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检索条件"任意字段=2023 International Conference on Computer Engineering and Distance Learning, CEDL 2023"

共 40626 条记录，以下是4971-4980 订阅

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Scoring Mechanism for Clustering Training in Wireless Federated learning 8

Scoring Mechanism for Clustering Training in Wireless Federa...

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8th international conference on computer and Communication Systems, ICCCS 2023

作者： Wang, Ye Zhang, Chenhu Tong, En Ni, Yiyang Zhao, Haitao Xia, Wenchao China Mobile Communications Group Jiangsu Co. Ltd Nanjing China Nanjing University of Posts and Telecommunications Jiangsu Key Laboratory of Wireless Communications Nanjing China School of Physics and Information Engineering Jiangsu Second Normal University Nanjing China

ISBN: (纸本)9781665456128

Federated learning (FL) is a distributed learning framework, which can be widely applied into wireless networks to improve network intelligence without disclosing users' private data. However, due to the scarce wireless resources, it is inefficient for massive user devices to participate in FL training simultaneously. To address this challenge, in this work, we introduce the client clustering scheme and decompose the training process into two stages. Firstly, we divide user devices into different clusters based on Density-Based Spatial Clustering of Applications with Noise (DBSCAN) and Low Energy Adaptive Clustering Hierarchy (LEACH) algorithms. Then a scoring mechanism considering user devices' locations, velocities, link retention time, and training latency is proposed to determine the cluster head among the devices in each cluster. Finally, simulation results validate the performance of the proposed client clustering method in terms of model accuracy and training latency. © 2023 IEEE.

关键词： client scheduling clustering federated learning scoring mechanism

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Analysis of the Cloudlet's Performance Using Machine learning with Cloud Management System 4

Analysis of the Cloudlet's Performance Using Machine Learnin...

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4th international conference on Computation, Automation and Knowledge Management, ICCAKM 2023

作者： Salve, Ritesh Paik, Sanket Kalaskar, Payal Urunkar, Rutuja Sable, Sahil William, P. Sanjivani College of Engineering Savitribai Phule Pune University Department of Electronics and Computer Engineering Pune India Sanjivani College of Engineering Savitribai Phule Pune University Department of Information Technology Pune India

ISBN: (纸本)9798350393248

One of the most widely used methods for hosting and offering services over networks and the Internet is cloud computing, which also includes edge computing. The hardware architecture is built upon the advanced computer technology. Many businesses and academic organizations employ cloud computing. Cloud and edge computing are hampered in terms of security and privacy. Customers frequently have security concerns about the cloud. There are numerous risks that could endanger sensitive user data kept in the cloud, even with privacy regulations in place. In addition to giving a summary of the numerous study findings on cloud privacy issues and tabulating the advantages and disadvantages of recent studies, we classify the current approaches to privacy issues in cloud environments as designs, tactics, and methodology. We also discuss open research topics and offer ideas for new directions to pursue. The principal objective of this research is to elucidate the current gaps in privacy knowledge and offer a more comprehensive grasp of the privacy issues related to cloud computing. The development of the cloud has brought up a number of security concerns that affect both people and corporations. Using machine learning (ML) is one way to secure the cloud. Machine learning methods can be used in a variety of contexts to identify and address cloud security risks. This study has improved IoT device network security detection by using machine learning more frequently. © 2023 IEEE.

关键词： Edge computing

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Temple Inscriptions Recognition and Transliteration in Devanagari Script 2

Temple Inscriptions Recognition and Transliteration in Devan...

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2nd IEEE international conference on Vision Towards Emerging Trends in Communication and Networking Technologies, ViTECoN 2023

作者： Babu, B. Sathish Shetty, Sannidhi Agarwal, Anushka Sreerama, Sai Lahari Bhustali, Vaishnavi K. Sanjana, Sanka RV College of Engineering Department of Artificial Intelligence and ML Bengaluru India RV College of Engineering Department of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350347982

Ancient inscriptions, palm scripts, manuscripts, etc., have vital information about India's rich culture. Recognition and understanding of these inscriptions have been challenging for epigraphers and professionals. The goal of the proposed research is to advance optical character recognition methods for archival Vatteluttu script inscriptions, which date back to the 4th or 5th century AD. This paper discusses a deep learning model to transliterate the ancient Tamil inscriptions (Vatteluttu Script), which can be extended further to other languages. The proposed work is beneficial to epigraphists, archaeological researchers, and the general public who are interested in this topic. The developed deep learning model has achieved an accuracy of 84.12%. © 2023 IEEE.

关键词： Optical character recognition

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Work-in-Progress Paper: Using Active learning Educational Software to Improve Hispanic Students learning at an HSI 53

Work-in-Progress Paper: Using Active Learning Educational So...

