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检索条件"任意字段=International Conference on Intelligent and Smart Computing in Data Analytics, ISCDA 2020"

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international conference on intelligent Emerging Methods of Artificial Intelligence and Cloud computing, IEMAICLOUD 2021

International Conference on Intelligent Emerging Methods of ...

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international conference on intelligent Emerging Methods of Artificial Intelligence and Cloud computing, IEMAICLOUD 2021

ISBN: (纸本)9783030929046

The proceedings contain 67 papers presendted at a virtual meeting. The special focus in this conference is on intelligent Emerging Methods of Artificial Intelligence and Cloud computing. The topics include: False Alarm Detection in Wind Turbine Management by K-Nearest Neighbors Model;classification Learner Applied to False Alarms for Wind Turbine Maintenance Management;agricultural Image Analysis on Wavelet Transform;deep Learning Algorithms;deep Reinforcement Learning;different Texture Segmentation Techniques: Review;Fully Protected Image Algorithm for Transmitting HDR Images Over a WSN;gabor Wavelets in Face Recognition and Its Applications;a Hybrid Model Based on Behavioural and Situational Context to Detect Best Time to Deliver Notifications on Mobile Devices;harris Corner Detection for Eye Extraction;human Computer Interface Using Electrooculogram as a Substitute;image Fusion Using Wavelet Transforms;review on Traction Control System;various Algorithms Used for Image Compression;wavelet Transformation for Digital Watermarking;singular Value Decomposition Based Image Compression;wavelet Transform for Signature Recognition;object Detection with Compression Using Wavelets;a Review on Deep Learning Models;smart IoT System for Chili Production Using LoRa Technology;real Time Based Target Detection Method;business analytics: Trends and Challenges;home Automation: IoT;A Review on Impact of COVID-19 on E-Commerce;data Security Techniques in Cloud computing;radio Frequency Identification Technology Used to Monitor the Use of Water Point for Grazing Cattle;a Study of Continuous Variable Transmission;review on Color Image Processing Techniques;building Demolition Technique;method of Noise Control for Building;peculiarities of Image Recognition by the Hopfield Neural Network;preface;introduction.

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Efficient Shapley Value Driven Federated Learning System for smart Grid

Efficient Shapley Value Driven Federated Learning System for...

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IEEE international conference on Power, intelligent computing and Systems (ICPICS)

作者： Qinqin Wu Yaoxin Pan Famao Mei Mingxin Lu Power Dispatching and Controlling Center of Guangdong Power Grid Company Limited Guangzhou China Nanjing University Nanjing China

ISBN: (数字)9798350374315

ISBN: (纸本)9798350374322

The integration of smart grids in the energy sector promises substantial improvements in efficiency, reliability, and sustainability through advanced data analytics and machine learning. Federated learning (FL) facilitates collaborative model training across decentralized datasets, crucial for smart grid applications while preserving data privacy. However, traditional FL approaches often grapple with fairness and resource allocation challenges among diverse participants, leading to inefficiencies. This paper introduces a Shapley value-driven federated learning system tailored for smart grids, addressing these challenges by incorporating Shapley values from cooperative game theory. This integration ensures a fair and transparent evaluation of each participant's contribution, promoting equitable resource distribution and fostering collaboration. We present the theoretical foundations, develop efficient algorithms for Shapley value computation in federated settings, and validate our system through extensive simulations and real-world case studies. Our findings demonstrate that the Shapley value-driven approach significantly enhances fairness, accuracy, and scalability in federated learning, making it a robust solution for optimizing smart grid operations. This work underscores the importance of fairness and collaboration in deploying advanced technologies in the energy sector, paving the way for more effective and equitable energy management solutions.

关键词： Training data privacy Accuracy Federated learning Computational modeling Collaboration smart grids Computational efficiency Resource management Sustainable development

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Wireless Communication Design Using Neural Networks and Deep Learning

Wireless Communication Design Using Neural Networks and Deep...

