检索结果-内蒙古大学图书馆

28th International Conference on Knowledge Based and Intelligent information and Engineering systems, KES 2024

作者： Wieckowski, Jakub Gajewski, Pawel Swaldek, Krzysztof Salabun, Wojciech National Institute of Telecommunications ul. Szachowa 1 Warsaw04-894 Poland Research Team on Intelligent Decision Support Systems Department of Artificial Intelligence and Applied Mathematics Faculty of Computer Science and Information Technology West Pomeranian University of Technology Szczecin ul. Zolnierska 49 Szczecin71-210 Poland Department of Computer Science Faculty of Computer Science and Telecommunications Cracow University of Technology Warszawska 24 Krakow31-155 Poland

This paper provides a comparative analysis of three selected Multi-Criteria Decision Analysis (MCDA) methods in a practical decision problem to examine the robustness of the obtained results. The Complex Proportional Assessment (COPRAS), Preference Ranking Organization METHod for Enrichment of Evaluations (PROMEHTEE) II, and Evaluation based on Distance from Average Solution (EDAS) methods were utilized for the evaluation purposes, while the Ranking Comparison (RANCOM) method was applied to determine criteria weights. The application was directed toward sustainable development management in selected European countries considering different energy sources in the consumption share per capita. The obtained results showed, that depending on the used MCDA method, the ranking of countries differed, thus showing that the evaluation process is sensitive to changing assessment methods. © 2024 The Authors.

关键词： Sustainable development goals

来源：评论

学校读者我要写书评

暂无评论

Reconstruction and Validation of the UX Factor Trust for the User Experience Questionnaire Plus (UEQ+) 19

Reconstruction and Validation of the UX Factor Trust for the...

引用

19th International Conference on Web information systems and Technologies, WEBIST 2023

作者： Hinderks, Andreas Schrepp, Martin Rauschenberger, Maria Thomaschewski, Jörg Computer Languages and Systems University of Sevilla Sevilla Spain SAP SE Germany Faculty of Technology University of Applied Science Emden/Leer Emden Germany

ISBN: (纸本)9789897586729

As digital technologies advance, user experience (UX) has become crucial for software and services success. The User Experience Questionnaire Plus (UEQ+) is a flexible tool used to evaluate UX through questionnaires tailored to specific problems, yet a critical factor often overlooked is Trust. Trust, understood as a user's belief in a software's ability to function consistently, securely, and with respect for user data privacy, is especially pivotal in areas like financial services, health informatics, and e-commerce platforms. This paper focuses on the construction and validation of Trust as a new factor in the UEQ+. During the construction phase, an initial collection of potential items was assembled for the trust factor. A subsequent study involving 405 participants facilitated the reduction of these items to four, a task accomplished via factor analysis. The proceeding stages involved two additional validation phases, enlisting a total of 897 participants, wherein the selected items were subject to validation. The culmination of this process resulted in a newly validated factor, Trust, which is constituted by the following items: insecure-secure, untrustworthy-trustworthy, unreliable-reliable, and non-transparent-transparent. Copyright © 2023 by SCITEPRESS - science and Technology Publications, Lda. Under CC license (CC BY-NC-ND 4.0)

关键词： Factor analysis

来源：评论

学校读者我要写书评

暂无评论

Comparative analysis of distance metrics for intuitionistic fuzzy sets in multi-criteria decision analysis 28th

Comparative analysis of distance metrics for intuitionistic ...

引用

28th International Conference on Knowledge Based and Intelligent information and Engineering systems, KES 2024

作者： Wieckowski, Jakub Niewiadomski, Daniel Kulis, Dominik Salabun, Wojciech National Institute of Telecommunications ul. Szachowa 1 Warsaw04-894 Poland Research Team on Intelligent Decision Support Systems Department of Artificial Intelligence and Applied Mathematics Faculty of Computer Science and Information Technology West Pomeranian University of Technology Szczecin ul. Zolnierska 49 Szczecin71-210 Poland Department of Computer Science Faculty of Computer Science and Telecommunications Cracow University of Technology Warszawska 24 Krakow31-155 Poland

This paper presents a comparative analysis of selected distance metrics within the intuitionistic fuzzy environment in Multi-Criteria Decision Analysis (MCDA). The Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) method, combined with intuitionistic fuzzy sets (IFS), was employed to investigate how different distance metrics influence decision-making outcomes. The study examines the sensitivity of the obtained recommendations to the variation in the calculation formula used to determine the distance between two IFS. The practical problem of selecting a site for a large-scale rooftop photovoltaic project served as the case study for this comparison. The results indicated that using different distance metrics within the IF-TOPSIS method led to varying preference score ranges and differing rankings, particularly in the middle portions of the rankings. The findings underscore the sensitivity of MCDA methods to the choice of distance metrics used in the calculation process. © 2024 The Authors.

