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15th International Conference on Computing Communication and Networking technologies, ICCCNT 2024

作者： Garg, Mohan Senthurya, S. Singh, Jagtej Bajpai, Anila Sharma, Nakul S. Yuvaraj, K. Chitkara University Centre of Research Impact and Outcome Punjab Rajpura140417 India Prince Shri Venkateshwara Padmavathy Engineering College Department of Computer Science and Engineering Chennai127 India Chitkara University Chitkara Centre for Research and Development Himachal Pradesh 174103 India School of Mangement - Ug Management Karnataka Bangalore India Vishwakarma Institute of Technology Department of Artificial Intelligence & Data Science Pune India Karpagam Academy of Higher Education Department of Computer Science Coimbatore641021 India

ISBN: (纸本)9798350370249

Digital personal Networks have been increasingly utilized in ATM networks for ease of communication. This paper studies the use of VPNs over ATM networks, providing a synopsis of their features and advantages and discussing bible safety threats. The paper also explains VPNs, their sub-types, and how they may be implemented in ATM surroundings;observing takes place once they provide additional protection capabilities while used on ATM networks;we also talk about the fate of VPNs throughout ATM networks, precisely due to the risk posed by using Internet-based providers. These novel results are encouraging and indicate that VPNs may also offer a solution for securing ATM transactions and protecting network data from threat actors. © 2024 IEEE.

关键词： Capabilities Defines Digital Discussing Examines Increasingly Malicious Stronger Transactions

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Model of the Dynamics of the State of educational Content Recommender System 13

Model of the Dynamics of the State of Educational Content Re...

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13th International Conference on Dependable Systems, Services and technologies, DESSERT 2023

作者： Muchacki, Mateusz Sitnikova, Oksana Yenhalychev, Serhii University of the National Education Commission Kracow Institute of Security and Computer Science Kraków Poland SET University Kyiv Ukraine Simon Kuznets Kharkiv National University of Economics Cybersecurity and Information Technologies Department Kharkiv Ukraine

ISBN: (纸本)9798350396119

the report presents a generalized model of the dynamics of the state of the educational content recommender system. It differs from the known ones by taking into account the factor of changes in user preferences over time and the possibility of adapting to them. The relevance of the report is caused by the need to take into account the dynamic nature of user preferences and changes in interests over time. To solve this problem, the authors propose to apply GERT-networks (Graphical Evaluation and Review Technique). GERT-networks allow describing the probability of the system staying in different states over time, which is important for understanding and predicting changes in user interests. As a result of modeling, analytical expressions for calculating the probability-time characteristics of the recommender system dynamics are derived. Experiments were conducted to estimate the probability distribution density function of the formation time of educational content recommendation. The results showed the possibility of estimating the maximum values of the probability distribution density of the task of forming educational content recommendations, and, accordingly, the indicator of the time of forming recommendations. © 2023 IEEE.

关键词： Recommender systems

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Optimizing Resource Utilization Using Vector Databases in Green Internet of Things

Optimizing Resource Utilization Using Vector Databases in Gr...

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2023 IEEE Globecom Workshops, GC Wkshps 2023

作者： Kumari, Rani Sah, Dinesh Kumar Cengiz, Korhan Nauman, Ali Ivković, Nikola Mihaljević, Ivan Birla Institutue of Technology Department of Computer Science and Engineering Ranchi India Siksha 'o' Anusandhan Department of Computer Science and Engineering Bhubaneswar India Istinye University Department of Computer Engineering Istanbul Turkey University of Hradec Kralove Faculty of Informatics and Management Department of Information Technologies Kralove50003 Czech Republic Yeungnam University Department of Information and Communication Engineering 280 Daehak-Ro Gyeongsan Gyeongbuk38541 Korea Republic of University of Zagreb Faculty of Organization and Informatics Pavlinska 2 Varaždin42000 Croatia

ISBN: (纸本)9798350370218

With the rapid proliferation of Internet of Things (IoT) devices and the ever-increasing volume of sensor data, optimizing resource utilization has become crucial for building sustainable and efficient IoT systems. In this study, we propose a novel approach for optimizing resource utilization in Green IoT through efficient storage and retrieval in vector databases. Our approach leverages specialized data structures, including k-d trees and ball trees, to achieve improved storage efficiency and accelerated retrieval performance for high-dimensional sensor data. We conducted extensive experiments to evaluate the effectiveness of our proposal, comparing it with traditional database approaches. The results demonstrate significant improvements in storage efficiency, with vector databases requiring considerably less storage space compared to traditional databases. Moreover, our approach enables fast and accurate retrieval of high-dimensional sensor data, reducing query times and enhancing real-time data analysis and decision-making capabilities. The technical achievements of our proposal offer promising prospects for the development of sustainable and efficient IoT systems in various domains, such as environmental monitoring, healthcare, and smart cities. Our work contributes to advancing the field of Green IoT by addressing the challenges of resource utilization and query performance through efficient storage and retrieval in vector databases. © 2023 IEEE.

