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Distance intelligent Learning Based on Big data Technology 16

Distance Intelligent Learning Based on Big Data Technology

引用

16th ieee international conference on Computational Intelligence and Communication Networks, CICN 2024

作者： Wang, Huanan Tianjin Xiqing Open University Tianjin300380 China

ISBN: (纸本)9798331505264

The popularity of the Internet and intelligent devices has become increasingly high, and distance intelligent learning has become an increasingly popular education method. However, providing personalized learning experiences and effective teaching support remains a challenge. The article utilized big data technology to analyze student behavior and learning data, providing personalized learning recommendations and teaching assistance. By analyzing learning data, students' learning preferences, difficulties, and needs were understood, providing them with targeted learning resources and support. By applying big data technology, distance intelligent learning can better meet the personalized needs of students, and improve learning effectiveness and teaching quality. The research results of the article indicated that after using cloud computing as a distance intelligent learning method, students achieved the highest academic performance of 88 points. Distance intelligent learning based on big data technology is of great significance for improving students' learning outcomes and experiences. © 2024 ieee.

关键词： Contrastive Learning

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Artificial Intelligence Based Smart Print on Demand (POD)

Artificial Intelligence Based Smart Print on Demand (POD)

引用

2024 ieee international conference on intelligent systems and advanced Applications, ICISAA 2024

作者： Garai, Pooja Gholap, Sakshi Gaikwad, Nikunj Chavan, Atharva Sagvekar, Janhavi Mane, Atharva Bhise, Suvarna Vasantdada Patil Pratishthan's College Of Engineering and Visual arts Department Of Artificial Intelligence and Data Science Mumbai India

ISBN: (纸本)9798331539948

This project presents the development of a cutting-edge Smart Print on Demand system that leverages advanced technologies such as TensorFlow, HTML, CSS, and JavaScript, integrated with the innovative DALL·E 3 model. The primary objective is to revolutionize the print-on-demand industry by introducing intelligent and dynamic content generation for customized print products. The project architecture incorporates TensorFlow, a powerful machine learning library, to enhance the system's ability to understand and respond to user preferences. The use of HTML and CSS ensures an intuitive and visually appealing user interface, providing a seamless and engaging experience for customers interacting with the print-on-demand platform. JavaScript is employed for dynamic and responsive features, enabling real-time updates and interactive functionalities. © 2024 ieee.

关键词： Tensors

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Performance Study on Normalization and Denormalization in MES System databases

Performance Study on Normalization and Denormalization in ME...

引用

2024 international conference on intelligent computing and data Mining, ICDM 2024

作者： Zhou, Fang Gao, Ying Shandong Water Conservancy Vocational College Department of Information Engineering Rizhao China University of International Business and Economics School of Statistics Beijing China Jinan University School of Business Jinan China

ISBN: (纸本)9798331506261

This study investigates the performance trade-offs between high normalization and denormalization in MES system databases. We constructed and compared two models: one adhered to the Third Normal Form (3NF) for high normalization, and the other incorporated moderate denormalization. Utilizing Apache JMeter for performance testing, our results demonstrate that denormalization markedly improves query efficiency, evidenced by a reduction in response times of approximately 98.92% and an increase in throughput by 96.88%. These findings offer valuable insights and practical guidance to database designers, highlighting the benefits of denormalization in balancing data consistency with enhanced query performance. © 2024 ieee.

关键词： database systems

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Research on the Human-Machine Collaborative Optimisation Algorithm for intelligent Traffic Signal Control systems

Research on the Human-Machine Collaborative Optimisation Alg...

引用

2024 international conference on computing, Robotics and System Sciences, ICRSS 2024

作者： Li, Zhuoxun Chen, Junju Wu, Kefan Wu, Zhanyi Liu, Qiqi School of Swinburne Shandong University of Science and Technology Shandong Jinan China

