检索结果-内蒙古大学图书馆

Information Technology (ACIT)

作者： Manel Guettala Samir Bourekkache Okba Kazar Saad Harous Belkacem Athamena Zina Houhamdi Department of Computer Science LINFI Laboratory University of Biskra Biskra Algeria Department of information Systems and Security College of Information Technology UAE University Department of Computer Science College of Computing and Informatics University of Sharjah College of Business Al Ain University Al Ain UAE College of engineering Al Ain University Al Ain UAE

ISBN: (数字)9798350320244

ISBN: (纸本)9798350320251

Information technology (IT) plays an important role in the education field, with ubiquitous computing technology the ubiquitous learning (U-learning) is now a practical possibility. Cloud computing can offer many benefits to the learning field, as it provides all resources such as software (SaaS), platforms (PaaS), and infrastructure (IaaS) as a service. The multi-agent systems approach is an excellent way to implement flexible systems whose structure evolves dynamically. Based on the discussion of the benefits of cloud computing and the multi-agent systems approach to support adaptive and personalized learning content, the paper presents a hybrid approach based on cloud computing that uses the multi-tenant architecture where tenants share the system software. Multi-agent system is used in this approach to support a ubiquitous learning system. A multi-tenant SaaS application addresses the unique requirements of each tenant by enabling customization at the tenant level with the adoption of the concept of virtualization (virtual machines). This paper explores the challenges of achieving performance isolation in the context of multi-tenant SaaS applications. As an output of this study, U-learning on the cloud-based multi-agent system architecture has been proposed. To achieve lower cost of resources and server allocation and provide isolation among learners in the ubiquitous learning environment.

关键词： Cloud computing Software as a service computer architecture Ubiquitous computing Virtual machining System software Servers

来源：评论

学校读者我要写书评

暂无评论

Machine Learning Techniques for Analysis of Mars Weather Data

Machine Learning Techniques for Analysis of Mars Weather Dat...

引用

International Conference on Electronics, computers and Artificial Intelligence (ECAI)

作者： Piyush Pant Anand Singh Rajawat S. B. Goyal Baharu Bin Kemat Traian Candin Mihălţan Chaman Verma Maria Simona Răboacă School of Computer Sciences and Engineering Sandip University Nashik India Faculty of Information Technology City University Petaling Jaya Malaysia City Graduate School City University Petaling Jaya Malaysia Faculty of Building Services Technical University of Cluj-Napoca Cluj-Napoca Romania Department of Media and Educational Informatics Faculty of Informatics Eötvös Loránd University Budapest ICSI Energy Department National Research and Development Institute for Cryogenics and Isotopic Technologies Romania

The exploration of Mars has provided vast amounts of weather data that present unique challenges for analysis and prediction. To address these challenges, this research paper focuses on the application of machine learning techniques for the analysis of Mars weather data. The objective is to develop models that can effectively extract patterns, uncover hidden relationships, and enable accurate predictions in the Martian weather system. The research begins with a comprehensive review of the available Mars weather data. The dataset, consisting of historical records, serves as the foundation for training and evaluating machine learning models. The analysis of the Mars weather dataset reveals the planet's icy and harsh climate, with average maximum temperatures of around -21°C and average minimum temperatures of -80°C. The temperature variations show that the minimum temperature fluctuates within a narrower range of 20-30°C over the course of Mars sols from 0 to 2000, while the maximum temperature experiences larger variations of about 40-50°C. During this time, the atmospheric pressure on Mars fluctuates between 720 Pa and 950 Pa. In addition, using the elbow method revealed that 3 clusters were the ideal number for identifying distinct patterns in the weather data. The linear regression model also attained an accuracy of 85%, demonstrating its efficacy in forecasting weather patterns on Mars.

关键词：

来源：评论

学校读者我要写书评

暂无评论

The Effect of UI and UX enhancement on bomberman game based on game experience questionnaire (GEQ)

The Effect of UI and UX enhancement on bomberman game based ...

