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Edge computing Enabled Technologies for Secure 6G smart Environment-An Overview 12th

Edge Computing Enabled Technologies for Secure 6G Smart Envi...

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12th international conference on Soft computing and Pattern Recognition, SoCPaR 2020 and 16th international conference on Information Assurance and Security, IAS 2020

作者： Jamil, Syed Usman Khan, M. Arif Rehman, Sabih ur Charles Sturt University Wagga Wagga Australia

ISBN: (纸本)9783030736880

In this article, we present a theoretical framework for a secure smart architecture based on futuristic edge resource allocation with a key focus on the task off-loading and intelligent enabling technologies. This article aims to highlight the growing need of embedded off-loading standards in the context of the Internet of Everything (IoE), fog, and cloud paradigms enabled technologies in the overall design framework of smart architecture. Efficient task off-loading strategies perform a vital role in mobility, heterogeneity, data and energy management, load balancing, establishing security mechanisms, reducing the propagation delay and information processing. The motive behind presenting such an embedded intelligent and secure architecture is to address the growing concerns of upcoming smart networks and its sub-network based on different futuristic scenarios, applications and devices. Furthermore, we discuss the off-loading criteria and enabling technologies, in view of the new advancement and critical requirements with rising demands of up-coming intelligence-based secure off-loading frameworks, in order to meet the requirements of the secure smart environment. Finally, through our discussion, we develop the specific research questions and explain its motivations in order to design and evaluate the off-loading criteria and its associated enabling technologies to accommodate the secure technological advancements and resource allocation in the sixth generation (6G) wireless communication era. © 2021, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Internet of Everything

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IoT, Heterogeneous data Processing, and AI Automation Synergy for Improved Efficiency and Maintenance: Revolutionizing Hospital Operations

IoT, Heterogeneous Data Processing, and AI Automation Synerg...

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IEEE international conference on Signal Processing, Communications and computing (ICSPCC)

作者： Niu Muqing Liu Wenting Gao Xiaole Zhao Zhiyuan Wang Jingjuan First Affiliated Hospital Henan University of Science and Technology Luoyang China Business School Henan University of Science and Technology Luoyang China

Modern healthcare is going through a big change because of the way that new tools are proposed. This paper works at how the Internet of Things (IoT), heterogeneous data transfer and processing, and Artificial Intelligence (AI) robotics could completely change how hospitals work. IoT technology has become a significant tool because it makes it possible to watch and collect data in realtime from hospital properties like medical equipment, wearables for patients, and smart sensors. This steady flow of different kinds of data brings both possibilities and problems. In our study, we talk about how significant it is to have strong data transmission and processing systems that can handle different data sources well. intelligent automation that is led by AI is at the heart of this change. AI systems can work with the huge amount of data created by IoT devices and use machine learning, predictive analytics, and cognitive computing to improve different parts of hospital management. This paper gives case studies and real-world examples of how IoT, diverse data processing, and AI automation have been used successfully in hospitals to show how these ideas can be used in the real world. IoT technology, diverse data processing, and AI automation are assembled to change the way hospitals work by making them more effective and easier to maintain. This study offers the significance of using these tools to keep up with the varying needs of current healthcare. As hospitals continue to change, this relationship will play an even bigger role in making sure that patients get superior care while making the best use of their resources.

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intelligent Detection of the Nanomaterials Spatial Structure with Synthetic Electron Microscopy Images 25

Intelligent Detection of the Nanomaterials Spatial Structure...

