检索结果-内蒙古大学图书馆

Virtualization and Exploration of the Garudeya Historical Objects Using Immersive Devices

学校读者我要写书评

暂无评论

Virtualization and Exploration of the Garudeya Historical Ob...

International Conference on computer engineering, Network and Intelligent Multimedia (CENIM)

作者： Surya Sumpeno Yanuar Ramadhani Achmadianto Ahmad Zaini Diana Purwitasari Computer Engineering Dept Institut Teknologi Sepuluh Nopember Surabaya Indonesia Informatics Dept. Institut Teknologi Sepuluh Nopember Surabaya Indonesia

ISBN: (纸本)9781728129662

Mpu Tantular Museum has a diverse collection of historical objects, one of which is the Garudeya gold jewelry. Garudeya jewelry is one of the artifacts of value that is the heritage of Hindu Buddhist kingdom in Indonesia. No wonder that some parts of Garudeya have been lost and damaged without repair. The replicas on display in the museum have a quality that is not comparable with the original object and the scope of exploration and interaction that can be carried out is very limited. The historical information presented in the museum is also unattractive and very monotonous causing the details of historical objects to be less in-formative. With the creation of virtual museum using immersive technology such as VR with HMD and Leap Motion Controller to represent Garudeya jewelry with a real nuance in the virtual world as a means of entertainment and unique information presentation. That fact is backed by the average respondents that are very attracted in the concept of this application by 70% and 90% of respondents have no difficulty in the exploration and presentation of User Interface. In terms of information presentation there is an increase of 33.3% of the correct answers from the respondents before and after trying the application.

关键词： Virtual Museum VR Garudeya Leap Motion Variable reluctance Exploration Museums Virtualization Respondents Jewelry presentation REPLICA Helmet mounted displays User Interface Device Component Entertainment

Enhance the Mammogram Images for Both Segmentation and Feature Extraction Using Wavelet Transform

学校读者我要写书评

暂无评论

Enhance the Mammogram Images for Both Segmentation and Featu...

2019 International Conference on Advanced Science and engineering, ICOASE 2019

作者： Zebari, Dilovan Asaad Haron, Habibollah Zeebaree, Subhi R.M. Zeebaree, Diyar Qader Faculty of Engineering School of Computing University Teknologi Malaysia Johor Malaysia University Teknologi Malaysia Faculty of Computing Department of Computer Science Johor Malaysia IT Dept. Technical College of Informatics Akre Duhok Scientific Research and Development Center Nawroz University Duhok Iraq

ISBN: (纸本)9781538693438

Breast cancer (BC) is a main killer disease for women and men. It can be cured and controlled only if it is detected at its early detection. BC initial identification can be realized by the help of computer support identification approaches. From the detailed study on previous researches, it is found that, there is no system producing high accuracy because of one or more reasons. Absence of effective preprocessing is the discussed reason that obstructs the detection accuracy of computer-Aided diagnosis (CAD) method. Noise removal and contrast enhancement are the two types of preprocessing. There is no system performs the preprocessing on mammogram image. This work is an attempt to develop an enhanced preprocessing method for CAD of breast cancer by incorporating suitable noise reduction and contrast enhancement methods in the conventional CAD system. Contrast enhancement after noise reduction double enhances the mammogram image and the proposed methods MSE value for the mammogram image mdb072 has been 1.44% reduced. Reduction in MSE increases the PSNR to 0.16%. Many mammogram images have been tested and the result shows that, increase in contrast, decrease in mean square error and increase in peak signal to noise ratio when comparing to existing methods. © 2019 IEEE.

