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Early Detection of Heart Disease Using Deep Learning Model 8

Early Detection of Heart Disease Using Deep Learning Model

引用

8th international conference on Smart Structures and Systems, ICSSS 2022

作者： Deepika, R. Srikaanth, P. Balaji Pitchai, R. Srm Institute of Science and Technology Kattankulathur Technology School of Computing Tamilnadu India Srm Institute of Science and Technology Department of Networking and Communications Kattankulathur India B v Raju Institute of Technology Department of Computer Science and Engineering Narsapur India

ISBN: (纸本)9781665497619

Identifying and detecting the early signs of diseases is difficult due to asymptomatic conditions. Detecting the signs early gives the patient a higher chance of surviving. there are several technologies to identify the heart diseases for timely treatment. So far, different techniques of machine learning is used to predict heart abnormalities. Machine learning models trained by large amounts of data in the healthcare field contain a lot of medical images that enable the medical professionals to identify even minimal changes with a high level of accuracy. this paper presents the deep learning technique used to predict the heart abnormalities. Deep learning technique helps in processing the image data through various tests which include electrocardiogram, echocardiogram etc., that detects the abnormalities in the heart. © 2022 IEEE.

关键词： Heart Deep learning Medical services Feature extraction Data models Intelligent structures Medical diagnostic imaging

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Design and Implementation of distributed Rendering System 19

Design and Implementation of Distributed Rendering System

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2022 IEEE SmartWorld, 19th IEEE international conference on Ubiquitous Intelligence and computing, 2022 IEEE international conference on Autonomous and Trusted Vehicles conference, 22nd IEEE international conference on Scalable computing and Communications, 2022 IEEE international conference on Digital Twin, 8th IEEE international conference on Privacy computing and 2022 IEEE international conference on Metaverse, SmartWorld/UIC/ATC/ScalCom/DigitalTwin/PriComp/Metaverse 2022

作者： Liu, Dan Wei, Lai Zheng, Qiuhong Ding, Peng Shen, Yun China Telecom Corporation Research Institute Department of Service and Application Innovation Beijing China

ISBN: (纸本)9798350346558

In the metaverse, users can produce and edit 3D digital content, whereas rendering is an important step towards the production of 3D digital content. Rendering is the computation of the picture seen by the user through computer graphics principles, while the huge demand of computing resources for rendering has inspired new rendering models. First, the current research status and problems of current rendering technologies are analyzed. the number of rendering services that can be started by a single machine is limited and cannot meet the demand of a large number of concurrent users. Second, this paper designs a distributed 3D rendering system and introduces the rendering scheme and virtual engine deployment scheme of the distributed rendering system, which achieves efficient distributed node management and rendering task offloading. then this paper proposes a load balancing strategy for distributed rendering, which can effectively distribute tasks and reasonably utilize resources. Finally, the practicality and effectiveness of the distributed drawing system are verified through experiments. the experiments show that the system improves the rendering speed and quality and supports multi-task simultaneous rendering. © 2022 IEEE.

关键词： three dimensional computer graphics

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Skadi: Heterogeneous Human-sensing System for Automotive IoT 8

Skadi: Heterogeneous Human-sensing System for Automotive IoT

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8th IEEE international conference on Smart computing (SMARTCOMP)

作者： Neumann, Dennis Peltonen, Ella Univ Siegen Siegen Germany Univ Oulu Oulu Finland

ISBN: (纸本)9781665481526

Over the past years, cars' computing, sensing, and networking capabilities have rapidly increased, and the automotive development aims for autonomous driving. However, the driver is still the focal point for decision making. It has to be alert at all times to avoid traffic accidents due to human factors like tiredness, inattentiveness, and intoxication. therefore, there is a need for a system that monitors the driver and intervenes before human failure can have a negative impact on traffic. A variety of commercially available wearable IoT devices, such as smartwatches, bracelets, and rings, are capable of monitoring human health conditions. However, those devices come with technological differences and manufacturer-specific implementations. this paper proposes a prototype for a human-sensing and health monitoring system based on wearable sensor devices. the aim is to find a solution that ignores the technological heterogeneity of IoT devices and generalises their implementation into the automotive system. Consequently, the data should be available to be analysed together with the data collected from the vehicular sensors. Our solution is compatible with open-source platforms Eclipse Hono and Kuksa.

