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Wearable smart Health Monitoring Transmitter and Portable Receiver Systems for Enhanced Wellness Insights

Wearable Smart Health Monitoring Transmitter and Portable Re...

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Automation, computing and Renewable Systems (ICACRS), international conference on

作者： T.C Sivaprasad R. Surendhar A. J. Heiner Electronics and Communication Engineering Saveetha Engineering College Chennai India

This study presents the development and evaluation of a smart wearable belt designed for comprehensive health monitoring and fall detection. The wearable integrates advanced sensors, including vibration and heartbeat, to give users real-time insights into their well-being. The vibration sensor, meticulously calibrated for precise fall detection, exhibited high sensitivity and responsiveness, distinguishing between daily activities and potential accidents. Simultaneously, the heartbeat sensor demonstrated accurate and consistent heart rate monitoring, contributing to proactive cardiovascular health management. The seamless integration of these sensors, supported by MQTT connectivity and secure data transmission, ensures a user-friendly experience. Cloud integration enhances data processing and analytics, empowering users with a comprehensive health dashboard. Results indicate the reliability of the fall detection system and the accuracy of heart rate monitoring. This intelligent wearable belt represents a promising advancement in health technology, offering a holistic solution for users seeking real-time health insights and proactive fall prevention. Ongoing research focuses on refining algorithms and incorporating user feedback for continuous improvement and broader applications in personalized health care.

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Crop Quality Prediction with GPU-DLCQPA: Comparing Deep Learning and Traditional Machine Learning Techniques

Crop Quality Prediction with GPU-DLCQPA: Comparing Deep Lear...

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Women in computing (InCoWoCo), international conference for

作者： Srinivasa Rao Alapati A.V.H Sai Prasad Guduri Padma Rao Mandula Aruna K. L. V. G. K. Murthy S. Kaliappan Siva Kumar Pathuri RICEFW Technologies Inc Okemos MI Department of CI(CSE-AIML) MRCET Secunderabad TS India Department of CSE SRKR Engineering College(A) Bhimavaram AP India Department of CSE Methodist College of Engineering & Technology Hyderabad Telangana India Department of CSE Vignan's Lara Institute of Technology & Science Guntur AP India Division of R & D Lovely Professional University G.T.Road Jalandhar Punjab India Department of CSE KLEF Vaddeswaram AP India

ISBN: (数字)9798331518943

ISBN: (纸本)9798331518950

smart agriculture, a field combining technology like drones, AI, IoT, and data analytics, aims to address challenges like rising food prices, water shortages, climate change, and labour scarcity. IoT sensors enable real-time crop health mon- itoring, while edge computing ensures timely decision-making. AI algorithms analyze data from sensors and drones to provide insights on crop diseases, pest infestations, and planting times, enabling farmers to make informed decisions, optimize resources, and minimize environmental impact. This paper compares deep learning and traditional machine learning techniques for crop quality prediction in smart agriculture. We proposed GPU- DLCQPA model, which uses GPU acceleration and YOLO classifier for accurate estimation of crop quality features from real-time images. The proposed deep learning model is compared with base classifiers in machine learning like Support Vector Machine (SVM) and Decision Trees. With an accuracy of almost 96% for crop quality prediction, our results show that GPU- DLCQPA performs much better than standard models in terms of accuracy, processing speed, and scalability. The potential of deep learning and GPU-accelerated computing to revolutionize agricultural practices and empower farmers to make data-driven decisions for yield and resource allocation optimization is the main objective of this study.

关键词： Deep learning smart agriculture Support vector machines YOLO Accuracy Crops Real-time systems intelligent sensors Water resources Drones

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intelligent On-Line Monitoring System of General Electrical Equipment Based on Remote Communication 6th

Intelligent On-Line Monitoring System of General Electrical ...