关键词： Wavelet transforms

Using LoRa Technology for IoT Monitoring Systems

学校读者我要写书评

暂无评论

Using LoRa Technology for IoT Monitoring Systems

International Conference on the Network of the Future (NOF)

作者： Christos Bouras Apostolos Gkamas Vasileios Kokkinos Nikolaos Papachristos Computer Technology Institute & Press "Diophantus" Patras Greece University Ecclesiastical Academy of Vella Greece Computer Engineering and Informatics Dept. Univ. of Patras Greece

ISBN: (数字)9781728144450

ISBN: (纸本)9781728144467

This paper presents technology comparison scenarios for Internet of Things (IoT) concepts on rescue monitoring. The study starts by comparing WiFi & LoRa as wireless technologies able to be used by smart devices for data transmission. The IoT end-devices used in these concepts have high requirements in battery saving and for this reason the usage of low-power modules is advisable. This paper focus in rescue monitoring and the goal in the current study is the usage of the two wireless technologies used for data transmission from IoT devices: the already known WiFi and the upcoming LoRa technology. During rescue monitoring, important concepts are the identification and individuals' rescue of particularly vulnerable groups or individuals belonging to population groups with a high probability of being lost. A LoRa based gateway and WiFi Router is used to connect the end-devices used in our scenarios to the Internet. The collected data on server application as captured from installed sensors on the IoT modules can be displayed to authorized users through a web or mobile application. The results through simulation and real time experiments indicate that LoRa could be an ideal candidate for rescue monitoring. This study is a first step in creating a more general ecosystem for rescue concepts including all the hardware and software using the LoRa technology as transmission method.

关键词： Wireless fidelity Monitoring Sensors Batteries Logic gates Servers Internet of Things

Privacy Preserved Blood Glucose Level Cross-Prediction: An Asynchronous Decentralized Federated Learning Approach

学校读者我要写书评

暂无评论

arXiv 2024年

作者： Piao, Chengzhe Zhu, Taiyu Wang, Yu Baldeweg, Stephanie E. Taylor, Paul Georgiou, Pantelis Sun, Jiahao Wang, Jun Li, Kezhi Institute of Health Informatics University College London LondonNW1 2DA United Kingdom Department of Psychiatry University of Oxford OxfordOX3 7JX United Kingdom Department of Diabetes & Endocrinology University College London Hospitals LondonNW1 2PG United Kingdom Centre for Obesity & Metabolism Dept of Experimental & Translational Medicine University College London LondonWC1E 6JF United Kingdom Centre for Bio-Inspired Technology Department of Electrical and Electronic Engineering Imperial College London LondonSW7 2AZ United Kingdom FLock.io LondonWC2H 9JQ United Kingdom Department of Computer Science University College London LondonWC1E 6EA United Kingdom

Newly diagnosed Type 1 Diabetes (T1D) patients often struggle to obtain effective Blood Glucose (BG) prediction models due to the lack of sufficient BG data from Continuous Glucose Monitoring (CGM), presenting a significant "cold start" problem in patient care. Utilizing population models to address this challenge is a potential solution, but collecting patient data for training population models in a privacy-conscious manner is challenging, especially given that such data is often stored on personal devices. Considering the privacy protection and addressing the "cold start" problem in diabetes care, we propose "GluADFL", blood Glucose prediction by Asynchronous Decentralized Federated Learning. We compared GluADFL with eight baseline methods using four distinct T1D datasets, comprising 298 participants, which demonstrated its superior performance in accurately predicting BG levels for cross-patient analysis. Furthermore, patients’ data might be stored and shared across various communication networks in GluADFL, ranging from highly interconnected (e.g., random, performs the best among others) to more structured topologies (e.g., cluster and ring), suitable for various social networks. The asynchronous training framework supports flexible participation. By adjusting the ratios of inactive participants, we found it remains stable if less than 70% are inactive. Our results confirm that GluADFL offers a practical, privacy-preserving solution for BG prediction in T1D, significantly enhancing the quality of diabetes management. Copyright © 2024, The Authors. All rights reserved.

