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检索条件"任意字段=2023 IEEE 3rd International Conference on Data Science and Computer Application, ICDSCA 2023"

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Dynamic data Rate Prediction Based on Congestion Severity in Wireless Body Area Networks 3

Dynamic Data Rate Prediction Based on Congestion Severity in...

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3rd international conference on Advances in Computing, Communication, Embedded and Secure Systems, ACCESS 2023

作者： Mekathoti, Vamsi Kiran Nithya, B. Subasri, S. Bhuvana Chandra, P. National Institue of Technology Department of Computer Science Trichy India

ISBN: (纸本)9798350346169

Wireless Body Area Network (WBAN) is a vital application of IoT and EDGE technologies, which enable remote patient monitoring by transmitting real-time health data to a hospital server. WBAN faces congestion as a severe threat due to its delay sensitivity, which leads to the loss of essential data. To address this issue, this paper proposes a Dynamic datarate Prediction Based on Congestion Severity (DPCS) in Wireless Body Area Networks which categorizes sensor nodes as normal or critical, based on the patient's health status. The data flow from these sensors is efficiently regulated with the aid of the critical function with four parameters such as buffer size, queue size, remaining energy of the node, and channel bandwidth. With these dynamic run time parameters, the proposed critical function yields four thresholds to predict the congestion severity. The proposed technique predicts an optimal data rate based on the node priority and the severity of the congestion, as opposed to having a constant data rate for all types of sensors. The proposed algorithm is simulated and its performance is compared with the existing algorithms in terms of throughput, packet loss ratio, remaining energy, and packet delivery ratio. © 2023 ieee.

关键词： Sensor nodes

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Vehicle Theft Detection Using Neural Pattern Recognition 3

Vehicle Theft Detection Using Neural Pattern Recognition

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3rd international conference on Pervasive Computing and Social Networking, ICPCSN 2023

作者： Jane Rubel Angelina, J. Subhashini, S.J. Kumar Rapuri, Anil Ravva, Vennela Rasala, Raghavendra Ray Adurgam, Sai Dcepak Kalasalingam Academy of Research and Education Dept of Computer Science and Engineering TamilNadu Krishnankoil India SRM Madurai College for Engineering and Technology Dept of Computer Science and Engineering TamilNadu Sivagangai India

ISBN: (纸本)9798350322842

Finding stolen cars is becoming increasingly important in many urban regions. An automated system for scanning license plates can recognize vehicle numbers without the need for human interaction. This work proposes a vehicle theft detection system based on neural pattern recognition, gaussian filter, and equilibrium optimization. The proposed architecture has significant speedup and higher accuracy rates. The proposed number plate recognition method has a maximum accuracy rate of 94% and an average reduction in the processing time of 32%. Pattern recognition is the process of detecting regularities and patterns in data using machine learning. These analogies may now be uncovered via statistical analysis, historical data, or machine-generated knowledge. © 2023 ieee.

关键词： Image segmentation

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Ubiquitous Computing Integration Models Enabling Wireless Networks of Sensors 3

Ubiquitous Computing Integration Models Enabling Wireless Ne...

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3rd international conference on Advance Computing and Innovative Technologies in Engineering, ICACITE 2023

作者： Gupta, Lovi Raj Khalid, Al Smadi Kumar, Ujjawal Mahadevuni, Sai Kumar Al-Chilibi, Hayder Alazzam, Malik Bader Lovely Professional University Phagwara India Jadara University Computer Science Jordan Jordan Faculty of Business Department of Business Intelligence Jordan Jordan Uttaranchal University Uttaranchal Institute of Technology India SR University School of Engineering Telangana Warangal India Al-Farahidi University Computer Science and IT Baghdad10021 Iraq Ajloun National University Information Technology College Jordan

ISBN: (纸本)9798350399264

Proposed system, pollution monitoring, the automobile industry, and sports are just a few of the application areas that have grown as a result of ubiquitous sensing and the distinctive features (Sensor systems). As the underlying significantly expanded the number of linked things with real-time communication and data computation, WSNs have grown in importance in recent years. However, owing to the scale and accessibility of IoT, building a complex challenge, and past methodologies established for Iot technologies cannot be implemented directly. In this paper, pairwise clusters models for Iot networks in the Iot paradigm are proposed: I a resource grouping model and (ii) a business clusters model where responsibilities are allocated to individual sensor nodes depending on how well they provide services. The end-to-end latency, and communication bandwidth balancing. © 2023 ieee.