关键词： Fuzzy models

来源：评论

学校读者我要写书评

暂无评论

A DES Homomorphic Scheme for Blackbox Data Analytics

A DES Homomorphic Scheme for Blackbox Data Analytics

引用

International Multidisciplinary information Technology and Engineering Conference (IMITEC)

作者： Luthando Mletshe Nthabiseng Modiba Lucky Makhoere Phumezo Ntlatywa Mnoneleli Nogwina Colin Chibaya Faculty of Natural and Applied Science Computer Science and Information Technology Kimberely South Africa

ISBN: (数字)9798350387988

ISBN: (纸本)9798350387995

Data confidentiality is a critical concern in corporate settings, particularly as data analysts seek to uncover trends and insights from sensitive datasets. Traditional approaches often require plaintext data, posing a significant risk to data privacy during analytics processes. In response to this challenge, homomorphic encryption schemes have emerged as a promising solution to enable secure data analytics while preserving confidentiality. In this work-in-progress paper, we present an ongoing implementation of a homomorphic scheme based on the Data Encryption Standard (DES) as a proof of concept. Our goal is to showcase how DES homomorphic encryption can facilitate secure data analytics by allowing computations on encrypted data without compromising confidentiality. While our work is still in the early stages, we aim to empower data custodians by enabling them to share complete cipher datasets with confidence, knowing that data analytics operations remain blackbox and preserve data availability. Overall, this work represents an ongoing effort to advance data security practices and contribute to the development of practical solutions for secure data analytics.

关键词： Data privacy Data analysis Random access memory Closed box Market research Environmental factors Homomorphic encryption information technology Standards Glass box

来源：评论

学校读者我要写书评

暂无评论

The Impact of Trendy Topics on information Diffusion 23

The Impact of Trendy Topics on Information Diffusion

引用

Proceedings of the 2023 12th International Conference on Software and information Engineering

作者： Sara G Fahmy Rana Khalil Hebat Allah Adel Moukhtar Doaa S Elzanfaly Khaled M Abdelgaber Department of Information Systems Faculty of Informatics & Computer Science The British University in Egypt Egypt Department of information systems Faculty of business information systems The Canadian International College Egypt Department of Information Systems Faculty of Computer & Artificial Intelligence Helwan University Egypt Faculty of Informatics & Computer Science The British University in Egypt Egypt

ISBN: (纸本)9798400708107

This paper presents a new information diffusion model that studies the impact of trendy topics on the diffusion process based on content analysis. The suggested model (Math 1) extends the SIR model by adding the topic weight (tw) that mirrors the topic significance reflected on the infection rate. In which, the new infection rate (λtw ) will be calculated by multiplying the normal infection rate (λ) with the topic weight (tw). An experimental study has been conducted on trendy and non-trendy topics and the results are compared to the classical SIR model. The simulation results of the Math 2 model using Mathematica show the early diffusion of the trendy topics. It has been concluded that the number of infected accounts will accelerate within a shorter time due to the impact of the trendy topics. Therefore, the proposed model (Math 3) better reflects the diffusion process in the presence of trendy topics.

关键词： information Diffusion SIR model SI_{t_w}R model Social Networks Structure of Network Textual Content Topic Identification Trend Extraction Trendy Topics

来源：评论

学校读者我要写书评

暂无评论

Optimizing Patient Outcomes with AI and Predictive Analytics in Healthcare 65

Optimizing Patient Outcomes with AI and Predictive Analytics...

