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5th International Conference on Electronics and Sustainable Communication Systems, ICESC 2024

作者： Karpagalakshmi, R.C. Sampoornam, M. Maria Vinya, Viyyapu Lokeshwari Monikapreethi, S.K. Yuvaraj, S. Srinivasan, C. Alliance College of Engineering and Design Alliance University Karnataka Bengaluru India J.J College of Engineering and Technology Department of Information Technology Tamil Nadu Tiruchirappalli India Vardhaman College of Engineering Department of Computer Science and Engineering Telangana Ranga Reddy India Rajalakshmi Institute of Technology Department of Electronics and Communication Engineering Tamil Nadu Chennai India Srm Institute of Science and Technology Faculty of Engineering and Technology Department of Electronics and Communication Engineering Tamil Nadu Kattankulathur India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Department of Computer Science and Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350379945

The increasing number of road accidents involving Vulnerable Road Users (VRUs) highlights the urgent need for innovative safety solutions. This study proposes a novel approach that leverages IoT and Convolutional Neural Network (CNN) technologies to enhance VRU safety. By deploying IoT sensors on vehicles and infrastructure, the proposed model can accurately track VRU locations and environmental conditions in real-time. A CNN-based model is developed to robustly detect and classify VRUs under various traffic scenarios, with continuous improvement through learning. The integration of IoT and CNN enables real-time communication and timely warnings, facilitating proactive interventions to prevent accidents. Experimental evaluations demonstrate the effectiveness of the proposed system in improving VRU safety. This research offers a promising solution for reducing road accidents and creating safer urban environments. © 2024 IEEE.

关键词： Convolutional neural networks

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Critical Factors of Immersive Learning in Higher Education: A Systematic Literature Review 10

Critical Factors of Immersive Learning in Higher Education: ...

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10th International Conference on ICT for Smart Society, ICISS 2023

作者： Sunardi Meyliana Ramadhan, Arief Rosmansyah, Yusep Bina Nusantara University BINUS Graduate Program Doctor of Computer Science Computer Science Department Jakarta Indonesia School of Information System Bina Nusantara University Information System Departement Jakarta Indonesia School of Computing Telkom University Bandung Indonesia School of Electrical Engineering and Informatics Bandung Institute of Technology Bandung Indonesia

ISBN: (纸本)9798350339543

Immersive learning has gained significant attention with the rising trend of spatial computing, particularly in the after-pandemic era. Numerous research has explored the potential of immersive learning in higher education, primarily on the educational sector. However, prior research has frequently focused too narrowly on the effects of technology and neglected to address the crucial element influencing successful immersive learning in higher education. This study seeks to pinpoint the crucial element contributing to the development of immersive learning experiences. The methodology uses a systematic literature review (SLR) from 2018 up to 2023 to investigate the critical factors of immersive Learning in Higher Education. From the 728 papers initially retrieved, 274 were considered potential candidates, and ultimately, 86 articles were selected based on their relevance to the research question. The results reveal that the critical factors include learning design, technology, immersion, engagement, interactivity, and usability. Academic interests will benefit from this SLR's consequences as institutions create models for designing suitable immersive learning, especially within the context of higher education. © 2023 IEEE.

关键词： E-learning

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Smartphone-based colorimetric detection of formaldehyde in the air

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Building Simulation 2024年第11期17卷 2007-2015页

作者： Meng Yang Jin Ye Tao Yu Ying Song Hua Qian Tianyi Liu Yang Chen Junqi Wang Shi-jie Cao Cong Liu School of Energy and Environment Southeast UniversityNanjing210096China School of Energy and Power Jiangsu University of Science and TechnologyZhenjiangJiangsu212100China Wuhan Second Ship Design and Research Institute Wuhan430205China Hubei Provincial Engineering and Technology Research Center for Food Quality and Safety Test Hubei Provincial Institute for Food Supervision and TestWuhan430075China Laboratory of Image Science and Technology the School of Computer Science and EngineeringSoutheast UniversityNanjingChina Jiangsu Provincial Joint International Research Laboratory of Medical Information Processing Southeast UniversityNanjingChina School of Cyber Science and Engineering Southeast UniversityNanjingChina Key Laboratory of Computer Network and Information Integration Southeast UniversityMinistry of EducationNanjingChina School of Architecture Southeast UniversityNanjingChina Jiangsu Province Engineering Research Center of Urban Heat and Pollution Control Southeast UniversityNanjingChina Global Centre for Clean Air Research(GCARE) University of SurreyGuildfordUK

