检索结果-内蒙古大学图书馆

Response to yarmolinsky, tzoulaki, gunter, et al

Journal of the National Cancer Institute 2024年第5期116卷 766-767页

作者： Jing Sun Xue Li Department of Big Data in Health Science School of Public Health and Center of Clinical Big Data and Analytics of The Second Affiliated Hospital Zhejiang University School of Medicine Hangzhou Zhejiang China.

来源：评论

学校读者我要写书评

暂无评论

Estimating the Fatality Burden of SARS-CoV2

引用

Digital Government: Research and Practice 2021年第2期2卷 1–8页

作者： Smyth, Barry Insight Centre for Data Analytics School of Computer Science University College Dublin Belfield Dublin 4 Ireland

As the world struggles with the SARS-CoV2 pandemic, public health officials and governments continue to refine the key metrics that are used to capture and compare the state of the pandemic and the effects of responses within and between countries and regions. This work presents a novel fatality metric, the COVID-19 burden, which normalises SARS-CoV2 fatalities with respect to historical mortality rates over the same period of time. We argue that this measure provides an improved basis for comparing fatality rates between countries, and we present an analysis of this measure across 174 countries, using data up to November 15, 2020, to better understand the impact of the virus in different countries and regions. © 2020 ACM.

关键词： COVID-19

来源：评论

学校读者我要写书评

暂无评论

Bayesian spatio-temporal modeling of African CO2 emissions (1990–2020): A hierarchical approach to identify determinants, regional trends, and local dynamics

引用

science of the Total Environment 2025年 969卷 178894页

作者： Afrifa-Yamoah, Ebenezer Osei, Prince Mensah Mathematical Applications & Data Analytics Group School of Science Edith Cowan University 270 Joondalup Drive WA6027 Australia

Africa's unique position in global CO2 emissions demands rigorous analysis for effective climate policy development. Despite contributing only 4% to global emissions, the continent faces disproportionate climate impacts while undergoing rapid development and complex economic transitions. Current research lacks extensive continental analysis of the spatial dependencies, temporal evolution of emission patterns, and their key drivers, which is fundamental for evidence-based climate policy and sustainable development strategies. We applied Bayesian hierarchical spatio-temporal modeling to analyze CO2 emissions across African countries (1990–2020), integrating rotated empirical orthogonal function (REOF) analysis with spatial autocorrelation techniques (Local Moran's I and LISA) to capture complex emission patterns. Our hierarchical framework incorporated demographic and environmental predictors, revealing urbanization as the dominant emission driver. Surface temperature and relative humidity showed significant positive associations, while forest cover demonstrated a strong negative relationship. Spatial analysis identified distinct emission clusters, with the first three REOF modes explaining 78% of total variance. Strong positive spatial autocorrelation in North Africa contrasts with negative patterns in Southern regions, suggesting regional development networks could influence emission trajectories. These findings highlight opportunities for low-carbon development during Africa's urbanization phase through integrated urban planning and forest preservation. The spatial dependencies highlight the importance of coordinated regional approaches to emission reduction, providing evidence for targeted climate policies that balance local contexts with regional interdependencies. © 2025 The Authors

关键词： Low emission

来源：评论

学校读者我要写书评

暂无评论

Open Architecture for Distributed and Cooperative Media Algorithms 3

Open Architecture for Distributed and Cooperative Media Algo...

引用

3rd International Conference for Innovation in Technology, INOCON 2024

作者： Bhambu, Pawan Manjunatha, R. Srivastava, Pratima Vivekananda Global University Department of Computer Science and Engineering Jaipur India School of Sciences Department of Data Analytics and Mathematical Sciences Karnataka Bangalore India Arka Jain University Department of Biotechnology Jharkhand Jamshedpur India

ISBN: (纸本)9798350381931

The Open structure for allotted and Cooperative Media Algorithms (OADCMA) is an open-deliver framework imparting a plug-in platform that lets customers, without problem, develop distributed and cooperative media algorithms to utilize the computational assets of diverse heterogeneous structures. It's miles designed for operations along with video capture, transmission, and machine operations. OADCMA affords unified APIs for allotted and cooperative media algorithms, which simplifies the implementation of distributed and cooperative media algorithms. Additionally, OADCMA permits green parallel computing while averting scalability problems. OADCMA helps a widespread fashion of verbal exchange networks, including allotted finite discipline computing. It is appropriate for an extensive kind of allotted and cooperative media programs together with video surveillance and broadcasting. © 2024 IEEE.

