检索结果-内蒙古大学图书馆

2nd International Conference on Self Sustainable Artificial Intelligence Systems, ICSSAS 2024

作者： Ronald, Bindu S. Raman, Ramakrishnan Vekariya, Daxa Baskar, V. Vijaya Rahul, J. Srinivasan, C. Symbiosis Law School Maharashtra Pune India Maharashtra Pune India Parul Institute of Engineering and Technology Parul University Faculty of Engineering and Technology Gujarat Vadodara India Sathyabama Institute of Science and Technology School of Electrical and Electronics Engineering Tamil Nadu Chennai India Agni College of Technology Department of Artificial Intelligence and Data Science Tamil Nadu Chennai India Saveetha Institute of Medical and Technical Sciences Saveetha School of Engineering Department of Computer Science and Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350368413

The increasing prevalence of hearing impairment across diverse age demographics, particularly in elderly populations, highlights the need for early diagnosis and ongoing therapy. Traditional audiology screening techniques often lack the necessary dynamic flexibility to meet personalized hearing health requirements over time. This system introduces an innovative method for dynamic audiology screening via IoT-based monitoring, using Support Vector Machine (SVM) algorithms for individualized hearing health management. The incorporation of IoT devices allows ongoing, real-time auditory data acquisition from the user's surroundings, recording essential metrics such as sound frequencies, noise levels, and auditory reactions. This data is analyzed using SVM to categorize and identify probable hearing impairments, providing great precision in differentiating between various degrees of hearing loss. The SVM model's strong classification skills render it especially effective in differentiating between varying levels of hearing loss, facilitating early identification and tailored therapy strategies. This tailored method of audiology screening boosts diagnostic precision and facilitates proactive management, perhaps improving long-term results for individuals with hearing impairments. © 2024 IEEE.

关键词： Electronic health record

来源：评论

学校读者我要写书评

暂无评论

Early Childhood Action Monitoring and Analytics System (ECAMS) 28

Early Childhood Action Monitoring and Analytics System (ECAM...

引用

28th IEEE Pacific Rim International Symposium on Dependable Computing, PRDC 2023

作者： Andarage, Isuru Supasan Naotunna Fernando, Dileepa Lokuarachchi, Buddhi Avishka Athuluwage, Malithi Gimhani Wijewickrama, Pavithra Sri Lanka Technological Campus Department of Data Science Padukka10500 Sri Lanka Nanyang Technological University School of Computer Science and Engineering Singapore Singapore Sri Lanka Technological Campus Faculty of Engineering Padukka10500 Sri Lanka Sabaragamuwa University of Sri Lanka Department of Sport Sciences and Physical Education Belihuloya Sri Lanka

ISBN: (纸本)9798350358766

The cultivation of fundamental movement skills (FMS) during the early stages of childhood bears immense importance in shaping an individual's involvement and achievements in sports throughout their entire life. Therefore, the assessment of motor skills in early childhood (age 3-5 years) significantly influences the advancement of sports within a nation. To this end, early childhood motor skill assessment has been performed manually posing efficiency challenges. The current automation approaches either involve invasiveness or depend on a substantial volume of annotated video training data. In this research, we introduce the Early Childhood Actions Monitoring and Analytics System (ECAMS) toolkit designed to evaluate early childhood motor skills, including activities like sitting up, running, walking, and jumping. This research employs computer vision and machine learning techniques integrated with the TGM2D (Test of Gross Motor Development-2nd Edition) toolkit to accurately detect motor skills from a given video input. This real-time analysis will offer accurate guidance to individuals involved in early childhood development, including educators, trainers, parents, and sports analysts. © 2023 IEEE.