Adverse impacts of exposure to formaldehyde on human health significantly increases attention in monitoring formaldehyde concentrations in the *** formaldehyde detection methods typically rely on large and costly instruments and requires high skills of expertise,preventing it from being widely accessible to *** study introduced a novel approach utilizing smartphone-based colorimetric *** of green channel signals of digital images by a smartphone successfully capture variation of purple color of 4-amino-3-hydrazino-5-mercapto-1,2,4-triazol solution,which is proportional to formaldehyde *** is because that green and purple are complimentary color pairs.A calibration curve was established between green channel signals and formaldehyde concentrations,with a correlation coefficient of *** limit of the smartphone-based method is 0.008 mg/m^(3).Measurement errors decrease as formaldehyde concentrations increase,with median relative errors of 34%,17%,and 6%for concentration ranges of 0–0.06 mg/m^(3),0.06–0.12 mg/m^(3),and 0.12–0.35 mg/m^(3),*** method replaced scientific instrumentation with ordinary items,greatly reducing cost and operation *** would provide an opportunity to realize onsite measurements for formaldehyde by occupants themselves and increase awareness of air quality for better health protection.

关键词： indoor air quality VOC RGB color identification machine vision

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IoT-Based Wearable Devices for Personal Safety and Accident Prevention Systems 2

IoT-Based Wearable Devices for Personal Safety and Accident ...

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2nd International Conference on Smart Technologies for Smart Nation, SmartTechCon 2023

作者： Deepa, A. Latha, R. Kumar, T. Senthil Manikandan, N.K. Preetha, J. Murugan, S. Department of Computer Science and Engineering Sathyabama Institute of Science and Technology Tamil Nadu Chennai India Department of Computer Science and Engineering K.Ramakrishnan College of Engineering Tamil Nadu Trichy India School of Information Technology and Engineering VIT University Tamil Nadu Vellore India Department of Computer Science Engineering Vel Tech Rangarajan Dr.Sagunthala R and D Institute of Science and Technology Tamil Nadu Chennai India Tamil Nadu Namakkal India Department of Biomedical Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Tamil Nadu Chennai India

ISBN: (纸本)9798350305418

This paper proposed an Internet of Things (IoT)-based wearable device system to protect the person's safety and prevent accidents. The wearable device and main processor are the Raspberry Pi, while other sensors monitor the user's surroundings and health. Such sensor types include the heart rate monitor, gyroscope, proximity sensor, and accelerometer. The wearable device provides the system with its most recent position information using its integrated GPS module. Real-time wearer tracking is possible with the Raspberry Pi using GPS data. The RF module makes wireless data transfer and communication between the wearable device's many parts possible. After gathering sensor data, the RF module transfers data wirelessly to a cloud-based database. The sensor data and alerts notification quickly send to the cloud. The cloud database is a flexible and easily available data management platform that saves up data storage, access, and analysis. To detect risks to security, the system analyzes data from sensors saved in the cloud. The system can generate real-time warnings and notifications in the case of a potential threat to the user's safety. The system allows continuous monitoring, wireless interaction, centralized data storage, and analytical processing. The potential of IoT technology to improve human safety and accident avoidance in a wide range of settings is shown by this integrated solution. The technology can improve security and reduce risks in various scenarios by providing real-time monitoring, wireless connection, efficient data storage, and analysis. © 2023 IEEE.