关键词： Security systems

来源：评论

学校读者我要写书评

暂无评论

Application of XGBoost and kernel principal component analysis to forecast oxygen content in ESR

引用

Journal of Iron and Steel Research International 2024年第12期31卷 2940-2952页

作者： Yu-xiao Liu Yan-wu Dong Zhou-hua Jiang Qi Wang Yu-shuo Li School of Metallurgy Northeastern UniversityShenyang110819LiaoningChina National Frontiers Science Center for Industrial Intelligence and Systems Optimization Northeastern UniversityShenyang110819LiaoningChina Key Laboratory of Data Analytics and Optimization for Smart Industry(Northeastern University) Ministry of EducationShenyang110000LiaoningChina Key Laboratory of Ecological Metallurgy of Multimetallic Mineral Northeastern UniversityShenyang110819LiaoningChina

A model combining kernel principal component analysis(KPCA)and Xtreme Gradient Boosting(XGBoost)was introduced for forecasting the final oxygen content of electroslag *** was employed to reduce the dimensionality of the factors influencing the endpoint oxygen content and to eliminate any existing correlations among these *** resulting principal components were then utilized as input variables for the XGBoost prediction *** KPCA-XGBoost model was trained and proven using data obtained from *** model structure was adapted,and hyperparameters were optimized using grid search *** model performance of the KPCA-XGBoost model is compared with five machine learning models,including the support vector regression *** findings demonstrated that the KPCA-XGBoost model exhibited the highest level of prediction accuracy,indicating that the incorporation of KPCA significantly enhanced the regression prediction performance of the *** accuracy of the KPCA-XGBoost model was 82.4%,97.1%,and 100%at errors of±1.5×10^(-6),±2.0×10^(-6),and±3×10^(-6)for oxygen content,respectively.

关键词： Electroslag remelting Oxygen content Machine learning Kernel principal component analysis XGBoost

来源：评论

学校读者我要写书评

暂无评论

SACNN-IDS: A self-attention convolutional neural network for intrusion detection in industrial internet of things

引用

CAAI Transactions on Intelligence Technology 2024年第6期9卷 1398-1411页

作者： Mimonah Al Qathrady Safi Ullah Mohammed S.Alshehri Jawad Ahmad Sultan Almakdi Samar M.Alqhtani Muazzam A.Khan Baraq Ghaleb Department of Information Systems College of Computer Science and Information SystemsNajran UniversityNajranSaudi Arabia Department of Computer Science Quaid-i-Azam UniversityIslamabadPakistan Department of Computer Science College of Computer Science and Information SystemsNajran UniversityNajranSaudi Arabia School of Computing Engineering and the Built EnvironmentEdinburgh Napier UniversityEdinburghUK ICESCO Chair Big Data Analytics and Edge Computing Quaid-i-Azam UniversityIslamabadPakistan

Industrial Internet of Things(IIoT)is a pervasive network of interlinked smart devices that provide a variety of intelligent computing services in industrial *** IIoT nodes operate confidential data(such as medical,transportation,military,etc.)which are reachable targets for hostile intruders due to their openness and varied *** Detection Systems(IDS)based on Machine Learning(ML)and Deep Learning(DL)techniques have got significant ***,existing ML and DL-based IDS still face a number of obstacles that must be *** instance,the existing DL approaches necessitate a substantial quantity of data for effective performance,which is not feasible to run on low-power and low-memory *** and fewer data potentially lead to low performance on existing *** paper proposes a self-attention convolutional neural network(SACNN)architecture for the detection of malicious activity in IIoT networks and an appropriate feature extraction method to extract the most significant *** proposed architecture has a self-attention layer to calculate the input attention and convolutional neural network(CNN)layers to process the assigned attention features for *** performance evaluation of the proposed SACNN architecture has been done with the Edge-IIoTset and X-IIoTID *** datasets encompassed the behaviours of contemporary IIoT communication protocols,the operations of state-of-the-art devices,various attack types,and diverse attack scenarios.

关键词： convolutional neural network deep learning industrial internet of things intrusion detection self-attention

来源：评论

学校读者我要写书评

暂无评论

Optimizing Protein Titer Production Using Animal Cells: Predictive Modeling and Recommendations for Enhanced Yield

Optimizing Protein Titer Production Using Animal Cells: Pred...

引用

IISE Annual Conference and Expo 2024

作者： Polk, Jackson E. Derakhshi, Mohammad Le, Huynh Wickham, Stephanie Razzaghi, Talayeh School of Industrial and Systems Engineering University of Oklahoma NormanOK United States Data Science and Analytics Institute University of Oklahoma NormanOK United States Cytovance Biologics Oklahoma CityOK United States

ISBN: (纸本)9781713877851

Current biomanufacturing processes rely heavily on human expertise, struggling to adapt to the growing complexity of bioprocessing. Decision support tools based on machine learning models play a vital role in optimizing the production and timely detection of anomalies in the production line and can save thousands of dollars and reduce quality control costs. We have performed a thorough analysis on a pharmaceutical dataset to identify significant variables that can affect protein titer production. Based on this analysis, we develop machine learning-based models for production of the protein titer in the biopharmaceutical/biotechnology-based manufacturing industry. We envision the practical applications of our models in other biopharmaceutical and biotechnology industries, leading to increased productivity and cost-effectiveness in protein production within animal cells. © IISE Annual Conference and Expo *** rights reserved.

