of Electrical Computer and Software Engineering Faculty of Engineering and Applied Science Ontario Tech University 2000 Simcoe Street North OshawaONL1H 7K4 Canada

This paper presents an overview of on-board conductive charger topologies for electric vehicles (EVs). Battery packs in electric and plug-in hybrid electric vehicles (EVs/PHEVs) need frequent energy refills to fulfil their duty cycles and overcome the range anxiety of vehicle owners. Due to the limited availability of fast charging infrastructures, all automotive manufacturers prefer an on-board battery charger in their vehicles. However, there are power level limitations for these chargers because of the weight, space and cost constraints. On-board charging systems can be either conductive or inductive. This paper reviews the topologies that are involved in conductive charging only. Globally, all automotive companies provide levels 1 and 2 for residential charging of vehicles. Single- and two-stage power converter topologies are summarised with their limitations. Furthermore, the current trends in the DC-DC converters for the two-stage conversion battery charger topologies are also outlined. This paper provides an overview of various topologies and configurations involved in EV charging with their limitations. © 2023 Inderscience Enterprises Ltd.

关键词： Topology

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Deep Reinforcement Learning-Based Robust Design for an IRS-Assisted MISO-NOMA System

IEEE Transactions on Machine Learning in Communications and ...

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IEEE Transactions on Machine Learning in Communications and Networking 2024年 2卷 424-441页

作者： Waraiet, Abdulhamed Cumanan, Kanapathippillai Ding, Zhiguo Dobre, Octavia A. University of York School of Physics Engineering and Technology YorkYO10 5DD United Kingdom Khalifa University Department of Computer and Communication Engineering Abu Dhabi United Arab Emirates Memorial University Faculty of Engineering and Applied Science St. John'sNLA1C 5S7 Canada

In this paper, we propose a robust design for an intelligent reflecting surface (IRS)-assisted multiple-input single output non-orthogonal multiple access (NOMA) system. By considering channel uncertainties, the original robust design problem is formulated as a sum-rate maximization problem under a set of constraints. In particular, the uncertainties associated with reflected channels through IRS elements and direct channels are taken into account in the design and they are modelled as bounded errors. However, the original robust problem is not jointly convex in terms of beamformers at the base station and phase shifts of IRS elements. Therefore, we reformulate the original robust design as a reinforcement learning problem and develop an algorithm based on the twin-delayed deep deterministic policy gradient agent (also known as TD3). In particular, the proposed algorithm solves the original problem by jointly designing the beamformers and the phase shifts, which is not possible with conventional optimization techniques. Numerical results are provided to validate the effectiveness and evaluate the performance of the proposed robust design. In particular, the results demonstrate the competitive and promising capabilities of the proposed robust algorithm, which achieves significant gains in terms of robustness and system sum-rates over the baseline deep deterministic policy gradient agent. In addition, the algorithm has the ability to deal with fixed and dynamic channels, which gives deep reinforcement learning methods an edge over hand-crafted convex optimization-based algorithms. © 2023 CCBY.

关键词： Heuristic algorithms

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The Least Square QR Method Improves Extreme Learning Machine

The Least Square QR Method Improves Extreme Learning Machine

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2023 IEEE World Conference on applied Intelligence and Computing, AIC 2023

作者： Subramani, Chinnamuthu Kuppili, Venkatanareshbabu Keshavamurthy, B.N. Jagannath, Ravi Prasad K. Prashanth, Gurusiddappa R. National Institute of Technology Goa Department of Applied Sciences Goa Ponda403401 India National Institute of Technology Goa Department of Computer Science and Engineering Goa Ponda403401 India National Institute of Technology Goa Department of Electronics and Communication Engineering Goa Ponda403401 India

ISBN: (纸本)9798350310061

The Extreme Learning Machine (ELM) is a fast and straightforward learning algorithm designed for single hidden layer feed-forward neural networks (SLFNs). The ELM randomly assigns input weights and calculates the output weights analytically using the Moore-Penrose generalized inverse (MP) (also called Pseudo inverse). However, the MP is not preferable for ill-posed systems as it is sensitive to perturbation and may produce inaccurate solutions. Furthermore, it is computationally expensive as it involves matrix inversion. Regularized ELM (RELM) was proposed to address the limitations of the MP. In RELM, the regularization parameter is the primary factor that controls the classifier's performance. Though the RELM is stable and produces accurate results, finding a n a ppropriate regularization parameter value is tedious and increases the computation. This work proposes a variant of ELM named LSQR-ELM algorithm, which balances the ELM and RELM by maintaining stability and accuracy and avoiding the regularization entirely. The LSQR-ELM is based on the Least Squares QR (LSQR) iterative technique. Since the LSQR finds p a rtial Q R rather than full QR, it reduces unnecessary computation. To verify the proposed algorithm's ability, comparisons are made with several existing methods including ELM, Minimum Residual Method (MT-ELM), SVD-based SG-ELM, Optimally Pruned ELM (OP-ELM), and K-nearest neighbor (KNN). Extensive experimen-tation on various datasets demonstrates that the LSQR-ELM algorithm outperforms ELM, SG-ELM, OP-ELM, and KNN on most datasets. Moreover, it exhibits comparable performance to MT-ELM while requiring significantly f e wer computations. © 2023 IEEE.

