检索结果-内蒙古大学图书馆

4th International Conference on informatics, Multimedia, Cyber and Information System, ICIMCIS 2022

作者： Maulana, Fairuz Iqbal Lestari, Dian Widartha, Vandha Pradwiyasma Haidar, Nirwana Dirgantara, Wahyu School of Computer Science Bina Nusantara University Computer Science Department Jakarta11480 Indonesia Airlangga University Faculty of Medicine Surabaya60132 Indonesia Telkom University Information System of Industrial Engineering Faculty Bandung Indonesia University of Madura Informatics Engineering Study Program Pamekasan Indonesia University of Merdeka Malang Department of Electrical Engineering Malang Indonesia

ISBN: (纸本)9781665473279

This study examines the mapping of research data on digital technology in the field of health education using bibliometric analysis method. Data was collected by identifying keywords in the Scopus database and sorting them out to sort the studies from the last 10 years (2012-2021). Through this step, a total of 1482 documents were obtained (articles, journals, proceedings, books, and others). The data is then processed using the VOSViewer instrument to obtain a visualization of the mapping analysis. This study also analyzes the network types of authors and co-authors through the VOSViewer instrument. The result of this study indicates that the most document types published within ten years are Articles (66.5%), Review (22.9%), Conference Paper (4.2%), and others. The most studied subjects are Medicine (55.7%), Nursing (9.0%), Health Professions (8.2%), Social sciences (7.8%), Engineering (3.9%), computer science (2.6%), Environmental science (2.6%), Biochemistry-Genetics and Molecular Biology (2.2%), Psychology (1.6%), and Dentistry (1.3%). This study offers a written communication process and the nature and direction of developing descriptive means of counting and analyzing the various phases of communication as well as recognizing the authorship and direction of its symptoms in documents on the subject of digital technology in the health sector. © 2022 IEEE.

关键词： Mapping

来源：评论

学校读者我要写书评

暂无评论

Vehicle Occurrence-Based Parking Space Detection

Vehicle Occurrence-Based Parking Space Detection

引用

IEEE International Conference on Systems, Man and Cybernetics

作者： Paulo R. Lisboa de Almeida Jeovane Honório Alves Luiz S. Oliveira Andre Gustavo Hochuli João V. Fröhlich Rodrigo A. Krauel Department of Informatics Federal University of Paraná (UFPR) Curitiba Brazil Graduate Program in Informatics Pontifical Catholic University of Paraná (PUCPR) Curitiba Brazil Department of Computer Science Santa Catarina State University (UDESC) Joinville Brazil

Smart-parking solutions use sensors, cameras, and data analysis to improve parking efficiency and reduce traffic congestion. computer vision-based methods have been used extensively in recent years to tackle the problem of parking lot management, but most of the works assume that the parking spots are manually labeled, impacting the cost and feasibility of deployment. To fill this gap, this work presents an automatic parking space detection method, which receives a sequence of images of a parking lot and returns a list of coordinates identifying the detected parking spaces. The proposed method employs instance segmentation to identify cars and, using vehicle occurrence, generate a heat map of parking spaces. The results using twelve different subsets from the PKLot and CNRPark-EXT parking lot datasets show that the method achieved an AP25 score up to 95.60% and AP50 score up to 79.90%.

关键词：

来源：评论

学校读者我要写书评

暂无评论

E-Government Interoperability: Provincial-Level Architecture Model to Enable Fast Healthcare Interoperability Resources (FHIR)

E-Government Interoperability: Provincial-Level Architecture...

引用

International Conference on Information Management and Technology (ICIMTech)

作者： Diana Wijayanti Satriawansyah Urbaya Taufiq Sitompul Verry Adrian Department of Information Systems BINUS Graduate Program Bina Nusantara University Jakarta Indonesia Country Health Information Systems and Data Use (CHISU) Program USAID Indonesia Jakarta Indonesia Department of Informatics University of Oslo Oslo Norway Computer Science Department Binus Graduate Program – Doctor Computer Science Bina Nusantara University Jakarta Indonesia Center of Data and Information Technology Jakarta Provincial Health Office Jakarta Indonesia

ISBN: (数字)9798350390025

ISBN: (纸本)9798350390032

The process of using ICT to provide services to the public is known as the Indonesian e-Government system, or Sistem Pemerintahan Berbasis Elektronik (SPBE). The e-Government initiative in Jakarta Provincial Health Office involves enhancing collaboration among public health entities for efficient data exchange and streamlined processes, especially between the Provincial and District Health Offices, public hospitals, government clinics, and primary health care centers (Puskesmas). Achieving interoperability requires standardized protocols and a well-defined architectural model to integrate data seamlessly. This study presents a provincial-level architectural model focused on improving electronic health records interoperability, aiming to promote the adoption of the national Fast Healthcare Interoperability Resources (FHIR) health information exchange platform and enhance the integrity of health data in Jakarta. The study methodology involves conducting literature reviews, observations, and discussions with representatives from healthcare facilities to develop the e-Government architecture model and prototype of the infrastructure layer aiming to facilitate the interoperability of Electronic Health Records (EHRs) across 93 healthcare facilities, all of which are part of the SPBE users.

