检索结果-内蒙古大学图书馆

EAI/Springer Innovations in Communication and Computing

学校读者我要写书评

暂无评论

Preface

EAI/Springer Innovations in Communication and Computing 2022年 V-VI页

作者： Haldorai, Anandakumar Ramu, Arulmurugan Mohanram, Sudha Lu, Joan Department of Computer Science and Engineering Sri Eshwar College of Engineering TN Coimbatore India Department of Computer Science and Engineering Presidency University KA Bengaluru India Sri Eshwar College of Engineering TN Coimbatore India Department of Computer Science School of Computing and Engineering Centre for Planning Autonomy and Representation of Knowledge University of Huddersfield Huddersfield United Kingdom

From Dialect Gaps to Identity Maps: Tackling Variability in Speaker Verification

学校读者我要写书评

暂无评论

arXiv 2025年

作者： Abdullah, Abdulhady Abas Badawi, Soran Abdullah, Dana A. Hamad, Dana Rasul Taher, Hanan Abdulrahman Muhamad, Sabat Salih Ahmed, Aram Mahmood Hassan, Bryar A. Aula, Sirwan Abdolwahed Rashid, Tarik A. Artificial Intelligence and Innovation Centre University of Kurdistan Hewlér Kurdistan Region Erbil Iraq Language Center Charmo University Kurdistan Region Chamchamal Iraq Department of Computer Science College of Science Knowledge University Kurdistan Region Erbil44001 Iraq Department of Information System ICTC Directorate Ministry of Higher Education and Scientific Research Kurdistan Region Iraq Computer Science Department Faculty of Science Soran University Soran Kurdistan Region Erbil Iraq Pedagogical Training Course for Professional Teacher Development Centre Duhok Polytechnic University Kurdistan Region Duhok Iraq Computer Science and Engineering Department School of Science and Engineering University of Kurdistan Hewler Kurdistan Region Erbil Iraq Department of Computer Science College of Science Charmo University Chamchamal Sulaimani Kurdistan Region46023 Iraq

The complexity and difficulties of Kurdish speaker detection among its several dialects are investigated in this work. Because of its great phonetic and lexical differences, Kurdish with several dialects including Kurmanji, Sorani, and Hawrami offers special challenges for speaker recognition systems. The main difficulties in building a strong speaker identification system capable of precisely identifying speakers across several dialects are investigated in this work. To raise the accuracy and dependability of these systems, it also suggests solutions like sophisticated machine learning approaches, data augmentation tactics, and the building of thorough dialect-specific corpus. The results show that customized strategies for every dialect together with cross-dialect training greatly enhance recognition performance. © 2025, CC BY.

关键词： Semantics

PULASki: Learning inter-rater variability using statistical distances to improve probabilistic segmentation

学校读者我要写书评

暂无评论

arXiv 2023年

作者： Chatterjee, Soumick Gaidzik, Franziska Sciarra, Alessandro Mattern, Hendrik Janiga, Gábor Speck, Oliver Nürnberger, Andreas Pathiraja, Sahani Data and Knowledge Engineering Group Faculty of Computer Science Otto von Guericke University Magdeburg Germany Genomics Research Centre Human Technopole Milan Italy Laboratory of Fluid Dynamics and Technical Flows Otto von Guericke University Magdeburg Germany Department of Biomedical Magnetic Resonance Faculty of Natural Sciences Otto von Guericke University Magdeburg Germany Department of Neurology Medical Faculty University Hospital Magdeburg Germany German Centre for Neurodegenerative Disease Magdeburg Germany Centre for Behavioural Brain Sciences Magdeburg Germany School of Mathematics & Statistics UNSW Sydney UNSW Data Science Hub Australia

In the domain of medical imaging, many supervised learning based methods for segmentation face several challenges such as high variability in annotations from multiple experts, paucity of labelled data and class imbalanced datasets. These issues may result in segmentations that lack the requisite precision for clinical analysis and can be misleadingly overconfident without associated uncertainty quantification. We propose the PULASki as a computationally efficient generative tool for biomedical image segmentation that accurately captures variability in expert annotations, even in small datasets. Our approach makes use of an improved loss function based on statistical distances in a conditional variational autoencoder structure (Probabilistic UNet), which improves learning of the conditional decoder compared to the standard cross-entropy particularly in class imbalanced problems. We analyse our method for two structurally different segmentation tasks (intracranial vessel and multiple sclerosis (MS) lesion) and compare our results to four well-established baselines in terms of quantitative metrics and qualitative output. These experiments are characterised by class-imbalanced, low signal to noise ratio data sets with highly ambiguous features. Empirical results demonstrate the PULASKi method outperforms all baselines at the 5% significance level. Our experiments are also of the first to present a comparative study of the computationally feasible segmentation of complex geometries using 3D patches and the traditional use of 2D slices. The generated segmentations are shown to be much more anatomically plausible than in the 2D case, particularly for the vessel task. Our method can also be applied to a wide range of multi-label segmentation tasks and and is useful for downstream tasks such as hemodynamic modelling (computational fluid dynamics and data assimilation), clinical decision making, and treatment planning. Copyright © 2023, The Authors. All rights reserved.

