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11th International Conference on Reliability, Infocom Technologies and Optimization, ICRITO 2024

作者： Rishabh, K. Parasher, Jai Singh, Sanskar Sharma, Vaishali Dabas, Pratibha Vashisht, Saransh Dr. Akhilesh das Gupta Institute of Professional Studies New Delhi Department of Computer Science and Engineering India

ISBN: (纸本)9798350350357

As the integration of technology in fitness becomes increasingly prevalent, our study addresses the vital aspect of exercise form through the lens of pose estimation. Employing a body landmark detection model, we devised an algorithm to accurately identify key body landmarks and their correctness in posture during exercises. This paper presents a comprehensive exploration of our methodology, emphasizing the algorithmic estimation of user poses based on the detected landmarks. Our approach enables real-time monitoring of exercise forms and provides instant critique to users on the correctness of their posture. The applications extend beyond mere tracking, fostering an interactive environment where users actively engage in perfecting their form throughout their workout sessions. This paper culminates with discerning observations into the potential future developments, emphasizing the need for more detailed landmark detection models to identify intuitive landmarks. This research contributes to the evolving landscape of technology-assisted fitness, offering a promising avenue for personalized and effective exercise routines. © 2024 IEEE.

关键词： Sports

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A quantitative study of gender representation and authors’ gender in a large-market print medium

A quantitative study of gender representation and authors’ ...

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2024 Computational Humanities Research Conference, CHR 2024

作者： Bartl, Christoph Rezagholi, Sharwin Schumacher, Mareike Department Computer Science University of Applied Sciences Technikum Wien Austria Institute of Literary Studies University of Stuttgart Germany

We analyse gender representation in articles published by the Austrian daily newspaper’Der Standard’ in the years 2021 and 2022. We use named entity recognition and automated gender classification of first names to count the number of female and male persons in articles. The analysis reveals the dominance of male persons in article content. We find that female authors exhibit a significantly higher tendency to mention female persons in their articles. © 2024 Copyright for this paper by its authors.

关键词： Gender of journalists gender representation in articles print newspaper content

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A Machine Learning Approach for Bit Error Rate Modeling in Impulsive Noise-Impaired IRS-NOMA Systems 19

A Machine Learning Approach for Bit Error Rate Modeling in I...

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19th Iranian Conference on Intelligent Systems, ICIS 2024

作者： Rahdari, Farhad Sheikh-Hosseini, Mohsen Institute of Science and High Technology and Environmental Sciences Graduate University of Advanced Technology Department of Computer and Information Technology Kerman Iran

ISBN: (纸本)9798331507565

Machine Learning (ML) has been proposed as a powerful tool for designing future cellular networks to meet their requirements. In the realm of 6G communications, IRSNOMA has emerged as an adaptive resource management solution for its performance in resource management. This study explores the use of ML methods to develop prediction models for bit error rate (BER) in IRS-NOMA systems, particularly in impulsive noise environments. The performance of the derived models has been investigated and compared by performing various simulation tests, revealing the effectiveness of ML models in predicting BER for both near and far users in IRS-NOMA systems. © 2024 IEEE.

关键词： Resource allocation

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A deep Q-learning model for sequential task offloading in edge AI systems

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Intelligent and Converged Networks 2024年第3期5卷 207-221页

作者： Dong Liu Shiheng Gu Xinyu Fan Xu Zheng Department of Computer Science and Engineering University of Electronic Science and Technology of ChinaChengdu 610000China Shenzhen Institute for Advanced Study University of Electronic Science and Technology of ChinaShenzhen 515100China Department of Information and Software Engineering University of Electronic Science and Technology of ChinaChengdu 610000China

Currently,edge Artificial Intelligence(AI)systems have significantly facilitated the functionalities of intelligent devices such as smartphones and smart cars,and supported diverse applications and *** fundamental supports come from continuous data analysis and computation over these *** the resource constraints of terminal devices,multi-layer edge artificial intelligence systems improve the overall computing power of the system by scheduling computing tasks to edge and cloud servers for *** efforts tend to ignore the nature of strong pipelined characteristics of processing tasks in edge AI systems,such as the encryption,decryption and consensus algorithm supporting the implementation of Blockchain ***,this paper proposes a new pipelined task scheduling algorithm(referred to as PTS-RDQN),which utilizes the system representation ability of deep reinforcement learning and integrates multiple dimensional information to achieve global task ***,a co-optimization strategy based on Rainbow Deep Q-Learning(RainbowDQN)is proposed to allocate computation tasks for mobile devices,edge and cloud servers,which is able to comprehensively consider the balance of task turnaround time,link quality,and other factors,thus effectively improving system performance and user *** addition,a task scheduling strategy based on PTS-RDQN is proposed,which is capable of realizing dynamic task allocation according to device *** results based on many simulation experiments show that the proposed method can effectively improve the resource utilization,and provide an effective task scheduling strategy for the edge computing system with cloud-edge-end architecture.

