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A health information systems architecture study in intellectual disability care: Commonalities and variabilities

Healthcare Analytics 2024年 5卷 100295-100295页

作者： Tummers, J. Tobi, H. Catal, C. Tekinerdogan, B. Schalk, B. Leusink, G. Department of Primary and Community Care Radboud University Medical Center P.O. Box 9101 Route 68 Nijmegen HB Nijmegen 6500 Netherlands Wageningen University & Research Biometris Droevendaalsesteeg 1 Wageningen 6708OB Netherlands Department of Computer Science and Engineering Qatar University 2713 Qatar Information Technology Group Wageningen University & Research Hollandseweg 1 KN Wageningen 6706 Netherlands

Care providers in intellectual disability care use various health information systems (HIS) to document the care they provide. This generates a substantial amount of structured and unstructured data with significant potential for reuse, which is currently underexploited. To enhance data reuse, it is important to understand the architecture of health information systems in intellectual disability care, including their commonalities and variabilities (diﬀerences), as well as to identify related privacy and security issues. Our study adopts a multiple-case study approach, examining the architectures of four health information systems in the Netherlands. We conducted interviews with seven stakeholders from four HISs and reviewed multiple documents concerning system infrastructure. We identified commonalities and differences between these systems and outlined the primary challenges regarding privacy and security for data reuse. For each HIS, four architectural views were developed: a context diagram, decomposition view, layered view, and deployment view. The study discusses crucial security and privacy aspects for data reuse in intellectual disability care and highlights several challenges that must be addressed to unlock the full potential of this data. This research provides initial guidelines for overcoming these challenges. © 2023 The Authors

关键词： Big data Data analytics Health information system information system analysis Intellectual disability Multiple-case study

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Crowdsensing-based Road Damage Detection Challenge (CRDDC-2022)

arXiv

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arXiv 2022年

作者： Arya, Deeksha Maeda, Hiroya Ghosh, Sanjay Kumar Toshniwal, Durga Omata, Hiroshi Kashiyama, Takehiro Sekimoto, Yoshihide Centre for Spatial Information Science The University of Tokyo 4-6-1 Komaba Tokyo Japan UrbanX Technologies Inc. Tokyo Japan Department of Civil Engineering Indian Institute of Technology Roorkee247667 India Department of Computer Science and Engineering Indian Institute of Technology Roorkee247667 India Faculty of Economics Osaka University of Economics Japan

This paper summarizes the Crowdsensing-based Road Damage Detection Challenge (CRDDC), a Big Data Cup organized as a part of the IEEE International Conference on Big Data’2022. The Big Data Cup challenges involve a released dataset and a well-defined problem with clear evaluation metrics. The challenges run on a data competition platform that maintains a real-time online evaluation system for the participants. In the presented case, the data constitute 47,420 road images collected from India, Japan, the Czech Republic, Norway, the United States, and China to propose methods for automatically detecting road damages in these countries. More than 60 teams from 19 countries registered for this competition. The submitted solutions were evaluated using five leaderboards based on performance for unseen test images from the aforementioned six countries. This paper encapsulates the top 11 solutions proposed by these teams. The best-performing model utilizes ensemble learning based on YOLO and Faster-RCNN series models to yield an F1 score of 76% for test data combined from all 6 countries. The paper concludes with a comparison of current and past challenges and provides direction for the future. © 2022, CC BY-SA.

关键词： Object detection

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Unsupervised hierarchical text summarization

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AIP Conference Proceedings 2022年第1期2670卷

作者： S. Divya N. Sripriya 1Department of Information Technology Sri Sivasubramaniya Nadar College of Engineering Kalavakkam India 603110