关键词： Ubiquitous computing

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Improving YOLOv5n for lightweight ship target detection 3

Improving YOLOv5n for lightweight ship target detection

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3rd ieee international conference on computer Systems, ICCS 2023

作者： Zhao, Xuemeng Song, Yinglei Shi, Sanxia Li, Shunxin Jiangsu University of Science and Technology School of Automation Jiangsu China

ISBN: (纸本)9798350313666

A lightweight target detection algorithm based on YOLOv5n is proposed for the current problems of low accuracy rate and high hardware and software limitations of ship target detection for near-shore port and river monitoring. First, the neck network of YOLOv5n uses Ghost convolutional structure instead of ordinary convolution and bottleneck structure in the C3 module to reduce network complexity and computation. Second, the SA attention mechanism is introduced in the backbone network to strengthen the feature extraction capability. Finally, the SIoU loss is used as the localization loss to speed up the model convergence and improve the accuracy of the algorithm. The experimental results show that the improved model volume is reduced by 13.2% and the average accuracy is improved by 3.8% on average. Deploying the algorithm acceleration to the edge device NVIDIA Jetson nano, the inference time is as low as 38ms for a single image with 640×640 resolution, which basically meets the requirements of real-time ship detection. © 2023 ieee.

关键词： Convolution

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Automated Shopping Cart: Reducing Long Queues One Cart At A Time 3

Automated Shopping Cart: Reducing Long Queues One Cart At A ...

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3rd international conference on Smart data Intelligence, ICSMDI 2023

作者： Gorai, Kuntal Santosh, S.V.S.C. Skanda, S. Murugan A S, Vijay Prajwala, T.R. Pes University Department of Computer Science Bengaluru India

ISBN: (纸本)9781665464871

Everyone has experienced waiting in long queues to get their items billed at a shopping cart. With this product, the proposed research work intends to reduce the time spent by shoppers in queues. Our automated shopping cart can reduce the time and manual labor required for weighing and billing the items a user wishes to buy. This article has developed a shopping cart with weight sensors built into the cart to measure the load of the product, and an android application with deep learning models(MobileNetV2 and EfficientNetV2) to classify the required item. The models were trained on a custom-built dataset with 51 classes. The mobile phone camera is used to capture an image of the product. The models are used to classify this item. Once the item is classified, the weight of the item is obtained from the load cells. A database analysis is performed to obtain the price of the item. With these details, the bill is generated as soon as the user is done with shopping without waiting in long queues. © 2023 ieee.

关键词： Deep neural networks

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2023 3rd international conference on Electrical Engineering and Control science, IC2ECS 2023

2023 3rd International Conference on Electrical Engineering ...

引用

3rd international conference on Electrical Engineering and Control science, IC2ECS 2023

ISBN: (纸本)9798350382426

The proceedings contain 322 papers. The topics discussed include: electromagnetic design of 10kV high voltage induction motor based on Ansoft Maxwell;efficiency promotion strategy for three-level ANPC-DAB-based DC-DC converter with wide-range multi-objective optimization method;nonlinear data classification of power Internet of Things considering transient and steady state;analysis of early warning indicators for high voltage power cable tunnel fire based on experimental data;real-time evaluation model construction of GIS conductor thermal state based on PSO-BP neural network;research and application of CPS-based automatic circulation system for the entire silk production process;and transmission line relay protection fault-tolerant information sharing and joint analysis method.

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Blockchain-based Secure Storage System for Medical Image data 3

Blockchain-based Secure Storage System for Medical Image Dat...

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3rd ieee international conference on Electronic Communications, Internet of Things and Big data, ICEIB 2023

作者： Lin, Chu-Hsing Li, Sin-Ping Lin, Yu-Chiao Tsai, Chiao-Hsu Department of Computer Science Tunghai University Taichung Taiwan China Medical University Hospital Taichung Taiwan

ISBN: (纸本)9798350333862

A large number of patient images are generated every day in medical applications such as ultrasound, computer Tomography scans, X-Ray, and so on. Medical data is related to patient privacy and personal rights. Thus, the security and privacy of medical images become an important issue. In order to comply with the regulations of the Personal data Protection Act, medical images need to be protected under a secure method. Therefore, we proposed a secure storage method for chest X-Ray images from Kaggle based on blockchain technology. We developed a smart contract to control role-based access permissions and authenticate medical images. We performed a cryptographic operation on the X-Ray image after each medical examination and stored the image fingerprint in the blockchain. Artificial intelligence was used to identify pneumonia-related diseases. We compared the required X-Ray retrieval time with the conventional PACS systems. The experimental results showed that the overhead of the proposed scheme using blockchain was only about 5 %. © 2023 ieee.

