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Multi-attribute Group Decision-making Based on Hesitant Bipolar-valued Fuzzy Information and Social Network

computer systems science & engineering 2023年第3期44卷 1939-1950页

作者： Dhanalakshmi R Sovan Samanta Arun Kumar Sivaraman Jeong Gon Lee Balasundaram A Sanamdikar Sanjay Tanaji Priya Ravindran Department of Computer Science and Engineering KCG College of TechnologyChennai600097Tamil NaduIndia Department of Mathematics Tamralipta MahavidyalayaWest Bengal721636India School of Computer Science and Engineering Vellore Institute of TechnologyChennai600127Tamil NaduIndia Division of Applied Mathematics Wonkwang UniversityIksan-SiJeonbuk54538Korea School of Computer Science and Engineering Center for Cyber Physical SystemsVellore Institute of TechnologyChennai600127Tamil NaduIndia Department of Instrumentation Engineering PDEAs College of EngineeringManjari412307PuneIndia Engineering and Research Services HCL TechnologiesChennai600119India

Fuzzy sets have undergone several expansions and generalisations in the literature,including Atanasov’s intuitionistic fuzzy sets,type 2 fuzzy sets,and fuzzy multisets,to name a *** can be regarded as fuzzy multisets from a formal standpoint;nevertheless,their interpretation differs from the two other approaches to fuzzy multisets that are currently *** fuzzy sets(HFS)are very useful if consultants have hesitation in dealing with group decision-making problems between several possible ***,these possible memberships can be not only crisp values in[0,1],but also interval values during a practical evaluation *** bipolar valued fuzzy set(HBVFS)is a generalization of *** paper aims to introduce a general framework of multi-attribute group decision-making using social *** propose two types of decision-making processes:Type-1 decision-making process and Type-2 decision-making *** the Type-1 decision-making process,the experts’original opinion is proces for thefinal ranking of *** Type-2 decision making processs,there are two major aspects we ***,consistency tests and checking of consensus models are given for detecting that the judgments are logically ***,the framework demands(partial)decision-makers to review their ***,the coherence and consensus of several HBVFSs are established forfinal ranking of *** proposed framework is clarified by an example of software packages selection of a university.

关键词： Group decision-making aggregation operators hesitant bipolar-valued fuzzy set

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A Detailed Study on Obstacle Detection and Avoidance Techniques for On-road Vehicles 1

A Detailed Study on Obstacle Detection and Avoidance Techniq...

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1st International Conference on computer, Power and Communications, ICCPC 2022

作者： Adhiyaman, Prasad Balasundaram, A. School of Computer Science and Engineering Vellore Institute of Technology Chennai India School of Computer Science and Engineering Centre for Cyber Physical Systems Vellore Institute of Technology Chennai India

ISBN: (纸本)9798350397840

Obstacle detection and avoidance are the important components of any autonomous land vehicle. The ability of an autonomous system to safely maneuver from an obstacle must be unerring to prevent any injuries to the driver or to a pedestrian. Hence it becomes cardinal to evaluate all the methodologies available to safely navigate a vehicle to safety. This paper performs a detailed study on the various obstacle detection methodologies and also on the various ways to avoid collision with the obstacle. This study provides a clear mapping of the domains and subdomains used for obstacle detection and obstacle avoidance. © 2022 IEEE.

关键词： computer vision

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Metaheuristic Optimization for Mobile Robot Navigation Based on Path Planning

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computers, Materials & Continua 2022年第11期73卷 2241-2255页

作者： El-Sayed M.El-kenawy Zeeshan Shafi Khan Abdelhameed Ibrahim Bandar Abdullah Aloyaydi Hesham Arafat Ali Ali E.Takieldeen Department of Communications and Electronics Delta Higher Institute of Engineering and TechnologyMansoura35111Egypt Faculty of Artificial Intelligence Delta University for Science and TechnologyMansoura35712Egypt Computer Science Department College of Engineering and Computer ScienceMustaqbal UniversityBuraidahSaudi Arabia Computer Engineering and Control Systems Department Faculty of EngineeringMansoura UniversityMansoura35516Egypt Department of Mechanical Engineering Qassim UniversityBuraidah51452Saudi Arabia