关键词： Medical imaging

Impacts of oxytactic microorganisms and viscous dissipation in Carreau nanoliquid featuring ferromagnetic nanoparticles

学校读者我要写书评

暂无评论

Partial Differential Equations in Applied Mathematics 2025年 13卷

作者： Tabrez, Muhammad Hussain, I. Khan, W.A. Khan, S.U. Waqas, M. Rashid, Rzgar Farooq Anwar, M.I. Ammar, Mohamed Ben Zamri, Nurnadiah Abiyev, Ruzimurod Garalleh, Hakim AL Department of Mathematics Mohi-ud-Din Islamic University Nerian Sharif Azad Jammu and Kashmir 12010 Pakistan Department of Mathematics Namal University Mianwali 42250 Pakistan NUTECH School of Applied Sciences and Humanities National University of Technology Islamabad 44000 Pakistan Department of Computer Science and Mathematics Lebanese American University Beirut Lebanon Faculty of Informatics and Computing Universiti Sultan Zainal Abidin Besut Campus Terengganu Besut 22200 Malaysia Department of Medical Laboratory Science College of Science Knowledge University Erbil 44001 Iraq Department of Medical Laboratory Technique College of Medical Technology AL-Kitab University Kirkuk Iraq Department of Mathematics School of Sciences and Humanities Nazarbayev University Astana Kazakhstan Center for Scientific Research and Entrepreneurship Northern Border University Arar 73213 Saudi Arabia Department of Mathematics and its Teaching Methods in Primary Education Tashkent State Pedagogical University Tashkent Uzbekistan Department of Mathematical Science College of Engineering University of Business and Technology Jeddah 21361 Saudi Arabia

Owing to rapid progress in field of nanotechnology, various mathematical model has been proposed regarding the flow of nanofluids. Based on boosted thermal properties, multidisciplinary applications of such materials has been claimed in various engineering and industrial processes. In current work, a theoretical analysis is performed for bioconvective flow of Carreau fluid with applications of magnetic dipole. The significance of magnetic dipole are presented with interaction of ferrofluid. The applications of viscous dissipation have also been entertained. The flow is subject to applications of stretched surface. Similarity variables are used for making transformation to obtain set of nonlinear ODEs from set of PDEs. The numerical computation is also made by using of well-known numerical method termed as bvp4c. Influences of various non dimensional fluid parameters like Brownian motion, viscous dissipation, Curie temperature, Weissenberg number, motile difference parameter and bioconvection Paclet number are examined and expressed graphically. It is perceived that ferrofluid's concentration declines when Brownian diffusive variable is augmented while opposite characteristics are reported for Schmidt number. Current results conveying applications in the biofuels, better heat transport medium, fertilizers, catalysts, electronics, paints, semiconductors, speakers, drug delivery, fertilizers etc. © 2024 The Author(s)

关键词： Bioconvection Carreau fluid Ferromagnetic flow Magnetic dipole Viscous dissipation

Digital Mahjong System: Towards Precise Cognitive Assessment with IoT Technologies 1

学校读者我要写书评

暂无评论

24th International Conference on Human-computer Interaction, HCII 2022

作者： An, Ning Hu, Enze Guo, Yanrui Yang, Jiaoyun Au, Rhoda Ding, Huitong School of Computer Science and Information Engineering Hefei University of Technology Hefei China Anhui Province Key Laboratory of Affective Computing and Advanced Intelligent Machine Hefei University of Technology Hefei China National Smart Eldercare International S&T Cooperation Base Hefei University of Technology Hefei China Intelligent Interconnected Systems Laboratory of Anhui Province Hefei University of Technology Hefei China Shenzhen Corecloud Innovation Technology Co. Ltd. Shenzhen China Key Laboratory of Knowledge Engineering with Big Data of Ministry of Education Hefei University of Technology Hefei China Department of Anatomy and Neurobiology Neurology and Framingham Heart Study Boston University School of Medicine Boston United States Department of Epidemiology Boston University School of Public Health Boston United States

