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Fog Assisted smart Video Surveillance System using Semantic Analysis for Abnormal Motion Detection

Fog Assisted Smart Video Surveillance System using Semantic ...

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international conference on Advanced computing and Communication Systems (ICACCS)

作者： R. S. Amshavalli J. Kalaivani Department of Computing Technology SRM Institute of Science and Technology Chennai India

ISBN: (数字)9798350384369

ISBN: (纸本)9798350384376

Recent Research in creating intelligent smart Closed Circuit Television (CCTV) surveillance systems that can counteract the rising levels of insecurity has increased dramatically as a result of the growing demand for video surveillance systems. However, the storage and processing capacity of analytical applications has been exceeded by the enormous volume of video data produced by these devices. By localizing data to the network's edges, this article suggests a smart surveillance system that exhibits high performance in terms of response time and bandwidth. In modern IoT era, the need for transforming the traditional system into smart surveillance system is high due to its demand for instant detection of anomalous occurrences by processing huge amount of video data on the go. Bringing the smartness into the system, makes it more automated and reliable. The challenging phases in making the system smart is storing and analyzing the video surveillance data since it calls for event recognition, visual interpretation, and the identification of relevant context. We employ sliding technique and adaptive contour based algorithms for detailed preprocessing layer that performs key frame extraction and background elimination respectively. And to address the issue of storage and instant processing, we also propose fog based smart surveillance framework that acts as the intermediator component between cloud and end users. The next major challenge is analyzing the incoming video data on the go. It can be addressed through semantic based algorithm for detecting the abnormal events. We propose an ontology based algorithm for detecting the unusual occurrences. Experiments are conducted on a real-time dataset to evaluate the suggested technique. The computed results demonstrate the superiority of the suggested fog based semantic analytics monitoring system over the traditional cloud-based monitoring solutions by attaining high prediction and accuracy rate coupled with low latency in

关键词： Visualization Accuracy TV Semantics Streaming media Video surveillance Prediction algorithms Motion detection Real-time systems Time factors

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MEC-EnergySaver: Unleashing Efficiency Through D2D and data Compression

MEC-EnergySaver: Unleashing Efficiency Through D2D and Data ...

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international Communication Systems and Networks and Workshops, COMSNETS

作者： Anindita Ghosh Poulomi Mukherjee Tanmay De Department of Computer Science and Engineering National Institute of Technology Durgapur

The shift from basic cell phones to smartphones has ushered in intelligent devices, alongside a surge in mobile apps that reshape daily life. Simultaneously, technologies like data analytics, AI and IoT have enabled smart homes and advanced transport systems, demanding substantial computational power from mobile devices. To tackle this challenge, mobile-edge computing (MEC) has emerged as a solution. Unlike remote cloud computing, MEC offers computing services at the network's edge, allowing mobile users to transfer their computing tasks to MEC servers located nearby. However, MEC systems face challenges in efficient computation offloading and distributing resources in a way that reduces energy use and processing times. The evolution of mobile devices and the rise of data-intensive applications have led to the need for efficient computing solutions like MEC, with the potential to integrate data compression for energy savings. This paper introduced a two-phase approach for request servicing in MEC systems. The first phase prioritizes Device-to-Device (D2D) connections based on device proximity, efficiently pairing devices for data exchange. Unpaired devices are directed to the nearest MEC node. In the second phase, requests not fulfilled via D2D connections are handled by strategically positioned MEC nodes, ensuring comprehensive coverage and minimizing network energy consumption.

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A Deep Learning Model for Arrhythmia Detection Using Ultra-Edge IoT Sensors

A Deep Learning Model for Arrhythmia Detection Using Ultra-E...

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Women in computing (InCoWoCo), international conference for

作者： Vishnu Prasad Goranthala K. Siva Sankar C. Brintha Malar Dept. of Computer Science & Engineering Noorul Islam Centre for Higher Education Thuckalay Tamil Nadu India Department of Information Technology Noorul Islam Centre for Higher Education Thuckalay Tamil Nadu India Department of Physics TDMNS College Vadakkuvalliyur Tamil Nadu India

ISBN: (数字)9798331518943

ISBN: (纸本)9798331518950

The proliferation of Internet of Things (IoT) devices in edge networks has highlighted their limitations in computational resources for ultra-edge analytics. This paper explores the advancement beyond traditional edge analytics, focused primarily on user-smartphones, by embedding intelligence into ultra-edge IoT sensors. The study particularly investigates smart IoT sensors for arrhythmia detection using Electrocardiogram (ECG) traces, exemplifying an enhanced intelligence application in mobile health. This paper introduces the E2DC-Net, a novel deep learning model leveraging a Multi-Scale Convolutional Neural Network (MSCNN), designed to enhance arrhythmia detection in ultra-edge IoT sensor networks. The methodology focuses on the MSCNN's ability to process complex ECG data by capturing multi-scale features, thereby improving diagnostic accuracy and reliability.

关键词： Deep learning Accuracy Arrhythmia Computational modeling Image edge detection Medical services Electrocardiography Internet of Things Wearable devices intelligent sensors

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Privacy Preserving Framework for Big data Management in smart Buildings

Privacy Preserving Framework for Big Data Management in Smar...