Recently,the path planning problem may be considered one of the most interesting researched topics in autonomous *** is why finding a safe path in a cluttered environment for a mobile robot is a significant requisite.A promising route planning for mobile robots on one side saves time and,on the other side,reduces the wear and tear on the robot,saving the capital *** route planning methods for the mobile robot have been developed and *** to our best knowledge,no method offers an optimum solution among the existing *** Swarm Optimization(PSO),a numerical optimization method based on the mobility of virtual particles in a multidimensional space,is considered one of the best algorithms for route planning under constantly changing environmental *** the researchers,reactive methods are increasingly common and extensively used for the training of neural networks in order to have efficient route planning for mobile *** paper proposes a PSO Weighted Grey Wolf Optimization(PSOWGWO)*** is a hybrid algorithm based on enhanced Grey Wolf Optimization(GWO)with *** order to measure the statistical efficiency of the proposed algorithm,Wilcoxon rank-sum and ANOVA statistical tests are *** experimental results demonstrate a 25%to 45%enhancement in terms of Area Under Curve(AUC).Moreover,superior performance in terms of data size,path planning time,and accuracy is demonstrated over other state-of-the-art techniques.

关键词： Mobile robot swarm optimization robot route planning neural networks

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Evolution of Web API Cooperation Network via Exploring Community Structure and Popularity

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第12期5卷 6659-6671页

作者： Kang, Guosheng Wang, Yang Liu, Jianxun Cao, Buqing Xiao, Yong Xu, Yu Hunan University of Science and Technology Hunan Provincial Key Laboratory for Services Computing and Novel Software Technology School of Computer Science and Engineering Xiangtan411201 China University of Cambridge Cambridge Baker Systems Genomics Initiative British Heart Foundation Cardiovascular Epidemiology Unit Department of Public Health and Primary Care CambridgeCB1 8RN United Kingdom

With the growing popularity of the Internet, Web applications have become increasingly essential in our daily lives. Web application programming interfaces (Web APIs) play a crucial role in facilitating interaction between applications. However, most Web service platforms are suffering from the imbalance of Web services now, many services of good quality but low popularity are difficult to be invoked even once and do not create direct connections with the users. Some graph-based Web service recommendation methods also often present a long-tailed distribution of recommended Web services due to limited Mashup-API invocation relationships. To relieve this problem and promote service recommendation, in this article, we propose a community structure and popularity-based approach by constructing an evolving cooperation network for Web APIs. We leverage the Louvain algorithm in community detection to assign community structure to each Web API and consider both the popularity and community structure in constructing the network. By optimizing the Barabαsi-Albert (BA) evolving network model, we demonstrate that our approach outperforms the BA, Bianconi-Barabαsi (BB), and popularity-similarity optimization (PSO) models in Web service clustering. Based on our proposed evolutionary network model for the evolutionary extension of API cooperation network and used for downstream Web service recommendation tasks, the experimental results also show that our recommended approach outperforms some other baseline models for Web service recommendation. © 2020 IEEE.

关键词： Web services

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Impact of Different UI on Foreign Language Learning Using iVR 6

Impact of Different UI on Foreign Language Learning Using iV...

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6th International Conference on Graphics and Interaction, ICGI 2024

作者： Peixoto, Bruno Goncalves, Guilherme Bessa, Maximino Pereira Bessa, Luciana C. Melo, Miguel Institute for Systems and Computer Engineering Technology and Science Porto Portugal University of Trás-os-Montes and Alto Douro Vila Real Portugal Bragança Polytechnic Institute Bragança Portugal Porto Portugal

ISBN: (纸本)9798331535995

This paper presents a study comparing different user interface modes (Controller-Based Selection, Object Interaction, and Voice Recognition) within immersive Virtual Reality (iVR) environments for foreign language learning. Given the rapid advancements and potential of iVR in education, there is a need for focused research on optimising user interfaces for effective learning experiences. This study aimed to identify optimal interfaces for integrating iVR applications as complementary educational tools while gauging student preferences. Participants engaged in interactive learning tasks across the three conditions, with assessments focused on System Usability, Presence, User Satisfaction, Cybersickness, Learning Outcomes, and Task Duration. Findings indicate high usability across all conditions, with a preference observed for Controller-Based Selection and Object Interaction. Object Interaction showed strong motivational appeal but required more time to complete tasks than Controller-Based. Therefore, for time-constrained educational settings, the Controller-Based Selection interface is practical due to its lower physical effort requirement. Despite recent advances, our study found Voice Recognition interaction to be the least preferred interaction method, indicating a need for further technological improvements to boost its acceptance and effectiveness in educational contexts. © 2024 IEEE.