引用

25th international conference on Soft computing and Measurements, SCM 2022

作者： Polyanichenko, Dmitry S. Chernov, Andrey V. Kartashov, Oleg O. Alexandrov, Alexandr A. Butova, Vera V. Butakova, Maria A. Institute at the Southern Federal University The Smart Materials Research Rostov-on-Don Russia

ISBN: (纸本)9781665496698

One of the priority tasks in the process of chemical synthesis is study of nanoparticles morphological features. The key characteristics of materials at the molecular level are the size and geometric shape of the structure of the nanomaterials studied. One of the most effective methods for characterizing the morphology of nanoparticles is the transmission electron microscopy method. The main problem of applying this approach under the conditions of streaming synthesis of nanomaterials is the laboriousness and routine processing of electron microscopy images by a researcher. The creation of methods for automating the determination of the shape and size of nanoparticles will reduce the level of time and resource costs for diagnostics based on the results of the chemical synthesis of nanomaterials. This fact will positively affect the efficiency of scientific research in the field of synthesis and diagnostics of new functional nanomaterials. This paper proposes an accelerated method for generating synthetic images of transmission electron microscopy as a basis for creating software tools to accelerate the process of diagnosing and characterizing metal-organic frameworks in the process of chemical synthesis based on the use of deep learning technologies. The approach of automatic generation of the spatial structure of synthesized materials at the nanolevel is considered, which makes it possible to simulate a wide range of possible outcomes during laboratory synthesis. Procedures are proposed for generating sets of synthetic transmission electron microscopy images with support for automatic segmentation and extraction of areas of interest for machine and deep learning applications. The data sets obtained were tested and evaluated to solve the designated problem using the Yolo v5 object detection deep learning algorithm. © 2022 IEEE.

关键词： Synthesis (chemical)

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Visual Imputation analytics for Missing Time-Series data in Bayesian Network

Visual Imputation Analytics for Missing Time-Series Data in ...

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

作者： Yeon, Hanbyul Son, Hyesook Jang, Yun Sejong Univ Dept Comp Engn Seoul South Korea

ISBN: (纸本)9781728160344

Bayesian network is derived from conditional probability and is useful in inferring the next state of the currently observed variables. If data are missed or corrupted during data collection or transfer, the characteristics of the original data may be distorted and biased. Therefore, predicted values from the Bayesian network designed with incomplete data are not reliable. Various techniques have been studied to resolve the imperfection in data using statistical techniques or machine learning, but since the complete data is unknown, there is no optimal way to impute missing values. In this paper, we present a visual analytics system that supports decision-making to impute missing values occurring in incomplete time series data. The visual analytics system allows data analysts to explore the cause of missing data in incomplete datasets. The system also enables us to compare the performance of a suitable imputation model with the Bayesian network accuracy and the Kolmogorov-Smirnov test. We evaluate how the visual analytics system supports the decision-making process for the data imputation through a use case.

关键词： Visual analytics Imputation Missing data Bayesian network

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Road analytics for Transportation Systems using smartphone Application 12

Road Analytics for Transportation Systems using Smartphone A...

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12th international conference on computing Communication and Networking Technologies, ICCCNT 2021

作者： Ugale, Hrishikesh Patil, Pushpak Chauhan, Shubham Rao, Neeraj Department of Electronics and Communication Engineering Visvesvaraya National Institute of Technology Nagpur440 010 India

ISBN: (纸本)9781728185958

Paper presents the design of a smartphone sensing-based IoT system to collect analytical information about the condition of roads using a smartphone application. The system uses inbuilt sensors in modern smartphones to analyze the condition of roads and to classify roads into three zones - Green zone i.e. good condition road, Yellow Zone i.e. moderate condition road, and Red zone i.e. poor condition road. Green zones are the desired zones, yellow zones are zones that would need maintenance and repairs soon, and red zones are terrible roads with potholes and rough surfaces that need immediate maintenance. This information is then mapped with GPS location and stored on the cloud. This data stored in the cloud can be used by government bodies to plan developmental strategies for transportation systems. This information can also be used for smart driving applications like real-time alerts to drivers and intelligent route recommendations. Paper briefly presents the process of collecting data using smartphone sensors, processing of collected data using various algorithms, mapping of data, the transmission of data on the cloud, and visualizing collected data on GUI. A prototype of the proposed system was developed and results obtained during real-time testing of the system are presented. © 2021 IEEE.