关键词： Accident prevention IoT RF module Sensors Wearable devices

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STAA-Net: A Sparse and Transferable Adversarial Attack for Speech Emotion Recognition

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IEEE Transactions on Affective Computing 2024年第2期16卷 861-874页

作者： Chang, Yi Ren, Zhao Zhang, Zixing Jing, Xin Qian, Kun Shao, Xi Hu, Bin Schultz, Tanja Schuller, Bjorn W. University of Bremen Germany Hunan University College of Computer Science and Electronic Engineering China Beijing Institute of Technology School of Medical Technology China Nanjing University of Posts and Telecommunications College of Telecommunications and Information Engineering China Imperial College London Glam - the Group on Language Audio & Music United Kingdom University of Augsburg Chair of Embedded Intelligence for Health Care and Wellbeing Germany

Speech contains rich information on the emotions of humans, and Speech Emotion Recognition (SER) has been an important topic in the area of human-computer interaction. The robustness of SER models is crucial, particularly in privacy-sensitive and reliability-demanding domains like private healthcare. Recently, the vulnerability of deep neural networks in the audio domain to adversarial attacks has become a popular area of research. However, prior works on adversarial attacks in the audio domain primarily rely on iterative gradient-based techniques, which are time-consuming and prone to overfitting the specific threat model. Furthermore, the exploration of sparse perturbations, which have the potential for better stealthiness, remains limited in the audio domain. To address these challenges, we propose a generator-based attack method to generate sparse and transferable adversarial examples to deceive SER models in an end-to-end and efficient manner. We evaluate our method on two widely-used SER datasets, Database of Elicited Mood in Speech (DEMoS) and Interactive Emotional dyadic MOtion CAPture (IEMOCAP), and demonstrate its ability to generate successful sparse adversarial examples in an efficient manner. Moreover, our generated adversarial examples exhibit model-agnostic transferability, enabling effective adversarial attacks on advanced victim models. The source code for this project is available at https://***/glam-imperial/STAA-Net-SER. © 2010-2012 IEEE.

关键词： Perturbation Methods Emotion Recognition Speech Processing Robustness Iterative Methods Feature Extraction Convolutional Neural Networks Convolution Long Short Term Memory Glass Box Speech Emotion Recognition Adversarial Attacks Sparsity Transferability Efficiency End To End Emotion Recognition Adversarial Attacks Emotional Speech Speech Emotion Recognition Neural Network Deep Neural Network Threat Model Adversarial Examples Attack Methods Popular Research Area Model Parameters Convolutional Neural Network Image Domain Imperceptible Short Time Fourier Transform Signal To Noise Ratio Values Audio Data Sparse Method Speech Samples Attack Success Rate Projected Gradient Descent Adversarial Perturbations Perturbation Magnitude Black Box Attacks Fast Gradient Sign Method White Box Attack Generator Training Foundation Model Automatic Speech Recognition System

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Health information Broadcast Distributed Pattern Association based on Estimated Volume 3

Health Information Broadcast Distributed Pattern Association...

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3rd International Conference on Artificial Intelligence and Smart Energy, ICAIS 2023

作者： Dhanalakshmi, R. Kalpana, A.V. Umamageswaran, J. Kumar, B. Praveen Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Computer Science and Engineering Tamil Nadu Chennai India Srm Institute of Science and Technology Kattankulathur Department of Data Science and Business Systems Tamil Nadu Chennai India R.M.K Engineering College Department of Information Technology Tamil Nadu Chennai India K L University Department of Computer Science and Engineering Andhra Pradesh India

ISBN: (纸本)9781665462167

Any study on dataset and inadequate representation are featured in the conventional data mining process. Consequently, data mining findings reveal high chances of challenges and huge root-mean-square approximation errors besides long utilization. The proposed algorithm for associating distribution patterns in streams of medical data is based on density calculations. The distance approach will be utilized to identify any orphaned or questionable data within the data stream, and the gathering of medical data will be used to achieve the aim. Data stream duplicates are compiled by sector match quality, and both the out-of-the-ordinary and the redundant data are eventually eliminated. The density of the information stream is then calculated based on histogram calculations. This investigates the flow of medical information from the viewpoints of concentration and dispersion, as well as the physical characteristics of information distribution, in combination with the results of data density estimate. The model is then built upon a neural network composite, the data distribution parameters in the clustering layer of the model are entered, and in-depth testing is carried out via the neural B.P. network (Back Propagation) on the mining layer of the model. Temporarily, all laws under the grouping of the hidden layer operation value besides the resulting output value are derived, and all rules under the grouping of the hidden layer value besides the conforming contribution value in the medical information stream pattern. The new findings show a curve of contour contiguous to the true curve of the likelihood density, a fair range of medical data dispersion and high medical data consistency, a lower probability of data redundancy, a low RMSEA for mining, a lower time for data recovery. © 2023 IEEE.

