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Performance Analysis and Improvement of Lithium Battery Charging System Based on Deep Learning Algorithms 3

Performance Analysis and Improvement of Lithium Battery Char...

引用

3rd ieee international conference on Distributed computing and Electrical Circuits and Electronics, ICDCECE 2024

作者： Lv, Hongtu Zhang, Hongyao Li, Gongju College Of Intelligent Systems Science And Engineering Hubei Minzu University Hubei Enshi China Jiangsu Fuyuan Technology Jiangsu Changzhou China Anhui Chery Ruifu Special Vehicle Technology Anhui Wuhu China

ISBN: (纸本)9798350318609

The research on traditional lithium battery charging systems has problems such as model simplification, insufficient data, insufficient accuracy, and poor real-time performance. Simplified electrochemical models cannot accurately reflect the complexity of actual systems, and limited experimental data limits the understanding of system behavior. Neglecting influencing factors leads to low prediction accuracy and a lack of real-time monitoring mechanisms to cope with dynamic changes in the system. These limitations affect the comprehensive analysis and optimization of system performance. This paper designed a Convolutional Neural Network - Gated Recurrent Unit (CNN-GRU) based on convolutional neural networks to extract local and global features from time series data, and combined it with time series prediction to predict charging temperature. In experimental verification, the quadratic mean error of the CNN-GRU algorithm model is 0.011, and the mean absolute deviation is 0.018. The error between the predicted electricity price and the actual value is within 2%. © 2024 ieee.

关键词： Learning algorithms

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Application of Bayes Rule for Precise Analysis of Class-Imbalanced Stroke data

Application of Bayes Rule for Precise Analysis of Class-Imba...

引用

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

作者： Viswapriya, S.E. Rajeswari, D. SRM Institute of Science and Technology School of Computing College of Engineering and Technology Department of Data Science and Business Systems Kattankulathur Chennai India

ISBN: (纸本)9798350319200

The interpretability of stroke risk prediction is still lagging and it can be solved by using deep analysisofdata with the application of the Bayes rule. The prediction model has to be produced with accuracy, clarity, and comprehensibility without ambiguity, especially in machine learning and data analytics. The class imbalance problem is a common issue affecting the raw data, which means the imbalanced categorization of outcome feature value. In many fields, such as fraud detection, network intrusion detection, and medical diagnostics, this class imbalance issue is present where negative class samples outnumber positive class samples. If some datasets have class imbalance and imperfect issues, there will be no proper classification pattern. There are many resampling methods to address this class imbalance problem. However, these resampling methods can make an even distribution of classes by generating or omitting some samples, but theycannot handle imperfect data. The probabilistic analysis can lead to the identification of issues that exist in the dataset and provide direction to medical experts on dealing with the condition to avoid *** strength of applying the Bayes rule will provide a meaningful interpretation of stroke data. This paper investigates the Baye rule's significance in stroke data analysis. © 2023 ieee.

关键词： Bayes rule Class imbalance probabilistic analysis Resampling Stroke data

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Privacy-Preserving QR Code Schemes for data Access and Aggregation in Smart Distribution Grid systems 9

Privacy-Preserving QR Code Schemes for Data Access and Aggre...

引用

9th international conference on intelligent computing and Signal Processing, ICSP 2024

作者： Zhang, Zhijun Gao, Yingjian Zhu, Weiping Lu, Kai State Grid Electric Power Research Institute Jiangsu Nanjing China State Grid JiangSu Electric Power Co. Ltd Jiangsu Nanjing China

ISBN: (纸本)9798350376548

The smart distribution grid is a type of electrical supply network that has been widely applied in life. Ensuring efficient and secure communication of information within the smart distribution grid has become one of the hot topics of research. This paper presents a QR code-based data access control and aggregation scheme for smart distribution grid. The scheme utilizes QR codes to store user's electricity consumption information and employs Hamming encoding along with information hiding techniques to embed user's identifiers into the QR codes for authentication by local gateways. Experimental results show that the scheme can effectively resist common image attacks. Compared to existing hiding schemes, this scheme demonstrates certain advantages in terms of security, robustness, and embedding capabilities. © 2024 ieee.

关键词： Smart power grids

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Fuse-Seg: semantic division of Cityscapes based on RGB and Fusion of Thermal data 4

Fuse-Seg: semantic division of Cityscapes based on RGB and F...

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4th international conference on Emerging systems and intelligent computing, ESIC 2024

作者： Bandi, Maheswari Reeja, S.R. VIT-AP University Scope India

ISBN: (纸本)9798350349856

Urban scene semantic division is an important stage in many automated vehicle applications. As deep learning technologies advance, it advances significantly. The majority of the available semantic segmentation networks only employ data from a single sensory modality, these are typically visible cameras output images in the RGB format. but, when illumination circumstances are not met, that is low lighting or total blackness, these networks' segmentation performance is susceptible to degradation. Our research shows those thermal pictures created using thermographic cameras resistant poor lighting conditions. Consequently, within this article we suggest a method called FuseSeg that improves the accuracy and robustness of the semantic division of Cityscapes environments by utilising both RGB and temperature data. RGB data represents the traditional color information captured by standard cameras, while thermal data captures the infrared emissions from objects in the scene. Combining thermal data with RGB data allows FuseSeg in order to get around restrictions of using RGB data alone, particularly in difficult situations like dim lighting or occlusions. By fusing the two modalities, the suggested approach aims to increase precision and reliability of urban scene understanding. © 2024 ieee.

