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Application and Optimization of Micro/Nano Electronic Sensors Based on Deep Learning in Human-computer Interaction 13

Application and Optimization of Micro/Nano Electronic Sensor...

引用

13th International conference of Information and Communication Technology, ICTech 2024

作者： Zhai, Aoyu Zhao, Zixuan Electronic Science and Technology Xi'an Jiaotong liverpool University China Computer Science and Technology Xi'an Jiaotong liverpool University China

ISBN: (纸本)9798350376258

the exponential growth of population and the use of electronic and electrical equipment have increased the requirement for energy production management, thereby increasing the need for precise energy management systems that can predict consumer use for future policy formulation. MEMS/NEMS technology, as a mature microstructure manufacturing technology, can form micro sensors and testing systems with small volume, light weight, and high performance. To improve the human-machine interaction experience of smart grids, this article applies micro/nano sensor technology to human-machine interaction optimization and proposes a data fusion model based on deep learning (DL). Based on the principle structure and training method of Deep Neural Network (DNN), a multi-source data system for power grid technical renovation projects has been constructed, which includes technical factors, economic factors, and environmental factors. this system can effectively analyze energy usage and combine multi-source data fusion technology to achieve intelligent evaluation of power grid projects. the experimental results show that the model designed in this paper is effective and superior, and has reference significance for improving the data fusion algorithm of micro and nano sensors in big data environments. ©2024 IEEE.

关键词： Smart power grids

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A Study on the Application of Interactive Whiteboards in Secondary School computer Practice Courses 13

A Study on the Application of Interactive Whiteboards in Sec...

引用

13th International conference on Information Technology in Medicine and Education, ITME 2023

作者： Zhang, Wenqing Sun, Jiande Lu, Ran Zhao, Liang Shandong Normal University School of Information Science and Engineering Jinan China Shandong Provincial Institute of Education Sciences Jinan China Shandong Normal University Faculty of Education Jinan China

ISBN: (纸本)9798350319156

With the continuous development of computer technology in China, informatization teaching has become an important part of teaching in secondary schools. With the deepening of education reform, the scientific integration of multimedia-assisted teaching and computerized teaching can effectively improve the quality of classroom teaching[1]. However, the mechanization of the introduction of multimedia-assisted teaching still makes the classroom teaching stays in the 'theoretical explanations in class + student exercises' mode, it is difficult to stimulate students' enthusiasm for learning. In order to solve the problems in the existing computer practical classroom teaching, this paper adds the interactive whiteboard as a supplement to multimedia-assisted teaching to the classroom teaching, and studies the application skills of the interactive whiteboard in the computer practical course, which provides a reference for teachers to use the interactive whiteboard for teaching. © 2023 IEEE.

关键词： Teaching

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Research on Quality Control of Electronic Medical Records Based on Feature Fusion Text Classification 24

Research on Quality Control of Electronic Medical Records Ba...

引用

5th International conference on Artificial Intelligence and computer Engineering, ICAICE 2024

作者： Sun, Feng Huang, Xiaofang School of Computer Science and Technology Southwest University of Science and Technology Sichuan Mianyang China

ISBN: (纸本)9798400718007

In China's healthcare system, the quality of medical records, as an important medical document that records the complete course of a patient's illness, is related to medical safety and clinical research. Since the traditional manual review of medical records for quality control is inefficient and time-consuming, we design a text classification model based on feature fusion to detect defects in medical records. the model introduces the text classification output of Bidirectional Encoder Represen-tations from Transformers (BERT) which makes the model more effective in understanding the overall meaning of a sentence. We introduced Bidirectional Long and Short Term Memory Net-works and Text Convolutional Neural networks in order to better capture the bi-directional semantic information in the text and to extract local features efficiently. Meanwhile, in order to extract the key features of the text and mitigate the degradation problem when the information is passed through the network, the model introduces the self-attention mechanism and residual connectivity. this feature fusion combining multiple techniques enables the model to better understand and classify the medical record defective text and improves the classification performance. the model was trained and tested with the Medical Record Defective Text dataset and the results showed an accuracy of 83.33%. © 2024 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： Convolutional neural networks

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A Versatile Low-Light Enhancement Module for Improved Workpiece Detection 13

A Versatile Low-Light Enhancement Module for Improved Workpi...

