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Application of Machine Learning Algorithms in Digital Twin Monitoring Systems: An Overview of Approaches, Methods, and Prospects

Application of Machine Learning Algorithms in Digital Twin M...

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intelligent computing and Next Generation Networks（ICNGN), international conference on

作者： Gulshat Amirkhanova Bayurzham Amirkhanov Gulnur Tyulepberdinova Timur Ishmurzin Artificial Intelligent and Big Data department Al-Farabi Kazakh National University Almaty Kazakhstan

ISBN: (数字)9798331529222

ISBN: (纸本)9798331529239

Digital Twins represent virtual replicas of real-world objects, processes, or systems, enabling continuous real-time monitoring, dynamic adaptation, and predictive analytics. Their integration has become central in industrial, urban, and transportation domains under the paradigms of Industry 4.0, the Industrial Internet of Things (IIoT), and smart cities. However, effectively harnessing the torrents of time-series data and complex system states integral to Digital Twins (DTs) requires advanced analytical tools. Machine learning (ML) methods-ranging from classical ensemble models to deep learning architectures and reinforcement learning-provide powerful mechanisms for anomaly detection, failure prediction, operational optimization, and scenario modeling. This review explores the essential ML algorithms, their application contexts, and the supportive infrastructures required for implementing ML-driven DT solutions. We also discuss interpretability techniques, address security and concept drift challenges, examine the role of MLOps in ensuring reliable deployment, and highlight future research directions, including physics-informed ML, multimodal data integration, and advanced transformers for time-series forecasting.

关键词： Machine learning algorithms smart cities Reviews Transportation Predictive models Transformers Digital twins Security Reliability Industrial Internet of Things

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Cherry detection algorithm based on improved YOLOv5s network 23

Cherry detection algorithm based on improved YOLOv5s network

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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： Gai, Rongli Li, Mengke Chen, Na School of Information Engineering Dalian University Dalian China

ISBN: (纸本)9781665494571

In order to realize the automatic fruit picking by robot in orchard environment, cherry target detection is carried out by studying the algorithm based on deep learning, so as to identify cherry and carry out picking operation. However, most of the existing methods are aimed at relatively sparse fruits and cannot solve the detection problem of small and dense fruits. In this paper, a YOLOv5s-cherry model based on YOLOv5s was proposed for cherry detection. By adjusting the CSP structure of BackBone and Neck, deepening the depth and width of the network, improving the ability of network feature extraction, feature fusion and learning ability of network feature extraction, so as to improve the detection ability of small and dense cherry fruit. The model is trained on the self-made cherry maturity classification data set. The trained YOLOv5s cherry network is used to extract the feature and location information of cherry images with different maturity, so as to realize the classification and detection of cherry. We tested the performance of the model, compared YOLOv5s-cherry with YOLOv5s and YOLOv4, and used F1 value as the evaluation value. The test results show that the F1 of YOLOv5s cherry is 0.08 higher than that of YOLOv4 algorithm and 0.03 higher than that of YOLOv5s algorithm. The improved YOLOv5s cherry model provides better detection performance for small and dense cherry detection, which is conducive to intelligent picking. © 2021 IEEE.

关键词： Training Solid modeling smart cities Object detection Feature extraction data models Robustness

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AI - Driven Predictive analytics for Dengue Outbreak Prevention: An Enhanced EWSORA-MLP Approach

AI - Driven Predictive Analytics for Dengue Outbreak Prevent...

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Progressive Innovations in intelligent Systems and data Science (ICPIDS), international conference on

作者： M. Rajkumar Yukti Juneja Shashwath K Balaji Aarna Tyagi Department of Smart Computing School of Information Technology & Engineering Vellore Institute of Technology VIT University Tamil Nadu India B. Tech-Information Technology School of Information Technology & Engineering Vellore Institute of Technology VIT University Tamil Nadu India

ISBN: (数字)9798331534691

ISBN: (纸本)9798331534707

Dengue fever poses a significant and increasing global health threat, and factors such as climate change, rapid urbanization, inadequate public health infrastructure and epidemic outbreaks often burden health systems in areas of rainy and sunny and tropical areas. This review focuses on the application of advanced predictive analytics in health care, specifically AI-driven approaches to dengue outbreak detection and prevention. We explore and compare machine learning models including self-order mapping, decision trees, and support vector machines, while Particle Swarm Optimization (PSO) and Correlation etc. We build performance using the best based patterns Feature focus selection (CFS). Combining soil variables, meteorological data, and real-time medical data, our approach provides a comprehensive and robust framework for dengue prediction These frameworks, if targeted, are helpful health authorities intervene, for better resource allocation and evidence-based policy delivery.

关键词： Machine learning algorithms Accuracy Prevention and mitigation Urban areas Predictive models Prediction algorithms data models Public healthcare Predictive analytics Viruses (medical)

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Empowering smart traffic avoidance using Natural language processing

Empowering smart traffic avoidance using Natural language pr...

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intelligent and Innovative Technologies in computing, Electrical and Electronics (IITCEE), international conference

作者： V. K. G. Kalaiselvi Vishnu Chandar K R Vishwa G Sharanya Tirunagari Shanmugasundaram Hariharan Murugaperumal Krishnamoorthy Department of Information Technology Sri Sairam Engineering College Tamilnadu India Department of Artificial Intelligence and Data Science Vardhaman College of Engineering Hyderabad India Department of Electrical and Electronics Engineering Vardhaman College of Engineering Hyderabad India

ISBN: (数字)9798331515911

ISBN: (纸本)9798331515928

The integration of Natural Language Processing (NLP) into smart traffic systems offers a novel approach to enhancing real-time traffic management and optimization. This paper explores how NLP can be leveraged to interpret and analyze vast amounts of unstructured traffic-related data, such as social media updates, traffic reports, and incident logs. A key component of this approach is the use of a BERT (Bidirectional Encoder Representations from Transformers) classifier, which enables more accurate extraction and classification of relevant traffic information from textual data. By processing this data in real time, the BERT-based NLP system can provide more precise traffic predictions, identify congestion patterns, and optimize traffic flow dynamically. The study highlights the potential of NLP, particularly through the use of BERT, in improving the efficiency and responsiveness of traffic management systems, ultimately leading to reduced travel times, lower emissions, and enhanced urban mobility. The app also includes user-friendly interfaces that allow drivers and city planners to access real-time updates, personalized route recommendations, and predictive analytics. The system’s adaptability ensures that it can respond quickly to unexpected incidents, such as accidents or road closures, further enhancing urban mobility. The study highlights the transformation potential of this app in improving the efficiency and responsiveness of traffic management systems, ultimately leading to reduced travel times, lower emissions, and a significant improvement in overall urban transportation experiences.

关键词： Social networking (online) Roads Urban areas Bidirectional control Transformers Natural language processing Real-time systems Encoding Predictive analytics Vehicles

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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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