检索结果-内蒙古大学图书馆

International Conference on Emerging Trends in Networks and computer Communications (ETNCC)

作者： Emmanuel Adetiba Uche Nnaji John S. Wejin Festus Ojesanmi Matthew B. Akanle Priscilla O. Ajayi Faith O. Sweetwiliams Surendra C. Thakur Ezekiel F. Adebiyi Department of Electrical and Information Engineering and Covenant Applied Informatics & Communication African Center of Excellene(CApIC-ACE) Covenant University Ota Nigeria HRA Institute for Systems Science Durban University of Technology Durban South Africa Department of Computer and Information Science & CApIC-ACE Covenant University Ota Nigeria Department of Electrical and Information Engineering & CApIC-ACE Covenant University Jalingo Nigeria Department of Computer Science Tareba State University Jalingo Nigeria Covenant Applied Informatics & Communication African Center of Excellene(CApIC-ACE) Covenant University Ota Nigeria Department of Electrical and Information Engineering Covenant University & CApIC-ACE Covenant University Ota Nigeria Center for Systems and Information Services & CApIC-ACE Covenant University Ota Nigeria Department of Information Technology and KZN e-Skills Co-Laboratory Durban University of Technology Durban South Africa Covenant University Bioinformatics Research (CUBRe) Covenant University Ota Ogun State Nigeria African Center of Excellence in Bioinformatics & Data Intensive Science Makerere University Kampala Uganda

ISBN: (数字)9798350353266

ISBN: (纸本)9798350353273

In the evolving landscape of cloud computing, federated cloud infrastructures present unique challenges and opportunities for resource and application monitoring. Monitoring the diverse array of cloud resources and applications within a federated cloud environment is crucial for delivering consistent Quality of Service (QoS) and continuous oversight across various cloud applications and infrastructure. Despite the various successes documented in the literature on multi-cloud monitoring, existing frameworks have not fully explored or provided enough insights into centralized monitoring of resources, applications, and network devices in a federated cloud environment. This paper introduces FedMonitor, an implementation architecture designed to enhance the monitoring and notification capabilities in federated cloud environments by building on the theoretical ideals in our earlier work (i.e. FEDARGOS-V1). FedMonitor leverages Prometheus and Grafana to crawl, manage and visualize metrics from diverse software services, hardware resources, and network devices within a federated cloud environment. The results demonstrate hat FedMonitor improves the effectiveness of resource and application monitoring in federated cloud environments.

关键词： Measurement Cloud computing Visualization computer architecture Quality of service Production Software Real-time systems Stakeholders Monitoring

来源：评论

学校读者我要写书评

暂无评论

Going the Extra Mile in Face Image Quality Assessment: A Novel Database and Model

arXiv

引用

arXiv 2022年

作者： Su, Shaolin Lin, Hanhe Hosu, Vlad Wiedemann, Oliver Sun, Jinqiu Zhu, Yu Liu, Hantao Zhang, Yanning Saupe, Dietmar School of Computer Science and Engineering Northwestern Polytechnical University China School of Science and Engineering University of Dundee DundeeDD1 4HN United Kingdom Department of Computer and Information Science University of Konstanz Konstanz78464 Germany School of Computer Science and Informatics Cardiff University CardiffCF24 4AG United Kingdom

An accurate computational model for image quality assessment (IQA) benefits many vision applications, such as image filtering, image processing, and image generation. Although the study of face images is an important subfield in computer vision research, the lack of face IQA data and models limits the precision of current IQA metrics on face image processing tasks such as face superresolution, face enhancement, and face editing. To narrow this gap, in this paper, we first introduce the largest annotated IQA database developed to date, which contains 20,000 human faces - an order of magnitude larger than all existing rated datasets of faces - of diverse individuals in highly varied circumstances. Based on the database, we further propose a novel deep learning model to accurately predict face image quality, which, for the first time, explores the use of generative priors for IQA. By taking advantage of rich statistics encoded in well pretrained off-the-shelf generative models, we obtain generative prior information and use it as latent references to facilitate blind IQA. The experimental results demonstrate both the value of the proposed dataset for face IQA and the superior performance of the proposed model. © 2022, CC BY.

