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On the continuous development of IoT in Big Data Era in the context of Remote Healthcare Monitoring & Artificial Intelligence 14

On the continuous development of IoT in Big Data Era in the ...

引用

14th international conference on Electronics, Computers and Artificial Intelligence, ECAI 2022

作者： Paraschiv, Elena-Anca Petrache, Cristian-Mihail Bica, Ovidiu National Institute for Research and Development in Informatics Communication Digital Applications and Systems Department Romania Doctoral School of Electronics Telecommunications and Information Technology University Politehnica of Bucharest Bucharest Romania

ISBN: (数字)9781665495356

ISBN: (纸本)9781665495356

the constant improvements in life expectancy have led to a considerable growth in the ageing population which significantly impacts the deliver of healthcare services and costs as well as an increase the medical professionals' burden. However, the most recent digital progresses have revealed a tremendous use of Internet of things (IoT), Big Data and Artificial Intelligence (AI) towards the development of complex systems that can facilitate and support the elderly and the medical staff. In this context, the purpose of this paper is to firstly present the capability IoT can provide in order to ease the remotely monitoring of the healthcare status, the Big Data and AI developments for early disease detection and the enlarged capacity of integrating IoT, Big Data and AI in the healthcare sector. Secondly, the paper discusses RO-SmartAgeing, a remote healthcare monitoring system which aims to support and offer a good quality of life for the staying-at-home elderly patient. Its purpose is not only for data collection, processing and visualization, but also to create a complex and reliblie system, augmented with AI capabilities. An application for demonstrating the use of IoT, Big Data and AI in the context of the RO-S martAgeing system is proposed. © 2022 IEEE.

关键词： Internet of things

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Oral Squamous Cell Carcinoma Diagnosis Using Spotted Hyena Optimizer Combined with Transfer Learning Approaches 15th

Oral Squamous Cell Carcinoma Diagnosis Using Spotted Hyena O...

引用

15th international conference on Soft Computing and Pattern Recognition, SoCPaR 2023 and 14th World Congress on Nature and Biologically Inspired Computing, NaBIC 2023

作者： Balakrishnan, Roshni M. Bharanidharan, N. Chakravarthy, S. R. Sannasi Kumar, V. Vinoth Patil, Swetha Rajaguru, Harikumar Department of Computer Science and Engineering Amrita School of Computing Bengaluru Amrita Vishwa Vidhyapeetham Bengaluru India School of Computer Science Engineering and Information Systems Vellore Institute of Technology Vellore India Department of Electronics and Communication Engineering Bannari Amman Institute of Technology Sathyamangalam India Department of Computer Science and Engineering PES University EC Campus Bengaluru India

ISBN: (纸本)9783031810794

Histopathological oral images are one of the popular methodologies to diagnose oral squamous cell carcinoma. Computerized diagnostic systems need to be developed in order to process huge number of histopathological oral images for each subject and helping the clinician in classifying the oral images as either normal class or oral squamous cell carcinoma class. this research work uses deep transfer learning approaches namely MobileNetV3, InceptionV2, and EfficientNetB3 for extracting the features from histopathological oral images. Spotted Hyena Optimizer is used as feature transform for improving classification accuracy. Two dense layers are used for classifying the transformed features. 2784 & 3632 number of histopathological oral images are considered in this study under the two classes as normal and oral squamous cell carcinoma respectively. With the proposed spotted Hyena Optimizer, the maximum accuracy attained is 93% for the features extracted through InceptionV2 model. Relatively, the highest accuracy attained is 89% if the optimizer is not used for the features extracted through MobileNetV3 model. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Transfer learning

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Blockchain-based Federated Learning for Bearing Fault Detection in the Industrial Internet of things 14

Blockchain-based Federated Learning for Bearing Fault Detect...

引用

14th international conference on Information and communication Technology Convergence, ICTC 2023

作者： Putra, Made Adi Paramartha Zainudin, Ahmad Alief, Revin Naufal Sampedro, Gabriel Avelino Kim, Dong-Seong Lee, Jae-Min Kumoh National Institute of Technology Department of It Convergence Engineering Gumi Korea Republic of Primakara University Informatic Enginering Denpasar Indonesia Kumoh National Institute of Technology Department of Electronic Engineering Gumi Korea Republic of University of the Philippines Open University Faculty of Information and Communication Studies Laguna Philippines

ISBN: (纸本)9798350313277

the reliability of Industrial Internet of things (IIoT) systems requires robust fault detection, which can be achieved with AI. However, the current centralized learning approach is inefficient. Federated Learning (FL) solves this problem by enabling distributed training without exposing individual information. this article proposes a parameter aggregation technique for bearing fault detection in IIoT using a lightweight smart contract with a PoA-based blockchain consensus. the findings indicate that the proposed system provides a secure aggregation process with an accuracy of 94.00% and a processing time of 1.54 s, which is suitable for the IIoT environment. © 2023 IEEE.

