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检索条件"任意字段=2023 International Research Conference on Smart Computing and Systems Engineering, SCSE 2023"

共 8062 条记录，以下是81-90 订阅

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The Wireless Network Signal Detection Using Transfer Learning 3

The Wireless Network Signal Detection Using Transfer Learnin...

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3rd international conference on smart Generation computing, Communication and Networking, smart GENCON 2023

作者： Sharma, Sorabh Centre for Interdisciplinary Research in Business and Technology Chitkara University Institute of Engineering and Technology Chitkara University Punjab India

ISBN: (纸本)9798350319125

Over the last decade, deep learning (DL) applications have exploded in the realm of wireless communications, particularly in the realms of channel estimation and signal identification. However, traditional DL methods need training for each new circumstance. As a result, updating to new wireless systems necessitates retraining. The acquisition of sufficient training data to achieve adequate detection efficiency may be time-consuming and expensive. Traditional DL methods sometimes need extensive amounts of time to train, even if substantial amounts of training data are available. Because of this, it's possible that DL approaches' efficacy and robustness may suffer. This letter presents a transfer learning architecture that may use information gained from an original system to enhance the training procedure of a target system that has access to less data for its training. In regards to channel estimation as well as signal identification, our simulation results show that the suggested solution is superior to state-of-the-art deep learning approaches. © 2023 IEEE.

关键词： DL techniques and Transfer learning Signal Detection Transfer Learning Wireless Networks

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smart Biomechanics System with IoT and Cloud computing for Injury Prevention and Muscle Fatigue Analysis 3

Smart Biomechanics System with IoT and Cloud Computing for I...

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3rd international conference on smart Generation computing, Communication and Networking, smart GENCON 2023

作者： Sangeethalakshmi, K. Sasi Kumar, C. Lakshmi, V. Vidya Giriprasad, S. Malathi, N. Velmurugan, S. R.M.K. College of Engineering and Technology Department of Electronics and Communication Engineering Tamil Nadu Chennai India Shree Sathyam College of Engineering and Technology Department of Computer Science and Engineering Salem Tamil Nadu Sankari India Velammal Engineering College Department of Electronics and Communication Engineering Tamil Nadu Chennai India Srm Institute of Science and Technology Department of Electronics and Communication Engineering Chennai Tamil Nadu Kattankulathur India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Department of Computer Science and Engineering Tamil Nadu Chennai India Tjs Engineering College Department of Electronics and Communication Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350319125

In sports training and performance analysis, muscular fatigue must be understanding and managed. For this, biomechanics is essential since it explains human movement and muscle exhaustion. In sports science, cloud computing has opened new avenues for muscle fatigue research and treatment. This research examines biomechanics and cloud computing for sports training muscle fatigue analysis. Wearable sensors, motion capture systems, and other data-collecting techniques are used to collect biomechanical data during sporting activities. After processing, analyzing, and storing this data on cloud platforms, players, coaches, and sports scientists may get real-time and distant insights. Cloud computing offers scalability, accessibility, and IoT techniques prediction models for muscle fatigue monitoring. By using the cloud, athletes may get personalized training, injury avoidance, and performance enhancement. Cloud-based sports biomechanics solutions provide data security and privacy issues, which the article discusses. It also shows how cloud-based biomechanical analysis might improve sports training and establish evidence-based muscle fatigue management solutions. © 2023 IEEE.

关键词： Injury prevention Muscle fatigue analysis Personalized training Remote access Sports biomechanics

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Emergency Vehicle Detection Using IoT in smart Cities

Emergency Vehicle Detection Using IoT in Smart Cities

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2023 international conference on Sustainable Emerging Innovations in engineering and Technology, ICSEIET 2023

作者： Thota, Mahesh Kumar Prathibhava Gujja, Sruthi Venugopal, Deeksha Ravula Bangalore University Uvce Department of Cse Bengaluru India Kakatiya Institute of Technology and Science Department of It India Department of CSE Uvce Bangalore University Warangal India

ISBN: (纸本)9798350329186

This abstract explores the use of IoT and edge computing for real-time video analytics in emergency vehicle detection within smart cities. It investigates the integration of sensor networks, computer vision algorithms, and edge computing infrastructure. Key considerations include low latency, capability, accuracy, and resource optimization. By harnessing these technologies, smart cities can enhance emergency response systems, improving efficiency and safety. © 2023 IEEE.

关键词： Wireless sensor networks

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smart Education based on Blockchain Technology

Smart Education based on Blockchain Technology

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2023 international conference on Sustainable computing and smart systems, ICSCSS 2023

作者： Dubey, Shubham Tiwary, Aditya Kumar Chandigarh University Be Cse Punjab India Chandigarh University Civil Engineering Punjab India

ISBN: (纸本)9798350333602

This research study explores the potential benefits and challenges of integrating blockchain technology into the education sector. Specifically, it examines the concept of 'smart education,' which utilizes blockchain to create a secure, decentralized, and transparent learning environment. This study provides an overview of blockchain technology and its applications in education, including the use of smart contracts, digital credentials, and decentralized learning platforms. It also discusses the potential advantages of smart education, such as increased security and privacy, improved student tracking and data analysis, and reduced costs. The ability to securely store, verify, and share educational credentials on a decentralised, transparent platform made possible by blockchain technology which has the potential to revolutionise the education sector. To fully realise the advantages of blockchain in education, there are several obstacles that must be overcome. The practical application of the technology is one of the main obstacles to its use in education. There are few set norms and protocols for using blockchain technology in education yet because it is still in its early phases. Getting educational institutions and businesses to use blockchain technology for the verification of educational credentials is another challenge. Due to a lack of knowledge or confidence in the technology, many educational institutions might be hesitant to adopt it. © 2023 IEEE.

