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检索条件"任意字段=3rd International Conference on Intelligent Data Engineering and Automated Learning"

共 20248 条记录，以下是4941-4950 订阅

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Improved YOLO framework for cell detection in bronchoalveolar lavage fluid 3

Improved YOLO framework for cell detection in bronchoalveola...

引用

3rd international conference on Control and intelligent Robotics, ICCIR 2023

作者： Zhang, Xin Li, Rui Wang, Chao Jia, Yujuan School of Computer and Communication Lanzhou University of Technology Gansu Lanzhou730050 China School of Information Engineering East China Jiao Tong University Jiangxi Nanchang330010 China School of Information Science and Engineering Shandong Normal University Shandong Jinan250358 China

ISBN: (纸本)9781510671867

In response to the limited amount of cell detection data and the phenomenon of overlapping and adhesion in the existing bronchoalveolar lavage fluid (BALF), a YOLOv5s-based target detection algorithm (YOLOv5s-GAN) was proposed to optimize the model structure and loss function and effectively address the overlapping and adhesion of BALF cells. The algorithm enhances the accuracy of cell detection in BALF by using a lightweight convolution GSconv module and a normalization-based attention mechanism (NAS) that does not require additional calculations or parameters. Additionally, the algorithm improves detection accuracy by using D-IoU to mitigate false detections due to overlapping cells. Experimental results showed that the algorithm achieved an average accuracy of 88.9% in detecting four types of BALF cells, which demonstrated its high practicality for cell detection in BALF. © 2023 SPIE.

关键词： Adhesion

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Analysis of data patterns of interwoven meltblown nonwoven materials based on polynomial fitting 3

Analysis of data patterns of interwoven meltblown nonwoven m...

引用

3rd international conference on Applied Mathematics, Modelling, and intelligent Computing, CAMMIC 2023

作者： Tan, Qiancheng Huang, Jiaoxia Wu, Sixuan Qin, Yonghui School of Mathematics and Computing Science Guilin University of Electronic Technology Guilin541004 China Guangxi Key Laboratory of Automatic Detecting Technology and Instruments Guangxi Colleges Universities Key Laboratory of Data Analysis and Computation Guilin University of Electronic Technology Guilin541004 China

ISBN: (数字)9781510667617

ISBN: (纸本)9781510667600

The meltblown nonwoven material (MNM) is an important raw material for mask production. With good filtration performance, its production process is simple, low cost, and light quality. The research on the process parameters and structural variables of the MNM is widely concerned by domestic and foreign enterprises, and the relationship between structural variables and product performance is also. To the end, we study the process parameters and product performance of the MNM by using some mathematical methods for the high quality and high efficiency of the industry. We use a multiple nonlinear regression model to build a statistical model based on the data provided by the enterprise to reveal the variation pattern between process parameters and structural variables. The coefficient of decidability was calculated, and the correlation coefficient was above 0.9, which proved the accuracy of the model. Finally, in order to explore the relationship between structural variables and product performance, we used MATLAB to make a correlation four-dimensional diagram, used fitting polynomials to finally fit a quadratic correlation function, and used Monte Carlo multi-objective planning programming to obtain the maximum filtration efficiency of 96.8272% when the acceptance distance was 39.5cm and the hot air speed was 1188r/min. © 2023 SPIE.

关键词： MATLAB

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TrackBot: Building an intelligent Multipurpose Line Follower Robot

TrackBot: Building an Intelligent Multipurpose Line Follower...

引用

Sentiment Analysis and Deep learning (ICSADL), international conference on

作者： C. B. Selva Lakshmi Sandheep S Mani Ashwin Raaj M Harish Kumar P Department of Computer Science and Engineering Velammal College of Engineering and Technology Madurai India

ISBN: (数字)9798350396157

ISBN: (纸本)9798350396164

Robots are the solutions for replacing humans from dangerous and manual labour. Repetitive, non-creative manual tasks can be automated by using robots. One of the principal approaches to automate robots is by line sensing principle. We create a robot that acquires the front view using a camera mounted on the vehicle then applying few processes in order to detect the lines thereby steering the vehicle autonomously.

关键词： Computer vision Sentiment analysis Roads Robot vision systems Manuals Robot sensing systems Sensors

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A Deep learning model for driver drowsiness detection in extremely low-light conditions 3

A Deep Learning model for driver drowsiness detection in ext...

