检索结果-内蒙古大学图书馆

arXiv 2022年

作者： SenthilKumar, Muthukrishnan Dadlani, Aresh Moradian, Masoumeh Khonsari, Ahmad Tsiftsis, Theodoros A. The Department of Applied Mathematics and Computational Sciences Psg College of Technology Coimbatore641004 India The Faculty of Arts - Interdisciplinary Studies University of Alberta EdmontonABT6G 2E7 Canada The Department of Electrical and Computer Engineering Nazarbayev University Astana010000 Kazakhstan The School of Computer Science Institute for Research in Fundamental Sciences Tehran Iran The Department of Electrical and Computer Engineering University of Tehran Iran The Institute for Research in Fundamental Sciences Tehran Iran The School of Intelligent Systems Science and Engineering Jinan University Zhuhai Campus Zhuhai519070 China

The timeliness of status message delivery in communications networks is subjective to time-varying wireless channel transmissions. In this paper, we investigate the age of information (AoI) of each source in a multi-source M/G/1queueingupdatesystem with active server failures. In particular, we adopt the method of supplementary variables to derive a closed-form expression for the average AoI in terms of system parameters, where the server repair time follows a general distribution and the service time of packets generated by independent sources is a general random variable. Numerical results are provided to validate the effectiveness of the proposed packet serving policy under different parametric settings. © 2022, CC BY.

关键词： Real time systems

来源：评论

学校读者我要写书评

暂无评论

Detection of effective genes in colon cancer: A machine learning approach

引用

Informatics in Medicine Unlocked 2021年 24卷

作者： Fahami, Mohammad Amin Roshanzamir, Mohamad Izadi, Navid Hoseini Keyvani, Vahideh Alizadehsani, Roohallah Department of Electrical and Computer Engineering Isfahan University of Technology 84156-83111 Isfahan Iran Department of Computer Engineering Faculty of Engineering Fasa University Fasa 74617-81189 Iran Department of Biology Faculty of Science Shahid Chamran University of Ahvaz Ahvaz Iran Institute for Intelligent Systems Research and Innovation (IISRI) Deakin University Australia

Nowadays, a variety of cancers have become common among humans which unfortunately are the cause of death for many of these people. Early detection and diagnosis of cancers can have a significant impact on the survival of patients and treatment cost reduction. Colon cancer is the third and the second main cause of women's and men's death worldwide among cancers. Hence, many researchers have been trying to provide new methods for early diagnosis of colon cancer. In this study, we apply statistical hypothesis tests such as t-test and Mann–Whitney–Wilcoxon and machine learning methods such as Neural Network, KNN and Decision Tree to detect the most effective genes in the vital status of colon cancer patients. We normalize the dataset using a new two-step method. In the first step, the genes within each sample (patient) are normalized to have zero mean and unit variance. In the second step, normalization is done for each gene across the whole dataset. Analyzing the results shows that this normalization method is more efficient than the others and improves the overall performance of the research. Afterwards, we apply unsupervised learning methods to find the meaningful structures in colon cancer gene expressions. In this regard, the dimensionality of the dataset is reduced by employing Principle Component Analysis (PCA). Next, we cluster the patients according to the PCA extracted features. We then check the labeling results of unsupervised learning methods using different supervised learning algorithms. Finally, we determine genes which have major impact on colon cancer mortality rate in each cluster. Our conducted study is the first which suggests that the colon cancer patients can be categorized into two clusters. In each cluster, 20 effective genes were extracted which can be important for early diagnosis of colon cancer. Many of these genes have been identified for the first time. © 2021 The Authors

关键词： Colon cancer Dimension reduction Machine learning Statistical hypothesis tests Unsupervised learning

来源：评论

学校读者我要写书评

暂无评论

Myocarditis Diagnosis: A Method using Mutual Learning-Based ABC and Reinforcement Learning

Myocarditis Diagnosis: A Method using Mutual Learning-Based ...

