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A study of community health model based on wearable care devices with information technology

A study of community health model based on wearable care dev...

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2022 international conference on intelligent Systems, Communications, and Computer Networks, ISCCN 2022

作者： Wu, Zijian Li, Jiasheng Zhang, Manli Clinical Research Management Department Affiliated Cancer Hospital Institute of Guangzhou Medical University No.78 Hengzhigang Road Yuexiu District Guangzhou510095 China Guangzhou Twinkle Culture Development Co. Ltd. Room 925 No. 26-29 Yuenan Street Huangsha Avenue Liwan District Guangzhou510150 China Hospital Office Affiliated Stomatological Hospital of Guangzhou Medical University No.195 Dongfengxi Road Yuexiu District Guangzhou510140 China

ISBN: (纸本)9781510657342

This paper presents a community health models and designs a corresponding system using wearable care devices depend on Internet of Things, Cloud computing, data analytics and other information technologies. For common chronic diseases, Electrocardiogram, respiration, pulse, temperature, and movement status signals are selected as important bases for reflecting the health status and determining the condition of the body. This paper designs the function of the wearable smart device and introduces the smart device's early warning function, built-in sensors, and various human monitoring components. The smart device can accurately judge the condition of the elderly. When an abnormality is found, it will send an alarm to the mobile phone in time, so that guardians and children can grasp it in real-time. Based on the user perspective, we propose strategies to meet user expectations and improve user information literacy;based on the social network perspective, we propose strategies to establish a long-term mechanism for smart medical operation and improve the information interaction network environment;based on the system service perspective, we propose strategies to optimize the system function design and innovate the service model. © 2022 SPIE.

关键词： data analytics

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Artificial Intelligence and data Science Approaches to Analyzing Human Resource Management in Hybrid and Remote Work Models

Artificial Intelligence and Data Science Approaches to Analy...

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

作者： Shripada Patil Pratibha Upadhye Sarika Rajabhau Khandekar Priyanka Darekar Asita Ghewari Uttara Bhattacharya NICMAR University Pune School of Business Dr. VishwanathKarad MIT World Peace University Dr. D. Y. Patil Vidyapeeth (Deemed to be University) Global Business School and Research Centre (GBS&RC) Pune. Indira Global School of Business Pune Dnyansagar Institute of Management & Research Pune Dr. DY Patil Institute Of Management And Research Pune

ISBN: (数字)9798331543624

ISBN: (纸本)9798331543631

Fresh challenges and opportunities are emerging in the field of Human Resource Management (HRM) as a result of the implementation of hybrid and remote work modes. This investigation investigates the use of artificial intelligence and data science tools to evaluate HRM practices in dynamic work environments. The study employs AI, machine learning, and advanced analytics to investigate critical HRM characteristics, including engagement, productivity, and performance, in hybrid and remote environments. The study underscores the importance of data-driven methodologies for optimising HRM policies, incorporating sentiment analysis into workforce planning, and utilising predictive analytics in workforce planning. The research aims to provide practical insights to human resources professionals and corporate executives in order to improve management strategies and cultivate a remote workforce that is both efficient and inclusive.

关键词： Analytical models Sentiment analysis Computational modeling data science Predictive models Remote working data models Planning Human resource management Predictive analytics

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smart Eye Testing 1

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international conference on intelligent and smart computing in data analytics, iscda 2020

作者： Hrushikesava Raju, S. Burra, Lakshmi Ramani Waris, Saiyed Faiayaz Kavitha, S. Dorababu, S. Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Vaddeswaram Guntur India Department of Computer Science and Engineering PVP Siddhartha Institute of Technology Kanuru Vijayawada India Department of Computer Science and Engineering Vignan’s Foundation for Science Technology and Research Vadlamudi Guntur India

ISBN: (数字)9789813361768

ISBN: (纸本)9789813361751

At present, most of the persons are suffering from eyesight. Their food habits and their genes will create eyesight. To test eyesight, there is a manual approach that consumes time as well as money. The noted disadvantages such as the need to take more care personally while interacting with an unknown environment and need to collaborate with other organizations for the charity of testing the eyesights. To avoid these inconveniences, the proposed approach is required which automatically tests the eyesight for right and left eyes using IoT. This smart vision approach using IoT will test eyesight for numerous users and will generate reports separately. That report sent to the user mobile. The virtual software developed uses IoT which enables checking of the sight functionality and the report is sent to the user mobile. In this, the virtual environment is created where IoT devices and networked computer vision devices are connected to test the eyesight. The proposed approach is demanded and is required in this modern and future culture too. Many benefits like consultancy price-cutting transport cost-cutting, time is reduced, speed up in generating reports are achieved. This is a future demanded revolutionary approach which directs many online spectacle shopping sites. This is considered a virtual doctor and serves lots of people since there is no tiredness because it is an automatic approach. © 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Internet of things

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international conference on Information Security, Privacy, and Digital Forensics, 2022

International Conference on Information Security, Privacy, a...