ISBN: (数字)9783031179020

ISBN: (纸本)9783031179013

To the best of our knowledge, this paper is the first to apply IoT technologies to transform the popular Mahjong game into a Digital Mahjong System (DMS) for digitally performing cognitive assessments. People have started exploring digital cognitive assessment tools for better objectivity and simplification. However, most of these tools are not friendly for older adults. This paper aims to address this issue by integrating IoT technologies with Mahjong to make cognitive assessment simpler and more engaging for older adults. DMS has the following features: 1) integrating motion tracking devices into Mahjong tiles and transferring them into Digital Mahjong Tiles (DMTs), which can precisely capture their moving trajectory during the assessment, 2) using a Near Field Communication reader to configure DMT’s primary data, 3) supporting Over-The-Air Device Firmware Update that can be done remotely via Bluetooth, and 4) developing a charging base that can charge 16 DMTs simultaneously using Qi wireless charging standard. With further testing, the DMS has the potential to become a digital platform to implement and test various cognitive assessment tools integrating Mahjong elements. © 2022, Springer Nature Switzerland AG.

关键词： Internet of things

Strategic COVID-19 vaccine distribution can simultaneously elevate social utility and equity

学校读者我要写书评

暂无评论

arXiv 2021年

作者： Chen, Lin Xu, Fengli Han, Zhenyu Tang, Kun Hui, Pan Evans, James Li, Yong Department of Computer Science and Engineering The Hong Kong University of Science and Technology Hong Kong SAR China Knowledge Lab & Department of Sociology University of Chicago ChicagoIL United States Santa Fe Institute Santa FeNM United States Department of Electronic Engineering Tsinghua University Beijing China Vanke School of Public Health Tsinghua University Beijing China

Balancing social utility and equity in distributing limited vaccines represents a critical policy concern for protecting against the prolonged COVID-19 pandemic. What is the nature of the trade-off between maximizing collective welfare and minimizing disparities between more and less privileged communities? To evaluate vaccination strategies, we propose a novel epidemic model that explicitly accounts for both demographic and mobility differences among communities and their association with heterogeneous COVID-19 risks, then calibrate it with large-scale data. Using this model, we find that social utility and equity can be simultaneously improved when vaccine access is prioritized for the most disadvantaged communities, which holds even when such communities manifest considerable vaccine reluctance. Nevertheless, equity among distinct demographic features are in tension due to their complex correlation in society. We design two behavior-and-demography-aware indices, community risk and societal harm, which capture the risks communities face and those they impose on society from not being vaccinated, to inform the design of comprehensive vaccine distribution strategies. Our study provides a framework for uniting utility and equity-based considerations in vaccine distribution, and sheds light on how to balance multiple ethical values in complex settings for epidemic control. © 2021, CC BY-NC-ND.

关键词： Vaccines

Curvature regularization to prevent distortion in graph embedding 20

学校读者我要写书评

暂无评论

Curvature regularization to prevent distortion in graph embe...

Proceedings of the 34th International Conference on Neural Information Processing Systems

作者： Hongbin Pei Bingzhe Wei Kevin Chen-Chuan Chang Chunxu Zhang Bo Yang College of Computer Science and Technology Jilin University China and Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education China Department of Electrical and Computer Engineering University of Illinois at Urbana-Champaign Department of Electrical and Computer Engineering University of Illinois at Urbana-Champaign and Department of Computer Science University of Illinois at Urbana-Champaign

ISBN: (纸本)9781713829546

Recent research on graph embedding has achieved success in various applications. Most graph embedding methods preserve the proximity in a graph into a manifold in an embedding space. We argue an important but neglected problem about this proximity-preserving strategy: Graph topology patterns, while preserved well into an embedding manifold by preserving proximity, may distort in the ambient embedding Euclidean space, and hence to detect them becomes difficult for machine learning models. To address the problem, we propose curvature regularization, to enforce flatness for embedding manifolds, thereby preventing the distortion. We present a novel angle-based sectional curvature, termed ABS curvature, and accordingly three kinds of curvature regularization to induce flat embedding manifolds during graph embedding. We integrate curvature regularization into five popular proximity-preserving embedding methods, and empirical results in two applications show significant improvements on a wide range of open graph datasets.