引用

International Symposium on Computational Intelligence and Informatics

作者： Saba Danaei Arsam Bostani Seyed Vahid Moravvej Fardin Mohammadi Roohallah Alizadehsani Afshin Shoeibi Hamid Alinejad-Rokny Saeid Nahavandi Adiban Institute of Higher Education Semnan Iran Department of mechanical engineering of biosystems Urmia university Department of exercise physiology & health science University of tehran Internship in UNSW BioMedical Machine Learning Lab Sydney NSW Australia Institute for Intelligent Systems Research and Innovation (IISRI) Deakin University Waurn Ponds Victoria Australia UNSW Data Science Hub The University of New South Wales (UNSW Sydney) Sydney New South Wales Australia BioMedical Machine Learning Lab The Graduate School of Biomedical Engineering UNSW Sydney Sydney NSW Australia

ISBN: (纸本)9798350398830

Myocarditis occurs when the heart muscle becomes inflamed and inflammation occurs when your body’s immune system responds to infections. It can be diagnosed using cardiac magnetic resonance image (MRI), a non-invasive imaging technique with the possibility of operator bias. This paper proposes a hybrid method of deep reinforcement learning-based algorithms and meta-heuristics algorithms. A mutual learning-based artificial bee colony (ML-ABC) is employed for initial weight, which adjusts the candidate food source generated with the higher fitness between two individuals determined by a mutual learning factor. Moreover, a sequential decision-making process investigates the imbalanced classification issue, in which a convolutional neural network (CNN) is used as the foundation for policy architecture. At first, initial weights are produced using the ML-ABC algorithm. After that, the agent receives a sample at each phase and classifies it, obtaining environmental rewards. The minority class receives more rewards than the majority class. Eventually, the agent discovers an ideal strategy with the aid of a specific reward function and a beneficial learning environment. We evaluate our proposed approach on the Z-Alizadeh Sani myocarditis dataset based on standard criteria and demonstrate that the proposed method gives superior myocarditis diagnosis performance.

关键词： Mechatronics Computational modeling Metaheuristics Reinforcement learning Myocardium Markov processes Classification algorithms

来源：评论

学校读者我要写书评

暂无评论

The Interrelationships between the length of stay, readmission, and post-acute care referral in cardiac surgery patients

引用

Healthcare Analytics 2022年 2卷 100062-100062页

作者： Sultana, Ineen Erraguntla, Madhav Kum, Hye-Chung Delen, Dursun Lawley, Mark Department of Industrial and Systems Engineering Texas A&M University College Station TX United States Data Scientist WVU Research Corporation West Virginia University United States School of Public Health Texas A&M University College Station TX United States Department of Management Science and Information Systems Spears School of Business Oklahoma State University United States Faculty of Engineering and Natural Sciences Istinye University Istanbul Turkey

Prolonged hospital stays, and readmission contribute to substantial healthcare cost. Hence, an assessment of the optimal inpatient length of stay (LOS) associated with lower readmission rate is important for healthcare providers. Post-acute care (PAC) facilities have promising potential to shorten the LOS;however, currently their influence on overall patient outcomes is not well understood. The primary goal of this study is to highlight the interrelated risk factors of LOS and readmission for cardiac patients. The study also examines the influence of PAC referral on LOS and readmission. In this paper, a cohort of 13,982 Coronary Artery Bypass Graft (CABG) and Valve Replacement (VR) patients from 49 hospitals in the U.S. were analyzed with respect to the association of healthcare delivery, demographics, PAC referral, and clinical conditions with LOS and readmission. A generalized linear mixed model and multinomial logistic regression model were developed to evaluate the readmission and LOS associative risk factors, respectively. Referral to PAC was included as a vital predictor in both models to examine its impact on optimal LOS and improving patient outcomes. The results indicate a non-uniform care distribution across census divisions and an inverse relationship between LOS and readmissions. The analytics showed that higher LOS patients were more often referred to PAC and yet they were more prone to readmission except for the patients who were referred to Long Term Care (LTC). This study identifies the effects of healthcare delivery, demographic, comorbidity, and PAC referral factors on readmission and LOS, so that personalized acute and post-acute care coordination can be achieved to improve overall patient outcomes. Interventions such as treating CABG and VR patients with multiple comorbidities for longer time in acute hospitals and increasing LTC referral can result in significant improvement. © 2022 The Author(s)

关键词： Cardiac surgery Length of stay Logistic regression Post-acute care Readmission

来源：评论

学校读者我要写书评

暂无评论

Foundation models and intelligent decision-making: Progress, challenges, and perspectives

