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

共 40714 条记录，以下是4961-4970 订阅

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Towards a Deep learning Pain-Level Detection Deployment at UAE for Patient-Centric-Pain Management and Diagnosis Support: Framework and Performance Evaluation 14

Towards a Deep Learning Pain-Level Detection Deployment at U...

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14th international conference on Ambient Systems, Networks and Technologies Networks, ANT 2023 and The 6th international conference on Emerging Data and Industry 4.0, EDI40 2023

作者： Ismail, Leila Waseem, Muhammad Danish Lab School of Computing and Information Systems Faculty of Engineering and Information Technology The University of Melbourne Australia Research Laboratory Department of Computer Science and Software Engineering College of Information Technology United Arab Emirates University United Arab Emirates National Water and Energy Center United Arab Emirates University United Arab Emirates

The outbreak of the COVID-19 pandemic revealed the criticality of timely intervention in a situation exacerbated by a shortage in medical staff and equipment. Pain-level screening is the initial step toward identifying the severity of patient conditions. Automatic recognition of state and feelings help in identifying patient symptoms to take immediate adequate action and providing a patient-centric medical plan tailored to a patient's state. In this paper, we propose a framework for pain-level detection for deployment in the United Arab Emirates and assess its performance using the most used approaches in the literature. Our results show that a deployment of a pain-level deep learning detection framework is promising in identifying the pain level accurately. © 2023 Elsevier B.V.. All rights reserved.

关键词： computer vision

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Image Denoising via Multiscale Feature Extraction and Triplet Attention based CNN 14

Image Denoising via Multiscale Feature Extraction and Triple...

引用

14th international conference on Electrical and Electronics engineering, ELECO 2023

作者： Ulu, Ahmet Dizdaroǧlu, Bekir Karadeniz Technical University Department of Computer Engineering Trabzon Turkey

ISBN: (纸本)9798350360493

Recent advances in deep learning have had a significant impact on the field of noise reduction and other image-processing tasks. One of the most important innovations of deep learning is the use of convolutional layers to extract features from images. However, most convolutional neural networks (CNNs) designed for noise reduction only perform feature extraction at a single scale. Additionally, the attention mechanism, which is commonly used for channel or spatial weighting of features, neglects the interdependence and interaction of features between different dimensions. This study proposes a new CNN network for noise reduction that addresses these limitations. The proposed network uses a triplet attention mechanism and multi-scale feature extraction to capture the interactions and interdependencies between different dimensions of features. Experimental results show that the proposed approach can effectively remove Gaussian noise from grayscale images and achieves state-of-the-art performance in both quantitative and qualitative evaluations. © 2023 IEEE.

关键词： Image denoising

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An Efficient Technique for the Better Recognition of Oral Cancer using Support Vector Machine 7

An Efficient Technique for the Better Recognition of Oral Ca...

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7th international conference on Intelligent Computing and Control Systems, ICICCS 2023

作者： Manikandan, J. Devakadacham, Sterlin Rani Shanthalakshmi, M. Arockia Raj, Y. Vijay, K. Rajalakshmi Engineering College Thandalam Computer Science and Engineering Chennai India R. M. D. Engineering College Computer Science and Engineering Kavaraipettai India Sri Venkateswara College of Engineering Computer Science and Engineering Chennai India PSNA College of Engineering and Technology Computer Science and Engineering Dindugal India

ISBN: (纸本)9798350397253

Accuracy is among the most important factors in a disease diagnosis. It is essential to select the characteristics that you find most pertinent for the highest accuracy. This study aims to more accurately predict the presence of the primary stage of squamous cell carcinoma using fewer indicators. Stages of oral cancer were first demonstrated to be predicted by 25 features. The variety of features that are obtained from various patient records is indirectly decreased in this study through the combination of the unified medical system using hybrid features selection techniques to identify the characteristics that are most useful for the identification of oral cancer. Hybrid feature selection has been used to condense 25 qualities into 14 features. The diagnosis of patients with oral cancer is then predicted using four classifiers: Updatable Naive Bayes, Multilayer Perceptrons, K-Nearest Neighbor, and Support Vector Machines. Also, the data show that, after adding feature development decisions with SMOTE during the preprocessing phases, the support vector machine's performance surpasses other machine learning techniques. © 2023 IEEE.

关键词： Support vector machines

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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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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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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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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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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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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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