关键词： Forecasting

GARNN: An Interpretable Graph Attentive Recurrent Neural Network for Predicting Blood Glucose Levels via Multivariate Time Series

学校读者我要写书评

暂无评论

arXiv 2024年

作者： Piao, Chengzhe Zhu, Taiyu Baldeweg, Stephanie E. Taylor, Paul Georgiou, Pantelis Sun, Jiahao Wang, Jun Li, Kezhi Institute of Health Informatics University College London LondonNW1 2DA United Kingdom Department of Psychiatry University of Oxford OxfordOX3 7JX United Kingdom Department of Diabetes & Endocrinology University College London Hospitals LondonNW1 2PG United Kingdom Centre for Obesity & Metabolism Dept of Experimental & Translational Medicine University College London LondonWC1E 6JF United Kingdom Centre for Bio-Inspired Technology Department of Electrical and Electronic Engineering Imperial College London LondonSW7 2AZ United Kingdom FLock.io LondonWC2H 9JQ United Kingdom Department of Computer Science University College London LondonWC1E 6EA United Kingdom

Accurate prediction of future blood glucose (BG) levels can effectively improve BG management for people living with diabetes, thereby reducing complications and improving quality of life. The state of the art of BG prediction has been achieved by leveraging advanced deep learning methods to model multi-modal data, i.e., sensor data and self-reported event data, organised as multi-variate time series (MTS). However, these methods are mostly regarded as "black boxes" and not entirely trusted by clinicians and patients. In this paper, we propose interpretable graph attentive recurrent neural networks (GARNNs) to model MTS, explaining variable contributions via summarizing variable importance and generating feature maps by graph attention mechanisms instead of post-hoc analysis. We evaluate GARNNs on four dataset, representing diverse clinical scenarios. Upon comparison with twelve well-established baseline methods, GARNNs not only achieve the best prediction accuracy but also provide high-quality temporal interpretability, in particular for postprandial glucose levels as a result of corresponding meal intake and insulin injection. These findings underline the potential of GARNN as a robust tool for improving diabetes care, bridging the gap between deep learning technology and real-world healthcare solutions. Copyright © 2024, The Authors. All rights reserved.

关键词： Forecasting

Understanding metric-related pitfalls in image analysis validation

学校读者我要写书评

暂无评论

arXiv 2023年

作者： Reinke, Annika Tizabi, Minu D. Baumgartner, Michael Eisenmann, Matthias Heckmann-Nötzel, Doreen Kavur, A. Emre Rädsch, Tim Sudre, Carole H. Acion, Laura Antonelli, Michela Arbel, Tal Bakas, Spyridon Benis, Arriel Blaschko, Matthew B. Buettner, Florian Cardoso, M. Jorge Cheplygina, Veronika Chen, Jianxu Christodoulou, Evangelia Cimini, Beth A. Collins, Gary S. Farahani, Keyvan Ferrer, Luciana Galdran, Adrian van Ginneken, Bram Glocker, Ben Godau, Patrick Haase, Robert Hashimoto, Daniel A. Hoffman, Michael M. Huisman, Merel Isensee, Fabian Jannin, Pierre Kahn, Charles E. Kainmueller, Dagmar Kainz, Bernhard Karargyris, Alexandros Karthikesalingam, Alan Kenngott, Hannes Kleesiek, Jens Kofler, Florian Kooi, Thijs Kopp-Schneider, Annette Kozubek, Michal Kreshuk, Anna Kurc, Tahsin Landman, Bennett A. Litjens, Geert Madani, Amin Maier-Hein, Klaus Martel, Anne L. Mattson, Peter Meijering, Erik Menze, Bjoern Moons, Karel G.M. Müller, Henning Nichyporuk, Brennan Nickel, Felix Petersen, Jens Rafelski, Susanne M. Rajpoot, Nasir Reyes, Mauricio Riegler, Michael A. Rieke, Nicola Saez-Rodriguez, Julio Sánchez, Clara I. Shetty, Shravya Summers, Ronald M. Taha, Abdel A. Tiulpin, Aleksei Tsaftaris, Sotirios A. van Calster, Ben Varoquaux, Gaël Yaniv, Ziv R. Jäger, Paul F. Maier-Hein, Lena Faculty of Mathematics and Computer Science Heidelberg University Heidelberg Germany Heidelberg Division of Intelligent Medical Systems Germany NCT Heidelberg A Partnership Between DKFZ University Medical Center Heidelberg Germany Heidelberg Division of Medical Image Computing Germany Heidelberg Division of Intelligent Medical Systems Germany MRC Unit for Lifelong Health and Ageing UCL Centre for Medical Image Computing Department of Computer Science University College London London United Kingdom School of Biomedical Engineering and Imaging Science King’s College London London United Kingdom Instituto de Cálculo CONICET – Universidad de Buenos Aires Buenos Aires Argentina Centre for Medical Image Computing University College London London United Kingdom McGill University Montreal Canada Division of Computational Pathology Dept of Pathology & Laboratory Medicine Indiana University School of Medicine IU Health Information and Translational Sciences Building Indianapolis United States University of Pennsylvania Richards Medical Research Laboratories FL7 PhiladelphiaPA United States Department of Digital Medical Technologies Holon Institute of Technology Holon Israel European Federation for Medical Informatics Le Mont-sur-Lausanne Switzerland Center for Processing Speech and Images Department of Electrical Engineering KU Leuven Leuven Belgium partner site Frankfurt/Mainz a partnership between DKFZ and UCT Frankfurt Marburg Germany Heidelberg Germany Goethe University Frankfurt Department of Medicine Germany Goethe University Frankfurt Department of Informatics Germany and Frankfurt Cancer Insititute Germany Department of Computer Science IT University of Copenhagen Copenhagen Denmark Leibniz-Institut für Analytische Wissenschaften – ISAS – e.V. Dortmund Germany Imaging Platform Broad Institute of MIT and Harvard CambridgeMA United States Centre for Statistics in Medicine University of Oxford Oxford United Kingdom Center for Biomedical In