Text Summarization is a process of shortening the longer text document(s) into a summary of informative sentences in an orderly way. Extractive summarization retrieves the important sentences without making any change to actual sentences. Each sentence in the document is represented as a vector of real numbers, which is termed as embedding. While embedding a sentence, values of specific features are analyzed and are plotted in n-dimensional space. This results in effective prediction of following and preceding sentences of the current sentence. Thus, the sentences with similar semantics lie closer to each other. Unsupervised Summarization groups the similar sentences by estimating the distance between the vectors and decides if sentence must be included in summary or not. Hierarchical Summarization constructs a tree-structure with the input text data, where the truncation of tree is done based on the number of clusters. Each cluster holds sentences that are semantically similar. By determining the nearest neighbor in each cluster, a specified number of sentences were retrieved from each cluster to be included in summary which holds at-least half the size of the input document(s). The performance of hierarchical summarization is measured on CNN/Daily Mail dataset and is determined using performance metrics. The evaluation score concludes that BIRCH algorithm performs better than the other Hierarchical Clustering techniques.

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Heterogeneous Ultra-Dense Networks with Traffic Hotspots: A Unified Handover Analysis

arXiv

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arXiv 2022年

作者： Zhou, He Zhou, Haibo Li, Jianguo Yang, Kai An, Jianping Shen, Xuemin The School of Information and Electronics Beijing Institute of Technology Beijing China School of Cyberspace Science and Technology Beijing Institute of Technology Beijing China The School of Electronic Science and Engineering Nanjing University Nanjing210023 China The Department of Electrical and Computer Engineering University of Waterloo WaterlooONN2L 3G1 Canada

With the ever-growing communication demands and the unceasing miniaturization of mobile devices, the Internet of Things is expanding the amount of mobile terminals to an enormous level. To deal with such numbers of communication data, plenty of base stations (BSs) need to be deployed. However, denser deployments of heterogeneous networks (HetNets) lead to more frequent handovers, which could increase network burden and degrade the users experience, especially in traffic hotspot areas. In this paper, we develop a unified framework to investigate the handover performance of wireless networks with traffic hotspots. Using the stochastic geometry, we derive the theoretical expressions of average distances and handover metrics in HetNets, where the correlations between users and BSs in hotspots are captured. Specifically, the distributions of macro cells are modeled as independent Poisson point processes (PPPs), and the two tiers of small cells outside and inside the hotspots are modeled as PPP and Poisson cluster process (PCP) separately. A modified random waypoint (MRWP) model is also proposed to eliminate the density wave phenomenon in traditional models and to increase the accuracy of handover decision. By combining the PCP and MRWP model, the distributions of distances from a typical terminal to the BSs in different tiers are derived. Afterwards, we derive the expressions of average distances from a typical terminal to different BSs, and reveal that the handover rate, handover failure rate, and ping-pong rate are deduced as the functions of BS density, scattering variance of clustered small cell, user velocity, and threshold of triggered time. Simulation results verify the accuracy of the proposed analytical model and closed-form theoretical expressions. Copyright © 2022, The Authors. All rights reserved.

关键词： Heterogeneous networks

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A Novel and Advantageous Recovery Solution for Deadlock Problem of Flexible Manufacturing Systems Based on Petri Nets Modeling Theory

A Novel and Advantageous Recovery Solution for Deadlock Prob...

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International Conference on Applied System Innovation (ICASI)

作者： Ching-Yun Tseng Ju-Chin Chen Yen-Liang Pan Department of Electronic Engineering National Kaohsiung University of Sciences and Technology Department of Computer Science and Information Engineering National Kaohsiung University of Sciences and Technology No. 415 Jiangong Rd. Sanmin Dist. Kaohsiung City Taiwan Mathematics and Physics Division General Education Center R.O.C. Air Force Academy. No. Sisou 1 Jieshou W. Rd. Gangshan Dist. Kaohsiung City Taiwan

Instead of the mass production industry in the past, over these decades, precision and automatic manufacturing have become more and more widely accepted in the production area. With highly variable productivity and flexibility, Flexible Manufacturing Systems (FMS) can decrease production costs and increase efficiency. Due to its resource sharing, unexpected system deadlock may occur in some situations. In research on system deadlock control, lots of existing literature use deadlock prevention as the primary control method, while it could block resources transporting and downscale the system reachability graph. This paper adopts a deadlock recovery policy as the direct control strategy based on control transition technology. This kind of control strategy and its benefit could be demonstrated through classical systems of simple sequential processes with resources (S 3 PR) nets and their Petri nets model.

