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Study on IOT based Smart Disease Detection Model on Machine Learning Techniques for Healthcare applications 7

Study on IOT based Smart Disease Detection Model on Machine ...

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7th international conference on Parallel, Distributed and grid computing, PDGC 2022

作者： Dhaka, Priyanka Sehrawat, Ruchi Guru Gobind Singh Indraprastha University Maharaja Surajmal Institute Ggsipu Janakpuri Delhi110058 India University School of Information and Communication Technology Ggsipu Delhi India

ISBN: (纸本)9781665454018

The wearable medical devices are integrated with the internet of things (IoT) for smart healthcare applications to improve the quality of service. The information regarding the patient's details updated through the IoT devices helps to provide the medication based on the present condition and hence the severity can be reduced instantly. Machine learning (ML) is utilized for making better decisions through clustering and classification methods that efficiently deal with the quality of information. Hence, this research introduces an analysis of smart disease prediction techniques for healthcare applications. The analysis of diseases such as heart disease, Alzheimer's disease, and diabetic retinopathy disease based on machine learning is employed by reviewing the conventional detection methods along with the achievements and research gaps. In addition, the analysis based on the performance metrics, dataset, and tools utilized is also devised for the development of the novel smart disease detection technique with more efficient accuracy of detection. © 2022 IEEE.

关键词： Machine learning

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Building Smart Systems to Satisfice User Intention in Situ: A Look Back to the Future

Building Smart Systems to Satisfice User Intention in Situ: ...

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1st IEEE international conference on Software Services Engineering (IEEE SSE)

作者： Liu, Lin Xie, Haihua Yang, Jingwei Li, Yanni Tsinghua Univ Sch Software Beijing Peoples R China Yanqi Lake Beijing Inst Math Sci & Applicat Beijing Peoples R China BNU HKBU United Int Coll Dept Stat & Data Sci Zhuhai Peoples R China China Southern Power Grid Energy Dev Res Inst Guangzhou Peoples R China

ISBN: (纸本)9798350340754

The world is evolving in a pace faster than ever, in terms of the ways in which human can interact with the rest of the world virtually or physically. Our exploration together with Carl along the road involves many models and methods that help better understand users' needs, desires and intentions, specify clearer system requirements and know-how, describe simpler service interface to find a match between requests and capabilities. There are implicit relations between these research topics, namely, building smarter software systems that can adapt to the needs and intentions of users, as well as the concrete operational context and environment. This paper may look retrofuturism in a sense, as we are looking forward to fulfillment the early AI and SE visions, by reviewing viable fundamental software requirements engineering principles and service system thinking methods, and at the same time by making sense of the on-going development of smart applications with the latest intelligent computing technologies.

关键词： Requirements Engineering Software Engineering Service computing Software Evolution Decision Making in Situ

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Application and Practice of Intelligent Temporary Construction Management Technology in UHV Project 5

Application and Practice of Intelligent Temporary Constructi...

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5th international conference on Artificial Intelligence Technologies and applications, ICAITA 2023

作者： Zhang, Haojie Jiang, Weijie Xu, Bin Zhang, Hyperplasia Wang, Shu Construction Branch of State Grid Zhejiang Electric Power Co. Ltd. Hangzhou310000 China

ISBN: (纸本)9781643684840

The purpose of this study is to explore the application of intelligent temporary construction management technology in extra-high voltage projects and analyze its effect through practical cases. Firstly, we will introduce the basic concept and development history of wisdom-based pro-construction management technology, and then elaborate its features and advantages in extra-high voltage projects. Finally, this paper will take a special high voltage project as an example for empirical analysis to explore the application of intelligent temporary construction management technology and its impact on construction progress. The wisdom of temporary construction management technology is a kind of intelligent construction management method based on big data, cloud computing and Internet of things. It can realize real-time collection, storage, processing and analysis of engineering data, so as to improve construction efficiency, reduce cost and guarantee quality. In extra-high voltage projects, intelligent temporary construction management technology has the following advantages: first, it can effectively avoid problems during construction;second, it can optimize the construction process and shorten the construction period;third, it can provide more comprehensive data to support decision-making. Therefore, intelligent construction management technology is one of the indispensable technical means in the field of extra-high voltage engineering. © 2024 The Authors.

