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9th Future Technologies Conference, FTC 2024

作者： Yuvaraj, Rajamanickam Faizal, Sahil Ratnavel, Rajalakshmi Yang, Wang Science of Learning in Education Centre Office of Education Research National Institute of Education Nanyang Technological University Singapore637616 Singapore Department of Computer Science and Engineering Tandon School of Engineering New York University New YorkNY11201 United States School of Computer Science and Engineering Vellore Institute of Technology Tamil Nadu Chennai India School of Computing and Information Systems Singapore Management University Singapore188065 Singapore

ISBN: (纸本)9783031731211

Measuring emotions in educational settings can provide important information in predicting and explaining student learning outcomes. Knowledge of student’s classroom emotions can also help teachers understand their students’ learning behaviors, improve their teaching methods, and optimize students’ learning and development. However, it can be highly challenging for teachers to monitor and accurately understand student emotions within classroom or group contexts, especially while they are actively teaching and attending to many students simultaneously. Video recording classroom activity can address that issue as high-definition cameras can be used to continuously record groups of students, enabling online or offline analyses of student emotions and supplementing teacher monitoring, retrieval, and interpretation of those processes. Teachers find it difficult to use existing emotion recognition methods to analyze student behaviors in videos due to a lack of user-friendly interfaces that facilitate automatic analysis. To address this challenge, we developed EmoRoom, an open-source tool designed to simplify the annotation and analysis of videos from an emotional perspective. EmoRoom tool offers a practical solution for quantifying and visualizing the frequency of emotions for individuals of interest. Furthermore, it can assist teachers in refining their teaching methods by offering a comprehensive overview of students’ emotional experiences during learning. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Students

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An efficient data aggregation scheme with local differential privacy in smart grid

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Digital Communications and Networks 2022年第3期8卷 333-342页

作者： Na Gai Kaiping Xue Bin Zhu Jiayu Yang Jianqing Liu Debiao He School of Cyber Science and Technology University of Science and Technology of ChinaHefeiAnhui230027China Department of Electronic Engineering and Information Science University of Science and Technology of ChinaHefeiAnhui230027China Department of Electrical and Computer Engineering University of Alabama in HuntsvilleHuntsvilleAL35899USA Key Laboratory of Aerospace Information Security and Trusted Computing of Ministry of Education School of Cyber Science and EngineeringWuhan UniversityWuhanBeihu430072China

By integrating the traditional power grid with information and communication technology, smart grid achieves dependable, efficient, and flexible grid data processing. The smart meters deployed on the user side of the smart grid collect the users' power usage data on a regular basis and upload it to the control center to complete the smart grid data acquisition. The control center can evaluate the supply and demand of the power grid through aggregated data from users and then dynamically adjust the power supply and price, etc. However, since the grid data collected from users may disclose the user's electricity usage habits and daily activities, privacy concern has become a critical issue in smart grid data aggregation. Most of the existing privacy-preserving data collection schemes for smart grid adopt homomorphic encryption or randomization techniques which are either impractical because of the high computation overhead or unrealistic for requiring a trusted third party.

关键词： Local differential privacy Data aggregation Smart grid Privacy preserving

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CPIMAC: MEC-assisted Message Dissemination Based on Traffic Prediction at Intersections 10

CPIMAC: MEC-assisted Message Dissemination Based on Traffic ...

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10th IEEE Smart World Congress, SWC 2024

作者： Liu, Bingyi Shao, Xun Zhao, Haoxiang Wang, Enshu Liu, Zhi Wuhan University of Technology Sanya Science and Education Innovation Park Sanya China Toyohashi University of Technology Department of Electrical and Electronic Information Engineering Aichi Japan Wuhan University School of Cyber Science and Engineering Wuhan China The University of Electro-Communications Department of Computer and Network Engineering Tokyo Japan

ISBN: (纸本)9798331520861

The Internet of Vehicles (IoV) is gaining increasing attention for its ability to improve traffic safety and efficiency by providing situational awareness and detecting potential hazards beyond the visual range. Time division multiple access (TDMA) based communication protocol effectively prevents transmission collisions in IoV. However, road intersections pose challenges for the implementation of TDMA-based MAC protocols, as vehicles converge from different directions, leading to severe communication access and merging collisions. To address this issue, we propose a distributed MAC protocol named slot collisions prediction based MAC at intersection (CPIMAC). Specifically, We combine mobile edge computing (MEC) with a CNN-BiLSTM-Attention neural network model to predict potential transmission collisions and propose corresponding collision avoidance algorithms. In addition, we design the head-tail beacon (HTB) algorithm to reduce slot collisions during vehicle waiting periods at intersections by adjusting beacon frequency. Finally, we conduct extensive experiments to evaluate the proposed protocol. The results show that transmission collisions can be reduced by 58.1 % compared to the existing protocols. © 2024 IEEE.

