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检索条件"任意字段=5th International Conference on Informational Processing in Sensor Networks"

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A Smart Vehicle Monitoring System Using Real-Time Data processing and Advanced sensor Integration 5

A Smart Vehicle Monitoring System Using Real-Time Data Proce...

引用

5th international conference on Electronics and Sustainable Communication Systems, ICESC 2024

作者： Sathish, M. Prabha Devi, D. Kanchana, R. Pranesh, R.K. Kumaraguru College of Technology Department of Computer Science and Engineering Tamil Nadu Coimbatore India Department of Computer Science and Engineering Tamil Nadu Sathyamangalam India Sphoorthy Engineering College Department of Artificial Intelligence and Machine Learning Telangana Hyderabad India Kumaraguru College of Technology Department of Information Science and Engineering Tamil Nadu Coimbatore India

ISBN: (数字)9798350379945

ISBN: (纸本)9798350379945

the 'Smart Vehicle Monitoring System' presents a comprehensive solution for enhancing road safety and user authentication in the realm of modern transportation. this system integrates advanced technologies to monitor crucial aspects of vehicle usage, ensuring both the rider's safety and the vehicle's safety. the core functionalities encompass monitoring the usage of safety gear such as helmets and detecting alcohol consumption. User authentication is achieved through a combination of fingerprint recognition and Radio-frequency identification (RFID). this multi-layered approach ensures that only authorized individuals can operate the vehicle. the proposed system further extends its capabilities to monitor essential vehicle parameters, including tire pressure and fuel levels. In the event of an accident, a vibration sensor is employed to swiftly detect impact, triggering an alert system through GSM communication. this timely notification ensures prompt assistance and emergency response. Wireless communication plays a vital role in the system, with the helmet connected to an Arduino Mega via the ESP protocol. this acilitates real-time data exchange, allowing for seamless integration of various components. To enhance user interface and information dissemination, the system utilizes an LCD to present real-time status updates. Additionally, the integration of a GPS module enables the sharing of live location data, contributing to improved navigation and overall safety. In conclusion, the Smart Vehicle Monitoring System not only prioritizes the safety of the rider but also addresses authentication concerns and provides a holistic approach to vehicle monitoring. through the amalgamation of cutting-edge technologies, this system aims to redefine the standards of safety and security in contemporary transportation. © 2024 IEEE.

关键词： Radio frequency identification (RFID)

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Bengali Fake Review Detection using Semi-supervised Generative Adversarial networks 5

Bengali Fake Review Detection using Semi-supervised Generati...

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5th international conference on Natural Language processing, ICNLP 2023

作者： Shawon, Md. Tanvir Rouf Shahariar, G.M. Shah, Faisal Muhammad Alam, Mohammad Shafiul Mahbub, Md. Shahriar Ahsanullah Unviersity of Science and Technology Dhaka Bangladesh

ISBN: (纸本)9798350302219

this paper investigates the potential of semi-supervised Generative Adversarial networks (GANs) to fine-tune pretrained language models in order to classify Bengali fake reviews from real reviews with a few annotated data. With the rise of social media and e-commerce, the ability to detect fake or deceptive reviews is becoming increasingly important in order to protect consumers from being misled by false information. Any machine learning model will have trouble identifying a fake review, especially for a low resource language like Bengali. We have demonstrated that the proposed semi-supervised GAN-LM architecture (generative adversarial network on top of a pretrained language model) is a viable solution in classifying Bengali fake reviews as the experimental results suggest that even with only 1024 annotated samples, BanglaBERT with semi-supervised GAN (SSGAN) achieved an accuracy of 83.59% and a f1-score of 84.89% outperforming other pretrained language models - BanglaBERT generator, Bangla BERT Base and BanglaElectra by almost 3%, 4% and 10% respectively in terms of accuracy. the experiments were conducted on a manually labeled food review dataset consisting of total 6014 real and fake reviews collected from various social media groups. Researchers that are experiencing difficulty recognizing not just fake reviews but other classification issues owing to a lack of labeled data may find a solution in our proposed methodology. © 2023 IEEE.

关键词： Generative adversarial networks

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Bayesian-based Security Distributed Estimation 2022

Bayesian-based Security Distributed Estimation

引用

5th international conference on Image and Graphics processing, ICIGP 2022

作者： Wang, Tiantian Chen, Feng Lai, Yingcheng Electronic and Information Engineering Southwest University Chongqing China Artificial Intelligence Southwest University Chongqing China

ISBN: (纸本)9781450395465

In recent years, the distributed estimation of wireless sensor networks has been widely studied, but there are often security threats in practical applications. For example, attackers damage data information in different ways and reduce the performance of network estimation. In order to solve this problem, this paper proposes an algorithm framework of attack detection based on distributed LMS. the algorithm classifies the states of adjacent nodes, and then realizes attack detection through Bayesian criterion. An adaptive detection threshold is proposed to improve the detection performance. the reliable information of the last time is used to replace the detected lossy information and fuse to ensure the performance of the algorithm. Finally, the simulation results of several algorithms under different attack models are given to prove the effectiveness of the algorithm. © 2022 ACM.

