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2024 IEEE International Conference on Intelligent systems and Advanced Applications, ICISAA 2024

作者： Mishra, Ajit Kumar Verma, Vikash Manivannan, R. Kumar, N. Krishna Srividya, Mudunuru Sharma, Sanskriti IES College of Technology Department of Electrical & Electronics Engineering M.P Bhopal462044 India GGITS Dept of Artificial Intelligence & Machine Learning M.P Jabalpur India Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology School of Computing Computer Science & Engineering TN Chennai India Guru Nanak Institutions Technical Campus Ibrahimpatam Dept.of CSE Telangana Hyderabad India MLR Institute of Technology Dept. of CSE Telangana Hyderabad India GGITS Dept of Computer Science & Business System M.P Jabalpur India

ISBN: (纸本)9798331539948

Modern artificial intelligence uses geographical data mining and regional analysis to analyze large datasets. Deep learning is a new geographic AI method. Example: Geographic Deep Mining Network (GDMN). The Spatial Feature Extractor (SFE), Temporal Geospatial Attention Mechanism (TGAM), and Geospatial data Fusion Module (GDFM) comprise this technique. We examined six popular approaches and found GDMN performed best. It worked successfully for several reasons. GDMN always outperforms its competitors in memory, F1 score, IoU, training time, and inference time. This is always true. These findings suggest that GDMN might boost geospatial AI's data mining and location analysis abilities. After all the effort, we have developed a solid basis for location intelligence, making it useful in various subject areas. Because it's versatile, mapping specialists may use GDMN for better environmental monitoring or city development. Deep learning helps GDMN make better space choices and uncover insights in large, challenging data sets that no one knew about. GDMN's cutting-edge approaches make space and time research and prediction simpler. This wide perspective offers a more full and all-encompassing picture of today's challenges than previous techniques. This makes it impossible to overstate how significant this work is to location intelligence. Its unmatched research and insights usher in a new era. © 2024 IEEE.

关键词： Adversarial machine learning

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Advancements in Artificial Intelligence for Better Image Analysis, Recognition, and Interpretation in the Field of Image Processing

Advancements in Artificial Intelligence for Better Image Ana...

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Communications and Information Technologies (GCCIT), Global Conference on

作者： V. Saravanan A. Adaikkammai G. Sasikala Iyswariya A A. Babisha M. Latha Department of Nano Electronics Materials and Sensors Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai Tamilnadu India Department of Computer Science and Business Systems R.M.D. Engineering College Department of ECE Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai Department of ECE R.M.K. Engineering College Chennai Department of Artificial Intelligence and Data Science Panimalar Engineering College Department of IT SRM Institute of Science and Technology

ISBN: (数字)9798350388916

ISBN: (纸本)9798350388923

The rapid advancement of artificial intelligence (AI) has brought about a significant revolution in image processing. This revolutionary technology has enabled the analysis, recognition, and interpretation of images in ways that were previously inconceivable. An investigation into the most recent developments in artificial intelligence techniques that have improved the efficiency and precision of image processing tasks is presented in this study. We survey the emergence of transformer architectures and the integration of deep learning models, namely Convolutional Neural Networks (CNNs), to explore how these technologies have transformed the understanding and application of images in several industries. An overview of existing image processing methods and the limits of those approaches is presented at the beginning of our research. This highlights the necessity of more advanced AI-driven solutions. After that, we examine the advancements that have been made in artificial intelligence, such as the incorporation of attention processes, the development of more effective CNN architectures, and the utilisation of generative adversarial networks (GANs) for the purpose of picture synthesis and augmentation. The purpose of this research is to investigate the impact that artificial intelligence has on particular applications of image processing, such as autonomous car navigation systems, facial recognition, medical imaging, and satellite images analysis. Specifically, we focus on the enhancements in diagnostic accuracy, environmental monitoring, security, and safety that arose as a result of the adoption of artificial intelligence in these sectors through the use of case studies. Furthermore, we discuss the issues that are associated with artificial intelligence in image processing. These challenges include the necessity for explainable AI models, the requirement for computational resources, and the protection of data privacy. A number of potential solutions to these problems

关键词： Industries Image recognition Image analysis Transformers Satellite navigation systems Generative adversarial networks Satellite images Convolutional neural networks Security Artificial intelligence

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Optimizing V2V Communication in 5G/6G Networks with Enhanced Deep Cooperative Q-Learning

Optimizing V2V Communication in 5G/6G Networks with Enhanced...