ISBN: (纸本)9798331511296

With the worsening of urban traffic congestion, improving traffic flow and resource utilisation efficiency has become an important challenge in the field of intelligent transportation. Traditional traffic signal control systems lack dynamic adaptability and are difficult to cope with complex and ever-changing traffic environments. To address this problem, this paper proposes a bionic ant path planning algorithm based on human-machine collaborative optimisation. The system obtains real-time road traffic data, including information such as signal status, traffic flow and obstacle distribution, through a data acquisition module, and uses an improved ant colony algorithm to achieve global path search and dynamic optimisation. At the same time, a human-machine collaborative interaction mechanism is designed to allow users to adjust path weight parameters in real time according to actual needs, enhancing the flexibility and adaptability of the system. Through simulation experiments, this paper verifies the performance of the proposed algorithm in different scenarios and compares it with traditional path planning algorithms. The results show that the ant colony algorithm based on human-machine collaborative optimisation has significant advantages in terms of convergence speed, path optimisation effect and dynamic environmental adaptability. This study provides an efficient, real-time and flexible path optimisation solution for intelligent traffic signal control systems, which can effectively improve traffic scheduling capabilities in complex dynamic environments. © 2024 ieee.

关键词： Traffic congestion

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An intelligent model to enhance user experience in E-learning systems

An intelligent model to enhance user experience in E-learnin...

引用

2024 international Research conference on Smart computing and systems Engineering, SCSE 2024

作者： Udugahapattuwa, D.P.D. Fernando, M.S.D. University of Moratuwa Department of Computer Science and Engineering Moratuwa Sri Lanka

ISBN: (纸本)9798350375688

E-learning systems improve day by day. Therefore, it is important to monitor and evaluate student performance to provide targeted content. This paper focuses on a model for intelligent E-learning systems that can identify student behaviors and provide personalized content recommendations. data mining is used to analyze user interactions and patterns in E-learning systems. This analysis helps identify areas for improvement and personalize the learning experience. The paper highlights the benefits of personalized learning, increased satisfaction, early intervention, and improved content recommendation within an E-learning system. These concepts form a model for student interaction and content recommendation aspects. © 2024 ieee.

关键词： data mining

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AK-Medoids-based Framework for Traffic Monitoring in Low Illumination Conditions 3

AK-Medoids-based Framework for Traffic Monitoring in Low Ill...

引用

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

作者： Ganesh Ram, R. Joshua Samuel, P. Vigneshwaran, V. Rajagopal, S. National Engineering College Kovilpatti India

ISBN: (纸本)9798331527549

A novel unsupervised framework employs YOLOv4 (You Only Look Once, Version 4) to enable real-time object detection on roadways, significantly enhancing intelligent traffic monitoring. The system efficiently identifies vehicles, pedestrians, and other road objects in various challenging conditions, such as low-light environments, varying weather, and high-traffic density. By adopting an unsupervised approach, the framework eliminates the need for large labeled datasets, ensuring scalable deployment across diverse road networks. With integrated difference image analysis and K-medoids clustering, the framework boosts detection accuracy, achieving an impressive 92% detection accuracy—representing a 7% improvement over YOLOv4 and a 12% improvement over YOLOv3. Difference images highlight significant frame-to-frame changes, essential in low-illumination scenarios, while K-medoids clustering refines object grouping and reduces detection noise. These techniques complement YOLOv4’s object detection capabilities, providing a robust solution for real-time monitoring. With edge computing for low-latency performance, the system is well-suited for large-scale deployment in intelligent Transportation systems. By combining unsupervised methods with advanced object detection, this framework delivers a scalable, efficient, and adaptive solution for continuous traffic monitoring, contributing to improved road safety. © 2025 ieee.

关键词： intelligent systems

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Modeling a Hybrid Stemmer for Kokborok

Modeling a Hybrid Stemmer for Kokborok

引用

2023 international conference on intelligent systems, advanced computing and Communication, ISACC 2023

作者： Debbarma, Pankaj Hrangkhawl, Pusparwng Debbarma, Swapan Tripura Institute of Technology Department of Computer Science and Engineering Narsingarh India Ambedkar College Department of Information Technology Fatikroy India National Institute of Technology Department of Computer Science and Engineering Agartala India

ISBN: (纸本)9798350399387

Kokborok is a resource scarce and vulnerable language and is spoken only by around a million people in the north-east Indian state of Tripura. Lots of unstructured textual data in Kokborok is now available however efficient data mining for such a resource is not applicable. Reasons are the absence of an efficient pre-processing step that can help in solving the information retrieval or natural language processing. In this work, we present a model of hybrid stemming algorithm for the vulnerable and resource-scarce Kokborok language and assess its performance using various methods. Evaluation of the hybrid stemmer was performed on a corpus of 52,482 words that resulted in achieving an accuracy of 89.28%. Recall and Precision of 89.28% and 89.61 %, respectively, with F1-measure of 89.44 has been found for the stemmer. © 2023 ieee.