关键词： Sports

来源：评论

学校读者我要写书评

暂无评论

Capturing the Complexity of Human Strategic Decision-Making with Machine Learning

SSRN

引用

SSRN 2024年

作者： Zhu, Jian-Qiao Peterson, Joshua C. Enke, Benjamin Griffiths, Thomas L. Department of Computer Science Princeton University NJ United States Faculty of Computing and Data Science Boston University MA United States Department of Economics Harvard University CambridgeMA United States Department of Computer Science Department of Psychology Princeton University NJ United States

Understanding how people behave in strategic settings–where they make decisions based on their expectations about the behavior of others–is a longstanding problem in the behavioral sciences. We conduct the largest study to date of strategic decision-making in the context of initial play in two-player matrix games, analyzing over 90,000 human decisions across more than 2,400 procedurally generated games that span a much wider space than previous datasets. We show that a deep neural network trained on these data predicts people’s choices better than leading theories of strategic behavior, indicating that there is systematic variation that is not explained by those theories. We then modify the network to produce a new, interpretable behavioural model, revealing what the original network learned about people: their ability to optimally respond and their capacity to reason about others are dependent on the complexity of individual games. This context-dependence is critical in explaining deviations from the rational Nash equilibrium, response times, and uncertainty in strategic decisions. More broadly, our results demonstrate how machine learning can be applied beyond prediction to further help generate novel explanations of complex human behavior. © 2024, The Authors. All rights reserved.

关键词： Adversarial machine learning

来源：评论

学校读者我要写书评

暂无评论

LaCour!: Enabling Research on Argumentation in Hearings of the European Court of Human Rights

arXiv

引用

arXiv 2023年

作者： Held, Lena Habernal, Ivan Trustworthy Human Language Technologies Germany Department of Computer Science Technical University of Darmstadt Germany Research Center Trustworthy Data Science and Security of the University Alliance Ruhr Faculty of Computer Science Ruhr University Bochum Germany

Why does an argument end up in the final court decision? Was it deliberated or questioned during the oral hearings? Was there something in the hearings that triggered a particular judge to write a dissenting opinion? Despite the availability of the final judgments of the European Court of Human Rights (ECHR), none of these legal research questions can currently be answered as the ECHR’s multilingual oral hearings are not transcribed, structured, or speaker-attributed. We address this fundamental gap by presenting LaCour!, the first corpus of textual oral arguments of the ECHR, consisting of 154 full hearings (2.1 million tokens from over 267 hours of video footage) in English, French, and other court languages, each linked to the corresponding final judgment documents. In addition to the transcribed and partially manually corrected text from the video, we provide sentence-level timestamps and manually annotated role and language labels. We also showcase LaCour! in a set of experiments that explore the interplay between questions and dissenting opinions. Apart from the use cases in legal NLP, we hope that law students or other interested parties will also use LaCour! as a learning resource, as it is freely available in various formats at https://***/datasets/TrustHLT/LaCour. © 2023, CC BY-SA.

关键词： Laws and legislation

来源：评论

学校读者我要写书评

暂无评论

Optimizing Patient Satisfaction Surveys and Feedback with Cloud Computing and NLP Techniques 8

Optimizing Patient Satisfaction Surveys and Feedback with Cl...

引用

8th International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud), I-SMAC 2024

作者： Jayanthi, L.N. Shanmugam, Kannan Suma, Challapalli Sagar, B.S. Maniraj, S.P. Ganeshbabu, T.R. Saveetha College of Liberal Arts and Sciences Department of Commerce Tamil Nadu Chennai India Vit Bhopal University School of Computing Science and Engineering Department of Gaming Technology Madhapradesh Bhopal India Department of Computer Science and Engineering Andhra Pradesh Bhimavaram India Reva University Department of Electrical and Electronics Engineering Karnataka Bengaluru India Faculty of Engineering & Technology Department of Data Science and Business Systems Chennai Tamil Nadu Kattankulathur India Department of Electronics and Communication Tamil Nadu Namakkal India

ISBN: (纸本)9798350376425

Healthcare quality assessment and identification of opportunities for improvement are crucially facilitated by patient satisfaction surveys. Traditional approaches sometimes face difficulties in handling substantial amounts of written comments and obtaining real useful data. It presents a new method that combines cloud computing with Natural Language Processing (NLP) approaches to enhance the analysis of patient satisfaction data. By using cloud platforms, it can effectively manage large datasets and provide scalable, cost-efficient processing. The use of NLP methods, such as text mining and sentiment analysis, enables the conversion of unstructured input into structured insights, thereby uncovering the fundamental patient feelings and patterns. This connection enables the analysis of data in real-time also improving the accuracy of interpreting feedback and facilitating fast enhancements in healthcare services. Future research will prioritise the improvement of NLP algorithms and the investigation of supplementary cloud-based technologies to enhance the accuracy and efficiency of patient satisfaction analysis to improved patient care and experience. © 2024 IEEE.

