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Disruptive Technologies (ICDT), International Conference on

作者： Sudeep Sharan Anh Tong Ngoc Minh Duc Thuan Nguyen Juan José Domínguez-Jiménez Peter Nauth Computer Science And Engineering Frankfurt University of Applied Sciences Frankfurt am Main Germany UCASE Software Engineering Group School of Engineering University of Cádiz Cádiz Spain

The optimal path for autonomous robots is highly needed, particularly in the industrial communities, manufacturing and social support industries. The meta-heuristic methods are strong and adaptable, and their employment in autonomous problems is taken into consideration. One of the methods used by the researchers is Ant Colony Optimization (ACO). However, this approach is less effective due to a few limitations. Thus, in order to mitigate its shortcomings and enhance the robustness of path planning, this paper aims to compare the different standard ACO algorithm with the new modified built algorithm, which is developed by taking the concept of ACO and extending it by condition-based rules, named as condition based-ant colony concept (CB-ACC), to solve path planning. The theoretical foundation of ACO with modification as another approach are used in this study to illustrate the ACO methods in the path planning, particularly in the static-complex environment. Simulation results show the comparison of the ACO approaches, including the Ant System (AS), the Ant Colony System (ACS) and the modified condition based-ant colony concept (CB-ACC), and how they are effective for static-complex environments compared to conventional ACO algorithms. Moreover, this paper presents the comparison results of the three approaches.

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Modeling the One Class of Radionuclide Migration Processes in Soils under Non-Isothermal Conditions Using Perturbation Theory Methods 13

Modeling the One Class of Radionuclide Migration Processes i...

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13th International Conference on Advanced computer Information Technologies, ACIT 2023

作者： Bomba, Andrii Ostapchuk, Oksana Vovk, Roman Kopnicky, Miroslav National University of Water and Environmental Engineering Department of Computer Science and Applied Mathematics Rivne Ukraine Ivano-Frankivsk National Technical University of Oil and Gas Department of Software Engineering Ivano-Frankivsk Ukraine Catholic University in Ružomberok Department of Catholic Theology Spišská Kapitula Slovakia

Based on the synthesis of complex analysis methods, perturbation theory, and characteristics, a new approach has been developed for accounting for osmosis and temperature in predicting the migration processes of radionuclides under non-isothermal conditions in quasi-ideal porous media (in curved regions) bounded by streamlines and equipotential lines. The solution to the corresponding degenerate problem was obtained based on the developed method of nonlinear inversion of boundary value problem solutions into quasi-conformal mappings. An algorithm for calculating a uniformly dynamic grid, streamlines, and speed of ideal filtration field was constructed. With the developed method of characteristics, formulas for the approximate solution of diffusion-convection mass transfer problems in perturbed osmosis and heat filtration fields were obtained. © 2023 IEEE.

关键词： Porous materials

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EEG-Based VR Scene Adaptive Generation System for Regulating Emotion 9

EEG-Based VR Scene Adaptive Generation System for Regulating...

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9th International Conference on Virtual Reality, ICVR 2023

作者： Liang, Hui Liu, ShiQing Wang, Yi Pan, Junjun Fu, Jialin Yuan, Yingkai Zhengzhou University of Lightindustry Department of Software Engineering Zheng zhou China General Hospital of Pingmei Shenma Group Department of Rehabilitation Pingdingshan China Beihang University Department of Computer Science and Engineering Peking China Zhengzhou University of LightIndustry College of Economics and Management Zheng zhou China Zhengzhou University of LightIndustry College of Computer and Communication Engineering Zheng zhou China

ISBN: (纸本)9798350345810

Research based on electroencephalogram (EEG) and virtual reality (VR) has developed rapidly in recent years. The integration of EEG and VR technology has been broadly employed in psychological intervention to treat various psychological conditions and alleviate stress. At present, there exist commercial-level EEG emotion recognition products, however, research on the correlation between the content of VR scenarios and emotions remains unavailable. Therefore, this work investigates VR scene construction based on EEG, which incorporates the recovery environment theory and virtual reality technology to construct different three-dimensional dynamic virtual reality interaction scenes. EEG equipment constructs an emotion-adaptive VR system based on EEG signals with the aid of VR interaction equipment. Herein, EEG data feedback was applied to automatically generate VR scenes, and the experimental findings revealed that the system has certain feasibility and application value. © 2023 IEEE.

