检索结果-内蒙古大学图书馆

Robust control and data reconstruction for nonlinear epidemiological models using feedback linearization and state estimation

引用

Mathematical Biosciences and Engineering 2025年第1期22卷 109-137页

作者： Csutak, Balázs Szederkényi, Gábor Faculty of Information Technology and Bionics Pázmány Péter Catholic University Práter u. 50/A BudapestH-1083 Hungary Systems and Control Laboratory HUN-REN Institute for Computer Science and Control Kende u. 13-17 BudapestH-1111 Hungary

It has been clearly demonstrated over the past years that control theory can provide an efficient framework for the solution of several complex tasks in epidemiology. In this paper, we present a computational approach for the state estimation based reference tracking control and historical data reconstruction using nonlinear compartmental epidemic models. The control model is given in nonlinear input-affine form, where the manipulable input is the disease transmission rate influenced by possible measures and restrictions, while the observed or computed output is the number of infected people. The control design is built around a simple SEIR model and relies on a feedback linearization technique. We examine and compare different control setups distinguished by the availability of state information, complementing the directly measurable data with an extended Kalman filter used for state estimation. To illustrate the capabilities and robustness of the proposed method, we carry out multiple case studies for output tracking and data reconstruction on Swedish and Hungarian data, all in the presence of serious model and parameter mismatch. Computation results show that a well-designed feedback, even in the presence of significant observation uncertainties, can sufficiently reduce the effect of modeling errors. © 2025 the Authors,

关键词： Feedback linearization

来源：评论

学校读者我要写书评

暂无评论

Shortest cover after edit

arXiv

引用

arXiv 2024年

作者： Mitani, Kazuki Mieno, Takuya Seto, Kazuhisa Horiyama, Takashi Graduate School of Information Science and Technology Hokkaido University Japan Department of Computer and Network Engineering University of Electro-Communications Japan Faculty of Information Science and Technology Hokkaido University Japan

This paper investigates the (quasi-)periodicity of a string when the string is edited. A string C is called a cover (as known as a quasi-period) of a string T if each character of T lies within some occurrence of C. By definition, a cover of T must be a border of T;that is, it occurs both as a prefix and as a suffix of T. In this paper, we focus on the changes in the longest border and the shortest cover of a string when the string is edited only once. We propose a data structure of size O(n) that computes the longest border and the shortest cover of the string in O(log n) time after an edit operation (either insertion, deletion, or substitution of some string) is applied to the input string T of length n, where is the length of the string being inserted or substituted. The data structure can be constructed in O(n) time given string T. Copyright © 2024, The Authors. All rights reserved.

关键词： Data structures

来源：评论

学校读者我要写书评

暂无评论

Deep Learning Metaphor Detection with Emotion-Cognition Association

Deep Learning Metaphor Detection with Emotion-Cognition Asso...

引用

2022 International Conference on Digital Transformation and Intelligence, ICDI 2022

作者： Razali, Md Saifullah Halin, Alfian Abdul Chow, Yang-Wai Norowi, Noris Mohd Doraisamy, Shyamala Faculty of Computer Science and Information Technology Universiti Putra Malaysia Selangor Malaysia Faculty of Engineering and Information Sciences University of Wollongong Wollongong Australia

ISBN: (纸本)9798350397000

The focal point of this work is to automatically detect metaphor instances in short texts. It is the study of extricating the most optimal features for the task by using a deep learning architecture and carefully hand-crafted contextual features. The first feature set is created using a Convolutional Neural Network (CNN) architecture. Then, three other feature sets are manually hand-crafted using contextual justifications. Next, all of the feature sets are combined. Finally, the combined feature sets undergo the classification process using Support Vector Machine, Logistic Regression, Decision Tree, K-Nearest Neighbour and Discriminatory Analysis. These well-known ma-chine learning classification algorithms are used at the same time for the purpose of comparison. The best algorithm for this task is found to be Support Vector Machine (SVM). The outcome of all the experiments using SVM are good in all the metrics used, with F1-measure of 0.83. Finally, comparison to existing works and performance of each feature sets are given. It is also found that a few sets performed well when they are used independently. However, even the sets that are not useful separately is proven to be very useful after the combination process. © 2022 IEEE.