关键词： Virtual environments

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Unpredictable Switching for Cyber-Physical Security Against Worst-case Attackers 63

Unpredictable Switching for Cyber-Physical Security Against ...

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63rd IEEE Conference on Decision and Control, CDC 2024

作者： Kanellopoulos, Aris Fotiadis, Filippos Vamvoudakis, Kyriakos G. Sandberg, Henrik Kth Royal Institute of Technology Division of Decision and Control Systems School of Electrical Engineering and Computer Science Stockholm10044 Sweden Georgia Institute of Technology Daniel Guggenheim School of Aerospace Engineering AtlantaGA30332 United States

ISBN: (纸本)9798350316339

In this paper, we investigate the problem of securing a system against actuator attacks. Specifically, we employ an unpredictability-based defense algorithm according to the principles of Moving Target Defense, while explicitly considering the worst-case attack that can be launched into the system. Consequently, we allow the system to alternate between different actuating modes, but also between optimal and robust control schemes. Via game-theoretic methods, we derive mixed strategies dictating the probabilities of utilizing different controllers for a given mode of operation, whereas an entropy-augmented optimization problem gives the probabilities of using these different modes. Finally, the overall stability of the system is analyzed, and simulation results showcase the efficacy of our proposed method. © 2024 IEEE.

关键词： Cyber attacks

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Real-time Stream Processing in IoT Environments 9

Real-time Stream Processing in IoT Environments

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9th International Conference on science, Technology, engineering and Mathematics, ICONSTEM 2024

作者： Rajasekar, P. Bhosale, Rupali Shekar Indhumathi, C. Sandeep, K.V. Prasanthi Kumari, N. Rajendiran, M. School of Computing Srm Institute of Science And Technology Department of Data Science And Business Systems Tamil Nadu Kattankulathur603203 India Bharati Vidyapeeth Institute of Technology Department of Electronics And Telecommunication Engineering Maharashtra Navi Mumbai400614 India Rmk College of Engineering And Technology Department of Computer Science And Business Systems Tamil Nadu Puduvoyal601206 India Dayananda Sagar College of Engineering Department of Electronics & Telecommunication Engineering Karnataka Bengaluru560111 India School of Computer Sciences University of Petroleum And Energy Studies Uttarakhand 248007 Dehradun India Panimalar Engineering College Department of Computer Science And Engineering Tamil Nadu Chennai600123 India

ISBN: (纸本)9798350365092

New IoT devices generate massive real-time data. Fast, real-time stream processing is needed for IoT data insight extraction. There are pros and downsides to real-time stream processing for the Internet of Things (IoT). This article analyzes present methods and technologies. The inquiry begins with large-scale, real-time IoT data analysis. Real-time analytics and bulk processing limits are important for IoT decision-making. We then investigate IoT stream processing frameworks, methodologies, and systems' complexity. We explore Internet of Things technology selection and tradeoffs between scalability, accuracy, latency, and real-time processing system scalability. It covers real-time stream processing system design and deployment and risk mitigation. Case studies and use cases highlight real-time IoT stream processing. Healthcare, smart city, and industrial IoT operational efficiency and proactive decision-making improve with real-time data. Finally, the report suggests the Internet of Things real-time stream processing research. New IoT data analytics protocols, methods, and technologies are highlighted. We teach real-time stream processing to IoT academics, practitioners, and decision-makers. They could then develop more efficient and scalable IoT data stream optimization solutions. © 2024 IEEE.

关键词： Edge computing

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Cyberbullying Classification Using a Natural Language Processing and Machine Learning Techniques

Cyberbullying Classification Using a Natural Language Proces...