关键词： Virtual reality

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Transformative effects of ChatGPT on modern education: Emerging Era of AI Chatbots

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Internet of Things and Cyber-Physical Systems 2024年第1期4卷 19-23页

作者： Gill, Sukhpal Singh Xu, Minxian Patros, Panos Wu, Huaming Kaur, Rupinder Kaur, Kamalpreet Fuller, Stephanie Singh, Manmeet Arora, Priyansh Parlikad, Ajith Kumar Stankovski, Vlado Abraham, Ajith Ghosh, Soumya K. Lutfiyya, Hanan Kanhere, Salil S. Bahsoon, Rami Rana, Omer Dustdar, Schahram Sakellariou, Rizos Uhlig, Steve Buyya, Rajkumar School of Electronic Engineering and Computer Science Queen Mary University of London London United Kingdom Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen China Raygun Performance Monitoring Wellington New Zealand Center for Applied Mathematics Tianjin University Tianjin China Department of Science Kings Education London United Kingdom Cymax Group Technologies BC Canada QM Academy Queen Mary University of London London United Kingdom Jackson School of Geosciences University of Texas at Austin AustinTX United States Centre for Climate Change Research Indian Institute of Tropical Meteorology Pune India Microsoft Hyderabad India Institute for Manufacturing Department of Engineering University of Cambridge Cambridge United Kingdom Faculty of Computer and Information Science University of Ljubljana Ljubljana Slovenia Machine Intelligence Research Labs AuburnWA United States Faculty of Computing and Data Science FLAME University Maharashtra Pune India Department of Computer Science and Engineering Indian Institute of Technology Kharagpur India Department of Computer Science University of Western Ontario London Canada Sydney Australia School of Computer Science University of Birmingham United Kingdom School of Computer Science and Informatics Cardiff University Cardiff United Kingdom Distributed Systems Group Vienna University of Technology Vienna Austria Department of Computer Science University of Manchester Oxford Road Manchester United Kingdom Laboratory School of Computing and Information Systems The University of Melbourne Australia

ChatGPT, an AI-based chatbot, offers coherent and useful replies based on analysis of large volumes of data. In this article, leading academics, scientists, distinguish researchers and engineers discuss the transformative effects of ChatGPT on modern education. This research discusses ChatGPT capabilities and its use in the education sector, identifies potential concerns and challenges. Our preliminary evaluation shows that ChatGPT perform differently in different subject areas including finance, coding, maths, and general public queries. While ChatGPT has the ability to help educators by creating instructional content, offering suggestions and acting as an online educator to learners by answering questions, transforming education through smartphones and IoT gadgets, and promoting group work, there are clear drawbacks in its use, such as the possibility of producing inaccurate or false data and circumventing duplicate content (plagiarism) detectors where originality is essential. The often reported "hallucinations" within GenerativeAI in general, and also relevant for ChatGPT, can render its use of limited benefit where accuracy is essential. What ChatGPT lacks is a stochastic measure to help provide sincere and sensitive communication with its users. Academic regulations and evaluation practices used in educational institutions need to be updated, should ChatGPT be used as a tool in education. To address the transformative effects of ChatGPT on the learning environment, educating teachers and students alike about its capabilities and limitations will be crucial. © 2023 The Authors

关键词： Machine learning

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Statistical Data Mining with Slime Mould Optimization for Intelligent Rainfall Classification

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computer Systems science & engineering 2023年第10期47卷 919-935页

作者： Ramya Nemani G.Jose Moses Fayadh Alenezi K.Vijaya Kumar Seifedine Kadry Jungeun Kim Keejun Han Department of Mathematics Vignan’s Institute of Information TechnologyVisakhapatnam530049India Department of Computer Science and Engineering University Institute of Engineering and Technology(UIET)Guru Nanak UniversityHyderabadIndia Department of Electrical Engineering College of EngineeringJouf UniversitySaudi Arabia Department of Computer Science and Engineering GITAM School of TechnologyVishakhapatnam CampusGITAM(Deemed to be a University)VishakhapatnamIndia Department of Applied Data Science Noroff University CollegeKristiansandNorway Artificial Intelligence Research Center(AIRC) College of Engineering and Information TechnologyAjman UniversityAjmanUnited Arab Emirates Department of Electrical and Computer Engineering Lebanese American UniversityByblosLebanon Department of Software Kongju National UniversityCheonan31080Korea Division of Computer Engineering Hansung UniversitySeoul02876Korea

Statistics are most crucial than ever due to the accessibility of huge counts of data from several domains such as finance,medicine,science,engineering,and so *** data mining(SDM)is an interdisciplinary domain that examines huge existing databases to discover patterns and connections from the *** varies in classical statistics on the size of datasets and on the detail that the data could not primarily be gathered based on some experimental strategy but conversely for other ***,this paper introduces an effective statistical Data Mining for Intelligent Rainfall Prediction using Slime Mould Optimization with Deep Learning(SDMIRPSMODL)*** the presented SDMIRP-SMODL model,the feature subset selection process is performed by the SMO algorithm,which in turn minimizes the computation *** rainfall *** neural network with long short-term memory(CNN-LSTM)technique is *** last,this study involves the pelican optimization algorithm(POA)as a hyperparameter *** experimental evaluation of the SDMIRP-SMODL approach is tested utilizing a rainfall dataset comprising 23682 samples in the negative class and 1865 samples in the positive *** comparative outcomes reported the supremacy of the SDMIRP-SMODL model compared to existing techniques.