关键词： Green IoT Indexing Techniques Resource Utilization Specialized Data Structures Vector Databases

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Advanced Remote Sensing and Generative Models for Comprehensive Water Quality management in a Changing Climate

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Remote Sensing in Earth Systems sciences 2024年第4期7卷 596-611页

作者： Thopanaiah, Chandrashekhara Kenkere Gireesh Babu C N Gurani, Vijayakumar Rajani, Thota Pavani, Avula Muthukumaran, D. Deivasigamani, Shanmugavel Department of CSE/ISE BMS Institute of Technology and Management Bengaluru India P.G. Department of Computer Science Karnatak University Dharwad India Department of English Koneru Lakshmaiah Education Foundation Vaddeswaram AP India Department of ECE Vel Tech Rangarajan Dr. Sagunthala R & amp D Institute of Science and Technology Chennai India Department of Artificial Intelligence and Data Science Jeppiaar Institute of Technology Anna University Chennai India

Water quality is a pivotal factor for maintaining human and ecological health. Traditional water quality assessments often depend on ground sampling and lab tests, which are costly, slow, and constrained by geographic limitations. The emergence of remote sensing technologies now allows for extensive and timely monitoring of water quality across vast regions. This study introduces an innovative approach that utilizes remote sensing data alongside a hybrid Generative Adversarial Network-Long Short-Term Memory (GAN-LSTM) model to transform the monitoring of water quality, focusing on pollution and sanitation management. We employed a comprehensive dataset from Kaggle, which includes 3276 data points and 10 essential water quality indicators, integrated with historical remote sensing data. The GAN model is designed to produce realistic synthetic datasets, which are then used by the LSTM model to predict water quality trends with high accuracy. The methodology achieved notable results, with an accuracy of 98%, precision of 97%, recall of 99%, and an F1-score of 98%. This approach leverages cutting-edge modeling techniques and extensive datasets to significantly improve the monitoring and management of water quality. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024.

关键词： GAN-LSTM Machine learning Pollution management Real-time monitoring Remote sensing Sanitation management Synthetic data Water potability Water quality monitoring

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Examining 6G Infrastructure Capabilities: Paving the Way for Future Connectivity

Examining 6G Infrastructure Capabilities: Paving the Way for...

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2023 International Conference on Power Energy, Environment and Intelligent Control, PEEIC 2023

作者： Rawat, Devyani Chaubey, Anurag Kumar, Prashant Hemelatha, S. Gajendran, P. Wankhede, D.S. Graphic Era Deemed to Be University Department of Management Studies Uttarakhand Dehradun India School of Commerce and Management Iimt University Meerut India Tmu Department of Ece India Karpagam Academy of Higher Education Department of Computer Science Engineering Coimbatore641021 India Karpagam College of Engineering Department of Electrical and Electronics Engineering Coimbatore641032 India Vishwakarma Institute of Information Technology Computer Engineering Pune India

ISBN: (纸本)9798350357769

6G structures are anticipated to be a major driver for the smart data society, providing functionality that is better to 5G and satisfying innovative features and apps. In this piece, we outline utilization situations and specifications for non - linear and non per third and sophisticated 6G networks as well as our view of what 6G will be. That can provide all-encompassing and limitless wireless communication, we show a massive and independent structured cabling that combines land, air, sea, and space communications. For network interfaces and creative vapour style, we also address artificial intelligence and machine learning. The Internet of Graphene, microwave frequency connectivity, very highly directional arrays myriad, numerous different, large erudite textures and holograms directional antennas, orbital rotational frequency - division, laser and that can be seen comms, nanotech, genomic messaging, and Ethereum - based frequency hopping are a few of the systems we identify as enticing for the 6F I environment. © 2023 IEEE.

关键词： 6G infrastructure Artificial intelligence Autonomous networks Directional arrays Holograms directional antennas Machine learning Microwave frequency connectivity Nanotech Quantum communications and computing SDN Smart data society Wireless communication

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A Systematic Approach for the Prediction of Personality Based on Attention Enhanced GCNN and LSTM Approach 2

A Systematic Approach for the Prediction of Personality Base...