关键词： Semantic Segmentation

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Decoding Ideology: Machine Learning-Based Detection of Extremist Content 5

Decoding Ideology: Machine Learning-Based Detection of Extre...

引用

5th international conference on intelligent computing, Communication, Networking and Services, ICCNS 2024

作者： Mussiraliyeva, Shynar Baisylbayeva, Kymbat Bolatbek, Milana Yeltay, Zhastay Al-Farabi Kazakh National University Dept. of Information Systems Almaty Kazakhstan

ISBN: (纸本)9798350354690

This paper explores the use of machine learning techniques to classify texts with regard to extremist ideology, such as radicalization, propaganda, and recruitment. The relevance of this topic is the effective identification and analysis of content that contributes to the spread of extremist ideas on the Internet. This article discusses various approaches to preprocessing textual data and selecting machine learning models. Text materials on social networks, Internet forums, and other online resources were used as a data set. Experiments have been conducted using various classification algorithms, including Naive Bayes classifier, SVM and Logistic regression, etc. The results of the study confirm the possibility of effective use of machine learning methods for automatic classification of texts according to their extremist ideology, which can be a useful tool for detecting and countering the spread of extremist ideas on the Internet. © 2024 ieee.

关键词： Adversarial machine learning

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intelligent Vessel Scheduling System for the Controlled inland Waterway Based CPS 25

Intelligent Vessel Scheduling System for the Controlled inla...

引用

ieee 25th international conference on intelligent Transportation systems (ITSC)

作者： Xie, Hui Wang, Dejun Gan, Shaojun Li, Zechen Liang, Shan Changjiang Luzhou Waterway Bur Yibin Sichuan Peoples R China Chongqing Univ Sch Automat Chongqing Peoples R China Changjiang Chongqing Waterway Bur Chongqing 401147 Peoples R China Beijing Univ Technol Coll Metropolitan Transportat Beijing Peoples R China Educ Minist China Key Lab Dependable Serv Comp Cyber Phys Soc Chongqing 400044 Peoples R China

ISBN: (数字)9781665468800

ISBN: (纸本)9781665468800

As an essential part of transportation systems, inland waterway transportation is economical and sustainable. However, traffic congestion and vessel collision often occur in controlled waterways, which has greatly restricted the development of inland waterway transportation. This paper designs a Cyber-Physical System based intelligent vessel scheduling system (IVSS) for controlled waterway traffic management and details the data acquisition, data transmission, and data processing process. IVSS has been applied in the upper reaches of the Yangtze River as a part of the Yangtze Digital Waterway construction project. The practical application confirms that IVSS enhances the controlled waterway traffic capacity by reducing the vessels' waiting time.

关键词： Wireless communication Processor scheduling data acquisition Transportation Reliability theory Control systems Real-time systems

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Leveraging Digital Twins and AI for Enhanced Clinical Decision Support in Endometrial Cancer Treatment 5

Leveraging Digital Twins and AI for Enhanced Clinical Decisi...

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5th international conference on IoT Based Control Networks and intelligent systems, ICICNIS 2024

作者： Palarimath, Suresh Maran, Pyingkodi Wilfred Blessing, N.R. Cibi, A. Kavitha, S.J. Steffi, S. College of Computing and Information Sciences University of Technology and Applied Sciences Salalah Oman Kongu Engineering College Department of Computer Application Erode India College of Computing and Information Sciences University of Technology and Applied Sciences Ibri Oman Virtusa Consulting Service Private Limited Data & Analytics Visualizaation - Delivery Chennai India Vels Institute of Science Technology and Advanced Studies Department of Computer Science and Engineering Chennai India Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Computer Science and Engineering Chennai India

ISBN: (纸本)9798331518097

Digital twins are a relatively new concept that have lately garnered interest in the industrial sector. Digital twins are virtual models that mirror actual products. In this research, we suggest using the technology of digital twins to the field of healthcare in order to enhance clinical decision support and to provide more individualized treatment for patients. When artificial intelligence (AI) is combined with digital twins, healthcare professionals are able to more effectively analyze large volumes of varied data and increase their ability to make diagnostic and therapeutic decisions. In this research, we provide a conceptual framework for leveraging digital twins and AI to solve existing limits in cancer care, notably endometrial cancer therapy. This research study mainly focuses on cancer care, and more specifically on endometrial cancer treatment. In addition, we analyze the possible challenges and opportunities that may arise throughout the process of integrating this technology in healthcare settings. Our overarching objective is to improve the standard of treatment as well as the clinical results for people who have cancer. © 2024 ieee.