引用

Innovation 2025年第6期6卷 100948页

作者： Huang, Jincai Xu, Yongjun Wang, Qi Wang, Qi [Cheems] Liang, Xingxing Wang, Fei Zhang, Zhao Wei, Wei Zhang, Boxuan Huang, Libo Chang, Jingru Ma, Liantao Ma, Ting Liang, Yuxuan Zhang, Jie Guo, Jian Jiang, Xuhui Fan, Xinxin An, Zhulin Li, Tingting Li, Xuefei Shao, Zezhi Qian, Tangwen Sun, Tao Diao, Boyu Yang, Chuanguang Yu, Chenqing Wu, Yiqing Li, Mengxian Zhang, Haifeng Zeng, Yongcheng Zhang, Zhicheng Zhu, Zhengqiu Lv, Yiqin Li, Aming Chen, Xu An, Bo Xiao, Wei Bai, Chenguang Mao, Yuxing Yin, Zhigang Gui, Sheng Su, Wentao Zhu, Yinghao Gao, Junyi He, Xinyu Li, Yizhou Jin, Guangyin Ao, Xiang Zhai, Xuehao Tan, Haoran Yun, Lijun Shi, Hongquan Li, Jun Fan, Changjun Huang, Kuihua Harrison, Ewen Leung, Victor C.M. Qiu, Sihang Dong, Yanjie Zheng, Xiaolong Wang, Gang Zheng, Yu Wang, Yuanzhuo Guo, Jiafeng Wang, Lizhe Cheng, Xueqi Wang, Yaonan Yang, Shanlin Fu, Mengyin Fei, Aiguo Institute of Computing Technology Chinese Academy of Sciences Beijing100190 China Department of Automation Tsinghua University Beijing100084 China Huazhong University of Science and Technology Wuhan430074 China School of Automation Beijing Institute of Technology Beijing100081 China School of Information Science and Engineering Dalian Polytechnic University Dalian116034 China National Engineering Research Center for Software Engineering Peking University Beijing100871 China Department of Oral Implantology Peking University School and Hospital of Stomatology Beijing100081 China Guangzhou511453 China College of Information and Electrical Engineering China Agricultural University Beijing100083 China IDEA Research International Digital Economy Academy Shenzhen518057 China Institute of Automation Chinese Academy of Sciences Beijing100190 China The State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China College of Science National University of Defense Technology Changsha410073 China Center for Systems and Control College of Engineering Peking University Beijing100871 China Gaoling School of Artificial Intelligence Renmin University of China Beijing100872 China College of Systems Engineering National University of Defense Technology Changsha410073 China Laboratory for Big Data and Decision National University of Defense Technology Changsha410073 China College of Computing and Data Science Nanyang Technological University Singapore639798 Singapore School of Electrical Engineering Chongqing University Chongqing400044 China Department of Mathematics The University of Hong Kong Hong Kong SAR999077 China School of Food Science and Technology Dalian Polytechnic University Dalian116034 China Centre for Medical Informatics University of Edinburgh EdinburghEH16 4UX United Kingdom Department of Stomatology Peking Union Medical College Hospital Chinese Academ

intelligent decision-making (IDM) is a cornerstone of artificial intelligence (AI) designed to automate or augment decision processes. Modern IDM paradigms integrate advanced frameworks to enable intelligent agents to make effective and adaptive choices and decompose complex tasks into manageable steps, such as AI agents and high-level reinforcement learning. Recent advances in multimodal foundation-based approaches unify diverse input modalities—such as vision, language, and sensory data—into a cohesive decision-making process. Foundation models (FMs) have become pivotal in science and industry, transforming decision-making and research capabilities. Their large-scale, multimodal data-processing abilities foster adaptability and interdisciplinary breakthroughs across fields such as healthcare, life sciences, and education. This survey examines IDM's evolution, advanced paradigms with FMs and their transformative impact on decision-making across diverse scientific and industrial domains, highlighting the challenges and opportunities in building efficient, adaptive, and ethical decision systems. © 2025 The Authors

关键词： Reinforcement learning

来源：评论

学校读者我要写书评

暂无评论

Blister Package Classification Using ResNet-101 for Identification of Medication

Blister Package Classification Using ResNet-101 for Identifi...

引用

International Computer science and engineering Conference (ICSEC)

作者： Narit Hnoohom Nagorn Maitrichit Pitchaya Chotivatunyu Virach Sornlertlamvanich Sakorn Mekruksavanich Anuchit Jitpattanakul Image Information and Intelligence Laboratory Mahidol University Nakorn Pathom Thailand Faculty of Data Science Asia AI Institute (AAII) Musashino University Tokyo Japan Faculty of Engineering Thammasat University Thailand Pathumthani Thailand School of Information and Communication Technology University of Phayao Phayao Thailand Faculty of Applied Science Intelligent and Nonlinear Dynamic Innovations Research Center King Mongkut's University of Technology North Bangkok Bangkok Thailand