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2024 International Conference on Advances in Computing Research on science engineering and Technology, ACROSET 2024

作者： Mankar, Nikhilesh Pramod Mahure, Tushar Joshi, Anushka A. Pande, Sagar Dhanraj Khamparia, Aditya Nathe, Ankita Shrikrushna Turjman, Fadi Al Pune India Independent Researcher Amravati India Babasaheb Bhimrao Ambedkar University Department of Computer Science Amethi India Kavikulguru Institute of Technology and Science Department of Information Technology Nagpur India Ai and Robotics Institute Near East University Artificial Intelligence Software Information Systems Engineering Departments Nicosia Cyprus University of Kyrenia Research Center for Ai and IoT Faculty of Engineering Kyrenia Turkey

ISBN: (纸本)9798350388800

The usage of online entertainment has increased dramatically after some time with the enhancement of the Internet and has turned into the most compelling systems administration stage in this century. Notwithstanding, the upgrade of a social network regularly makes adverse consequences on society that add to two or three terrible peculiarities, for example, online maltreatment, badgering cyberbullying, cybercrime, and internet savaging. Cyberbullying habitually prompts genuine mental and actual misery, especially for ladies and kids, and even occasionally make them to use self-destruction methods. Multiple number of episodes have as of late happened worldwide because of online badgering, like sharing private talks, bits of hearsay, and sexual comments. In this way, the distinguishing proof of harassing messages or messages via online entertainment has gained a significant measure for development for consideration with in scientists. The ground or motive for this exploration is to create a full-fledged way or a method to differentiate online harmful and tormenting drafts by combining various methods and techniques in the domain of natural language processing and AI © 2024 IEEE.

关键词： Adversarial machine learning

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RobFL: Robust Federated Learning via Feature Center Separation and Malicious Center Detection 40

RobFL: Robust Federated Learning via Feature Center Separati...

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40th IEEE International Conference on Data engineering, ICDE 2024

作者： Zhou, Ting Liu, Ning Song, Bo Lv, Hongtao Guo, Deke Liu, Lei National University of Defense Technology Science and Technology Laboratory on Information Systems Engineering Changsha China School of Software Shandong University Jinan China Shandong Research Institute of Industrial Technology Jinan China

ISBN: (纸本)9798350317152

In recent years, the integration of federated learning and deep learning technologies has become increasingly prevalent in privacy-preserved scenarios, such as smart health applications and automatic financial support. However, the inherent robustness issue in deep learning poses potential risks to federated learning systems when subjected to various attack methods. These attacks can inflict damage during the training and testing phases, perturbing models and inputs. To enhance the robustness of existing federated learning systems, we propose a novel framework called RobFL. This framework incorporates a unique feature learning module -feature center separation learning -that is specifically designed to increase the margins between different classes in the feature space, thereby augmenting the difficulty of attacks employing imperceptible perturbations on inputs. Furthermore, we design a malicious center detection method to detect malicious clients and mitigate their adverse impact. Extensive experiments substantiate the robustness of our proposed framework, RobFL, demonstrating its resilience against both evasion attacks and poisoning attacks. © 2024 IEEE.

关键词： Anomaly detection

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Pseudo-label Assisted Optimization of Multi-branch Network for Cross-domain Person Re-identification 20

Pseudo-label Assisted Optimization of Multi-branch Network f...

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20th IEEE International Conference on Mechatronics and Automation, ICMA 2023

作者： Wang, Zhengyang Guo, Shuxiang Shang, Xue Ye, Xiufen Harbin Engineering University College of Intelligent Systems Science and Engineering Harbin China National University of Singapore Department of Electrical and Computer Engineering Singapore Beijing Institute of Technology School of Life Science Beijing China

ISBN: (纸本)9798350320831

Person re-identification plays an important role in the field of robot intelligence perception and public safety. Its main task is to identify person targets under cross-camera. However, the domain diversity between different datasets poses a clear challenge for adapting a model trained on one dataset to another. Currently, person re-identification methods based on domain adaptive learning and pseudo-label have made good progress on this problem. Unfortunately, inferior pseudo-labels and source domain noise affect the performance. In order to improve the quality of generated pseudo-labels and enhance the feature representation capability of the model, we propose a pseudo-label assisted optimization of a multi-branch person re-identification method. The multi-branch network is able to extract and represent more effective global and local features, and the generated pseudo-labels are optimized by using cosine similarity and DBSCAN clustering on the feature vectors, thus improving the consistency of the supervised information to enhance the cross-domain recognition performance. We also use a loss function combining cross-entropy loss and triplet loss to make the best feature learning. Experiments show that our method performs well in the Market-to-Duke and Duke-to-Market cross-domain recognition tasks. © 2023 IEEE.

关键词： Intelligent robots

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