引用

6th international conference on Innovative computing, IC 2023

作者： Cui, Xinhe Xia, Fei Meng, Fanbo Huang, Xiaobo An, Xin Information Communication Branch of State Grid Liaoning Electric Power Co. Ltd Liaoning Shenyang China

ISBN: (纸本)9789819920914

With the in-depth development of smart grid and the continuous improvement of the demand for power supply reliability, the scale of power communication network is expanding rapidly, the power communication equipment is diversified, the proportion of traditional power communication services such as voice, stability and protection is declining, and the remote control business, information management business and intelligent communication business of transmission and distribution network are increasing. The development of power communication will also usher in a qualitative leap. Based on the analysis of the intelligent on-line monitoring system of telecommunication general electrical equipment, this paper discusses the functional requirements of the intelligent on-line monitoring system, the hardware structure of the monitoring terminal, and the design and implementation of the database server. Through the remote on-line condition monitoring algorithm of the communication tower, the TCP throughput and delay data of openflow switch and goose switch are tested, The results show that the measured TCP throughput of openflow switch is about 500 mbit/s, and the maximum is no more than 570 mbit/s. The measured TCP throughput is in line with the reality. The TCP throughput of goose Ethernet switch in the same network environment is obviously not as large as that of openflow switch. Therefore, the throughput performance of openflow switch is good;For the high-speed switch, the delay of 1518 bytes and below is required to be less than 1ms in the least ideal case. Openflow switch has smaller delay, so it is feasible to apply it to the on-line monitoring system of substation equipment. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： intelligent systems

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Analysis of Hainan Tourism Carbon Balance Information System Based on Big data 3rd

Analysis of Hainan Tourism Carbon Balance Information System...

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3rd international conference on Advanced intelligent Technologies, ICAIT 2022

作者： Wang, Lina Zhang, Qiuyue Cai, Guoqin Wang, Yitong Pan, Yongqiang School of Economy and Management Hainan Normal University Hainan Haikou571158 China

ISBN: (纸本)9789819952021

In the context of big data, this paper applies advanced intelligent technologies such as cloud computing and big data and green computing, based on the time series data related to tourism in Hainan Province 2010–2020, a carbon emission information system measurement model of tourism and tourism ecosystem carbon absorption information system is constructed, and the carbon balance information system of tourism in Hainan Province is preliminarily calculated to clarify the dilemma of carbon emission reduction in tourism, and explore the low-carbon development mode of tourism. The results show that: (1) from 2010 to 2020, the carbon emissions of tourism in Hainan Province increased from 3.02972 million tons to 4.61407 million tons, of which tourism transportation, warehousing, and post and telecommunications are the main sources of carbon emissions information system. (2) From 2010 to 2020, the total carbon absorption of the tourism ecosystem in Hainan Province increased from 2.78928 million tons to 3.52312 million tons, of which the tourism waters and tourism forest ecosystems are the main carbon sinks, and the capacity of tourism farmland and grassland ecosystems is relatively weak. (3) The estimated annual carbon deficit of the tourism industry is 693600 tons. It presents a serious carbon imbalance;Hainan tourism industry has become a significant carbon source, and energy conservation, emission reduction, and ecological exchange rate increase will be an effective way for future development. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Carbon

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An Innovative smart and Sustainable Low-cost Irrigation System for Smallholder Farmers' Communities 3

An Innovative Smart and Sustainable Low-cost Irrigation Syst...

引用

3rd international conference on Embedded and Distributed Systems, EDiS 2022

作者： Dahane, Amine Benameur, Rabaie Kechar, Bouabdellah Research Laboratory in Industrial Computing and Networks University of Oran 1 Oran Algeria

ISBN: (纸本)9798350397543

The agricultural sector has several difficulties today in ensuring the safety of the food supply. However, the Internet of Things (IoT) has recently come to light as a promising remedy with several cutting-edge uses in smart farming. The study presents the design and development of a low-cost and full-featured fog-IoT/AI system. The system has been created using open-source platforms that monitor agro-field data in real-Time. However, the smallholder community is hesitant to adopt technology-based solutions. The PRIMA INTEL-IRRIS project aims to make digital and smart agricultural technologies more appealing and available to these communities by advancing the idea of intelligent irrigation 'in-The-box' This study explains a low-cost fog-IoT/AI system of version 1.0 fully targeted toward smallholder farmer communities (SFCs) and how it may provide the notion of intelligent irrigation 'in-The-box' concept. © 2022 IEEE.