关键词： Prototypes Medical services Data models Security Public healthcare Electronic medical records Interoperability Information exchange Standards Electronic government

来源：评论

学校读者我要写书评

暂无评论

Mammogram Breast Cancer Classification Based on Deep-Convolutional Neural Network

Mammogram Breast Cancer Classification Based on Deep-Convolu...

引用

International Seminar on Application for Technology of Information and Communication (ISemantic)

作者： Avissa Aurellia Amadea Christy Atika Sari Eko Hari Rachmawanto Wellia Shinta Sari Mohammed S. Noori Mohamed Ayad Alkhafaji Study Program in Informatics Engineering University of Dian Nuswantoro Semarang Indonesia Study Program in Information Systems University of Dian Nuswantoro Semarang Indonesia Department of Computer Engineering Technology Northern Technical University Mosul Iraq Department of Computer Science National University of Science and Technology DhiQar Iraq

Based on WHO’s data, breast cancer is one of the most deadly diseases that has claimed many victims, especially women. This disease begins with the presence of an undetected and eventually turns into malignant (cancer). This happens due to ignorance of the importance of having a medical check-up even though in good health. Doctors and researchers can prevent the development of tumor cells through treatment that begins with radiological examinations to identify the possibility of a person being affected by this disease. One of the most frequently used techniques is Mammography. This technique can detect the presence of tumor cells using advanced technology and several methods in displaying the patient’s diagnostic results on low-dose X-rays in the form of mammogram images. The technology is inseparable from the methods used to identify the presence of tumor cells. In this study, we have proposed the CNN method based on the deep-CNN model to identify mammogram images in the detection of breast cancer cells with average evaluation results in terms of accuracy, precision, recall, specificity, and f-measure on mammogram image datasets of 99.52%, 99.72%, 99.31%, 99.72%, and 99.5%. These results showed that this method has a good performance in breast cancer detection.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Gene Name Recognition in Gene Pathway Figures Using Siamese Networks

Gene Name Recognition in Gene Pathway Figures Using Siamese ...

引用

Medical Artificial Intelligence (MedAI), IEEE International Conference on

作者： Micheal Olaolu Arowolo Muhammad Azam Fei He Mihail Popescu Dong Xu Department of Public Health Sciences Health Informatics Program Xavier University of Louisiana New Orleans Louisiana USA Department of Electrical Engineering and Computer Science Bond Life Sciences Center University of Missouri Columbia Missouri USA Department of Health Management and Informatics University of Missouri Columbia Missouri USA

ISBN: (数字)9798350377613

ISBN: (纸本)9798350377620

The number of scientific papers related to biology is rapidly increasing, and with it, the number of pathway figures containing crucial biological information. However, manually annotating these figures is a time-consuming process, and current optical character recognition (OCR) methods are not effective in identifying gene names within these figures. To improve gene name recognition from pathway figures, we utilized a Siamese network that maps image segments to a library of pictures containing known gene name, like object recognition in many photo applications. We combined the triplet convolution neural network and the spatial pyramid pooling (TSPP-Net) to create a CNN triple loss function. We trained and tested the developed TSPP-Net network using 563 gene images from 45 pathway pictures, 1000 images of alphanumeric characters and gene names from HUGO and KEGG. To benchmark against major available OCR technologies, we used 20 randomly selected curated pathway genes. Additionally, we compared several models, such as VGG19, VGG16, Resnet-50 Resnet-18, Densnet-121, and Xception for the Siamese backbone network model. VGG16 achieved the best performance with an accuracy of 93%, which is much higher than the results obtained using OCR. Our code is publicly available at https://***/MuhammadAzam636/Siamese-Network.

关键词： Image recognition Accuracy Sensitivity Text recognition Biological system modeling Optical character recognition Neural networks Libraries Object recognition Biological information theory

来源：评论

学校读者我要写书评

暂无评论

High-Capacity Double-Layer Data Hiding Technique Based on 3D Magic Cube 25th

High-Capacity Double-Layer Data Hiding Technique Based on 3D...

