检索结果-内蒙古大学图书馆

arXiv 2023年

作者： Ghayoula, Ridha Fattahi, Jaouhar Smida, Amor Gmati, Issam El Pricop, Emil Ziadia, Marwa Unit of Research in High Frequency Electronic Circuits and Systems University of Tunis El Manar Tunis2092 Tunisia Department of Electrical and Computer Engineering Laval University Quebec Canada Department of Computer Science and Software Engineering Laval University Quebec Canada Department of Medical Equipment Technology College of Applied Medical Sciences Majmaah University Majmaah11952 Saudi Arabia College of Engineering Al Gunfudha Umm Al Qura University Saudi Arabia Automatic Control Computers and Electronics Department Petroleum-Gas University of Ploiesti Romania

FPGA is a hardware architecture based on a matrix of programmable and configurable logic circuits thanks to which a large number of functionalities inside the device can be modified using a hardware description language. These functionalities must often be secured especially when the context is sensitive (military, banking, medical, legal, etc.). In this paper, we put forward an efficient implementation of SIMON’s block cipher algorithm using Xilinx Vivado 2018.2. The proposed design is analyzed through simulation on Xilinx Artix-7. A prototype of our design is implemented using the xc7a35tcsg324-1 FPGA chip. Performance and results are discussed. © 2023, CC BY.

关键词： Field programmable gate arrays (FPGA)

来源：评论

学校读者我要写书评

暂无评论

Multiple perspectives of spiritual intelligence: A systematic literature review

引用

Social Sciences and Humanities Open 2024年 9卷

作者： Fidelis, Andréa Moreira, António Carrizo Vitória, Andreia Department of Economics Management Industrial Engineering and Tourism University of Aveiro Aveiro 3810-193 Portugal GOVCOPP—Research Unit on Governance Competitiveness and Public Policies University of Aveiro Aveiro 3810-193 Portugal INESCTEC—Institute for Systems and Computer Engineering Technology and Science Porto 4200-465 Portugal NECE-UBI—Research Center for Business Sciences Universidade da Beira Interior Covilhã Portugal

This paper aims to understand the influence of sociocultural perspectives on the perception and conceptualization of spiritual intelligence (SI). To do so, a systematic literature review was carried out, and 64 journal articles and reviews form the sample for data analysis. Four different perspectives (Western, Eastern, Islamic, and Hindu) were identified. In the Western perspective, an egocentric and utilitarian approach to the construct prevails. In the Islamic perspective, religion is understood as the only possible expression of SI. In the Eastern perspective, the point of view of the common good and the connection with nature prevails. In the Hindu perspective, however, knowledge is the key to expanding spiritual awareness. The results indicate that the authors' perspective interferes in the understanding of the conceptualization of SI and its dimensions, and in the interpretation of the research results. Finally, a framework for analyzing SI research is proposed. © 2024 The Authors

关键词： Spiritual intelligence Spirituality Systematic literature review

来源：评论

学校读者我要写书评

暂无评论

Scalable Crystal Structure Relaxation Using an Iteration-Free Deep Generative Model with Uncertainty Quantification

arXiv

引用

arXiv 2024年

作者： Yang, Ziduo Zhao, Yi-Ming Wang, Xian Liu, Xiaoqing Zhang, Xiuying Li, Yifan Lv, Qiujie Chen, Calvin Yu-Chian Shen, Lei Department of Mechanical Engineering National University of Singapore Singapore117575 Singapore Artificial Intelligence Medical Research Center School of Intelligent Systems Engineering Shenzhen Campus of Sun Yat-sen University Shenzhen518107 China Department of Physics National University of Singapore Singapore117551 Singapore -Preferred Program School of Electronic and Computer Engineering Peking University Shenzhen Graduate School Shenzhen518055 China State Key Laboratory of Chemical Oncogenomics School of Chemical Biology and Biotechnology Peking University Shenzhen Graduate School Shenzhen518055 China Department of Medical Research China Medical University Hospital Taichung40447 Taiwan Department of Bioinformatics and Medical Engineering Asia University Taichung41354 Taiwan Guangdong L-Med Biotechnology Co. Ltd. Guangdong Meizhou514699 China Research Institute Chongqing401123 China