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2022 international conference on Innovative computing, intelligent Communication and smart Electrical Systems, ICSES 2022

作者： Singh, Rajesh Khurana, Virender Sambasiva Reddy, M. Yadav, Ranjeet Jangir, Rachit Kapila, Dhiraj Uttaranchal University Uttaranchal Institute of Technology India Rohtak Department of Computer Science India Department of Electronics and Communication Engineering Andhra Pradesh Mylavaram521230 India Government Polytechnic Daman Varkund Nani Daman UT Administration of Dadra and Nagar Haveli & Daman and Diu Nagar Haveli India Government Polytechnic College Department of Computer Science & Engineering Rajasthan Sikar India Lovely Professional University Department of Computer Science & Engineering Punjab Phagwara India

ISBN: (数字)9781665474139

ISBN: (纸本)9781665474139

According to this report, the most innovative and active area of the communications business is wireless communications. data may be sent wirelessly between two places without the need of physical media like wires or cables. Business network providers have had to adjust and adapt as a result of the big data age. Computer science, which is used in a variety of industrial and scholarly scientific fields, is one of the effective AI technologies for analysing this flood of data. This article goes in-depth on big dataset processing, technological breakthroughs, and potential 5G network service usage. Identify the needs of phone users using sophisticated analytics, and then apply that knowledge to increase the effectiveness of "shared wireless communications."Particularly striking is the extensive, comprehensive, data-driven computer vision framework it provides, which covers extraction of features, simulation models, and forecast enhancements. The proposed framework's main advantages include its reliance on a sizable data set that faithfully captures the spectrum and other demanding user requirements, the logic of robust computational models, the formulation of issues in the context of wireless networks, and the capacity to design a method. Through a communication system, data is often sent from the transmitter to the receiver across very long distances. Through wireless communication, the transmitter may be positioned anywhere within a few metres. We'll examine existing strategies, their advantages and disadvantages, as well as upcoming developments in underwater sensor networks. We have discovered a fresh approach to doing research. © 2022 IEEE.

关键词： data acquisition

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A Generic Digital Twin Framework for Collaborative Supply Chain Development 5

A Generic Digital Twin Framework for Collaborative Supply Ch...

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5th international conference on computing and Big data, ICCBD 2022

作者： Lu, Xin Wang, Wei Li, Weidong Jing, Yanguo Li, Xiaoxia Bournemouth University Department of Computing and Informatics Bournemouth United Kingdom School of Engineering Scienceion University of Skovde Skovde Sweden School of Mechanical Engineering University of Shanghai for Science and Technology Shanghai China Computing and Digital Industries Leeds Trinity University Faculty of Business Leeds United Kingdom College of Informatics Huazhong Agricultural University Wuhan China

ISBN: (纸本)9781665457163

Current Supply Chains (SCs) are complex and diverse along with fragile to SC disruptions. This leads urgently needs to develop an intelligent, transparent, collaborative and resilient SC system to cope with unexpected SC disruptions. Digital twin (DT) is one of the most promising solutions to develop smart SCs that has been extensively studied recent years. However, SCDT paradigm is still at an early stage. This paper presents a generic and modularized five layers DT framework to provide a flexible and collaborative solution, which can be compatible with different DT systems in various SCs. The feasibility of the proposed framework is validated through a practical implementation in a distributed eyewear industry. © 2022 IEEE.

关键词： Risk assessment

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Moss-Based Biotechnological Air Purification Control System

Moss-Based Biotechnological Air Purification Control System

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2022 international conference on Electrical and computing Technologies and Applications, ICECTA 2022

作者： Kusdavletov, Sanzhar Sapargali, Abdulla Yedilkhan, Didar Yermekov, Altynbek Astana IT University Department of Intelligent Systems and Cybersecurity Astana Kazakhstan Astana IT University Department of Computer Engineering Astana Kazakhstan Astana IT University 'Smart City' Research Center Astana Kazakhstan

ISBN: (数字)9781665456005

ISBN: (纸本)9781665456005

Nowadays, air pollution is one of the acute problems that humanity faces as the world further develops and more technology comes into use. Pollution is produced both outside and inside buildings mostly from the machinery we use for convenience. In this paper, a non-standard approach is being explored to solve the problem at hand. Since excessive ventilation can lead to more pollution and infection, and is also a loss of energy, it was decided to focus not on ventilation, but on air purification. To purify the air, a filter created by living plants has been used. For the project, we chose moss, which is unpretentious and small, unlike trees and bushes. Due to the autonomy and scale, our solution is easy to use and brings enormous ecological and social impact. During the research, a biotechnological prototype is created, where it is possible to observe and control moss growth and its productivity. For the algorithm to work correctly, data about the environment is required. For this reason, in this paper, we have described not only the modeling of the system we suggest but also the process of collecting information from the biofilter. By reducing pollution, we will shortly save and stabilize the ecological situation. In this project, the effective biofilter is developed based on various solutions from biology, control engineering, as well as internet of things fields. © 2022 IEEE.

