检索结果-内蒙古大学图书馆

20th International Conference on the Economics of Grids, Clouds, Systems, and Services, GECON 2024

作者： Amin, Maryam Noor, Umara Fatima, Manahil Rashid, Zahid Altmann, Jörn Department of Software Engineering Faculty of Computing and Information Technology International Islamic University Islamabad44000 Pakistan Technology Management Economics and Policy Program College of Engineering Seoul National University Seoul Korea Republic of College of Engineering Seoul National University Seoul Korea Republic of Integrated Major in Smart City Global Convergence Seoul National University Seoul Korea Republic of

ISBN: (纸本)9783031812255

Despite technological advancements, ensuring aircraft safety remains a challenge, however, Machine learning (ML)-based approaches for predicting future incidents play a crucial role in addressing flight safety. As ML models increase in complexity, their decision-making process becomes less transparent, posing significant challenges to trustworthiness. While simpler models demonstrate lower accuracy, more intricate models such as deep neural networks achieve higher accuracy but sacrifice interpretability. In this study, we enhance trustworthiness in aircraft safety prediction by leveraging a dataset of past accidents and incidents to prevent similar accidents from occurring in the future. To achieve this, we apply Random Forest and Extreme Gradient Boosting models to classify different categories of aircraft incidents. Additionally, we apply two powerful explainable artificial intelligence (XAI) techniques: Local Interpretable Model-Agnostic Explanations (LIME) and Shapley Additive exPlanations (SHAP) to provide insights into both local and global predictions made by the models. Notably, our results reveal high accuracy in these predictions while maintaining trustworthiness. This research contributes to the advancement of XAI and offers valuable insights for safety-critical applications and decision support systems. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Aircraft accidents

来源：评论

学校读者我要写书评

暂无评论

Getting Situated: Comparative Analysis of Language Models With Experimental Categorization Tasks 44

Getting Situated: Comparative Analysis of Language Models Wi...

引用

44th Annual Meeting of the Cognitive Science Society: Cognitive Diversity, CogSci 2022

作者： Edinger, Andy Goldstone, Robert L. Program in Cognitive Science & Luddy School of Informatics Computing and Engineering Indiana University 1101 E. 10th Street BloomingtonIN47405 United States Program in Cognitive Science Department of Psychological and Brain Sciences Indiana University 1101 E. 10th Street BloomingtonIN47405 United States

Common critiques of natural language processing (NLP) methods cite their lack of multimodal sensory information, claiming an inability to learn situated, action-oriented relations through language alone. Barsalou's (1983) theory of ad hoc categories, which are formed from to achieve goals in real-world scenarios, correspond theoretically to those types of relations with which language models ought to have great difficulty. Recent NLP models have developed dynamic approaches to word representations, where the same word can have different encodings depending on the context in which it appears. Testing these models using categorization tasks with human response data demonstrates that situated properties may be partially captured through semantic analysis. We discuss possible ways in which different notions of situatedness may be distinguished for future development and testing of NLP models. © 2022 The Author(s). This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY)

关键词： Semantics

来源：评论

学校读者我要写书评

暂无评论

Patient Engagement through Mobile Health Interventions of Diabetes: A Systematic Literature Review 33

Patient Engagement through Mobile Health Interventions of Di...

引用

33rd Australasian Conference on Information Systems: The Changing Face of IS, ACIS 2022

作者： Alhubayshi, Arwa Almutairi, Nawaf Win, Khin Than Information Technology Department Medical Cities Program – MOI Riyadh Saudi Arabia School of Computing and Information Technology University of Wollongong Wollongong Australia Faculty of Public Health and Health Informatics University of Hail Hail Saudi Arabia

This paper aims to review the applicability of the Healthcare Information and Management Systems Society (HIMSS) patient engagement framework and to recognise the patient engagement features presented in current mHealth interventions for improving engagement in diabetes self-management. Scopus, Web of Science and PubMed databases were searched for relevant studies. The selected studies were then analysed using the Healthcare Information and Management Systems Society (HIMSS) patient engagement framework. Twenty engagement features were identified from 29 studies reviewed. Half of the HIMSS patient engagement framework categories were seen in the studies. This review shows that mHealth interventions have achieved the third and fourth phases of the HIMSS patient engagement framework. In addition, this review suggests improvements in patient engagement facilitation, including integrating patient-generated data into electronic health records, collaboration access to the health records, and peer support through online community forums. Finally, the future patient engagement framework is discussed for mHealth technology. Copyright © 2022 Arwa Alhubayshi, Nawaf Almutairi, Khin Than Win.

关键词： mHealth

来源：评论

学校读者我要写书评

暂无评论

Rapid Monitoring for Flood Inundation Detection-Based Sentinel-1 SAR Image Data and Exposure Assessment in Urban Building Area: Makassar City, Indonesia 3rd

Rapid Monitoring for Flood Inundation Detection-Based Sentin...