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53rd IEEE ASEE Frontiers in Education international conference, FIE 2023

作者： Stepanova, Anna Anwar, Saira Hammond, Tracy Texas A&m University Department of Computer Science & Engineering College StationTX United States Texas A&m University Department of Multidisciplinary Engineering College StationTX United States

ISBN: (纸本)9798350336429

In this work-in-progress paper we explored the effect of active learning software FossilSketch, and variations in learning modes on Hispanic students' learning. Our institutional data showed that Hispanic students have consistently lower GPAs at graduation, and their average grades are lower across all geological courses, compared to all other ethnicities. However, in 2021, when students were in hybrid learning mode, we observe improvement in grades across all geological courses for Hispanic students. FossilSketch is an educational software application for teaching undergraduate micropaleontology, introduced at the University starting in 2021. Analysis of Hispanic student survey responses showed that their perception of learning micropaleontology improved with the introduction of FossilSketch. When looking at class mode, we conclude that hybrid mode was the most favourable learning mode for Hispanic students, when the flexibility of class mode during 2021-2022 and use of FossilSketch improved Hispanic students' attitudes to learning, which is additionally supported by improved assessment scores in the class where FossilSketch was used, and across all geological courses. © 2023 IEEE.

关键词： active learning geoscience education HSI

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A Novel Hybrid Evolutionary Mating Algorithm for Covid19 Confirmed Cases Prediction based on Vaccination

A Novel Hybrid Evolutionary Mating Algorithm for Covid19 Con...

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2023 international conference on Next-Generation Computing, IoT and Machine learning, NCIM 2023

作者： Ahmed, Marzia Mohamad, Ahmad Johari Rahman, Mostafijur Sulaiman, Mohd Herwan Kashem, Mohammod Abul Faculty of Electrical & Electronics Engineering Technology Pahang Malaysia Daffodil International University Department of Software Engineering Dhaka Bangladesh Green University of Bangladesh Department of Computer Science and Engineering Bangladesh DUET Department of Computer Science and Engineering Gazipur Bangladesh

ISBN: (纸本)9798350316001

Microorganisms may cause illness when they enter the body, multiply, and spread to other parts. The rapid spread of COVID-19 to neighboring countries is examined in this research. Anticipating a positive COVID-19 occurrence helps in determining risks and creating countermeasures. As a result, developing robust mathematical models with small error margins for predictions is crucial. Based on these findings, a combined method of evaluating confirmed cases of COVID-19 with universal immunization is recommended. First, the best hyperparameter values of the RBF kernel-based LSSVM (least square support vector machine) were determined using the most recent Evolutionary Mating Algorithm (EMA). After that, LSSVM will complete the task of prediction. This hybrid method has been utilized for time series forecasting in Malaysia since the country's immunization program against COVID-19 got underway. We evaluate our results next to those of well-known methodologies in nature-inspired metaheuristics. © 2023 IEEE.

关键词： COVID-19

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Clustering Driven Multi-Hop Graph Attention Network for Speaker Diarization 4

Clustering Driven Multi-Hop Graph Attention Network for Spea...

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4th international conference on Intelligent Computing and Human-computer Interaction, ICHCI 2023

作者： Mao, Qingqing Mao, Qirong School of Computer Science and Communication Engineering Jiangsu University Jiangsu China

ISBN: (纸本)9798350343113

Recently, the segmented utterance-level modeling approach based on Graph Attention Network (GAT) has been proved to be effective in Clustering-based Speaker Diarization (CSD). However, these existing methods only rely on the message passing by a single neighbor per layer, ignoring the influence of sub-region and global information. In this paper, we propose clustering driven multi-hop Graph Attention Network (CD-MGAT) with the multi-hop neighbor module and the clustering-oriented prototype module, which effectively explores the sub-region and global information for each segmented utterance. Specifically, the developed modules can adaptively interact with each other by clustering-consistency loss, which ensures the consistency of learning between the prototype and speaker embedding. Extensive experiments demonstrate the effectiveness of our solution on the AMI datasets. © 2023 IEEE.

关键词： Message passing

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Use of Digital Word Games as a Tool for Improving Vocabulary Skills

Use of Digital Word Games as a Tool for Improving Vocabulary...

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2023 international conference on Advancement in Computation and computer Technologies, InCACCT 2023

作者： Gupta, Meenu Kumar, Rakesh Kumar, Uttam Singh, Akash Chandigarh University Department of Computer Science and Engineering Punjab India

ISBN: (纸本)9798350396485

With the advancement in technology and availability in the last few decades, it has become extremely convenient for us to use computer software and mobile apps to learn new things and improve our existing skills. One such way to improve our vocabulary and spelling skills is using digital word games. Word games have always been a great way to learn and improve language skills like vocabulary and spelling. Digital word games like Hangman, spelling quiz, synonym-matching, and many more help people enhance their vocabulary in a fun way. With the high popularity of mobile and computer games in the last few decades, digital word games have also seen a tremendous increase in their acceptance by people. It is not a surprise that even parents and teachers tend to encourage the use of these learning activity games for students, especially for young ones. This paper discusses the importance and scope of these digital games in the education sector. This paper also details the designing, working, and development implementation of a theme-based computer game that can be used as a tool to improve language skills using an experiment to evaluate and compare the effectiveness of such digital word games. © 2023 IEEE.

关键词： computer games

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Enhancing Security and Privacy in Cloud - Based Healthcare Data Through Machine learning 1

Enhancing Security and Privacy in Cloud - Based Healthcare D...