关键词： Database systems

来源：评论

学校读者我要写书评

暂无评论

RAFA-Net: Region Attention Network For Food Items And Agricultural Stress Recognition

arXiv

引用

arXiv 2024年

作者： Bera, Asish Krejcar, Ondrej Bhattacharjee, Debotosh Department of Computer Science and Information Systems Birla Institute of Technology and Science Pilani Pilani Campus Rajasthan333031 India Center for Basic and Applied Science Faculty of Informatics and Management University of Hradec Kralove Rokitanskeho 62 Hradec Kralove500 03 Czech Republic Skoda Auto University Na Karmeli 1457 Mlada Boleslav293 01 Czech Republic Universiti Teknologi Malaysia Kuala Lumpur Malaysia Department of Computer Science and Engineering Jadavpur University Kolkata32 India

Deep Convolutional Neural Networks (CNNs) have facilitated remarkable success in recognizing various food items and agricultural stress. A decent performance boost has been witnessed in solving the agro-food challenges by mining and analyzing of region-based partial feature descriptors. Also, computationally expensive ensemble learning schemes fusing multiple CNNs have been studied in earlier works. This work proposes a region attention scheme for modeling long-range dependencies by building a correlation among different regions within an input image. The attention method enhances feature representation by learning the usefulness of context information from complementary regions. Spatial pyramidal pooling and average pooling pair aggregate partial descriptors into a holistic representation. Both pooling methods establish spatial and channel-wise relationships without incurring extra parameters. A context gating scheme is applied to refine the descriptiveness of weighted attentional features, which is relevant for classification. The proposed Region Attention network for Food items and Agricultural stress recognition method, dubbed RAFA-Net, has been experimented on three public food datasets, and has achieved state-of-the-art performances with distinct margins. The highest top-1 accuracies of RAFA-Net are 91.69%, 91.56%, and 96.97% on the UECFood-100, UECFood-256, and MAFood-121 datasets, respectively. In addition, better accuracies have been achieved on two benchmark agricultural stress datasets. The best top-1 accuracies on the Insect Pest (IP-102) and PlantDoc-27 plant disease datasets are 92.36%, and 85.54%, respectively;implying RAFA-Net’s generalization capability. © 2024, CC BY.

关键词： Plant diseases

来源：评论

学校读者我要写书评

暂无评论

3D laplacian surface deformation for template fitting on craniofacial reconstruction 20

3D laplacian surface deformation for template fitting on cra...

引用

8th International Conference on computer and Communications Management, ICCCM 2020

作者： Suputra, Putu Hendra Sensusiati, Anggraini Dwi Yuniarno, Eko Mulyanto Purnomo, Mauridhi Hery Purnama, I. Ketut Eddy Department of Electrical Engineering Institut Teknologi Sepuluh Nopember Surabaya Indonesia Department of Informatics Universitas Pendidikan Ganesha Singaraja Indonesia Department of Radiology Universitas Airlangga Surabaya Indonesia Department of Electrical Engineering Department of Computer Engineering Institut Teknologi Sepuluh Nopember Surabaya Indonesia

ISBN: (纸本)9781450387668

computer-based craniofacial reconstruction utilizes medical imaging for forensic identification and medical reconstruction surgery, enables sophisticated three-dimension object representation and visualization. Reconstruction process involves deformation in their imagery how the shape of a person's face based on its properties and landmark correspondences. We propose the Laplacian surface deformation to deform the facial template to the cranial surface on several constraints that simulates fitting face to the skull using landmark correspondences. The range of variation of the landmarks on the human face lies in narrow intervals. In other words, small differences can affect the shape and facial expressions. Thus, a Laplacian surface model gives better result than a volumetric Laplacian in the matter of accuracy. © 2020 ACM.