关键词： Internet of things

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Greensense: IoT-Enabled smart Monitoring And Early Detection System For Optimal Plant Health In Greenhouse

Greensense: IoT-Enabled Smart Monitoring And Early Detection...

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Advances in data Engineering and intelligent computing Systems (ADICS), international conference on

作者： Venkatesh R Aadhira K R Kathiravan M Department of Computer Science & Engineering Hindustan Institute of Technology and Science Chennai India

ISBN: (数字)9798350364828

ISBN: (纸本)9798350364835

The emergence of Agriculture 4.0 signifies a transformative shift in traditional farming practices, integrating cutting-edge technologies like IoT, machine learning, and robotics to support productivity and sustainability. This era leverages real-time data and automation for precision farming, predictive analytics, and resource optimization, crucial for addressing global food security challenges while minimizing environmental impact. GreenSense represents progress within Agriculture 4.0. It seeks to revolutionize greenhouse management by integrating IoT and advanced machine learning algorithms. Through sensors, actuators, and a Raspberry Pi 4 board, this system offers a comprehensive solution for monitoring and optimizing plant health in controlled environments. The motivation behind this system stems from the imperative to enhance agricultural practices sustainably in the face of population growth. By enabling real-time monitoring and swift anomaly detection through application, this paper empowers proactive intervention to mitigate risks and optimize growing conditions. Integration of the YOLOv5 algorithm with 97% accuracy enhances precision in leaf disease and pest detection using OpenCV, reducing crop losses effectively. Beyond greenhouse management, this paper contributes to advancing sustainable agriculture. Its scalable and adaptable nature promotes resource-efficient practices and environmental care. Finally, a user-friendly interface is tailored to facilitate efficient data collection from sensors, enabling comprehensive monitoring of crop conditions, including pest and disease detection.

关键词： YOLO Machine learning algorithms Green products Crops Agriculture Real-time systems Monitoring

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smart Tourism Development Strategy Under the Background of Big data

Smart Tourism Development Strategy Under the Background of B...

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international conference on Multi-modal Information analytics, MMIA 2021

作者： Ren, Linlin Shanghai Xingjian College Shanghai200072 China

ISBN: (纸本)9783030748104

Under the background of big data, smart tourism emerges at the right moment and has become the focus of attention of tourism industry and tourism enthusiasts. This paper analyzes the status and problems on the development of smart tourism in China, and explores effective approaches to the development of smart tourism under the background of big data. This paper proposes the construction of cloud service platform, management mode, information sharing mechanism and the strengthening of information security management. In order to provide suggestions for the innovation-driven development of smart tourism mode in the future. © 2021, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Big data

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Autonomous Vehicle Traffic Flow Optimization Using AI and 5G Communication Networks

Autonomous Vehicle Traffic Flow Optimization Using AI and 5G...

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Device Intelligence, computing and Communication Technologies, (DICCT), international conference on

作者： Jayant Shekhar Rama Prabha K. P S. Prashanth Umamaheswari K Rahul S G K. Gayathri Department of CSE Sharda School of Engineering and Technology Sharda University Greater Noida UP India School of Computer Science and Engineering Vellore Institute of Technology Chennai Tamil Nadu India Department of Aeronautical Engineering Excel Engineering College Namakkal Tamil Nadu India Department of Information Technology V.S.B. Engineering College Karur Tamilnadu India Department of Electronics and Communication Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai Tamil Nadu India Department of Artificial Intelligence and Data Science St. Joseph's Institute of Technology Chennai Tamil Nadu India