In computational molecular and materials science, determining equilibrium structures is the crucial first step for accurate subsequent property calculations. However, the recent discovery of millions of new crystals and complex twisted structures has challenged traditional computational methods, both ab initio and machine-learning-based, due to their computationally intensive iterative processes. To address these scalability issues, here we introduce DeepRelax, a deep generative model capable of performing geometric crystal structure relaxation rapidly and without iterations. DeepRelax learns the equilibrium structural distribution, enabling it to predict relaxed structures directly from their unrelaxed ones. The ability to perform structural relaxation at the millisecond level per structure, combined with the scalability of parallel processing, makes DeepRelax particularly useful for large-scale virtual screening. We demonstrate DeepRelax's reliability and robustness by applying it to five diverse databases, including oxides, Materials Project, two-dimensional materials, van der Waals crystals, and crystals with point defects. DeepRelax consistently shows high accuracy and efficiency, validated by density functional theory calculations. Finally, we enhance its trustworthiness by integrating uncertainty quantification. This work significantly accelerates computational workflows, offering a robust and trustworthy machine-learning method for material discovery and advancing the application of AI for science. Code for DeepRelax is available at https://***/Shen-Group/DeepRelax. Copyright © 2024, The Authors. All rights reserved.

关键词： Point defects

来源：评论

学校读者我要写书评

暂无评论

Effects of Dataset Curation on Body Condition Score (Bcs) Determination with a Vision Transformer (Vit) Applied to Rgb+Depth Images

SSRN

引用

SSRN 2024年

作者： Winkler, Zachary Boucheron, Laura E. Utsumi, Santiago Nyamuryekung’e, Shelemia McIntosh, Matthew Estell, Richard E. Klipsch School of Electrical and Computer Engineering New Mexico State University Las CrucesNM88003 United States Department of Animal and Rangeland Science New Mexico State University Las CrucesNM88003 United States PB 115 N-1431Ås Norway United States Department of Agriculture-Agricultural Research Service Jornada Experimental Range Las CrucesNM88003 United States United States Department of Agriculture-Agricultural Research Service Food Systems Research Unit BurlingtonVT05405 United States

Body condition score (BCS) has been a useful tool in estimating the body energy reserves, reproductive performance, and health of cattle for many years now. This categorical metric, while useful, requires one or more experienced observers to visually inspect cows and assess variability of body fat deposits regularly, and this process can be very time consuming, subject to bias among observers, and difficult to implement in extensive ranching systems where the sighting and systematic inspection of cattle is not possible. Availability of low-cost RGB+depth cameras has encouraged the use of computer vision and machine learning algorithms as a potential solution to those barriers. Obtaining BCS ground truth training data for herds of rangeland cattle, however, can be difficult. Furthermore, judging experts may not always agree on a cow’s BCS categorical assessment, and the processing and curation of BCS data itself can pose problems for the successful training, validation, and testing of machine learning models. In this study we use the vision transformer (ViT) BEiT to assess the BCS of Raramuri Criollo cattle and discuss the challenges associated with the method, needs for dataset processing and curation, and similarities between BCS classes. Special consideration is given to procedures for BCS dataset curation, as this aspect of machine learning has significant influence on the overall performance of the network and presents a unique challenge for image classification models. © 2024, The Authors. All rights reserved.

关键词： Learning algorithms

来源：评论

学校读者我要写书评

暂无评论

Beyond the Brain: MIDS Extends BIDS to Multiple Modalities and Anatomical Regions 32

Beyond the Brain: MIDS Extends BIDS to Multiple Modalities a...

引用

32nd Medical Informatics Europe Conference, MIE 2022

作者： Saborit-Torres, Jose Manuel Nadal-Almela, Silvia Montell-Serrano, Joaquim Angel Oliver-Garcia, Elena Carceller, Hector Gómez-Ádrian, Jon Ander Caparrós-Redondo, Marisa García-García, Francisco Domenech-Fernández, Julio De La Iglesia-Vayá, Maria Unidad Mixta de Imagen Biomédica FISABIO-CIPF Fund. Para El Fomento de la Invest. Sanitaria y Biomedica de la Comunidad Valenciana-Valencia Spain Cibersam Isc Iii Av. Blasco Ibáñez 15 València46010 Spain Universitat de Valencia Burjassot46100 Spain Department of Computer Systems and Computation Universitat Politècnica de València-València Spain Bioinformatics & Biostatistics Unit Principe Felipe Research Center-Valencia Spain Hospital Arnau de Vilanova-Valencia Spain General Directorate of Research Innovation Technology and Quality Subdirectorate General of Information Systems for Health-Valencia Spain

