检索结果-内蒙古大学图书馆

Design and Implementation of a Comprehensive RAG-Driven Dashboard Within the ILO System for data Visualization and Query Support: A Case Study of the University of Petra

Design and Implementation of a Comprehensive RAG-Driven Dash...

引用

computational Intelligence Approaches and Applications (ICCIAA), International Conference on

作者： Mohammad Abu-Arqoub Abed Alkarim Banna Nuha El-Khalili Mohammad Al-Shaikh Hasan Department of Computer Science University of Petra Amman Jordan Data Science & Artificial Intelligence Dept. University of Petra Amman Jordan Software Engineering Department University of Petra Amman Jordan Computer Science Department Brunel University London London UK

ISBN: (数字)9798331523657

ISBN: (纸本)9798331523664

This article presents a comprehensive study on designing and implementing a dashboard for enhanced data visualization and query support at the University of Petra. The system leverages retrieval-augmented generation (RAG) and large language models (LLMs) to support diverse document types, including curricula, course descriptions, and program outcomes, alongside intended learning outcome (ILO)-related files. Our implementation demonstrates significant improvements in data accessibility and query response times while maintaining high accuracy in information retrieval and visualization. Through extensive evaluation, we show that this innovative approach transforms data management processes in higher education by enabling natural language interactions with educational data systems, building upon established business intelligence frameworks while introducing advanced AI capabilities.

关键词： Large language models Retrieval augmented generation Natural languages data visualization Transforms Educational technology Information retrieval data systems Business intelligence Time factors

来源：评论

学校读者我要写书评

暂无评论

Process Quality Assurance of Artificial Intelligence in Medical Diagnosis

Process Quality Assurance of Artificial Intelligence in Medi...

引用

Intelligent Systems and Computer Vision (ISCV)

作者： Negin Moghadasi Misagh Piran Rupa S. Valdez Stephen Baek Negar Moghaddasi Thomas L. Polmateer James H. Lambert Dept. of Systems and Information Engineering University of Virginia Charlottesville USA Dept. of Radiology Nuclear Medicine and Molecular Imaging Heart and Diabetes Center North-Rhine Westphalia Ruhr University of Bochum Bad Oeyhausen Germany School of Data Science University of Virginia Charlottesville USA Dept. of Dentistry Western University of Health Sciences Pomona USA Dept. of Systems and Information Engineering CCALS University of Virginia Charlottesville USA Dept. of Systems and Information Engineering University of Virginia

ISBN: (数字)9798350350180

ISBN: (纸本)9798350350197

While artificial Intelligence (AI) has shown promising results in the healthcare field, it is undeniable that there are risks associated with AI in healthcare that society must acknowledge. This paper presents a comprehensive systems modeling framework aimed at evaluating trust and being responsive to reasons for mistrust and distrust in AI-assisted medical diagnosis, with a specific focus on the diagnosis of cardiac sarcoidosis, utilizing Explainable Artificial Intelligence (XAI) techniques. The design includes two primary sections: 1. Identifying the most and least disruptive scenarios to the system, as well as the most important initiatives for the system. 2. Utilizing XAI techniques such as SHapley Additive exPlanations (SHAP), Local Interpretable Model-agnostic Explanations (LIME), and Anchors models to provide explanations on how machine learning models justify their outcomes. The findings indicate the significance of employing explainable AI in critical domains like healthcare, where lives of patients are at risk. XAI can be employed to analyze the outcomes for AI users, determining the significance of features, improving comprehension of AI outputs, enhancing transparency, explainability, and interpretability of AI outputs, and facilitating data assessment.

关键词： Computer vision Quality assurance Additives Explainable AI Systems modeling Medical diagnosis Artificial intelligence

来源：评论

学校读者我要写书评

暂无评论

Improving Image Classification Using Attention Mechanisms in Deep Learning Models

Improving Image Classification Using Attention Mechanisms in...