ISBN: (纸本)9781665411981

This research aimed to fine-tune image classification with deep learning techniques to verify the dispensing of prescriptions in hospitals. The proposed approach will be able to help pharmacies reduce the errors that lead to patients receiving the wrong medications. The image classification model uses a double-side transformed image dataset with download from Highlighted Deep Learning (HDL) paper. The dataset collected two-hundred seventy-two images for types of medicine blister packs, including 72 images of front-side and backside merged with a horizontal cropped background, which were used for training the model. The blister package image dataset uses a deep learning model with a ResNet-101 pre-trained model from the TensorFlow framework. The experimental results indicated that the TensorFlow framework achieved higher precision, recall, and F1-score than the Caffe framework. A ResNet-101 model with histogram equalization in the front and backside has the highest accuracy at 100 percent.

关键词： Deep learning Training Computer science Histograms Hospitals Image processing Computational modeling

来源：评论

学校读者我要写书评

暂无评论

Cosmological phase-space analysis of f(G)-theories of gravity

arXiv

引用

arXiv 2025年

作者： Papagiannopoulos, Giannis Luongo, Orlando Leon, Genly Paliathanasis, Andronikos Department of Physics National & Kapodistrian University of Athens Zografou Campus GR 157 73 Athens Greece School of Science and Technology University of Camerino Via Madonna delle Carceri Camerino62032 Italy Department of Nanoscale Science and Engineering University at Albany-SUNY AlbanyNY12222 United States INAF - Osservatorio Astronomico di Brera Milano Italy Sezione di Perugia Perugia06123 Italy Al-Farabi Kazakh National University Al-Farabi av. 71 Almaty050040 Kazakhstan Departamento de Matemáticas Universidad Católica del Norte Avda. Angamos 0610 Casilla Antofagasta1280 Chile Institute of Systems Science Department of Mathematics Faculty of Applied Sciences Durban University of Technology Durban4000 South Africa School for Data Science and Computational Thinking Department of Mathematical Sciences Stellenbosch University Stellenbosch7602 South Africa South Africa

The impact of topological terms that modify the Hilbert-Einstein action is here explored by virtue of a further f(G) contribution. In particular, we investigate the phase-space stability and critical points of an equivalent scalar field representation that makes use of a massive field, whose potential is function of the topological correction. To do so, we introduce to the gravitational Action Integral a Lagrange multiplier and model the modified Friedmann equations by virtue of new non-dimensional variables. We single out dimensionless variables that permit a priori the Hubble rate change of sign, enabling regions in which the Hubble parameter either vanishes or becomes negative. In this respect, we thus analyze the various possibilities associated with a universe characterized by such topological contributions and find the attractors, saddle points and regions of stability, in general. The overall analysis is carried out considering the exponential potential first and then shifting to more complicated cases, where the underlying variables do not simplify. We compute the eigenvalues of our coupled differential equations and, accordingly, the stability of the system, in both a spatially-flat and non-flat universe. Quite remarkably, regardless of the spatial curvature, we show that a stable de Sitter-like phase that can model current time appears only a small fraction of the entire phase-space, suggesting that the model under exam is unlikely in describing the whole universe dynamics, i.e., the topological terms appear disfavored in framing the entire evolution of the universe. © 2025, CC BY.

关键词： Topology

来源：评论

学校读者我要写书评

暂无评论

Learning enables adaptation in cooperation for multi-player stochastic games

arXiv

引用

arXiv 2020年

作者： Huang, Feng Cao, Ming Wang, Long Center for Systems and Control College of Engineering Peking University Beijing100871 China Center for Data Science and System Complexity Faculty of Science and Engineering University of Groningen Groningen9747 AG Netherlands

Interactions among individuals in natural populations often occur in a dynamically changing environment. Understanding the role of environmental variation in population dynamics has long been a central topic in theoretical ecology and population biology. However, the key question of how individuals, in the middle of challenging social dilemmas (e.g., the "tragedy of the commons"), modulate their behaviors to adapt to the fluctuation of the environment has not yet been addressed satisfactorily. Utilizing evolutionary game theory and stochastic games, we develop a game-theoretical framework that incorporates the adaptive mechanism of reinforcement learning to investigate whether cooperative behaviors can evolve in the ever-changing group interaction environment. When the action choices of players are just slightly influenced by past reinforcements, we construct an analytical condition to determine whether cooperation can be favored over defection. Intuitively, this condition reveals why and how the environment can mediate cooperative dilemmas. Under our model architecture, we also compare this learning mechanism with two non-learning decision rules, and we find that learning significantly improves the propensity for cooperation in weak social dilemmas, and, in sharp contrast, hinders cooperation in strong social dilemmas. Our results suggest that in complex social-ecological dilemmas, learning enables the adaptation of individuals to varying environments. Copyright © 2020, The Authors. All rights reserved.

