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

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Neural Network Algorithms in the Construction of intelligent Financial Sharing Platform 3

Neural Network Algorithms in the Construction of Intelligent...

引用

3rd IEEE international conference on Distributed Computing and Electrical Circuits and Electronics, ICDCECE 2024

作者： Fu, Yuanyuan Weifang Engineering Vocational College Shandong Qingzhou China

ISBN: (纸本)9798350318609

The current financial sharing platform has no advantages when dealing with nonlinear relationships. The purpose of this research is to apply neural network algorithms to build an intelligent financial sharing platform to improve the accuracy of financial data processing and forecasting. In this study, a multi-layer perceptron is used as the main neural network algorithm, and a financial data set is used for model training and verification. It performed preprocessing and feature engineering of financial data, and then divided the data into training sets and test sets. After the division is completed, the paper continued to build the multilayer perceptron model, added cross-verification and parameter adjustment techniques to optimize the performance and generalization capabilities of the model, and then evaluated the accuracy of the model after the platform was built. The experimental results show that the accuracy rate of the multilayer perceptron algorithm is between 92% and 98%, and the intelligent financial sharing platform built on the multilayer perceptron algorithm has achieved significant improvements in financial data classification and prediction tasks. © 2024 IEEE.

关键词： Multilayer neural networks

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Research on intelligent Construction Construction engineering System under Computer Digitization Technology 3

Research on Intelligent Construction Construction Engineerin...

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3rd international conference on Artificial Intelligence and Autonomous Robot Systems, AIARS 2024

作者： Sun, Liangjing Han, Lei Shandong University of Engineering and Vocational Technology Jinan Shandong China

ISBN: (纸本)9798350376173

The overall framework of large-scale intelligent building integration system based on IBS technology is proposed. Then, the whole architecture is deeply researched and designed. The realization method of the platform is introduced in detail from the client program design, the main functions of the client and the data transmission mode based on OPC technology. The debugging results show that all performance indexes of the system are good. © 2024 IEEE.

关键词： information domain information system intelligent building OPC protocol system integration

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Next-Gen Dermatology: Revolutionizing Dermatological Diagnostics with AI and Emergency Care Solutions 3

Next-Gen Dermatology: Revolutionizing Dermatological Diagnos...

引用

3rd international conference on intelligent and Innovative Technologies in Computing, Electrical and Electronics, IITCEE 2025

作者： Yashas, D. Kashyap, M. Shivani Charan, P.R. Hemanth Kumar, G.P. Abhishek, B.K. Deekshith, C. New Horizon College of Engineering Dept of Artificial Intelligence and Machine Learning Bengaluru India New Horizon College of Engineering Dept of Information Science and Engineering Bengaluru India Bnm Institute of Technology Dept of Artificial Intelligence and Machine Learning Bengaluru India Siddaganga Institute of Technology Dept of Artificial Intelligence and Data Science Tumkur India Vidyavardhaka College of Engineering Dept of Computer Science and Engineering Mysore India

ISBN: (纸本)9798331515911

Skin health conditions should be diagnosed and treated on time so as to prevent complications and promote better health results. This paper presents an AI-based system that makes use of Convolutional Neural Networks with the InceptionV3 Architecture for predicting skin diseases at higher levels. The model, which is based on the large DermaNet dataset, classifies skin disorders fast and effectively. The system is coupled with a simple Telegram based application for the users to get a complete solution. A user can upload a photo of the affected skin area and get a diagnosis within seconds, an explanation of the disease and how severe it is in percentage. The bot also has a location and contact sharing feature that assists users in finding nearby doctors and clinics that can provide further assistance. It further provides on the spot appropriate cures for the particular condition diagnosed. Moreover, this makes it possible for the customer to fill the healthcare void in particular the neglected ones by making it easy for them to access real time high quality services. By equipping users with relevant information and appropriate actions required at the right time, this project has showcased how AI has the ability to transform the dermatological field. © 2025 IEEE.

关键词： Convolutional neural networks

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Human Activity Recognition with EfficientNetB3: A Model-Driven Approach to Complex Activity Classification 3

Human Activity Recognition with EfficientNetB3: A Model-Driv...