关键词： Learning algorithms

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Autonomic IoT Pet Health Monitoring System

Autonomic IoT Pet Health Monitoring System

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2024 International Symposium on Networks, computers and Communications, ISNCC 2024

作者： Shultz, Michael Rouff, Christopher Tekeoglu, Ali Johns Hopkins University Whiting School of Engineering Computer Science Department United States Johns Hopkins University Applied Physics Laboratory Jhu Whiting School of Engineering United States Leidos Innovations Center Johns Hopkins University Whiting School of Engineering United States

ISBN: (纸本)9798350364910

Internet of Things (IoT) devices have seen an uptick in popularity in recent years for individual health tracking, allowing for a person to monitor their health in real time. Simultaneously, pet care devices have also become popular. These devices have become increasingly connected and automated to allow for easier care of pets. This includes allowing an owner to not have to worry about forgetting to feed or overfeeding their pet, or to monitor a pet's health condition. In this paper, we propose an approach for using the principles of self-health monitoring overlaid on existing IoT connected pet care devices to provide an autonomic system that can adjust itself to the needs of a pet as it detects changes in the pet's health. © 2024 IEEE.

关键词： Electronic health record

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Measurement of Dissemination Characteristics of LTE-V2X and ITS-G5 for Safety-Critical Vehicle-to-Vehicle Communication 5

Measurement of Dissemination Characteristics of LTE-V2X and ...

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5th International Conference on Advanced Communication Technologies and Networking, CommNet 2022

作者： Stellwagen, Jochen Deegener, Matthias Kuhn, Michael Frankfurt University of Applied Sciences Facility 2: Computer Science and Engineering Frankfurt a.M. Germany Darmstadt University of Applied Sciences Department of Electrical Engineering Darmstadt Germany

ISBN: (数字)9781665450546

ISBN: (纸本)9781665450546

Vehicle-to-vehicle communication enables the exchange of information between road users with the aim of improving safety and efficiency in the traffic environment. The highly dynamic environment with fast moving participants presents major challenges for the transmission technologies that are used. Furthermore, safety-critical applications in particular, place high demands on the reliability of the systems. Currently, there are two competing technologies for Vehicle-to-Vehicle communication: ITS-G5 and LTE-V2X. These two technologies have the potential to be used together due to their specific characteristics. As a result, they can complement each other constructively. In preparation for a comprehensive study of such a hybrid utilization, measurements were carried out to determine the characteristics of each technology in real traffic environment. The results of these measurements with both systems show, on the one hand, the characteristic properties of the technologies. On the other hand, they provide the information needed for the subsequent development of situation- and environment-dependent transmission models, that can be used in simulation environments to investigate complex traffic scenarios. This investigation should provide a direct comparison between different transmission technologies for the V2X communication. The measurement is focused on the successful transmission of information. The goal is to characterize the technologies by parameters that can be directly compared with the requirements of V2X use cases. The measurement results should be later used to create dissemination models for a simulation environment to investigate the communication for several use cases in a larger scale, especially in hybrid V2X networks. © 2022 IEEE.

关键词： Vehicle to vehicle communications

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Archery Algorithm:A Novel Stochastic Optimization Algorithm for Solving Optimization Problems

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computers, Materials & Continua 2022年第7期72卷 399-416页

作者： Fatemeh Ahmadi Zeidabadi Mohammad Dehghani Pavel Trojovsky Štěpán Hubálovsky Victor Leiva Gaurav Dhiman Department of Mathematics and Computer Sciences Sirjan University of TechnologySirjanIran Department of Mathematics Faculty of ScienceUniversity of Hradec Králové50003Hradec KrálovéCzech Republic Department of Applied Cybernetics Faculty of ScienceUniversity of Hradec Králové50003Hradec KrálovéCzech Republic School of Industrial Engineering Pontificia Universidad Católica de ValparaísoValparaíso2362807Chile Department of Computer Science Government Bikram College of CommercePatialaPunjabIndia