引用

3rd International Conference on Radioscience, Equatorial Atmospheric Science and Environment, INCREASE 2023

作者： Sekarjati, Dhanu Hanif, Muhammad Komala, Siffa Anastasya Giofandi, Eggy Arya Putri, Febita Safera Bunga, Brigitta Audrynne Rombe Putklang, Wasana Wilis, Ratna Geoinformatics Department of Computer Science College of Computing Khon Kaen University Khon Kaen Thailand Department of Geophysics and Meteorology Faculty of Mathematics and Natural Science IPB University Bogor Indonesia Graduate Program of Regional Planning Science Faculty of Agriculture IPB University Bogor Indonesia Department of Geography Faculty of Social Science Universitas Negeri Padang Padang Indonesia

ISBN: (纸本)9789819707393

Real-time monitoring remains a challenge in providing accurate information, especially in the case of urban flood inundation disasters. The accuracy of data for disaster risk reduction has a positive impact on reducing the loss of livelihood or the regional economy. Understanding flood levels and their impact on urban areas can be crucial information for flood mitigation and effective urban planning. The research aims to conduct a rapid monitoring investigation for flood inundation detection in Makassar City, Indonesia, using Sentinel-1 SAR image data and assess the exposure of urban building areas to flooding. By identifying flood inundation areas using backscatter values, it was determined that Tamalanrea District exhibits a high density and is prone to flooding as a result of urban expansion. Additionally, Biringkanaya District, which is adjacent to Tamalanrea, was also impacted by the flood. The most severely affected area was found to be Tamalanrea District, with an inundation area covering 73,114 m2. It also recorded the highest extent of built-up area affected by flooding, reaching 29,475 m2. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Floods

来源：评论

学校读者我要写书评

暂无评论

Recommending View Bundles in Data Marketplaces

Recommending View Bundles in Data Marketplaces

引用

2022 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2022

作者： De Carvalho, Thiego Buenos Aires Martins, Denis M. Lima De Lima Neto, Fernando B. Vossen, Gottfried University of Pernambuco Computing Engineering Program Recife Brazil University of Münster Ercis Machine Learning and Data Engineering Münster Germany University of Münster Ercis Databases and Information Systems Münster Germany

ISBN: (数字)9781665452588

ISBN: (纸本)9781665452588

For companies to have a competitive advantage, they need to extract relevant information from data and for that, they need to complement their own data with other data sources. Data marketplaces are platforms on which data providers and data consumers do business. However, every data interaction incurs a monetary cost. Therefore, data consumers are interested in buying a set of interesting views that fit their (goal and) budget. Views allow data to be represented visually, enabling users to make sense of patterns and insights. Besides allowing for easier cost control, buying a set of views bundled together increases the chance of finding what consumers want over buying them view-by-view. Selecting the suitable views to compose an interesting bundle is non-trivial, due to the vast number of view combinations that potentially meet the data consumer's needs. In this paper, we address the problem of view bundle recommendation in data marketplaces, in which the utility of a bundle depends on the interplay among candidate views. We propose the use of Self-Organizing Maps as a means to compute this interplay and use a Genetic Algorithm to design near-optimal bundles. Our empirical results demonstrate that our approach can effectively aid data consumers to find relevant view bundles under budget constraints. © 2022 IEEE.

关键词： Genetic algorithms

来源：评论

学校读者我要写书评

暂无评论

Computer Vision-based Approach for Automated Recognition of Interference Patterns in Digital Inline Holography Microscopy

Computer Vision-based Approach for Automated Recognition of ...

引用

Annual IEEE Symposium on Computer-Based Medical Systems

作者： Manoella Rockembach Rodrigo Ferraz Souza Aldo von Wangenheim Antonio Carlos Sobieranski Department of Computing – DEC Federal University of Santa Catarina Araranguá Brazil Brazilian National Institute for Digital Convergence – INCoD Federal University of Santa Catarina Florianópolis Brazil Department of Computing - DEC Graduate Program in Information and Communication Technologies - PPGTIC Federal University of Santa Catarina Araranguá Brazil

ISBN: (数字)9798350384727

ISBN: (纸本)9798350384734

Digital Inline Holographic Microscopy (DIHM) is a lensless shadow imaging technique where a coherent light-source is utilized to illuminate samples and record interference patterns in a digitalizing device. Those patterns, called holograms, carry volumetric information regarding the inspected sample, requiring complex numerical methods to make them distinguishable as occurs in a conventional bright-field microscopic image. However, for many applications, the real-time analysis of holograms is computationally expensive due to the nature of numerical diffraction methods to reconstruct the signals into visual information. To mitigate this problem, in this paper we investigate the use of deep learning approaches to classify those interference patterns, directly from the raw holograms, without the requirement of phase-recovering methods for diffraction. In our approach, we investigated the use of distinct Convolutional Neural Networks (CNNs) architectures and its adaptability to correctly classify holograms in an experimental environment with a dataset generated from synthetic interference patterns produced by the Fresnel Diffraction Method. The computer-generated dataset was produced from 26 classes, resulting in a total of 520 samples after the data augmentation procedure. The obtained results demonstrated the feasibility of the proposed approach to properly classify samples with 96.8% of precision, directly from the holographic interference patterns, avoiding the need for computationally expensive diffraction methods.