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Next Generation Communication & Information Processing (INCIP), International Conference on

作者： Priyameet Kaur Keer N. Lakshmi Deepthi Ginni Nijhawan Neeraj Chahuan Ali Albawi Madhu Palati Master of Business Administration New Horizon College of Engineering Bangalore India Computer Science and Engineering (Data Science) Institute of Aeronautical Engineering Hyderabad India Lovely Professional University Phagwara India Lloyd Institute of Engineering & Technology Greater Noida Uttar Pradesh Radiology Techniques Department College of Medical Technology The Islamic University Najaf Iraq Dept. of EEE BMS Institute of Technology and Management Bangalore

ISBN: (数字)9798331528140

ISBN: (纸本)9798331528157

Understanding the physical channel characteristics of V2V communications is important, but it is neither cost-effective nor practical to have full information about them. Since there is always delay and data-loss during communications, which are further accelerated when the wireless channel status and device parameters remain uncalibrated frequently, full or even partial information of the environment is not feasible, even for a short period. Moreover, the vehicular mobility can also introduce abrupt changes to communication. These uncertainties dictate that a system should be well-and adaptively-designed to handle changing information. V2V communications that exploit this information naturally serve as candidates for artificial Intelligence (AI) algorithms. Specifically, there are applications of cooperative learning strategies that are suitable, including machine learning, deep reinforcement learning, and deep multi-agent reinforcement learning. This paper applies novel Enhanced Deep Cooperative Q-Learning (DCO-DQN) model for V2V communication, to achieve the best trade-offs (latency/ reliability/safety) under various states, including short and long memory, asymptotic and non-asymptotic, possible and unpredictable outcomes scenarios. The proposed framework incorporates spatial wisdom and historical information into Euclidean spaces, accounting for the above behavior and challenges. For this reason, future V2V communication in 5G/6G networks may benefit from adopting our approach, as previously described. A thorough comparative study has been conducted to illustrate our advantages and shortcomings. Our analysis also offers insights on hybrid techniques and decentralized approaches, as possible means for future development.

关键词： Wireless communication 6G mobile communication Q-learning Uncertainty 5G mobile communication Vehicular ad hoc networks Interference Linear programming Safety Optimization

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Blockchain technology in Automobile Insurance Claim systems Research 1

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5th International Conference on Smart Computing and Communication, SmartCom 2020

作者： Deng, Han Wang, Chong Wu, Qiaohong Nie, Qin Huang, Weihong Zhang, Shiwen Big Data Development and Research Center Guangzhou College of Technology and Business Guangzhou528138 China College of Computer Science and Electronic Engineering Hunan University Changsha410000 China Department of Accounting Guangzhou College of Technology and Business Guangzhou528138 China School of Computer Science and Engineering Hunan University of Science and Technology Xiangtan411201 China

ISBN: (数字)9783030747176

ISBN: (纸本)9783030747169

Recently, academics and industries are increasingly paying attention to blockchain technology because it is used to improve traditional companies. Insurance company is one type of the traditional and inflexible companies. Its operation is not transparent, based on paper contracts, relying on human intervention and other characteristics resulting in low efficiency. The transparency, tamper-proof, decentralization and other advantages of blockchain-based Internet of Things and smart contract technology enable people to improve the traditional processes and structures of the insurance industry. This paper summarizes the application of block chain technology in the auto insurance claim systems. By comparing the implementations of various systems, the existing achievements and their advantages and disadvantages are described. In addition, this paper prospects the key directions of future research on the implementation of blockchain. © 2021, Springer Nature Switzerland AG.

关键词： Smart contract

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Enabling Protection for Critical Infrastructure through Security and Privacy in the Industrial Internet of Things

Enabling Protection for Critical Infrastructure through Secu...

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International Conference on Communication and Electronics systems (ICCES)

作者： B. Yasotha T. Vijayanandh P Thirumoorthy M.Sangeetha Priya Sasidaran S S.B. Prakalya Department of Data Science and Business Systems SRMIST Kattankulathur Department of Computer Science & Engineering School of Computing Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Avadi Chennai Tamil Nadu India Department of Electrical and Electronics Engineering Dhanalakshmi College of Engineering Chennai India Department of Computer Science and Engineering (Cyber Security) Saveetha Engineering College Chennai Department of Information Technology Panimalar Engineering College Chennai Department of Electronics and Communication Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai India

ISBN: (数字)9798350377972

ISBN: (纸本)9798350377989

Industrial Internet of Things can improve critical infrastructure in energy, transportation, and manufacturing. However, IIoT device and system integration opened security weaknesses that bad actors may exploit, causing huge disruptions and financial losses. IoT network security and privacy must be prioritized to protect key infrastructure. IIoT security issues and solutions are examined in this research. First, we take a look at the state of security and the unique risks that industrial IoT devices are up against, including hacking, illegal access, and cyber-physical assaults. Then, to lessen the impact of these dangers, we investigate cutting-edge security solutions, such as authentication, encryption, and access control systems. Building a robust security architecture specifically for IIoT applications is one of the main goals of the project. To improve safety and anonymity, this system uses cutting-edge innovation like blockchain, AI, and edge computing. We also talk about how privacy-preserving methods like differential privacy and anonymization are important for keeping sensitive information safe while allowing data sharing for analysis and operations. We assess the efficacy of the suggested security protocols and procedures using a battery of case studies and simulations. Reducing the attack surface and improving the resilience of IIoT systems can be achieved by a multi-layered security approach that includes effective privacy protections. We conclude by outlining our research's larger implications, which include the importance of standardization, regulatory compliance, and stakeholder collaboration in ensuring the safe and privacy-protecting deployment of IIoT technologies in vital infrastructure. Protecting vital infrastructure from cyber threats is our ultimate goal, and our work helps build IIoT systems that are more secure and dependable.