引用

65th IEEE Annual International Scientific Conference on Power and Electrical Engineering of Riga Technical University, RTUCON 2024

作者： Janjua, Jamshaid Iqbal Ghazal, Taher M. Abushiba, Walid Abbas, Sagheer Lahore Pakistan College of Arts & Science Applied Science University P.O.Box 5055 Manama Bahrain Center for Cyber Security Faculty of Information Science and Technology Selangor Bangi43600 Malaysia College of Engineering Applied Science University Bahrain PMU Department of Computer Science Al Khobar Saudi Arabia

ISBN: (纸本)9798350365771

Healthcare's integration with the emerging artificial intelligence, predictive analytics and health information exchange (HIE) system is currently undergoing a revolution. These emerging technologies are offering better diagnostic accuracies, more personalized treatments, and better patients' outcomes. Artificial Intelligence supported by data from either Electronic Health Records or other big data sources are increasing the accuracy of forecasting critical patient conditions such as disease developments, hospital readmission rates, and mortality risks in patients. These AI-empowered models ensure seamless data sharing between the care providers and promote operational interoperability. Challenges to overcome include issues of privacy of the data, the biases of the prediction algorithms, and the proper integration of the model into the currently established operational environments of healthcare providers. With these technologies combined, it will become possible to optimize patient's outcomes with the provision of data to empower real-time medical interventions. © 2024 IEEE.

关键词： Electronic health record

来源：评论

学校读者我要写书评

暂无评论

Swallowing Assessment using High-Resolution Cervical Auscultations and Transformer-based Neural Networks 46

Swallowing Assessment using High-Resolution Cervical Auscult...

引用

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

作者： Anwar, Ayman Khalifa, Yassin Lucatorto, Erin Coyle, James L. Sejdic, Ervin University of Toronto Faculty of Applied Science & Engineering Department of Electrical and Computer Engineering TorontoON Canada University of Pittsburgh Center for Research Computing and Information Technology Analytics PittsburghPA United States Cairo University Faculty of Engineering Systems and Biomedical Engineering Department Giza Egypt University of Pittsburgh School of Health and Rehabilitation Sciences Department of Communication Science and Disorders PittsburghPA United States University of Toronto North York General Hospital Faculty of Applied Science & Engineering Department of Electrical and Computer Engineering TorontoON Canada

ISBN: (纸本)9798350371499

Swallowing assessment is a crucial task to reveal swallowing abnormalities. There are multiple modalities to analyze swallowing kinematics, such as videofluoroscopic swallow studies (VFSS), which is the gold standard method, and high-resolution cervical auscultation (HRCA), which is a noninvasive technique that uses a triaxial accelerometer attached to the patient's neck. Deep learning models play an essential role in data driven analysis of swallowing landmarks using VFSS and/or HRCA as input data. Most of these models utilize convolutional and recurrent neural networks. Here, we investigate the ability of transformers to analyze swallowing kinematics;specifically upper esophageal sphincter opening and laryngeal vestibule closure using HRCA signals. We tested the model using an independent test dataset to assess the generalizability of the proposed network. The proposed network achieved an average detection accuracy higher than 90% and 85% for both segmentation tasks, which outperform the hybrid neural networks from the literature, and the model obtained high-performance measures for the independent dataset, showing the transformers' ability to generalize on unseen data. © 2024 IEEE.

关键词： Recurrent neural networks

来源：评论

学校读者我要写书评

暂无评论

Improved Dwarf Mongoose Optimization for Constrained Engineering Design Problems

引用

Journal of Bionic Engineering 2023年第3期20卷 1263-1295页

作者： Jeffrey O.Agushaka Absalom E.Ezugwu Oyelade N.Olaide Olatunji Akinola Raed Abu Zitar Laith Abualigah School of Mathematics Statisticsand Computer ScienceUniversity of KwaZulu-NatalKing Edward AvenuePietermaritzburg CampusPietermaritzburg3201KwaZulu-NatalSouth Africa Department of Computer Science Federal University of LafiaLafia950101Nigeria Unit for Data Science and Computing North-West University11 Hoffman StreetPotchefstroom2520South Africa Sorbonne Center of Artificial Intelligence Sorbonne University-Abu Dhabi38044Abu DhabiUnited Arab Emirates Hourani Center for Applied Scientific Research Al-Ahliyya Amman UniversityAmman19328Jordan Faculty of Information Technology Middle East UniversityAmman11831Jordan Faculty of Information Technology Applied Science Private UniversityAmman11931Jordan School of Computer Sciences Universiti Sains Malaysia11800Pulau PinangMalaysia