ISBN: (纸本)9781643682846

Brain Imaging Data Structure (BIDS) provides a valuable tool to organise brain imaging data into a clear and easy standard directory structure. Moreover, BIDS is widely supported by the scientific community and has been established as a powerful standard for medical imaging management. Nonetheless, the original BIDS is restricted to magnetic resonance imaging (MRI) of the brain, limiting its implantation to other techniques and anatomical regions. We developed Medical Imaging Data Structure (MIDS), conceived to extend BIDS methodology to other anatomical regions and multiple imaging systems in these areas. The MIDS standard was developed to store and manage medical images as an extension of BIDS. It allows the user to handily save studies of multiple anatomical regions and imaging techniques. Besides, MIDS improves the classification of multiple images within the structure, allowing the possibility to unify them in a single study to apply on them preprocessing or artificial intelligence algorithms. Finally, the results generated are saved in the derivatives folder. © 2022 European Federation for Medical Informatics (EFMI) and IOS Press.

关键词： Standardization

来源：评论

学校读者我要写书评

暂无评论

Beyond a Single Mode: GAN Ensembles for Diverse Medical Data Generation

arXiv

引用

arXiv 2025年

作者： Tronchin, Lorenzo Löfstedt, Tommy Soda, Paolo Guarrasi, Valerio Unit of Computer Systems and Bioinformatics Department of Engineering Università Campus Bio-Medico di Roma Rome Italy Department of Computing Science Umeå University Umeå Sweden Department of Diagnostics and Intervention Radiation Physics Biomedical Engineering Umeå University Umeå Sweden

The advancement of generative AI, particularly in medical imaging, confronts the trilemma of ensuring high fidelity, diversity, and efficiency in synthetic data generation. While Generative Adversarial Networks (GANs) have shown promise across various applications, they still face challenges like mode collapse and insufficient coverage of real data distributions. This work explores the use of GAN ensembles to overcome these limitations, specifically in the context of medical imaging. By solving a multi-objective optimisation problem that balances fidelity and diversity, we propose a method for selecting an optimal ensemble of GANs tailored for medical data. The selected ensemble is capable of generating diverse synthetic medical images that are representative of true data distributions and computationally efficient. Each model in the ensemble brings a unique contribution, ensuring minimal redundancy. We conducted a comprehensive evaluation using three distinct medical datasets, testing 22 different GAN architectures with various loss functions and regularisation techniques. By sampling models at different training epochs, we crafted 110 unique configurations. The results highlight the capability of GAN ensembles to enhance the quality and utility of synthetic medical images, thereby improving the efficacy of downstream tasks such as diagnostic modelling. © 2025, CC BY-SA.

关键词： Multiobjective optimization

来源：评论

学校读者我要写书评

暂无评论

Prediction of Stroke Risk Within 7-Years Follow Up Using Machine Learning Models*

Prediction of Stroke Risk Within 7-Years Follow Up Using Mac...

引用

Data Science and engineering in Healthcare, Medicine and Biology, IEEE EMBS Special Topic Conference on

作者： Konstantina-Helen Tsarapatsani Antonis Sakellarios Vasilis D Tsakanikas Henrik Rudolf Hans J. Trampisch Vasileios C. Pezoulas George K. Matsopoulos Dimitrios I. Fotiadis Biomedical Research Institute Foundation for Research and Technology-Hellas (FORTH) Dept. of Materials Science and Engineering Unit of Medical Technology and Intelligent Information Systems University of Ioannina GR 45110 Ioannina Greece University Medical Center Rostock Institute for Biostatistics and Informatics in Medicine and Ageing Research Department of Medical Informatics Biometry and Epidemiology Ruhr University Bochum Bochum Germany School of Electrical and Computer Engineering National Technical University of Athens Athens Greece