关键词： Sentiment analysis

来源：评论

学校读者我要写书评

暂无评论

SortedAP: Rethinking evaluation metrics for instance segmentation

SortedAP: Rethinking evaluation metrics for instance segment...

引用

International Conference on computer Vision Workshops (ICCV Workshops)

作者： Long Chen Yuli Wu Johannes Stegmaier Dorit Merhof Institute of Imaging & Computer Vision RWTH Aachen University Germany Faculty of Informatics and Data Science University of Regensburg Germany

Designing metrics for evaluating instance segmentation revolves around comprehensively considering object detection and segmentation accuracy. However, other important properties, such as sensitivity, continuity, and equality, are overlooked in the current study. In this paper, we reveal that most existing metrics have a limited resolution of segmentation quality. They are only conditionally sensitive to the change of masks or false predictions. For certain metrics, the score can change drastically in a narrow range which could provide a misleading indication of the quality gap between results. Therefore, we propose a new metric called sortedAP, which strictly decreases with both object- and pixel-level imperfections and has an uninterrupted penalization scale over the entire domain. We provide the evaluation toolkit and experiment code at https://***/looooongChen/sortedAP.

关键词：

来源：评论

学校读者我要写书评

暂无评论

A Hybrid Blockchain Framework for Secure, Scalable, and Energy-Efficient Digital Identity Management with Token-based Selective Disclosure

A Hybrid Blockchain Framework for Secure, Scalable, and Ener...

引用

Electronics and Renewable Systems (ICEARS), International Conference on

作者： Velmurugan M Shinzeer C K G. Maya R. Ramya R Kanimozhi Syed Ismail Abdul Lathif Department Of Computer Science & Engineering Vel Tech Rangarajan Dr Sagunthala R&D Institute of Science And Technology Chennai Department of School of Computer Applications Lovely Professional University Phagwara Punjab Faculty of Management SRM Institute of Science and Technology Kattankulathur Department of Electronics and Communication Engineering K.Ramakrishnan College of Engineering Samayapuram Computer Science and Engineering Muthayammal Engineering College Rasipuram Department of Data Science and Business Systems SRM Institute of Science and Technology Chennai

ISBN: (数字)9798331509675

ISBN: (纸本)9798331509682

data breaches, identity theft, and the lack of user control within traditional digital identity management systems, it begs a more secure and decentralized alternative. In this study we propose a blockchain based digital identity management framework that implements privacy, security and transparency by employing distributed ledger technology. To provide tamper proof identity storage and automated verification, the proposed model uses cryptographic hashing and smart contracts. The use of a novel token-based access mechanism enables users to safely share their identity data with third parties, while keeping sensitive information out of sight. Security metrics are enhanced by 30% for data integrity with 45% reduction in unauthorized access attempts all through simulations. The framework also achieves improved efficiency reducing identity verification time by 40 percent compared to traditional systems. It offers a promising approach to address important vulnerabilities in centralized identity management in line with the development of marketable and user-friendly digital identity solutions.

关键词： Resistance Privacy Fault tolerance Smart contracts Fault tolerant systems Throughput Energy efficiency Blockchains Identity management systems Interoperability

来源：评论

学校读者我要写书评

暂无评论

Development of Reliable TOPSIS Method Using Intuitionistic Z-Numbers 1

引用

12th World Conference on Intelligent Systems for Industrial Automation, WCIS 2022