Validation metrics are key for tracking scientific progress and bridging the current chasm between artificial intelligence (AI) research and its translation into practice. However, increasing evidence shows that particularly in image analysis, metrics are often chosen inadequately. While taking into account the individual strengths, weaknesses, and limitations of validation metrics is a critical prerequisite to making educated choices, the relevant knowledge is currently scattered and poorly accessible to individual researchers. Based on a multi-stage Delphi process conducted by a multidisciplinary expert consortium as well as extensive community feedback, the present work provides the first reliable and comprehensive common point of access to information on pitfalls related to validation metrics in image analysis. While focused on biomedical image analysis, the addressed pitfalls generalize across application domains and are categorized according to a newly created, domain-agnostic taxonomy. The work serves to enhance global comprehension of a key topic in image analysis validation. © 2023, CC BY.

关键词： computer vision

Runtime QoS service for application-driven adaptation in network computing

学校读者我要写书评

暂无评论

arXiv 2019年

作者： Al-Hawari, Feras Manolakos, Elias Electrical and Computer Engineering Department Northeastern University Boston United States Dept. of Informatics and Telecommunications University of Athens Athens Greece

—A distributed application executing on a Network of Workstations (NOW) needs to be resource state aware to possibly adapt itself accordingly in order to keep satisfying the desired Quality of Service (QoS) demands throughout its lifespan. We implemented a QoS service to enable application-driven adaptation for performance and fault tolerance at runtime. The service is associated with lightweight middleware that monitors the state and load of all application entities (e.g., machines, tasks, and logical network links). Moreover, it makes its services available to an application task via an anonymous and simple to use QoS API. We present a Manager-Worker application that uses our fault tolerance QoS API to adapt for Worker faults in order to avoid application deadlock at runtime. Moreover, we show how a dynamic application-level scheduler can easily utilize the QoS API to find efficient schedules. Furthermore, we quantified the overhead of the QoS middleware in various scenarios to demonstrate that it has minor impact on the performance of the application it is servicing. Copyright © 2019, The Authors. All rights reserved.

关键词： Fault tolerance

Explorable Virtual Diorama of Indonesian Prehistoric Human Life using Apple ARKit based Mixed Reality

学校读者我要写书评

暂无评论

Explorable Virtual Diorama of Indonesian Prehistoric Human L...

