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Rabble Based Autonomous Assistance Using Machine Learning Algorithms 3

Rabble Based Autonomous Assistance Using Machine Learning Al...

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3rd international conference on Advance Computing and Innovative Technologies in engineering, ICACITE 2023

作者： Sutariya, Kamal Vishal Gupta, Mohan Lal, Bechoo Rahim Alatba, Sura Sriramakrishnan, G.V. Tripathi, Vikas Parul University Parul Institute of Engineering and Technology Department of CSE Gujarat Vadodara India Teerthanker Mahaveer University College of Computing Science and Information Technology Uttar Pradesh Moradabad India Department of CSE AP India Al-Turath Universiy College Computer Science Department Baghdad Iraq Mohan Babu University CSE Tirupati India Graphic Era Deemed To Be University Department of CSE Uttarakhand Dehradun India

ISBN: (纸本)9798350399264

In order to properly control rabbles, we shall investigate computer vision and mechanical aid technologies with lending devices here. Machine intelligence, commonly referred to as AI, or algorithms, enables software to improve prediction performance while requiring a deliberate goal to do so. Machine-learning algorithms anticipate new output using historical data as their input. Since information is so crucial, creating novel approaches for effectively administering the neighbourhood, pervasive data gathering systems of today is a crucial next stage for actors and thus is entirely independent. Therefore, deep learning and its autonomous rabbel control support network will be discussed here. With the availability of information expanding through time, education for application in numerous sectors has become more widespread. Many fields have benefited from the use of machine vision, encompassing research and academia database and process visualisation, biomedical imaging, and more. These methods have been applied to safety, computer components, internet websites, and mobile applications. Thus, we can draw an opinion regarding an autonomous assistance system for effective kernel maintenance that is based upon deep ***, COVID-19 is causing mayhem throughout the world. There have found over than 110 million cases, or the number grows daily. Due to the outbreak, or more 4 million people died. The three most affected nations are the Usa, India, and Brazil, and many agencies are working to develop a vaccine. The world is seeking get back to normal after a string of shutdowns while upholding safety precautions. As a consequence, it is necessary to keep an eye on anyone's hat and temperature within public spaces like malls, hospitals, offices, shrines, and the like. Costs increase as a consequence of the lengthy process and extensive contact with people. Consequently, a system is required that can detect respirators, gauge temperatures, and manage the crowd tha

关键词： computer vision

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Detailed review on Integrated Healthcare Prediction System Using Artificial Intelligence and Machine Learning 3

Detailed review on Integrated Healthcare Prediction System U...

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3rd international conference on Advance Computing and Innovative Technologies in engineering, ICACITE 2023

作者： Parasar, Deepa Ali, Arif Pillai, N. Muthuvairavan Shahi, Archana Alfurhood, Badria Sulaiman Pant, Kumud Amity University Maharashtra Cse India Dev Bhoomi Uttarakhand University Department of Cse Uttarakhand Dehradun India R.M.D Engineering College Computer Science and Business Systems Kavarapettai India Chitkara University Chitkara Business School India Princess Nourahbint Abdulrahman University College of Computer and Information Sciences Department of Computer Sciences Saudi Arabia Graphic Era Deemed to Be University Department of Biotechnology Uttarakhand Dehradun India

ISBN: (纸本)9798350399264

The intricacy and expansion of statistics in increased care sector will lead to an increase in the application of artificial intelligence (AI). There are several different AI techniques in use today by consumers, health professionals, and sciences companies. Guidelines for care and prognosis, patient and commitment, and organizational chores are the key application areas. Challenges will prevent the work of health professionals from being substantially computerized for a considerable length of time, despite the fact that AI can do healthcare tasks in many instances equally as well or better than people. Ethics issues and the implementation of artificial intelligence in healthcare are also discussed. AI technology is essential for producing effective healthcare applications. The definition of several effective software programmers may help in the growth of the health predictive model. computer vision and cloud services are critical elements for building smart healthcare *** collection and analysis of a wide range of health care data may provide useful insights quickly. Artificial intelligence is also crucial in the process of illness detection and prognosis. This smart and efficient approach may help with early illness diagnosis. Sensors connected through wireless networks make it possible to track the actions of a whole population. Sensors embedded in the human body may aid in the recognition of emotions and facial expressions by using neural networks. There is a plethora of technological implementations that contribute to a more advanced data processing *** primary focus of this research is to determine how effectively various efficient technology's function when used in the development of healthcare prediction models. AI uses a wide variety of sensor-based methods to boost healthcare's efficacy. Another area of study has substantial implications for the healthcare system. To help identify problems in biomedical goods, modelling tools may be pro