关键词： Internet of things Vehicular Systems Automotive computing

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Markov Blanket Composition of SLOs 8

Markov Blanket Composition of SLOs

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8th IEEE international conference on Edge computing and Communications, EDGE 2024

作者： Sedlak, Boris Pujol, Victor Casamayor Donta, Praveen Kumar Dustdar, Schahram Distributed Systems Group Vienna1040 Austria

ISBN: (纸本)9798350368499

Smart environments use composable microservices pipelines to process Internet of things (IoT) data, where each service is dependent on the outcome of its predecessor. To ensure Quality of Service (QoS), individual services must fulfill Service Level Objectives (SLOs);however, SLO fulfillment is dependent on resources (e.g., processing or storage), which are scarcely available within the Edge. Hence, when distributing services over heterogeneous devices, this raises the question of where to deploy each service to best fulfill both its own SLOs as well as those imposed by dependent services. In this paper, we maximize SLO fulfillment of a pipeline-based application by analyzing these dependencies. To achieve this, services and hosting devices alike are extended with a Markov blanket (MB) - a probabilistic view into their internal processes - which are composed into one overarching model. Given a mutable set of services, hosts, and SLOs, the composed MB allows inferring the optimal assignment between services and edge devices. We evaluated our method for a smart city scenario, which assigned pipelined services (e.g., video processing) under constraints from subsequent services (e.g., consumer latency). the results showed how our method can support infrastructure providers by optimizing SLO fulfillment for arbitrary devices currently available. © 2024 IEEE.

关键词： Markov processes

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BERT: A Paradigm Shift in Natural Language Processing 8th

BERT: A Paradigm Shift in Natural Language Processing

引用

8th international conference on Information System Design and Intelligent Applications, ISDIA 2024

作者： Shinde, Rohan Helen Victoria, A. Department of Networking and Communication School of Computing SRM Institute of Science and Technology Kattankulathur India

ISBN: (纸本)9789819786657

the field of natural language processing has seen a significant evolution. Due to the emergence of deep learning techniques, it has become easier to perform various tasks in the field. One of the most prominent innovations in this area is the use of bidirectional characterizations from the transformer model, i.e., Bidirectional Encoder Representations from Transformers (BERT) model. the BERT model uses a transformer structure as a framework to process text. It can capture meaning and context both from the right and the left sides of a sequence, and it has been shown that it can perform exceptionally well in various NLP applications, such as language comprehension and sentiment analysis. the paper explores the BERT model’s architecture, pre-training steps, and the model’s performance in various tasks. It also covers how it has revolutionized the field by overcoming previous benchmarks. the paper also explores the various limitations and opportunities of the BERT framework. It concludes by showing how it represents a significant shift in the field of NLP. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Deep learning

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Research on Low Delay Distribution Automation Transmission Technology Based on Edge computing 8

Research on Low Delay Distribution Automation Transmission T...

引用

8th international conference on Control Science and Systems Engineering, ICCSSE 2022

作者： Honglin, Xue Jianliang, Zhang Yang, Li Junwei, Ma Huifang, Wang Yinghao, Gao Sheng, Bi Information Communication Branch Company Shanxi Electric Power Company Taiyuan China

ISBN: (纸本)9781665484138

Smart grid is a distribution network based on integrated, high-speed communication network. It aims to use advanced sensing and measurement technologies to realize more reliable, safe and efficient operation of power grid. With the development and application of 5G technology, smart grid system has also begun to use 5G technology to replace distributed wired communication and save system cost. In this paper, firstly, the non orthogonal multiple access (NOMA) transmission mode is adopted in the wireless access network to transmit power information, so as to increase the transmission capacity and system throughput. Secondly, the greedy algorithm based on LODCO is used to allocate the power distribution task to each edge node through mobile edge computing, and realize real-time, fast and efficient user response through distributed computing to improve the overall transmission performance of the system. © 2022 IEEE.

关键词： Smart power grids

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Evaluating distributed Computation Offloading Scalability for Multiple Robots 8

Evaluating Distributed Computation Offloading Scalability fo...

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8th IEEE international conference on Fog and Mobile Edge computing (FMEC)

作者： Ayoub, Fatima Villing, Rudi Maynooth Univ Dept Elect Engn Maynooth Kildare Ireland

ISBN: (纸本)9798350316971

Perception in robotics using modern deep learning approaches is often computationally expensive. Cloud robotics and edge robotics provide possible solutions to this. In particular, computation offloading permits compute-constrained robots to offload compute tasks from the robot itself to more capable servers (the remote brain). However, the scalability of computation offloading for robots sharing a WiFi network has typically not been considered. In this work, we investigated the scalability of offloading to the cloud and edge for deployments of multiple robots in real-world settings and network conditions. We interpret typical network performance metrics such as latency, throughput, and packet loss in terms of their effect on robot computations to help to understand this question. To characterize the problem further, we introduce three different static offloading profiles based on the sensing capabilities of currently available robot platforms and examined these in a number of experiments on both simulated and physical wireless networks. Our results show that WiFi network capacity is much less than advertised when subjected to offloading data traffic and indicates limitations on the number of robots that can concurrently use computation offloading in a given space and the amount of data that they can transfer. this has significant implications for the dense deployment of robots that depend on computation offloading to meet their required service level.