关键词： Deep learning Visualization Diffraction Microscopy Neural networks Holography Data augmentation

来源：评论

学校读者我要写书评

暂无评论

Enhancing waste sorting and recycling efficiency: robust deep learning-based approach for classification and detection

引用

Neural computing and Applications 2025年第6期37卷 4567-4583页

作者： Sayem, Faizul Rakib Islam, Md. Sakib Bin Naznine, Mansura Nashbat, Mohammad Hasan-Zia, Mazhar Kunju, Ali K Ansaruddin Khandakar, Amith Ashraf, Azad Majid, Molla Ehsanul Kashem, Saad Bin Abul Chowdhury, Muhammad E. H. Department of Electrical and Electronic Engineering University of Dhaka Dhaka Bangladesh Department of Biomedical Engineering Military Institute of Science and Technology Dhaka Bangladesh Department of Computer Science & Engineering Rajshahi University of Engineering & Technology Rajshahi6204 Bangladesh Chemical Engineering Department University of Doha for Science and Technology Doha Qatar Department of Electrical Engineering Qatar University Doha2713 Qatar Computer Applications Department Academic Bridge Program Qatar Foundation Doha Qatar Department of Computing Science AFG College With the University of Aberdeen Doha Qatar

Given the severity of waste pollution as a major environmental concern, intelligent and sustainable waste management is becoming increasingly crucial in both developed and developing countries. The material composition and volume of urban solid waste are key considerations in processing, managing, and utilizing city waste. Deep learning technologies have emerged as viable solutions to address waste management issues by reducing labor costs and automating complex tasks. However, the limited number of trash image categories and the inadequacy of existing datasets have constrained the proper evaluation of machine learning model performance across a large number of waste classes. In this paper, we present robust waste image classification and object detection studies using deep learning models, utilizing 28 distinct recyclable categories of waste images comprising a total of 10,406 images. For the waste classification task, we proposed a novel dual-stream network that outperformed several state-of-the-art models, achieving an overall classification accuracy of 83.11%. Additionally, we introduced the GELAN-E (generalized efficient layer aggregation network) model for waste object detection tasks, obtaining a mean average precision (mAP50) of 63%, surpassing other state-of-the-art detection models. These advancements demonstrate significant progress in the field of intelligent waste management, paving the way for more efficient and effective solutions. © The Author(s) 2024.

关键词： Deep learning for waste management Intelligent waste management Object detection in waste management Sustainable waste processing Urban solid waste Waste image classification

来源：评论

学校读者我要写书评

暂无评论

Aedes Aegypti Egg Classification Model Using Support Vector Machine 15

Aedes Aegypti Egg Classification Model Using Support Vector ...

引用

15th International Conference on Computer Research and Development, ICCRD 2023

作者： Gumiran, Cherry R. Fajardo, Arnel C. Medina, Ruji P. Dao, Minh S. Gumiran, Joel M. Graduate Program Technological Institue of the Philippines Quezon City Philippines Isabela State University Cauayan College of Computing Studies Information and Communications Technology Philippines Graduate Prgram Technological Institue of the Philippines Quezon Philippines National Institute of Information and Communications Technology Big Data Integration Research Center Tokyo Japan

ISBN: (纸本)9781665487504

Aedes aegypti is the dengue fever vector, affecting 3.9 billion people worldwide. Found primarily in subtropical areas such as the Philippines. Numerous approaches and procedures had used to identify this disease carrier throughout its life cycle. However, recognizing it early, primarily during the egg stage, helps monitor the disease carrier's existence. On the other hand, classifying the egg remains an issue due to its resemblance to another mosquito species. As a result, extracted traits of the Aedes egg had added to the classification model. Furthermore, the Support Vector Machine algorithm's effectiveness as a classifier improves classification accuracy, mainly when the feature extracted has been added. Thus, this paper used the Support Vector Machine in designing a model to classify Aedes aegypti eggs. This model differentiates it from non-Aedes with a classification accuracy of 98.23%, which is greater than the classification accuracy compared to previous studies using the pixel and region-based method. In addition, the newly developed model had increased accuracy compared to the model in which the features had omitted from the classification process by 3.26 percent. In addition, the model was validated on a non-Aedes dataset in which it exhibited similar characteristics. As a result, the model reveals improved categorization and is determined to be non-Aedes. Thus, this model demonstrates a considerable boost in classification accuracy. © 2023 IEEE.