引用

Advancement in Electronics & Communication engineering (AECE), International Conference on

作者： Vinod Sharma Atul Kumar Mishra Anil Kumar Nanda Shakti Madhuri Sharma Srinju M Dept of CSE SLP University Gwalior India Dept. of Commerce & Management Madhav University Pindwara India Dept. of EEE Mangalayatan University Aligarh India Dept. of Engineering and Applied Sciences Usha Martin University Ranchi India Dept. of Life Sciences CHRI Campus University Gwalior India Dept. of AI and Data Science Dhanalakshmi Srinivasan University Coimbatore India

ISBN: (数字)9798350364729

ISBN: (纸本)9798350364736

In this study, we explore the application of attention mechanisms to enhance deep learning models in the context of image classification. We assess several types of attention mechanisms across diverse image classification datasets. Our findings indicate substantial enhancements in model accuracy facilitated by these attention mechanisms. Additionally, we delve into implementation challenges such as computational overhead and the necessity for extensive datasets. Furthermore, we investigate the potential benefits of transfer learning and fine-tuning pretrained models integrated with attention mechanisms to optimize performance and streamline training efforts.

关键词： Deep learning Training Attention mechanisms Accuracy computational modeling Transfer learning Streaming media Image classification Context modeling

来源：评论

学校读者我要写书评

暂无评论

Talent management in agile software development: The state of the art 3

Talent management in agile software development: The state o...

引用

3rd East Indonesia Conference on Computer and Information Technology, EIConCIT 2021

作者： Gandomani, Taghi Javdani Mashmool, Amir Dashti, Maedeh Khosravi, Samiyeh Sarpiri, Mona Najafi Radnejad, Mahsa Afshari, Mahnaz Mansouri, Shahin Shahrekord University Dept. of Computer Science Shahrekord Iran University of Birjand Dept. of Computer Engineering Birjand Iran Shahrekord University Data Science Research Center Shahrekord Iran University of Birjand Dept. of Computer Engineering Birjand Iran Isfahan Branch Islamic Azad University Dept. of Computer Engineering Isfahan Iran Dolatabad Branch Islamic Azad University Dept. of Computer Engineering Isfahan Iran

ISBN: (纸本)9781665405140

With increasing competition in the software market, software companies are using new management strategies to increase their competitiveness. In recent years, with the prevalence of agile methodologies in software development, paying attention to human resources, and selecting and managing talented human resources have become inevitable. This should be achieved by adopting appropriate talent management processes. However, in software engineering, talent management has not yet been seriously considered. Due to the critical role of agile methodologies in human resources, and the authority and freedom of individuals in agile teams, the benefit of talent management in these methodologies is vital. As a starting point, this article tries to provide a concise overview of talent management in agile software development and address the ambiguities and research gaps facing software researchers in this regard the initial results of this study show that several ambiguities such as appropriate models of talent management applicable in agile methodologies, how to use them, effective factors in talent assessment, and the relationship between talent management and the quality of teamwork are some of the most noticeable research gaps in this area. © 2021 IEEE.

关键词： Human resource management

来源：评论

学校读者我要写书评

暂无评论

Improved Dynamic Coupled Graph Convolutional Recurrent Networks for Traffic Forecasting

Improved Dynamic Coupled Graph Convolutional Recurrent Netwo...

引用

IEEE International Conference on Big data

作者： Seunghoon Han Sungsoo Kim Sungsu Lim Dept. of Computer Science & Engineering Chungnam National University Daejeon South Korea Smart Data Research Section ETRI Daejeon South Korea

Traffic forecasting is a crucial application of the Intelligent Transportation System (ITS), with research focusing on various methods, from classical statistical approaches to graph-based methods integrated with RNN-based approaches to capture spatial and temporal correlations simultaneously. During the traffic data collection phase, the absence of vehicles on each road or sensor malfunctions can result in the collection of traffic time series data as zeros. However, storing such zero values makes accurate traffic prediction more challenging. To address this challenge, we present a novel model for improving traffic forecasting using graph convolutional recurrent neural networks. The proposed method is evaluated on two real-world public benchmark datasets and compared with six baseline models, showcasing its superior performance.