关键词： smart contract

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research on the Development Path and Construction Effect Evaluation Index System of smart Waterway

Research on the Development Path and Construction Effect Eva...

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2023 international conference on smart Transportation and City engineering, STCE 2023

作者： Di, Xiaojian Zhang, Jixiu Liang, Yue Li, Baidan Transport Planning and Research Institute Ministry of Transport Beijing China

ISBN: (纸本)9781510673540

China has unveiled guidelines on developing a comprehensive transport network, with the aim of creating a modern, high-quality and comprehensive national transport system. Amid efforts to optimize the country's waterway infrastructure layout, the total length of the high-grade waterways is projected to reach approximately 25,000 km by 2035. It is expected that the construction of high-grade waterways will experience rapid growth from the 14th Five Year Plan period to the mid-century. In order to guide and standardize the construction of smart waterways, this study analyzes the connotation of smart waterways. Furthermore, it elucidates the technical framework of smart waterways and establishes an evaluation index system to assess the effectiveness of their construction. This found paves the way for the integrated and coordinated development of smart waterways. © 2024 SPIE.

关键词： Navigation systems Artificial intelligence Information technology Transportation Network security

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Security Challenges and Future Privacy Perspectives in Cloud computing for smart City Environments

Security Challenges and Future Privacy Perspectives in Cloud...

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2023 international conference on Power Energy, Environment and Intelligent Control, PEEIC 2023

作者： Tayal, Ankit Bhatia, Gaurav Singh, Jay Bansal, Mohit Anitha, G. Gl Bajaj Institute of Technology & Management Electrical Engineering Department Gr. Noida India Gl Bajaj Institute of Technology & Management Computer Application Department Gr. Noida India Institute of Aeronautical 5Engineering Department of Computer Science and Information Technology Hyderabad India

ISBN: (纸本)9798350357769

The context of smart cities and cloud computing is essential to understanding the broader landscape in which security challenges and privacy perspectives play pivotal roles in enhancing the quality of life for the residents in smart cities. smart cities use information and communication technologies (ICT) to optimize resource efficiency. They leverage data, sensors, and connectivity to improve various aspects of urban living, including transportation, energy usage, public services, and sustainability. Cloud computing is an integral part of the infrastructure of smart cities that enables the collection, storage, analysis, and sharing of data and services. This research paper indicates many factors and challenges in the development of cloud-enabled smart city areas and provides potential recommendations. A guideline for future studies on this research topic has also been provided to serve the comparative analysis of the various findings and results. © 2023 IEEE.

关键词： Cloud computing Cyber Security Data computing Internet of Things (IoT) smart City

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Effective Information Capture and Retrieval Through Machine Learning-Based systems 3

Effective Information Capture and Retrieval Through Machine ...

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3rd international conference on smart Generation computing, Communication and Networking, smart GENCON 2023

作者： Sharmila, P. Kadam, Rutuja S. Bhushan, Bharat Vinay Kumar, S.B. Singh, Shweta Hassan, Inzimam Ul Prince Shri Venkateshwara Padmavathy Engineering College Department of Science and Humanities Chennai127 India Pune India Centre of Interdisciplinary Research in Business and Technology Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Faculty of Engineering and Technology Department of Electronics and Communication Engineering Karnataka Bangalore India Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh India Vivekananda Global University Department of Computer Science & Engineering Jaipur India

ISBN: (纸本)9798350319125

Effective information capture and retrieval is crucial for organizations to meet their business needs and remain competitive in today's fast-paced digital world. Traditional information management systems, while useful, are often limited in their ability to handle large amounts of data and provide accurate and relevant results. This paper proposes the use of machine learning-based systems to enhance the process of information capture and retrieval. These systems leverage the power of artificial intelligence to automatically learn from data patterns and improve their performance over time. This approach has shown promising results in various industries, such as finance, healthcare, and e-commerce. Through the utilization of machine learning algorithms, organizations can achieve more efficient and accurate information capture and retrieval, leading to better decision-making and improved business outcomes. The benefits and challenges associated with using machine learning-based systems for information management are discussed, along with potential future research directions in this field. Overall, this paper highlights the importance of utilizing machine learning-based systems for effective information capture and retrieval and the potential impact they can have on organizational success. © 2023 IEEE.

关键词： accurate extracting generating inferences recognizing

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Exploiting Bytecode Analysis for Reentrancy Vulnerability Detection in Ethereum smart Contracts

Exploiting Bytecode Analysis for Reentrancy Vulnerability De...

