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Face Recognition and Facial Temperature Detection System Based on Neural Network Algorithms 5th

Face Recognition and Facial Temperature Detection System Bas...

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5th international conference on Artificial Intelligence in China, AIC 2023

作者： Guo, Ziqiang Wang, Qu Fu, Meixia Dan, Lin Wang, Qixia Wei, Hanming Zhao, Liang Shanxi Information Industry Technology Research Institute Co. Ltd Taiyuan030012 China School of Automation and Electrical Engineering Institute of Industrial Internet University of Science and Technology Beijing Beijing100083 China

ISBN: (纸本)9789819975440

In response to the issues of proximity between the measurer and the measured individual, as well as low measurement efficiency in traditional methods of body temperature measurement, a face recognition and facial temperature detection system based on neural network algorithms has been designed. Regarding hardware design, it utilizes a dual-camera system with infrared capabilities and an integrated built-in blackbody module to capture thermal imaging data. Regarding software design, the collected data is input into a neural network algorithm model for analysis and processing. It has been demonstrated through experimentation that the system can automatically capture and rapidly output dynamic facial images and body temperatures. this system enhances information technology in prevention and control work while reducing the risk of disease transmission. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Temperature measurement

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NNA and Activation Equation-Based Prediction of New COVID-19 Infections 5

NNA and Activation Equation-Based Prediction of New COVID-19...

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5th international Congress on Human-Computer Interaction, Optimization and Robotic Applications, HORA 2023

作者： Jasim Shaban, Faris Ali Imam ja'Afar Al_Sadiq University Computer Technology Engineering Department Baghdad Iraq

ISBN: (纸本)9798350337525

At 2019, China had a large number of severe cases of pneumonia, particularly in Wuhan. A SARS virus was detected after a thorough realization of sample from the sick people. Due to the form of the virus, which resembled a crown, it was given the name CORONA;the abbreviation COVID-19 stands for 2019 CORONA VIRUS. the World Health Organization WHO classified it as COVID-19, a pandemic, on March, 2020. In this study, artificial neural networks - which function similarly to the network of human neurons - are built to imitate how the human brain functions. Due to this, neural networks were used to connect the diagnosis to the symptoms, where the platform and knowledge-based system were found to be compatible, the symptoms that depend on the diagnosed disease were represented as numerical data, and after the network had been trained, the system was found to be appropriate for the accurate diagnosis of the disease. Our current study includes two primary phases: the training phase of neurons, which includes inputting the training data and generating random weights whose value is less than 1 for each of these inputs, and applying the neural network algorithm to them. the testing phase, where the two inputs were entered without the results to assess how well the proposed system works. three statistical calculations R, RMSE, MAPE were made in order to evaluate the performance of the existing system and its findings. © 2023 IEEE.

关键词： COVID-19

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Web-plugin to Detect Clickbait in News Articles using RNN and LSTM 5

Web-plugin to Detect Clickbait in News Articles using RNN an...

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

作者： Krishneth, A. Dharaneesh, J.D. Jisnu, S. Sivaganesan, D. Psg Institute of Technology and Applied Research Department of Computer Science and Engineering Coimbatore India

ISBN: (纸本)9798350321425

'Clickbaits' are either false or misleading headlines that aim to attract users' attention and entice them to click on a link or a news article that isn't really interesting as the title makes it up to be. A clickbait title can degrade the user experience and spread disinformation or misinformation on the Internet. In this study, a novel approach for Clickbait detection in news articles using machine learning technique of Recurrent Neural Network and Long-Short Term Memory is proposed. the clickbait detection process takes place in two distinct steps, first to identify the headlines in a website using a web plug-in, these headlines are then fed into a machine learning model which can predict if the headline is a clickbait or not. this a binary classification problem where the model is trying to predict which of the two categories the input belongs to. the algorithm is put into action using a web plugin which is triggered when popular news websites are visited and fetch the headlines using DOM method, the headlines are then sent to the server for prediction upon which the headlines get marked either red or green which indicates the headlines to be either clickbait or not respectively which, this makes the user aware if a headlineis clickbait or not with just a glance. the result obtained by the above method is an improvement over the existing methods and the user interface provided by the web plugin removes any manual labor from the user side and offers an engaging yetsimplistic user experience. © 2023 IEEE.