关键词： Time division multiple access

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Effective Traffic Model for Intelligent Traffic Monitoring Enabled Deep RNN Algorithm for Autonomous Vehicles Surveillance Systems 4

Effective Traffic Model for Intelligent Traffic Monitoring E...

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4th International Conference on Smart Electronics and Communication, ICOSEC 2023

作者： Leelavathy, S. Nithya, M. Dhaya, R. Muthuselvan, S. Kanthavel, R. Rajakumari, K. Department of Computer Science and Engineering Chennai India Salem India Department of Computer Science and Engineering Salem India Kcg College of Technology Department of Information Technology Chennai India School of Cse Hindustan Institute of Technology of Science and Technology Chennai India Bharath Institute of Higher Education and Research Chennai India

ISBN: (纸本)9798350300888

Regardless of the terrain, weather, visibility, or road surface conditions, improving detection and forecast accuracy is a critical priority for autonomous cars. Little shifts in the images to be collected should be understood even in low-light settings because the vehicle needs more data to make a binding judgment. As a subfield of AI and DL, Recurrent Neural Networks (RNNs) are structured with temporally aware connections between their nodes. As a result, the network can engage in sequential action driven entirely by internal incentives for an extended period. Moreover, cars require enormous computational capacity and a substantial quantity of data sets in an autonomous environment, such as unexpected events, opposing movement, lane conditions, and indestructible barriers. This study presents an effective deep recurrent neural network (Deep RNN) technique for autonomous cars to meet the need for recurrent nodes and capture sequence patterns within the acquired data. In addition, this study shows how to use deep RNN for efficient data annotation. Simulated findings show a significant drop in error when the proposed Deep RNN is utilized in autonomous cars. © 2023 IEEE.

关键词： Recurrent neural networks

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Advanced deep learning approach for enhancing crop disease detection in agriculture using hyperspectral imaging

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Journal of Intelligent and Fuzzy Systems 2024年第2期46卷 3281-3294页

作者： Zekrifa, Djabeur Mohamed Seifeddine Lamani, Dharmanna Chaitanya, Gogineni Krishna Kanimozhi, K.V. Saraswat, Akash Sugumar, D. Vetrithangam, D. Koshariya, Ashok Kumar Manjunath, Manthur Sreeramulu Rajaram, A. Department of Bioinformatics Higher School of Food Science and Agri-Food Industry Ahmed Hamidouche Av Oued Smar Algiers Algeria Department of Computer Science & Information Technology School of Computing and Information Technology REVA University Rukmini Knowledge Park Kattigenahalli Karnataka Bengaluru India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Andhra Pradesh Vaddeswaram India Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Tamil Nadu Chennai India Department of Computer Science Engineering Dr. K. N. Modi University Jaipur Rajasthan Newai India Department of Electrical and Electronics Engineering Karunya Institute of Technology and Sciences Deemed to be University Coimbatore India Department of Computer Science and Engineering University Institute of Engineering Chandigarh University Punjab Mohali India Department of Plant Pathology School of Agriculture Lovely Professional University Punjab Phagwara India Department of Electronics and Communication Engineering E.G.S. Pillay Engineering College Tamilnadu Nagapattinam India

Crop diseases pose significant challenges to global food security and agricultural sustainability. Timely and accurate disease detection is crucial for effective disease management and minimizing crop losses. In recent years, hyperspectral imaging has emerged as a promising technology for non-destructive and early disease detection in crops. This research paper presents an advanced deep learning approach for enhancing crop disease detection using hyperspectral imaging. The primary objective is to propose a hybrid Autoencoder-Generative Adversarial Network (AE-GAN) model that effectively extracts meaningful features from hyperspectral images and addresses the limitations of existing techniques. The hybrid AE-GAN model combines the strengths of the Autoencoder for feature extraction and the Generative Adversarial Network for synthetic sample generation. Through extensive evaluation, the proposed model outperforms existing techniques, achieving exceptional accuracy in crop disease detection. The results demonstrate the superiority of the hybrid AE-GAN model, offering substantial advantages in terms of feature extraction, synthetic sample generation, and utilization of spatial and spectral information. The proposed model’s contributions to sustainable agriculture and global food security make it a valuable tool for advancing agricultural practices and enhancing crop health monitoring. With its promising implications, the hybrid AE-GAN model represents a significant advancement in crop disease detection, paving the way for a more resilient and food-secure future. © 2024 – IOS Press. All rights reserved.