International Conference on computer engineering, Network and Intelligent Multimedia (CENIM)

作者： Surya Sumpeno Bima Panji Yudasmara Diana Purwitasari Computer Engineering Dept Institut Teknologi Sepuluh Nopember (ITS) Surabaya Indonesia Informatics Dept Institut Teknologi Sepuluh Nopember (ITS) Surabaya Indonesia

ISBN: (纸本)9781728129662

Prehistoric human diorama which are being diplayed in archaeology museums in Indonesia are still static and inexplorable. This research aims to design a virtual diorama application about Indonesian prehistoric human habitat and life which applies Apple ARKit based Mixed Reality (MR) technology. Virtual diorama application lets users to explore dynamic virtual diorama from their ARKit supported iOS devices. Virtual diorama application has an ability to 3D scan the environment around user by taking point cloud data through ARKit. Scanning result is used to generate virtual environment which its objects are arranged according with real-world object arrangement by using heightmap from scanning result. This application uses “Magic Door” concept which the user can get in and out virtual diorama through a virtual door. Test results show that virtual environment arranger system has a success rate of 89% with average global translation of 5,04 cm and global rotation chance of 13,33%. Questionnaire testing to 30 persons showed that 43,33% of respondents were helped by virtual environment arrangements which were matched with real world environment arrangement in prevention of collisions with objects in the real world. There are 36,67% of respondents agree that the displayed dynamic prehistoric human helps respondents in understanding prehistoric human life and 46,67% of the respondents agree that Mixed Reality provides a new experience in learning prehistoric human life.

关键词： Three-dimensional displays Cameras Virtual environments Flowcharts Tools Testing

Application of explainable artificial intelligence in medical health: A systematic review of interpretability methods

学校读者我要写书评

暂无评论

informatics in Medicine Unlocked 2023年 40卷

作者： Shahab S Band Atefeh Yarahmadi Chung-Chian Hsu Meghdad Biyari Mehdi Sookhak Rasoul Ameri Iman Dehzangi Anthony Theodore Chronopoulos Huey-Wen Liang Future Technology Research Center National Yunlin University of Science and Technology Douliu Taiwan Department of Information Management International Graduate School of Artificial Intelligence National Yunlin University of Science and Technology Taiwan Department of Information Management National Yunlin University of Science and Technology Taiwan Department of Computer Science Texas A&M University Corpus Christi Tx USA Department of Computer Science Rutgers University NJ USA Department of Computer Science University of Texas San Antonio TX 78249 USA Dept of Computer Engineering & Informatics University of Patras Greece Department of Physical Medicine and Rehabilitation National Taiwan University (NTU) College of Medicine Taipei Taiwan

This paper investigates the applications of explainable AI (XAI) in healthcare, which aims to provide transparency, fairness, accuracy, generality, and comprehensibility to the results obtained from AI and ML algorithms in decision-making systems. The black box nature of AI and ML systems has remained a challenge in healthcare, and interpretable AI and ML techniques can potentially address this issue. Here we critically review previous studies related to the interpretability of ML and AI methods in medical systems. Descriptions of various types of XAI methods such as layer-wise relevance propagation (LRP), Uniform Manifold Approximation and Projection (UMAP), Local Interpretable Model-agnostic Explanations (LIME), SHapley Additive exPlanations (SHAP), ANCHOR, contextual importance and utility (CIU), Training calibration-based explainers (TraCE), Gradient-weighted Class Activation Mapping (Grad-CAM), t-distributed Stochastic Neighbor Embedding (t-SNE), NeuroXAI, Explainable Cumulative Fuzzy Class Membership Criterion (X-CFCMC) along with the diseases which can be explained through these methods are provided throughout the paper. The paper also discusses how AI and ML technologies can transform healthcare services. The usability and reliability of the presented methods are summarized, including studies on the usability and reliability of XGBoost for mediastinal cysts and tumors, a 3D brain tumor segmentation network, and the TraCE method for medical image analysis. Overall, this paper aims to contribute to the growing field of XAI in healthcare and provide insights for researchers, practitioners, and decision-makers in the healthcare industry. Finally, we discuss the performance of XAI methods applied in medical health care systems. It is also needed to mention that a brief implemented method is provided in the methodology section.

关键词： Explainable artificial intelligence (XAI) Interpretability Health ML Methods Review