关键词： Long short-term memory

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data-driven modeling fast-slow coupling delayed dynamics using noisy data and sparse enhancement 5

Data-driven modeling fast-slow coupling delayed dynamics usi...

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5th international conference on Industrial Artificial Intelligence, IAI 2023

作者： Tang, Huifang Wu, Yuqiang Dong, Yingming Cheng, Cheng Li, Xiuting Huazhong Agricultural University College of Science Wuhan China Yunnan Mechanical Research and Design Institute of Machinery Research Institute Group Yunnan Key Laboratory of Mechatronics Applied Technology Yunnan China Ministry of Education of China Huazhong University of Science and Technology Autonomous Intelligent Unmanned System Engineering Research Center Wuhan430074 China Huazhong University of Science and Techonology School of Artificial Intelligence and Automation Wuhan430074 China

ISBN: (纸本)9798350325294

In this work, we focus on fast-slow delayed dynamical systems, which are usually described by coupled delayed differential equations with two-time scales. the purpose of this article is to propose a data-driven discovery method to identify the fast-slow delayed differential equations from noisy measurements. the proposed method blends delay dictionary-based sparse identification of nonlinear dynamics (SINDy) framework and a fixed interval sampling strategy. Specifically, we leverage the SINDy methodology to describe the fast-slow delayed differential equations by a few active terms of a large dictionary containing the delay terms and many candidate nonlinear basis functions. To identify the active terms and the corresponding coefficients, a sequential thresholded re-weighted 2 least squares algorithm is used to solve the resulting sparse regression problem to enhance the sparsity. Once a sparse model with fixed delay is selected, the proposed method further estimates the delayed time by calculating the reconstruction error from the sparse model until the one that makes the reconstruction error minimum. We verify the effectiveness of the proposed method with several classical fast-slow coupling delayed systems. the experimental results show that the proposed method can well automate the fast-slow delayed differential equations from noisy data and the delayed terms also can be detected accurately. © 2023 IEEE.

关键词： Dynamical systems

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Design and Implementation of a Distributed technology-Based big data Analysis System 24

Design and Implementation of a Distributed Technology-Based ...

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2024 5th international conference on big data Economy and Information Management, BDEIM 2024

作者： Song, Zhengju Housing and Urban Rural Construction Administration Bureau of Dongying Shandong Dongying China

ISBN: (纸本)9798400711862

A new big data analysis method for big data is proposed to address the storage, processing, and security issues of massive data in the big data environment. the main research objects are Hadoop HDFS and Spark, with scalability, fault tolerance, and efficiency as the goals. A hierarchical approach is adopted to achieve data collection, storage, processing, and visualization integration. On this basis, it is proposed to improve the overall performance of the system through data block storage and parallel processing, achieving high-speed, efficient, and robust fault tolerance. the system also incorporates security measures for data protection and integrity across multiple nodes. Performance testing reveals that the system excels in data handling, resource allocation, and fault tolerance across various data scales, while identifying areas for optimization. the research results of this project will provide valuable references for practical problems in the field of big data, and also provide new ideas for the scalability development of big data. © 2024 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： big data analysis system distributed architecture Hadoop HDFS

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Class Imbalance of Bio-Medical data by Using PCA-Near Miss for Classification 5

Class Imbalance of Bio-Medical Data by Using PCA-Near Miss f...

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

作者： Tumuluru, Praveen Daniel, Ravuri Mahesh, Gundabathula Lakshmi, Kalavala Deekshitha Mahidhar, Pakalapati Kumar, Muttum Vinay Koneru Lakshmaiah Education Foundation Department of Cse AP Vaddeswaram India Prasad V. Potluri Siddhartha Institute of Technology Department of Cse Vijayawada India