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2nd International Conference on Edge Computing and Applications, ICECAA 2023

作者： Sharma, Prashant Sagvekar, Vijaya Alone, Vinod N. Krishna, P. Vamsi Thulasimani, T. Sethumadhavan, R. Mandsaur University Faculty of Engineering Department of Computer Science and Engineering Madhya Pradesh Mandsaur India Pacific Academy of Higher Education and Research University Pacific Hills Computer Engineering India Vppcoe & Va Computer Engineering Department Sion Maharashtra Mumbai India School of Management Malla Reddy University Telangana Hyderabad India Bannari Amman Institute of Technology Department of Mathematics Erode Tamilnadu Sathyamangalam India Dayananda Sagar College of Engineering Department of Management Studies Karnataka Bangalore India

ISBN: (纸本)9798350347579

Individuals can be distinguished from one another in significant ways, and one such way is by one's personality. Predicting someone's character traits is a perennial topic of study. Personality prediction using social media data has great potential because it eliminates the need for users to fill out lengthy questionnaires, saving both time and improving the method's trustworthiness. As a result, understanding personality is a topic that piques the curiosity of scientists. There are many real-world uses for personality prediction. The popularity of social media platforms continues to grow. Every day, more and more pictures and text are uploaded on the internet. Most recent efforts have been concentrated on feature extraction, feature selection, and model training. The proposed method employs AGCNN-LSTM for measuring performance. The suggested model performs better than the CNN and LSTM models against which it is benchmarked. © 2023 IEEE.

关键词： Long short-term memory

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A comprehensive review of deep learning power in steady-state visual evoked potentials

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Neural Computing and Applications 2024年第27期36卷 16683-16706页

作者： Al-Qaysi, Z.T. Albahri, A.S. Ahmed, M.A. Hamid, Rula A. Alsalem, M.A. Albahri, O.S. Alamoodi, A.H. Homod, Raad Z. Shayea, Ghadeer Ghazi Duhaim, Ali M. Tikrit Iraq Technical College Imam Ja’afar Al-Sadiq University Baghdad Iraq Baghdad Iraq Department of Management Information Systems College of Administration and Economics University of Mosul Mosul Iraq Australian Technical and Management College Melbourne Australia Computer Techniques Engineering Department Mazaya University College Nasiriyah Iraq Applied Science Research Center Applied Science Private University Amman Jordan Department of Oil and Gas Engineering Basra University of Oil and Gas Basra1004 Iraq Ministry of Education Thi-Qar Education Directorate Thi Qar Iraq MEU Research Unit Middle East University Amman Jordan Baghdad Iraq

Brain–computer interfacing (BCI) research, fueled by deep learning, integrates insights from diverse domains. A notable focus is on steady-state visual evoked potential (SSVEP) in BCI applications, requiring in-depth assessment through deep learning. EEG research frequently employs SSVEPs, which are regarded as normal brain responses to visual stimuli, particularly in investigations of visual perception and attention. This paper tries to give an in-depth analysis of the implications of deep learning for SSVEP-adapted BCI. A systematic search across four stable databases (Web of science, PubMed, scienceDirect, and IEEE) was developed to assemble a vast reservoir of relevant theoretical and scientific knowledge. A comprehensive search yielded 177 papers that appeared between 2010 and 2023. Thence a strict screening method from predetermined inclusion criteria finally generated 39 records. These selected works were the basis of the study, presenting alternate views, obstacles, limitations and interesting ideas. By providing a systematic presentation of the material, it has made a key scholarly contribution. It focuses on the technical aspects of SSVEP-based BCI, EEG technologies and complex applications of deep learning technology in these areas. The study delivers more penetrating reporting on the latest deep learning pattern recognition techniques than its predecessors, together with progress in data acquisition and recording means suitable for SSVEP-based BCI devices. Especially in the realms of deep learning technology orchestration, pattern recognition techniques, and EEG data collection, it has effectively closed four important research gaps. To increase the accessibility of this critical material, the results of the study take the form of easy-to-read tables just generated. Applying deep learning techniques in SSVEP-based BCI applications, as the research shows, also has its downsides. The study concludes that a radical framework will be presented which, include

关键词： Deep learning

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Design and Application Research of Bionic Mechanical Finger 5

Design and Application Research of Bionic Mechanical Finger

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5th IEEE Eurasia Conference on IOT, Communication and Engineering, ECICE 2023