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13th International conference of Information and Communication Technology, ICTech 2024

作者： Yin, ZhenYu Lin, DaHeng Wang, XiaoHui Xin, Qi Zhang, XueMei Shenyang Institute of Computing Technology Chinese Academy of Sciences Shenyang110168 China School of Computer Science and Technology University of Chinese Academy of Sciences Beijing100049 China Liaoning Key Laboratory of Domestic Industrial Control Platform Technology on Basic Hardware & Software China School of Computer Science and Technology Shenyang University of Chemical Technology Shenyang110168 China

ISBN: (纸本)9798350376258

this study presents a versatile Low-Light Enhancement Module (LLEM) to enhance object detection models, particularly for workpieces in challenging lighting conditions. the LLEM seamlessly integrates into existing models, addressing low-light challenges through a denoising branch, a global branch, and a Structural Perception loss for image quality improvement. the denoising branch utilizes Residual Denoising Modules to effectively reduce noise and preserve low-level features. Meanwhile, the global branch focuses on color recovery and gamma correction. Experimental results show YOLOv3+LLEM significantly improves detection performance, achieving a mAP increase from 88.0% to 91.9%. this enhancement, especially with the introduction of Structural Perception loss, maintains computational efficiency, validating its practicality for industrial workpiece detection in low-light conditions. ©2024 IEEE.

关键词： Image enhancement

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A Review on Opinion Mining Approaches: Using Deep Learning for Large Scale Data 13

A Review on Opinion Mining Approaches: Using Deep Learning f...

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13th International conference on System Modeling and Advancement in Research Trends, SMART 2024

作者： Singh, Navjot Bathla, Gaurav Chandigarh University Computer Science and Engineering India

ISBN: (纸本)9798350380583

Opinion mining, another name for sentiment analysis (SA), is a branch of computer science. that focuses on analyzing the opinions and feelings expressed in text, audio and video-Sentiment analysis involves determining the sentiment expressed by person. the objective of the opinion-mining field is to conduct subjectivity analysis, indicating whether a document is subjective or objective. Subjectivity implies the presence of sentiment, while objectivity signifies content devoid of sentiment. Currently, an abundance of information about a specific product is available, with a single product often garnering hundreds of reviews across various webpages. Numerous websites, such as ***, ***, ***, among others, aggregate user information and expert opinions to publish reviews. Experts meticulously analyse reviews, extract opinions, and generate ratings related to the dataset provided by the requesting agencies. However, handling the vast amount of data is a labour intensive task for experts. the continuously growing volume of web data poses challenges in extracting precise opinions from content. Hence, there is a need to design a system that can efficiently perform these tasks with human-like accuracy. this paper analyses various approaches which are capable of handling and analysing large amounts of reviews and generate the aspect level opinion mining (AOM). © 2024 IEEE.

关键词： Opining Mining Review Subjectivity Text Data

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Battery Management System for Polish Hybrid Residential Photovoltaic Power Plants 13th

Battery Management System for Polish Hybrid Residential Pho...

引用

13th International conference on Image Processing and Communications, IP and C 2023, 13th International conference on computer Recognition Systems, CORES 2023

作者： Parczyk, Pawel Burduk, Robert Department of Systems and Computer Networks Wroclaw University of Science and Technology Wybrzeże Wyspiańskiego 27 Wroclaw Poland

ISBN: (纸本)9783031416293

Growing share of Renewable Energy Sources (RES) in Polish electricity system has motivated the Polish government to make changes in the law. those changes decrease the economic profit of using photovoltaic power plants, but incentivize the use of hybrid systems equipped with batteries. In this paper, the state-of-the-art battery management system, which maximizes self-consumption has been compared to our own time-based proposal, which is focused on economic profit. We used real-life measurements from a 6kWp residential power plant to compare both battery management systems. the results show that controlling batteries with our model allows us to save more money. © 2023, the Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Hybrid systems

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the Impact of Parameters on the Efficiency of Keypoints Detection and Description 13

The Impact of Parameters on the Efficiency of Keypoints Dete...

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13th IEEE International conference on Electronics and Information Technologies, ELIT 2023

作者： Fesiuk, Andriy Furgala, Yuriy Department of Electronics and Computer Technologies Ivan Franko National University of Lviv Lviv Ukraine

ISBN: (纸本)9798350383096

this paper comprehensively investigates the efficiency and performance of the keypoints detection and description methods in computer vision and image processing. Four widely used methods - SIFT, SURF, ORB, and BRISK are compared and analyzed. the study aims to assess the influence of different parameters on detecting and describing keypoints, specifically focusing on the limitation of the number of keypoints and Lowe’s ratio values. the effectiveness of each method is evaluated by analyzing the similarity coefficient for pairs of images with varying degrees of similarity. It has been found that the similarity coefficient is also rising, with Lowe’s ratio rising. At the same time, both Lowe’s ratio and detected keypoints number limitation significantly impact the achieved methods’ performance. the results obtained from the experiments provide valuable insights into the pros and cons of each keypoints method. Furthermore, we suggest how to optimize parameter settings to achieve optimal performance. the findings of this research can benefit researchers and developers working in computer vision and image processing. © 2023 IEEE.

关键词： computer vision

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Databases for Digital Infrastructure of Small Modular Reactors Considering Dynamic Subject Area 13

Databases for Digital Infrastructure of Small Modular Reacto...

