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Classification of Indicators Based on Morbidity and Mortality

Classification of Indicators Based on Morbidity and Mortalit...

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2023 international conference on Innovative computing, intelligent Communication and Smart Electrical systems, ICSES 2023

作者： Deepak Kumar, A. Jayakumar, Shruti Balamurali, A. Mithun, P. Janagiraman, S. Rajesh Kanna, R. St. Joseph's Institute of Technology Department of Computer Science and Engineering Chennai India SRM Institute of Science and Technology Department of Computer Science and Engineering Chennai Ramapuram India

ISBN: (纸本)9798350319200

In our evolving world, human vulnerability to diseases remains a critical concern, with an overwhelming volume of health data generated by applications. Managing this massive and complex big data requires advanced processing methods. This is particularly evident in medicine, where predicting outcomes is vital for patient care [15]. The intricate and variable nature of healthcare data presents challenges, necessitating the development of sophisticated algorithms and tools. These advancements have the potential to revolutionize disease prevention, personalized treatment, and healthcare [17] system optimization. Enhancing analytical capabilities is urgent, enabling healthcare professionals to uncover valuable patterns that improve patient care [8]. Efforts are underway to efficiently analyze medical big data [11], promising a brighter future for healthcare. Ultimately, of all the algorithms used, Decision Tree analysis proves to be the most effective approach after enhancing using bagging classifier. © 2023 ieee.

关键词： Bagging Classifier Big data Decision Tree Machine Learning

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Secure-JobChain: Revolutionizing Job Recruitment with Blockchain Technology

Secure-JobChain: Revolutionizing Job Recruitment with Blockc...

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2024 ieee international conference on intelligent systems and advanced Applications, ICISAA 2024

作者： Pavithra, S. Geethanjali, S. Vidya, B. Suria, S. Rahul, B.S. Kishore, R. Computer Science and Engineering Rajiv Gandhi College of Engineering and Technology Pondicherry India SRM Institute of Science and Technology Data Science and Business Systems Kattankulathur India SRM Institute of Science and Technology Electronics and Communication Engineering Kattankulathur India

ISBN: (纸本)9798331539948

The traditional job recruitment process is time consuming and also undergoes certain difficulties such as data privacy, lack of transparency, and inefficient credential verification. These difficulties lead to mistrust and also delay the process of hiring. There is a need for improvement in this process that could benefit both the companies and the job applicants. In this paper, a decentralized recruitment platform based on blockchain technology is proposed. This proposed technique will overcome the above-mentioned difficulties by ensuring secure, transparent, and efficient connections between the recruitment companies and the prospective candidates. This solution uses the merits of blockchain i.e., immutability and automation to record job postings, applications, and hiring decisions on an immutable ledger, protecting personal data with cryptographic techniques and automating qualification verification with smart contracts. By implementing the decentralization concepts in Job recruitment, it is proven that the transparency in the interview process is improved by 9 0 and fraud risks are reduced considerably. Moreover, the efficiency of the entire job recruitment process is improved largely. Thus, the decentralized solution proposed in this paper demonstrates its potential in revolutionizing the job recruitment process by providing a secure, transparent and efficient hiring ecosystem. © 2024 ieee.

关键词： Smart contract

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A Systematic Review of 5G Applications and Impacts in Automotive Manufacturing and Mobility 2

A Systematic Review of 5G Applications and Impacts in Automo...

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2nd international conference on computing and data Analytics, ICCDA 2024

作者： Torkzadeh, Moein Hossein Sherimon, Vinu English Language Center University of Technology and Applied Sciences Muscat Oman College of Computing and Information Sciences University of Technology and Applied Sciences Muscat Oman

ISBN: (纸本)9798331542573

The integration of 5G and Internet of Vehicles (IOV) technologies will quicken intelligent transportation system developments, improving urban mobility and safety. This systematic literature review paper summarizes the current research on 5G's uses and impacts in the automotive manufacturing and mobility industries, emphasizing its potential to change transportation scenarios. This systematic literature review examines how 5G technology may impact automobile manufacturing and transportation, with a focus on IOV applications. A comprehensive search across ieee Xplore, Google Scholar, and ProQuest, yielded 31 relevant publications. Five papers were chosen after applying an exclusion and inclusion criteria to demonstrate how 5G will improve vehicle communication, autonomous driving, and traffic management. The review emphasizes the relevance of Vehicle-to-Everything (V2X) communication technology in enabling real-time information transmission among intelligent connected vehicles, a key to future autonomous driving environmental sensing. © 2024 ieee.

关键词： 5G mobile communication systems

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An intelligent Monitoring and Warning Framework in Drone Swarm Digital Twin systems 59

An Intelligent Monitoring and Warning Framework in Drone Swa...