Finding a suitable solution to an optimization problem designed in science is a major ***,these must be addressed utilizing proper *** on a random search space,optimization algorithms can find acceptable solutions to *** Algorithm(AA)is a new stochastic approach for addressing optimization problems that is discussed in this *** fundamental idea of developing the suggested AA is to imitate the archer’s shooting behavior toward the target *** proposed algorithm updates the location of each member of the population in each dimension of the search space by a member randomly marked by the *** AA is mathematically described,and its capacity to solve optimization problems is evaluated on twenty-three distinct types of objective ***,the proposed algorithm’s performance is compared *** approaches,including teaching-learning based optimization,marine predators algorithm,genetic algorithm,grey wolf optimization,particle swarm optimization,whale optimization algorithm,gravitational search algorithm,and tunicate swarm *** to the simulation findings,the AA has a good capacity to tackle optimization issues in both unimodal and multimodal scenarios,and it can give adequate quasi-optimal solutions to these *** analysis and comparison of competing algorithms’performance with the proposed algorithm demonstrates the superiority and competitiveness of the AA.

关键词： Archer meta-heuristic algorithm population-based optimization stochastic programming swarm intelligence population-based algorithm Wilcoxon statistical test

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Discovering High Utility Episodes in Sequences

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2023年第3期4卷 473-486页

作者： Gan, Wensheng Lin, Jerry Chun-Wei Chao, Han-Chieh Yu, Philip S. Jinan University College of Cyber Security Guangzhou510632 China Pazhou Lab Guangzhou510330 China Western Norway University of Applied Sciences Department of Computing Mathematics and Physics Bergen5063 Norway National Dong Hwa University Department of Electrical Engineering Hualien974 Taiwan University of Illinois at Chicago Department of Computer Science ChicagoIL60607 United States

Sequence data are more commonly seen than other types of data (e.g., transaction data) in real-world applications. For the mining task from sequence data, several problems have been formulated, such as sequential pattern mining, episode mining, and sequential rule mining. As one of the fundamental problems, episode mining has often been studied. The common wisdom is that discovering frequent episodes is not useful enough. In this article, we propose an efficient utility mining approach, namely, UMEpi: utility mining of high-utility episodes from complex event sequences. We propose the concept of remaining utility of episodes and achieve a tighter upper bound, namely, episode-weighted utilization (EWU), which will provide better pruning. Thus, the optimized EWU-based pruning strategies can achieve better improvements in mining efficiency. The search space of UMEpi w.r.t. a prefix-based lexicographic sequence tree is spanned and determined recursively for mining high-utility episodes, by prefix-spanning in a depth-first way. Finally, extensive experiments on four real-life datasets demonstrate that UMEpi can discover the complete high-utility episodes from complex event sequences. Furthermore, the improved variants of UMEpi significantly outperform the baseline in terms of execution time, memory consumption, and scalability. © 2020 IEEE.

关键词： Artificial intelligence

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ACPPS: Autonomic Computing based Phishing Protection System 14

ACPPS: Autonomic Computing based Phishing Protection System

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2024 IEEE 14th Annual Computing and Communication Workshop and Conference, CCWC 2024

作者： Mazur, Jordan Rouff, Christopher Tekeoglu, Ali Johns Hopkins University Whiting School of Engineering Computer Science Department United States Johns Hopkins University Applied Physics Laboratory & Jhu Whiting School of Engineering United States Leidos Innovations Center Johns Hopkins University Whiting School of Engineering United States

ISBN: (纸本)9798350360134

Phishing attacks are one of the most common initial access vectors malicious actors utilize to get into personal and corporate systems. The weakest component of a system in terms of security is the human user, which is the targeted. We propose a mechanism, Autonomic Computing based Phishing Protection System (ACPPS), that is able to prevent the malicious activity from succeeding regardless of the user actions. Utilizing system monitoring, self-protection, and defined policies, ACPPS will interrupt malicious processes and system calls from completing. It is able to analyze anomalous behavior and make decisions that protect both the individual system and the network from attackers. Initially, the proof of concept is paired down to handle basic phishing attempts against Windows computers from common botnets. The preliminary results of ACPSS implementation indicate that the autonomic system can be expanded to encompass increasingly sophisticated forms of attacks, across less prevalent system types compared to Windows that was utilized in this study. © 2024 IEEE.

关键词： computer crime

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