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19th IEEE international conference on Pervasive computing and Communications (IEEE PerCom)

作者： Inibhunu, Catherine McGregor, Carolyn Ontario Tech Univ Oshawa ON Canada Univ Technol Sydney Ultimo NSW Australia

ISBN: (纸本)9781665404242

There are many possibilities for smart buildings that have well provisioned and managed internet of things (IoT) frameworks where seamless data acquisition from sensors, processing to analytics can bring benefits to vast domains. Specifically, in building management, data captured from sensors, actuators and multiple devices within a building can be analyzed and utilized for resource planning as well as informed services provision while maintaining the security and privacy of IoT data sources and context. To facilitate such a process, a robust framework for acquisition, processing and analysis of data from vast IoT systems is necessary. This is an extremely complex procedure that requires augmentation of multiple computation layers for management of IoT data coupled with privacy preservation capabilities. In this paper, an overview of IoT and the data process flow from acquisition, collection, processing, integration and the technological enablers is presented. In particular this work examines methods proposed in the literature for management of IoT data from acquisition to analytical applications, highlights challenges and opportunities that are still open for research and proposes a privacy preserving computation framework that can be explored for robust processing of IoT data in a smart building ecosystem leading to effective provision of services while maintaining privacy of IoT data sources and context.

关键词： IoT frameworks smart buildings privacy preserving IoT edge computing big data architectures intelligent buildings privacy preserving computing

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The Internet of Healthcare Things (IoHT): Developing Patient Care and Medical Outcomes with Connected Healthcare Technologies

The Internet of Healthcare Things (IoHT): Developing Patient...

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2025 international conference on intelligent Control, computing and Communications, IC3 2025

作者： Arunachalam, P. Yugandhar, Alli. Nag, D. Manesh Sri Reddy, P. Venkateswar VelTech Rangarajan Dr. Sugunthala R&d Institute of Science and Technology Department of Biomedical Engineering Avadi Tamilnadu Chennai India

ISBN: (纸本)9798331527495

Patient care and the outcomes of medical procedures are undergoing a transformation as a result of connected health care technologies. The Internet of Healthcare Things (IoHT), which is a subset of the Internet of Things (loT) that focuses on applications related to medical and health care, is the driving force behind this transition. The objective of this research is to analyze the development of technologies related to the Internet of Things (loT) and the impact that these technologies have had on the healthcare industry. To be more specific, the research studies how these technologies can improve patient monitoring, increase the effectiveness of treatment, and contribute to personalized medicine. Wearable technology, remote monitoring systems, and intelligent medical equipment are all subjects that are studied in this study, which focuses on clinical and home care settings. The study also concentrates on the usage of these technologies. Both the huge amounts of health data that are generated by devices connected to the Internet of Things (loT) and the role that data analytics and cloud computing play in the processing and interpretation of this data are analyzed as well. In terms of improving patient outcomes, lowering healthcare expenses, and increasing the efficiency with which healthcare is given, the research highlights the benefits of Internet of Health Technologies (loHT). In order to achieve this goal, a comprehensive analysis of case studies and applications that are grounded in the actual world is carried out. According to the findings, the Internet of Things has the potential to bring about a revolution in the healthcare industry. This opportunity arises from the fact that it makes it possible to implement proactive and preventive treatment approaches. Nevertheless, the research has also addressed the challenges that are associated with the Internet of Health Technologies (loHT). These challenges include issues over the privacy and security of data, as

关键词： Cloud computing in Medicine Connected Health care Technologies Equipment Healthcare Costs Patient Outcomes Personalized Medicine Remote Patient Monitoring smart Medical Wearable Devices

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Generating Natural Language Adversarial Examples Based on the Approximating Top-K Combination Token Substitution 24

Generating Natural Language Adversarial Examples Based on th...

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24th IEEE international conference on High Performance computing and Communications, 8th IEEE international conference on data Science and Systems, 20th IEEE international conference on smart City and 8th IEEE international conference on Dependability in Sensor, Cloud and Big data Systems and Application, HPCC/DSS/smartCity/DependSys 2022

作者： Qiu, Panfeng Wu, Xi Zhao, Yongxin East China Normal University Shanghai Key Laboratory of Trustworthy Computing Shanghai China The University of Sydney Australia

ISBN: (纸本)9798350319934

Deep Neural Networks (DNNs) have been widely used in Natural Language Processing (NLP) applications. However, due to the lack of interpretability, recent studies have shown that the DNN-based models used in NLP are vulnerable to adversarial attacks by adding subtle perturbations into inputs. Among the various existing adversarial attack methods, it is still challenging on how to maintain the high similarity between generated adversarial text and the original text while ensuring both grammatical correctness and semantic preservation. In this paper, we propose a novel attack method based on the approximating Top-K combination token substitution to generate adversarial text. We extend the sequential substitution that is commonly used in the existing methods into a combination substitution, and combine it with Monte Carlo simulation to significantly expand the search space. Furthermore, based on the part-of-speech information, we combine the synonym token substitution strategy and the language model based substitution strategy to generate adversarial texts that are semantically consistent with the original texts. Extensive experiments illustrate that our method outperforms previous methods regarding attack efficiency, perturbation rate, and semantic similarity. Moreover, training on adversarial samples generated by our approach can effectively improve the robustness of the model. © 2022 IEEE.