关键词： Forecasting

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Ground extraction and road obstacle segmentation method based on point cloud data 3

Ground extraction and road obstacle segmentation method base...

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3rd international conference on Intelligent Computing and Human-computer Interaction, ICHCI 2022

作者： Li, XiaoWei Yang, HuiCheng Feng, HaiHong Xu, ShuQi School of Electrical Engineering Anhui Polytechnic University Wuhu China Wuhu Angong Cognitive Technology Co LTD China

ISBN: (纸本)9781510661301

With the rise of artificial intelligence and other technologies, unmanned driving has become an important direction of future automobile development. The ground extraction of roads is a key task. The traditional method of ground extraction is limited to flat roads and the speed is slow. In view of the under-segmentation problem between the ground point cloud and the multi-objective object point cloud, we propose a ground segmentation algorithm for point cloud data based on multi-region segmentation. In this paper, the ground data can be accurately found by using the method of concentric region division for point cloud data. At the same time, it also has good robustness for uneven road surface, so as to provide a good foundation for the subsequent segmentation of road obstacles. For the extraction of road obstacles, we adopt the DBSCAN clustering method based on sub-region. According to the characteristics of near-density and distant point cloud, we adopt adaptive parameter selection for different regions to improve the accuracy of obstacle extraction. In order to verify the results, the use of KITTI data set on ubuntu18.04 using ROS system to complete the test, can maintain about 28fps processing speed. © 2023 SPIE.

关键词： Extraction

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Fault Identification Method for the Whole Map of Station Area Considering Node Correlation 3

Fault Identification Method for the Whole Map of Station Are...

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3rd international conference on Energy engineering and Power Systems, EEPS 2023

作者： An, Yuzheng Hu, Liehao Qin, Yingjie Zhang, Yongjun Zhong, Kanghua South China University of Technology School of Electrical Power Guangzhou China Dispatching and Control Center of Guangdong Power Grid Co. Ltd System Analysis Department of Electric Guangdong China

ISBN: (纸本)9798350313857

With the introduction of digital grid, transparent grid, and smart grid concepts, a large number of sensors are used in power systems. As the "last mile"of the power grid, the safe operation of the low-voltage distribution network is directly related to the personal safety of power users. In the traditional low-voltage distribution network, fault identification is reported by customers combined with manual on-site inspection, which is time-consuming and laborious. At the same time, the danger has increased. For this reason, this paper proposes a short-circuit identification method for low-voltage distribution network based on the classification and identification of the whole map, combined with the operation data of the low-voltage distribution network. Firstly, users in the station area are used as nodes to automatically generate the topological adjacency matrix of the station area;secondly, the intelligent equipment of the terminal in the low-voltage station area is used to collect and transmit the characteristic data when a fault occurs to the main station, and jointly form graph data in the adjacency matrix;Finally, input the data of all nodes in the station area to the graph convolutional neural network, obtain the feature representation of the station area through average graph pooling, and identify the short circuit type of the station area. Finally, it is verified by simulation that this method has an accuracy rate of 97% in the research and judgment of short-circuit fault types in low-voltage stations, which is effective. © 2023 IEEE.