引用

2020 International Seminar on Application for Technology of Information and Communication, iSemantic 2020

作者： Zulfa, Nafa Yuniasri, Dini Damayanti, Putri Herumurti, Darlis Yunanto, Andhik Ampuh Department of Informatics Institut Teknologi Sepuluh Nopember Keputih Sukolilo Surabaya City East Java60117 Indonesia Department of Informatics and Computer Engineering Politeknik Elektronika Negeri Surabaya Keputih Sukolilo Surabaya City East Java60117 Indonesia

ISBN: (纸本)9781728190686

User Interface (UI) and User Experience (UX) are aspects that cannot be separated from video game development. In this study, we have improved the UI and UX of the Bomberman game and compared it to the UI and UX of the original Bomberman game. In evaluating the UI and UX results, we use the Game Experience Questionnaire Method (GEQ). There are six aspects used in the GEQ method, i.e., aspects of the challenge, competition, immersion, playfulness, social experiment, and enjoyment. We use 65 respondents to rate the UI and UX of the original Bomberman game. The questionnaire's results were taken into consideration in improving the UI and UX of the game to be developed. GEQ also provided to get the result of the improvement game. Likert scale calculation concluded that the 62 respondents rated the success of the Bomberman game development with UI and UX that has been improved. The 62 respondents indicated that the UI and UX of games provide more fun and enjoyable than the original Bomberman game. © 2020 IEEE.

关键词： User interfaces

来源：评论

学校读者我要写书评

暂无评论

Automatic detection of lung nodule in CT scan slices using CNN segmentation schemes: A study

Automatic detection of lung nodule in CT scan slices using C...

引用

2022 International Conference on Machine Learning and Data engineering, ICMLDE 2022

作者： Kadry, Seifedine Herrera-Viedma, Enrique Crespo, Rubén González Krishnamoorthy, Sujatha Rajinikanth, Venkatesan Faculty of Applied Computing and Technology Noroff University College Kristiansand94612 Norway Research Institute in Data Science and Computational Intelligence University of Granada Andalusian Granada Spain Computer Science Department School of Engineering and Technology Universidad Internacional de la Rioja 26006 Spain Zhejiang Bioinformatics International Science and Technology Cooperation Center Wenzhou-Kean University Zhejiang Province China Wenzhou Municipal Key Lab of Applied Biomedical and Biopharmaceutical Informatics Wenzhou-Kean University Zhejiang Province China Department of Electronics and Instrumentation Engineering St. Joseph's College of Engineering OMR Tamil Nadu Chennai600119 India

The lung is one of the prime respiratory organs in human physiology, and its abnormality will severely disrupt the respiratory system. Lung Nodule (LN) is one of the abnormalities, and early screening and treatment are necessary to reduce its harshness. The proposed work aims to implement the Convolutional-Neural-Network (CNN) segmentation methodology to extract the LN in various lung CT slices, such as axial, coronal, and sagittal planes. This work consists of the following phases;(i) Image collection and pre-processing, (ii) Ground-truth generation, (iii) CNN-supported segmentation, and (iv) Performance evaluation and validation. In this work, the merit of pre-trained CNN segmentation schemes is verified using (i) One-fold training and (ii) Two-fold training methods. The test images for this study are collected from The Cancer Imaging Archive (TCIA) database. The experimental investigation is executed using Python®, and the outcome of this study confirms that the VGG-SegNet helps to get better values of Jaccard (>88%), Dice (>93%), and Accuracy (>96%) compared to other CNN methods. © 2023 The Authors. Published by Elsevier B.V.