关键词： Lung cancer

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Secure Electronic Health Record Storage in the Cloud Based on Multiple Fragmentation and Reconstruction Using Blockchain with Cryptographic Techniques 4

Secure Electronic Health Record Storage in the Cloud Based o...

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4th international conference on Ubiquitous computing and intelligent Information systems, ICUIS 2024

作者： Mercy, W. Annabel, L. Sherly Puspha Kcg College of Technology Anna University Dept. of Cse Chennai India St. Joseph's College of Engineering Dept. of Artificial Intelligence and Machine Learning Chennai India

ISBN: (纸本)9798331529635

data privacy is an important factor in cloud data storage. Still there are some security issues such as hacking that may take place with respect to data. In the existing techniques the time and cost consumption are more in computing and data storage. data privacy is a challenging one against hacking and to avoid hacking data can be fragmented over multiple locations when storing and reconstructed while accessing the data. In the existing implementation single field query is used to access the Electronic Health Records (EHR). It will take more time to fetch the data and the fetched data also will be huge with some unwanted data. This paper proposes multi field skyline conditional query to access the accurate EHR data for minimizing the accessing time to improve the performance by implementing B+-Tree indexing and skyline query. Cloud data storage privacy also will be improved with Cryptography and Blockchain techniques. Secure Hashing Algorithm (SHA 512), Consensus algorithm and Smart Contracts are used for data integrity and fault tolerance mechanism. The possible goals include effective data storage and access, reducing security breach, storage overhead, avoiding hacking, enhancing data privacy. © 2024 ieee.

关键词： B+ - Tree Blockchain Cloud Storage Consensus algorithm data Fragmentation data Reconstruction Electronic Health Records Secure Hashing Algorithm Skyline Query Smart Contracts

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A Review of Emerging Trends in Energy data Management systems 11

A Review of Emerging Trends in Energy Data Management System...

引用

11th international conference on Internet of Things: systems, Management and Security, IOTSMS 2024

作者： Karagiannis, Vasileios Nagy, Bela Jodkowski, Agnes Kranner, Margit Ignjatović, Dražen Austrian Institute of Technology Center for Digital Safety & Security Vienna Austria

ISBN: (纸本)9798350366501

The integration of the Internet of Things (IoT) into various sectors, such as manufacturing, healthcare, agriculture, and transportation, has significantly enhanced the ability to gather data, process it through edge and cloud computing, and deliver IoT services to end users. However, the energy sector has yet to fully leverage these advancements. The primary associated challenge is the acquisition and processing of energy data which can be subject to stringent regulations imposed by data protection laws. This can hinder access to energy data for IoT service providers focusing on energy optimizations and analytics, thus hampering the development of energy IoT services. This challenge is particularly pertinent to manufacturing scenarios where energy-efficient processes are crucial for sustainable development. For example, in the context of battery production, energy data and services can be critical for optimizing processes and enhancing the manufacturing efficiency of battery cells. Thus, energy data can be essential for achieving sustainability and reducing costs as well as ensuring the effective use of energy resources, and minimizing the environmental footprint. To address the challenge of accessing energy data, emerging trends emphasize the application of data sovereignty principles and data policies that facilitate secure and compliant data sharing. This paper delves into these trends, offering a thorough review of existing approaches for sharing energy data and identifying related open problems. Overall, this work provides guidance for practitioners developing advanced energy data management systems and for researchers aiming to design novel approaches based on the latest advances in energy data management. © 2024 ieee.

关键词： computing Continuum data Management data Spaces Energy data Internet of Things

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Securing the internet of things communication and data transmission by using Blockchain based Architecture 3

Securing the internet of things communication and data trans...

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3rd international conference on intelligent and Innovative Technologies in computing, Electrical and Electronics, IITCEE 2025

作者： Sheetal Kumari, Vasantha Badiger, Veena S Presidency College Department of Computer Applications Bengaluru India

ISBN: (纸本)9798331515911

Due to the rapid growth of technology usage of Internet of Things devices has revolutionized in multiple domains and areas, but still there are few challenges to address like security and performance issues. Most of the traditional approaches are not sufficient in solving those issues related to IoT ecosystems. This paper aims to present a block chain technology-based solution to the problems of security challenges and improvement in integrity, confidentiality, and availability for IoT systems. The features of Block chain in the sense of an immutable nature help secure the IoT system to share data with others, enhance authentication, and transparency. The integration of block chain with IoT devices can also ensure data provenance thus creating an environment for trust with users and stakeholders. To build this blockchain-based IoT system, the proposed architecture ensures a framework of not only ensuring the security of the IoT ecosystem but also sustainability. Future work will be based on designing prototypes and applications within real-world environments to validate the effectiveness of these proposed solutions in various IoT scenarios. © 2025 ieee.

关键词： Blockchain Technology challenges IoT architecture security

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