引用

25th International computer Symposium on New Trends in computer Technologies and Applications, ICS 2022

作者： Weng, Hao-Yu Tsai, Min-Yi Wang, Wei-Jen Huang, Cheng-Ta Department of Computer Science and Information Engineering National Central University Taoyuan City320 Taiwan Department of Information Management Yuan Ze University Taoyuan City320 Taiwan International Bachelor Program in Informatics Yuan Ze University Taoyuan City320 Taiwan

ISBN: (纸本)9789811995811

Information technology and the Internet have progressed rapidly in people’s lives, the privacy of information has become an important issue due to the accessibility of data. Therefore, to enhance information security and privacy protection, this paper proposes a new high-capacity method for data hiding. The proposed method is based on 3D magic cube with double layers embedding. In the field of data hiding, the reference matrix-based methods attract many researchers because of its high-capacity. Recently, as the needs of the high payloads of the stego-image, the multi-layers-based technique is applied to provide larger embedding capacity. However, the image after data embedding will cause serious distortions, especially on multi-layers embedding, hence, keeping the image at a good visual quality and achieving high-payloads are the main challenges for data hiding. In this scheme, we take advantage of 3D magic cube and 3D reference space with double layers embedding for our data hiding technique to improve the embedding payloads. The experimental results show that our method not only provides good embedding capacity (4.00 bpp) but also keeps the image at acceptable quality (In terms of PSNR > 30). © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Embeddings

来源：评论

学校读者我要写书评

暂无评论

Intrusion Detection using Deep Neural Network Algorithm on the Internet of Things 11

Intrusion Detection using Deep Neural Network Algorithm on t...

引用

11th IEEE International Conference on Communication, Networks and Satellite, COMNETSAT 2022

作者： Ikhwan, Syariful Wibowo, Adi Warsito, Budi School of Postgraduate Studies Diponegoro University Doctoral Program of Information System Semarang Indonesia Institut Teknologi Telkom Purwokerto D3 Telecommunication Engineering Banyumas Indonesia Diponegoro University Computer Science Informatics Department of Science and Mathematics Faculty Semarang Indonesia Diponegoro University Statistics Department of Science and Mathematics Faculty Semarang Indonesia

ISBN: (纸本)9781665460309

The increasing use of IoT devices on future networks is very helpful for humans in their lives. However, the increase in devices connected to IoT networks also increases the potential for attacks against those networks. Vulnerabilities in Internet of Things (IoT) networks can be exposed at any time. Artificial intelligence can be used to protect the IoT network by being able to detect attacks on the network so that they can be prevented. In this study, network detection was carried out using the Deep Neural Network (DNN) algorithm. The test was carried out using the UNSW Bot-IoT dataset with a comparison of training data of 75% of the overall data. The results obtained show the ability of the algorithm to detect attacks on average with 99.999% accuracy. The validation loss and training loss look very small. In this study, there is a validation loss that still occurs in overfitting, but the difference is very small. © 2022 IEEE.

关键词： Internet of things

来源：评论

学校读者我要写书评

暂无评论

A BCLC Staging System for Hepatocellular Carcinoma using Swin Transformer and CT Imaging 46

A BCLC Staging System for Hepatocellular Carcinoma using Swi...

引用

46th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2024

作者： Chang, Shun-Cheng Wang, Pochuang Wang, Weichung Su, Tung-Hung Kao, Jia-Horng Lin, Che Taipei10617 Taiwan Department of Computer Science and Information Engineering Ntu Taipei10617 Taiwan Institute of Applied Mathematical Sciences Ntu Taipei10617 Taiwan Division of Gastroenterology and Hepatology Department of Internal Medicine National Taiwan University Hospital Taipei10048 Taiwan Department of Electrical Engineering Ntu Taipei10617 Taiwan Center for Advanced Computing and Imaging in Biomedicine Ntu Taipei10617 Taiwan Smart Medicine and Health Informatics Program Ntu Taipei10617 Taiwan

ISBN: (纸本)9798350371499

The Barcelona Clinic Liver Cancer (BCLC) staging system plays a crucial role in clinical planning, offering valuable insights for effectively managing hepatocellular carcinoma. Accurate prediction of BCLC stages can significantly ease the workload on radiologists. However, few datasets are explicitly designed for discerning BCLC stages. Despite the common practice of appending BCLC labels to clinical data within datasets, the inherent imbalance in BCLC distribution is further amplified by the diverse purposes for which datasets are curated. In this study, we aim to develop a BCLC staging system using the advanced Swin Transformer model. Additionally, we explore the integration of two datasets, each originally intended for separate objectives, highlighting the critical challenge of preserving class distribution in practical study designs. This exploration is pivotal for ensuring the applicability of our developed staging system in the designed clinical settings. Our resulting BCLC staging system demonstrates an accuracy of 55.81% (±7.8%), contributing to advancing medical image-based research for predicting BCLC stages. © 2024 IEEE.