关键词： Privacy Collaboration Standardization Critical infrastructure Encryption Security Stakeholders Protection Computer crime Industrial Internet of Things

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Practice and case analysis of vehicle network interaction technology in microgrid operation optimization 4

Practice and case analysis of vehicle network interaction te...

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4th International Conference on Energy, Power and Electrical engineering, EPEE 2024

作者： Zhang, Lin Liu, Jiran Li, Junyi Liu, Jingsheng Wang, Jiayi Luo, Yuchen University of Malaya Department of Mechanical Engineering Kuala Lumpur Malaysia Xinjiang University of Finance and Economics School of Statistics and Data Science Xinjiang Uygur Autonomous Region Urumqi City China Shandong University of Science and Technology Swinburne College Shandong Province Jinan City China Yili Normal University College of Electronics and Engineering Ili Kazak Autonomous Prefecture Yining City China Northwest A&f University College of Natural Resources and Environment Yangling Agricultural High-tech Industries Demonstration Zone Shaanxi Province Xianyang City China Shanghai Normal University Business School Shanghai Municipality China

ISBN: (纸本)9798331518066

Guided by the goals of carbon peaking and carbon neutrality, the power distribution system is gradually evolving into a new type of regional power system that integrates functions such as power collection, transmission, storage and trading, showing brand-new forms and characteristics. [1] In this study, the application of vehicle network interaction technology in a microgrid project in Dongying City was analyzed in depth, and a variety of vehicle network interaction modes were selected to optimize the microgrid operation status through real-time data monitoring and control strategy adjustment. During the survey, detailed data on microgrid operation over several months were collected, including the charging and discharging time, frequency, power load changes, and other information on trams. The influencing factors of vehicle network interaction were discussed through data association analysis. This study found that the correct configuration and implementation of the vehicle network interaction strategy can effectively reduce the peak load of the grid, make the internal load of the microgrid more balanced, improve the overall stability of the system, and reduce the energy cost. At the same time, the technology can also promote the efficient use of electric vehicles, and provide two-way regulation capability for grid energy storage. The case study shows that after the implementation of the vehicle network interaction technology, the operation cost of the microgrid project has decreased by about 15% and the utilization rate of renewable energy has increased by nearly 10%. Through quantitative analysis, this paper constructs a vehicle network interaction optimization model suitable for different sizes and properties of microgrid, proposes a set of systematic operation process and optimization strategy, and puts forward corresponding management and policy suggestions according to the experimental results, in order to provide reference for the development of microgrid. © 2

关键词： Microgrids

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Optimizing Supply Chain Operations through Machine Learning: Opportunities and Challenges

Optimizing Supply Chain Operations through Machine Learning:...

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Pervasive Computational Technologies (ICPCT), International Conference on

作者： D. Santhakumar Gopalakrishnan Soundararajan Suman Chintala E. Bharath Krishna Murthy Inumula Kuldeep Chouhan Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Thandalam – Chennai Tamil Nadu Muscat College Ruwi Sultanate of Oman Business Intelligence Architect USA Department of Artificial Intelligence & Data Science C K College of Engineering & Technology Anna University Cuddalore Tamil Nadu Symbiosis Institute of International Business (SIIB) Symbiosis International (Deemed University) Pune Maharashtra India Department of Computer Science and Applications Sharda School of Engineering and Technology Sharda University Greater Noida India

ISBN: (数字)9798331508685

ISBN: (纸本)9798331519476

In order to create enduring partnerships that are essential to attaining long-term economic success, supply chain management, or SCM, is essential. Strict standards and decision-making procedures are necessary for efficient SCM, and they have a big influence on the final results. In order to efficiently manage inventory levels and logistical procedures, this study offers a complex ML system that combines demand forecasts and optimisation methods. For predicting demand, the approach uses a Generative Adversarial Networks (GAN) model, taking advantage of its capacity to process time-series data and identify intricate trends. The algorithm achieves an impressive 96.4% forecasting of demand accuracy by examining extensive historical sales data and a variety of supply chain variables. In order to optimise inventory rules, the investigation also uses optimisation techniques, particularly Genetic Algorithms (GA) in conjunction with Simulated Annealing (SA). The model demonstrated significant improvements when implemented to an actual commercial logistics system. Notably, it reduced storage expenses by 19% and increased order fulfillment rates by 24%, proving its capacity for combining service standards with financial efficiency. The findings highlight the significant impact of ML on improving supply chain efficiency and show how it can result in significant cost savings and improve service quality.