关键词： Electric power distribution

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3rd international conference on Computational Electronics for Wireless Communications, ICCWC 2023

作者： Bera, Ruchira Sen, Soumya Khosla, Mamta Raman, Ashish Kumar, Naveen Department of Basic Science and Humanities Seacom Engineering College West Bengal Howrah India Department of Electronics and Communication Engineering Dr B. R. Ambedkar National Institute of Technology Jalandhar India Department of Electronics and Nanoscale Engineering University of Glasgow Glasgow United Kingdom

ISBN: (纸本)9789819719426

Higher education institutions must modify existing English curricula to fit the needs of today’s learners who grew up with the Internet. This entails raising consciousness and making use of the advantages offered by technology and resources from the Internet+ era. Positive changes have resulted from the present-day college English teaching approach, renewing guidance, modernizing it, and improving effectiveness. In higher education, assessing English instructors is essential since both instructors and students value passing required English exams. computer-based remote instruction has increased online learning, advancing digitalization and lifetime learning. This article discusses problems with college English instruction, including low efficacy, uneven competency, and a mismatch between grading and instruction. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Contrastive Learning

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A Case Study on the Marketing Implications of using Smart Gas Leakage Detectors in Industrial Facilities 3

A Case Study on the Marketing Implications of using Smart Ga...

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3rd international conference on Innovative Mechanisms for Industry Applications, ICIMIA 2023

作者： Annjan Kaur, Simran Sharma, Babita Mongia, Anupal Chandigarh University Department of Computer Science and Engineering Mohali India Mody University of Science and Technology School of Business Lakshmangarh India

ISBN: (纸本)9798350343632

Gas leakages are a major safety hazard in industrial facilities. They can cause fires, explosions, and other disasters, which can lead to significant property damage, injuries, and even death. Smart gas leakage detectors can help to prevent these accidents by detecting gas leaks early on. This research study explores the marketing implications of using a smart gas leakage detector made of Arduino Uno and MQ-135 gas sensor in industrial facilities. The benefits of using a smart gas leakage detector for industrial facilities, such as improved safety, efficiency, and compliance are also discussed. Further, this study discusses how a smart gas leakage detector can be used to market industrial facilities to potential customers. It concludes that smart gas leakage detectors can have a positive impact on the marketing of industrial facilities. They can help to improve the safety and security of facilities, which can make them more attractive to potential customers. They can also help to demonstrate the commitment of facilities to safety, which can build trust with customers. The different approaches of different sensors are also discussed, sensors such as MQ-2, MQ-3, MQ-4 and MQ-135. A comparative study of the sensors as also done to support the same. © 2023 IEEE.

关键词： Arduino Uno Gas leakage detection IOT MQ-135 Safe Society

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Voltage Transformers in Modern Power Systems: A Review 1

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3rd international Joint conference on Energy, electrical and Power engineering, CoEEPE 2023

作者： Xu, Minrui Li, Zhixin Lu, Shufeng Huang, Tianchao Lu, Zigang Chen, Gang Yuan, Jiaxin Zhou, Hang Ma, Guangchen Marketing Service Center State Grid Jiangsu Electric Power Co. Ltd. Nanjing China State Key Laboratory of Power Grid Environmental Protection School of Electrical Engineering and Automation Wuhan University Wuhan China

ISBN: (数字)9789819739400

ISBN: (纸本)9789819739394

Voltage transformers occupy a paramount position within the realm of power systems, serving a multitude of critical functions such as relay protection, electrical energy metering, and power maintenance. Under the "dual-carbon" imperative, the rapid integration of new energy sources, exemplified by wind and solar power, has led to a slew of challenges for voltage transformers, such as an expanded voltage fluctuation range, increased frequency variation scope, and exacerbated harmonic disturbances. This article introduces Optical Voltage Transformers (OVTs), Capacitive Voltage Transformers (CVTs) and Electromagnetic Voltage Transformers (PTs) used in power systems. Analyzes their principles, summarizes their respective strengths and limitations, and offers a perspective on the future trajectory of voltage transformers. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Relay protection

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Construction and Application Research of Safe Building Information Sharing System Based on Blockchain technology 3

Construction and Application Research of Safe Building Infor...