关键词： Smart contract

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application and Improvement of LSTM in Power System Prediction

Application and Improvement of LSTM in Power System Predicti...

引用

data science and computer application (icdsca), ieee international conference on

作者： Rui Nie Hongyi Li College of Software Engineering Beihang University Beijing China School of Mathematical Science Beihang University Beijing China

In previous power system predictions, the accuracy of predicting extreme electricity consumption peaks was often insufficient. This study aims to explore the differences in power data prediction among three models: BiLSTM, BiLSTM with self-attention mechanism, and self-attention BiLSTM optimized by WOA. By analyzing and predicting three years of continuous power data, this paper compares the prediction accuracy and stability of these three models and provides a detailed comparison and evaluation of their performance. The BiLSTM model with the self-attention mechanism performs better in terms of prediction accuracy and stability in power data prediction, according to experimental findings. This model can better capture the long-term dependency relationship of time series data and automatically adjust attention, thus predicting future power loads more accurately. In addition, this study found that the self-attention BiLSTM model optimized by WOA can further improve prediction accuracy and stability, and its performance is better than that of traditional BiLSTM models. The experimental findings of this study demonstrate that the WOA optimization and self-attention mechanism play a significant role in enhancing the performance of BiLSTM models in power data prediction, and can increase the accuracy to over 80%. These results can provide reference for future deep learning-based power load prediction and valuable guidance for other time series prediction tasks.

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Chewing Detection Using Brightness Changes in Video based on Deep Learning 3

Chewing Detection Using Brightness Changes in Video based on...

引用

3rd ieee international conference on computer Communication and Artificial Intelligence, CCAI 2023

作者： Nakada, Daiki Ogawa, Tomomi Tokyo Denki University Faculty of Science and Engineering Tokyo Japan

ISBN: (纸本)9798350335262

Chewing well is known to be beneficial for human health. However, a simple method to measure the number of chews for health guidance has not been established. In this paper, we propose a simple method to measure the number of chews using a photographic device such as a smart phone. When a video of chewing during eating is filmed, the brightness of the chewer's face changes as the jaw moves up and down due to chewing. When the values are graphed, the change in brightness results in a waveform shape that is easy to understand. Since the number of chews can be estimated from the number of waves in the waveform, the number of chews is measured using a neural network that counts the number of waves. To compensate for the small amount of data, we use a large amount of pseudowaveforms, such as sine waves. Then, a learning model that determines the number of repetitions is created, and a large amount of pseudo-waveform data is used for pre-training. The parameters of the trained model are determining by transfer learning so that the model can be applied to a small amount of data. As a result of learning the video waveform data, we were able to measure 96.6% of the data within ± 2 by moving all the parameters of the trained model. © 2023 ieee.

关键词： Luminance

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Enhancing Remote Oversight of Plants with a Compact IIoT System 3

Enhancing Remote Oversight of Plants with a Compact IIoT Sys...

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3rd ieee international conference on ICT in Business Industry and Government, ICTBIG 2023

作者： Singh, Shweta Ganorkar, Akshaya M Anujhna, B. Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh India Presidency University Department of Electronics and Communication Engineering Karnataka Bangalore India Department of Computer Science and Information Technology Bangalore India

ISBN: (纸本)9798350343274

It is impossible to overestimate the importance of remotely monitoring industrial facilities in the context of the Industrial Internet of Things. The goal of this line of research is to improve remote monitoring by using real-time data analytics, predictive maintenance, and energy optimization with a tiny Industrial Internet of Things (IIoT) system. The three most important algorithms are an energy optimization method using a proportional-integral-derivative (PID) controller, a predictive maintenance strategy based on machine learning models, and a real-time data streaming and analysis tool. The proposed method is said to be able to use these algorithms in real time, which enables it to schedule preventive maintenance, identify anomalies, and use energy effectively. Effective, sustainable, and data-driven plant management support should be expected to result in minimal downtime, lower operating costs, and ecologically conscious operations. © 2023 ieee.

关键词： Proportional control systems

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