关键词： Robotics Cloud robotics Computational offloading Wireless network performance

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A Survey of Mobile Edge computing for the Metaverse: Architectures, Applications, and Challenges 8

A Survey of Mobile Edge Computing for the Metaverse: Archite...

引用

IEEE 8th international conference on Collaboration and Internet computing (CIC)

作者： Wang, Yitong Zhao, Jun Nanyang Technol Univ Sch Comp Sci & Engn Singapore Singapore

ISBN: (纸本)9781665473002

Metaverse is an emerging virtual universe where humans can have real-time interactions and solid social links like in the physical world, and it opens up a new era of Internet and interactions. In Metaverse, an immersive and photorealistic environment promotes social activities, including education, meetings, and shopping of digital avatars based on critical technologies, including 3D rendering, extended reality, digital twins, artificial intelligence, and Blockchain. However, the limitations of computation, storage, and energy resources restrict the development of Metaverse, and a series of system issues (e.g., latency, security, and battery-life) continue to arise. As a result, how to find corresponding measurements to mitigate unsatisfactory influences becomes the focus. Mobile edge computing (MEC) as a distributed computing paradigm offloads computation-intensive tasks to the edge of the network. It brings the resources as close as possible to the end devices, addressing the shortcomings mentioned above. In this paper, we propose a comprehensive survey of the MEC-based Metaverse. Particular emphasis is given to the technologies convergence, architectures, and application scenarios, e.g., BoundlessXR and CloudXR. Significantly, we introduce the potential future directions for developing Metaverse systems.

关键词： Metaverse Edge computing Extended Reality Augmented Reality Virtual Reality Blockchain

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Fine-Tuning BERT-Based Models for Negative Content Identification on Indonesian Tweets 8

Fine-Tuning BERT-Based Models for Negative Content Identific...

引用

8th international conference on Information Technology and Digital Applications, ICITDA 2023

作者： Hidayatullah, Ahmad Fathan Kalinaki, Kassim Aslam, Muhammad Muzamil Zakari, Rufai Yusuf Shafik, Wasswa Universitas Islam Indonesia Department of Informatics Yogyakarta Sleman55584 Indonesia School of Digital Science Universiti Brunei Darussalam Bandar Seri BegawanBE1410 Brunei Department of Computer Science Mbale Uganda Borderline Research Laboratory Kampala Uganda Kampala Uganda

ISBN: (纸本)9798350344691

Social media platforms like Twitter have become substantial sources of user-generated content, enabling people to easily express their emotions and opinions. However, this freedom has increased the spread of harmful content, such as abusive language, sexually explicit content, and hate speech. this poses challenges for content moderation and user safety. In order to guarantee a safer, more receptive, and more pleasurable online environment for users of all ages, it is essential to develop a system capable of recognizing abusive and sexually explicit material on Twitter. Despite the growing importance of content moderation, a research gap exists in Indonesian tweets, with limited comprehensive studies on negative content identification. this research addresses this gap by evaluating the effectiveness of Bidirectional Encoder Representations from Transformers (BERT) models in the Indonesian context, which were primarily developed for English and other languages. this research aims to identify abusive, adult, and neutral content in Indonesian tweets by examining and fine-tuning BERT-based models to maintain a healthy online environment for optimal tweet classification. Based on our experiments, the BERT-based models showed promising results in detecting negative tweets. Among the BERT-based models, IndoBERTweet achieved the best precision, recall, and macro F1 scores with 97.03, 96.88, and 96.94, respectively. © 2023 IEEE.

关键词： Social networking (online)

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Attack-Model-Agnostic Defense Against Model Poisonings in distributed Learning 19

Attack-Model-Agnostic Defense Against Model Poisonings in Di...

引用

作者： Xu, Hairuo Shu, Tao Auburn University Department of Computer Science and Software Engineering AuburnAL United States

ISBN: (纸本)9798350346558

the distributed nature of distributed learning renders the learning process susceptible to model poisoning attacks. Most existing countermeasures are designed based on a presumed attack model, and can only perform under the presumed attack model. However, in reality a distributed learning system typically does not have the luxury of knowing the attack model it is going to be actually facing in its operation when the learning system is deployed, thus constituting a zero-day vulnerability of the system that has been largely overlooked so far. In this paper, we study the attack-model-agnostic defense mechanisms for distributed learning, which are capable of countering a wide-spectrum of model poisoning attacks without relying on assumptions of the specific attack model, and hence alleviating the zero-day vulnerability of the system. Extensive experiments are performed to verify the effectiveness of the proposed defense. © 2022 IEEE.

关键词： Zero-day attack

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