检索结果-内蒙古大学图书馆

Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

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

ISBN: (数字)9798350371499

ISBN: (纸本)9798350371505

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.

关键词： Liver cancer Accuracy Computed tomography Biological system modeling Transformers Planning Biomedical imaging

来源：评论

学校读者我要写书评

暂无评论

Blockchain Technology? Visual Art Marketing Through Non-Fungible Token (NFT) from the Perspectives of Islamic Shariah in Malaysia: A Need Assessment Analyses

Blockchain Technology? Visual Art Marketing Through Non-Fung...

引用

computing and Information Technology (ICCIT-1441), 2020 International Conference on

作者： Ishak Ramli Mohamad Noorman Masrek Mohd Daud Abdul Rahim Thuraiya Mohd Mohd Ashrof Zaki Yaacob Mohd Radzuan Mohd Rafee Issarezal Ismail Mohamad Rahimi Osman Mohd Khalik Mustafa Muhammad Redza Rosman Program of Fine Art College of Creative Arts Universiti Teknologi MARA Perak Branch Perak Malaysia School of Information Management College of Computing Informatics and Mathematic Universiti Teknologi MARA Shah Alam Selangor Malaysia Program of Fine Art College of Creative Arts Universiti Teknologi MARA Perak Branch Seri Iskandar Campus Perak Malaysia Program of Real Estate Management College of Buit Environment Universiti Teknologi MARA Perak Branch Seri Iskandar Campus Perak Malaysia Division of Zakat Charity and Waqf Academy of Contemporary Islamic Studies Universiti Teknologi MARA Shah Alam Selangor Malaysia Program of Photography College of Creative Arts Universiti Teknologi MARA Perak Branch Seri Iskandar Campus Perak Malaysia Academy of Contemporary Islamic Studies Universiti Teknologi MARA Shah Alam Selangor Malaysia Program of Fashion Design College of Creative Arts Universiti Teknologi MARA Perak Branch Seri Iskandar Campus Perak Malaysia Program of Building Technology College of Built Environment Universiti Teknologi MARA Perak Branch Seri Iskandar Campus Perak Malaysia

ISBN: (数字)9798350353839

ISBN: (纸本)9798350353846

The development of blockchain technology across the globe has seen to be vast, including those related to visual arts. In Islam, anything new: especially those that never existed before, needs to be, obligatory discussed from the Islamic Shariah perspective. Therefore, the vast development of blockchain technology relating to non-fungible token (NFT) towards marketing visual art needs assessment from the Islamic Shariah perspective as there has not been any empirical studies carried out towards it. This paper intends to discuss a need assessment analyses regarding marketing visual art through the NFT platform blockchain technology from the Islamic Shariah perspective in Malaysia. Using the systematic literature review technique, researchers have discussed briefly regarding the development of visual arts, NFT and its trend, and reviewing Islamic perspectives on NFT. As a result, it is necessary for the Islamic Council of Malaysia to explain in detail the marketing of visual arts through NFT. A proper paperwork is crucial to give an overview on the practices regarding NFT that are 1) crypto currency; 2) artwork visual appearance; 3) copyright and consent issue; 4) pricing and value; and 5) royalty and blockchain record - to market of visual arts for Islamic Shariah assessment; in ensuring the muamalat activities amongst Muslims are centred on the faith of Islam.

关键词： Visualization Art Pricing Market research Cryptocurrency Nonfungible tokens Information technology Systematic literature review

来源：评论

学校读者我要写书评

暂无评论

Software Effort Estimation with Use Case Points Using Ensemble Machine Learning Models

Software Effort Estimation with Use Case Points Using Ensemb...

引用

Electrical, Computer and Energy Technologies (ICECET), International Conference on

作者： Bhaskar Marapelli Anil Carie Sardar M. N. Islam College of Computing and Informatics Wolkite university Wolkite Ethiopia School of Computer Science and Engineering VIT-AP University Amaravathi India ISILC and Decision Sciences and Modelling Program Victoria University Melbourne Australia

ISBN: (纸本)9781665442329

Software development process includes estimating effort as a crucial task. The Use Case Point Analysis (UCPA) is a well know size metric that can be used to calculate effort. The software size is measured using use case diagrams in the UCPA method, using the calculated software size the effort required to complete the project is estimated. The traditional effort estimation with statistical methods is not accurate when compared to the real effort. In-accurate estimation of effort leads to problems with cost calculation and human resources calculation and it might lead to project failure. Machine learning techniques based on regression might help in estimating the effort with accuracy. In this study we proposed a method to identify best performing regression based machine learning model using two data sets Dataset1, Dataset2. Ensembles of different Machine learning methods is created and compared with individual methods and other ensemble methods to find an accurate estimation model. The results shows the individual model SVR and ensemble model GBR gives the best performance with a regression score above 98% with both data sets.

