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

共 20250 条记录，以下是251-260 订阅

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Multivariate Time series Forecasting for Cloud Machine Utilization 3

Multivariate Time series Forecasting for Cloud Machine Utili...

引用

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

作者： Naik, Archana Sooda, Kavitha Department of Computer Science and Engineering Nitte Meenakshi Institute of Technology Bangalore India Department of Computer Science and Engineering B.M.S. College of Engineering Bangalore India

ISBN: (纸本)9798331527549

The cloud machine utilization prediction using a multivariate time series model is explored in this study. The cloud machine usage data set samples are selected with varied patterns. The long sequence forecasting model Informer is investigated for the forecasting. The model results are benchmarked against LSTM, GRU, Prophet, and XGBoost across the diverse datasets. The performance is evaluated using RMSE. Experimental results show that the Prophet model performs best overall due to its ability to capture both seasonal and irregular patterns accurately. The RMSE value for Informer is 0.13, Prophet is 0.097, XGBoost is 0.096, LSTM is 0.1725, and GRU is 0.1547. © 2025 IEEE.

关键词： Time series

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Attribute Extraction of Geographic Entities Based on the LERT Pre-trained Model 24

Attribute Extraction of Geographic Entities Based on the LER...

引用

3rd international conference on Artificial Intelligence and intelligent Information Processing, AIIIP 2024

作者： Li, Zeyu North China Institute of Computing Technology Beijing China

ISBN: (纸本)9798400707308

Geographic information systems(GIS) are now changing from two-dimensional expression to three-dimensional expression, and the geographic data gradually transforms into a more intuitive and more applied method of expression. In order to support the data expression in GIS, attributes extraction from unstructured Chinese geographic texts is needed. Manual extraction or pattern-matching methods have been insufficient to get geographic attributes, therefore deep learning methods for attribute extraction are required to meet the needs of the supplementary for entity properties. This paper took LERT for pre-training in the word-embedding layer and the BiLSTM module combined with attention mechanism for the feature extraction layer, finally obtained the attribute-value pairs as the output. The experimental results show that the accuracy, recall rate and F1 value of the proposed model on the test set improved compared with the existing model, proving the effectiveness of the proposed model. © 2024 Copyright held by the owner/author(s).

关键词： Adversarial machine learning

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Dual-Plate Verification System for Vehicle Identifcation using IoT and Embedded Systems 3

Dual-Plate Verification System for Vehicle Identifcation usi...

引用

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

作者： Sampath, A.V.S.S. Malagati, Nischal S. Kanapathi, V. Belwal, Meena Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Bengaluru India

ISBN: (纸本)9798331527549

This research uses a dual-plate verification system for improving vehicle security by using IoT and embedded systems. The system compares front and rear license plates to detect any anomaly, which signals unauthorized modifications. Using real-time image processing and IoT-based data communication, this solution gives us an accurate plate matching system, making sure of reliable vehicle identity verification. Its possible applications include traffic monitoring, parking management, and detecting any unlawful activity. This project is unique because it integrates the dual plate verification system with an Arduino IoT setup. For hassle-free text recognition, this system uses deep learning frameworks combined with a wireless mobile setup that serves as a camera. The system’s accuracy is shown through experiments, displaying its value in improving transportation security with automation. The number plates were detected, and the respective penalties for the vehicles were imposed accordingly. © 2025 IEEE.

关键词： Anomaly detection

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Detecting and Analyzing Threats for Network Traffic Control using Machine learning Approaches 3

Detecting and Analyzing Threats for Network Traffic Control ...

引用

3rd international conference on Innovative Sustainable Computational Technologies, CISCT 2023

作者： Mathur, Richa Dutta, Ritam Poornima University Dept. of Computer Science & Engineering Rajasthan Jaipur303905 India

ISBN: (纸本)9798350303360

Today big data playing pivotal role in helping mankind with large amount of dataset that can be beyond human perception. Big data is ultra-fast and growing rapidly. This data is more valuable because it has hidden facts and insights after its process. But various challenges exist when we deal with such data with the help of traditional tools and technologies. Earlier the traditional network security solutions were insufficient to address different kinds of threats and network attacks. To resolve and restrict cyber threats and attacks is the most increasing concern in today's world of cyber security. With the advent of Machine learning (ML) technology, an automated and up-to-date security system has been provided through which we can detect, analyze and secure data for network traffic and cyber security. In this paper, a comprehensive analysis on various machine learning algorithms have been investigated to identify the glitches and provides intelligent data analysis report and automation in cybersecurity to extract valuable insights from cyber data. © 2023 IEEE.