关键词： Game theory

来源：评论

学校读者我要写书评

暂无评论

CBLNER: A Multi-models Biomedical Named Entity Recognition System Based on Machine Learning 1

引用

15th International Conference on intelligent Computing, ICIC 2019

作者： Lejun, Gong Xiaolin, Liu Xuemin, Yang Lipeng, Zhang Yao, Jia Ronggen, Yang Jiangsu Key Lab of Big Data Security and Intelligent Processing School of Computer Science Nanjing University of Posts and Telecommunications Nanjing210023 China Faculty Intelligent Science and Control Engineering Jinling Institute of Technology Nanjing211169 China

ISBN: (数字)9783030269692

ISBN: (纸本)9783030269685

Biomedical named entities is fundamental recognition task in biomedical text mining. This paper developed a system for identifying biomedical entities with four models including CRF, LSTM, Bi-LSTM and BiLSTM-CRF. The system achieved the following performance in test data Genia V3.02: CRF with an F score of 75.91%, LSTM with an F score of 71.69%, BiLSTM with a F score of 74.37%, BiLSTM-CRF with a F score of 76.81%. Experimental results show the performance of BiLSTM-CRF model is better than other three models. Compared with CRF model, Bi-LSTM-CRF model has better recognition effect for biological entities in long text and entities that modified by modifiers. Therefore, CBLNER system lays a foundation for further relationship and event extraction, and could also provide reference for entity recognition research in other fields. © 2019, Springer Nature Switzerland AG.

关键词： Character recognition

来源：评论

学校读者我要写书评

暂无评论

Recording provenance of workflow runs with RO-Crate

arXiv

引用

arXiv 2023年

作者： Leo, Simone Crusoe, Michael R. Rodríguez-Navas, Laura Sirvent, Raül Kanitz, Alexander De Geest, Paul Wittner, Rudolf Pireddu, Luca Garijo, Daniel Fernández, José M. Colonnelli, Iacopo Gallo, Matej Ohta, Tazro Suetake, Hirotaka Capella-Gutierrez, Salvador de Wit, Renske Kinoshita, Bruno P. Soiland-Reyes, Stian CA Pula Italy Vrije Universiteit Amsterdam Amsterdam Netherlands DTL Projects Netherlands Forschungszentrum Jülich Germany Barcelona Supercomputing Center Barcelona Spain Biozentrum University of Basel Basel Switzerland Swiss Institute of Bioinformatics Lausanne Switzerland VIB Data Core Gent Belgium Faculty of Informatics Masaryk University Brno Czech Republic Institute of Computer Science Masaryk University Brno Czech Republic BBMRI-ERIC Graz Austria Ontology Engineering Group Universidad Politécnica de Madrid Madrid Spain Computer Science Department Università degli Studi di Torino Torino Italy Database Center for Life Science Joint Support-Center for Data Science Research Research Organization of Information and Systems Shizuoka Japan Institute for Advanced Academic Research Chiba University Chiba Japan Sator Incorporated Tokyo Japan Department of Computer Science The University of Manchester Manchester United Kingdom Informatics Institute University of Amsterdam Amsterdam Netherlands

Recording the provenance of scientific computation results is key to the support of traceability, reproducibility and quality assessment of data products. Several data models have been explored to address this need, providing representations of workflow plans and their executions as well as means of packaging the resulting information for archiving and sharing. However, existing approaches tend to lack interoperable adoption across workflow management systems. In this work we present Workflow Run RO-Crate, an extension of RO-Crate (Research Object Crate) and *** to capture the provenance of the execution of computational workflows at different levels of granularity and bundle together all their associated objects (inputs, outputs, code, etc.). The model is supported by a diverse, open community that runs regular meetings, discussing development, maintenance and adoption aspects. Workflow Run RO-Crate is already implemented by several workflow management systems, allowing interoperable comparisons between workflow runs from heterogeneous systems. We describe the model, its alignment to standards such as W3C PROV, and its implementation in six workflow systems. Finally, we illustrate the application of Workflow Run RO-Crate in two use cases of machine learning in the digital image analysis domain. © 2023, CC BY.

关键词： Workflow management

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：