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Exploring Psychosocial Effects of the COVID-19 on University Students Using Visual analytics

Exploring Psychosocial Effects of the COVID-19 on University...

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2021 IEEE international conference on Innovative computing, intelligent Communication and smart Electrical Systems, ICSES 2021

作者： Rahman, Mosiur Akter, Sharmin Kabir, Parizatbinta City University Department of Computer Science and Engineering Dhaka Bangladesh

ISBN: (纸本)9781665435215

The coronavirus disease of 2019 (COVID-19) eruption has perpetrated desolation on educational systems all across the globe. We conducted a fast longitudinal study to find, evaluate, and synthesize research on the repercussions of this epidemic on university sophomores' psychological disorders. We created an interactive simulation to gain a better understanding of the intellectual well-being of university students. Collaborative boards analyze and exhibit actual data as visualizations, statistics, and prose, with a variety of user involvement possibilities. The widgets make it possible to derive meaningful data and present it in a simple and easy-To-understand style. We created an interactive statistics interface to show not only just the latest trends but also crucial metrics and forecasts for the future fortnight. Our panel is simple to use and optimized for effectiveness. It can forcibly temporize values and deploy on any remote server. In this study, we have compared data of pre and during COVID. The data are divided into four categories such as 1) educational impact, 2) family pressure, 3) social and mental health, and 4) stress. Our study found that during prevalent psychological impact is more negative than pre-stage. © 2021 IEEE.

关键词： Coronavirus

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Road extraction method of vehicle trajectory data based on geo-referenced videos 22

Road extraction method of vehicle trajectory data based on g...

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

作者： Du, Zhongxin Qiu, Ye Yu, Qingbin Chen, Yingjie Ma, Mengru Ding, Wei Jinan China Jinan Rail Transit Group Co. LTD. Jinan China Jinan China

ISBN: (纸本)9781450396295

With the increasing use of driving recorder equipment, people need new methods for the analysis of vehicle trajectories. The extraction of road information from vehicle trajectory data is one of the focuses in the field of geographic information. In this paper, we proposed two vehicle trajectory extraction methods, a fast vehicle trajectory extraction method based on GPS points and a vehicle trajectory extraction method based on the field of view. First, we gave a problem definition for the video trajectory display method. Then we expounded on the field of view of the Geo-referenced video [1] and its related information. The first method connects the location points, and each segment of the trajectory line indicates the current driving direction of the vehicle. The other method introduces the concept of perspective on this basis. It not only shows the direction of the trajectory line but also extracts the perspective of keyframes to accurately describe the trajectory of the vehicle. Next, we used a time and distance-based spatiotemporal clustering algorithm to extract points and demonstrate them through experimental results. We visualized the extracted vehicle trajectories and displayed them on a map. Finally, we compared the efficiency and accuracy of the traditional vehicle trajectory extraction method and the two methods proposed in this paper. The results showed that the vehicle trajectory extraction methods proposed in this paper are superior to the traditional vehicle trajectory display method in accuracy. © 2022 ACM.

关键词： Trajectories

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Cultivate smart and Healthy Ageing 9th

Cultivate Smart and Healthy Ageing

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9th EAI international conference on IoT Technologies for HealthCare, HealthyIoT 2022

作者： Andreou, Andreas Mavromoustakis, Constandinos X. Markakis, Evangelos Mastorakis, George Batalla, Jordi Mongay Pallis, Evangelos Dobre, Ciprian Department of Computer Science University of Nicosia and University of Nicosia Research Foundation Nicosia Cyprus Department of Electrical and Computer Engineering Hellenic Mediterranean University Heraklion Crete Greece Department of Management Science and Technology Hellenic Mediterranean University Agios Nikolaos Crete Greece Warsaw University of Technology Warsaw Poland Department of Industrial Design and Production Engineering School of Engineering University of West Attica Athens Greece National Institute for Research and Development in Informatics Bucharest Romania