关键词： Deep neural networks

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

International Conference on Machine Learning and Big Data An...

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

ISBN: (纸本)9783030627454

The proceedings contain 126 papers. The special focus in this conference is on Machine Learning and Big data analytics for IoT Security and Privacy. The topics include: Development of Industrial Chain of Internet of Things Based on 5G Communication Technique;path Choice of smart City Construction from the Perspective of Economic Growth;computer Audit Quality Control in Big data Era;MOOC System in the Era of Big data Improves the Effectiveness of College Physical Education;Risks and Prevention in the Application of AI;improvement of Microblog Recommendation System Based on Interaction Strategies of Agricultural E-Commerce Enterprise;exploration and Construction of "One Ring, Three Deductions" Innovation and Entrepreneurship Talent Cultivation Model for Higher Vocational Art Design Major Based on Information Technology;research Progress of Neuroimaging Techniques in Organizational Behavior Under the Background of smart City;recommendation Strategies for smart Tourism Scenic Spots Based on smart City;control Strategy of Environmental Control System in Power Transmission;design and Implementation of Self-service Tourism Management Information System Based on B/S Architecture;MATLAB Software in the Numerical Calculation of Civil Engineering;Low Latency V2X Application of MEC Architecture in Traffic Safety;cloud computing Technology for the Network Resource Allocation on the Research of Application;talent Evaluation Model of College Students Based on Big data Technology;application of Cloud Class in Comprehensive English Teaching in the Context of Internet Plus;the Application of 3D Printing Technology in Sculpture;the Development Strategy of Current Medical Scientific Research Information;on the Development of the Industry Trend of "Ai+Education".

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

International Conference on Machine Learning and Big Data An...

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

ISBN: (纸本)9783030627423

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ResNet-50: The Deep Networks for Automated Breast Cancer Classification using MR Images

ResNet-50: The Deep Networks for Automated Breast Cancer Cla...

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

作者： Tejaswini Das Debasish Swapnesh Kumar Nayak Anindita Kar Lambodar Jena Tripti Swarnkar Dept. of Computer Application FET-ITER Siksha ‘O’ Anusandhan (Deemed to be) University Bhubaneswar India Dept. of Computer Science and Engineering FET-ITER Siksha ‘O’ Anusandhan (Deemed to be) University Bhubaneswar India Dept. of Data Science (CSE) FET-ITER Siksha ‘O’ Anusandhan (Deemed to be) University Bhubaneswar India

One in every four cancer cases in women is breast cancer, which is the most prevalent malignancy in this group globally. In 2020, breast cancer will account for one in every eight new instances of cancer, according to projections of 2.3 million new cases. Breast cancer identification, both early and accurate, is critical to improve patient outcomes and survival rates. Magnetic resonance imaging (MRI) has developed as a valuable method for diagnosing breast cancer. Deep learning algorithms, notably convolutional neural networks (CNNs), have demonstrated exceptional effectiveness in a variety of medical image processing applications, including breast cancer classification. The ResNet-50 architecture has received a lot of interest in this context because of its excellent performance in image recognition tasks. ResNet-50 is a deep residual network that introduces skip connections and residual learning to solve the difficulty of training very deep neural networks. A total number of 1480 cancer MRI samples are collected from the Kaggle database, having two classes, which include healthy and malignant scans. It is observed that our proposed model with the data pre-processing techniques has achieved a classification accuracy of 92.01%.

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Enhancing Energy Efficiency in Solar Water Heating Systems for Sustainable Homes with IoT Technology

Enhancing Energy Efficiency in Solar Water Heating Systems f...

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

作者： Robert Halam Ramakrishnan Raman Symbiosis Law School (SLS) Pune & Symbiosis International (Deemed University) Pune Maharashtra India Symbiosis Institute of Business Management Pune & Symbiosis International (Deemed University) Pune Maharashtra India

Sustainable home techniques and methods are becoming more popular as homeowners recognize the need to perform their part to slow global warming and save their energy bills. One effective solution for lowering household energy use and carbon emissions is installing a solar water heating system. To increase the effectiveness, convenience, and sustainability of solar water heating systems, it investigates how to combine these technologies. It discusses the limitations of traditional solar water heating systems, such as their inefficiency, high maintenance costs, and complicated operation. The potential for sensors, data analytics, and remote control to improve energy gathering and distribution is explored in the Internet of Things (IoT)-enabled solar water heating systems. It also proceeds through how predictive maintenance algorithms might help extend the system's life and reduce repair bills. It provides a household installation to evaluate the economics and benefits of IoT-enabled solar water heating systems. Our research shows that energy efficiency has significantly increased, potentially saving up to 30% more money than traditional systems. IoT technology's real-time monitoring and management functions give households more agency over and understanding of energy use.

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