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检索条件"任意字段=International Conference on Data and Knowledge Systems for Manufacturing and Engineering"

共 37467 条记录，以下是4581-4590 订阅

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Question-Centric Evaluation of Descriptive Answers using Attention-Based Architecture 12

Question-Centric Evaluation of Descriptive Answers using Att...

引用

12th international conference on Cloud Computing, data Science and engineering, Confluence 2022

作者： Oasis, Adithya S Abishai Ebenezer, M. Sharma, Dhruv Sada, Rithvik Arya, Arti Pes University Computer Science And Engineering Bangalore India

ISBN: (纸本)9781665437011

Evaluating descriptive student answers is a long and important process for examiners in an educational institution. The amount of resources and time that goes into correcting answer scripts is tremendous. To go along with it, anxiety and further implications of exam scores highlight the need for instant yet accurate exam scores for each student. This calls for the necessity to create automatic evaluation systems to reduce the burden on educational institutions. Current systems use various NLP methods to compare similarity between the student's answer and the expected answer without considering the context in which the question has been asked. In addition to the expected answer, the approach put forward suggests a method for evaluating the answer with the help of the question. This is accomplished using attention networks to extract relevant features in the expected answer and the student's answer using the question. These features are then passed through a neural network classifier to evaluate the student's answer. The proposed architecture yielded an accuracy of 95.88% (+-1) thus proving that knowledge of the question while evaluating a student's answer leads to a more effective answer evaluation. © 2022 IEEE.

关键词： Feedforward neural networks

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A Robust and Accurate Multivariate Time Series Anomaly Detection in Fluctuating Cloud-Edge Computing systems 24

A Robust and Accurate Multivariate Time Series Anomaly Detec...

引用

24th IEEE international conference on High Performance Computing and Communications, 8th IEEE international conference on data Science and systems, 20th IEEE international conference on Smart City and 8th IEEE international conference on Dependability in Sensor, Cloud and Big data systems and Application, HPCC/DSS/SmartCity/DependSys 2022

作者： Song, Yujia Xin, Ruyue Zhang, Rui Chen, Juan Zhao, Zhiming School of Computer and Software Engineering Xihua University Chengdu China Amsterdam Netherlands

ISBN: (纸本)9798350319934

Cloud-edge computing uses edge infrastructure to extend cloud computing further away from the data source, compensating for some of the limitations of conventional cloud computing. Multivariate time series anomaly detection can reflect the operating status of edge cloud computing systems. Many anomaly detection methods have been developed but still face challenges, especially irregular fluctuations in data leading to false positive detection. In addition, good detection robustness is necessary to capture complex data patterns in cloud-edge computing systems, but few existing methods focus on it. Furthermore, monitoring data usually lack labels, while manual data labeling is ineffective. To address these issues, we propose an unsupervised multivariate time series anomaly detection method: Correlative-GNN with Multi-head Self-attention and Auto-Regression Ensemble Method (CGNN-MSAR). Our approach provides parallel Graph Neural Networks(GNNs) to learn inter-dependencies in multivariate time series from feature and time dimensions to achieve fewer false positives without any prior knowledge. Moreover, Our method integrates a multi-head self-attention mechanism and AR models to improve the robustness of the model. Extensive experiments have been conducted on five publicly available datasets. Specifically, CGNN-MSAR outperforms its competitors with an F1-Score of 0.86 on average and is 18.2% better than state-of-the-art baseline methods. © 2022 IEEE.

关键词： Anomaly detection

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IoT Integration and Digital Twin Technology in Smart manufacturing: Advancements and Prospects

IoT Integration and Digital Twin Technology in Smart Manufac...

引用

international Multidisciplinary Information Technology and engineering conference (IMITEC)

作者： Peter Onu Anup Pradhan Nelson Sizwe Madonsela Mechatronics Engineering Programme Bowen University Iwo Nigeria Quality and Operaions Managment University of Johannesburg Johannesburg South Africa

ISBN: (数字)9798350387988

ISBN: (纸本)9798350387995

Integrating Internet of Things (IoT) technologies and Digital Twin (DT) systems has become a mission-critical technological advancement in smart manufacturing. This study explores recent information technology solutions and prospects of IoT integration and DT technology within smart manufacturing environments. Through a comprehensive review of literature and expert insights, this research posits the potential of these technologies in optimizing manufacturing processes, enhancing productivity, and enabling predictive maintenance strategies. Additionally, the study explores the critical role of data analytics in ensuring seamless integration and efficient functioning of IoT and DT systems. This is a contribution to the body of literature on smart manufacturing. It outlines critical areas for future research, including but not limited to ethical implications of AI algorithms, data ownership, privacy concerns, and fair use of intellectual property.