关键词： Medical imaging

来源：评论

学校读者我要写书评

暂无评论

Independent Vector Extraction Constrained on Manifold of Half-Length Filters

Independent Vector Extraction Constrained on Manifold of Hal...

引用

European Signal Processing Conference (EUSIPCO)

作者： Zbyněk Koldovský Jaroslav Čmejla Tülay Adali Stephen O'Regan Acoustic Signal Analysis and Processing Group Faculty of Mechatronics Informatics and Interdisciplinary Studies Technical University of Liberec Czech Republic Department of Electrical and Computer Engineering University of Maryland Baltimore County Baltimore MD USA Naval Surface Warfare Center Carderock Division West Bethesda Maryland USA

Independent Vector Analysis (IVA) is a popular extension of Independent Component Analysis (ICA) for joint separation of a set of instantaneous linear mixtures, with a direct application in frequency-domain speaker separation or extraction. The mixtures are parameterized by mixing matrices, one matrix per mixture. This means that the IVA mixing model does not account for any relationships between parameters across the mixtures/frequencies. The separation proceeds jointly only through the source model, where statistical dependencies of sources across the mixtures are taken into account. In this paper, we propose a mixing model for joint blind source extraction where the mixing model parameters are linked across the frequencies. This is achieved by constraining the set of feasible parameters to the manifold of half-length separating filters, which has a clear interpretation and application in frequency-domain speaker extraction.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Simultaneous Super-Resolution and Denoising on MRI via Conditional Stochastic Normalizing Flow

Simultaneous Super-Resolution and Denoising on MRI via Condi...

引用

IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Zhenhong Liu Xingce Wang Zhongke Wu Yi-Cheng Zhu Alejandro F. Frangi School of Artificial Intelligence Beijing Normal University Beijing China Department of Neurology Peking Union Medical College Hospital Beijing China Division of Informatics Imaging and Data Sciences School of Health Sciences Christabel Pankhurst Institute Department of Computer Science School of Engineering The University of Manchester UK Department of Cardiovascular Sciences Medical Imaging Research Centre (MIRC) Department of Electrical Engineering KU Leuven Leuven Belgium lan Turing Institute London UK

Magnetic resonance imaging (MRI) scans often suffer from noise and low-resolution (LR), which affect the diagnosis and treatment results obtained for patients. LR images and noise come together with MRI, and the existing methods solve image super-resolution (SR) reconstruction and denoising tasks in a step-by-step manner, which influences the overall real distribution of the MRI data. In this paper, we present a simultaneous SR and denoising algorithm based on a stochastic normalizing flow (SNF), named the MR image SR and denoising model based on an SNF (SRDSNF). SRDSNF adds the encoded information of the input image as the conditional information to each reverse step of the stochastic normalizing flow, which realizes a consistent description of the spatial distribution between the reconstruction result and the input image. We introduce rangenull space decomposition and subsequence sampling strategies to enhance the consistency of the input and output data and increase the generation speed of the model. Simultaneous SR and denoising tasks experiment is carried out using the BrainWeb and NFBS datasets. The experimental results show that good SR and denoising results are obtained with fewer sampling steps, these results are consistent with the ground truths, and the structural similarity and peak signal-to-noise ratio of the results are also higher than those of the comparison methods. The proposed method demonstrates potential clinical promise.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Exploring the Roles of Agents and Multi-Agent in Improving Mobile Ad Hoc Networks 4

Exploring the Roles of Agents and Multi-Agent in Improving M...