关键词： Control systems

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Big data analytics and machine learning techniques to manage the smart grid

Big data analytics and machine learning techniques to manage...

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international conference on Computer Communication and Informatics (ICCCI)

作者： M. Elavarasi Ramakrishna Kolikipogu Mahesh Kotha M. V. B. T. Santhi school of computing sciences Vels University Pallavaram Chennai India Information Technology Chaitanya Bharathi Institute of Technology (A) Hyderabad India Department of CSE (AI&ML) CMR Technical Campus Hyderabad India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Vaddeswaram AP India

Big data is a crucial part of the Energy Internet. Integrating renewables and smart grids is vital and exciting. Any power plant must collect and evaluate data to make educated choices. This study examines big data analytics in renewable energy based power plants. This paradigm implements big data analytics for renewable energy utilities and smart grids. The authors describe a five-step machine-learning approach to predict smart grid dependability. Using 64,000 occurrences and 12 attributes from an intelligent grid data system, we expected the system’s stability using three machine-learning algorithms. The penalized linear regression model achieves 95% accuracy using 70% of the training data. The random forest model is 84% accurate vs 77% for the decision tree. CNN and gradient-boosted decision tree models obtained 86% classification accuracy. This study’s small dataset prevents extensive data analyses. Cloud computing and real-time event analysis are well-suited for a data analytics infrastructure. Future research should include data from additional countries and renewable energy sources.

关键词： Renewable energy sources Computational modeling Training data Big data Stability analysis Real-time systems smart grids

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Transforming HR in Finance by Leveraging IoT and AI for Strategic Talent Management

Transforming HR in Finance by Leveraging IoT and AI for Stra...

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Innovative computing, intelligent Communication and smart Electrical Systems (ICSES), international conference on

作者： Devadutta Indoria Barani Dakshinamoorthy M. Karthik Manish Sharma S. Kaliappan G. Manikandan Department of Commerce Vikram Dev University Jeypore Odisha India Department of Human Resource Management &OB Sharda School of Business Studies Sharda university Greater Noida Uttar Pradesh India Department of Management Studies Shri Vishnu Engineering College for Women Bhimavaram Andhra Pradesh India Department of Computer Science and Engineering Graphic Era Deemed to be University Dehradun Uttarakhand India Division of Research and Development Lovely Professional University Phagwara Punjab India Department of Electronics and Communication Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai India

ISBN: (数字)9798331543624

ISBN: (纸本)9798331543631

Financial institutions have recognized the value of AI and the IoT in enhancing strategic human resource management. The paper presents a comprehensive integration framework that utilizes AI and IoT technologies. The focus is on key aspects of human resources operations, such as employee recruitment, retention, employee engagement, and performance evaluations. Productivity and efficiency can be maximized by 72% and 92%, respectively, through the application of AI algorithms and predictive analytics in IoT devices used in the collection of real-time data for HR operations. The proposed framework will be diverse in performance indicators, have appropriate strategies for change management, robust security measures, and pipelined data meant for long-term use. It consequently contributes to the design of a dynamic and personalized human resource management strategy based on insights derived from data.

关键词： Performance evaluation Technological innovation Organizations Prediction algorithms Real-time systems Internet of Things Security Artificial intelligence Predictive analytics Recruitment

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Analysis and Prediction Transient Population in Expressway Service Area based Long Short-Term Memory 23

Analysis and Prediction Transient Population in Expressway S...