关键词： Wireless sensor networks

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Optimized design and implementation of sensor for portable external insulation equipment fouling degree measurement device 5

Optimized design and implementation of sensor for portable e...

引用

5th international conference on Telecommunications, Optics, and Computer Science, TOCS 2024

作者： He, Jun Gou, Xiang Zhu, Jun Li, Wenhao Huang, Shansong Zeng, Tingting Yang, Mei Zhou, Shichao Zhong, Shuangshuang Zeng, Decai Sichuan Shuneng Electric Power Co. Ltd. High Tech Branch Chengdu610000 China Sichuan Shuneng Dianke Energy Technology Co. Ltd. Chengdu610000 China

ISBN: (纸本)9781510691667

this study carried out a series of optimization design and implementation for the existing portable external insulated equipment dirtiness measurement device for the deficiencies in the sensor design. By adopting new conductive materials, array layout, differential amplification technology, multilevel filtering technology and wireless transmission technology, the measurement accuracy, stability and anti-interference ability of the sensor are significantly improved. Experimental verification and field test results show that the new sensor has obvious advantages in contact resistance stability, response time and signal processing capability, effectively reducing the operation and maintenance costs of power systems, extending the service life of equipment, and generating significant economic and social benefits. this study provides strong support for the measurement of the degree of fouling of the external insulation equipment of the power system, and has high practical value and promotion prospects. © 2025 SPIE.

关键词： Telephone interference

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Forecasting Naturally Occurring Forest Fires using AI and Machine Learning in Wireless sensor networks 5

Forecasting Naturally Occurring Forest Fires using AI and Ma...

引用

5th international conference on Inventive Research in Computing Applications, ICIRCA 2023

作者： Akila, T. Menaka, M. Vasanth, P. Victoria Jancee, B. Mahendra College of Engineering Department of Information Technology Tamil Nadu Salem India Kingston Engineering College Department of Information Technology Tamil Nadu Vellore India Sri Venkateswara College of Engineering Department of Electronics and Communication Engineering Tamil Nadu Chennai India St.Joseph's College of Engineering Department of Electronics and Communication Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350321425

Wildfires are recurrent and sometimes devastating in regions of subtropical deciduous forests. Protecting forest areas are essential to ensure the health and vitality of the ecosystem. the goal of this work is to detect forest fires based on Artificial Intelligence (AI) and Machine Learning (ML) algorithms. the information obtained from the images combined with the forest spatial data will be used to make the detection. this article introduces an AI and ML based algorithm to forecast the forest fire in the wireless sensor network. the Convolutional Neural Network (CNN) algorithm is a better image classification algorithm for detecting fire. the experimental results illustrate that the proposed approach enhances the accuracy of fire detection. Furthermore, the proposed system using the CNN algorithm is compared to several ML algorithms such as Support Vector Machine (SVM), Decision Tree (DT), Random Forest (RF), and K-Nearest Neighbor (KNN) algorithms. From the results, it is observed that the proposed algorithm minimizes the false positive and false negative ratio, and provides an accuracy of 97.35%. © 2023 IEEE.

关键词： Fires

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Gray Wolf Optimization Algorithm to Enhance Data Aggregation in Wireless sensor Network 5

Gray Wolf Optimization Algorithm to Enhance Data Aggregation...

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5th international conference on Inventive Research in Computing Applications, ICIRCA 2023

作者： Loganathan, Sathyapriya Victoria Jancee, B. Senkamalavalli, R. Subbu Lakshmi, T.C. Srm Institute of Science and Technology Department of Computing Technologies Tamil Nadu Kattankulathur India St.Joseph's College of Engineering Department of Electronics and Communication Engineering Tamil Nadu Chennai India East Point College of Engineering and Technology Department of Computer Science Engineering Karnataka Bangalore India Francis Xavier Engineering College Department of Information Technology Tamil Nadu Tirunelveli India

ISBN: (纸本)9798350321425

Wireless sensor networks (WSNs) have become increasingly popular as distributed systems using small, lightweight nodes with limited resources for monitoring environmental conditions or measuring physical parameters. To minimize energy utilization, data aggregation can be employed to reduce the data quantity forward to the base station (BS). To solve these issues, a Gray wolf optimization algorithm (GWOA) is proposed to enhance data aggregation in WSN. Increasing the network lifetime, node reliability, and throughput with the selection of best cluster heads (CHs) utilizing the Gray Wolf Optimization (GWO) algorithm is the major objective of this research. this approach selects the CH by applying the GWO algorithm established on the node remaining energy, available bandwidth, and distance. the GWO-based clustering protocol minimizes dead nodes and energy utilization and raises the clustering rounds and throughput. the simulation results demonstrate that the increases the node reliability and network throughput. © 2023 IEEE.

关键词： Energy efficiency

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Exploring the Potential of Deep Learning Algorithms in Medical Image processing: A Comprehensive Analysis 5th

Exploring the Potential of Deep Learning Algorithms in Medic...