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international conference on Information Security, Privacy, and Digital Forensics, 2022

ISBN: (纸本)9789819950904

The proceedings contain 29 papers. The special focus in this conference is on Information Security, Privacy, and Digital Forensics. The topics include: Lightweight Malicious Packet Classifier for IoT Networks;cyber Security Issues and Challenges on Non-fungible Tokens;the Rise of Public Wi-Fi and Threats;digital Forensic Investigation on Ponzi Schemes;holistic Cyber Threat Hunting Using Network Traffic Intrusion Detection Analysis for Ransomware Attacks;Cyber Security Attack Detection Framework for DODAG Control Message Flooding in an IoT Network;Application of Digital Forensic Evidence in Hit and Run: A Comparative Study with Special Reference to § 304 Part II of IPC;a Forensic Video Upscaling Colorizing and Denoising Framework for Crime Scene Investigation;a Review of Face Detection Anti Spoofing Techniques on Varied data Sets;re-examining Laws Pertaining to Admissibility of Digital Evidence in Investigations;ethereum Blockchain-Based Medicine Supply Chain;machine Learning Algorithms for Attack and Anomaly Detection in IoT;a Mini Review on—Physically Unclonable Functions: The Hardware Security Primitives;an intelligent Analysis of Mobile Evidence Using Sentimental Analysis;Forensics Analysis of TOR Browser;phishing Classification Based on Text Content of an Email Body Using Transformers;vehicle Theft Detection and Tracking Using Surveillance Video for the Modern Traffic Security Management System;Resilient Risk-Based Adaptive Authentication and Authorization (RAD-AA) Framework;survey on Blockchain Scalability Addressing Techniques;anti-money Laundering analytics on the Bitcoin Transactions;fog Forensics: A Comprehensive Review of Forensic Models for Fog computing Environment;memory Forensics for Artefacts Recovery from Ether Transactions;a Lightweight Intrusion Detection and Electricity Theft Detection System for smart Grid.

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The Role of Cloud computing in Real-Time Big data analytics for smart Cities and Infrastructure Automation

The Role of Cloud Computing in Real-Time Big Data Analytics ...

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international conference on computing and Networking Technology (ICCNT)

作者： Sumeet Singh Sarpal V. Mathan Kumar Vivek Pimplapure Shrinivas Patil Vijay Marathe H. Mary Henrietta Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Department of Computer Science Karpagam Academy of Higher Education Coimbatore India Department of Management Dr. Ambedkar Institute of Management Studies And Research Nagpur India School of Mangement - PG Management JAIN (Deemed to be University) Bangalore Karnataka India Department of Electrical Prince Shri Vishwakarma Institute of Technology Pune India Department of Mathematics Saveetha Engineering College Chennai Tamil Nadu India

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

With the enhancement of Big data and smart Cities, Cloud computing has emerged as an advanced technology. Innovative city operations require real-time processing of vast volumes of data from various sources like sensors, IOT devices, or social media so that it can be analyzed and acted on in real-time with better automation for its infrastructure against different parameters. This article examines the potential of cloud computing to enable real-time big data analytics for smart cities and infrastructure automation. The cloud allows for real-time big data analytics, collecting input from disparate sources across a business ecosystem and analyzing it immediately for actionable insights. The need for large amounts of data combined with the nature of many Iota devices necessitates processing and storage at scale, a task uniquely suited to cloud computing due to its ability to provide scalability, flexibility, and lower cost essential qualities in building intelligent cities or automated infrastructure. Cloud computing offers a space for shared data and collaborative analysis, allowing the city government to open up its vast browser-based datasets so that other agencies (both within government and outside it), researchers, and even citizens can access this information, in turn driving innovation or assisting with strategic planning. Moreover, the evolution not just in cloud-based machine learning and artificial intelligence algorithms but also device-side AI/ML allows for predictive analytics, which can be helpful to preemptively discern potential failures to prevent them pro-actively, thus augmenting city operations efficacy as well as resource utilization besides operational efficiency of infrastructural management. Cloud computing is integral for real-time and big data analytics and innovates cities, organizations, and infrastructure.