关键词： Industries Technological innovation Machine learning algorithms Reviews Digital twins Internet of Things Security Information technology Artificial intelligence Smart manufacturing

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Differential Privacy in Spatial Task Assignment for Cloud Crowdsourcing

Differential Privacy in Spatial Task Assignment for Cloud Cr...

引用

knowledge engineering and Communication systems (ICKECS), international conference on

作者： S Santhosh S Vadivel P C Yoga B Mohamed Ajmal Computer Science and Engineering K. S. Rangasamy College of Technology Tiruchengode India

ISBN: (数字)9798350359688

ISBN: (纸本)9798350359695

Spatial crowdsourcing platforms enable efficient task assignment based on the geographical locations of workers. However, ensuring the privacy of users’ location data poses a significant challenge. This paper introduces a novel approach to address this challenge by integrating differential privacy principles into the task assignment process. Our proposed framework aims to balance task assignment efficiency with robust privacy protection. We develop an algorithm that considers both task requirements and users’ privacy preferences, optimizing task-worker assignments while preserving the privacy of location data. Experimental evaluation using real-world datasets demonstrates the effectiveness of our approach in enhancing privacy without compromising task assignment efficiency. By providing a principled solution to privacy preservation in cloud-based crowdsourcing platforms, this study contributes to advancing the field and fostering trust among users.

关键词： Crowdsourcing knowledge engineering Privacy Differential privacy Communication systems Task analysis Protection

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Machine Learning for Enhanced Cyber Security 5

Machine Learning for Enhanced Cyber Security

引用

5th international conference on Smart systems and Inventive Technology, ICSSIT 2023

作者： Vargheese, M. Nallasivan, G. Ponkumar, D. David Neels Ponnithish, N. Devi, P. Karunya Arun, M. Psn College of Engineering and Technology Department of Cse Tamil Nadu Tirunelveli India RandD Institute of Science and Technology Dept. of Ece Vel Tech Rangarajan Dr. Sagunthala Chennai India

ISBN: (纸本)9781665474672

Cyber security is a big issue in current society since exploiting computer network vulnerabilities has become simple thanks to technological advances and human ***, several types of assaults are occurring, such as DOS attacks, probing, R2U, R2L viruses, port scanning, buffer overflow, CGI attacks, and floods, among others. A strong foundation is required to build a system for detecting and preventing these threats. The majority of the most recent ways for implementing IDS for computer security are covered in this article. Intrusion Detection systems are the best answer for cyber-attacks. In a continuously changing environment, machine learning-based intrusion detection systems exhibit excellent accuracy. This study provide a broad overview of machine-learning algorithms, focusing on how they may be used for sophisticated data processing and automation in cybersecurity, with a particular focus on their ability to extract useful insights from cyber data. In addition to, this study investigates a variety of practical applications wherein information knowledge, mechanization, and decision-making might be used to provide proactive, next-generation cyber security. Our paper concludes by emphasizing the potential of computer vision in security in the future and pointing the way toward further research. Ultimately, this study intends to focus on how the status of learning algorithms and related approaches might inform future advancements in the field of cybersecurity. © 2023 IEEE.

关键词： Network security

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Construction of Large-Scale Chinese-English Bilingual Corpus and Sentence Alignment 2nd

Construction of Large-Scale Chinese-English Bilingual Corpus...

引用

2nd EAI international conference on Application of Big data, Blockchain, and Internet of Things for Education Informatization, BigIoT-EDU 2022

作者： Jie, Sun School of International Culture and Education Heilongjiang University Heilongjiang Province Harbin150088 China

ISBN: (纸本)9783031239465

With the development of computer and Internet, applications based on bilingual (or multilingual) parallel corpora are increasing in the field of natural language processing. In addition to the application of machine translation, the construction of parallel corpus is also of great value for bilingual dictionary compilation, word meaning disambiguation and cross language information retrieval. At present, the bilingual corpus of word alignment and sentence alignment has a large scale, and the related alignment algorithms are also relatively mature. In contrast, the chunk level alignment algorithm remains to be studied, and the chunk level alignment corpus required by the alignment algorithm is quite lacking. The construction of bilingual corpus and its automatic alignment are of great significance to the development of computational linguistics. At present, the existing bilingual corpora at home and abroad, especially Chinese-English bilingual corpora, are not large, the processing standards are not unified, and there is no general bilingual corpus that can be used publicly. It has laid a solid foundation for the large-scale establishment of bilingual language information and knowledge base with unified standards and norms, multi fields, multi genres and sentence level alignment. © 2023, ICST Institute for Computer Sciences, Social Informatics and Telecommunications engineering.

关键词： knowledge based systems

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Predictivity of Category Based Human Navigation and the Effect of Navigation Path Length on the Prediction Accuracy in knowledge Networks 3

Predictivity of Category Based Human Navigation and the Effe...

