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2024 IEEE International Conference on Recent Advances in science and engineering technology, ICRASET 2024

作者： Usha, S.M. Kumar, D. Mahesh Kavitha, M. Pavithra, G.S. Mallikarjunaswamy, S. Sharmila, N. Rekha, V. Umashankar, M.L. Jss Academy of Technical Eduction Dept of Electronics and Communication Engineering Bengaluru India Jss Academy of Technical Eduction Dept of Computer Science and Engineering Bengaluru India Jss Science and Technology University Dept of Electronics and Electrical Engineering Mysuru India Department of Computer Science and Engineering Bangalore India Wipro Ltd Wipro Engineering Edge Department Doddakannelli Bengaluru India

ISBN: (纸本)9798350388602

The integration of renewable energy sources and distributed power generation in smart grids has significantly increased the complexity of fault detection and prediction. Traditional methods like Supervisory Control and Data Acquisition (SCADA) and Rule-Based Detection (RBD) face challenges in processing the vast amounts of real-time data generated by modern grids, often leading to delayed responses and low fault detection accuracy. Furthermore, conventional techniques such as Fault Tree Analysis (FTA) are rule-dependent and lack adaptability to dynamic grid conditions, resulting in inefficient fault prediction and grid stability issues. To overcome these challenges, this paper introduces an Intelligent Fault Detection and Prediction Model (IFDPM), utilizing supervised learning techniques to enhance the detection and prediction of faults in smart grids. The IFDPM leverages machine learning algorithms to process real-time grid data, improving both the accuracy and speed of fault identification. By continuously adapting to the grid's operational environment, the proposed model predicts faults before they occur, enabling timely preventive actions. Through extensive simulations and testing, the IFDPM has demonstrated a 0.25% improvement in fault detection accuracy and a 0.20% reduction in response time compared to conventional methods. This approach provides a more reliable and efficient framework for managing fault events in next-generation smart grids. © 2024 IEEE.

关键词： Prediction models

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Generalization-Enhanced Few-Shot Object Detection in Remote Sensing

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IEEE Transactions on Circuits and Systems for Video technology 2025年第6期35卷 5445-5460页

作者： Lin, Hui Li, Nan Yao, Pengjuan Dong, Kexin Guo, Yuhan Hong, Danfeng Zhang, Ying Wen, Congcong China Academy of Electronics and Information Technology Beijing100846 China China Meteorological Administration National Satellite Meteorological Center Innovation Center for FengYun Meteorological Satellite Beijing100081 China Tsinghua University State Key Laboratory of Hydroscience and Engineering Department of Hydraulic Engineering Beijing100084 China Aerospace Information Research Institute Chinese Academy of Sciences Beijing100094 China University of Chinese Academy of Sciences School of Electronic Electrical and Communication Engineering Beijing100049 China University of Science and Technology Beijing School of Automation and Electrical Engineering Beijing100083 China New York University Abu Dhabi Department of Electrical and Computer Engineering Abu Dhabi United Arab Emirates University of Science and Technology of China School of Cyber Science and Technology Anhui Hefei230052 China

Object detection is a fundamental task in computer vision that involves accurately locating and classifying objects within images or video frames. In remote sensing, this task is particularly challenging due to the high resolution, multi-scale features, and diverse ground object characteristics inherent in satellite and UAV imagery. These challenges necessitate more advanced approaches for effective object detection in such environments. While deep learning methods have achieved remarkable success in remote sensing object detection, they typically rely on large amounts of labeled data. Acquiring sufficient labeled data, particularly for novel or rare objects, is both challenging and time-consuming in remote sensing scenarios, limiting the generalization capabilities of existing models. To address these challenges, few-shot learning (FSL) has emerged as a promising approach, aiming to enable models to learn new classes from limited labeled examples. Building on this concept, few-shot object detection (FSOD) specifically targets object detection challenges in data-limited conditions. However, the generalization capability of FSOD models, particularly in remote sensing, is often constrained by the complex and diverse characteristics of the objects present in such environments. In this paper, we propose the Generalization-Enhanced Few-Shot Object Detection (GE-FSOD) model to improve the generalization capability in remote sensing FSOD tasks. Our model introduces three key innovations: the Cross-Level Fusion Pyramid Attention Network (CFPAN) for enhanced multi-scale feature representation, the Multi-Stage Refinement Region Proposal Network (MRRPN) for more accurate region proposals, and the Generalized Classification Loss (GCL) for improved classification performance in few-shot scenarios. GE-FSOD demonstrates superior robustness and accuracy in remote sensing FSOD tasks through these enhancements. Extensive experiments on the DIOR and NWPU VHR-10 datasets show that our

关键词： Labeled data

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A Comparative Study on Facial Emotion Recognition using Deep Neural Networks 15

A Comparative Study on Facial Emotion Recognition using Deep...