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1st international conference on Artificial Intelligence for Innovations in Healthcare Industries, ICAIIHI 2023

作者： Shukla, Aasheesh Pokhariya, Hemant Singh Michaelson, Jacob Srivastava, Arun Pratap Narayanamma, Laxmi Srivastava, Amit Gla University Department of Electronics and Communication Mathura India Graphic Era Deemed to Be University Department of Computer Science & Engineering Uttarakhand Dehradun India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai India Lloyd Institute of Engineering and Technology Greater Noida India Institute of Aeronautical Engineering Department of Information Technology Telangana Hyderabad India Lloyd Law College Greater Noida India

ISBN: (纸本)9798350330915

It is becoming more and more important for healthcare providers to protect the integrity and security of sensitive medical data as they use cloud computing for data processing and storage. This work explores the field of machine learning algorithms that are secure and privacy-preserving when applied to healthcare information in cloud environments. We investigate sophisticated cryptography, federated learning, and differentiating privacy techniques using an interpretive philosophy and a method based on deduction. Our results highlight the computational expense associated with cryptographic protocols, while also revealing their nuanced performance and potential for enabling secure calculations. Federated learning is shown to be effective in collaborative model training, providing a workable approach to privacy-preserving data analysis over-dispersed healthcare datasets. Differential privacy systems require careful parameter calibration because they demonstrate a delicate balance between data value and privacy preservation. © 2023 IEEE.

关键词： computation federated learning protocols Technique vast amounts

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PhD Forum Abstract: Integrating Prior Knowledge and Machine learning Techniques for Efficient AIoT Sensing 22

PhD Forum Abstract: Integrating Prior Knowledge and Machine ...

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22nd ACM/IEEE international conference on Information Processing in Sensor Networks, IPSN 2023

作者： Luo, Wenjie School of Computer Science and Engineering Nanyang Technological University Singapore Singapore

ISBN: (纸本)9798400701184

Recent advances in machine learning have inspired the development of deep neural network (DNN)-based smart sensing applications for the Artificial Internet of Things (AIoT). However, the effectiveness of DNNs relies on the availability of large, labeled data to uncover useful feature representations. The widespread use of DNN models in computer vision (CV), natural language processing (NLP), and voice sensing can be attributed to the massively available labeled training datasets. Despite the abundance of IoT sensing data, the human-uninterpretable property of AIoT data makes it difficult to construct labeled datasets for DNN model training. Additionally, variations in sensor hardware or DNN models' deployment environments introduce domain shifts, making generalized machine learning algorithms even more difficult to develop. The scarcity of labeled training data and run-time domain shifts are two main challenges in developing effective machine learning algorithms for AIoT sensing. The goal of my research is to address the above challenges for AIoT sensing applications. Two main research methodologies are involved. The first is to leverage the latest state-of-the-art machine learning techniques to develop effective models for smart sensing. The second approach involves integrating known prior knowledge into machine learning algorithms to develop more accurate and reliable DNN models for AIoT sensing applications. © 2023 Owner/Author.

关键词： Internet of things

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Implementing Machine learning Techniques to Enhance Real-Time Data Analysis Services 3

Implementing Machine Learning Techniques to Enhance Real-Tim...

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3rd international conference on Smart Generation Computing, Communication and Networking, SMART GENCON 2023

作者： Jaisudha, J. Dhabliya, Dharmesh Narasimhayya, B.E. Singh, Akhilendra Pratap Sinha, Sanjay Kumar Vashisht, Nitish Prince Shri Venkateshwara Padmavathy Engineering College Department of Electronics and Communication Engineering Chennai127 India Vishwakarma Institute of Information Technology Department of Information Technology Maharashtra Pune India Computer Science and Engineering Faculty of Engineering and Technology Karnataka Bangalore India Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh India Vivekananda Global University Department of Computer Science & Engineering Jaipur India Chitkara University Institute of Engineering and Technology Chitkara University Centre of Interdisciplinary Research in Business and Technology Punjab India

ISBN: (纸本)9798350319125

System learning techniques have emerged to automatically extract data from massive volumes of statistics and enable predictive analytics. Gadget-gaining knowledge has, in particular, been applied to records-driven real-time analytics offerings to facilitate accurate, short, and efficient selection-making. This paper discusses how machine studying strategies can be implemented to improve the accuracy and speed of real-time facts analysis. Conventional statistics analysis models are regularly misguided because of latency troubles and the absence of contextual statistics. Machine getting-to-know techniques have been followed to lessen latency issues, contain contextual facts, and enhance the accuracy of predictions. The paper offers a top-level view of some commonly used machine learning algorithms, such as supervised getting to know, unsupervised mastering, decision timber, neural networks, and support vector machines. It also explains how they can beautify actual-time analytics services. It also covers how strategies, including regularization and dimensionality discount, may be applied to enhance the model's overall performance. The paper gives case research of real-time analytics packages in finance, healthcare, and retail to illustrate the efficacy and added cost of gadget learning in facts analytics offerings. © 2023 IEEE.

关键词： Support vector machines

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