ISBN: (纸本)9798350321425

Class imbalance is a significant problem in many real-time domains dealing with healthcare data, which leads to poor classification performance and may have severe consequences. this work addresses the class imbalance problem in healthcare data, which often leads to poor classification performance and potentially serious consequences. this study proposes a hybrid PNM model that integrates the Near-Miss sampling technique and Principal Component Analysis to overcome this challenge. this study aims to investigate the effectiveness of the PNM model in improving classifier performance and compare it with several baseline classifiers. Further, the experiments are conducted on a real-world healthcare dataset containing various healthcare records. the results showed that the PNM model outperformed the baseline classifiers regarding the precision, recall, F1 score, and area under the curve (AUC). Specifically, the PNM model achieved a precision of - 0.85, recall of - 0.82, F1 score - 0.83, and AUC - 0.91, while the best-performing baseline classifier achieved a precision of 0.72, recall of 0.64, F1 score of 0.68, and AUC of 0.83. Our study demonstrates that the PNM model offers a promising approach to addressing the class imbalance in healthcare data and improving classifier performance. Integrating the Near-Miss sampling technique and Principal Component Analysis enables the model to achieve a better balance among the majority and minority classes, resulting in more accurate classification. the PNM model has the potential to be applied to various healthcare domains, such as disease diagnosis, patient risk stratification, and treatment prediction. © 2023 IEEE.

关键词： Principal component analysis

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Intelligent Operational Decision Support System for Sugar Factory Resource Efficiency 5

Intelligent Operational Decision Support System for Sugar Fa...

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5th IEEE international conference on Modern Electrical and Energy System, MEES 2023

作者： Lutska, Nataliia Zaiets, Nataliia Vlasenko, Lidiia Junge, Stefan National University of Food Technologies Dept. of Automation and Computer Technologies of Control Systems Kyiv Ukraine National University of Life and Environmental Sciences of Ukraine Dept. of Automation and Robotic Systems Kyiv Ukraine State University of Trade and Economics Dept. of Software Engineering and Cyber Security Kyiv Ukraine Berlin University of Applied Sciences Dept. of Packaging Technology Berlin Germany

ISBN: (纸本)9798350359787

With the economic crisis that is looming over the world due to changes in the energy sector and a reduction in the supply of certain types of products, energy and resource efficiency issues are becoming more relevant for the food industry. Also important is the preservation of the environment and the implementation of Cleaner Production trends. Most enterprises follow the concept of Industry 4.0 and increase efficiency through the implementation of intelligent methods, in particular machine learning. the paper proposes a neural network model of sugar production. the model makes it possible to predict the main estimates of resource efficiency used to correct the technological modes of production by the operator-technologist. the structure of the operational decision support system has been developed and is used by personnel to model and predict changes in resource efficiency estimates and KPIs calculated on their basis. Its feature is the integration of methods for achieving resource efficiency, improving environmental performance and ensuring KPIs. To do this, at the initial stage, an analysis of the main flows that affect resource efficiency and an assessment of their importance was carried out. Ensuring Production Performance is confirmed by the simulation and the adequacy of the created neural network accuracy of more than 95%. © 2023 IEEE.

关键词： Decision support systems

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Numerical analysis of inter-valley electrons transferring behavior in GeSn alloys for optoelectronic applications 5

Numerical analysis of inter-valley electrons transferring be...

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

作者： Huang, Shihao Li, Jiapeng Li, Hailin Sun, Qinqin Xie, Wenming Li, Qiongyu Zhao, Fengyan School of Electronic Electrical Engineering and Physics Fujian University of Technology Fuzhou350118 China Fujian Key Laboratory of Automotive Electronics and Electric Drive Fujian University of Technology Fuzhou350118 China

ISBN: (纸本)9781510691667

the electron-phonon interaction in GeSn alloys provides a handle to tune its optoelectronic properties for a variety of applications. A comprehensive understanding of how the inter-valley scattering between Γ and L valleys modulates electron dynamics in GeSn alloys is needed. this letter focuses on the theoretical analysis of inter-valley scattering mechanisms between Γ and L valleys, and hence on the electron transmission dynamics in GeSn alloys. Firstly, the electronic band structure of GeSn alloys in the full Brillouin zone has been reproduced by 30-band k•p method. Secondly, inter-valley scattering calculations have been shown that scattering rate of electrons from Γ valley to L valley is higher than that from L valley to Γ valley. thirdly, electron transmission dynamics indicates that inter-valley scattering help to enhance the electron population in Γ valley for GeSn materials with low Sn composition. Results are beneficial to understand the microscopic mechanism of carrier scattering in GeSn materials, and can be a potential theoretical approach for optimizing the performance of GeSn-based optoelectronic devices. © 2025 SPIE.