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15th International Conference on Advances in Computing, Control, and Telecommunication Technologies, ACT 2024

作者： Asha, S. Sundarrajan, R. Department of Computer Science and Engineering Kalasalingam Academy of Research and Education Srivilliputtur Tamilnadu India Department of Information Technology Kalasalingam Academy of Research and Education Srivilliputtur Tamilnadu India

ISBN: (纸本)9798331300579

Emotions are strongly associated with individuals' mood and *** the field of Human computer Interaction, human face plays a very vital *** to studies made by researchers' majority of the information conveyed through facial expressions than verbal *** day-to-day life, human expresses different types of feelings such as Happiness, Anger, Sadness, Fear, Disgust and Surprise which is considered as "Universal Emotions".It has always been difficult for computers to recognize human ***, a substantial effort was made by the researchers to build the Facial Emotion Recognition system and which was considered as the best tool for recognizing emotions through facial *** this paper, a detailed study on different methods that can be used in facial emotion recognition is *** this study, the literature is collected from various reputable research *** survey paper is based on the current approaches to face detection and feature extraction techniques for FER and also presented the real-time applications. © Grenze Scientific Society, 2024.

关键词： Convolutional neural networks

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Securing Electronics Gadgets using Blockchain based Framework 2

Securing Electronics Gadgets using Blockchain based Framewor...

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2nd IEEE International Conference on Emerging Research in Computational science, ICERCS 2024

作者： Prakash, P. N. Senthil Lanitha, B. Sundaram, Karthic Vellore Institute of Technology School of Computer Science and Engineering Chennai India Karpagam Academy of Higher Education Department of Artificial Intelligence and Data Science Coimbatore India KPR Institute of Engineering and Technology Department of Information Technology Coimbatore India

ISBN: (纸本)9798331534950

The usage of electronic gadgets has grown dramatically in the current digital era. Devices like mobile phones and smart watches are unavoidable and has ingrained themselves into every person's daily routine. Almost everyone owns at least one portable electronic device and these devices are usually expensive. Due to this widespread use, there is also an increase in the number of lost or stolen cases reported. In a recent study, it has been revealed that the number of devices lost is mostly because of misplacing in public places like home, office, public transport. Such incidents will not only cause financial loss but it may also lead to data breaches stored in the portable devices. Hence there is a need for a secure eco-system which helps to identify the genuine owners of electronic devices. A blockchain-based framework is deployed on an Ethereum-based network and adopts the Proof of Stake (PoS) mechanism to protecting gadgets. Further, it is designed using the smart contract defined using solidity programming language. The system enables the owner's details to be recorded upon purchase of a device. The complete specification details of the devices along with owner details is contained in a block and then appended to network. Later on, if the owner wants to resale the device, then the transfer of ownership information is stored in a block and added to the network. The proposed system provides an appropriate interface to check the genuine owner. The proposed framework has been implemented using solidity language, Remix IDE and Sepolia testnet. Obtained results highlights the suitability of the method and its performance in terms of gas consumption and execution latency. © 2024 IEEE.

关键词： Ethereum

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Optimizing Diagnostic Accuracy in Ultrasound Imaging Through Cloud-Enabled Deep Learning Techniques 4

Optimizing Diagnostic Accuracy in Ultrasound Imaging Through...

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4th International Conference on Soft Computing for Security Applications, ICSCSA 2024

作者： Kalpana, V. Girija, M. Tharageswari, K. Yobu, D. Revathi, J. Aruna, R. Department of Computer Science and Engineering Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Chennai India Jeppiaar University School of Engineering and Technology Chennai India Department of Computer Science and Engineering Karpagam Academy of Higher Education Coimbatore India Computer Science and Engineering Cyber Security J.J College of Engineering and Technology Tricy India Department of Mathematics Kongu Engineering College Erode India Department of Computer Science and Engineering School of Computing Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Chennai India