ISBN: (纸本)9783031286629

The essential criterion for developing an intelligent age-friendly environment is evaluating and monitoring the provision of services. Our research aims to allow the deployment of a broad range of digital healthcare solutions interconnected in an IoT network ecosystem. Through their interoperability, we will be able to cultivate age-friendly smart homes for older adults. Thus, we will enable interpreted living and wellbeing. The intelligent integrations of digital solutions will allow the acquisition and evaluation of health-related data. Standardization, interoperability and scalability of the integration will increase efficiency in healthcare, improving older adults’ Quality of Life (QoL). Also, interpreting their needs will allow key stakeholders to optimize the Quality of Service (QoS). The proposed Internet of Health (IoH) framework will encapsulate sustainable and affordable innovative solutions and their benefits for a comfortable, meaningful, and independent life in an intelligent environment. Therefore, the frame mediated by edge computing, in-home and community settings will be able to interact with healthcare networks contributing to hospitalizations and institutional care. © 2023, ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering.

关键词： Internet of things

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An Efficient Ensemble Model for Forecasting Time Series Analysis of Crypto Currency data Using Machine Learning

An Efficient Ensemble Model for Forecasting Time Series Anal...

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2021 IEEE international conference on Innovative computing, intelligent Communication and smart Electrical Systems, ICSES 2021

作者： Saravanakumar, C. Lakshmi, S. Kanaga Prakash, M. Vijayakumar, M. Arun, C. St. Joseph's Institute of Technology Department of Information Technology Chennai India Sri Muthukumaran Institute of Technology Department of Management Studies Chennai India Data Science and Analytics Center Karpagam College of Engineering Coimbatore India St. Joseph's Institute of Technology Department of Computer Science and Engineering Chennai India R.M.K College of Engineering and Technology Department of Electronics and Communication Engineering Chennai India

ISBN: (纸本)9781665435215

Time series data are analyzed for predicting the future based on the consistent time period using statistical methods. These analyses are based on the data which are extracted from different sources. Existing method for performing the forecasting process does not fit into the crypto currency data. GARCH and ARIMA models with intelligent mechanisms provide the solution but it is not at the proper level. RNN model takes the data from preexisting intervals and decides the next time series data which is represented as a directed graph. Two types of behavior are considered namely spatial and temporal. The linear model uses the homogeneous data and heterogeneous data analysis is based on the nonlinear model. The nodes and layers are connected based on the input and corresponding activation unit. The memory model is attached to the RNN model for making persistent storage for keeping the processed data for a longer time. The proposed model uses the ensemble method for integrating various RNN models for achieving a high level of accuracy. © 2021 IEEE.

关键词： Time series analysis

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Multi-Reference Based Target Tracking for TDOA Systems

Multi-Reference Based Target Tracking for TDOA Systems

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IEEE international conference on Big data and smart computing (BigComp)

作者： Baek, Jaeuk Lee, ChangEun Park, Sangjoon ETRI Intelligent Convergence Res Lab Daejeon 34129 South Korea

ISBN: (纸本)9781728189246

In military and civilian applications, target localization is one of the crucial techniques to improve spatial awareness and enable location services without human intervention. However, in a noisy and cluttered environment, it is challenging to achieve high estimation accuracy due to inherent measurement error and clutter-reflected measurement via non-line of sight (NLOS) link between radar sensors and target. In this paper, we propose a multi-reference based target localization (MRTL) process to estimate the location of targets using time difference of arrival (TDOA) measurement. Compared to single reference TDOA systems where target localization may fail in some cases due to sensor-target geometry and TDOA error, the proposed MRTL can he a reliable and robust solution by utilizing multiple reference sensors simultaneously. Simulation results are provided to demonstrate the performance of our proposed MRTL process.

关键词： TDOA Real time localization Target tracking Radar system

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Internet of Thing (IoT) and data analytics with Challenges and Future Applications

Internet of Thing (IoT) and Data Analytics with Challenges a...