关键词： computerized tomography

来源：评论

学校读者我要写书评

暂无评论

A Container-Based of Edge Device Monitoring on Kubernetes 10th

A Container-Based of Edge Device Monitoring on Kubernetes

引用

10th International Conference on Frontier Computing, FC 2020

作者： Fathoni, Halim Yen, Hao-Yi Yang, Chao-Tung Huang, Chin-Yin Kristiani, Endah Department Industrial Engineering and Enterprise Information Tunghai University Taichung City407302 Taiwan Departement of Ekonomi dan Bisnis Politeknik Negeri Lampung Bandar Lampung Indonesia Department Computer Science Tunghai University Taichung City407302 Taiwan Departement of Informatics Krida Wacana Christian University Jakarta11470 Indonesia

ISBN: (纸本)9789811601149

The concept of the Internet of Things has widely used in various applications in life from home to big cities that have a variety of edge devices to facilitate edge computing. This paper attempts to use Kubernetes to establish an environment for collecting the internal resource usage status of various node devices and implementing monitoring and visualization. The deep learning models are deployed on heterogeneous devices for performance evaluation and verification. In this experiment, Kubernetes used to maintain a Docker container that used to deploy all applications mentioned above. All the data then visualize through Grafana. © 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Edge computing

来源：评论

学校读者我要写书评

暂无评论

Graph-in-Graph (GiG): Learning interpretable latent graphs in non-Euclidean domain for biological and healthcare applications

arXiv

引用

arXiv 2022年

作者： Mullakaeva, Kamilia Cosmo, Luca Kazi, Anees Ahmadi, Seyed-Ahmad Navab, Nassir Bronstein, Michael M. The Department of Computer Science Technical University of Munich Munich Germany The Department of Environmental Sciences Informatics and Statistics Ca' Foscari University of Venice Venice Italy The Informatics Departement Usi University of Lugano Lugano Switzerland Nvidia Munich Germany The Whiting School of Engineering Johns Hopkins University Baltimore United States The University of Oxford United Kingdom

Graphs are a powerful tool for representing and analyzing unstructured, non-Euclidean data ubiquitous in the healthcare domain. Two prominent examples are molecule property prediction and brain connectome analysis. Importantly, recent works have shown that considering relationships between input data samples have a positive regularizing effect for the downstream task in healthcare applications. These relationships are naturally modeled by a (possibly unknown) graph structure between input samples. In this work, we propose Graph-in-Graph (GiG), a neural network architecture for protein classification and brain imaging applications that exploits the graph representation of the input data samples and their latent relation. We assume an initially unknown latent-graph structure between graph-valued input data and propose to learn end-to-end a parametric model for message passing within and across input graph samples, along with the latent structure connecting the input graphs. Further, we introduce a degree distribution loss that helps regularize the predicted latent relationships structure. This regularization can significantly improve the downstream task. Moreover, the obtained latent graph can represent patient population models or networks of molecule clusters, providing a level of interpretability and knowledge discovery in the input domain of particular value in healthcare. Copyright © 2022, The Authors. All rights reserved.

关键词： Graphic methods

来源：评论

学校读者我要写书评

暂无评论

BiomedGPT: A Unified and Generalist Biomedical Generative Pre-trained Transformer for Vision, Language, and Multimodal Tasks

arXiv

引用

arXiv 2023年

作者： Zhang, Kai Zhou, Rong Adhikarla, Eashan Yan, Zhiling Liu, Yixin Yu, Jun Liu, Zhengliang Chen, Xun Davison, Brian D. Ren, Hui Huang, Jing Chen, Chen Zhou, Yuyin Fu, Sunyang Liu, Wei Liu, Tianming Li, Xiang Chen, Yong He, Lifang Zou, James Li, Quanzheng Liu, Hongfang Sun, Lichao Department of Computer Science and Engineering Lehigh University PA United States School of Computing University of Georgia GA United States Samsung Research America CA United States Department of Radiology Massachusetts General Hospital Harvard Medical School MA United States Department of Biostatistics Epidemiology and Informatics University of Pennsylvania PA United States PolicyLab Children’s Hospital of Philadelphia PA United States Center for Research in Computer Vision University of Central Florida FL United States Department of Computer Science and Engineering University of California Santa CruzCA United States McWilliams School of Biomedical Informatics UTHealth HoustonTX United States Department of Radiation Oncology Mayo Clinic AZ United States University of Pennsylvania PA United States PA United States Leonard Davis Institute of Health Economics PA United States Department of Biomedical Data Science Stanford University School of Medicine CA United States Department of Computer Science Stanford University CA United States