关键词： smartphones

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Application of smart computing Systems for smart Cities and Urban Infrastructure: Framework, data Management, and smart Monitoring Attributes 3rd

Application of Smart Computing Systems for Smart Cities and ...

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3rd international conference on Advancements in smart computing and Information Security, ASCIS 2024

作者： Bhogayata, Ankur Thoriya, Amit Vora, Tarak Meva, Divyakant Marwadi University Gujarat Rajkot360003 India

ISBN: (纸本)9783031863011

smart cities require the utilization of advanced computing technology to address the intricate challenges posed by the rapid expansion of metropolitan areas. This study discusses the use of intelligent computing systems in smart cities, specifically emphasizing their conceptual frameworks, essential components, and actual implementations. smart computing systems consist of several technologies, including Internet of Things (IoT) devices, artificial intelligence (AI), and big data analytics. These technologies collaborate to enhance urban infrastructure and services. The aim of this article is to provide a comprehensive understanding of how intelligent computing systems might potentially transform urban living and make substantial contributions to the progress of sustainable, efficient, and resilient smart cities. The study explores essential components, including data collection and analysis, real-time decision-making, and resource optimization through automation and predictive analytics. The article represents the framework of IoT, integration of various smart components, operation of devices, working with data collected, smart monitoring of urban infrastructures, application of smart computing aspects for smart homes and smart cities by discussing real time example. The article additionally examines the challenges and potential associated with the adoption of these technologies, including concerns over data privacy, the need for robust cyber security measures, and the importance of scalable solutions. The article represents an overall framework for further research and progress in intelligent computing systems for smart cities and infrastructures. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Decision making

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smart Healthcare analytics Solutions Using Deep Learning AI

Smart Healthcare Analytics Solutions Using Deep Learning AI

引用

international conference on Recent Trends in Machine Learning, IOT, smart Cities and Applications, ICMISC 2020

作者： Subiksha, K.P. Ramakrishnan, M. Olive Tree Consultives and Freelancing Bengaluru India School of Information Technology Madurai Kamara University Madurai India

ISBN: (纸本)9789811572333

Machine learning is widely used in various applications such as business organizations, e-commerce, and healthcare industry, scientific and engineering for predicting and discovering relationships among data. In the healthcare industry, the predictive analytics in machine learning is mainly used for disease prediction. ML techniques help in predicting relationships in the electronic health record (EHR) data. The clinical process guidelines in the corpus may be considered as one of the inputs, and various healthcare parameters can be feature scaled, and the resultant architecture provides a positive impact in healthcare systems decision making. The objective of this work is to determine suitable features and optimal classifier design for a Deep Learning Healthcare Diagnosis system (DLHDS) to differentiate the endothelial dysfunction. It is used to predict under-perfused as wells as over-perfused tissues during dynamic contrast material–enhanced magnetic resonance (MR) imaging of the peripheral vascular and muscular system. Early detection of disease and by mapping the drug side effects with patient histories is the needed approach for future prescriptions. The real-time applications of knowledge acquisition of healthcare data research require deep learning healthcare diagnosis systems. This paper presents deep learning service share model architecture for the generalizations of knowledge processing which is available in form of cloud and by using various parameters which enhances the assistive intelligence. By using semi-supervised machine learning techniques, features are reduced based on their nature of correlation in EHR and analyzed by ML techniques classifier and achieved an accuracy of 97%, and it outperforms the other existing prediction models, and this improves the throughput in the prediction of diseases. © 2021, Springer Nature Singapore Pte Ltd.

关键词： Predictive analytics

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Multi-keyword Cipher-text Retrieval Method for smart Grid Edge computing 3

Multi-keyword Cipher-text Retrieval Method for Smart Grid Ed...