引用

4th International Symposium on Agents, Multi-Agents Systems and Robotics, ISAMSR 2021

作者： Jubair, Mohammed Ahmed Alabdullah, Abdullah I. M. Mostafa, Salama A. Mustapha, Aida Hassan, Mustafa Hamid Gunasekaran, Saraswathy Shamini College of Information Technology Imam ja'Afar Al-Sadiq University Department of Computer Technical Engineering Al-Muthanna Iraq University of Mosul College of Science Department of Physics Mosul Iraq Faculty of Computer Science and Information Technology Universiti Tun Hussein Onn Malaysia Johor Malaysia Faculty of Applied Sciences and Technology Universiti Tun Hussein Onn Malaysia Johor Malaysia College of Computer Science and Informatics Universiti Tenaga Nasional Kajang43000 Malaysia

ISBN: (纸本)9781728166544

We are experiencing a rapid evolution and advancement of technologies that are capable of supporting the formulation and implementation of robust and adaptive network architectures designed for mobile and dynamic environments. An example of such technologies is the Mobile Ad-Hoc Network (MANET), which supports self-organizing multi-hop heterogeneous network routing services and organization. The importance of such technologies for the future of DoD networking is evident, particularly in the front edge of the battlespace where there is a need for robust and self-organizing network. Another area of such technological advancement is the Multi-Agent Systems (MAS), which are capable of supporting both team-based and independent problem solving in many different environments. These two technologies have been extensively explored, but their combination is largely unexplored. Consequently, this paper aims to investigate the joint operation of agents and multi-agent in the MANET environment. The paper also discusses the research efforts that have been made to optimally improve the technologies. © 2021 IEEE.

关键词： Multi agent systems

来源：评论

学校读者我要写书评

暂无评论

A deep learning approach to Lung Nodule Growth Prediction using CT image combined with Demographic and image features 23

A deep learning approach to Lung Nodule Growth Prediction us...

引用

7th International Conference on Medical and Health informatics, ICMHI 2023

作者： Li, Ai-Hsien Adams Nguyen, Duc-Khanh Lai, Yen-Jun Chien, Ting-Ying Chiu, Yen-Ling Chan, Chien-Lung Yang, Pan-Chyr Division of Cardiology Far Eastern Memorial Hospital Taipei220 Taiwan Graduate Program in Biomedical Informatics Yuan Ze University Taoyuan City Taiwan Department of Information Management Yuan Ze University Taoyuan320 Taiwan Department of Radiology Far Eastern Memorial Hospital Taipei220 Taiwan Department of Computer Science and Engineering Yuan Ze University Taoyuan City Taiwan Innovation Center for Big Data and Digital Convergence Yuan Ze University Taoyuan City Taiwan Graduate Institute of Medicine and Graduate Program in Biomedical Informatics Yuan Ze University Taoyuan Taiwan Department of Medical Research Far Eastern Memorial Hospital Taipei Taiwan Innovation Center for Big Data and Digital Convergence Yuan Ze University Taoyuan320 Taiwan Institute of Biomedical Sciences Academia Sinica Taipei106 Taiwan Department of Internal Medicine College of Medicine National Taiwan University Taipei100 Taiwan Center of Genomic Medicine National Taiwan University Taipei100 Taiwan

ISBN: (纸本)9798400700712

Lung cancer remains a significant global public health concern, being the leading cause of cancer-related deaths worldwide. Despite recent medical advancements, the disease still has a high mortality rate, making early detection and treatment critical for improving patient outcomes. Accurate prediction of nodule growth is key to early lung cancer treatment, but current assessments offer unclear indications of future growth, leading physicians to recommend costly and anxiety-provoking follow-up appointments every 3 to 12 months. To address this need, this study develops an ensemble deep learning approach that combines demographic data with existing CT images to predict whether lung nodules will grow, potentially reducing unnecessary examinations and easing the burden on patients. Our study, conducted on 862 patients with 1004 nodules, produced promising preliminary results with Accuracy, Sensitivity, Precision, F1 Score, and AUC of 0.66, 0.66, 0.67, 0.66, and 0.71, respectively. The proposed method provides a promising support system to empower patients to make informed decisions about seeking medical attention and helps physicians facilitate early treatment regimens, leading to improved patient outcomes and potentially saving lives. © 2023 ACM.