关键词： Statistical data mining predictive models deep learning rainfall prediction parameter tuning

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Attribute-Based Data Sharing Scheme with Flexible Search Functionality for Cloud-Assisted Autonomous Transportation System

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IEEE Transactions on Industrial Informatics 2023年第11期19卷 10977-10986页

作者： Xiong, Hu Wang, Hanxiao Meng, Weizhi Yeh, Kuo-Hui University of Electronic Science and Technology of China School of Information and Software Engineering Network and Data Security Key Laboratory of Sichuan Province Chengdu610054 China Advanced Cryptography and System Security Key Laboratory of Sichuan Province Chengdu610025 China Technical University of Denmark Department of Applied Mathematics and Computer Science Kongens Lyngby2800 Denmark National Dong Hwa University Department of Information Management Hualien97401 Taiwan National Sun Yat-sen University Department of Computer Science and Engineering Kaohsiung804201 Taiwan

The existing group public key encryption with equality test schemes could only support one-to-one data sharing and are not suitable for cloud-assisted autonomous transportation systems, which demand one-to-many data sharing. To tackle this problem efficiently, in this article, we put forward the group-attribute-based encryption with equality test (G-ABEET) scheme. The presented G-ABEET allows sensors equipped in vehicles to encrypt traffic data with an expressive access policy before sharing it. Only users with attributes required by the access policy ought to access the shared ciphertexts, thus achieving selective one-to-many data sharing. Meanwhile, the authorized cloud server could provide group users with equality tests over the ciphertexts, realizing ciphertext search ability. Furthermore, with the group mechanism, the G-ABEET scheme could resist offline message recovery attacks. Besides, in the standard model, we give rigorous security proof of the G-ABEET construction. The feasibility and efficiency of G-ABEET are demonstrated by experimental simulations. © 2005-2012 IEEE.

关键词： Cloud computing

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A Review of Algorithms's Complexities on Different Valued Sorted and Unsorted Data

A Review of Algorithms's Complexities on Different Valued So...

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2023 International Conference on IT and Industrial Technologies, ICIT 2023

作者： Shabbir, Attia Majeed, Asad Iftikhar, Mahnoor Ali, Raja Hashim Arshad, Usama Shabbir, Muhammad Zeeshan Zeeshan Ijaz, Ali Ali, Nisar Aftab, Ali Gik Institute of Engg. Sciences & Technology Faculty of Computer Science & Engineering Topi Pakistan University of Europe for Applied Sciences Dept. of Technology & Software Engineering Berlin Germany Department of Electrical Engineering Lahore Pakistan University of Regina Faculty of Electronic Systems Engineering Regina Canada

ISBN: (纸本)9798350315974

Sorting and searching are critical processes for effecttive data analysis. In this paper, we evaluate the performance of various sorting and searching algorithms and compare their time and space complexities on both sorted and unsorted data. The algorithms we analyzed include six common sorting algorithms (insertion, radix, bucket, merge, bubble, and quick sort) and three search algorithms (linear, binary, and jump search). The results of our study provide insights into the best algorithms to use for different input sizes and types of data. It was found that for small input sizes, all algorithms perform similarly, but for larger input sizes, insertion and radix sorts are better for time complexity while bubble sort is better for space complexity. Additionally, jump search outperformed linear and binary search algorithms in both time and space complexity. Besides, difference between time and space complexity of sorted and unsorted data was significant. © 2023 IEEE.

关键词： Computational complexity

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Crime Based Evaluation of GPS Network Using Machine Learning Techniques 4

Crime Based Evaluation of GPS Network Using Machine Learning...

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4th International Conference on Smart Systems and Inventive Technology, ICSSIT 2022

作者： Rahul Monika Durgapal, Vartika Hari Department Of Software Engineering Delhi Technological University Delhi India Department of Computer Science Shaheed Rajguru College of Applied Sciences for Women University of Delhi Delhi110096 India