关键词： edge computing task scheduling reinforcement learning Rainbow Deep Q-Learning(RainbowDQN)

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An Ensemble Deep Learning and Modified Wild Horse Optimization based Pixel Selection for Secured Content-based Medical Image Retrieval

An Ensemble Deep Learning and Modified Wild Horse Optimizati...

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Automation, Computing and Renewable Systems (ICACRS), International Conference on

作者： S. Ramya V. Sumalatha Department of Computer Science Vels Institute of Science Technology and Advanced Studies (VISTAS) Chennai India Department of Computer Applications Vels Institute of Science Technology and Advanced Studies (VISTAS) Chennai India

ISBN: (数字)9798331532420

ISBN: (纸本)9798331532437

Due to the restricted capacity for focus on the Human Visual System, clinical specialists may ignore minor lesions in different Medical Images (MI), making MI-based diagnosis a difficult process that could negatively impact clinical therapy. On the other hand, this issue could be handled through an effective Content-Based MI Retrieval (CBMIR) approach to examine comparable cases from the prior medical database. The retrieval engine utilizes Feature Vectors (FV), which are High-Level (HL) image representations maintained by a CBIR structure, to match and rank Query Images (QI) based on similarity. In this research work, an Ensemble Learning (EL) and Modified Wild Horse Optimization (MWHO) based pixel selection with Henon chaotic map (MWHO-HCE) is developed. Next, the image retrieval process includes a series of processes, namely Densenet-121-based feature extraction, Enhanced Sparrow Search Optimization Algorithm (ESSO) based Hyper Parameter (HP) optimizer, and Ensemble deep learning classifier (EDLC) such as Resnet50 inspectionV2 and Lenet are used to classify the lung diseases for the given dataset. Utilizing the elcap test image database, the suggested technique's effectiveness is evaluated. The study outcomes indicate the effectiveness of the suggested strategy for MIR (MI Retrieval).

关键词： Deep learning Computational modeling Pulmonary diseases Image retrieval Feature extraction Real-time systems Iron Optimization Medical diagnostic imaging Residual neural networks

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The Large Language Models on Biomedical Data Analysis: A Survey

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IEEE Journal of Biomedical and Health Informatics 2025年第6期29卷 4486-4497页

作者： Lan, Wei Tang, Zhentao Liu, Mingyang Chen, Qingfeng Peng, Wei Chen, Yiping Phoebe Pan, Yi Guangxi University School of Computer Electronic and Information Nanning530004 China Kunming University of Science and Technology Faculty of Information Engineering and Automation Kunming650500 China La Trobe University Department of Computer Science and Information Technology MelbourneVIC3086 Australia Chinese Academy of Sciences Shenzhen Institute of Advanced Technology Shenzhen518055 China

With the rapid development of Large Language Model (LLM) technology, it has become an indispensable force in biomedical data analysis research. However, biomedical researchers currently have limited knowledge about LLM. Therefore, there is an urgent need for a summary of LLM applications in biomedical data analysis. Herein, we propose this review by summarizing the latest research work on LLM in biomedicine. In this review, LLM techniques are first outlined. We then discuss biomedical datasets and frameworks for biomedical data analysis, followed by a detailed analysis of LLM applications in genomics, proteomics, transcriptomics, radiomics, single-cell analysis, medical texts and drug discovery. Finally, the challenges of LLM in biomedical data analysis are discussed. In summary, this review is intended for researchers interested in LLM technology and aims to help them understand and apply LLM in biomedical data analysis research. © 2013 IEEE.

关键词： Data consistency

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Gastrointestinal Disease Classification And Analysis Using GI-Net Model

Gastrointestinal Disease Classification And Analysis Using G...

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2022 International Conference on Edge Computing and Applications, ICECAA 2022

作者： Malviya, Animesh Dutta, Malay Kishore Department of Computer Science and Engineering Center for Advanced Studies Lucknow India

ISBN: (数字)9781665482325

ISBN: (纸本)9781665482325

Detecting gastrointestinal illnesses accurately is crucial to early cancer detection and treatment. In spite of this, manual analysis is time-consuming, requiring the assistance of a gastrointestinal. A multi-class classification framework for screening gastrointestinal diseases is proposed that is efficient and robust. A neural network known as gastrointestinal GI-Net was developed to extract features that could differentiate between normal and diseased images taken by endoscopy device. In order to achieve the most optimal classification network, a variety of optimization techniques are used. For the classification network to be more effective, the framework can handle the challenges present in the dataset. It is 88% accurate in diagnosing using unseen endoscopy images. In comparison with other deep learning networks, the developed architecture is highly effective. Compared to other networks, in limited computation environments, the proposed network is likely to perform better. © 2022 IEEE.