关键词： Bearing fault detection blockchain federated learning industrial internet of things

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Clustering-Beamforming Algorithm for Security Performance in Multiple Flying RISs-supported systems 14

Clustering-Beamforming Algorithm for Security Performance in...

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14th international conference on Information and communication Technology Convergence, ICTC 2023

作者： Sim, Yuna Cho, Jihun Liu, Huaping Moon, Sangmi Sin, Seungseok Kim, Kyunam Kim, Jihyung Hwang, Intae Chonnam National University Deptartment of Ict Convergences System Engineering Gwangju Korea Republic of Oregon State University United States Artificial Intelligence Korea Nazarene University Department of It Cheonan Korea Republic of Alps Electric Korea Co. Ltd. Gwangju Korea Republic of Electronics and Telecommunications Research Institute Spatial Wireless Transmission Research Section Daejeon Korea Republic of Chonnam National University Department of Electronic Engineering and Department of Ict Convergence System Engineering Gwangju Korea Republic of

ISBN: (纸本)9798350313277

this paper introduces an algorithm for improving the security performance in a system by combining unmanned aerial vehicles (UAVs) and reconfigurable intelligent surfaces (RISs), which are emerging technologies in the context of 6G networks. Multiple flying RISs are utilized in this approach. Among them, a DNN-based algorithm is presented to deploy flying RISs in appropriate locations to cover the maximum number of users possible. through simulation experiments, the superiority of the proposed algorithm is confirmed in terms of accuracy and execution time. In the future, research will be conducted on algorithms optimizing beamforming from the perspective of the base station (BS) and achieving active beamforming within the RIS to further enhance the security performance. © 2023 IEEE.

关键词： Deep neural network Reconfigurable intelligent surface Secrecy rate Unmanned aerial vehicles

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Disaggregated Database Management systems 1

引用

14th TPC Technology conference on Performance Evaluation and Benchmarking, TPCTC 2022, held in conjunction with the 45th international conference on Very Large Data Bases, VLDB 2022

作者： Ghandeharizadeh, Shahram Bernstein, Philip A. Borthakur, Dhruba Huang, Haoyu Menon, Jai Puri, Sumit USC Los AngelesCA United States Microsoft Research RedmondWA United States Rockset San MateoCA United States Google Mountain ViewCA United States Fungible Santa ClaraCA United States Liqid BroomfieldCO United States

ISBN: (数字)9783031295768

ISBN: (纸本)9783031295751

Modern applications demand high performance and cost efficient database management systems (DBMSs). their workloads may be diverse, ranging from online transaction processing to analytics and decision support. the cloud infrastructure enables disaggregation of monolithic DBMSs into components that facilitate software-hardware co-design. this is realized using pools of hardware resources, i.e., CPUs, GPUs, memory, FPGA, NVM, etc., connected using high-speed networks. this disaggregation trend is being adopted by cloud DBMSs because hardware re-provisioning can be achieved by simply invoking software APIs. Disaggregated DBMSs separate processing from storage, enabling each to scale elastically and independently. they may disaggregate compute usage based on functionality, e.g., compute needed for writes from compute needed for queries and compute needed for compaction. they may also use disaggregated memory, e.g., for intermediate results in a shuffle or for remote caching. the DBMS monitors the characteristics of a workload and dynamically assembles its components that are most efficient and cost effective for the workload. this paper is a summary of a panel session that discussed the capability, challenges, and opportunities of these emerging DBMSs and disaggregated hardware systems. © 2023, the Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Database systems

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Wireless network for Industrial application using ESP32 as Gateway

Wireless network for Industrial application using ESP32 as G...

引用

international conference on Computing and Networking Technology (ICCNT)

作者： Lopamudra Samal Pallavi Bute Electronics and Communication Engineering NIT Rourkela India Information Technology Department Shri Sant Gajanan Maharaj College of Engineering Shegaon

Modern lighting systems in industrial environments harness the potential of IoT technologies and intelligent industry concepts, facilitated by robust WiFi networks. these systems seamlessly incorporate WiFi-enabled LED lighting, intelligent controls, and real-time monitoring capabilities. By capitalizing on the data transmission capabilities of WiFi networks and employing advanced analytics, industries can make informed decisions, implement predictive maintenance strategies, and optimize operational efficiency. the seamless integration with other smart industry components, facilitated by WiFi network connectivity, allows for comprehensive optimization and fosters sustainable practices. Ultimately, these cutting-edge lighting systems establish interconnected workspaces, empowered by WiFi networks, that enhance productivity, improve well-being, and demonstrate a steadfast commitment to environmental stewardship. In this paper WiFi network is design using ESP32 for smart industrial application, All ESP32 node are behaving like Gateway .

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Active User Detection for Uplink NOMA communication: Deep Learning Approach 14

Active User Detection for Uplink NOMA Communication: Deep Le...