引用

3rd international conference on Communications, Information, Electronic and Energy systems, CIEES 2022

作者： Cifci, Evren Gokturk, Mehmet Gebze Technical University Computer Engineering Department Kocaeli Turkey

ISBN: (数字)9781665491495

ISBN: (纸本)9781665491495

In web sites containing large amount of information, systems which help visitors access the contents they are interested in, significantly increase the usability of the sites. Knowing the category of the content that the user wants to access is important to offer more qualified suggestions and advertisements to the user. On the other hand, as the user continues to navigate in the site, more navigation data is obtained from him, which may potentially be used for better *** this study, the predictability of users' category-based navigation and the effect of the length of the navigation path on the prediction accuracy in knowledge networks is investigated using LSTM classifiers. © 2022 IEEE.

关键词： Forecasting

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Study on Intelligent Decision Architecture of Online data Acquisition and Analysis System

Study on Intelligent Decision Architecture of Online Data Ac...

引用

Intelligent Computing and Next Generation Networks（ICNGN), international conference on

作者： Chenglong Yu Daoyuan Liu Hongpo Yan Qiang Li Maozhen Gong Technology and Application Beijing Institute of Computer Beijing China Jincheng Nanjing Engineering Institute of Aircraft System Nanjing China

ISBN: (数字)9798331529222

ISBN: (纸本)9798331529239

To better improve the production capacity and control efficiency of discrete manufacturing workshops, a decision system architecture based on online acquisition and data analysis is proposed, and its architecture and operation process are described. Furthermore, the key supporting function such as manufacturing data detection and acquisition based on internet of things, data analysis for production control, production optimization decision based on data analysis were further studied. Ultimately, a prototype system was built, as well as its functionality was validated by the past actual data. It has good reference value to provides technical support and software foundation for subsequent research and performance verification.

关键词： Schedules data analysis systems operation Prototypes systems architecture Process control Computer architecture Production control Software systems manufacturing

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A hybrid approach for a movie recommender system using Content-Based, Collaborative and knowledge-Based Filtering methods

A hybrid approach for a movie recommender system using Conte...

引用

Smart Information systems and Technologies (SIST), IEEE international conference on

作者： Aruzhan Amangeldieva Chingis Kharmyssov Computer Science and Engineering Astana IT University Astana Kazakhstan Computer Engineering Astana IT University Astana Kazakhstan

ISBN: (数字)9798350374865

ISBN: (纸本)9798350374872

Rapid evolution of information technology has rapidly increased the significance of Internet-generated data. As a result, finding specific information within this vast data landscape has become increasingly challenging for individuals. Recommender systems have emerged as precious tools in response to this challenge, offering assistance in navigating and extracting relevant information from the abundance of available data. As user engagement increases in importance, recommender systems will be considered an integral part of personalized marketing. The most common methods leverage product features (Content-Based), user similarity (Collaborative Filtering), and personal information (knowledge-based). However, with the increasing popularity of Neural Networks, companies have started experimenting with new Hybrid Recommendation systems that combine them all. This study thoroughly assesses the strengths and limitations of prevalent filtering methods, aiming to introduce an advanced hybrid recommender system. By integrating content-based, collaborative, and knowledge-based techniques, the hybrid model notably enhances recommendation accuracy, effectively addressing challenges like the "cold start" problem and data sparsity issues.

关键词： Technological innovation Accuracy Collaborative filtering knowledge based systems Neural networks Collaboration Information filters

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Intelligent Attendance System with Fake Face Prevention Using Ageitgey and Real-Time database Integration

Intelligent Attendance System with Fake Face Prevention Usin...

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Research Methodologies in knowledge Management, Artificial Intelligence and Telecommunication engineering (RMKMATE), international conference on

作者： Suresh T Darwin Nesakumar A Deepak P Praveen G Sanjay P Department of Electronics and Communication Engineering R.M.K. Engineering College Thiruvallur India

ISBN: (数字)9798331598488

ISBN: (纸本)9798331598495

The Next-Gen Attendance System employs advanced facial recognition technology to provide a secure, efficient, and reliable way to track attendance. By eliminating proxy attendance, it ensures data integrity and offers real-time updates for both parents and teachers. The system prioritizes user privacy with robust encryption, adhering to strict data protection standards. It integrates smoothly with existing institutional systems, allowing for scalability and future enhancements. This solution boosts operational efficiency by automating attendance processes, enhancing accuracy, and reducing the administrative workload. Designed with flexibility and security in mind, the system represents a significant leap forward in attendance management technology.

关键词： Accuracy Face recognition Scalability Prevention and mitigation Medical services Reliability engineering Real-time systems Security Synchronization Standards

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