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Computer Science and Emerging Technologies (CSET), international conference on

作者： Mohammmadnour Mosbah Aljarrah Farah H. Zawaideh Murad Magableh Hassan Al Wahshat Rajina R. Mohamed Archana K V MIS BI and Data Analysis Department Faculty of Business and Financial Science Irbid National University Irbid Jordan Department of BI and Data Analysi Faculty of Financial Sciences and Business Irbid National University Irbid Jordan Department of Management Information System Faculty of Business Ajloun National University Ajloun Jordan College of Computing dan Informatics Universiti Tenaga Nasional Malaysia Jain Deemed To Be University Bangalore India

The Internet of Things (IoT) has emerged as a transformative technology, enabling seamless connectivity and communication between smart devices and the digital world. IoT's integration with data analytics has opened up a myriad of possibilities for extracting valuable insights from the vast volumes of data generated by connected devices. This research paper explores the convergence of IoT and data analytics, shedding light on the challenges encountered during their integration and envisioning future applications that can revolutionize various domains. The paper begins with a comprehensive review of the IoT ecosystem and its significance in shaping the modern era of interconnected devices. It highlights the diverse range of IoT applications across industries, including smart cities, healthcare, agriculture, manufacturing, transportation, and more. Moreover, it delves into the underlying technologies and communication protocols that enable IoT devices to gather, transmit, and process data efficiently. As data generated by IoT devices continues to escalate exponentially, effective data analytics becomes paramount to extract meaningful insights and support informed decision-making. The research delves into various data analytics techniques, such as machine learning, artificial intelligence, and big data analytics, illustrating their relevance in harnessing the potential of IoT-generated data. However, amidst the promises, several challenges impede the seamless amalgamation of IoT and data analytics. Issues concerning data privacy, security, interoperability, scalability, and data quality are thoroughly examined. The research emphasizes the need for robust frameworks and standards to address these challenges and ensure the secure and efficient functioning of IoT ecosystems. In the final section, this paper envisions the future applications of IoT and data analytics. It envisions intelligent and autonomous systems that adapt to dynamic environments, leading to advancement

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The 5th Artificial Intelligence of Things (AIoT) Workshop 22

The 5th Artificial Intelligence of Things (AIoT) Workshop

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28th ACM SIGKDD conference on Knowledge Discovery and data Mining (KKD)

作者： Zhang, Jian Tang, Jian Chen, Yiran Liu, Jie Ye, JiePing Wolf, Marilyn Narayanan, Vijaykrishnan Srivastava, Mani Jordan, Michael I. Bahl, Victor Microsoft Redmond WA 98052 USA Midea Grp Foshan Peoples R China Duke Univ Durham NC 27706 USA Harbin Inst Tech Shenzhen Peoples R China Beike Shenzhen Peoples R China Univ Nebraska Lincoln NE 68583 USA Penn State Univ University Pk PA 16802 USA UCLA Los Angeles CA 90024 USA Univ Calif Berkeley Berkeley CA 94720 USA

ISBN: (纸本)9781450393850

With advancement of recent network and chip technologies, IoT devices are becoming smarter with increasing compute power, bandwidth, and storage available on the device. This enables intelligent decision making and information transferring on the devices and unleashes the power of AIoT (Artificial Intelligence of Things) that supports applications such as smart city/agriculture/manufacturing/health care and self-driving scenarios. The AIoT Workshop is a forum for researchers, scientists, engineers, and practitioners to share and learn AI powered IoT solutions. The AIoT is a multi-disciplinary area, which include but not limited to IoT, AI/ML, embedded systems, networking, and stream analytics. The 5th AIoT workshop will be hosted in person in conjunction with the 28th ACM SIGKDD international conference on Knowledge Discovery and data Mining (KDD 2022). The workshop program consists of keynotes, invited talks, accepted technical paper presentations, as well as a digital agriculture hackathon panel.

关键词： iot ai/ml edge computing 5g networking self-power embedded systems

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Unlocking the Potential of Time Series IoT (Internet of Things) data: The Transformative Role of AI and Machine Learning in smart Cities

Unlocking the Potential of Time Series IoT (Internet of Thin...