关键词： Decision making

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An Efficient Blockchain-Based Security Framework for PUF-Enabled IoT Devices in Smart grid Infrastructure 7th

An Efficient Blockchain-Based Security Framework for PUF-Ena...

引用

7th international conference on Emerging Research in computing, Information, Communication and applications, ERCICA 2022

作者： Prasanna Kumar, M. Nalini, N. Department of ISE SSIT Tumakuru India Department of CSE NMIT Bengaluru India

ISBN: (纸本)9789811954818

In the Internet of Things (IoT), security and privacy are important considerations owing to the huge scale and scattered nature of IoT networks. The Internet of Things allows Internet-connected objects to send data to private blockchain networks, which create tamper-proof records of shared transactions. In many IoT applications, the combination of blockchain with IoT has a lot of potential for building a truly decentralised solution. Smart grids based on IoT are expected to become crucial infrastructure in the future (Wang et al. in IEEE Syst J 12:2403–2416, Sep. 2018). PUFs, or physically unclonable functions, have emerged as a viable lightweight alternative to classical cryptography for authentication. In this paper, we have proposed a security framework for authenticating smart metres in smart grids using the unique features of blockchain and physically unclonable functions without requiring complicated cryptographic solutions. In a smart grid implementation, our protocol delivers reasonable security and efficiency. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Blockchain

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Improving Global Precipitation in Numerical Weather Prediction Systems based on Deep Learning Techniques 25

Improving Global Precipitation in Numerical Weather Predicti...

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25th IEEE international conferences on High Performance computing and Communications, 9th international conference on Data Science and Systems, 21st IEEE international conference on Smart City and 9th IEEE international conference on Dependability in Sensor, Cloud and Big Data Systems and applications, HPCC/DSS/SmartCity/DependSys 2023

作者： Zhao, Haixiang Zhang, Guojing Du, Ming Wang, Xiaoying Qinghai University Department of Computer Technology and Applications Xining810016 China Geospatial Information Technology and Application Qinghai Provincial Key Laboratory Xining810016 China

ISBN: (纸本)9798350330014

Accurate weather forecasts are crucial for many aspects of people's lives. Traditional short-term precipitation forecasting methods mainly rely on numerical weather predictions based on meteorological dynamics modelling. However, numerical weather predictions based on high-resolution physical models require hours of computation on supercomputers. Deep learning networks possess strong feature extraction capabilities, directly mapping images, meteorological data, and other information to high-dimensional feature spaces. Neurons extract abstract features, enabling continuous training and learning for predictions. Therefore, deep learning provides a new solution for short-term precipitation forecasting. In the past, deep learning has been used for precipitation forecasting by employing radar echo extension methods;however, these methods are limited to short-term forecasts and do not utilize other meteorological data, resulting in inaccurate predictions. This study proposes a model containing an attention mechanism, a convolutional extended short-term neural network (Att-PredRNN), based on 5.625° (32×64 grid points) resolution ECMWF reanalysis data and three years of global meteorological and daily precipitation data. The network uses multiple meteorological fields as input factors to predict spatial precipitation distribution for 24-hour precipitation forecasting. The model significantly improves the capture of spatial and temporal correlations through the attention matrices focusing on different parts of the input sequence. Experimental results show that the proposed Att-PredRNN model outperforms other comparison models in terms of RMSE, CSI, POD, and FAR overall and dramatically enhances the accuracy of short-term precipitation forecasts. Our code and data are publicly available. © 2023 IEEE.

关键词： Supercomputers

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Deep Learning applications for Wind Energy Forecasting in Smart grids 10

Deep Learning Applications for Wind Energy Forecasting in Sm...