关键词： Photoelectric devices

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Smart Home Garden Monitoring System Based on Internet of things 5

Smart Home Garden Monitoring System Based on Internet of Thi...

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5th IEEE international conference on Advances in Computing, Communication Control and Networking, ICAC3N 2023

作者： Bhargav, M.S.S. Varma, A.K.C. Venkateswara Rao, Ch. Srinivas, Ch.V.V.S. Orugu, Rangarao Shaik, Abdul Rahaman B V Raju Institute Of Technology Department Of Ece Narsapur India Vishnu Institute Of Technology Department Of Ece Bhimavaram India

ISBN: (纸本)9798350330861

A smart home gardening system using IoT cloud technology can greatly enhance the efficiency and convenience of managing garden. It enables the monitor and control various aspects of the garden remotely, making it easier to maintain healthy plants and optimize resource usage. In order to ensure optimum plant development while also using the most amount of irrigation resources, the aim of this effort is to create a prototype for continually managing soil moisture levels and monitoring temperature, humidity, and sun light intensity. the monitoring software blink application has been used, and the sensor data will be available online. Given that water is a need for plant existence, it must not be used excessively. It is possible to program the Arduino Uno to examine some signals from sensors, like moisture and rainfall. With the help of blink user interface has been done which can be used to regulate and monitor the irrigation of water to plants, and an IoT module can be used to display the soil moisture levels and humidity continually on a webpage. the proposed system has various sensors (soil moisture, temperature, humidity, and light depending resistor) for monitoring the garden plants. Controlling and monitoring the water supply and keeping an eye on the plants are the key goals of this work which is envisioned through blink application, where the interfacing between prototype and monitoring environment has been enabled through it. the experimental results shows that the proposed system for garden monitoring is best suitable and economic and accurate in terms of monitoring. © 2023 IEEE.

关键词： Soil moisture

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Wearable Sensors for Non-Invasive Sport Monitoring: An Overview of the STEX Project 5

Wearable Sensors for Non-Invasive Sport Monitoring: An Overv...

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5th IEEE international Flexible Electronics technology conference, IFETC 2023

作者： Angeli, Martina Aurora Costa Petrelli, Mattia Scarton, Alessandra Pogliaghi, Silvia Ferrari, Luca Bochicchio, Gianluca Inam, Akm Sarwar Altana, Antonio Madagalam, Mallikarjun Biasi, Roberto Petti, Luisa Lugli, Paolo Faculty of Engineering Free University of Bozen-Bolzano Bolzano39100 Italy Microgate Srl Bolzano39100 Italy Department of Neuroscience Biomedicine and Movement Science University of Verona Verona37100 Italy Department of Applied Science and Technology Politecnico di Torino Torino10129 Italy

ISBN: (纸本)9798350332094

In this work, we present the research carried out within the STEX ("Smart TEXtiles for monitoring muscle activity") project, aimed at the development of wearable non-invasive sensors for real-Time monitoring of muscle activity. the project's activities performed involved the design, fabrication, characterization and validation of two different sensors: (i) piezoresistive textile-based strain sensors for the detection of the breathing rate (BR) and (ii) flexible electrolyte-gated carbon nanotube fieldeffect transistor (EG-CNTFET)-based sensors for NH+ 4 detection in sweat. the piezoresistive textile-based strain sensors proved able to follow the chest movements during quiet breathing with a sensitivity of 22-2%, while a preliminary pilot study confirmed the possibility of using them to estimate the breath-by-breath BR during cycling activity. the EG-CNTFET-based NH+ 4 sensors showed a linear detection range for ammonium from 0.01 to 10 mM in artificial sweat, which covers the entire range of physiological concentrations of interest, with average sensitivities of 0.346 uA/decade and 96.94% coefficient of determination. © 2023 IEEE.

关键词： Electrolytes

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