关键词： Support vector machines

来源：评论

学校读者我要写书评

暂无评论

U-Net and SegNet performances on lesion segmentation of breast ultrasonography images

引用

Research on Biomedical Engineering 2021年第2期37卷 171-179页

作者： Vianna, Pedro Farias, Ricardo de Albuquerque Pereira, Wagner Coelho Biomedical Engineering Program COPPE/Federal University of Rio de Janeiro Av. Horácio Macedo 2030 Room H327 Rio de JaneiroRJ Brazil Systems and Computing Engineering Program COPPE/Federal University of Rio de Janeiro Rio de JaneiroRJ Brazil

Purpose: Screening is the predominant strategy for the early detection of breast cancer. However, image analysis depends on the experience of the radiologist, inserting subjective factors in the evaluation of findings. Most biopsies performed on women with suspicious lesions on breast imaging tests show benignity. This medical technique is invasive and consumes time and resources;therefore, there is a demand to reduce these unnecessary procedures. Computer-aided diagnostics has been increasingly used as a second-reader tool lately, decreasing the diagnostic uncertainty of specialists. Based on this fact, the development of such systems is crucial and can be accomplished by the refinement of its steps, i.e., more accurate segmentation and classification. Considering this scenario, the present work describes the implementation of two well-known convolutional neural networks, U-Net and SegNet, for the segmentation of lesions found in breast ultrasonography. Defining which architecture is most appropriate for this task can ultimately help in reducing the number of biopsies performed. Methods: Convolutional neural networks are used in segmentation by classifying each pixel in an image, based on self-trained weights. Using a dataset of 2054 images, obtained in partnership with the National Cancer Institute, we compared the automatic segmentation performed by the networks with a manual segmentation made by a specialist. Results: Among the two proposed architectures, U-Net obtained better results in this task, obtaining a dice coefficient of 86.3%, and took 68.3% less training time than the SegNet, which achieved a dice score of 81.1%. Conclusion: The two networks can segment ultrasound images with useful accuracy depending on their configuration, with U-Net being faster to train and more accurate. © 2021, Sociedade Brasileira de Engenharia Biomedica.

关键词： Semantic Segmentation

来源：评论

学校读者我要写书评

暂无评论

Fuzzy correlational analysis for dynamic consolidation of virtual machines in cloud computing environment

引用

ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2023年第PartD期126卷

作者： Bertei, Alex Foss, Luciana Bedregal, Benjamin Reiser, Renata Univ Fed Pelotas Lab Ubiquitous & Parallel Syst Pelotas RS Brazil Fed Univ Rio Grande de Norte Dept Informat & Appl Math Postgrad Program Syst & Computing Natal RN Brazil

This theoretical research in fuzzy correlation analysis integrates data uncertainty analysis by measuring the strength of the linear relationship restricted to two fuzzy sets. As the relevant contribution to this research area, this paper presents the axiomatic definition of the n-dimensional generalized fuzzy correlation coefficient (nGCC), assigning to n input fuzzy sets an output value in the interval [-1,1]. Thus, the properties of general overlap functions and fuzzy negations are studied, discussing binary non-normed restricted dissimilarity functions, and the n-dimensional non-normed conjunctive functions. This study provides new methods for the n-dimensional generalized fuzzy correlation coefficient analysis, regarding their applications in solving multi-criteria and decision-making problems founded on fuzzy logic extensions. The n-dimensional non-normed conjunctive aggregation functions are also introduced, as range domain extensions from [0,1] to [-1.1], covering the interpretation of negative to positive variable associations. The proposal correlation analysis promotes a better evaluation and data selection even when more than one algorithm is applied to evaluate the reduction methods in the defuzzification process based on Interval-valued Fuzzy Logic. We also investigate the relevance to determine a reliable result from the fuzzy inference system based on the nGCC methodology. This correlation methodology applied to the Interval Fuzzy Load Balancing for Cloud computing (Int-FLBCC) model contributes as a flexible approach for virtual machines dynamic consolidation enabling improvements in resource usage and power efficiency, improving the computational system's energy efficiency. So, the nGCC methodology extends the Int-FLBCC model by adding other degrees of reliability to the results obtained with diverse evaluations through n-dimensional generalized fuzzy correlation coefficient expressions, exploring average aggregations as arithmetic and exponential me

关键词： Pearsen's correlation coefficient Cloud computing Fuzzy set n-dimensional fuzzy correlation analysis Multi-criteria Decision-making Interval-valued fuzzy logic

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：