ISBN: (纸本)9798331515720

This research aims to study predicting and optimizing the diagnostic accuracy by cloud-enabled deep learning techniques in ultrasound imaging for kidney disease diagnosis. The main findings are that CNN gives 92% accurate outcomes in terms of sensitivity, precision, specificity, AUC-ROC, and other terms used for predictive outcomes in the current research. The other outcomes are 88% accuracy and 91% specificity in predicting kidney diseases by using the RNN model. The other two models used for learning are transfer learning and federated learning which also provide good outcomes which are 94% accurate and 91% accurately in diagnosing with the above-said models. These learning approaches optimize or increase the accuracy rates such as 93% by the transfer learning approach and 90% accuracy outcomes by the federated learning mechanism. These above-given models have been taken based on advanced technologies that are used to predict or forecast the diseases through learning processes. The success of these learning mechanisms is explained by the reliable processes used in the learning of the models that are possible by the neural network used in the learning mechanisms. The high accuracy rates of the models show the successfulness of such clinics, who are in rural areas and are unable to meet their necessary outcome-oriented goals, then measures can be taken for running tele-clinics using the above models of learning techniques to diagnose the diseases at the correct time by 'n' number of people in rural areas. Mine is one of them working as a health worker for which measures need to be taken for finding an accurate solution that operates on cloud-enabled deep learning. This model help run family life and be able to diagnose certain conditions at desired rural clinics out of the above learning models. These tools of learning are studied on cloud-based deep learning techniques and performed well in diagnosing kidney diseases on rural tele-medicine and these learning techni

关键词： Ultrasonic imaging

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SITSL: Strategic Image Tag Recommendation Framework Encompassing Semantics Oriented Learning and Reasoning 2

SITSL: Strategic Image Tag Recommendation Framework Encompas...

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2nd IEEE International Conference for Women in Innovation, technology and Entrepreneurship, ICWITE 2024

作者： Kumari, Rupa Deepak, Gerard National Institute of Technology Department of Electronics and Communication Engineering Meghalaya India Manipal Institute of Technology Bengaluru Manipal Academy of Higher Education Department of Computer Science and Engineering Manipal India

ISBN: (纸本)9798350383287

This paper introduces the concept of a strategic structure for Web Image Tagging that is oriented toward semantics and knowledge centric. It encompasses strategies like LDA (Latent Dirichlet Allocation) based topic modelling and ontology generation through the dataset terms and further help in the metadata generation. The generated metadata is classified using autoencoders classifier to yield the classified instances. In the proposed model, the classifier for logistics is applied in the dataset of web images that integrates features through Lin Similarity from the ontologies generated in the perfect global dataset. The LDA also enhances the topics and contextualizes that for classified instances of the dataset. The normalized compression distance with lance William index helps in semantics similarity computation for semantics measure-based reasoning and learning. Shuffle frog algorithm helps in Optimization helps in refining the initial solutions into a much more optimal solution state to produce in the top-tier images. So, to produce in the top-tier tags for the images and overall precision of dash with a measure of dash and an FDR of dash has been achieved by the proposed framework which is the best-in-class model for web image tagging. © 2024 IEEE.

关键词： Semantics

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Effect of temperature on heavy ion-induced single event transient on 16-nm FinFET inverter chains

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Chinese Physics B 2023年第4期32卷 504-510页

作者：蔡莉池雅庆叶兵刘郁竹贺泽王海滨孙乾孙瑞琪高帅胡培培闫晓宇李宗臻刘杰 Institute of Modern Physics Chinese Academy of SciencesLanzhou 730000China University of Chinese Academy of Sciences Beijing 100049China College of Computer Science National University of Defense TechnologyChangsha 410073China College of IoT Engineering Hohai UniversityChangzhou 213022China

The variations of single event transient(SET)pulse width of high-LET heavy ion irradiation in 16-nm-thick bulk silicon fin field-effect transistor(Fin FET)inverter chains with different driven strengths are measured at different ***-dimensional(3D)technology computer-aided design simulations are carried out to study the SET pulse width and saturation current varying with *** and simulation results indicate that the increase in temperature will enhance the parasitic bipolar effect of bulk Fin FET technology,resulting in the increase of SET pulse *** the other hand,the increase of inverter driven strength will change the layout topology,which has a complex influence on the SET temperature effects of Fin FET inverter *** experimental and simulation results show that the device with the strongest driven strength has the least dependence on temperature.