Traditional biomedical artificial intelligence (AI) models, designed for specific tasks or modalities, often exhibit limited flexibility in real-world deployment and struggle to utilize holistic information. Generalist AI holds the potential to address these limitations due to its versatility in interpreting different data types and generating tailored outputs for diverse needs. However, existing biomedical generalist AI solutions are typically heavyweight and closed source to researchers, practitioners, and patients. Here, we propose BiomedGPT, the first open-source and lightweight vision-language foundation model, designed as a generalist capable of performing various biomedical tasks. BiomedGPT achieved state-of-the-art results in 16 out of 25 experiments while maintaining a computing-friendly model scale. We also conducted human evaluations to assess the capabilities of BiomedGPT in radiology visual question answering, report generation, and summarization. BiomedGPT exhibits robust prediction ability with a low error rate of 3.8% in question answering, satisfactory performance with an error rate of 8.3% in writing complex radiology reports, and competitive summarization ability with a nearly equivalent preference score to human experts. Our method demonstrates that effective training with diverse data can lead to more practical biomedical AI for improving diagnosis and workflow efficiency. © 2023, CC BY.

关键词： Visual languages

来源：评论

学校读者我要写书评

暂无评论

Temporal clustering with external memory network for disease progression modeling

arXiv

引用

arXiv 2021年

作者： Zhang, Zicong Yin, Changchang Zhang, Ping Department of Computer Science and Engineering The Ohio State University Columbus United States Department of Biomedical Informatics The Ohio State University Columbus United States

Disease progression modeling (DPM) involves using mathematical frameworks to quantitatively measure the severity of how certain disease progresses. DPM is useful in many ways such as predicting health state, categorizing disease stages, and assessing patients’ disease trajectory, etc. Recently, with the wider availability of electronic health records (EHR) and the broad application of data-driven machine learning methods, DPM has attracted much attention yet remains two major challenges: (i) Due to the existence of irregularity, heterogeneity, and long-term dependency in EHRs, most existing DPM methods might not be able to provide comprehensive patient representations. (ii) Lots of records in EHRs might be irrelevant to the target disease. Most existing models learn to automatically focus on the relevant information instead of explicitly capture the target-relevant events, which might make the learned model suboptimal. To address these two issues, we propose Temporal Clustering with External Memory Network (TC-EMNet) for DPM that groups patients with similar trajectories to form disease clusters/stages. TC-EMNet uses a variational autoencoder (VAE) to capture internal complexity from the input data and utilizes an external memory work to capture long-term distance information, both of which are helpful for producing comprehensive patient health states. Last but not least, the k-means algorithm is adopted to cluster the extracted comprehensive patient representation to capture disease progression. Experiments on two real-world datasets show that our model demonstrates competitive clustering performance against state-of-the-art methods and is able to identify clinically meaningful clusters. The visualization of the patient representations shows that the proposed model can generate better patient health states than the baselines. © 2021, CC BY-NC-ND.

关键词： K-means clustering

来源：评论

学校读者我要写书评

暂无评论

Information Quality Assessment for the Medical Wearable Devices: Validation Study 14

Information Quality Assessment for the Medical Wearable Devi...