关键词： Endoscopy

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Redundant Data Detection and Deletion to Meet Privacy Protection Requirements in Blockchain-Based Edge Computing Environment

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China Communications 2024年第3期21卷 149-159页

作者： Zhang Lejun Peng Minghui Su Shen Wang Weizheng Jin Zilong Su Yansen Chen Huiling Guo Ran Sergey Gataullin Cyberspace Institute Advanced Technology Guangzhou UniversityGuangzhou 510006China College of Information Engineering Yangzhou UniversityYangzhou 225127China School Math&Computer Science Quanzhou Normal UniversityQuanzhou 362000China Computer Science Department City University of Hong KongHong Kong 999077China School of Computer and Software Nanjing University of Information Science and TechnologyNanjing 21004China Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education School of Computer Science and TechnologyAnhui UniversityHefei 230601China Department of Computer Science and Artificial Intelligence Wenzhou UniversityWenzhou 325035China Guangzhou University Library Guangzhou UniversityGuangzhou 510006China Central Economic and Mathematics Institute Russian Academy of Sciences MIREA-Russian Technological University Moscow RegionRussia

With the rapid development of information technology,IoT devices play a huge role in physiological health data *** exponential growth of medical data requires us to reasonably allocate storage space for cloud servers and edge *** storage capacity of edge nodes close to users is *** should store hotspot data in edge nodes as much as possible,so as to ensure response timeliness and access hit rate;However,the current scheme cannot guarantee that every sub-message in a complete data stored by the edge node meets the requirements of hot data;How to complete the detection and deletion of redundant data in edge nodes under the premise of protecting user privacy and data dynamic integrity has become a challenging *** paper proposes a redundant data detection method that meets the privacy protection *** scanning the cipher text,it is determined whether each sub-message of the data in the edge node meets the requirements of the hot *** has the same effect as zero-knowledge proof,and it will not reveal the privacy of *** addition,for redundant sub-data that does not meet the requirements of hot data,our paper proposes a redundant data deletion scheme that meets the dynamic integrity of the *** use Content Extraction Signature(CES)to generate the remaining hot data signature after the redundant data is *** feasibility of the scheme is proved through safety analysis and efficiency analysis.

关键词： blockchain data integrity edge computing privacy protection redundant data

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A Psycholinguistic Evaluation of Language Models' Sensitivity to Argument Roles

A Psycholinguistic Evaluation of Language Models' Sensitivit...

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2024 Findings of the Association for Computational Linguistics, EMNLP 2024

作者： Lee, Eun-Kyoung Rosa Nair, Sathvik Feldman, Naomi H. Department of Linguistics University of Maryland College Park United States Institute for Advanced Computer Studies University of Maryland College Park United States

ISBN: (纸本)9798891761681

We present a systematic evaluation of large language models' sensitivity to argument roles, i.e., who did what to whom, by replicating psycholinguistic studies on human argument role processing. In three experiments, we find that language models are able to distinguish verbs that appear in plausible and implausible contexts, where plausibility is determined through the relation between the verb and its preceding arguments. However, none of the models capture the same selective patterns that human comprehenders exhibit during real-time verb prediction. This indicates that language models' capacity to detect verb plausibility does not arise from the same mechanism that underlies human real-time sentence processing. © 2024 Association for Computational Linguistics.

关键词： Computational linguistics

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AI-Driven Assessment and Evaluation: Enhancing Sustainability in Educational Practices 3

AI-Driven Assessment and Evaluation: Enhancing Sustainabilit...

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3rd International Conference on Automation, Computing and Renewable Systems, ICACRS 2024

作者： Chinnasamy, P. Pushpalatha, M. Nirmala, V. Vidhya, V. Ayyasamy, Ramesh Kumar Priya, Divya Department of Computer Science and Engineering School of Computing Kalasalingam Academy of Research and Education Tamilnadu India Department of Computer Science and Technology Karpagam College of Engineering Coimbatore India Department of MCA Annamacharya PG College of Computer Studies Rajampet Kadapa India India Kampar Perak Malaysia Department of Computer Science and Engineering MLR Institute of Technology Hyderabad India

ISBN: (纸本)9798331532420

AI-powered educational technologies are emerging as transformative forces in the quickly changing field of education, where innovation is essential to keeping ahead of the competition. Assessments are one area that is undergoing revolutionary transformation. Artificial Intelligence (AI) is redefining the way we evaluate learning outcomes as traditional evaluation approaches struggle to keep up with the demands of the digital age. The field of artificial intelligence-powered learning assessments has a lot of promising future developments ahead of it. This research proposes novel technique in assessment and evaluation based on online education system using Artificial intelligence in machine learning techniques. Here, the input has been analysed as student performance data based on online education and processed for removing of missing values with noise removal. Then this data has been analysed using spatio LSTM convolutional fuzzy neural network for classification of evaluation and assessment model. the experimental analysis has been carried out for various student performance analysis dataset in terms of training accuracy, average precision, specificity, F-measure and recall. Proposed method training accuracy 97%, Average precision 94%, F-measure 90%, RECALL 93%, SPECIFICITY 96%. © 2024 IEEE.

关键词： Adversarial machine learning

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