ISBN: (数字)9798331543358

ISBN: (纸本)9798331543365

Real-time optimization of autonomous vehicle (AV) traffic flow using 5G communication networks and AI algorithms could overcome the limitations of static traffic enforcement, which uses outdated technologies to manage fluctuating traffic demands. It allows dynamic flow. Such commodities cannot adapt in real time because to their rigorous handling requirements, which disrupts traffic dynamics and creates construction delays and traffic congestion. The study suggests transmitting real-time vehicle status data to nearest intelligent crossings via vehicular wireless networks (VWNs). These intersections enhance traffic flow and reduce 5G network congestion using AI algorithms. Predictive analytics will help AVs change routes while on the road to maximize travel efficiency. It finds that peak congestion, average route duration, and fuel use reduce vehicle CO2 emissions by 66.7%, 22%, and 15%, respectively. It determined that the proposed system was a viable design route for a deployable smart city transportation solution because it outperformed traditional techniques in economic and environmental criteria.

关键词： 5G mobile communication smart cities Heuristic algorithms Real-time systems Artificial intelligence Vehicle dynamics Autonomous vehicles Optimization Traffic congestion intelligent sensors

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Privacy Protection Under the Construction of smart Cities in China—From the Perspectives of Legislation

Privacy Protection Under the Construction of Smart Cities in...

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international conference on Machine Learning and Big data analytics for IoT Security and Privacy, SPIoT 2020

作者： Liu, Ruiyi Institute for Disaster Management and Reconstruction Sichuan University Chengdu China

ISBN: (纸本)9783030627423

Taking a normative analyzing approach, this study summarized the framework of laws and regulations concerning privacy protection and analyzed the legislative problems in the privacy protection in a smart city scenario. Since the laws and regulations for privacy protection are scattered in the Constitution and other existing laws, the privacy protection in China is inevitably scattered in laws of particular areas. Major problems exist in almost all sections in collecting, spreading, storing and using personal information. Basic principles, including collection limitation, data quality, purpose specification, security safeguards, openness, should be followed for the improvement of the legislation of Chinese concerning privacy issues in smart cities. © 2021, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： smart city

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Searching Top-K Similar Moving Videos 22

Searching Top-K Similar Moving Videos

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6th international conference on High Performance Compilation, computing and Communications, HP3C 2022

作者： Yu, Qingbin Du, Zhongxin Ma, Mengru Chen, Yingjie Ding, Wei Jinan China Jinan China

ISBN: (纸本)9781450396295

The application of sensors enables mobile devices to generate amounts of content-aware data, such as trajectory, gyro, and video data. Moving video is an emerging new type of moving object that can provide a potential source for geo-referenced applications. Measuring the similarity of moving videos is widely used in traffic management, tourist recommendations, and location-based advertising. Our prior work proposed two similarity measures, the Largest Common View Subsequences can accurately calculate similar moving videos, and the View Vector Subsequences can fast calculate similar moving videos. In this paper, we proposed the searching top-k similar moving videos (K-SSMV). First, we give the problem definition of searching top-k similar moving videos. Then, we illustrated the strategy of searching for moving videos. Specifically, we sorted the video pairs by the most similar videos. Since the similar videos would be less than k, we first calculated the similarity of moving videos by the Largest Common View Subsequences algorithm, if the number of similar videos is less than k, we will adopt the View Vector Subsequences algorithm to compute the similarity of moving videos. Next, we mixed the candidate videos calculated by the Largest Common View Subsequences and the View Vector Subsequences algorithms, the searching top-k similar moving videos algorithm picked the top-k similar videos from candidate videos. Finally, we evaluated the performance of our proposed algorithms on the accuracy and computational cost. The experiments verified that our algorithms can efficiently search top-k similar moving videos. © 2022 ACM.

关键词： sensor data similar moving videos top-k

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