关键词： Support vector machines Statistical analysis Computational modeling Estimation Predictive models Boosting Software

来源：评论

学校读者我要写书评

暂无评论

Deep learning outperforms classical machine learning methods in pediatric brain tumor classification through mass spectra

引用

Intelligence-Based Medicine 2024年 10卷 100178-100178页

作者： Santos Bezerra, Thais Maria Silva de Deus, Matheus Cavalaro, Felipe Ribeiro, Denise Seidinger, Ana Luiza Cardinalli, Izilda Aparecida de Melo Porcari, Andreia de Souza Queiroz, Luciano Pedrini, Helio Meidanis, Joao Internship Program in Pediatric Oncology Boldrini Children's Center Sao Paulo Brazil School of Medical Sciences University of Campinas Sao Paulo Brazil Institute of Computing University of Campinas Sao Paulo Brazil MS4Life Laboratory of Mass Spectrometry Health Sciences Postgraduate Program Sao Francisco University Sao Paulo Brazil Boldrini Research Center Boldrini Children's Center Sao Paulo Brazil Boldrini Children's Center Sao Paulo Brazil Scylla Informatics Sao Paulo Brazil

Pediatric brain tumors are the most common cause of death among all childhood cancers and surgical resection usually is the first step in disease management. During surgery, it is important to perform safe gross resection of tumors, retaining as much brain tissue as possible. Therefore, appropriate resection margin delineation is extremely relevant. Currently available methods for tissue analysis have limited precision, are time-consuming, and often require multiple invasive procedures. Our main goal is to test whether machine learning techniques are capable of classifying the pediatric brain tissue chemical profile generated by DESI-MSI, which is mainly lipidic, into normal or abnormal tissue and into low- and high-grade malignancy subareas within each sample. Our experiments show that deep learning methods outperform classical machine learning methods in the task of classifying brain tissue from DESI-MSI mass spectra, both in normal versus abnormal tissue, and, for malignant tissues, in low-grade versus high-grade malignancy. Our conclusion are based on the analysis of 34,870 annotated spectra, obtained from the neoplastic and non-neoplastic microanatomical stratification of individual samples from 116 pediatric patients who underwent brain tumor surgical resection at the Boldrini Children's Center between 2000 and 2020. Support Vector Machines, Random, Forests, and Least Absolute Shrinkage and Selection Operator (LASSO) were among the classical machine learning techniques evaluated. © 2024

关键词： LASSO Neural networks Random forests Support vector machines

来源：评论

学校读者我要写书评

暂无评论

Fair Patient Model: Mitigating Bias in the Patient Representation Learned from the Electronic Health Records

arXiv

引用

arXiv 2023年

作者： Sivarajkumar, Sonish Huang, Yufei Wang, Yanshan Intelligent Systems Program School of Computing and Information University of Pittsburgh PittsburghPA United States Department of Electrical and Computer Engineering University of Pittsburgh PittsburghPA United States Department of Health Information Management University of Pittsburgh PittsburghPA United States Department of Biomedical Informatics University of Pittsburgh PittsburghPA United States Clinical and Translational Science Institute University of Pittsburgh PittsburghPA United States

Objective: To pre-train fair and unbiased patient representations from Electronic Health Records (EHRs) using a novel weighted loss function that reduces bias and improves fairness in deep representation learning models. Methods: We defined a new loss function, called weighted loss function, in the deep representation learning model to balance the importance of different groups of patients and features. We applied the proposed model, called Fair Patient Model (FPM), to a sample of 34,739 patients from the MIMIC-III dataset and learned patient representations for four clinical outcome prediction tasks. Results: FPM outperformed the baseline models in terms of three fairness metrics: demographic parity, equality of opportunity difference, and equalized odds ratio. FPM also achieved comparable predictive performance with the baselines, with an average accuracy of 0.7912. Feature analysis revealed that FPM captured more information from clinical features than the baselines. Conclusion: FPM is a novel method to pre-train fair and unbiased patient representations from EHR data using a weighted loss function. The learned representations can be used for various downstream tasks in healthcare and can be extended to other domains where bias and fairness are important. Copyright © 2023, The Authors. All rights reserved.