关键词： data mining

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CC Link IE Based Remote Monitoring and Control of Windmills Using Slot Type Controller 10

CC Link IE Based Remote Monitoring and Control of Windmills ...

引用

10th international conference on advanced computing and Communication systems, ICACCS 2024

作者： Jagatap, Govind Vishwanath Ingale, Sanjaykumar Patil, Deepak T. Mane, Atish Bandpatte, Sagar Chandu Hariharan, R. Bharati Vidyapeeth College of Engineering Department of Mechanical Engineering Navi Mumbai400614 India Sanjay Ghodawat University Department of Mechanical Engineering Kolhapur India Bharati Vidyapeeth college of Architecture Navi Mumbai India Bharati Vidyapeeth College of Engineering Department of Mechanical Engineering Pune Lavale India Bharati Vidyapeeth Deemed to be University College of Engineering Department of Chemical Engineering Pune India Sri Eshwar College of Engineering Department of Electrical and Electronics Engineering Coimbatore641 202 India

ISBN: (纸本)9798350384369

There are millions of wind mills are operated in the world and producing the electricity to meet out the demand. Generation of electricity and the transmission of the same is an easy one to all the power sector companies or power engineers. But the monitoring of windmills from the remote location and exchanging the data between the control station and the windmill is somehow tedious task. Even though the wireless communication and SCADA are implemented in most of the windmills, the lack of speed or data transmission and acquisition of real time data without any dead band is a challenging one. In ordered to over this drawback, CC-Link IE based wired transmission is introduced. It uses the modular master controller called 'R04 CPU', CC-Link networking module and few remote I/O Modules near the windmill station. It reduces the loss of data and improves the communication speed, data handling capacity and security. © 2024 ieee.

关键词： SCADA systems

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Elastic Stack: A Comprehensive Overview 6

Elastic Stack: A Comprehensive Overview

引用

6th ieee international conference on Recent Advances in intelligent Computational systems, RAICS 2024

作者： Shaji, Ayda Sara George, Mithu Mary Toc H Institute of Science and Technology Department of Computer Science and Engineering Kerala India Muthoot Institute of Technology and Science Department of Computer Science and Engineering Kerala India

ISBN: (纸本)9798350381689

This document thoroughly explores the Elastic Stack, comprising Elasticsearch, Logstash, Kibana, and Beats, emphasizing its effectiveness in real-time data analysis, log handling, and visualization. It examines the essential components, architecture, and practical uses in different fields, offering in-depth technical insights into each element, integration methods, and practical examples. Readers will gain a comprehensive grasp of the Elastic Stack's capabilities and its application potential in various scenarios by the conclusion of this paper. © 2024 ieee.

关键词： data handling

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Research on Efficient Traffic Light Detection System in intelligent Connected Vehicles 5

Research on Efficient Traffic Light Detection System in Inte...

引用

5th international conference on Big data and Artificial Intelligence and Software Engineering, ICBASE 2024

作者： Zheng, Ke Tian, Chuanyin Li, Qingxia Zhao, Chunsheng Catarc Intelligent & Connected Technology Co. Ltd. Tianjin China

ISBN: (纸本)9798331506612

This paper uses a method based on the YOLOv5s network to study the real-time traffic light detection task in the vehicle-mounted intelligent system. Due to the limitation of computing resources on the actual vehicle and the particularity of the traffic light detection task, this paper optimizes the YOLOv5s network and proposes an explicit multi-scale fusion module and a cross-long-order spatial attention module to improve the model's detection ability for small targets such as traffic lights. At the same time, it reduces the number of channels and scales of the network, and improves the detection efficiency while ensuring accuracy. On the optimized network, by using public datasets and self-collected datasets for training, the loss of computing power of the on-board computing unit is reduced while ensuring high accuracy. Finally, on the intelligent connected vehicle platform, efficient and accurate real-time traffic light detection function is realized. © 2024 ieee.

关键词： intelligent systems

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