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A Comprehensive Methodology for Evaluating Conversation-Based Interfaces to Relational Databases (C-BIRDs)

A Comprehensive Methodology for Evaluating Conversation-Base...

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Intelligent Systems Conference, IntelliSys 2020

作者： Owda, Majdi Owda, Amani Yousef Gasir, Fathi Department of Computing and Mathematics Manchester Metropolitan University Chester Street ManchesterM1 5GD United Kingdom Department of Electrical and Electronic Engineering The University of Manchester Sackville Street ManchesterM13 9PL United Kingdom Computer Science Department Faculty of Information Technology Misurata University Misurata Libya

ISBN: (纸本)9783030551865

Evaluation can be defined as a process of determining the significance of a research output. This is usually done by devising a well-structured study on this output using one or more evaluation measures in which a careful inspection is performed. This paper presents a review of evaluation techniques for Conversational Agents (CAs) and Natural Language Interfaces to Databases (NLIDBs). It then introduces the developed customized evaluation methodology for Conversation-Based Interface to Relational Databases (C-BIRDs). The evaluation methodology created has been divided into two groups of measures. The first is based on quantitative measures, including two measures: task success and dialogue length. The second group is based on a number of qualitative measures, including: prototype ease of use, naturalness of system responses, positive/negative emotion, appearance, text on screen, organization of information, and error message clarity. Then an elaboration is carried out on the devised methodology by adding a discussion and recommendations on the sample size, the experimental setup and the scaling in order to provide a comprehensive evaluation methodology for C-BIRDs. In conclusion the evaluation methodology created is better way for identifying the strengths and weaknesses of C-BIRDs in comparison to the usage of single measure evaluations. © 2021, Springer Nature Switzerland AG.

关键词： Birds

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Architecting Health information Systems in Intellectual Disability Care

SSRN

引用

SSRN 2023年

作者： Tummers, Joep Tobi, Hilde Catal, Cagatay Tekinerdogan, Bedir Schalk, Bianca Leusink, Geraline Information Technology Group Wageningen University & Research Hollandseweg 1 Wageningen6706 KN Netherlands Biometris Wageningen University & Research Droevendaalsesteeg 1 Wageningen6708OB Netherlands Department of Computer Science and Engineering Qatar University 2713 Qatar Department of Primary and Community Care Radboud University Medical Center P.O. Box 9101 Route 68 Nijmegen6500 HB Netherlands

Introduction: Care providers in intellectual disability care use a wide variety of health information systems to register their provided care. This leads to a large set of structured and unstructured data with a high potential for re-use that cannot be utilized in the current state. To support re-use of the data we need to know what the health information system architectures in intellectual disability care look like and what their commonalities and variabilities (diﬀerences) are. Furthermore, privacy and security issues for data re-use need to identiﬁ***: We used a multiple-case study approach to identify the architectures of four health information systems in the Netherlands. We interviewed seven stakeholders from four unique HISs and gathered multiple documents regarding the system infrastructure. Commonalities and diﬀerences between the systems and the main challenges regarding the privacy and security for data re-use were ***: For each HIS of the four cases four architectural were derived: a context diagram, decomposition view, layered view, and deployment view. We discuss multiple important aspects of security and privacy for data re-use in intellectual disability ***: Multiple challenges must be solved before the full potential of data in intellectual disability care can be unlocked. This research provides ﬁrst guidelines on how these problems can be overcome. © 2023, The Authors. All rights reserved.

关键词： Big data

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A five-layer deep convolutional neural network with stochastic pooling for chest CT-based COVID-19 diagnosis

A five-layer deep convolutional neural network with stochast...