关键词：

Automated SSIM Regression for Detection and Quantification of Motion Artefacts in Brain MR Images

学校读者我要写书评

暂无评论

arXiv 2022年

作者： Sciarra, Alessandro Chatterjee, Soumick Dünnwald, Max Placidi, Giuseppe Nürnberger, Andreas Speck, Oliver Oeltze-Jafra, Steffen The Medicine and Digitalization - MedDigit Group Department of Neurology Medical Faculty University Hospital Magdeburg Germany the Department of Biomedical Magnetic Resonance Faculty of Natural Sciences Otto von Guericke University Germany The Data and Knowledge Engineering Group Faculty of Computer Science Otto von Guericke University Germany The Genomics Research Centre Human Technopole Milan Italy The Faculty of Computer Science Otto von Guericke University Germany The Department of Life Health and Environmental Sciences University of L'Aquila Italy The Center for Behavioral Brain Sciences Magdeburg Germany The German Center for Neurodegenerative Disease Magdeburg Germany

Motion artefacts in magnetic resonance brain images can have a strong impact on diagnostic confidence. The assessment of MR image quality is fundamental before proceeding with the clinical diagnosis. Motion artefacts can alter the delineation of structures such as the brain, lesions or tumours and may require a repeat scan. Otherwise, an inaccurate (e.g. correct pathology but wrong severity) or incorrect diagnosis (e.g. wrong pathology) may occur."Image quality assessment" as a fast, automated step right after scanning can assist in deciding if the acquired images are diagnostically sufficient. An automated image quality assessment based on the structural similarity index (SSIM) regression through a residual neural network is proposed in this work. Additionally, a classification into different groups - by subdividing with SSIM ranges - is evaluated. Importantly, this method predicts SSIM values of an input image in the absence of a reference ground truth image. The networks were able to detect motion artefacts, and the best performance for the regression and classification task has always been achieved with ResNet-18 with contrast augmentation. The mean and standard deviation of residuals' distribution were µ = −0.0009 and σ = 0.0139, respectively. Whilst for the classification task in 3, 5 and 10 classes, the best accuracies were 97, 95 and 89%, respectively. The results show that the proposed method could be a tool for supporting neuro-radiologists and radiographers in evaluating image quality quickly. Copyright © 2022, The Authors. All rights reserved.

关键词： Image quality

A novel cascade heat integration configuration for electricity/freshwater/hydrogen outputs using SOFC-GT, multi-effect desalination, and PEM electrolysis using machine learning optimization

学校读者我要写书评

暂无评论

Case Studies in Thermal engineering 2025年 72卷

作者： Zuo, Zhaoyang Wang, Junhua Samad, Sarminah Othman, Nashwan Adnan Almadhor, Ahmad Ghandour, Raymond Alsayer, Ibrahim A Abduvalieva, Dilsora Alkhalaf, Salem Babiker, Samah G. School of Mechanical Engineering Xijing University Shaanxi Xi'an710123 China School of Computer Science South China Business College Guangdong University of Foreign Studies Gangzhou Guangdong510545 China Department of Management College of Business Administration Princess Nourah Bint Abdulrahman University Riyadh 11671 Saudi Arabia Department of Computer Engineering College of Engineering Knowledge University Erbil44001 Iraq Department of Computer Engineering Al-Kitab University Altun Kupri Iraq Department of Computer Engineering and Networks College of Computer and Information Sciences Jouf University Saudi Arabia College of Engineering and Technology American University of the Middle East Egaila54200 Kuwait Department of Chemical and Materials Engineering Engineering College Northern Border University Arar91431 Saudi Arabia Department of Mathematics and Information Technologies Tashkent State Pedagogical University Bunyodkor avenue 27 Tashkent100070 Uzbekistan Department of Computer Engineering College of Computer Qassim University Buraydah Saudi Arabia Department of Electronic Physics Faculty of Applied Science Red Sea University Port Sudan Sudan

The current study presents an innovative integrated multi-generation system tailored for residential communities based on a novel cascade heat integration configuration. The proposed system is designed for scalability, making it adaptable for various residential communities, from small housing complexes to larger urban developments. Its modular architecture allows flexible integration with existing infrastructure, enabling widespread deployment in diverse geographic and economic conditions. Utilizing a real-Time machine learning framework, the system dynamically optimizes energy distribution and operational efficiency, making it feasible for large-scale implementation while maintaining economic viability and environmental benefits. Hence, a real-Time machine learning framework is implemented to optimize key operational parameters dynamically, ensuring efficient fuel utilization, energy distribution, and load balancing across the system. The ML model continuously processes sensor data to adjust SOFC current density, GT pressure ratio, and ORC steam pressure, maximizing exergy efficiency while minimizing operational costs and CO2 emissions. Additionally, it enhances system reliability through predictive maintenance and demand-based power allocation, enabling a more adaptive and cost-effective multi-generation process. Noteworthy outcomes highlight an exergy efficiency improvement from 48 % to 60 % and a power output increase from 6.1 MW to 11.6 MW. Additionally, the levelized cost of electricity is notably reduced from 8 to 3 cents/kWh. The system also provides extra benefits by generating freshwater and hydrogen, addressing various societal challenges. © 2025 The Authors.

关键词： Predictive maintenance