关键词： Cloud computing Technological innovation Automation smart cities Social networking (online) Scalability Government Big data Strategic planning Real-time systems

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Evaluating a Mobile Game for Urban Exploration Utilising Mobile Edge computing (MEC) 37

Evaluating a Mobile Game for Urban Exploration Utilising Mob...

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37th international BCS Human-Computer Interaction conference, BCS HCI 2024

作者： Doumanis, Ioannis Tsioutas, Kostantinos University of Central Lancashire Preston United Kingdom Athens University of Economics and Business Athens Greece

Mobile gaming designed for urban exploration can quickly change how people interact with their city and others. These games help people explore and learn about their city while engaging and interacting with others in a fun and enjoyable way. However, the players’ perceived quality of such games depends on various human, system and contextual factors. This paper presents the results of an empirical evaluation of a mobile game that encourages people to explore Bristol, UK. The quality of player experience (QoE) in urban environments requires studying end-to-end system effects, including players’ experiences and the quality of mobile service (QoS). To evaluate the players’ QoE, we implemented two networking scenarios on Bristol’s smart city network: traditional IP and a Mobile Edge computing (MEC) scenario using POINT, a content-centric network platform. The MEC-based mobile game offers better interaction latency than the IP version. We evaluated the impact of each scenario on the player’s perceived Quality of Experience (QoE) and usability of the game in two field experiments. The results show that aspects of the players’ QoE were significantly better with the MEC-based mobile game than with the IP version. However, players rated the usability of the two versions similarly. Behavioural analytics data indicate that the improved QoE of the MEC-based version resulted in a higher volume of video content production and recordings. Additionally, we found the time of day influenced the players’ QoE, impacting the MEC version more than the IP version. Future work will focus on improving the game’s usability to fully utilise MEC’s advantages. © Doumanis et al.

关键词： empirical evaluation Mobile Edge computing QoE QoS scavenger hunt game usability

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Efficient Parcel Damage Detection via Faster R-CNN: A Deep Learning Approach for Logistical Parcels' Automated Inspection 20th

Efficient Parcel Damage Detection via Faster R-CNN: A Deep L...

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20th EAI international conference on Mobile and Ubiquitous Systems - computing Networking and Services (EAI MobiQuitous)

作者： Chen, Zhi Du, Cuifeng Guan, Quanlong Zhou, Yuyu Hoo, Vichen Huang, Xiujie Li, Zhefu Lv, Shuanghuan Wu, Xiaofeng Zhuang, Xiaotian Jinan Univ Coll Informat Sci & Technol Guangzhou Peoples R China Jinan Univ Guangdong Inst Smart Educ Guangzhou Peoples R China Jinan Univ Network & Educ Technol Ctr Guangzhou Peoples R China Guangdong Key Lab Data Secur & Privacy Preserving Guangzhou Peoples R China Cetc Potevio Sci & Technol Co Ltd Guangzhou Peoples R China Guangzhou Polytech Sports Guangzhou Peoples R China Guangdong Macao Adv Intelligent Comp Joint Lab Guangzhou Peoples R China Beijing JD Zhenshi Informat Technol Co Ltd Beijing Peoples R China

ISBN: (纸本)9783031639913;9783031639920

Parcel damage detection poses a significant challenge in warehouse automation and conveyor belt transportation, with a particular focus on leveraging the potential of Internet of Things (IoT) technologies. With this purpose in mind, we introduce RPA R-CNN, an improved deep learning-based object detection method that seamlessly integrates IoT capabilities. Our proposed approach enhances the feature extraction network of the Faster R-CNN model and utilizes advanced post-processing techniques to further refine the extracted features. By employing a meticulously curated dataset specifically designed for parcel detection, we train our model and conduct rigorous experimental verification. Encouragingly, the outcomes convincingly demonstrate that the RPA R-CNN method surpasses the performance of the original model in various metrics in domestic or cross-border logistics parcel detection. These promising results not only offer a practical solution to the challenging problem of identifying parcel damage but also underscore the immense potential of integrating IoT principles to optimize automation and enhance operational efficiency within the domestic or cross-border logistics industry.