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3rd international conference on intelligent data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Kaur, Jashanpreet Rani, Shalli Singh, Gurpreet Archana Reddy, R. Chitkara University Institute of Engineering and Technology Chitkara University Punjab Rajpura140401 India School of Computer Science and Artificial Intelligence SR University Telangana Warangal506371 India

ISBN: (纸本)9798331527549

Human Activity Recognition (HAR) is an application that encompasses surveillance to health care monitoring. The paper propounds deep learning (DL) in the classification of 15 different activities including calling, cycling, dancing, and running etc. through the use of EfficientNetB3 for the convolutional neural network. The dataset comprises more than 12,000 images with one specific category per image. In the experiment, different forms of data augmentation were used to avoid overfitting and enhance generalization. EfficientNetB3 has excellent balance between accuracy and computational efficiency in handling intricate complex patterns with fewer parameters and thus adopted for this experiment. The model was trained from scratch for 50 epochs, resulting in an accuracy of 90.10%, which was higher than that of traditional and hybrid methods of HAR. From a comparative analysis, the proposed model outperforms all competing approaches, such as YOLOv5 with LDA and SVM, as well as hierarchical classification models. The results show that DL based models work very well in discrimination of complex human activities. In an attempt to further make the system more versatile, future work will concentrate on optimization of the model for real-time HAR, fusion with sensor data, and extension to larger datasets with more classes of activities. © 2025 IEEE.

关键词： Convolutional neural networks

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Predicting Employee Attrition in a Multi-company Setting 25th

Predicting Employee Attrition in a Multi-company Setting

引用

25th international conference on intelligent data engineering and automated learning

作者： Gomes, Adriano Silva, Luis M. Cruz, Joao Pedro Univ Aveiro Dept Econ Management Ind Engn & Tourism P-3810193 Aveiro Portugal Univ Aveiro Ctr Res & Dev Math & Applicat CIDMA Dept Math P-3810193 Aveiro Portugal

ISBN: (纸本)9783031777301;9783031777318

This paper describes the creation of a database and a machine learning model to predict employee attrition. Our proposal deals with attrition by considering 3 classes (voluntary, involuntary and no attritors) giving a more complete view of the loss of qualified personnel to the Human Resources Management. Of the several machine learning models tested to solve the problem, XGBoost stood out as the best performing one on a dataset with more than four thousand employees and twenty-one features collected from three independent companies from different industrial sectors. The model, evaluated on a 20-run experiment, achieved an overall mean accuracy of 78.5%, corresponding to the correct classification of 52.6% of the voluntary attritors, 78.9% of the involuntary attritors and 81.6% of the non-attritors, showing that voluntary attritors are harder to discriminate.

关键词： Employee attrition Machine learning Real data Human resources management Industry 4.0

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Artificial Intelligence and Supervised learning based Solid Waste Segregation Mechanism using ESP 32 3

Artificial Intelligence and Supervised Learning based Solid ...

引用

3rd international conference on Ubiquitous Computing and intelligent Information Systems, ICUIS 2023

作者： Srinevasan, M. Parameshwari, V. Abirami, T. Divyaa, N. Nandha Engineering College Dept of Ece Erode India M. Kumarasamy College of Engineering Dept of Ece Karur India Velalar College of Engineering and Technology Dept of Ece Erode India

ISBN: (纸本)9798350306989

Garbage separation must take place at the household level because it is time-consuming and frequently unsuccessful to do it at the disposal. This Proposed method attempts to decrease mannual effort in segregation of waste in order to achieve proper waste segregation with the least amount of effort. This proposed system suggests an automatic trash segregator system powered by IOT. In addition to detecting and classifying household rubbish into dry, moist, and metallic waste streams, it aids in real-time monitoring the levels of garbage in trash discharges. A metallic sensor is then used to determine whether there is any metal in the garbage after an Ultrosonic [radar based] has detected the arrival of the trash. Additionally, the trash is separated using the capacitive detector into dry and moist residue. The conveyor helps to move the waste to the proper bins before the platform rotates once it has been detected and segregated using a supervised learning-based algorithm. Additionally, the system uses ESP 32 to display a note to clean the trash can when it is occupied and to show the amount of compost in the trash discharges on the LCD screen. Experimental results show that the proposed method is effective in categorizing waste into moist and dry waste as well as metal and non-metal waste. © 2023 IEEE.

关键词： Supervised learning

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Federated Dynamic Client Selection Based on Comprehensive Performance Evaluation 3

Federated Dynamic Client Selection Based on Comprehensive Pe...