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3rd international conference on intelligent Robotics, Mechatronics and Automation Systems, IRMAS 2023

作者： Anoop, P.P. Deivanathan, R. School of Mechanical Engineering VIT Chennai India

Accidents occurring at night account for a significant percentage when compared to those that happen during the daytime. One of the leading causes of such accidents is driver drowsiness, which could result from inadequate sleep or exhaustion. Continuous monitoring of the facial expressions of the driver can detect signs of drowsiness, and an alarm system can alert the driver and prevent potential accidents. Various research studies have been conducted in this area. However, the low lighting conditions at night make it challenging to obtain detailed images, making it difficult to apply the proposed models effectively. This paper presents an effective model for detecting driver drowsiness using deep learning techniques. The study utilizes AlexNet as a classifier and attains an impressive accuracy of 98.3% after hyperparameter tuning. © Published under licence by IOP Publishing Ltd.

关键词： Accidents

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Augmented Reality (AR) and Virtual Reality (VR) for Personalized learning

Augmented Reality (AR) and Virtual Reality (VR) for Personal...

引用

intelligent data Communication Technologies and Internet of Things (IDCIoT), international conference on

作者： Aditya Rahul Gaikwad Rohan Maruti Gaikwad Dasharathraje Naganath Jodh Simran Khiani Department of Computer Engineering G. H Raisoni College of Engineering and Management Pune India Head of Department (Computer Engineering) G. H Raisoni College of Engineering and Management Pune India

ISBN: (数字)9798331527549

ISBN: (纸本)9798331527556

To develop a specific learning environment using augmented reality (AR) and virtual reality (VR) technology, we create immersive and interactive educational experiences that enhance engagement, improve understanding of complex concepts, and cater to diverse learning styles for students. The system will be able to meet the needs of each learner by gathering data on their learning objectives, preferences, and past knowledge. Interactive multiple-choice questions will be used to evaluate students' performance and identify areas for development. Based on the findings, the website will recommend relevant study guides and classes. AR and VR technology will be used to develop immersive learning environments that include interactive simulations, 3D visualizations, and collaborative virtual worlds. To improve learning results and student engagement, the platform's architecture will prioritize data security, accessibility, and usability.

关键词： Solid modeling Three-dimensional displays Scalability Education data visualization Collaboration Internet of Things Usability Augmented reality Immersive learning

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NAS for automated ML Deployment on Extreme Edge Devices 3rd

NAS for Automated ML Deployment on Extreme Edge Devices

引用

3rd international conference on Signal and data Processing, ICSDP 2023

作者： Mahendran, Gayathri Thirumaran, Gokhulnath Ali, Syed Imam Jadon, Vishwas Raj SSN College of Engineering Tamil Nadu Chennai India SRM Institute of Science and Technology Tamil Nadu Kattankulathur India Amity School of Engineering and Technology Noida India

ISBN: (纸本)9789819795772

Machine learning (ML) and Artificial Intelligence are omnipresent topics nowadays. Especially since small personal devices, such as smartphones, are capable of running computation intensive tasks on their own, they can improve our lives in many different ways, e.g. computer vision or speech detection. Deploying neural network models on tiny and therefore performance, memory and power-constrained devices, such as microcontrollers, is currently an important research topic. For mobile and other embedded target devices, Neural Architecture Search, or NAS for short, is a novel technique to find appropriate network models without requiring manual error-prone adjustments by engineers, which allows to automate the workflow from the definition of a real work problem to a deployable solution. While there are a lot of comprehensive surveys on NAS methods in general, none of them considers the application of those ideas on edge devices like microcontrollers (MCUs). This work provides an overview of the recent developments and compares different approaches, which appeared over the last few years, against one another and with well-known reference applications intended for the usage on mobile phones. In the end some opportunities for future work on this topic are discussed. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： AutoML MCUNet Once-for-All SpArSe The MicroNet models

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RGBot: A Machine learning Enabled Requirements Gathering Technique in Agile-Based Lifecycle 3

RGBot: A Machine Learning Enabled Requirements Gathering Tec...

引用

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

作者： Patidar, Anil Suman, Ugrasen School of Computer Science and Information Technology Devi Ahilya Vishwavidyalaya Indore India

ISBN: (纸本)9798350343274

The creation of mobile app has emerged as an integral aspect of modern organizations in a wide range of fields. A greater percentage of mobile app development (MAD) industries are turning to agile practices to increase app quality and shorten development times. Agile-based MAD relies heavily on efficient requirements gathering from stakeholders, and it has a significant impact on whether or not a mobile app is successful. Conventional methods of gathering requirements are typically labor-intensive and error-prone, prolonging the building process, driving up expenses and may struggle to maintain the track of frequent variations that characterize agile-based MAD. Machine learning (ML) approaches are becoming increasingly prevalent in gathering requirements as a means of automating the monotonous task of requirement engineering. The ability of ML approaches to process massive volumes of data, identify trends, and arrive at precise forecasts has the potential for significant advancement in gathering of requirements. This study intends to offer a methodological proposal for automating requirements gathering through the practice of a ML-based interactive bot, which we have named as requirements gathering bot (RGBot). The RGBot streamlines MAD process by acquiring requirements and optimizing customer experiences to meet stakeholders' expectations. © 2023 IEEE.