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Cognitive computing in Engineering, Communications, Sciences and Biomedical Health Informatics (IC3ECSBHI), international conference on

作者： Dheeraj Singh Vikramaditya Singh Shivam Aditya Singh DIGITALU3 Delhi Technical University

ISBN: (数字)9798331518523

ISBN: (纸本)9798331518530

The integration of the Internet of Things (IoT) within urban environments is reshaping how cities operate, yet the vast data generated often remains underutilized due to significant barriers to effective data management. Despite the widespread use of IoT devices, more than 75 percent of businesses do not follow a data-driven methodology. This is mainly due to obstacles such as the complexity of data integration, a lack of standardization, and insufficient technical skills. This paper examines the obstacles and highlights the transforming impact of Artificial Intelligence (AI) and Machine Learning (ML) in converting IoT data into actionable insights that improve decision-making processes across diverse industries in smart cities. By addressing the complexities of data integration, AI and machine learning (ML) offer scalable solutions enabling real-time analytics, predictive modeling, and effi- cient resource management. This paper discusses specific applications of AI and ML in different industries, such as manufacturing, healthcare, energy, and agriculture. For example, AI-powered predictive maintenance in manufacturing helps reduce maintenance costs by up to 25 percent, while intelligent energy management in buildings can optimize energy consumption, enabling reduced costs by up to 30percent. In healthcare, AI-powered monitoring improves patient outcomes, and in agriculture, data-driven precision techniques can increase agricultural yields by 15percent. These figures indicate how important the role of Machine Learning is expected to be across verticals of the industry in the next few decades. The IoT trends indicate that implementing edge an-alytics further enhances the capabilities and efficiency of AI and ML. It allows for quick decision-making at the main source of data, therefore reducing latency and bandwidth demands. This paper is concluded by discussing the importance of AI and ML in unlocking the full potential of IoT investments, driving efficiencies, redu

关键词： Costs smart cities Time series analysis Machine learning Medical services Market research Real-time systems Manufacturing Internet of Things Artificial intelligence

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Artificial Intelligence Technology Promotes Sustainable Development of Agricultural Economy 14th

Artificial Intelligence Technology Promotes Sustainable Deve...

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14th international conference on Frontier computing, FC 2024

作者： Lu, Xinyong Li, Changgui School of Economics and Trade Zhongkai College of Agricultural Engineering Guangdong Guangzhou510225 China Business School Guilin University of Technology Guangxi Guilin541004 China

ISBN: (纸本)9789819623907

With the progress of the times, traditional research methods cannot well meet the urgent needs of sustainable development of agricultural economy. Therefore, this article aims to study the application of AI technology to promote sustainable development of agricultural economy. Firstly, this article utilizes support vector machines in machine learning techniques to establish a prediction model and optimize the planting optimization techniques in the R area;secondly, it uses deep reinforcement learning technology to construct an intelligent scheduling system for R area, optimizing the scheduling of intelligent agricultural machinery operations;next, this article constructs a long-term and short-term memory network model to make intelligent market predictions based on market data in the R region. Finally, this article takes the irrigation system in R area as an example to conduct experimental evaluation of sustainable development indicators for agricultural economy. The results showed that the irrigation efficiency and water resource utilization rate of irrigation systems using AI (Artificial Intelligence) technology were 24.05% and 25.64% higher than traditional irrigation systems, respectively. It can be seen that the application of AI technology can effectively promote the sustainable development of agricultural economy. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： smart agriculture

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Blockchain-Based Consent Management for Privacy Persevering and Transparency in intelligent Surveillance Systems

Blockchain-Based Consent Management for Privacy Persevering ...

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Blockchain computing and Applications (BCCA), international conference on

作者： Fehmi Jaafar Darine Amayed Wissam Salhab Hajer Bouani Mohamed Cheriet Department of Computer Science Quebec University at Chicoutimi Canada Systems Engineering Department École de Technologie Supérieure Canada ENSI L'École Nationale des Sciences de l'Informatique Tunis

Video cameras are becoming ubiquitous. They'd be interconnected within a larger pervasive infrastructure supported by artificial intelligence which helps ensure smart services relaying in real-time video analysis and image processing. However, such services and technologies involve a set of privacy concerns. If privacy concerns can be addressed, enabling real-time analytics on video streams in public spaces can be valuable in supporting many applications for security, public safety, and urban space management. This study aims to highlight privacy concerns in image and video processing in the context of intelligent IoT systems by proposing a blockchain-based approach for data transparency and privacy management. As a result, our proposed approach will provide IoT customers the ability to manage their consent using a distributed ledger securely.

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