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59th Annual ieee international conference on Communications (ieee ICC)

作者： Demirbaga, Umit Aujla, Gagangeet Singh Singh, Maninderpal Singh, Amritpal Sun, Hongjian Camp, Joseph Univ Cambridge Dept Med Cambridge England Bartin Univ Dept Comp Engn Bartin Turkiye Univ Durham Dept Comp Sci Durham England Southern Methodist Univ Elect & Comp Engn Dept Dallas TX USA Univ Durham Dept Engn Durham England

ISBN: (纸本)9781728190549

In drone swarms, where multiple drones collaborate closely to achieve shared objectives within constrained spatial domains, the intricacies of these interrelated actions can lead to potential issues. Despite rigorous pre-deployment planning, the inherent probability of complications persists. These complications stem from onboard computational resources, hardware failures, and network communication disruptions. While the malfunction of an individual drone may seem inconsequential, it can escalate into a substantial predicament when it disrupts the seamless coordination of the entire swarm. Therefore, the need to proactively monitor drones for predictive failure analysis and the subsequent examination of failed drones to mitigate future occurrences becomes imperative. This paper introduces a comprehensive framework for systematically collecting and processing data within drone swarms. The framework gathers critical information about onboard characteristics and communication metrics. These data points are subjected to advanced analysis using Complex Bayesian Networks to probabilistically uncover complex and hidden relationships between random features. The results demonstrate exceptional accuracy, with influences ranging from 99% to 79%, that ensures the reliability and effectiveness of the predictive capabilities in enhancing drone safety and network performance.

关键词： Drone swarms Digital twin systems Metric dependencies Bayesian networks Performance analysis

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Research on data acquisition and Remote Monitoring System Based on Digital Twin and OPC UA Technology 3

Research on Data Acquisition and Remote Monitoring System Ba...

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3rd international conference on Digital Society and intelligent systems, DSInS 2023

作者： Wang, Zixuan Zhao, Wei Zhang, Zikuan Zhang, Yicheng Tianjin Key Laboratory of High Speed Cutting and Precision Processing Tianjin300222 China

ISBN: (纸本)9798350331387

para>Addressing the challenges of inconsistent communication protocols of underlying manufacturing equipment, difficulty in data collection integration, and low degree of remote real-time visualization monitoring in the production process of intelligent manufacturing lines, a data collection and remote monitoring system based on digital twin technology and OPC UA technology is being researched. Based on the twin model of the intelligent manufacturing line and the OPC UA client-server architecture, the overall framework of the data collection system and remote monitoring system is designed;the key technologies for implementing this system are researched, namely, multi-dimensional modeling technology oriented to digital twins, data collection technology based on OPC UA, and remote monitoring technology with two and three-dimensional collaborative representation. Based on these technologies, the virtual-real mapping of the physical production line and the virtual model within the digital twin platform is realized, completing the data collection and cloud storage of each manufacturing equipment in the production line and the remote monitoring of the two-dimensional visualization panel and three-dimensional model collaborative representation in the virtual environment. © 2023 ieee.

关键词： data acquisition

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Modeling datasets in Distributed Monitoring systems 11

Modeling Datasets in Distributed Monitoring Systems

引用

11th ieee international conference on intelligent data acquisition and advanced computing systems - Technology and Applications (IDAACS)

作者： Bekeneva, Yana A. St Petersburg Electrotech Univ LETI Ul Prof Popova 5 St Petersburg 197376 Russia

ISBN: (纸本)9781665426053

Automated detection of abnormal events in processes in real time is an urgent and important task. To identify various kinds of anomalies, various data Mining methods are often used. Often, to obtain high accuracy of the analysis, preliminary training is required using a large amount of data and representative samples. However, it is not always possible to obtain a representative sample of various kinds of anomalies from real sources. This paper proposes an approach to modeling datasets generated by distributed heterogeneous monitoring systems.

关键词： monitoring systems vehicle movement generation datasets modeling system anomalies data mining

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intelligent Cyber-Physical systems: Integrating Machine Learning and Federated Learning for Enhanced Performance 4

Intelligent Cyber-Physical Systems: Integrating Machine Lear...