引用

5th international conference on Communication and Intelligent Systems, ICCIS 2023

作者： Pal, Ganesh Prasad Pal, Raju Department of Computer Science and Engineering Jaypee Institute of Information Technology Noida India Department of Computer Science and Engineering School of Information and Communication Technology Gautam Buddha University Greater Noida India

ISBN: (纸本)9789819720781

In this study, we comprehensively examine the potential of deep learning algorithms in the domain of medical image processing. through a systematic analysis of existing literature, we explore the applications, methodologies, and outcomes of these algorithms across diverse medical specialties. Our analysis underscores the transformative impact of convolutional neural networks and transfer learning techniques in enhancing diagnostic accuracy, image segmentation, and disease detection. We discuss challenges such as data variability, ethics, and interpretability while emphasizing the importance of interdisciplinary collaboration. By synthesizing findings and identifying future directions, we highlight the promising role of deep learning in revolutionizing healthcare diagnostics and treatment planning. After conducting a thorough analysis of various literature and empirical studies, we have evaluated the capabilities and drawbacks of deep learning models in managing different medical imaging modalities such as X-rays, MRIs, and CT scans. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： COVID-19

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Multi-Class ECG Signal processing and Classification using CWT based on various Deep Neural networks 5

Multi-Class ECG Signal Processing and Classification using C...

引用

5th IEEE international conference on Electrical, Computer and Communication Technologies, ICECCT 2023

作者： Kumar, Subramanyam Shashi Ramachandran, Prakash School of Electronics Engineering Vellore Institute of Technology University Vellore Tamil Nadu India

ISBN: (纸本)9781665493604

the basic functioning of heart can be read through Electrocardiogram (ECG) Signal, this signal gives an idea whether the functioning of heart is normal or abnormal and type abnormality can also be identified, which helps to diagnose the patients in time. this work investigates a deep-learning model using 2DCNN to classify various category of ECG signal. this proposed CNN model is trained and tested to classify three different classes of heart arrhythmia such as cardiac arrhythmia (ARR), congestive heart failure (CHF) and normal sinus rhythms (NSR). the time domain ECG signal is preprocessed and further it is transformed in to time-frequency scalogram by utilizing continuous wavelet transform (CWT), these scalogram is remodeled and saved as RGB images with necessary dimensions. Later these converted RGB images are fed to the input of various 2DCNN models such as alexnet, vgg16, squeezenet and googlenet to classify arrhythmia type. ECG Recordings from MIT BIH database were chosen and used for training and testing dataset. the performance of proposed scheme is evaluated on various CNN networks, a reasonable classification accuracy of 99.33 % was acheived by alex net. © 2023 IEEE.

关键词： Electrocardiograms

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Deep Learning based Channel Estimation for Full-Duplex Backscatter Communication Systems 5

Deep Learning based Channel Estimation for Full-Duplex Backs...

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5th international conference on Artificial Intelligence in Information and Communication (ICAIIC)

作者： Jung, Chae Yoon Kang, Jae Mo Kim, Dong In Sungkyunkwan Univ SKKU Dept Elect & Comp Engn Dept Superintelligence Engn Suwon South Korea Kyungpook Natl Univ Dept Artificial Intelligence Daegu South Korea

ISBN: (纸本)9781665456456

A novel deep learning (DL) based channel estimation method is proposed for full-duplex backscatter communication systems to realize the wireless-powered sensor networks (WPSN) for internet of things (IoT). We aim to minimize the power consumption at a sensor node by reflecting the supplied power signal from an access point (AP), which is called backscatter communication. Moreover, by adopting the frequency-shifted modulation technique during backscatter transmission, full-duplex communication is performed between the AP and the sensor node. However, this incurs a problem that the uplink and downlink channels are cascaded, which results in degrading the performance of beamforming. In order to overcome this problem, we propose a novel channel estimation method that extracts separate uplink and downlink channels from the cascaded channels. We formulate the problem for joint channel estimation and pilot optimization, and then design the DL based channel estimator, which is composed of feedforward neural network(FNN) and convolutional neural network(CNN), for compensating non-linearity and non-convexity. Finally, we analyze the performance of the proposed DL based channel estimator compared to the conventional channel estimator.

关键词： Beamforming channel estimation (CE) deep learning (DL) full-duplex backscatter communication internet of things (IoT) wireless-powered sensor networks (WPSN)

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Fifth international conference on Control, Robotics, and Intelligent System, CCRIS 2024

Fifth International Conference on Control, Robotics, and Int...

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5th international conference on Control, Robotics, and Intelligent System, CCRIS 2024

ISBN: (纸本)9781510685864

the proceedings contain 39 papers. the topics discussed include: adaptive optimal output regulation: a parametric Lyapunov equation approach;evaluation on parameter-efficient continual instruction tuning of large language models;research on enhancing forecast accuracy of gold futures using LSTM neural networks across various time periods;FedGKD: personalized federated learning through grouping and distillation;the development of a question-answering system for learning article using natural language processing;KG-CQAM: knowledge graph and mind mapping-based complex question answering for large language models;and PCEM-SQL: enhancing text-to-SQL capabilities by large language models and prompt engineering with self-consistency and self-evaluation mechanism.

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