关键词： heavy ion single event effect single event transient Fin FET inverter chain

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Methodology for Determining Proper Tyre Pressure 15

Methodology for Determining Proper Tyre Pressure

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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Selvaraj, S.Kailasam Vineetha, V. Vinod, M. Vardhan, M.Vishnu Kumar, K.Vijay Reddy, N.Vinay Kumar Kalasalingam Academy of Research and Education Department of Information Technology Tamilnadu Virudhnagar India Kalasalingam Academy of Research and Education Department of Computer Science and Engineering Tamilnadu Virudhnagar India

ISBN: (纸本)9798350370249

This paper presents the design & implementation of a Tyre pressure monitor system (TPMS) utilizing a BMP280 sensor for automotive applications. The TPMS is composed of a transmitter unit installed in tyre and receiver unit mounted inside vehicle. The transmitter end consists of a BMP280 sensor, a microcontroller, and control buttons. It continuously measures the tyre pressure and temperature, transmitting the data wirelessly to the receiving unit. The receiver unit comprises a microcontroller, a display screen, LED indicators, and a buzzer for alerts. Upon receiving data from the transmitter, the receiver displays tyre pressure and temperature readings in display. Experimental results demonstrate the functionality, accuracy, and reliability of the proposed TPMS, providing timely alerts to drivers, thereby enhancing vehicle safety and performance. © 2024 IEEE.

关键词： Microcontrollers

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iHofman: a predictive model integrating high-order and low-order features with weighted attention mechanisms for circRNA-miRNA interactions

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BMC Biology 2025年第1期23卷 1-19页

作者： Yu, Chang-Qing Jiang, Chen Wang, Lei You, Zhu-Hong Wang, Xin-Fei Wei, Meng-Meng Shi, Tai-Long Liang, Si-Zhe School of Information Engineering Xijing Univerity Xi’an 710123 China Guangxi Key Lab of Human-Machine Interaction and Intelligent Decision Guangxi Academy of Science Nanning 530007 China School of Computer Science and Technology China University of Mining and Technology Xuzhou 221116 China School of Computer Science Northwestern Polytechnical University Xi’an 710129 China College of Computer Science and Technology Jilin University Changchun 130012 China

Background: Increasing research indicates that the complex interactions between circular RNAs (circRNAs) and microRNAs (miRNAs) are critical for diagnosing and treating various human diseases. Consequently, accurately predicting potential circRNA-miRNA interactions (CMIs) has become increasingly important and urgent. Traditional biological experiments, however, are often labor-intensive, time-consuming, and prone to external influences. Results: To tackle this challenge, we present a novel model, iHofman, designed to predict CMIs by integrating high-order and low-order features with weighted attention mechanisms. Specifically, we first extract sequence and structural information representations using FastText and GraRep, respectively, and capture high-order and low-order features from sequence information representations using stacked autoencoders. Subsequently, weighted attention mechanisms are applied for feature fusion, focusing on the most relevant information. Finally, multi-layer perceptron is employed to accurately infer potential CMIs. In the fivefold cross-validation (CV) experiment on the baseline dataset, iHofman achieved an accuracy of 82.49% with an AUC of 0.9092. iHofman also demonstrates solid performance on other CMI datasets. In case studies, 26 of the top 30 CMIs with the highest iHofman predictive scores were confirmed in relevant literature. Conclusions: The above experimental results indicate that iHofman can effectively predict potential CMIs and has achieved outstanding performance compared with existing methods. It provides a reliable supplementary approach for subsequent biological wet experiments. © The Author(s) 2025.

关键词： CircRNA CircRNA-miRNA interaction FastText GraRep MiRNA Weighted attention mechanisms

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Research on Image Subject Accessing Model under Foggy Weather 14

Research on Image Subject Accessing Model under Foggy Weathe...

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14th International Conference on Electronics, Communications and Networks, CECNet 2024

作者： Li, Zhiyi Zhang, Songtao Meng, Fanlei Zhang, Lijuan School of Instrument Science and Electrical Engineering Jilin University Jilin China Computer Science and Technology Guohao Academy Tongji University Shanghai200092 China College of Internet of Things Engineering Wuxi University Wuxi214105 China School of Computer Science and Engineering Changchun University of Technology Changchun130012 China

ISBN: (纸本)9781643685632

Imaging results under outdoor conditions are easily affected by foggy weather which may produce blur, low contrast, intensity shift and other visual degradation effects. This paper proposed an accessing model of fog density based on discrete wavelet transform features of multi-channel mean subtracted contrast normalized coefficients. Besides, corner feature intensity is utilized to extract highlevel features of foggy images. The accessing model used support vector regression to predict fog density with the help of a fog density accessing dataset collected from human eyes. The experimental results show that the subjective accessing method proposed in this paper has a high correlation with human eyes accessing results on the testing dataset, and is better than the general referenceless image spatial quality evaluator model. The accessing model can reflect the image quality as human eyes in foggy scenes. © 2024 The Authors.

关键词： Support vector regression

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