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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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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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2024 ieee 6th international conference on Power, intelligent computing and systems, ICPICS 2024

2024 IEEE 6th International Conference on Power, Intelligent...

引用

6th ieee international conference on Power, intelligent computing and systems, ICPICS 2024

ISBN: (纸本)9798350374315

The proceedings contain 299 papers. The topics discussed include: research on lightning fault analysis and lightning protection application of 500 kV transmission line;algorithm-based optimization of YOLOv5s model for robot trajectory detection study;boosting the feature extraction power of convolutional networks via mixed-weighting;deep learning based optimization of UAV orbital paths;fault prediction for distribution transformers based on stacking;electric field distribution and optimization design of XLPE cable joints under typical defect;research on auxiliary terminal system of power communication operation and maintenance based on augmented reality technology;an improved path planning algorithm for ship track planning based on grey wolf algorithm;and optimizing motion capture data in animation sequences using machine learning techniques.

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Privacy Analysis of Federated Learning via Dishonest Servers 9

Privacy Analysis of Federated Learning via Dishonest Servers

引用

9th ieee international conference on Big data Security on Cloud (BigdataSecurity) / 9th ieee international conference on High Performance and Smart computing (ieee HPSC) / 9th ieee international conference on intelligent data and Security (ieee IDS)

作者： Jeter, Tre' R. Thai, My T. Univ Florida Dept Comp & Informat Sci & Engn Gainesville FL USA

ISBN: (纸本)9798350312935

Federated Learning (FL) has gained popularity for its ability to improve model training while protecting user privacy. However, recent studies have shown that. FL can be vulnerable to active reconstruction attacks by dishonest servers. Specifically, a dishonest server can obtain users' private data in numerous ways via gradient inversion based on the core neural network concept of neuron activation. Addressing this style of attack is imperative to preserve user privacy and remains a major challenge due to its sophisticated nature. In this paper, we examine various active reconstniction attacks by a dishonest server and provide comprehensive evaluations to demonstrate their effectiveness and practicality, highlighting the risks associated with FL systems.

关键词： Federated Learning data Privacy data Leakage Dishonest Servers Reconstruction Attacks

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Secure data Transmission Using Steganography by AES AlgorithmTitle

Secure Data Transmission Using Steganography by AES Algorith...

引用

2024 international conference on Advances in data Engineering and intelligent computing systems, ADICS 2024

作者： Manoharan, L. Tamezheneal, R. Velmurugan, S. Dwibedi, Rajat Kumar Saravanapandian, M. Rani, L.Pattathu Hindustan Institute Of Technology And Science Department Of Electronics & Communication Engineering Chennai India Sri Sairam Engineering College Department Of Electronics & Communication Engineering Chennai India T.J.S. Engineering College Department Of Electronics & Communication Engineering T.J.S. Nagar Peruvoyal India Aarupadai Veedu Institute Of Technology Department Of Electronics & Communication Engineering Chennai India Psr Engineering College Department Of Electronics & Communication Engineering Sivakasi India Saveetha School Of Engineering Department Of Electronics & Communication Engineering Chennai India

ISBN: (纸本)9798350364828

With the rapid advancement of data transmission and communication technology, there has been a parallel increase in concerns surrounding privacy and security. Many encryption techniques were introduced to reduce the risk of data loss and malicious attacks. Steganography is also one of those techniques that cover up the existence of certain data in a cover document or media ensuring its safety. This paper presents an image steganography technique that allows the secure transmission of data using the AES algorithm. This advanced encryption-based image steganographic mechanism ensures data encryption with the help of the segmentation key or the steganographic key. The masked-up data is extracted using the same segmentation key from the receiver end to view the data. This technique helps in transmitting confidential data without being accessed by unintended recipients, thereby preserving information secrecy. © 2024 ieee.

关键词： Steganography

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Big data-Driven intelligent Structure of Two-ticket System for Safety Production in Power Grid Enterprise 3

Big Data-Driven Intelligent Structure of Two-ticket System f...

引用

3rd international conference on Electronics and Information Technology, EIT 2024

作者： Huang, Chengwei Yang, Jian Xie, Wenhai Han, Lei Deng, Yan Hezhou Power Supply Bureau of Guangxi Power Grid Co. Ltd. Guangxi China Hohai University College of Information Science and Engineering Changzhou China

ISBN: (纸本)9798350369151

As power systems become more intelligent, the intelligent structure of two-ticket system in power grids is key to improving efficiency and safety. This paper investigates the integration of edge computing, artificial intelligence, IoT, and big data into a three-layer system: data collection, edge computing, and central management. Each layer enhances real-time performance, reliability, and intelligence. The system reduces latency through local processing and optimizes decision-making at the central level. Application scenarios demonstrate its practicality and potential for improving power grid operations. © 2024 ieee.