引用

2023 IEEE international conference on Dependable, Autonomic and Secure computing, 2023 international conference on Pervasive Intelligence and computing, 2023 international conference on Cloud and Big Data computing, 2023 international conference on Cyber Science and Technology Congress, DASC/PiCom/CBDCom/CyberSciTech 2023

作者： Tahir, Usman Siyal, Fiza Ianni, Michele Guzzo, Antonella Fortino, Giancarlo University of Camerino Italy Modeling Electronics and Systems University of Calabria Department of Computer Engineering Italy

ISBN: (纸本)9798350304602

Reentrancy is a type of attack that can occur in smart contracts, enabling untrusted external code execution within the contract. This method exploits a vulnerability that allows an attacker to repeatedly invoke a function in the contract, resulting in an infinite loop and potentially leading to fund theft. Therefore, the reentrancy attack represents a critical concern in blockchain security, prompting the development of various methods for analyzing and detecting reentrancy vulnerabilities over the last decade. Among these methods, the most recent ones leverage the advantages of AI and deep learning techniques. Nonetheless, several limitations persist in existing approaches. Many current methods rely on complex code analysis rules, resulting in a high number of false positives and false negatives. Additionally, the feature engineering process involving word embedding techniques can lead to the loss of critical information. Lastly, the majority of proposed methods necessitate access to the actual source code of the smart contracts for analysis. In this study, we introduce a straightforward and lightweight approach to address these limitations in reentrancy detection. Our approach employs an image-based detection method utilizing deep learning. The pipeline of our method involves disassembling the smart contracts into opcodes and transforming them into RGB images. These images are then used to train a VGG16 CNN model to detect similarities between images labeled as either 'Vulnerable' or 'Not Vulnerable'. To address class imbalance, we implement image augmentation techniques to expand the training dataset. Experimental results conducted on a publicly available dataset demonstrate that our model achieves a significantly high accuracy rate of 99.07%. © 2023 IEEE.

关键词： smart contract

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A DIGITAL TWIN DEVELOPMENT FRAMEWORK FOR A smart SALTWATER GREENHOUSE

A DIGITAL TWIN DEVELOPMENT FRAMEWORK FOR A SMART SALTWATER G...

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ASME international Design engineering Technical conferences / 43rd Annual Computers and Information in engineering conference (IDETC-CIE) / 19th IEEE/ASME international conference on Mechatronic and Embedded systems and Applications (MESA)

作者： Cao, Shiang Chen, YangQuan Univ Calif Merced Dept Mech Engn Mechatron Embedded Syst & Automat MESA Lab 5200 N Lake Rd Merced CA 95343 USA

ISBN: (纸本)9780791887356

Agriculture in arid areas is often limited by the scarcity of freshwater and increasing soil salinization problems. A saltwater greenhouse that uses saltywater instead of freshwater is a promising solution for agriculture in such areas. When used in agriculture irrigation tail water processing, the salt-water greenhouse can be an effective tool for salt extraction which is an important means to mitigate the salinity accumulation problem. However, to optimize greenhouse production and energy efficiency, it is essential to incorporate smartness into the system using existing developments in AI/ML, big data, cloud/edge computing, etc. Therefore, developing a smart saltwater greenhouse leveraging these emerging, cutting edge and groundbreaking technologies becomes a center objective of today's greenhouse development in general and saltwater greenhouse in particular. In this overview framework paper, we present a Digital Twin development framework for a smart saltwater greenhouse. First, the background of the saltwater greenhouse project is introduced together with the basic concept of smart systems and Digital Twins. The greenhouse control system is introduced with a Digital Twin in mind. We then propose the DT development framework dedicated to the saltwater greenhouse. A five-step DT development framework is reviewed and those new smartness features that could be enabled by using DT are also explained. Finally, we conclude the DT framework with some future research efforts.

关键词： Saltwater Greenhouse Digital Twin smart System

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Algorithms and methods of using intelligent systems in fire safety

Algorithms and methods of using intelligent systems in fire ...

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international conference on Artificial Intelligence, Blockchain, computing and Security, ICABCS 2023

作者： Nurmukhamedov, T. Hudayberdiev, M. Koraboshev, O. Sodikov, S. Hudayberdiev, K. Tashkent State Transport University Tashkent Uzbekistan Tashkent University of Information Technologies Tashkent Uzbekistan Digital technologies and Artificial Intelligence Development Research Institute Tashkent Uzbekistan University of Science and Technology Moscow Russia

ISBN: (纸本)9781032669663

The development, selection and implementation of intelligent fire safety systems should be based on a detailed analysis of the fire hazard characteristics of technological processes and the proposed means of protection. Currently, there are many ways to ensure fire safety. Including "smart fire extinguishing" technologies. "smart firefighting" means using the Internet of Things, big data and other technical means to integrate various elements such as firefighting, social supervision and firefighting management, and Internet firefighting and rescue. This article analyzes various ways to ensure the required level of fire safety, which is the main issue for complex objects. © 2024, CRC Press/Balkema. All rights reserved.

关键词： Intelligent systems

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