引用

10th international conference on Engineering and Emerging Technologies, ICEET 2024

作者： Wadood, Hamid Haris, Muhammad Hassan, Aqib Malik, Muhammad Osama Yousaf, Hamza Ullah, Kaleem University of Alabama at Birmingham Department of Computer Science Alabama United States Institute for Insight - J Mack Robinson College of Business Georgia State University AtlantaGA United States School of Computing and Mathematical Sciences University of Greenwich London United Kingdom University of Tulsa Department of Business Analytics Oklahoma United States Stetson-Hatcher School of Business Mercer University GA30341 United States University of Engineering and Technology Department of Energy System Engineering Peshawar Pakistan

ISBN: (纸本)9798331532895

Driven by the pressing need for sustainable and reliable energy sources, this paper critically evaluates deep learning's role in advancing wind energy forecasting, highlighting its potential to address current energy demands through improved prediction accuracy. Due to the inherent unpredictability of wind, conventional forecasting models often fall short;however, deep learning models offer enhanced reliability by effectively identifying complex, embedded patterns within wind data. The paper examines techniques such as time-series recurrent neural networks, restricted Boltzmann machines, convolutional neural networks, and autoencoders, each targeting distinct challenges in wind forecasting to strengthen grid integration and ensure energy reliability. Future directions-including hybrid model approaches and model interpretability-are discussed, underscoring deep learning's promise as a powerful tool for optimizing wind energy utilization and supporting a more sustainable, low-carbon energy infrastructure. © 2024 IEEE.

关键词： Wind forecasting

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Research on Neural Network Adaptive Noise Cancellation Technique Based on BP Algorithm 24

Research on Neural Network Adaptive Noise Cancellation Techn...

引用

8th international conference on High Performance Compilation, computing and Communications, HP3C 2024

作者： Zhang, Hong Dong, Yanling Pang, Simiao Li, Shengzhu Changchun Institute of Technology National and Local Joint Engineering Research Center for Measurement Control and Safe Operation of Intelligent Distribution Networks China Zhejiang Datang Wusha Mountain Power Generation Co. Ltd China State Grid Jilin Electric Power Company China

ISBN: (纸本)9798400716904

Adaptive filter is a kind of filter with high filtering accuracy that has the ability to adapt to the environment, adapt to the system and environmental changes, and has a wide range of applications in noise cancellation research. In this paper, we mainly utilize BP network for the filtering of adaptive algorithm, and build a BP neural network of 1-4-1 structure with 1 node in the input layer and 1 node in the output layer and 4 nodes in the hidden layer according to the basic principle of adaptive filtering, and explain in detail the method of creating, training and simulating the neural network, and establish the simulation model of adaptive filtering of the BP network based on Simulink. The simulation results verify that the adaptive filter can estimate the required statistical characteristics and automatically adjust its own parameters following the changes in the statistical characteristics. The BP neural network achieves the desired optimal filtering effect by adaptively changing the weight coefficients of the filter for the noisy signals, thus effectively eliminating the interference of the external signals and improving the signal-to-noise ratio of the input signals. © 2024 ACM.

关键词： Wiener filtering

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Application of LoRa Network for Data Transmission in Intelligent Smart grid Systems 8th

Application of LoRa Network for Data Transmission in Intelli...

引用

8th international conference on Emerging Research in computing, Information, Communication and applications, ERCICA 2023

作者： Soham, K. Dhaval, V. Dhanamjayulu, C. Rafikiran, Shaik Hussaian Basha, C.H. Prashanth, V. School of Electrical Engineering VIT University Vellore632014 India Tirupati India Nitte Meenakshi Institute of Technology Karnataka Bengaluru560064 India