关键词： Deep learning

来源：评论

学校读者我要写书评

暂无评论

Multivariate Information Theory Uncovers Synergistic Subsystems of the Human Cerebral Cortex

arXiv

引用

arXiv 2022年

作者： Varley, Thomas F. Pope, Maria Faskowitz, Joshua Sporns, Olaf Department of Psychological and Brain Sciences Indiana University BloomingtonIN47405 United States School of Informatics Computing & Engineering Indiana University BloomingtonIN47405 United States Program in Neuroscience Indiana University BloomingtonIN47405 United States Indiana University Network Science Institute Indiana University BloomingtonIN47405 United States

One of the most well-established tools for modeling the brain as a complex system is the functional connectivity network, which examines the correlations between pairs of interacting brain regions. While powerful, the network model is limited by the restriction that only pairwise dependencies are visible and potentially higher-order structures are missed. In this work, we explore how multivariate information theory can reveal higher-order, synergistic dependencies in the human brain. Using the O-information, a measure of whether the structure of a system is redundancy- or synergy-dominated, we show that synergistic subsystems are widespread in the human brain. We provide a mathematical analysis of the O-information to locate it within a larger taxonomy of multivariate complexity measures. We also show the O-information is related to a previously established measure, the Tononi-Sporns-Edelman complexity, and can be understood as an expected difference in integration between system scales. Highly synergistic subsystems typically sit between canonical functional networks, and may serve to integrate those networks. We then use simulated annealing to find maximally synergistic subsystems, finding that such systems typically comprise ≈10 brain regions, also recruited from multiple canonical brain systems. Though ubiquitous, highly synergistic subsystems are invisible when considering pairwise functional connectivity, suggesting that higher-order dependencies form a kind of "shadow structure" that has been unrecognized by established network-based analyses. We assert that higher-order interactions in the brain represent a vast and under-explored space that, made accessible with tools of multivariate information theory, may offer novel scientific insights. © 2022, CC BY-NC-ND.

关键词： Redundancy

来源：评论

学校读者我要写书评

暂无评论

Framework to Creation of Inclusive and Didactic Digital Material for Elderly 15

Framework to Creation of Inclusive and Didactic Digital Mate...

引用

15th Iberian Conference on Information Systems and Technologies, CISTI 2020

作者： Eliseo, Maria Amelia Oyelere, Solomon Sunday Silva, Camilla Almeida Da Silveira, Ismar Frango Tomczyk, Lukasz Hercovici, Mariana Amato, Cibelle A. De La Higuera Akyar, Ozgur Yasar Martins, Valeria Farinazzo Mackenzie Presbyterian University Computing and Informatics Department São Paulo Brazil University of Eastern Finland School of Computing Joensuu Finland Institute of Educational Studies Pedagogical University of Cracow Cracow Poland Mackenzie Presbyterian University Developmental Disorders Graduate Program São Paulo Brazil Hacettepe University Faculty of Sport Sciences Ankara Turkey

ISBN: (纸本)9789895465903

The elderly population has been increased due better health conditions and quality of life. With a healthier aging, the elderly start to have a more active and participative life in society. They pursue, among other things, to acquire new knowledge and despite being a generation which access to existing technology was restricted, there is an increase in the use of digital technologies, especially in the educational field. However, with advancing age, cognitive and motor skills decrease. Given this scenario, the creation of accessible didactic materials that include the elderly population is necessary. This paper shows a framework to create inclusive digital material for elderly learning. The framework will help teachers to create digital contents that support elderly learners' personalized educational goals. © 2020 AISTI.

关键词： E-learning

来源：评论

学校读者我要写书评

暂无评论

Predicting Neurological Outcomes of Comatose Cardiac Arrest Patients Using Transformer Neural Networks with EEG Data

Predicting Neurological Outcomes of Comatose Cardiac Arrest ...