关键词： Generative adversarial networks

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MERN Stack Web-Based education Management information Systems for Pacific Island Countries

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SN computer Science 2023年第1期4卷 70页

作者： Badru, Lawal Olarotimi Vasudevan, Vani Lingam, Govinda Ishwar Khan, M.G.M. The School of Information Technology Engineering Mathematics and Physics The University of South Pacific Suva Fiji Department of Computer Science and Engineering Nitte Meenakshi Institute of Technology Karnataka Bengaluru India The School of Pacific Arts Communication and Education The University of South Pacific Suva Fiji

Generation of quality data to aid planning, assessing the education performance, monitoring programs implementation and learning outcomes are the basic functions of the education Management information System (EMIS). This study examines the existing EMIS in the Pacific Island Countries PICs and proffers solution. A SWOT analysis on the selected PICs EMIS through the published technical reports and policy documents from government and donors’ between years 2000 and 2021, revealed that EMIS in PICs have not progressed beyond the stages of collecting demographic data and generating basic indicators. Fiji EMIS which has prospect of leading other PICs EMIS could only generate few indicators manually, and the findings indicate that these indicators are not being considered in decision making. To solve these defects, we proposed data-driven microservices architecture developed with MERN (MongoDB, ExpressJS, ReactJS, and NodeJS) stack on 13 NoSQL collections, tested with pseudonymised data from Fiji Ministry of education (consisting of 98.6% Learners, 100.2% Schools and 99.5% Teachers in post). Our solution renders dynamic visualized production-ready education data and 28 UNESCO standard indicators to guide decision making and this may serve as a model for PICs EMIS. © 2022, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.

关键词： education indicators Fiji Microservices architecture School management solution Web-based application

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IoT-Embedded Deep Learning Model for Real-Time Remote Health Monitoring and Early Identification of Diseases 3

IoT-Embedded Deep Learning Model for Real-Time Remote Health...

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3rd International Conference on Technological Advancements in Computational Sciences, ICTACS 2023

作者： Sharma, Meenakshi Latha, B. Gupta, Ankur Hasan, Dler Salih Rajan, C. Munagala, Madhu Inderprastha Engineering College Department of Electronics & Communication Engineering Ghaziabad India Ksr Institute for Engineering and Technology Electronics and Communication Engineering Tiruchengode India School of Computer Science Engineering and Technology Bennett University Greater Noida India University of Salahaddin-Erbil Department of Computer Science and Information Technology Erbil Iraq K.S. Rangasamy College of Technology Department of Information Technology Tiruchengode India Koneru Lakshmaiah Education Foundation Ap Department of English Vaddeswaram India

ISBN: (纸本)9798350342338

The present research introduces a novel method for developing early cardiac disease diagnostics that blends Internet of Things (IoT) technologies with potent deep learning algorithms. The research demonstrates how a Convolutional Neural Network (CNN) model may effectively use sensor data to precisely detect and forecast a variety of cardiac issues. The basis of this study is a well curated dataset derived from a cohort of 30 patients monitored over a 10-day period. When it comes to identifying cardiac metrics like the Normal Beat, Premature Beat, Early Ventricular Contraction, Unexplained Beat, and Ventricular Fusion, the CNN model exhibits astounding accuracy, precision, recall, and F1 scores. The results show the possibility for early treatments to avoid significant health problems, changing healthcare from reactive to proactive and customised methods. The blending of cutting-edge technology and medical knowledge creates new opportunities for early diagnosis and intervention, revolutionising patient care. This study lays the path for a future where rapid and accurate heart health evaluations become essential to overall wellbeing by using the capabilities of the CNN model and IoT devices. Wide-ranging effects of this effort include increased life quality and the coming of a new generation with greater healthcare. © 2023 IEEE.

关键词： CNN Deep learning health monitoring IoT Performance evaluation

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Integrated Multi-Energy Scheduling Strategy for Smart Community based on Twin Delayed Deep Deterministic Policy Gradient

Integrated Multi-Energy Scheduling Strategy for Smart Commun...

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2023 International Conference on Smart Electrical Grid and Renewable Energy, SEGRE 2023

作者： Wen, Mengfei Wang, Yujie Chen, Jie Zhu, Ren Changsha Preschool Education College School of Information Technology Changsha China Central South University School of Computer Science and Engineering Changsha China Changsha Preschool Education College School of Preeducation Changsha China

ISBN: (纸本)9798350323269

In the realm of energy internet, the pivotal roles of community integrated multi-energy systems cannot be overstated, as it is instrumental in mitigating energy expenses, augmenting the utilization of renewable energy sources, and upholding equilibrium in supply and demand dynamics. A multi-energy scheduling strategy based on improved twin delayed deep deterministic policy gradient algorithm is proposed in this study, which can effectively decrease the energy cost of the community integrated multi-energy system energy. Firstly, the groundwork entails the establishment of a comprehensive model encompassing diverse energy conversion and storage devices deployed within the community. Moreover, the purpose of the scheduling strategy is to achieve the minimization of the energy cost. The cost minimization problem is mathematically formalized as a Markov decision process. Then the improved TD3 method is employed to address the optimization problem, where convolutional neural networks and attention mechanism are introduced to extract temporal features and improve the exploration efficiency of TD3. Finally, the simulation results are empirically validate the efficiency of the proposed community integrated multi-energy scheduling strategy, specifically in reducing the system's energy costs and ensuring equilibrium between energy supply and demand. © 2023 IEEE.