关键词：

来源：评论

学校读者我要写书评

暂无评论

UATTA-ENS: Uncertainty Aware Test Time Augmented Ensemble for PIRC Diabetic Retinopathy Detection

arXiv

引用

arXiv 2022年

作者： Seth, Pratinav Khan, Adil Gupta, Ananya Mishra, Saurabh Kumar Bhandari, Akshat Dept. of Data Science and Computer Applications India Dept. of Mechanical Engineering India Dept. of Electronics and Comm. Engineering India Dept. of Computer Science and Engineering India Manipal Institute of Technology Manipal Academy of Higher Education Manipal India

Deep Ensemble Convolutional Neural Networks has become a methodology of choice for analyzing medical images with a diagnostic performance comparable to a physician, including the diagnosis of Diabetic Retinopathy. However, commonly used techniques are deterministic and are therefore unable to provide any estimate of predictive uncertainty. Quantifying model uncertainty is crucial for reducing the risk of misdiagnosis. A reliable architecture should be well-calibrated to avoid overconfident predictions. To address this, we propose a UATTA-ENS: Uncertainty-Aware Test-Time Augmented Ensemble Technique for 5 Class PIRC Diabetic Retinopathy Classification to produce reliable and well-calibrated predictions. Implementation is available at https://***/ptnv-s/UATTA-ENS. © 2022, CC BY.

关键词： Diagnosis

来源：评论

学校读者我要写书评

暂无评论

Sustainable Energy Efficient Routing Protocol using Chimp algorithm and SOM for Underwater Wireless Sensor Networks

Sustainable Energy Efficient Routing Protocol using Chimp al...

引用

Recent Advances in science and engineering Technology (ICRASET), International Conference on

作者： T Philip Paul Arunodhayam Akhil Raj Vadde Thimmampali Prasanna Kumar Salaja Silas Shalem Preetham Gandu Chamarti Harshith Kumar Dept. of Computer Science and Engineering Karunya Institute of Technology and Sciences Coimbatore India Data Analytics Engineering College of Engineering and Computing George Mason University Fairfax VA United States

Underwater Wireless Sensor Networks (UWSNs) are increasingly being used for various applications, such as environmental monitoring, underwater exploration, and oceanographic research. protocols that consume less energy for underwater wireless sensor networks (UWSNs) are still a difficult undertaking because of the harsh conditions present underwater and the restricted battery capacity of the sensor nodes. This research paper proposes an optimized multi-Hop routing protocol using Chimp Optimization and Self-Organizing Map (SOM). The proposed technique is compared with three popular routing protocols - AODV, DSDV, and DSR in terms of network lifetime and packet delivery ratio. Chimp Optimization algorithm as well as SOM are used to improve network performance. The findings indicate that the put-forward technique exceeds the performance of existing routing protocols, as it increases network lifetime, PDR and PLR while decreasing packet loss ratio. The network lifetime of the put-forward technique is 1039.42 seconds, which is higher than AODV, DSDV, and DSR. Similarly, the PDR of the proposed technique is 97.38%, which is comparatively higher than the three routing protocols. Moreover, the PLR of the proposed technique is 2.62%, which is much lower than AODV, DSDV, and DSR. The proposed technique based on Chimp Optimization and SOM can effectively optimize the multi-Hop routing protocol in underwater wireless sensor networks to achieve sustainable energy efficiency, thus providing important insights for researchers and practitioners to enhance the performance of underwater wireless sensor networks.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Performance Evaluation of data Imputation Methods for Graph Deep Learning-Based Traffic Prediction

Performance Evaluation of Data Imputation Methods for Graph ...

引用

IEEE International Conference on Big data

作者： Jeongseon Kim Sungsoo Kim Sungsu Lim Dept. of Computer Science & Engineering Chungnam National University Daejeon South Korea Smart Data Research Section ETRI Daejeon South Korea

We present five approaches for interpolating missing data in real-world traffic datasets using basic, statistical, and generative model methods. The effectiveness of these approaches is evaluated using graph deep learning models, and the results show that imputing missing data improves the performance of traffic prediction models, especially when dealing with a higher proportion of missing data. The experiments are conducted on two real-world datasets, METR-LA and PEMS-BAY, to evaluate the proposed methods comprehensively. The results demonstrate that imputing missing data significantly enhances the performance of traffic prediction models, particularly when dealing with a higher proportion of missing data.