引用

13th International conference on Dependable Systems, Services and Technologies, DESSERT 2023

作者： Brezhniev, Yevhen Kharchenko, Vyacheslav Dotsenko, Serhii Nor, Dmytro Networks and Cybersecurity of National Aerospace University KhAI Department of Computer Systems Kharkiv Ukraine Ukrainian State University of Railway Transport UkrDUZT Department of Specialized Computer Systems Kharkiv Ukraine

ISBN: (纸本)9798350396119

the paper analyses the digital infrastructure (DIS) of small modular reactors (SMRs), their main systems (Instrumentation and Control Systems of SMR, Supervision Information and Control System, System of Exchanging and Processing Information of Crisis Center, System of Physical Security and Protection, System of Radiation Control, UAV Fleet Control System, System of Consumption Management), and requirements to databases as a part of the DIS. It describes methods of forming dynamic subject areas (DSA) considering the specific features of SMR DIS. the results of DSA analysis and suggestions for developing an integrative database for SMR are presented. © 2023 IEEE.

关键词： Database systems

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LBPNET: Detection of Fake Face Currency through LBP-Based Convolutional Neural networks 5

LBPNET: Detection of Fake Face Currency through LBP-Based Co...

引用

5th International conference on Emerging Systems and Intelligent Computing, ESIC 2025

作者： Reddy, Y.V. Bhaskar Kumar, C. Satya Ashok, Rayalla Mandlem, Sai Nithin Reddy, P. Ashok Reddy, M.Babu Department of Computer Science and Engineering Mylavaram India Vardhaman College of Engineering Department of Computer Science and Engineering India Krishna University Department of Computer Science Machilipatnam India

ISBN: (纸本)9798331522100

the escalating sophistication of counterfeit face currency demands innovative solutions for robust detection. In this study, we propose LBPNET, a novel approach employing local Binary Pattern (LBP) feature extraction and Convolutional Neural networks (CNNs) to address the challenges posed by realistic fake currencies. LBP descriptors are extracted from currency images, capturing subtle patterns invisible to the human eye. these descriptors serve as inputs for a CNN, named LBPNET, trained to discern genuine from counterfeit face currencies. the model undergoes a comprehensive training phase, incorporating diverse currency images, to establish a generalized and adaptive detection system. In testing, new currency images are evaluated using the trained LBPNET to identify the presence of fake or non-fake currency, showcasing the model's adaptability to emerging counterfeit variations. this approach integrates machine learning and image processing to enhance detection capabilities, offering a promising solution for the evolving landscape of counterfeit face currency across forensic and social media domains. © 2025 IEEE.

关键词： Feature Selection

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Adaptive CSS Using Deep Learning for Flexible Web Designs and Performance Improvement 13

Adaptive CSS Using Deep Learning for Flexible Web Designs an...

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13th International conference on System Modeling and Advancement in Research Trends, SMART 2024

作者： Vadlamani, Satish Jain, Arpit Sharma, Vivek Kankanampati, Phanindra Kumar Byri, Ashvini Ramachandran, Ramya Osmania University India Koneru Lakshmaiah Education Foundation Department of Computer Science & Engineering A.P. India School of Computer Science & Engineering Galgotias University Department of Cse Gr. Noida India B106 Sri Harika Towers Fci Colony Miyrapur Hyderabad500049 India Parel Mumbai400012 India Thiruthangal Via Tamil Nadu Sivakasi626130 India

ISBN: (纸本)9798350380583

Complexities in new Web applications require new creative ways of changing usability, responsiveness, and interactivity requirements. Classical web design approaches rely heavily on static templates and predefined CSS rules, which, in reality, rarely provide enough flexibility to adapt to new user requirements and varied usage scenarios. In this paper, we discuss possible ways of using techniques of deep learning and artificial intelligence to influence the design of web pages by automatically generating customized and adaptive styles in CSS. In particular, we describe a general architecture of a hybrid deep learning model for real-time creation and optimization of CSS rules, which exploits a combination of Convolutional Neural networks and Deep networks. the CNN captures all the significant user interaction features and environmental data to effect the change in design components according to the user's behavior and context;then, the DQN algorithm dynamically updates the CSS based on real-time feedback. Finally, Bayesian optimization is used to adjust the hyperparameters effectively to further enhance the model's capabilities. this specific result of the testing only shows how the AI -driven strategy enhances performance measures, such as higher responsiveness, engagement, and even satisfaction, in addition to further enriching the aesthetic appeal and usability of a website. therefore, our study is an essential step towards the emergence of AI-driven web design, which proves that deep learning and traditional web development techniques can be combined to build more innovative, more malleable web interfaces. this research thus allows for intelligent customized online experiences across a range of devices and environments and may better meet the different needs of its users. © 2024 IEEE.

关键词： Deep reinforcement learning

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