关键词： Smart power grids

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Secure Multimedia data systems in the Era of Artificial Intelligence: Significant Progress and Vision for the Future 5

Secure Multimedia Data Systems in the Era of Artificial Inte...

引用

5th ieee international conference on Trust, Privacy and Security in intelligent systems and Applications (TPS-ISA)

作者： Thuraisingham, Bhavani Univ Texas Dallas Richardson TX 75083 USA

ISBN: (纸本)9798350323856

Multimedia data systems manage complex data types such as text, images, audio, and video. Extensive research and development efforts in multimedia data management systems have been carried out since the1980s. Furthermore, significant progress has been made on securing such systems. For example, techniques for multilevel security for such systems as well as enforcing access control policies have been developed. More recently, with the advent of big data technologies, data science and Artificial Intelligence, there is a need for managing, analyzing, and securing massive data systems with heterogeneous data types. This paper first describes the progress that has been made on securing multimedia data systems and then describes our vision for securing such systems in the era of Artificial Intelligence.

关键词： Multimedia data systems Security and Privacy Machine Learning Big data Blockchain Artificial Intelligence Quantum computing

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Driver Action Recognition in Low-Light Conditions: A Multi-View Fusion Framework 7

Driver Action Recognition in Low-Light Conditions: A Multi-V...

引用

ieee 7th international conference on advanced Technologies, Signal and Image Processing (ATSIP)

作者： Abdullah, Karam Jegham, Imen Mahjoub, Mohamed Ali Ben Khalifa, Anouar Univ Mosul Coll Educ Pure Sci Dept Comp Sci Mosul Iraq Univ Sousse Ecole Natl Ingn Sousse LATIS Lab Adv Technol & Intelligent Syst Sousse 4011 Tunisia Univ Jendouba Inst Natl Technol & Sci Kef Le Kef 7100 Tunisia

ISBN: (纸本)9798350351491;9798350351484

As big data continues to rise, the fusion of information emerges as one of the primary concerns in the fields of computer vision and intelligent transportation systems. A multi-view driver action recognition system can be a key to road safety, especially at nighttime. In fact, driver monitoring from a single view is one of the most challenging task due to the high amount of issues present including occlusion and illumination variation. A multiview driver action recognition system can provide more abundant and complementary driver behavior information which helps to prevent dramatic traffic injuries. In this work, we introduce a multi-viewpoint information fusion system that reliably classifies drivers' actions at nighttime. Experiments on the public and unique realistic dataset, 3MDAD, demonstrate that our proposed approach achieves high recognition accuracy compared to state-of-the-art methods.

关键词： Driver Action Recognition Nighttime Fusion Multi-view Deep Learning Hybrid Network

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Optimized data Processing for Hard Drive Failure Prediction 5

Optimized Data Processing for Hard Drive Failure Prediction

引用

5th international conference on Emerging systems and intelligent computing, ESIC 2025

作者： Sivakumar, M. Ragavendra, R. Satapathy, Sandeep Kumar Mishra, Shruti Mallick, Pradeep Kumar Vellore Institute of Technology School of Computer Science & Engineering Tamilnadu Chennai India Vellore Institute of Technology Centre for Advanced Data Science Tamil Nadu Chennai India Kiit University School of Computer Engineeing Odisha India

ISBN: (纸本)9798331522100

With the rise of big data, efficient data handling techniques have become increasingly crucial. Hard drive failure prediction is a critical domain where accurate and timely data handling is paramount. This research paper introduces a data analysis and pre-processing approach for hard drive failure prediction based on machine learning techniques. A large dataset encompassing hard drive operational data, metadata, and error rates was collected. Extensive pre-processing was performed, including data cleaning and transformation. Multiple Machine Learning algorithms, including random forests, were employed to train predictive models for hard drive failure. The results demonstrate the effectiveness of our approach in achieving high accuracy for hard drive failure prediction, highlighting the significance of our pre-processing methods. Additionally, this paper compares existing machine learning algorithms and explores the role of big data in enhancing hard drive failure prediction. The utilization of big data and advanced data handling techniques has led to improved accuracy and predictive capabilities, making it an indispensable factor in this field. © 2025 ieee.

关键词： data curation

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