关键词：

来源：评论

学校读者我要写书评

暂无评论

An Architecture for Dynamic Load Balancing in Cloud Environment

An Architecture for Dynamic Load Balancing in Cloud Environm...

引用

International Conference on Edge Computing and Applications (ICECAA)

作者： Gudivada Lokesh K. K. Baseer Dept. of Computer Science and Engineering Mohan Babu University (Erstwhile Sree Vidyanikethan Engineering College) Tirupati Department of Data Science School of Computing Mohan Babu University (Erstwhile Sree Vidyanikethan Engineering College) Tirupati

Clouds are highly customizable infrastructures that offer a platform as a service and let customers subscribe on a pay-as-you-go basis to their requirements. The straightforward service-oriented cloud computing model is gaining popularity around the world. The number of people using the Cloud is constantly growing. Clouds use modern data centers to manage a massive number of users. The reliability of the Cloud depends on load balancing. Balancing virtual machine loads lowers energy consumption and task rejections by optimizing resource utilization. One can increase performance while using fewer resources using load balancing, resource management, quality of service, etc. The difficulty of overloading and underloading virtual machines in cloud computing can be lessened by load balancing in the *** research study thoroughly examined the load-balancing algorithms found in the literature. First, the traditional approaches are analyzed before moving on to more recent work on load balancing with heterogeneous techniques. Along with the tools available for the current investigation, various metrics are used to evaluate the load-balancing algorithms. The proposed article will primarily serve to assist in the development of new algorithms in the future.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Systems Modeling of Trust in AI-Enabled Medical Diagnosis

Systems Modeling of Trust in AI-Enabled Medical Diagnosis

引用

Annual IEEE Systems Conference

作者： Negin Moghadasi Misagh Piran Rupa S. Valdez Negar Moghaddasi Davis C. Loose Thomas L. Polmateer James H. Lambert Dept. of Systems and Information Engineering University of Virginia Johnson & Johnson Medtech Charlottesville USA Dept. of Radiology Nuclear Medicine and Molecular Imaging Heart and Diabetes Center North-Rhine Westphalia Ruhr University of Bochum Bad Oeyhausen Germany Dept. of Systems and Information Engineering School of Data Science University of Virginia Charlottesville USA Dept. of Dentistry Western University of Health Sciences Pomona USA Dept. of Engineering Systems and Environment University of Virginia Charlottesville USA Dept. of Engineering Systems and Environment CCALS University of Virginia Charlottesville USA Dept. of Systems and Information Engineering University of Virginia Charlottesville USA

ISBN: (数字)9798350358803

ISBN: (纸本)9798350358810

The progression of artificial intelligence, particularly in healthcare domain, has brought significant progress to treatment, drug discovery, disease diagnosis and more. The main contribution of this paper is the development of a scenario-based preferences risk register framework for quantifying AI related risks in disease diagnosis. Two case studies, one involving physician and the other one with involving patients as the main stakeholders, are compared to evaluate the effectiveness of the framework and to evaluate how the initiatives ranking orders will evolve based on the most and least disruptive scenarios for each cases. The framework is applied to realistic case studies on cardiac sarcoidosis, identifying success criteria, initiatives, emergent conditions, and the most and least disruptive scenarios. The success criteria align with the National Institute of Standards and Technology AI Risk Management Framework (NIST AI RMF) seven trustworthy AI characteristics. Finally, the framework identifies the most and least disruptive scenarios and the most important initiatives for both cases. Results show that Non-Interpretable AI and Lack of Human-AI Communications, Privacy Attacks, and Human Errors in Design, Develop, Measurement and Implementation are the most disruptive scenarios in both cases, however, scenario Cyber Security Threats also counted as other most disruptive scenarios to the initiatives ranked by patients. This adaptable framework has the potential not only to transfer findings to global healthcare systems but also to extend its effectiveness to various other domains, such as transportation, finance, environment, and design.

关键词： Privacy Measurement uncertainty Transportation NIST Systems modeling Registers Medical diagnosis

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：