A stroke, also known as brain attack, occurs when blood supply to your brain is interrupted. Primary prevention relies on prompt prediction of a stroke. While currently there are several clinical risk scores, machine learning (ML) models seems to be more suitable tools for accurate prediction of stroke events. Therefore, this work focuses on the prediction of stroke within 7 years follow-up in patients who have not suffered from a stroke or TIA event at baseline. LightGBM (LGBM), Extreme Grading Boosting (XGBoost), Support Vector Machine (SVM) and Decision Tree were employed in the getABI dataset, which includes 5,897 participants. The performance of models was calculated by Accuracy (ACC), Sensitivity (SENS), Specificity (SPE) and area under the receiver operating characteristic curve (AUC) of each model. According to the comparison analysis’s results, LGBM has been shown to be the most trustworthy algorithm, with accuracy 68 %. Moreover, sex, age, status of peripheral artery disease (PAD), history of myocardial infarction, angina pectoris, amputation and diabetes and pulse status of different arteries can be used as a simple and cost-effective way to predict *** Relevance: A fatal medical emergency, stroke may be anticipated using artificial intelligence, and the sooner it is predicted, the more cerebrovascular disease occurrences can be avoided.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Towards the use of multiple ROIs for radiomics-based survival modelling: finding a strategy of aggregating lesions

arXiv

引用

arXiv 2024年

作者： Wilk, Agata Malgorzata Swierniak, Andrzej d’Amico, Andrea Suwiński, Rafal Fujarewicz, Krzysztof Borys, Damian Department of Systems Biology and Engineering Silesian University of Technology Akademicka 16 Gliwice44-100 Poland Department of Biostatistics and Bioinformatics Maria Sklodowska-Curie National Research Institute of Oncology Gliwice Branch Wybrzeze AK 15 Gliwice44-102 Poland Department of Nuclear Medicine and Endocrine Oncology PET Diagnostics Unit Maria Sklodowska-Curie National Research Institute of Oncology Gliwice Branch Wybrzeze AK 15 Gliwice44-102 Poland II-nd Radiotherapy and Chemotherapy Clinic Maria Sklodowska-Curie National Research Institute of Oncology Gliwice Branch Wybrzeze AK 15 Gliwice44-102 Poland

Background. Radiomic features, derived from a region of interest (ROI) in medical images, are valuable as prognostic factors. Selecting an appropriate ROI is critical, and many recent studies have focused on leveraging multiple ROIs by segmenting analogous regions across patients –— such as the primary tumour and peritumoral area or subregions of the tumour. These can be straightforwardly incorporated into models as additional features. However, a more complex scenario arises for example in a regionally disseminated disease, when multiple distinct lesions are present. Aim. This study aims to evaluate the feasibility of integrating radiomic data from multiple lesions into survival models. We explore strategies for incorporating these ROIs and hypothesise that including all available lesions can improve model performance. Methods. While each lesion produces a feature vector, the desired result is a unified prediction. We propose methods to aggregate either the feature vectors to form a representative ROI or the modeling results to compute a consolidated risk score. As a proof of concept, we apply these strategies to predict distant metastasis risk in a cohort of 115 non-small cell lung cancer patients, 60% of whom exhibit regionally advanced disease. Two feature sets (radiomics extracted from PET and PET interpolated to CT resolution) are tested across various survival models using a Monte Carlo Cross-Validation framework. Results. Across both feature sets, incorporating all available lesions — rather than limiting analysis to the primary tumour — consistently improved the c-index, irrespective of the survival model used. Conclusion. Lesions beyond the primary tumour carry information that should be utilised in radiomics-based models to enhance predictive ability. © 2024, CC BY-NC-ND.