关键词： Data mining

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A Systematic Review of Deep Learning Approaches for Vessel Segmentation in Retinal Fundus Images 2

A Systematic Review of Deep Learning Approaches for Vessel S...

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2nd International Conference on Artificial Intelligence, Computational Electronics and Communication System, AICECS 2023

作者： Hegde, Govardhan Prabhu, Srikanth Gupta, Shourya Prabhu, Gautham Manuru Palorkar, Anshita Srujan, Metta Venkata Bhandary, Sulatha V Department of Computer Science and Engineering India Department of Data Science and Computer Applications India Department of Information and Communication Technology Manipal Institute of Technology Karnataka Manipal576104 India Department of Ophthalmology Kasturba Medical College Karnataka Manipal576104 India

Retinal vessel segmentation (RVS) is important to accurately differentiate retinal vasculature for diagnosing and monitoring various ocular and systemic diseases. The traditional methods for RVS have mostly involved supervised learning, although semi-supervised and unsupervised techniques are on the rise. This paper reviews the increase in complexity of developments in RVS primarily after 2020. The methods were chosen to cover both the gradual transition over time and a variety of unorthodox or combinatorial approaches. This includes convolutional neural networks, encoder-decoder models, generative models, and other multi-modal or hybrid techniques. CNN approaches discussed employ Zero Phase Component Analysis, Global Contrast Normalization, and reinforcement learning. Encoder-decoder models include approaches such as the use of skip and residual connections, spatial attention, and atrous enhancement U-Net. Generative models propose short link connections, recurrent residual blocks, and multi-scale features to refine convolutional blocks. Hybrid methods involve the use of connectivity features, the MISODATA Algorithm, cross-domain adaptation, and multiple filters (such as morphological, match, and Gabor). All the frameworks are compared based on their performance on the benchmark dataset DRIVE to provide a comprehensive understanding of the current state of RVS. © Published under licence by IOP Publishing Ltd.

关键词： Diagnosis

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An Intelligent Systemic Circulation Monitoring System for Neonatal Illness Severe Severity Evaluation

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IEEE Access 2023年 11卷 127468-127478页

作者： Chen, Hsiu-Lin Lin, Bor-Shing Chen, Kai-Huei Lin, Bor-Shyh Kaohsiung Medical University Hospital Department of Pediatrics Kaohsiung80756 Taiwan National Taipei University Department of Computer Science and Information Engineering New Taipei City237303 Taiwan Institute of Imaging and Biomedical Photonics National Yang Ming Chiao Tung University Tainan71150 Taiwan

Illness severity is a major determinant of neonatal mortality. In neonatal intensive care, severity-of-illness scales, primarily relied on subjective judgment by experienced physicians, are used to assess the mortality risk prediction and therapeutic intensity. Because cardiovascular morbidity and other disease state may contribute to hemodynamic abnormalities, the illness severity may reflect on the state of hemodynamic abnormalities. Several diagnostic approaches, such as laser Doppler flowmetry and orthogonal polarization spectral, have been developed to evaluate the local information of blood flow or vascular distribution on skin surface, but they may not efficiently evaluate the systemic circulation. In this study, an intelligent systemic circulation monitoring system was proposed to non-invasively evaluate the blood perfusion state and further estimate the illness severity. In the proposed system, a wireless sensing device was designed to real-time monitor the changes of hemoglobin parameters under the altered external pressure, and several indexes were defined from the hemodynamic response to present the blood perfusion state. The relationship between the blood perfusion state and the illness severity was also investigated in this study. Moreover, the technique of neural network was also applied in the classification of illness severity. The experimental results indicate the differences between many defined blood perfusion indexes in neonates with different illness severity were exactly significant and the mild and severe illness severity group could also be efficiently recognized by the neural network technique. Therefore, the proposed system may contain the potential of future clinical applications in the evaluation of illness severity or other diseases. ©;2023 The Authors.

关键词： Pediatrics

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Convolutional Neural Network and Long Short-Term Memory based Ensemble Learning for modulation classification in 5G Network 2

Convolutional Neural Network and Long Short-Term Memory base...