关键词： Distribution transformers

来源：评论

学校读者我要写书评

暂无评论

ReViT: A Hybrid Approach for BCLC Staging of Hepatocellular Carcinoma Using 3D CT with Multiple Instance Learning

ReViT: A Hybrid Approach for BCLC Staging of Hepatocellular ...

引用

2024 IEEE-EMBS International Conference on Biomedical and Health informatics, BHI 2024

作者： Chang, Shun-Cheng Yu, Hsin-Pei Hsieh, Yi-Hsien Wang, Pochuang Wang, Weichung Su, Tung-Hung Kao, Jia-Horng Lin, Che Taipei10617 Taiwan Department of Computer Science and Information Engineering Ntu Taipei10617 Taiwan Institute of Applied Mathematical Sciences Ntu Taipei10617 Taiwan National Taiwan University Hospital Division of Gastroenterology and Hepatology Department of Internal Medicine Taipei10048 Taiwan Department of Electrical Engineering Ntu Taipei10617 Taiwan Center for Advanced Computing and Imaging in Biomedicine Ntu Taipei10617 Taiwan Smart Medicine and Health Informatics Program Ntu Taipei10617 Taiwan

ISBN: (纸本)9798350351552

Deep learning has revolutionized medical imaging, offering advanced methods for accurate diagnosis and treatment planning. The BCLC staging system is crucial for staging Hepatocellular Carcinoma (HCC), a high-mortality cancer. An automated BCLC staging system could significantly enhance diagnosis and treatment planning efficiency. However, we found that BCLC staging, which is directly related to the size and number of liver tumors, aligns well with the principles of the Multiple Instance Learning (MIL) framework. To effectively achieve this, we proposed a new preprocessing technique called Masked Cropping and Padding(MCP), which addresses the variability in liver volumes and ensures consistent input sizes. This technique preserves the structural integrity of the liver, facilitating more effective learning. Furthermore, we introduced Re ViT, a novel hybrid model that integrates the local feature extraction capabilities of Convolutional Neural Networks (CNNs) with the global context modeling of Vision Transformers (ViTs). Re ViT leverages the strengths of both architectures within the MIL framework, enabling a robust and accurate approach for BCLC staging. We will further explore the trade-off between performance and interpretability by employing TopK Pooling strategies, as our model focuses on the most informative instances within each bag. © 2024 IEEE.

关键词： Convolutional neural networks

来源：评论

学校读者我要写书评

暂无评论

Investigating Student and Teacher Perceptions in e-Learning with Learning Analytics and Ontologies

International Journal of Emerging Technologies in Learning

引用

International Journal of Emerging Technologies in Learning 2023年第8期18卷 26-47页

作者： Costa, Laécio A. Silveira, Aleph C. Souza, Marlo Salvador, Laís N. Santos, Celso A. S. Informatics Collegiate Instituto Federal do Sertão Pernambucano PE Petrolina Brazil Computer Science Graduate Program Federal University of Bahia BA Salvador Brazil Department of Informatics Federal University of Espírito Santo ES Vitória Brazil

This work is an approach that brings together Learning Analytics and Ontologies for a data classification that promotes improvements and behavioral changes for students and teachers on e-Learning platforms. Combining training courses, dashboards, user’s evaluations, and based on Design science Research (DSR) methodology, artifacts were created. One of the most important artifacts of our work is the Sapes tool that aims to improve students’ perceptions of their learning path and to promote a better teacher overview to follow their students’ progress. The results showed high approval by the participating students and teachers, who perceived the Sapes tool as a good facilitator of the teaching-learning process, with possibilities for self-monitoring, dynamization of the learning sequence and better interactivity with colleagues, highlighted as absent in standard e-Learning courses. In addition, the application changed the behavior of users towards the content provided by the teacher, with students performing self-management and self-regulation that were not commonly performed previously. © 2023, International Journal of Emerging Technologies in Learning. All Rights Reserved.

关键词： Ontology

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：