引用

Computers in Cardiology (CinC)

作者： Jefferson Dionisio Che Lin Lian-Yu Lin Wen-Chau Wu Smart Medicine and Health Informatics Program National Taiwan University (NTU) Taipei Taiwan Department of Electrical Engineering NTU Graduate Institute of Communication Engineering NTU School of Medicine NTU Center for Advanced Computing and Imaging in Biomedicine NTU Center for Biotechnology NTU Department of Internal Medicine NTU Hospital Taipei Taiwan Institute of Medical Device and Imaging NTU Department of Radiology College of Medicine NTU Graduate Institute of Clinical Medicine NTU

As part of the 2023 PhysioNet Challenge, our team FINDING_MEMO utilized Transformer to predict outcomes using patient EEG data since it excels at dealing with sequential data like EEG. We mainly used the Transformer encoder block's multi-head self-attention to generate representations from the input and leverage several hidden layers to form the final prediction. Using the latest EEG from every patient, our team achieved the challenge score of 0.42 with the hidden validation set (ranked 36th out of 73 invited teams) and obtained a result of 0.37 (ranked 29th out of 36 qualified teams). Our results show a consistent performance across varying EEG recording durations in both the validation and test set. Our team also had the second-best score when evaluated, with only 12 hours of available recordings in the test set. Such promising results showcase the models' generalizability and clinical potential in predicting outcomes for comatose patients, especially for limited available EEG recordings.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Dense Video Captioning Using Unsupervised Semantic Information

arXiv

引用

arXiv 2021年

作者： Estevam, Valter Laroca, Rayson Pedrini, Helio Menotti, David Federal Institute of Paraná PR Irati84500-000 Brazil Federal University of Paraná Department of Informatics PR Curitiba81531-970 Brazil Pontifical Catholic University of Paraná Postgraduate Program in Informatics PR Curitiba80215-901 Brazil University of Campinas Institute of Computing SP Campinas13083-852 Brazil

We introduce a method to learn unsupervised semantic visual information based on the premise that complex events can be decomposed into simpler events and that these simple events are shared across several complex events. We first employ a clustering method to group representations producing a visual codebook. Then, we learn a dense representation by encoding the co-occurrence probability matrix for the codebook entries. This representation leverages the performance of the dense video captioning task in a scenario with only visual features. For example, we replace the audio signal in the BMT method and produce temporal proposals with comparable performance. Furthermore, we concatenate the visual representation with our descriptor in a vanilla transformer method to achieve state-of-the-art performance in the captioning subtask compared to the methods that explore only visual features, as well as a competitive performance with multi-modal methods. Our code is available at https://***/valterlej/dvcusi. © 2021, CC BY-NC-ND.

关键词： Unsupervised learning

来源：评论

学校读者我要写书评

暂无评论

Currents Trends in Use of Collaborative Learning in Teaching of Robotics and programming – A Systematic Review of Literature

Currents Trends in Use of Collaborative Learning in Teaching...

引用

Frontiers in Education (FIE) Conference

作者： Joethe Moraes de Carvalho José Francisco de Magalhães Netto Post-Graduate Program in Informatics (PPGI) Institute of Computing (ICOMP) Federal University of Amazonas (UFAM) CMA-Federal Institute of Amazonas (IFAM) Manaus AM Brazil Post-Graduate Program in Informatics (PPGI) Institute of Computing (ICOMP) Federal University of Amazonas (UFAM) Manaus AM Brazil

ISBN: (数字)9781728189611

ISBN: (纸本)9781728189628

This Research Full paper presents a Systematic Mapping Study of actions that are being carried out in the academic environment, aiming at teaching robotics and programming through collaborative procedures at a distance. Recent studies point to an increasing utilization of collaborative activities in teaching-learning process of robotics and programming, favoring the STEM fields, with encouraging results regarding the improvement of student's skills and better use of educational institutions infrastructure. Our goal is to collect information about state of the art on collaborative learning, employed through Groupware. To carry out this investigation, goals were defined by the Systematic Mapping Process, with the purpose of providing a better process understanding. The Research Questions were stipulated to be answered after the results analysis and the Search Strings, which allowed to select publications that satisfy the objective of this research articles in main digital repositories, all carried out in the last 5 years. After applying filters, 22 articles were considered more relevant according to the research objective. The results show the activities, modalities, methodologies, accomplished pedagogical concepts and the conclusions obtained. This work aims to assist researchers who seek information on referred topic.

关键词： Libraries Collaboration Robots programming profession Collaborative work Collaborative software Systematics

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：