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23rd IEEE international conference on High Performance computing and Communications, 7th IEEE international conference on data Science and Systems, 19th IEEE international conference on smart City and 7th IEEE international conference on Dependability in Sensor, Cloud and Big data Systems and Applications, HPCC-DSS-smartCity-DependSys 2021

作者： Wang, Jin-Qiang Zhang, Kai Zhi, Peng Xi, Guolong Tang, Xiaorun Zhou, Qingguo School of Information & Engineering Lanzhou University Key Laboratory of Smart Transportation of Gansu Province Lanzhou China Gansu Longyuan Information Technology Co. Ltd Lanzhou China

ISBN: (纸本)9781665494571

The expressway service areas are widely built in the expressway system, as an important place for regulating road traffic flow, alleviating traffic pressure, providing short breaks and adjustments. However, the current intelligent transportation analysis and prediction are mainly concentrated on urban roads, and relatively few expressways. This paper comprehensively analyzes the operating data of the expressway service area, including ingredients such as the number of cars, passenger consultation data, weather conditions and water consumption, to predict the transient population in the expressway service area based on Long Short-Term Memory(LSTM). The experimental results show that the Mean Absolute Percentage Error(MAPE) of the transient population prediction is 10.25%, which further verifies the effectiveness of the model. © 2021 IEEE.

关键词： Analytical models Fluctuations smart cities Roads Sociology Transportation Predictive models

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Cognitive computing for smart City Infrastructure Management

Cognitive Computing for Smart City Infrastructure Management

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Advances in computing, Communication and Applied Informatics (ACCAI), international conference on

作者： P Murthi Jyoti Jayesh Chavhan Prithu Sarkar Kritika Khandelwal Shaik Chandini Joshuva Arockia Dhanraj Department of Civil Engineering School of Engineering SR University Warangal Telangana India Computer Engineering SIES Graduate School Of Technology Mumbai University Maharashtra Amity School of Communication Amity University Kolkata Department of Business Administration Manipal University Jaipur Jaipur-Ajmer Express Highway Dehmi Kalan Near GVK Toll Plaza Jaipur Rajasthan Department of Civil Engineering Gulf American University UAE University Centre for Research & Development (UCRD) Chandigarh University Mohali Punjab India

ISBN: (数字)9798350389449

ISBN: (纸本)9798350389456

The accelerated urbanisation and the increasing complexity of municipal infrastructure require the implementation of innovative strategies to regulate and improve urban environments. By integrating By integrating Cognitive computing, which combines AI, machine learning, and data analytics, could completely change how smart cities manage their infrastructure. The main goal of this project is to see what the possible benefits are of adding cognitive computing to the systems that are already in place, architecture of smart cities, resource allocation, enable predictive maintenance, facilitate informed decision-making, and provide real-time monitoring. We demonstrate the potential of cognitive systems to enhance the durability, duration, and efficacy of urban infrastructure through the examination of case studies and contemporary technological advancements. The results suggest that cognitive computing is the most effective solution for addressing current urban challenges and, as a result, is the most suitable choice for the development of intelligent cities in the future.

关键词： data analysis smart cities Decision making Machine learning Real-time systems Resource management Cognitive systems

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intelligent Manufacturing Core Enterprise-dominated Collaborative dataspace Framework for New Energy Vehicle

Intelligent Manufacturing Core Enterprise-dominated Collabor...

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2022 international conference on data analytics, computing and Artificial Intelligence, ICDACAI 2022

作者： Zhang, Ruirui Yan, Hairui Xiao, Zhongdong Zhou, Guanghui Chang, Fengtian Yang, Shuqi Fei, Xuedong Yang, Xinrui Xi'an Jiaotong University Xi'an China

ISBN: (数字)9781665454704

ISBN: (纸本)9781665454704

With the development of new generation intelligent manufacturing and network technologies, the green manufacturing industry, represented by New Energy Vehicle (EV), is transforming to large-scale collaborative network production under regional industrial zone. It promotes the core manufacturing enterprises and related upstream and downstream enterprises to manage the massive, heterogeneous and scattered collaboration data for the whole life-cycle processes, namely design, manufacturing, services and sales, and thus to provide the collaborative services. However, the traditional data management manners are fixed, single and private, and mainly depend on data models and lack on-demand integration, which is unable to cope with the increasing demands for big data management and collaboration relations. Therefore, this paper focuses on EV domain, and studies on the new generation core enterprise-dominated collaborative dataspace management framework. Firstly, the data management requirements for the whole life cycle business collaboration of EV enterprises zone are given. Then, according to the requirements, the collaboration framework is developed, which includes resource layer, space layer, service layer and subject layer. Finally, the collaborative design case is applied to verify the feasibility and practicability of dataspace framework, which provides theoretical and application references for the data integration and management, and thus to urge the core enterprise-dominated whole life cycle business collaboration. © 2022 IEEE.

关键词： Life cycle

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