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3rd international conference on Cloud Computing, Big data Application and Software engineering, CBASE 2024

作者： Tang, Jianqing Guangdong Open University Guangdong Polytechnic Institute Guangdong China

ISBN: (纸本)9798331506582

Federated learning, as a distributed machine learning approach, enables the full exploitation of data value while protecting data privacy. However, traditional Federated learning methods are significantly impacted in terms of model accuracy and convergence speed when dealing with Non-Independent and Identically Distributed (Non-IID) data. This paper introduces a Federated Dynamic Client Selection method based on Comprehensive Performance Evaluation (FedDCP), which dynamically adjusts the probability of client participation in training by integrating the clients' computational capabilities, historical performance, and current performance. This approach optimizes model accuracy, convergence speed, and runtime. Experimental results indicate that the FedDCP method outperforms existing Federated Averaging optimization methods in terms of model accuracy and convergence speed on the MNIST, Fashion-MNIST, and Cifar-10 datasets, with a similar runtime, demonstrating its effectiveness. © 2024 IEEE.

关键词： Federated learning

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Research on intelligent Manufacturing Production Lines for Automobile Wheel Hubs by Digital Twin Models 3

Research on Intelligent Manufacturing Production Lines for A...

引用

3rd international conference on data Analytics, Computing and Artificial Intelligence, ICDACAI 2024

作者： Zhuming, Cao Huanbo, Dong Jide, Jia Shouhua, Yi Xiamen China Metalworking Practice Center School of Advanced Engineering University of Science and Technology Beijing Beijing China Center of Modern Engineering Training Hunan University Changsha China

ISBN: (纸本)9798331519032

Due to the complex geometric shapes, high precision design dimensions, and specific material characteristics of automobile wheel hubs, there are stringent requirements for processing procedures, efficiency, and stability. To meet these demands, enterprises commonly adopt intelligent manufacturing production lines. Key challenges include establishing adaptive intelligent capabilities for the "processing-measurement-compensation-processing" cycle, constructing digital twin models of wheel hub production lines for real-time synchronization in virtual reality production, optimizing equipment and process interfaces, and improving fixture systems. Integrating discrete CNC machining, inspection, and assembly organically is crucial for smart manufacturing engineers. This article provides targeted discussions on core technologies and development directions in this field. © 2024 IEEE.

关键词： Smart manufacturing

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Research on Deep learning-based Driver Fatigue Driving Behavior Recognition Methods 3

Research on Deep Learning-based Driver Fatigue Driving Behav...

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3rd international conference on Big data, Information and Computer Network, BDICN 2024

作者： Sun, Miao Liu, Fan He, Haijun Yang, Zhuomin Lou, Wen Guo, Dudu School of Intelligent Manufacturing Modern Industry Xinjiang University Urumqi China Xinjiang Uygur Autonomous Region Public Security Department Traffic Police Corps Urumqi China Traffic Management Research Institute of the Ministry of Public Security Key Laboratory of Road Traffic Safety Ministry of Public Security Wuxi China School of Traffic and Transportation Engineering Xinjiang University Urumqi China Xinjian Key Laboratoryof Gn. Construction and Smart Traffic Control of Transportation Infrastructure Urumqi China

ISBN: (纸本)9798350363821

Aiming at the problem of motor vehicle drivers' fatigue driving, this paper proposes a deep learning-based fatigue driving behavior recognition method. The four parameters of PERCLOS parameter, blinking frequency, continuous eye closure time and continuous mouth opening time are used as the recognition indexes to establish a driver fatigue driving recognition model based on deep learning, and the experimental results show that the method proposed in this paper has a better recognition effect in different light environments. © 2024 IEEE.

关键词： Deep reinforcement learning

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Design of intelligent elderly care robot system based on ROS 3

Design of intelligent elderly care robot system based on ROS

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3rd international conference on Electronic Information engineering and data Processing, EIEDP 2024

作者： Fan, Yu Chen, Yuanbin Chen, Chin-Ta Zhao, Jianchao Electronic and Electrical Engineering College Zhao Qing University Zhao Qing China

ISBN: (纸本)9781510680531

In order to cope with the increasing challenge of population aging worldwide, intelligent elderly care robots have become a solution that has attracted much attention. This paper constructed an intelligent elderly care Robot based on ROS (Robot Operating System), which could patrol within the specified scope, monitor the status of the elderly, potential security risks, collect physiological data of the elderly, and upload these data to the Internet of things platform in real time to share with family members or guardians. It has a comprehensive nursing function for the elderly. The design is expected to provide effective technical support and practical research for communities and social institutions, and promote the transformation and upgrading of existing pension models toward diversification and wisdom. © 2024 SPIE.

关键词： Machine learning

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