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2nd IEEE International Conference on Networks, Multimedia and information Technology, NMITCON 2024

作者： Devi, T. Alkhayyat, Ahmad Armoogum, Vinaye Buvaneswari, B. Manjula, B.M. Saveetha Institute of Medical and Technical Sciences Saveetha School of Engineering Department of Computer Science and Engineering Chennai India The Islamic University College of Technical Engineering Department of Computers Techniques Engineering Najaf Iraq University of Technology School of Innovative Technologies and Engineering Mauritius Panimalar Engineering College Department of Information Technology Chennai India Nitte Meenakshi Institute of Technology Department of Ece Bengaluru India

ISBN: (纸本)9798350372892

Modulation classification is important for its fundamental role in dynamic spectrum access which supports 5G (fifth generation) wireless communications. When the receiver receives multiple signals, multipath fading exists at the receiver side it is a big challenge to classify the modulation type. When many signals are transmitting to the receiver side, there exists a heterogeneous traffic, where there is a lot of disturbances occur. Due to these reasons, the block error rate for modulation increases simultaneously. To overcome the changing demands like multipath fading, minimize block error rate and to reduce the traffic. In this research a modulation classification algorithm is developed using the combination architectures of Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) based Ensemble Learning (EL). Dimensionality is reduced through principal component analysis;it also minimizes the training time. The proposed method outperformed same as the ordinary CNN in terms of classification accuracy, but it reduced the training time with the help of Principal Component Analysis (PCA). The results proved that the proposed CNN-LSTM-EL achieved the best classification accuracy of 99.5 % than all other existing methods like CNN, LSTM. © 2024 IEEE.

关键词： Principal component analysis

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Edge-AI: IoT Request Service Provisioning in Federated Edge Computing Using Actor-Critic Reinforcement Learning

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IEEE Transactions on engineering Management 2024年 71卷 12519-12528页

作者： Baghban, Hojjat Rezapour, Amir Hsu, Ching-Hsien Nuannimnoi, Sirapop Huang, Ching-Yao National Yang Ming Chiao Tung University Department of Electronics Engineering and Institute of Electronics Hsinchu30010 Taiwan Asia University Department of Computer Science and Information Engineering Taichung41354 Taiwan National Yang Ming Chiao Tung University Department of Computer Science Hsinchu30010 Taiwan National Yang Ming Chiao Tung University Logitech Inc. Hsinchu30010 Taiwan China Medical University Department of Medical Research and China Medical University Hospital Taichung404 Taiwan Foshan University Guangdong-Hong Kong-Macao Joint Laboratory for Intelligent Micro-Nano Optoelectronic Technology School of Mathematics and Big Data Foshan528000 China National Yang-Ming Chiao Tung University Department of Electronics Engineering Institute of Electronics Hsinchu30010 Taiwan National Yang Ming Chiao Tung University Department of Electronics Engineering Institute of Electronics Hsinchu30010 Taiwan

Edge computing plays a critical role in the Internet of Things (IoT) environment as it potentially supports the time-critical IoT applications' resources as well as latency requirements. In the near future, most edge service providers are envisioned to receive revenue from deploying these applications with the expenditures proportional to placing the offloaded requests from IoT devices and allocating the required resources. One way to maximize the edge profit and minimize the response latency is to integrate the edge nodes and form the edge federation. Therefore, edge service providers can have interoperability to distribute the IoT requests on the appropriate edge nodes in the light of providing satisfactory service levels to meet their objectives. Since the edge nodes are volatile and IoT time-critical applications are increasing, the edge nodes are envisioned to face massive traffic from IoT devices. Therefore, exploiting the traditional dynamic requests placement approaches cannot meet the SLA requirement of both IoT devices and edge service providers. In this article, we designed an intelligent reinforcement learning-based request service provisioning system (i.e., here, we call Edge-AI) as part of a smart edge orchestrator in the edge federation. We implement the proposed method, which is called DRL-Dispatcher, and compare it with greedy and random algorithms in edge federation. The experimental results show that the proposed DRL-Dispatcher performs better in terms of profit and low response latency as compared with the baseline approaches. © 1988-2012 IEEE.

关键词： Internet of things

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