关键词： Supply chain management Costs Accuracy Supply chains Demand forecasting Machine learning Predictive models Generative adversarial networks Standards Genetic algorithms

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An adaptive transformer model for anomaly detection in wireless sensor networks in real-time

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Measurement: Sensors 2023年 25卷

作者： Siva Kumar, A. Raja, S. Pritha, N. Raviraj, Havaldar Babitha Lincy, R. Jency Rubia, J. Department of Data Science and Business Systems School of Computing SRM Institute of Science and Technology Tamilnadu Kattankulathur India School of Electrical & Communication Department of Electronics & Communication Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Tamilnadu India Department of ECE Panimalar Engineering College Tamilnadu India Department of BME KLE Dr. M S Sheshgiri College of Engineering and Technology Belgaum India Computer and Communication Engineering Department Sri Eshwar College of Engineering Tamilnadu India Department of ECE K. Ramakrishnan College of Engineering Tamilnadu India

Defense, environmental monitoring, healthcare, home automation, and other fields are just a few of the many that make use of wireless sensor networks. There are sensor nodes in these networks that are sent out into the field to collect data and relay it back to the network's hub. The data streams may be infused with anomalous data due to interferences, malfunctioning nodes, etc. The security of the network depends on the prompt identification of any irregularities. Due to noise, missing data, and the dynamic nature of the network, identifying and detecting abnormal data is *** study introduces a dynamic context-capturing deep learning model for sensor data anomaly detection, constructed as a Transformer with a spatio-temporal attention mechanism. The proposed model is trained and tested with a public dataset built from real-time sensor data captured in a water treatment plant. With an accuracy of 0.09142, F1 of 0.9109, and precision of 0.9191, the STA-Tran model outperforms the best existing methods. © 2022

关键词： Anomaly detection

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Retraction Note: A systematic Survey on Implementation of Fuzzy Regression Models for Real Life Applications

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Archives of Computational Methods in engineering 2025年 1-2页

作者： Khan, Mufala Kumar, Rakesh Aledaily, Arwa N. Kariri, Elham Viriyasitavat, Wattana Yadav, Kusum Dhiman, Gaurav Kaur, Amandeep Sharma, Ashutosh Vimal, S. Department of Mathematics Lovely Professional University Phagwara India College of Computer Science and Engineering University of Ha’il Hail Saudi Arabia College of Computer Science and Engineering Prince Sattam Bin Abdulaziz University Al‑Kharj Saudi Arabia Business Information Technology Division Department of Statistics Faculty of Commerce and Accountancy Chulalongkorn University Bangkok Thailand Department of Computer Science Government Bikram College of Commerce Patiala India Department of Electrical and Computer Engineering Lebanese American University Byblos Lebanon University Centre for Research and Development Department of Computer Science and Engineering Chandigarh University Gharuan India Department of Computer Science and Engineering Graphic Era Deemed to be University Dehradun India Chitkara University Institute of Engineering and Technology Chitkara University Rajpura India School of Computer Science University of Petroleum and Energy Studies Dehradun India Department of Artificial Intelligence and Data Science Ramco Institute of Technology Rajaplayam India

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Perceptual Video Summarization Using Keyframes Extraction Technique

Perceptual Video Summarization Using Keyframes Extraction Te...

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Innovative Practices in technology and Management (ICIPTM), International Conference on

作者： Rajitha Jasmine R Padmaja Nimmagadda K. Sudhakar Benitha Christinal J P. Rajasekar Syed Musthafa A Department of Information Technology R. M. K. Engineering College Kavaraipettai India Department of Electronics and Communication Mohan Babu University Tirupati India Department of Computer Science Madanapalle Institute of Technology Madanapalle India Department of Information Technology Panimalar Engineering College Chennai India Department of Data Science and Business Systems SRM Institute of Science and Technology Chengalpattu India Department of Information Technology M. Kumarasamy College of Engineering Karur India

The growth of video content in recent years is a challenging problem due to increased memory storage and time consuming for analyzing content of the video. Therefore, there is a need to reduce the content for human usage. This paper presents a keyframes technique that uses co-occurrence matrix and permutation computation for perceptual video summarization (PVS). The Human Visual system (HVS) needs to incorporate the PVS. It helps to allow for the importance of perceptually significant contents. The proposed method uses different kinds of videos to evaluate the effectiveness of the work. The subjective evaluation scores have evaluated the proposed work.

关键词： Color Visual systems Feature extraction

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