作者： Alam, Nik Muhammad Farhan Hakim Nik Badrul Khalif, Ku Muhammad Naim Ku Jaini, Nor Izzati Centre for Mathematical Sciences Universiti Malaysia Pahang Gambang Pahang Kuantan Malaysia Faculty of Computer and Mathematical Sciences Universiti Teknologi MARA Pahang Pahang Bandar Tun Abdul Razak Jengka Malaysia Centre of Excellence for Artificial Intelligence and Data Science Universiti Malaysia Pahang Gambang Pahang Kuantan Malaysia

ISBN: (数字)9783031515217

ISBN: (纸本)9783031515200

Technique for order of preference by similarity to ideal solution (TOPSIS) is a multi-criteria decision-making (MCDM) method which is developed based on the distance measure from the positive and negative ideal solutions. This paper extends the TOPSIS for handling data in form of intuitionistic Z-numbers (IZN). IZN consists of restriction and reliability components which are characterized by the intuitionistic fuzzy numbers. The distance measure between IZN is proposed using the convex compound of the distances for the restriction and reliability parts. The supplier selection problem in an automobile manufacturing company is adopted to illustrate the proposed model. Sensitivity analysis is performed for the validation of the proposed model and its result shows that the proposed model gives a consistent ranking of alternatives. The strength of the proposed model is the preservation of decision information in form of IZN which does not possess the conversion into regular fuzzy number to avoid the loss of information. © 2024, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Sensitivity analysis

来源：评论

学校读者我要写书评

暂无评论

Emotional Reactivity Classification Using Artificial Intelligence Based on Twitter in Bahasa Indonesia

Emotional Reactivity Classification Using Artificial Intelli...

引用

Social sciences and Intelligent Management (SSIM), IEEE International Conference on

作者： Greta V. Paramita Esther W. Andangsari Ade P. Kemala Moondore M. Ali Trisa M. Putri Katarina I. Puspita Psychology Department Faculty of Humanities Bina Nusantara University Jakarta Indonesia Data Science Program School of Computer Science Bina Nusantara University Jakarta Indonesia

Twitter is one of Indonesia's most widely used social media to express positive and negative emotions. Negative emotions are referred to as emotional reactivity, an example of which is depression. To detect emotional reactivity, Artificial Intelligence (AI) was used. With deep learning pretrained model methods (IndoBERT and IndoBERTweet), AI successfully classified emotional reactivity. AI satisfactorily detected emotional reactivity on Twitter.

关键词：

来源：评论

学校读者我要写书评

暂无评论

QoE-oriented Soft Caching with Content Recommendation for Edge Computing Networks

QoE-oriented Soft Caching with Content Recommendation for Ed...

引用

2024 IEEE Global Communications Conference, GLOBECOM 2024

作者： Wang, Chenyang Chen, Yan Sun, Chuan Jia, Bosen Wang, Xiaofei Taleb, Tarik Leung, Victor C. M. Shenzhen University College of Computer Science & Software Engineering Shenzhen China Shenzhen518132 China Ruhr University Bochum Faculty of Electrical Engineering and Information Technology Bochum Germany Nanyang Technological University College of Computing and Data Science Singapore China Unicom Digital Technology Co. Ltd Beijing China Tianjin University College of Intelligence and Computing Tianjin China University of British Columbia Vancouver Canada

ISBN: (纸本)9798350351255

Mobile Edge Caching (MEC) can potentially alleviate Internet transmission congestion by delivering content at the network edge. However, current MEC solutions suffer from low resource utilization efficiency and often fail to meet user Quality of Experience (QoE), primarily due to dynamic user requests and obsessive pursuit of direct caching hits. Given the prevalence of recommendation systems, users often lack precise requests when using recommendation-based applications like TikTok and Taobao, insted passively enjoying recommended content. In this paper, we introduce a recommendation-enabled MEC architecture to enhance resource utilization and QoE. We develop a recommendation-enabled soft caching model and formulate the optimization problem as maximizing joint system revenue. To address this, we propose an attention-assisted federated learning deep Q-network algorithm. We conduct the simulations by using the real-world MIND dataset. The results demonstrate that our proposed algorithm outperforms existing baselines, demonstrating its effectiveness in improving resource utilization and QoE. © 2024 IEEE.

关键词： Mobile edge computing

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：