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作者： Zhang, Yu-Dong Satapathy, Suresh Chandra Liu, Shuaiqi Li, Guang-Run School of Informatics University of Leicester LeicesterLE1 7RH United Kingdom Department of Information Systems Faculty of Computing and Information Technology King Abdulaziz University Jeddah21589 Saudi Arabia School of Computer Engg KIIT Deemed To University Bhubaneswar India College of Electronic and Information Engineering Hebei University BaodingHebei071002 China Department of Imaging Jinhu People’s Hospital No. 160 Jinhu Shenhua Street Huai’an China

Till August 17, 2020, COVID-19 has caused 21.59 million confirmed cases in more than 227 countries and territories, and 26 naval ships. Chest CT is an effective way to detect COVID-19. This study proposed a novel deep learning model that can diagnose COVID-19 on chest CT more accurately and swiftly. Based on traditional deep convolutional neural network (DCNN) model, we proposed three improvements: (i) We introduced stochastic pooling to replace average pooling and max pooling;(ii) We combined conv layer with batch normalization layer and obtained the conv block (CB);(iii) We combined dropout layer with fully connected layer and obtained the fully connected block (FCB). Our algorithm achieved a sensitivity of 93.28% ± 1.50%, a specificity of 94.00% ± 1.56%, and an accuracy of 93.64% ± 1.42%, in identifying COVID-19 from normal subjects. We proved using stochastic pooling yields better performance than average pooling and max pooling. We compared different structure configurations and proved our 3CB + 2FCB yields the best performance. The proposed model is effective in detecting COVID-19 based on chest CT images. © 2020, Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： COVID-19

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How to Learn More? Exploring Kolmogorov-Arnold Networks for Hyperspectral Image Classification

arXiv

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arXiv 2024年

作者： Jamali, Ali Roy, Swalpa Kumar Hong, Danfeng Lu, Bing Ghamisi, Pedram The Department of Geography Simon Fraser University 8888 University Dr BurnabyBCV5A 1S6 Canada The Department of Computer Science and Engineering Alipurduar Government Engineering and Management College West Bengal 736206 India The Aerospace Information Research Institute Chinese Academy of Sciences Beijing100094 China The School of Electronic Electrical and Communication Engineering University of Chinese Academy of Sciences Beijing100049 China Helmholtz Institute Freiberg for Resource Technology Machine Learning Group Freiberg09599 Germany Lancaster University LancasterLA1 4YR United Kingdom

Convolutional Neural Networks (CNNs) and vision transformers (ViTs) have shown excellent capability in complex hyperspectral image (HSI) classification. However, these models require a significant number of training data and are computational resources. On the other hand, modern MultiLayer Perceptrons (MLPs) have demonstrated great classification capability. These modern MLP-based models require significantly less training data compared to CNNs and ViTs, achieving the state-of-the-art classification accuracy. Recently, Kolmogorov-Arnold Networks (KANs) were proposed as viable alternatives for MLPs. Because of their internal similarity to splines and their external similarity to MLPs, KANs are able to optimize learned features with remarkable accuracy in addition to being able to learn new features. Thus, in this study, we assess the effectiveness of KANs for complex HSI data classification. Moreover, to enhance the HSI classification accuracy obtained by the KANs, we develop and propose a Hybrid architecture utilizing 1D, 2D, and 3D KANs. To demonstrate the effectiveness of the proposed KAN architecture, we conducted extensive experiments on three newly created HSI benchmark datasets: QUH-Pingan, QUH-Tangdaowan, and QUH-Qingyun. The results underscored the competitive or better capability of the developed hybrid KAN-based model across these benchmark datasets over several other CNN- and ViT-based algorithms, including 1D-CNN, 2DCNN, 3D CNN, VGG-16, ResNet-50, EfficientNet, RNN, and ViT. The code are publicly available at https://***/aj1365/HSIConvKAN © 2024, CC BY.

关键词： Complex networks

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Architecting Dutch Health information Systems in Intellectual Disability Care: Towards Data Re-Use

SSRN

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SSRN 2023年

关键词： Software architecture

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