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14th IEEE international conference on Wireless communications and Signal Processing (WCSP)

作者： Yu, Chuanhang Li, Chenglin Wu, Gang Univ Elect Sci & Technol China Natl Key Lab Sci & Technol Commun Chengdu Peoples R China

ISBN: (纸本)9781665450850

Recently, with the rapid increase in the number of internet of things (IoT) devices, wireless communications based on orthogonal multiple access (OMA) are greatly limited by limited spectrum resources and the data conflicts and loss caused by large-scale IoT device. In order to support massive user devices, non-orthogonal multiple access (NOMA) have been suggested. In this paper, we focus on the problem of active user detection (AUD) of NOMA-based systems in IoT scenarios, and propose a long short-term memory (LSTM) based detection algorithm to solve the problems of low accuracy and high complexity of conventional AUD algorithms. the simulation results demonstrate that the proposed AUD algorithm based on LSTM network is superior to the conventional orthogonal matching pursuit (OMP) algorithm and the sparse Bayesian learning (SBL) algorithm in terms of both the error detection rate and the missed detection rate. Moreover, when the pilot scheme is designed for sparse code multiple access (SCMA), these two rates are further reduced compared with traditional pilot scheme. Furthermore, we present a detailed complexity analysis and comparison between conventional and deep learing (DL) AUD algorithm, which show the DL-based method greatly reduces the computational complexity.

关键词： internet of things non-orthogonal multiple access sparse code multiple access active user detection long short-term memory

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conference Committees

Conference Committees

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international conference on Computational Intelligence and communication networks (CICN)

Lists the conference organizers, sponsors, patrons, speakers, and/or committee members.

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Design of a Robust Cooperative Adaptive Cruise Control System on EDSA Bus Carousel for Improving the Current MRT Line 3 Operations 14

Design of a Robust Cooperative Adaptive Cruise Control Syste...

引用

14th IEEE international conference on Humanoid, Nanotechnology, Information Technology, communication and Control, Environment, and Management, HNICEM 2022

作者： Bejar, Althea Tiffanie A. Ouyang, Jon Kinsly T. Christopher Gumban, Mavin Concepcion, Ronnie Rhay Vicerra, Ryan Carpio, Joy De la Salle University Department of Manufacturing Engineering and Management Manila Philippines University of California Systems Engineering Department BerkeleyCA United States

ISBN: (纸本)9781665464932

the train service efficiency in the Philippines is claimed to be one of the lowest in Asia. this can be attributed to the constant down time especially in the Mass Rapid Transit 3 (MRT 3) that is mainly caused by train breakdowns. As a result, the whole system slows down, and furthermore increases wait time. In line with this, the Philippine government offered an alternative along EDSA to the MRT, this system is known as the EDSA Carousel which makes use of a dedicated lane for buses throughout EDSA. Although this system alleviated some of the foot traffic in the MRT 3, there are things that still need to be improved in the EDSA Carousel itself. the main objective of this study is to explore possible improvement of the service through the application of a Cooperative Adaptive Cruise Control (CACC) system. Its benefits include lessening passenger load in MRT 3 thus resulting in less breakdowns, an alternative for commuters, its system modularity, and increased road safety. this study improves existing models of CACC to fit its use in the EDSA carousel by CACC system bus clustering which is presented in two parts: (1) the application of CACC within a cluster and (2) cluster to cluster control system. In terms of its aim to increase system efficiency, the EDSA carousel can overtake or bypass a broken vehicle in the system immediately ensuring lesser down times which cannot be applied to the MRT 3 operations. © 2022 IEEE.

关键词： Cooperative communication

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Application of Software Defined networks for Collection of Process Data in Industrial Real-Time systems 14th

Application of Software Defined Networks for Collection of P...

引用

14th international conference on Computational Collective Intelligence (ICCCI)

作者： Smolka, Ireneusz Stoj, Jacek Fojcik, Marcin Silesian Tech Univ Gliwice Poland Western Norway Univ Appl Sci Forde Norway

ISBN: (纸本)9783031162107;9783031162091

Modern computer systems are expected to be more and more advanced and therefore their complexity is ever increasing. Some solutions make control systems more open and flexible. Whereas the main goal of others is to improve reliability. Such services may adding failure detection and even failure prediction features. Knowing the possibility of failures with a high level of accuracy may bring excellent management and economic benefits and refers to predictive maintenance. However, it is based on process, and diagnostic data gathered from the monitored system, which sometimes is not straightforward. In this paper, we present a way of obtaining knowledge about the system state without introducing significant modifications to the system itself. It is based on Software Defined networks SDN, which allows for controlling of communication network traffic on the software level as well as obtaining information about the state of the network or even about the user data that are sent in it. Data gathered in such a way may be forwarded to machine learning services, e.g., to perform predictive maintenance. the conceptual solution presented in this paper was verified during some experimental research performed in a system based on Beckhoff industrial controllers and EtherCAT communication network. In the role of SDN switch and SDN controller Raspberry Pi development boards were used.

关键词： Software Defined Network SDN Networked control system Real-time systems Industrial Ethernet Industry 4.0 EtherCAT

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