关键词： Life cycle

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The Investigation of Student's Online learning Adaptability Level Prediction Based on AutoML

The Investigation of Student's Online Learning Adaptability ...

引用

Electrical engineering, Big data and Algorithms (EEBDA), IEEE international conference on

作者： Yan Zhao Pengbo Wang Beijing Polytechnic Beijing China Beijing Information Science and Technology University Beijing China

In the rapidly evolving realm of online education, developing an accurate method for assessing students' adaptability is of paramount importance to the educational field. It enables educational institutions to better understand student performance and needs, allowing for the provision of timely support and resources. Therefore, this study employed automated Machine learning (AutoML) technology to predict students' adaptability to online learning. Utilizing Baidu's AI development platform named EasyDL, this research conducted an in-depth analysis of 1, 205 data samples provided by the Kaggle platform. The model's performance was evaluated using key indicators e.g. accuracy, precision, recall, and F1-score, and it was compared with traditional machine learning methods to establish its superiority. The experimental results demonstrate that AutoML technology effectively predicts students' adaptability to online learning, with the model achieving an accuracy rate of 90.9%. While there is still room for improvement in predicting high adaptability levels in students, the model significantly underscored the importance of critical features such as age, location, type of educational institution, and class duration. These findings not only validate the efficacy of AutoML technology in the educational sector but also lay an empirical foundation for future research.

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Harnessing AI to Achieve the Best Results from data Science 3

Harnessing AI to Achieve the Best Results from Data Science

引用

3rd international conference on Smart Generation Computing, Communication and Networking, SMART GENCON 2023

作者： Valarmathy, A.S. Mokashi, Mandar K. Jakhar, Sunil Kumar Singh, Vaishali Asha, K.S. Kaur, Ashmeet Prince Shri Venkateshwara Padmavathy Engineering College Department of Electrical and Electronics Engineering Chennai127 India Vishwakarma Institute of Information Technology Department of Information Technology Pune India Vivekananda Global University Department of Mechanical Engineering Jaipur India Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh India Karnataka Bangalore India Centre of Interdisciplinary Research in Business and Technology Chitkara University Institute of Engineering and Technology Chitkara University Punjab India

ISBN: (纸本)9798350319125

The integration of artificial intelligence (AI) with records science can result in extra efficient and accurate outcomes in the evaluation of large datasets. AI techniques have already been validated to be helpful to facts and technological know-how via automating certain information preprocessing, function engineering, version training, and parameter tuning tasks. In this paper, we discuss the automation process for data analysis, which involves the use of AI techniques such as machine learning, natural language processing, and predictive modeling to automate the data analysis process. This not only saves time and resources but also eliminates the potential for human errors. We also highlight the benefits of this approach, including improved accuracy, efficiency, and scalability. Furthermore, we explore the role of data scientists in this automation process and how they can work together with AI to achieve the best results from data science. Finally, we discuss some potential challenges and limitations of automation in data analysis and suggest ways to overcome them. By harnessing AI to automate the data analysis process, organizations can tap into the full potential of their data and make informed decisions that drive business growth and success. By using AI, information scientists can gain entry to the kingdom of the art strategies to progress further in their data science journey. © 2023 IEEE.

关键词： Contextual Integration Relationships Scientists Techniques

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Exploration of Training Practice for Rural Teachers in the New Era Under the Guidance of Core Literacy 3

Exploration of Training Practice for Rural Teachers in the N...

引用

3rd international conference on Digitalization and Management Innovation, DMI 2024

作者： Wang, Ziqi Chen, Mei School of Education Inner Mongolia Normal University Inner Mongolia Hohhot China

ISBN: (纸本)9781643685823

The introduction of new curriculum standards for various subjects in compulsory education has accelerated the pace of new curriculum reform. How to better cultivate students' core literacy and enhance their ability to solve practical problems in real life has become the primary task that teachers need to solve. However, teachers in rural areas often tend to adopt traditional teacher led teaching methods, and have limited learning opportunities, making it difficult for them to receive guidance from new ideas and technologies from the outside world. Implementing teaching reforms guided by literacy poses certain challenges. To this end, a special training program for rural teachers has been launched in pilot schools, which comprehensively cultivates the teaching content organization ability, learning evaluation integration teaching ability, intelligent technology application ability, classroom data analysis ability, etc. of rural teachers, in order to enhance their professional literacy from multiple aspects, bring new learning methods to rural students, and promote the development of students' literacy. © 2025 The Authors.

关键词： Federated learning

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