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2020 3rd international Symposium on Power Electronics and Control Engineering, ISPECE 2020

作者： Zhu, Jing Wu, Tao Li, Jintao Liu, Yanbin Jiang, Qixin School of Communication and Information Engineering Chongqing University of Posts and Telecommunications China School of Cybersecurity and Information Law Chongqing University of Posts and Telecommunications Chongqing China School of Computer Science Chongqing University of Posts and Telecommunications Chongqing China Guangdong Provincial Key Laboratory of Power System Network Security Guangzhou China Electric Power Institute CSG Guangzhou China

With the continuous deepening of China's power grid intelligent upgrading and the rise of edge computing technology, power grid terminals can carry out data storage and intelligent calculation at the edge of the power grid. However, the generated data by users may cause the leakage of user privacy information. The power grid edge system can retrieve the user's electricity data on demand, so as to improve the level of privacy protection and reduce the demand of transmission and computing resources through the localized storage of data. Therefore, how to carry out privacy protection and on-demand retrieval is a key problem to realize the intelligent power grid edge. In this paper, a multi-keyword cipher-text retrieval scheme (MCR- SGEC) for smart grid edge computing is proposed, which can achieve accurate matching of index and trap door, and has low spatial complexity. At the same time, on this basis, a multi-keyword fuzzy cipher-text retrieval method for smart grid edge computing (MFSE-SGEC) is proposed, which allows users to have spelling errors, which makes the user experience better. © Published under licence by IOP Publishing Ltd.

关键词： Edge computing

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Collaborative Workflow-Based Enterprise Information System

Collaborative Workflow-Based Enterprise Information System

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international conference on Application of intelligent Systems in Multi-modal Information analytics, MMIA 2020

作者： Huang, Bin Teng, Qilong Tang, You Electrical and Information Engineering College JiLin Agricultural Science and Technology University Jilin132101 China Smart Agricultural Engineering Research Center of Jilin Province Jilin132101 China Electronic Information Engineering Jilin Petrochemical Engineering Efficiency Jilin China

ISBN: (纸本)9783030514303

Based on the study of collaborative workflow technology, this paper analyzes the characteristics of enterprise supply chain management system, studies its requirements for workflow management system, and designs a workflow management system for enterprise supply chain management system integration platform. © 2021, Springer Nature Switzerland AG.

关键词： Information use

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NoSQL data Lake: A Big data Source from Social Media 1

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20th international conference on Hybrid intelligent Systems, HIS 2020 and 12th World Congress on Nature and Biologically Inspired computing, NaBIC 2020

作者： Dabbèchi, Hichem Haddar, Nahla Zaaboub Elghazel, Haytham Haddar, Kais MIRACL Laboratory FSEG University of Sfax B.P. 1088 Sfax3018 Tunisia LIRIS UMR 5205 University of Lyon 1 Villeurbanne France MIRACL Laboratory FSS University of Sfax B.P. 242 Sfax3021 Tunisia

ISBN: (数字)9783030730505

ISBN: (纸本)9783030730499

Big data analytics has recently emerged as an important research area due to the advent of user-generated content platforms. In recent years, we have witnessed an exponential increase in data production all over the world. This phenomenon is known as big data. Social media platforms, like Facebook, Twitter, and YouTube represent the most popular source of big data. Big data coming from social networking sites can be immensely useful for all companies by providing valuable insights on consumer preferences and changing trends. This information presents enormous opportunities for companies that have implemented Big data management solutions. For this reason, how to store, manage, and transform social media posts into knowledge for decision-makers becomes an important research problem. On the other hand, the massive amount of data generated by users using social media platforms has led to the emergence and development of new technologies and techniques of data management. data Lake is one of the latest technologies that was introduced to address this challenge in the last period. In this paper, we provide a NoSQL data lake design approach. More precisely, we start by introducing the main concepts of data lake. We also discuss the advantages of data lakes and their impact on big data analytics. Then, we describe some of the recent literature reviews on data lake design approaches. Finally, we provide a NoSQL data lake using MongoDB that allows storing big data collected from social networks such as Facebook, Twitter, and Youtube. © 2021, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Lakes

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