关键词： Lung cancer

来源：评论

学校读者我要写书评

暂无评论

On the robustness of EEG tensor completion methods

引用

Science China(Technological Sciences) 2021年第9期64卷 1828-1842页

作者： DUAN Feng JIA Hao ZHANG ZhiWen FENG Fan TAN Ying DAI YangYang CICHOCKI Andrzej YANG ZhengLu CAIAFA Cesar F. SUN Zhe SOLE-CASALS Jordi College of Artificial Intelligence Nankai UniversityTianjin 300350China Skolkowo Institute of Science and Technology Moscow 121205Russia College of Computer Science Hangzhou Dianzi UniversityHangzhou 310018China Department of Informatics Nicolaus Copernicus UniversityTorun 87-100Poland Systems Research Institute Polish Academy of SciencesWarsaw 01-447Poland College of Computer Science Nankai UniversityTianjin 300350China Instituto Argentino de Radioastronom´ıa IAR-CCT La Plata CONICET/CIC-PBA/UNLPVilla Elisa 1894Argentina Computational Engineering Applications Unit Head Office for Information Systems and CybersecurityRIKENWako-Shi 351-0106Japan Department of Psychiatry University of CambridgeCambridge CB28AHUK Data and Signal Processing Research Group University of Vic-Central University of CataloniaCatalonia 08500Spain

During the acquisition of electroencephalographic(EEG) signals, data may be missing or corrupted by noise and artifacts. To reconstruct the incomplete data, EEG signals are firstly converted into a three-order tensor(multi-dimensional data) of shape time × channel × trial. Then, the missing data can be efficiently recovered by applying a tensor completion method(TCM).However, there is not a unique way to organize channels and trials in a tensor, and different numbers of channels are available depending on the EEG setting used, which may affect the quality of the tensor completion results. The main goal of this paper is to evaluate the robustness of EEG completion methods with several designed parameters such as the ordering of channels and trials, the number of channels, and the amount of missing data. In this work, the results of completing missing data by several TCMs were compared. To emulate different scenarios of missing data, three different patterns of missing data were ***, the amount of missing data on completion effects was analyzed, including the time lengths of missing data and the number of channels or trials affected by missing data. Secondly, the numerical stability of the completion methods was analyzed by shuffling the indices along channels or trials in the EEG data tensor. Finally, the way that the number of electrodes of EEG tensors influences completion effects was assessed by changing the number of channels. Among all the applied TCMs, the simultaneous tensor decomposition and completion(STDC) method achieves the best performance in providing stable results when the amount of missing data or the electrode number of EEG tensors is changed. In other words, STDC proves to be an excellent choice of TCM, since permutations of trials or channels have almost no influence on the complete results. The STDC method can efficiently complete the missing EEG signals. The designed simulations can be regarded as a procedure to validate whether or

关键词： electroencephalogram corrupted data missing data tensor completion tensor decomposition

来源：评论

学校读者我要写书评

暂无评论

A Vision of DevOps Requirements Change Management Standardization

A Vision of DevOps Requirements Change Management Standardiz...

引用

IEEE International Conference on Software Quality, Reliability and Security Companion (QRS-C)

作者： Muhammad Azeem Akbar Arif Ali Khan Sajjad Mahmood Saima Rafi Department of Software Engineering LUT University Lappeenranta Finland M3S Empirical Software Engineering Research Unit University of Oulu Oulu Finland Information and Computer Science Department King Fahd University of Petroleum and Minerals Dhahran Saudi Arabia Interdisciplinary Research Center for Intelligent Secure Systems King Fahd University of Petroleum and Minerals Dhahran Saudi Arabia Department of Informatics and Systems University of Murcia Murcia Spain

DevOps (development and operations) aims to shorten the software development process and provide continuous delivery with high software quality. To get the potential gains of DevOps, the software development industry considering global software development (GSD) environment to hire skilled human resources and round-the-clock working hours. However, due to the lack of frequent communication and coordination in GSD, the planning and managing of the requirements change process becomes a challenging task. As in DevOps, requirements are not only shaped by development feedback but also by the operations team. This means requirements affect development, development affects operations and operations affect requirements. However, DevOps in GSD still faces many challenges in terms of requirement management. The purpose of this research project is to develop a DevOps requirement change management and implementation maturity model (DevOps-RCMIMM) that could assist the GSD organizations in modifying and improving their requirement management process in the DevOps process. The development of DevOps-RCMIMM will be based on the existing DevOps and RCM literature, industrial empirical study, and understanding of factors that could impact the implementation of the DevOps requirement change management process in the domain of GSD. This vision study presents the initial results of a systematic literature review that will contribute to the development of maturity levels of the proposed DevOps-RCMIMM.

关键词： Systematics Requirements management Standards organizations Software quality Standardization Organizations Software reliability

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：