关键词： Adversarial machine learning

来源：评论

学校读者我要写书评

暂无评论

Bayesian Optimization with Multi-objective Acquisition Function for Bilevel Problems 30th

Bayesian Optimization with Multi-objective Acquisition Func...

引用

30th Irish Conference on Artificial Intelligence and Cognitive science, AICS 2022

作者： Dogan, Vedat Prestwich, Steven Confirm Centre for Smart Manufacturing School of Computer Science and Information Technology University College Cork Cork Ireland Insight Centre for Data Analytics School of Computer Science and Information Technology University College Cork Cork Ireland

ISBN: (纸本)9783031264375

A bilevel optimization problem consists of an upper-level and a lower-level optimization problem connected to each other hierarchically. Efficient methods exist for special cases, but in general solving these problems is difficult. Bayesian optimization methods are an interesting approach that speed up search using an acquisition function, and this paper proposes a modified Bayesian approach. It treats the upper-level problem as an expensive black-box function, and uses multiple acquisition functions in a multi-objective manner by exploring the Pareto-front. Experiments on popular bilevel benchmark problems show the advantage of the method. © 2023, The Author(s).

关键词： Multiobjective optimization

来源：评论

学校读者我要写书评

暂无评论

Beyond Correlation: Evaluating Multimedia Quality Models with the Constrained Concordance Index

arXiv

引用

arXiv 2024年

作者： Ragano, Alessandro Martinez, Helard Becerra Hines, Andrew The Insight Centre for Data Analytics The School of Computer Science University College Dublin Dublin Ireland

This study investigates the evaluation of multimedia quality models, focusing on the inherent uncertainties in subjective Mean Opinion Score (MOS) ratings due to factors like rater inconsistency and bias. Traditional statistical measures such as Pearson’s Correlation Coefficient (PCC), Spearman’s Rank Correlation Coefficient (SRCC), and Kendall’s Tau (KTAU) often fail to account for these uncertainties, leading to inaccuracies in model performance assessment. We introduce the Constrained Concordance Index (CCI), a novel metric designed to overcome the limitations of existing metrics by considering the statistical significance of MOS differences and excluding comparisons where MOS confidence intervals overlap. Through comprehensive experiments across various domains including speech and image quality assessment, we demonstrate that CCI provides a more robust and accurate evaluation of instrumental quality models, especially in scenarios of low sample sizes, rater group variability, and restriction of range. Our findings suggest that incorporating rater subjectivity and focusing on statistically significant pairs can significantly enhance the evaluation framework for multimedia quality prediction models. This work not only sheds light on the overlooked aspects of subjective rating uncertainties but also proposes a methodological advancement for more reliable and accurate quality model evaluation. © 2024, CC BY.

关键词： Image quality

来源：评论

学校读者我要写书评

暂无评论

Reinforcement Learning with UAV Assistance for Optimized Computation Offloading in Mobile Edge Computing 34

Reinforcement Learning with UAV Assistance for Optimized Com...

引用

34th Conference of Open Innovations Association FRUCT, FRUCT 2023

作者： Alabsi, Aisha Hawbani, Ammar Wang, Xingfu Alsamhi, Saeed Hamood Zhao, Liang Al-Dubai, Ahmed University of Science and Technology of China School of Computer Science and Technology China National University of Ireland Insight Centre for Data Analytics Galway Ireland Shenyang Aerospace University School of Computer Science China Computing School of Edinburgh Napier University United Kingdom

ISBN: (纸本)9789526524603

With the rise of computing-intensive applications like online gaming and telemedicine on user equipment (UE) and the evolution of 5G technology, there is a surge in demand for greater computing resources and power. Yet, UEs have limited resources and batteries. Mobile Cloud Computing (MCC) has emerged as a method to enhance UEs computing capabilities and conserve energy by transferring tasks to the cloud. Mobile Edge Computing (MEC) further aids by reducing delays, although it faces issues like limited resources and unpredictable network conditions. Unmanned Aerial Vehicles (UAVs) offer a remedy by serving as mobile stations for MEC, but optimal offloading decisions in UAV-assisted MEC remain intricate. Addressing this, I propose using Reinforcement Learning (RL), specifically Q-Learning, Deep Q Network (DQN), and Deep Deterministic Policy Gradient (DDPG), to enhance decision-making for offloading. Our focus is on energy efficiency and reduced service delay, and our simulations prove our method's efficacy in UAV-assisted MEC environments. © 2023 FRUCT Oy.

关键词： Mobile edge computing

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：