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4th international conference on Sustainable Expert systems, ICSES 2024

作者： Deore, Prajwal Mallupalli, Praneeth Reddy Parashar, Jyoti Ekatpure, Shubham Rajendra Varun, D. Kushwah, Virendra Singh Ramrao Adik Institute of Technology Maharashtra India Cbit Hyderabad India Dr. Akhilesh Das Gupta Institute of Professional Studies New Delhi India Kulicke and Soffa Industrial Engineering PA United States Madras institute of technology Tamil Nadu India Vit Bhopal University School of Cse & Ai Sehore India

ISBN: (纸本)9798331540364

The incorporation of Machine Learning (ML) and Artificial Intelligence (AI) into Cyber-Physical systems (CPS) promises to revolutionize their capabilities, enabling autonomous decision-making, predictive analytics, and real-time data processing. Traditional CPS are prone to high latency and data privacy concerns due to centralized processing models. Federated learning facilitates united model training amongst numerous decentralized CPS nodes, confirming data confidentiality and security while minimizing bandwidth usage. The architecture includes core components such as data acquisition modules, federated ML model training, interference engines, and robust communication protocols. The results indicate enhanced predictive accuracy, reduced downtime, and increased operational efficiency. The challenges of deploying AI/ML in CPS, including computational resource constraints, real-time processing requirements, and the need for explainability and transparency in ML decisions, are also addressed. The importance of robust data management and secure communication channels to maintain system integrity and reliability is discussed. Future research directions include integrating advanced AI techniques such as reinforcement learning and exploring the ethical implications of deploying AI/ML in CPS. This research contributes to the field by offering a structured methodology for incorporating federated learning into CPS architectures, paving the way for more intelligent, autonomous, and resilient systems capable of adapting to complex and evolving environments. © 2024 ieee.

关键词： Federated learning

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Exploring the Performance of NOMA systems with intelligent Surface Assistance 5

Exploring the Performance of NOMA Systems with Intelligent S...

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5th ieee international conference on Communication, computing and Industry 6.0, C2I6 2024

作者： Subhashini, G. Rajalakshmi, R. Kumari, L. Krishna Sivakumar, P. Aswathi, I. Ramco Institute of Technology Department of Ece India Kalasalingam Academy of Research and Education Department of Ece India

ISBN: (纸本)9798350379884

This study presents a comprehensive analysis of the Bit Error Rate (BER) performance in a Non-Orthogonal Multiple Access (NOMA) system enhanced by a Reconfigurable intelligent Surface (RIS) to support uplink power domain communication. By leveraging the RIS's ability to intelligently reconfigure the wireless propagation environment, the proposed system aims to optimize spectral efficiency and enhance communication reliability. The BER performance is evaluated under various conditions, considering key factors such as channel fading, interference, and power allocation schemes. Through detailed mathematical modeling and extensive simulations, the study demonstrates that integrating RIS can substantially reduce BER, leading to more robust and efficient wireless communication. The findings suggest that RIS-enhanced NOMA systems have the potential to revolutionize future wireless communication networks by offering significant improvements in signal quality and data transmission reliability. © 2024 ieee.

关键词： Rayleigh fading

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A Power Consumption Prediction Method for data Center Refrigeration System Based on LSTM 2

A Power Consumption Prediction Method for Data Center Refrig...

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2nd international conference on Signal Processing and intelligent computing, SPIC 2024

作者： Tian, Yuxing Gao, Lin Lu, Sijun Shenyang Ligong University School of Information Science and Engineering Shenyang China Shen Kan Engineering & Technology Corportion Shenyang China

ISBN: (纸本)9798350368888

The energy consumption problem caused by data centers is becoming more and more serious, and air conditioning systems rank second only to IT equipment in terms of power consumption. Accurate prediction of air conditioning power consumption in data centers not only helps enterprises grasp the cost level of data centers, but also helps data centers to save energy and build green data centers. This paper presents an energy consumption prediction method for air-conditioning in data center based on LSTM network. This paper also utilizes simulation software to model data center facilities and simulate the power consumption necessary to maintain the air conditioning system under various conditions. The experimental results demonstrate that utilizing the LSTM neural network model results in more accurate predictions for air conditioning in data center facilities compared to the BP neural network model. © 2024 ieee.

关键词： air conditioning system Date center neural network power consumption prediction

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Climatic data acquisition using Sensor Interfaced Raspberry System 6

Climatic Data Acquisition using Sensor Interfaced Raspberry ...

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6th international conference on intelligent computing and Control systems, ICICCS 2022

作者： Bernatin, T. Sriram, J.V.S. Tejas Sai, P. Nisha, Sahaya Anselin Revathy Jayapriya Sathyabama Institute of Science and Technology Dept. of Ece Chennai India Sathyabama Institute of Science and Technology Dept. of It Chennai India Sathyabama Institute of Science and Technology Dept. of Maths Chennai India

ISBN: (纸本)9781665410359

This paper presents the cutting-edge weather conditions observing arrangements and makes data accessible to the world. The innovation behind it is Internet of Things (IoT), which is the first and best answer to associate with the Internet and interface the world to the Internet. This might incorporate electronic gadgets, sensors, electronic gadgets, and the sky is the limit from there. This framework is liable for endlessly checking the climate like temperature, relative hotness, light power, and the CO2 level, sending data to the site and setting up the sensor data in the information. Cutting-edge data from the executed framework can be seen online from any place on the planet. © 2022 ieee.

关键词： data acquisition

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