作者： Shu, Kuen-Ming Li, Meng-Hua Yeh, Chung-Yi Yan, Yuan-Shao Chen, Zhi-Kun Cheng, Po-Hung Lin, Po-Yung National Formosa University Dept. of Mechanical and Computer-Aided Engineering Yunlin Taiwan National Formosa University Smart Machinery and Intelligent Manufacturing Research Center Department of Industrial Management Yunlin Taiwan National Changhua University of Education Dept. of Computer Science and Information Engineering Changhua Taiwan

ISBN: (纸本)9798350314694

We designed and manufactured a bionic mechanical finger made of aluminum as the skeleton. Its front end was equipped with a piezoelectric ceramic sheet as a pressure sensor, and then the whole finger was covered with silicone and made into a similar shape as a human finger. The elasticity of the silicone makes it smooth to avoid damage to objects or discomfort to the human body in applications. The stepping motor was used to control the finger to grasp the object. When the fingertip touches the object, the pressure sensor generates a feedback signal to the controller and calculates, integrates, and analyzes the measured value. When the value exceeds, the stepper motor stops rotating, and the bionic finger is positioned to clamp the object. In this study, ANSYS Workbench software was used to simulate the pressure test of various parts of the silicone-coated mechanical finger. Compared with the actual pressing experiment, the designed bionic finger in this study was successfully applied to fragile objects. © 2023 IEEE.

关键词： piezoelectric ceramics pressure sensing robotic fingers silicone

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Using DL Measure and Distance to Assess Text Mining Document Classification Problems 2

Using DL Measure and Distance to Assess Text Mining Document...

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2nd IEEE Global Conference on Communications and Information technologies, GCCIT 2024

作者： Deshpande, Vivek Yuvaraj, K. Kalia, Akhilesh Praveena, J. Gupta, Shivangi Swadimath, Uma C. Vishwakarma Institute of Technology Department of Computer Engineering Maharashtra Pune India Karpagam Academy of Higher Education Department of Computer Science Coimbatore641021 India Chitkara University Centre of Research Impact and Outcome Punjab Rajpura140417 India Prince Shri Venkateshwara Padmavathy Engineering College Department of Science and Humanities Chennai127 India Quantum University Research Center Quantum University Uttarakhand Roorkee247667 India School of Mangement - Pg Management Karnataka Bangalore India

ISBN: (纸本)9798350388916

In this study, we take an asymptotic approach to the issue of document categorization in text mining. Some intriguing aspects may be shown graphically through the formalisation of statistical hypothesis testing as a text mining task. Due to its experimental efficacy in numerous case studies, various heuristics are used to practical analysis in the text mining issue. This method will provide the necessary theoretical explanation for the efficacy of text mining approaches. Documents are sorted using a distance metric in word vector area and deep learning algorithms. The study also examines the effectiveness of distance measures from the fresh perspective of asymptotic analysis. © 2024 IEEE.

关键词： Adversarial machine learning

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Tech-Enabled Banking Revolt:The Transformational Era of IT in the Financial Sector 7

Tech-Enabled Banking Revolt:The Transformational Era of IT i...

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7th International Conference on Image Information Processing, ICIIP 2023

作者： Ghantasala, G. S. Pradeep Kunchala, Anjaneyulu Vidyullatha, Pellakuri Banu, Shaik Rehana Bhaumik, Amiya Banu, Shaik Balkhis Gupta, Gaurav Alliance University Department of Computer Science And Engineering Bengaluru India Koneru Lakshmaiah Education Foundation Department of Computer Science And Engineering Guntur AP Vaddeswaram India Lincoln University College Dept of Business Management Malaysia Lincoln University College Faculty of Busniess And Accountancy Dept of Business Management Malaysia Fatima College of Heaith Sciences Dept of Physiotherapy Al Ain United Arab Emirates Shoolini University of Biotechnology And Management Sciences Yogananda School of Artificial Intelligence Computers And Data Science HP Solan India

ISBN: (纸本)9798350371406

Information technology is been widely spread all over the world. The benefits that the IT sector provides are unmeasurable. Though there are numerous benefits there are scams going around with the software developed under IT. The growing need for IT has made IT one of the necessary systems that need to be provided in an organization to enhance productivity and progress. The developed countries are making full use of IT in all sectors. The banking sector plays a vital role in the growth of the nation's economy working along the side of IT will only result in the enhancement of the banking sector in many perspectives. The present paper views on the impact of IT on the banking sector. © 2023 IEEE.

关键词： Banking sector Banking Technology Information Communication Technology Information Technology

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