关键词： Cybersecurity

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intelligent Tutoring System for English Language Acquisition Based on Computer Algorithms 3

Intelligent Tutoring System for English Language Acquisition...

引用

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

作者： Li, Xing Hunan University of Medicine NO.492 Jinxi South Road Hecheng District Hunan Huaihua China

ISBN: (纸本)9798331519032

With the continuous deepening of computer application, computer-assisted instruction (CAI) has made remarkable progress in foreign language learning. Foreign language learning mainly relies on memory-based learning methods, and CAI has a unique advantage in assisting memory by providing information in the form of visuals, images, sounds and animations, thus constructing a multidimensional knowledge layer in the brain and enhancing learning efficiency. At present, CAI English learning software mainly focuses on memorizing words and quizzes, but tutoring software specifically for college English level exams is scarce. In this paper, based on computer algorithms, combining collaborative filtering algorithm and convolutional neural network algorithm, an intelligent tutoring system for English language acquisition is designed and realized. The experimental data show that students using the intelligent tutoring system show more significant improvement in English proficiency improvement than traditional learning methods, and the average improvement score of the experimental group is about 6 points higher than that of the control group, further verifying the effectiveness of the system in improving learning efficiency. © 2024 IEEE.

关键词： Adversarial machine learning

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Research on user interest prediction and recommendation algorithm based on artificial intelligence algorithm 3

Research on user interest prediction and recommendation algo...

引用

3rd IEEE international conference on Electrical engineering, Big data and Algorithms, EEBDA 2024

作者： Yang, Yiming Tianjin University of Technology Tianjin China

ISBN: (纸本)9798350380989

With the development of the Internet, more and more data are generated by users on the network, and the interests of users are becoming more and more diverse. How to predict the user's interest according to the user's behavior data and recommend relevant content to the user has become a hot research direction in the Internet field. In this paper, 'Research on User interest prediction and recommendation Algorithm based on artificial intelligence algorithm', the user interest prediction and recommendation algorithm is deeply studied and discussed. Firstly, this paper summarizes and analyzes the relevant algorithms of user interest prediction. Traditional user interest prediction algorithms are mainly modeled based on user behavior data and content characteristics, such as collaborative filtering algorithm and content-based recommendation algorithm. However, these algorithms have certain limitations when dealing with massive data and diverse user interests, so it is necessary to introduce artificial intelligence algorithms to improve prediction accuracy and recommendation effect. Secondly, this paper proposes a solution based on artificial intelligence algorithm for user interest prediction and recommendation. Firstly, feature extraction and representation learning of user behavior data are carried out by deep learning algorithm, so as to achieve accurate prediction of user interest. Secondly, the model algorithm is used to optimize the decision-making strategy of the recommendation system, so as to improve the personalization and accuracy of the recommendation. By introducing artificial intelligence algorithm, hidden information in user behavior data can be better mined, and the accuracy and effect of user interest prediction and recommendation can be improved. Finally, this paper verifies the effectiveness of the user interest prediction and recommendation algorithm based on artificial intelligence through experiments. The experimental results show that the introduct

关键词： Artificial intelligence data mining intelligent recommendation interest prediction

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Construction of intelligent Fault Diagnosis System for Pumping Station Based on Fusion Algorithm 3

Construction of Intelligent Fault Diagnosis System for Pumpi...

引用

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

作者： Chen, Taiwu Dong, Fei Wang, Haojie Xu, Zhiming Liu, Erjun Lianyungang Linhong Water Conservancy Project Management Office Jiangsu Lianyungang222006 China Tongyu River North-extended Water Supply Project Management Division of Lianyungang Lianyungang Jiangsu 222005 China Lianyungang Urban Water Engineering Management office Lianyungang Jiangsu 222005 China

ISBN: (纸本)9798331519032

The purpose of this article is to study and implement an intelligent fault diagnosis system of pumping station based on fusion algorithm, so as to improve the accuracy and efficiency of fault diagnosis of pumping station. Firstly, this article points out the limitations of traditional fault diagnosis methods, such as insufficient information from a single sensor and low diagnostic accuracy. Then this article designs and implements an intelligent fault diagnosis system for pumping stations, which includes data acquisition, preprocessing, feature extraction, fusion algorithm diagnosis and user interaction. Among them, the fusion algorithm adopts the multi-modal information fusion method based on deep learning, and realizes intelligent identification and classification of pump station faults by comprehensively analyzing the characteristic parameters from different sensors. The experimental results show that the fusion algorithm shows high accuracy in fault diagnosis of pumping stations, and the false alarm rate and false alarm rate are low. The conclusion is that the intelligent fault diagnosis system of pumping station based on fusion algorithm has high practicability and reliability, and can provide strong support for the operation and maintenance management of pumping station. © 2024 IEEE.