关键词： Faster R-CNN Parcel Damage Detection IoT

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data Mining for Predictive analytics and Optimization of Treatment Plans in Cardiovascular Disease Management using Neural Networks

Data Mining for Predictive Analytics and Optimization of Tre...

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Advancements in smart, Secure and intelligent computing (ASSIC), international conference on

作者： Seeniappan Kaliappan V. Balaji G. Bhavani Bharathi Srinivas Aluvala Department of Mechatronics Engineering KCG College of Technology Karapakkam Chennai Tamil Nadu India Department of Mechanical Engineering Loyola Institute of Technology Chennai Tamil Nadu India School of Computer Science & Artificial Intelligence SR University Warangal Telangana

This section presents an integrated method that merges data mining techniques and neural networks to augment predictive analytics and optimize treatment strategies for managing cardiovascular diseases. Algorithm 1 focuses on data preprocessing and feature selection, essential for refining the patient data. It standardizes the input data by normalization and selects critical features using information gain, enhancing the subsequent neural network's efficiency in predicting disease progression. Algorithm elucidates the training of a neural network, utilizing the pre-processed data and disease progression labels. The neural network undergoes iterative training to minimize the loss function, resulting in a trained model capable of accurate predictions. Algorithm emphasizes the utilization of the trained neural network to optimize personalized treatment plans for cardiovascular disease management. By predicting disease progression and evaluating treatment options, it tailors' treatment plans to individual patient needs, thereby enhancing the effectiveness of healthcare interventions. The proposed method is demonstrated to surpass traditional techniques such as Linear Regression, Decision Trees, Support Vector Machines, K-Nearest Neighbors, Random Forest, and Logistic Regression in terms of critical performance metrics. Leveraging the power of neural networks, the proposed method showcases superior accuracy, precision, recall, Fl score, and AUC-ROC. Moreover, a comprehensive evaluation involving sensitivity, specificity, Matthews Correlation Coefficient (MCC), Negative Predictive Value (NPV), Youden's Index, and Cohen's Kappa consistently emphasizes its advanced performance, validating its potential to revolutionize cardiovascular disease management.

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A Matching Scheme from Supply and Demand Sides of Electronic Health Records Based on Blockchain 7

A Matching Scheme from Supply and Demand Sides of Electronic...

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7th international conference on intelligent computing and Signal Processing, ICSP 2022

作者： Zhao, Yang Du, Kai Shandong University School of Economics Jinan China

ISBN: (纸本)9781665478571

This paper proposes a double-chains-parallelized electronic health record (EHR) matching system which combines asymmetric encryption technology (homomorphic encryption) and blockchain technology. In this double-chains parallelized system, EHR-supply-chain includes sharing nodes and EHR-demand-chain includes the nodes to be matched. The system achieves decentralization, safe and efficient sharing, voluntary circulation of value, effective growth of EHRs' resources by smart contracts in blockchain. This system manages to get effective supervision of EHR in the chains and give some ideas for solving the security problems in the health data sharing process. © 2022 IEEE.

关键词： smart contract

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Predictive analytics for Parkinson's Disease Detection Leveraging Deep Learning Algorithms

Predictive Analytics for Parkinson's Disease Detection Lever...

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

作者： T. Kalpana R. Thamilselvan K. Chitra S. Priyanka R.Reshmi Jahaan T. Thenmalar Department of Computer Application Kongu Engineering College Erode Department of Computer Science and Engineering Kongu Engineering College Erode

ISBN: (数字)9798331543624

ISBN: (纸本)9798331543631

Parkinson disorder is a neurological disease that progresses gradually which is typified by the brain's dopamine-producing neurons becoming exhausted. This neuronal loss results by symptoms including tremors, muscle stiffness, bradykinesia, and balance difficulties. Early detection and accurate prediction of PD progression are especially crucial for improving management and intervention strategies. This study builds a prediction model with the use of MRI datasets. To detect Parkinson's disease from MRI data, this study uses deep learning algorithms, such as Inception, Xception, and VGG19, and evaluates how well they perform in terms of accuracy.

关键词： Deep learning Neurological diseases Accuracy Parkinson's disease Magnetic resonance imaging Neurons Predictive models Muscles Prediction algorithms Predictive analytics

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