关键词： Internet of Energy

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A Quaternion Two-Stream R-CNN Network for Pixel-Level Color Image Splicing Localization

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Chinese Journal of Electronics 2021年第6期30卷 1069-1079页

作者： CHEN Beijing JU Xingwang GAO Ye WANG Jinwei Engineering Research Center of Digital Forensics Ministry of Education Nanjing University of Information Science &Technology School of Computer Nanjing University of Information Science & Technology Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology (CICAEET) Nanjing University of Information Science & Technology

Recently, Zhou et al. designed a twostream faster Region-Convolutional neural networks(RCNN) model RGB-N for color image splicing localization in CVPR2018. However, the RGB-N locates spliced regions only at block-level and ignores the entirety and inherent correlation of three channels. Therefore, an improved quaternion two-stream R-CNN model is proposed to solve these drawbacks: a mask branch combining fully convolutional network and condition random field is added for locating spliced regions at pixel-level; quaternion representation of color images is used to process color spliced images in a holistical way. In addition, feature pyramid network based on quaternion residual network is considered to extract multi-scale features for color spliced images; attention region proposal network is combined with attention mechanism and is designed to pay more attention to the spliced regions; a high-pass filter designed for image splicing detection specifically is adopted to replace steganalysis rich model filter in the RGB-N to obtain noise input for the noise stream. Experimental results on a new synthetic dataset and three standard forgery datasets demonstrate that the proposed method is superior to the existing methods in the abilities of localization, generalization, and robustness.

关键词： quaternion residual network image classification feature extraction image splicing detection convolutional neural nets RGB-N block-level color spliced images steganalysis rich model filter spliced regions noise stream feature pyramid network image segmentation attention region proposal network quaternion two-stream R-CNN network improved quaternion two-stream R-CNN model two-stream faster region-convolutional neural networks image colour analysis quaternion representation fully convolutional network object detection pixel-level color image splicing localization

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Enhancing COVID-19 Diagnosis through Deep Learning on a coupled Edge-Cloud Computing technique using CT Scan Images

Enhancing COVID-19 Diagnosis through Deep Learning on a coup...

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2024 International Conference on Smart Devices, ICSD 2024

作者： Gangadharan, S.M.P. Mohammad, Faz Sunil Reddy, G. Kumar, Kaushal Singh, Jagendra Haridass, R. Liverpool John Moores University Liverpool United Kingdom KCC Institute of Legal and Higher Education Department of Information Technology Greater Noida India SR University School of Computer Science & Artificial Intelligence Warangal India Raj Kumar Goel Institute of Technology Department of Computer Science Engineering Ghaziabad India Bennett University School of Computer Science Engineering & Technology Greater Noida India Karpagam College of Engineering Department of Mechanical Engineering Coimbatore India

ISBN: (纸本)9798350394412

The present research looks at how deep learning models may improve the accuracy of COVID-19 diagnosis using CT scan pictures. The project aims to improve the detection of COVID-19-specific patterns and features by using the capabilities of the VGG19, DenseNet201, and VGG16 models. The training and assessment were based on a collection of 2,521 CT scan images from patients with cardiac problems as well as those who tested positive for COVID-19. To assess the models' diagnostic abilities, performance metrics such as accuracy, precision, recall, and specificity are used. DenseNet201 regularly beats the competition across all key measures, revealing its potential for accurate COVID-19 diagnosis. The suggested edge-cloud architecture further integrates the models into real clinical scenarios. Given the important role of false negatives and false positives in medical diagnostics, the results highlight the need of looking at more than just accuracy. These models are useful diagnostic tools, but they should not be used in place of hands-on medical treatment. This study adds to the increasing body of knowledge at the interface of artificial intelligence and healthcare by providing insights into how deep learning models may be used to improve COVID-19 diagnostic accuracy and perhaps modify medical practices. To fully realize the promise of these findings in enhancing patient care and healthcare systems, more cooperation between machine learning researchers and healthcare practitioners is required. © 2024 IEEE.

关键词： COVID-19

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