关键词： Deep learning

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DeepSegRecycle: Deep learning and ImageProcessing for automated Waste Segregation and Recycling 3

DeepSegRecycle: Deep Learning and ImageProcessing for Automa...

引用

3rd international conference on Advancement in Electrical and Electronic engineering, ICAEEE 2024

作者： Forhad, Shamim Jahan, Israt Biswas, Parag Rashid, Abdur Ahsan, Iraj Ahmed Fahim, Lutful Hassan Noman, Shibly Shuvo, Md. Riazat Kabir Department of EEE Dhaka Bangladesh Department of EEE Dhaka Bangladesh CA United States Yangzhou University Department of SE Jiangsu Province China Jiangsu Normal University Department of SE Xuzhou China Department of EEE Dhaka Bangladesh Department of CSE Dhaka Bangladesh

ISBN: (纸本)9798350388282

Effective waste management is a critical issue for sustainable development, and automated waste segregation and recycling systems can potentially revolutionize how we handle waste. This study introduces DeepSegRecycle, a unique approach that autonomously separates garbage for recycling using deep learning techniques. In this study, the GoogleNet CNN model has been used to identify different types of garbage. We also provide the findings from our performance evaluation of the system, which show its great accuracy of 97.3% in processed models and 93.78% accuracy in real-life testing scenarios. The effectiveness of garbage classification was verified by testing 1000 picture data of each category, ensuring precision in the evaluation. Our study emphasizes the promise of deep learning and image processing techniques for solving the worldwide waste management problem. By lowering the quantity of the garbage that goes to landfills and encouraging the circular economy, DeepSegRecycle has the potential to improve the environment and human health significantly. © 2024 IEEE.

关键词： Automation

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Optimisation Techniques in Investment Risk Management :Scipy, SLSQP and Modern Portfolio Theory 3

Optimisation Techniques in Investment Risk Management :Scipy...

引用

3rd international conference on intelligent Systems, Advanced Computing, and Communication, ISACC 2025

作者： Garg, Aayushi Singh, Aditya Pratap Saraswat, Anushka Kumar, Avnish Taluja, Anuradha Ajay Kumar Garg Engineering College Department of Computer Science Ghaziabad India Ajay Kumar Garg Engineering College Department of Computer Science & Engineering Ghaziabad India

ISBN: (纸本)9798331523893

Organizations achieve successful investment risk management by achieving optimal risk exposures and return objectives at the same time. The research examines optimization methodologies through its comparison of SciPy's optimization suite to SLSQP while incorporating Modern Portfolio theory. This research analyzes historical financial data to examine both risk-adjusted return metrics along with computational efficiency metrics. The research study provides detailed mathematical definitions about optimization procedures and performs quantitative assessments on data output and suggests future work that integrates machine learning with economic domains. The research demonstrates that these optimization approaches present critical methods to achieve maximum portfolio diversity. © 2025 IEEE.

关键词： Risk management

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What Students Should Learn and Teachers Must Know About Artificial Intelligence 25th

What Students Should Learn and Teachers Must Know About Arti...

引用

25th international conference on intelligent data engineering and automated learning

作者： Opel, Simone Linxen, Andrea Beecks, Christian Fern Univ Hagen D-58097 Hagen Germany

ISBN: (纸本)9783031777370;9783031777387

Systems based on artificial intelligence have become prominent in nearly all domains. However, knowledge of the inner workings of these intelligent systems is not as widespread, partly because the associated issues have been discussed only to a limited extent in computer science education. In order to gain an overview of AI in curricula and to see what competencies teachers need to teach this content, the AIrelated content of the computer science curricula of the German federal states was analysed and compared with existing approaches. Proposals for further training courses are derived from this to enable teachers to teach AI competently.

关键词： Curriculum AI Secondary School Teacher Education learning Students Further Education Curriculum Analysis

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