This paper proposes a modified version of the Dwarf Mongoose Optimization Algorithm (IDMO) for constrained engineering design problems. This optimization technique modifies the base algorithm (DMO) in three simple but effective ways. First, the alpha selection in IDMO differs from the DMO, where evaluating the probability value of each fitness is just a computational overhead and contributes nothing to the quality of the alpha or other group members. The fittest dwarf mongoose is selected as the alpha, and a new operator ω is introduced, which controls the alpha movement, thereby enhancing the exploration ability and exploitability of the IDMO. Second, the scout group movements are modified by randomization to introduce diversity in the search process and explore unvisited areas. Finally, the babysitter's exchange criterium is modified such that once the criterium is met, the babysitters that are exchanged interact with the dwarf mongoose exchanging them to gain information about food sources and sleeping mounds, which could result in better-fitted mongooses instead of initializing them afresh as done in DMO, then the counter is reset to zero. The proposed IDMO was used to solve the classical and CEC 2020 benchmark functions and 12 continuous/discrete engineering optimization problems. The performance of the IDMO, using different performance metrics and statistical analysis, is compared with the DMO and eight other existing algorithms. In most cases, the results show that solutions achieved by the IDMO are better than those obtained by the existing algorithms.

关键词： Improved dwarf mongoose Nature-inspired algorithms Constrained optimization Unconstrained optimization Engineering design problems

来源：评论

学校读者我要写书评

暂无评论

Robust Multiresolution and Multistain Background Segmentation in Whole Slide Images 23rd

Robust Multiresolution and Multistain Background Segmentatio...

引用

Proceedings of the 23rd Polish Conference on Biocybernetics and Biomedical Engineering, PCBEE 2023

作者： Jurgas, Artur Wodzinski, Marek Atzori, Manfredo Müller, Henning Information Systems Institute Sierre Switzerland Faculty of Electrical Engineering Automatics Computer Science and Biomedical Engineering AGH University of Science and Technology Krakow Poland Department of Neuroscience University of Padova Padova Italy Medical Faculty University of Geneva Geneva Switzerland

ISBN: (纸本)9783031384295

Background segmentation is an important step in analysis of histopathological images. It allows one to remove irrelevant regions and focus on the tissue of interest. However, background segmentation is challenging due to the variability of stain colors and intensity levels across different images, modalities, and magnification levels. In this paper, we present a learning-based model for histopathology background segmentation based on convolutional neural networks. We compare two multiresolution approaches to deal with the variability of magnification in histopathology images: (i) model that uses upscaling of smaller patches of the image, and (ii) model simultaneously trained on multiple resolution levels. Our model is characterized by solid performance both in multiresolution and multistain dyes (H &E and IHC), achieving good performance on publicly available dataset. The quantitative scores are, in terms of the Dice score, close to 94.71. The qualitative analysis presents strong performance on previously unseen cases from different distributions and various dyes. We freely release the model, weights, and ground-truth annotations to promote the open science and reproducible research. © 2024, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Pathology

来源：评论

学校读者我要写书评

暂无评论

Data Augmentation Impact on Deep learning Performance for Stress Detection

Data Augmentation Impact on Deep learning Performance for St...

引用

IEEE International Conference on Intelligent Computing and information systems (ICICIS)

作者： Eman Hamdi Mohamed Mabrouk Sherine Rady Mostafa Aref Computer Science Department Faculty of Computer and Information Science Cairo Egypt Information Systems Department Faculty of Computer and Information Science Cairo Egypt

The mental health of individuals has a major influence on society. Mental disorders such as depression and anxiety are related to issues and distress to function in work, social, or family gatherings. Motivated by helping people to early detect stress, we propose a deep learning model for stress detection that is trained and tested on Stress Annotated Dataset (SAD). Also, different techniques of textual data augmentation are applied to generate more data samples to study the impact on the stress detection task. The model achieves competitive results in the task of stress detection and proves that data augmentation has a great effect on the performance. As the accuracy is raised by 6.1% to 10.4 % by applying various techniques of data augmentation.

关键词：

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：