关键词： Forecasting

来源：评论

学校读者我要写书评

暂无评论

Unveiling the Post-Covid Economic Impact Using NLP Techniques

Unveiling the Post-Covid Economic Impact Using NLP Technique...

引用

International Conference on Electronics, computers and Artificial Intelligence (ECAI)

作者： Kanishk Barhanpurkar Nikita Mandlik Anand Singh Rajawat S.B. Goyal Traian Candin Mihaltan Chaman Verma Maria Simona Raboaca Thomas J. Watson College of Engineering and Applied Science Binghamton University USA School of Computer Science & Engineering Sandip University Nashik India Faculty of Information Technology City University Petaling Jaya Malaysia Faculty of Building Services Technical University of Cluj-Napoca Cluj-Napoca Romania Department of Media and Educational Informatics Faculty of Informatics Eötvös Loránd University Budapest Hungary ICSI Energy Department National Research and Development Institute for Cryogenics and Isotopic Technologies Ramnicu Valcea Romania

The research paper presents a novel analysis of textual data based on Natural Language Processing (NLP) techniques to analyse New York Times articles from January 2019 to May 2023. The purpose of this paper is to gain an understanding of the economic impact that follows Covid-19 disease. New York Times (NYT), it was started in 1884 is one of the most prominent newspapers in the world. Additionally, we have used the New York Times Archive API to collect the data for the given timeframe. By analysing sentiment analysis, topic modelling, entity recognition, and keyword extraction, valuable insights can be gathered into market trends, industry shifts, and policy interventions. The authors have created a data science pipeline using Amazon Web Services (AWS), which enables data collection, storage, and visualization. It contributes to a better understanding of the pandemic's short-term and long-term economic effects. The results of this study demonstrate Natural Language Processing techniques' potential as a tool for financial analytics, assisting policymakers, economists, and businesses in formulating recovery strategies.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Semi-automated spine and intervertebral disk detection and segmentation from whole spine MR images

引用

informatics in Medicine Unlocked 2021年 27卷

作者： Badarneh, Alaa Abu-Qasmeih, Isam Otoom, Mwaffaq Alzubaidi, Mohammad A. Department of Biomedical Systems and Informatics Engineering Yarmouk University Irbid 21163 Jordan Department of Computer Engineering Yarmouk University Irbid 21163 Jordan

Intervertebral disks are found between the vertebrae in the spine from the second cervical vertebra to the sacrum. Intervertebral disks permit various movements of the vertebral column to keep it flexible and to absorb vertical shock. The spine can be exposed to several types of compression forces that can adversely affect disk function and cause diseases or disorders, such as herniated disks. Mainly to diagnose these disorders we use Magnetic Resonance Imaging (MRI), since it's the preferred method for extracting the spinal information needed. In this paper, a computer Aided Diagnosis (CAD) system for intervertebral disk detection and segmentation, using sagittal MR images of the whole spine has been proposed. The system consists of three major steps as follows: spine region extraction, intervertebral disk detection, and intervertebral disk segmentation. A 6th order polynomial Iterative Randomized Hough Transform (IRHT) approach is used to segment the spine sub-image from a whole spine T1/T2-weighted MR image in a relatively short time. Finally, an elliptical Hough Transform (HT) is used to detect the intervertebral disks from the segmented spine image. The proposed system used a whole spine images dataset consisting of 31 spine T1/T2 MR images. The accuracy measures of intervertebral disk detection and disk segmentation are 94.65% and 90%, respectively. The results show that the proposed system is robust and accurate for spine segmentation and intervertebral disks detection with high performance. Finally, the proposed system is a useful tool for radiologists in detecting as well as segmenting intervertebral disk in whole spine MR images. © 2021 The Authors

关键词： Detection Intervertebral disk Polynomial IRHT Segmentation T2-weighted MR image Whole spine

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：