引用

14th International Conference on Innovations in Information Technology, IIT 2020

作者： Alrae, Rawhi Abutalib, Manar Nasir, Qassim University of Sharjah College of Engineering Engineering Management Department Sharjah United Arab Emirates University of Sharjah College of Computing Informatics Department of Computer Science Sharjah United Arab Emirates University of Sharjah College of Engineering Department of Electrical Engineering Sharjah United Arab Emirates

ISBN: (纸本)9781728181844

Wearable systems are promising Internet of Things (IoT) solutions that have attracted researchers' attention in the last decade. They are getting more consideration after the COVID-19 pandemic. Information Quality (IQ) is essential for obtaining value from wearable IoT systems. In our earlier work, we proposed a complete framework that systemically developed a total IQ assessment of the IoT systems. The framework was initially validated by conducting a comparison study. Therefore, this paper aims to validate the utility of the earlier framework by using a single-case experiment. This experiment studies the use of the framework in defining IQ assessment for medical wearable IoT devices. The methodology of the experiment involves conducting literature surveys. This paper successfully uses wearable systems as a case to test the assessment processes of the IQ framework for the IoT systems. However, the case effectively indicates the importance of the framework pillars: the data quality dimensions and the IoT elements by assessing the medical wearables. © 2020 IEEE.

关键词： Internet of things

来源：评论

学校读者我要写书评

暂无评论

Automatic Detection of Cardiac Ejection Rate in Left Ventricular Photography Using Deep Learning 4

Automatic Detection of Cardiac Ejection Rate in Left Ventric...

引用

4th IEEE Eurasia Conference on Biomedical engineering, Healthcare and Sustainability, ECBIOS 2022

作者： Lin, Wenyuan Hu, Hsiang-Wei Tung, Nai-Yun Huang, Ren-Syuan Lin, Wei-Ming Hu, You-Ming Huang, Sheng-Yao Kao, Shin-Yu Lee, Chun-Yi Shih, Shao-Ni Wu, Chin-Yu Chang, Wei-Ting International Academia of Biomedical Innovation Technology Taipei Taiwan Chang Gung University Department of Medical Biotechnology and Laboratory Science Taoyuan Taiwan Department of Biomedical Engineering National Cheng Kung University Tainan Taiwan National Cheng Kung University Department of Biomedical Engineering Tainan Taiwan National Central University Department of Economics Taoyuan Taiwan Hualien Tzu Chi General Hospital Department of Radiation Oncology Hualien Taiwan Taipei Medical University Depaertment of Biomedical Informatics Taipei Taiwan National Tsing Hua University Department of Computer Science Hsinchu Taiwan National Tsing Hua University Department of Chinese Literature Hsinchu Taiwan Chinese Culture University Department of Electrical Engineering Taipei Taiwan Division of Cardiology Department of Internal Medicine Chi-Mei Medical Center Taiwan Institute of Clinical Medicine College of Medicine National Cheng Kung University Taiwan Department of Biotechnology Southern Taiwan University of Science and Technology Tainan Taiwan

ISBN: (数字)9781728195797

ISBN: (纸本)9781728195797

Left ventricular angiography is an assessment of cardiac structure and function that is often performed during cardiac catheterization. In addition to obtaining the index of left ventricular ejection rate (LVEF), left ventricular angiography also assists in the interpretation of cardiac hypertrophy, valve regurgitation, and regional myocardial systolic dysfunction. This also assists the clinician in the treatment and the judgment of opening the blood vessels. Although left ventricular angiography has been a well-established and internationally recognized detection method, it is not easy to train cardiac catheterization professionals, as image analysis requires sufficient experience but it is time-consuming. In this study, the deep learning models are introduced to reduce clinical interpretation errors and provide information on cardiac function and local cardiac systolic abnormalities in a short period. Then, clinicians can easily make medical decisions and judgments. The models in this study are expected to reduce 90% of operation time and improve the accuracy from 50% to 99%. It also helps detect myocardial hypoxia and determine whether to perform coronary surgery. Automatic and accurate detection of cardiac ejection rate, cardiac hypertrophy1, and abnormal local contraction is enabled to immediately warn and feedback to clinicians to make the most accurate treatment decisions. © 2022 IEEE.

关键词： Angiography

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：