ISBN: (纸本)9789819976218

As new ways to generate renewable energy cost effectively are being developed, high load devices such as electric vehicles (EVs) are becoming common. EVs require high current and voltage for charging, due to the large size of the batteries and the requirement for fast charging. This has made intelligent smart grids necessary, as traditional grids are incapable of dealing with the disturbances in the grid produced by the load required by these devices. Our paper proposes an intelligent smart grid which is interconnected using LoRa network and uses a long short-term memory (LSTM)-based neural network for processing, forecasting and predicting the disturbances produced in the network. The LoRa modules will be used in a mesh configuration;this will remove the need for a repeater and also give us greater network coverage. A LoRa access point will be placed, which will collect data from the individual modules and send them to the power station. Here, the LSTM algorithm will be applied on the data collected thereby doing the necessary predictions. The use of LoRa has make the system independent of cellular networks and due to its low power consumption, and efficiency have been improved. LSTM algorithm is capable of dealing with not only individual data but also complete time series. It can also deal with missing data, which makes it much more beneficial. Overall, our proposed system can greatly benefit both consumers and electricity companies. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Forecasting Long short-term memory LoRa Neural networks Smart grid

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Cutting-Edge Predictive Methods for Enhancing Short-Term Load Prediction

Cutting-Edge Predictive Methods for Enhancing Short-Term Loa...

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2024 international conference on Electrical and Computer Engineering Researches, ICECER 2024

作者： Ghazal, Taher M. Janjua, Jamshaid Iqbal Abushiba, Walid Ahmad, Munir Abbas, Tahir Ateeq, Karamath College of Arts & Science Applied Science University P.O.Box 5055 Manama Bahrain Universiti Kebangsaan Malaysia Center for Cyber Security Faculty of Information Science and Technology Bangi Selangor Malaysia Lahore Pakistan College of Engineering Applied Science University Bahrain College of Informatics Korea University Seoul02841 Korea Republic of TIMES Institute Department of Computer Science Multan Pakistan School of Computing Skyline University College University City Sharjah Sharjah United Arab Emirates

ISBN: (纸本)9798331539733

Efficient management and cost-related factors in the power sector call for accurate short-term load forecasting as it enables better planning with the electric grid and achieving stability within it. This paper looks into the state-of-the-art forecasting strategies that enhance load forecasts made in shorter time frames. Such techniques include but are not limited to machine learning applications, effective data models, and multi-dimensional modeling leveraging historical, meteorological, and economic data. Their review is carried out in detail with their advantages and disadvantages and their application in practice. Furthermore, this study strives to understand the barriers that need to be overcome in the quest to increase the accuracy of the forecasting models especially concerning renewable energy sources. We illustrate how new trends relate to future developments of predictive models about supporting the decision-making in operating power systems and improving the reliability and sustainability of energy systems. © 2024 IEEE.

关键词： Prediction models

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Low Voltage Fault Traversal Control Strategy for DC Transformer Based on Voltage Balancing Circuit 5

Low Voltage Fault Traversal Control Strategy for DC Transfor...

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5th IEEE international conference on Power, Intelligent computing and Systems, ICPICS 2023

作者： Liu, Shangke Wang, Zheng Zhao, Rui Bai, Bin Liu, Yuanyuan State Grid Ningxia Electric Power Co. Ltd Eco-tech Research Institute Yinchuan China

ISBN: (纸本)9798350333442

Low voltage DC bipolar short circuit fault, as one of the most severe faults in DC transformer, may cause low-voltage overcurrent blocking of the converter in the DC transformer, and even damage power electronic power devices. This article studies a DC transformer topology based on voltage sharing circuit and analyzes the working mechanism of the new topology. Using this as a prototype, the discharge circuit of low-voltage short circuit faults is discussed and divided into two fault stages. A low-voltage fault traversal control strategy is proposed for the stage two discharge mechanism, which switches the control quantity from DC voltage to DC current. Combined with phase shift control modulation, the fault current can be limited within the threshold range. After fault recovery, the delay switches the control quantity from DC current to DC voltage, and the fault traversal control logic switches smoothly, making it easy for engineering applications. The simulation results verify the effectiveness and correctness of the low-voltage fault traversal control strategy proposed in this paper. © 2023 IEEE.

关键词： DC transformers

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