ISBN: (纸本)9781665401180

With the increased usage of data transmission, data leakage and privacy protection are becoming increasingly critical. Data comes in a variety of forms, and the amount of protection required for each one differs. With the rapid development and demand of Global Positioning S ystem (GPS) networks in every of big data technology, the vulnerability of the transportation system to unintentional and intentional GPS disruption is also increasing. The GPS signal is degraded and lost as a result of opposing interests' attacks. Potential assaults vary from GPS signal jamming and spoofing to GPS ground stations and satellite disruption. S ecurity issues with respect to data increases during data transmission. Data travelling across an organization's network is frequently sensitive and internet being the storehouse of every kind of data with methods for accessing the precise information serves as an auspicious medium for the hackers [19]. Network security solutions must deal with complicated data in this setting in order to detect and minimize any threats in real time. There are several techniques to the problem of data security such as cryptographic algorithms, classification-based models, and block-chain methods [1]-[6]. Through this research, crime rates are evaluated in a GPS network data with different other parameters using machine learning (ML) algorithms. Moreover, the inferred features effecting the crimes during data transmission are extracted. S ome of the techniques used includes techniques such as 'Decision Tree (DT)', 'K Nearest Neighbors (KNN)', 'Random Forest (RF)', 'Multivariable Regression (MR)' and 'Naïve Bayes (NB)'. It was seen that the best accuracy was given by RF - of approximately 84.25%. © 2022 IEEE

关键词： Crime

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A Comparative Analysis of Goal-Oriented Requirements engineering and Model-Based Systems engineering Frameworks for Managing Requirements of Systems of Systems

A Comparative Analysis of Goal-Oriented Requirements Enginee...

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Information Technology (ACIT)

作者： Suhair AlHajHassan Samer Murrar Ahmad Samhan Mohammed Odeh Ali Elrashidi Software Engineering Department Applied Science Private University Amman Jordan Computer Science Department Applied Science Private University Amman Jordan Software Engineering Department Zarqa University Zarqa Jordan Global Academy for Digital Health Bristol United Kingdom Department of Electrical Engineering University of Business and Technology Jeddah Saudi Arabia

ISBN: (数字)9798331540012

ISBN: (纸本)9798331540029

Goal-oriented requirements engineering (GORE) for Systems of Systems (SoS) includes combining individual operational systems local goals to achieve higher-level goals. GORE offers a structured approach to managing complex requirements, ensuring that strategic goals are translated into operational tasks. This paper provides a review of GORE frameworks, including those incorporating Model-Based Systems engineering (MBSE), that could be used in the management of complex Systems of Systems. The paper analyzes recent GORE frameworks such as CGS4Adaptation which combines Goals and SysML for managing adaptive Socio-Cyber-Physical Systems (SCPSs); GORE-based approach to Energy Management Systems (EnMS); Model-Based and Goal-Oriented Approach for the conceptual design of smart grid services; GORE and reference architecture approach for microgrid systems; and Adaptation-Oriented Requirement Modeling approach (ADORE). A comparative analysis is conducted to assess the extent of effectiveness these frameworks provide for improving SoS traceability, adaptability, and system design integrity. The paper concludes with key findings on the strengths and limitations of the considered frameworks, on the basis of which, major conclusions on how GORE combined with MBSE can be used for managing SoS requirements in Smart Grid (SG) and socio-cyber-physical systems could be drawn. This review paper also contributes to the requirements engineering domain by outlining effective strategies for designing and managing complex, adaptive Systems of Systems.

关键词： Adaptation models Analytical models Adaptive systems Reviews Microgrids Smart grids Requirements engineering Information technology System analysis and design System of systems

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From Quantum software Handcrafting to Quantum software engineering

From Quantum Software Handcrafting to Quantum Software Engin...

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software Analysis, Evolution and Reengineering - Companion (SANER-C), IEEE International Conference on

作者： Giuseppe Bisicchia Jose García-Alonso Juan M. Murillo Antonio Brogi Department of Computer Science University of Pisa Pisa Italy Quercus Software Engineering Group University of Extremadura Cáceres Spain

ISBN: (数字)9798350351576

ISBN: (纸本)9798350351583

Quantum Computing holds the potential to change our world. Following the quantum wave, software engineers have recognised the opportunity to establish a new discipline of Quantum software engineering. Despite the significant progress achieved, Quantum Computing's widespread adoption still faces critical hurdles. In this paper, we outline two of these challenges. (1) Quantum programming continues to be a complex art mastered by a select few experts. We suggest that the primary culprit can be pinpointed in the absence of high-level quantum software abstractions which forces developers to work with low-level quantum concepts and reason in terms of matrix multiplications. (2) The scarce collaboration among quantum software engineers resulted in a lack of platform and software interoperability. While a diversity of research proposals fuels scientific progress, it can hinder the development and adoption of innovative technologies, potentially fragmenting the collective efforts and confining them within isolated research groups. We believe that overcoming these issues is crucial for fostering innovation, advancing Quantum software engineering, and Quantum Computing as a whole.

关键词： Technological innovation Quantum computing Collaboration Programming software Proposals Fuels

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