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3rd international conference on Applied Mathematics, Modeling and computer Simulation, AMMCS 2023

作者： Qi, Ya Chongqing Vocational Institute of Engineering Chongqing China

ISBN: (纸本)9781643684581

In recent years, blockchain, as one of the emerging information technologies, has been increasingly integrated into the whole process of development in various economic and social fields and industries, playing an important role in global resource reorganization, global economic structure reshaping and global competition pattern change. This paper proposes a hybrid chain model combining DPoS-Quorum consensus algorithm for coalition chains and PANDA consensus algorithm for public chains in conjunction with the current situation of construction supply chain, and experiments prove that this paper's method, while ensuring data security and privacy security, can accelerate the data circulation and sharing efficiency of construction supply chain. © 2023 The Authors.

关键词： Blockchain

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Classification of thyroid ultrasound standard section based on ResNet-cbam 3

Classification of thyroid ultrasound standard section based ...

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3rd international conference on Digital Signal and computer Communications, DSCC 2023

作者： Zhu, Yifeng Yao, Lanzhen Zhou, Xingyu An, Xiaofeng School of Electronic Information Engineering Changchun University of Science and Technology Jilin Province Changchun China JiLin Engineering Normal University Jilin Province Changchun China

ISBN: (数字)9781510666566

ISBN: (纸本)9781510666559

For organ ultrasound examination, it is very important to accurately obtain the standard section of classified organ ultrasound images. In this paper, a method based on ResNet-cbam is proposed to identify the classified ultrasonic standard plane. By collecting thyroid ultrasound images, which are divided into TPTI transverse section isthm us, TPRT transverse section right thyroid lobe, TPLT transverse section left thyroid lobe and lateral thyroid lobe longitudinal section. After image denoising and enhancement preprocessing, several models are first used for experiments, which show that ResNet-cbam has the best classification and recognition effect. By constantly adjusting the ResNet-cbam model structure, the number of iterations comparative experiments on changing the learning rate and activation function show that the best experimental effect is when Resnet18-cbam and the learning rate lr is 0.001 and the activation function is relu() function. Finally, the accuracy of classification and recognition is 89%, which proves that ResNet-cbam can recognize the standard section of thyroid well. © 2023 SPIE.

关键词： Ultrasonics

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An AI-Centred Adaptive Cognitive Network Traffic Modelling & Measurement Technique for EIS 3

An AI-Centred Adaptive Cognitive Network Traffic Modelling &...

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3rd international conference on Advance Computing and Innovative Technologies in engineering, ICACITE 2023

作者： Bharathi, L. Vasoya, Nimish H. Kansal, Lavish Gehlot, Anita Swathi, Bajila Khan, Suyeb Ahmed Ramachandra College of Engineering Department of Electronics and Communication Engineering Andhra Pradesh Eluru India Children's University Department of Student Startup Gujarat Gandhinagar382021 India Lovely Professional University Phagwara India Uttaranchal University Uttaranchal Institute of Technology India SR University School of Computer Science and Artificial Intelligence Telangana Warangal India Hoshiar Nagar Amritsar Baba Kuma Singh Ji Engineering College India

ISBN: (纸本)9798350399264

The IoT is the foundation of the Enterprise Information System (EIS), which collects a lot of business information. For EIS, timely, accurate data analysis transfer are crucial. For net management traffic optimization in EIS, Network infrastructure traffic is crucial. Nevertheless, the needs for fine-grain traffic monitoring are in conflict with the monitoring overheads precision. Long-standing promises of artificial intelligence (AI) include the ability to recognise the physical attributes of network activity take appropriate steps in response. In order to decrease network monitoring overheads increase measurement precision, we present an AI centred between light Adapt Measuring System (ALAMM) for SDN in this study. Initially, we simulate forecast the network congestion information using AI. We suggest an adaptive strategy to choose the rate of traffic sample centred on the outcomes of traffic forecasting. Next, we instruct switching devices to evaluate the coarse-grain traffic of flow pattern networks using the measuring primitives. In order to retrieve the Fine grained (FG) assessment optimise the measured values, the matrices refilling optimizing approach is finally suggested. Simulated outcomes demonstrate that our method can accurately and efficiently determine network activity. © 2023 IEEE.

关键词： Software defined networking

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