关键词： Support vector machines

来源：评论

学校读者我要写书评

暂无评论

Systematic Literature Review for Smart Mobile Learning in programming Courses

International Journal of Interactive Mobile Technologies

引用

International Journal of Interactive Mobile Technologies 2024年第12期18卷 129-142页

作者： Al Farsi, Ghaliya Tawafak, Ragad M. Alyoussef, Ibrahim Yaussef Al-Rahmi, Waleed Mugahed Information Technology Department Al Buraimi University College Buraimi Oman Education Technology Department King Faisal University Alahsa Saudi Arabia Computer Science Department Community College King Saud University Riyadh Saudi Arabia Faculty of Social Sciences and Humanities School of Education Universiti Teknologi Malaysia Skudai Malaysia

Advancements in technology are currently expediting the learning process for students, prompting an investigation into the integration of technology into student education. This study explores the impact of technological evolution on education. E-learning, a method of education that delivers information to students through modern communication platforms such as computers, networks, and multimedia audio components, is being examined. Whether utilized remotely or in traditional classroom settings, this revolution has made the world increasingly dependent on electronic screens in an era marked by the convergence of media technologies, information, culture, and technology. This paper outlines the development of an e-learning system customized for mobile devices, aimed at improving learning within the university environment. With mobile phones now ubiquitous among students, e-learning has become a potent tool for advancing educational objectives. Furthermore, e-learning aids in elevating student proficiency levels. The research conducted involved implementing and evaluating an application, which produced satisfactory results. As this application becomes integrated into university curricula to support learning, it is anticipated to substantially enhance students’ learning processes and performance, as evidenced by survey results following its implementation. © 2024 by the authors of this article.

关键词： Students

来源：评论

学校读者我要写书评

暂无评论

Comprehensive Analysis and Remediation of Insecure Direct Object References (IDOR) Vulnerabilities in Android APIs

Comprehensive Analysis and Remediation of Insecure Direct Ob...

引用

Cryptography, Informatics, and Cybersecurity (ICoCICs), IEEE International Conference on

作者： Semi Yulianto Roni Reza Abdullah Benfano Soewito Computer Science Department Binus Graduate Program Computer Science Bina Nusantara University Jakarta Indonesia Faculty of Informatics Engineering National Institute of Science and Technology Jakarta Indonesia Computer Science Department Binus Graduate Program. Master of Computer Science Bina Nusantara University Jakarta Indonesia

The escalating visibility of secure direct object reference (IDOR) vulnerabilities in API security, as indicated in the compilation of OWASP Top 10 API Security Risks, highlights a noteworthy peril to sensitive data. This study explores IDOR vulnerabilities found within Android APIs, intending to clarify their inception while evaluating their implications for application security. This study combined the qualitative and quantitative approaches. Insights were obtained from an actual penetration test on an Android app into the primary reasons for IDOR vulnerabilities, underscoring insufficient input validation and weak authorization methods. We stress the frequent occurrence of IDOR vulnerabilities in the OWASP Top 10 API vulnerability list, highlighting the necessity to prioritize them in security evaluations. There are mitigation recommendations available for developers, which recognize its limitations involving a possibly small and homogeneous selection of tested Android applications, the testing environment that could cause some inaccuracies, and the impact of time constraints. Additionally, the study noted insufficient threat modeling and root cause analysis, affecting its generalizability and real-world relevance. However, comprehending and controlling IDOR dangers can enhance Android API security, protect user data, and bolster application resilience.

关键词：

来源：评论

学校读者我要写书评

暂无评论

AI-Based Feature Extraction and Cosine Similarity for Automation of Student Learning Assessment 24

AI-Based Feature Extraction and Cosine Similarity for Automa...

引用

24th International Arab Conference on information technology, ACIT 2023

作者： Wijanarko, Bambang Dwi Bachtiar Hassan, Rosilah Binti Murad, Dina Fitria Ihsan, Ridho Bramulya Heryadi, Yaya Kuncoro, Engkos Achmad Julianti, Eka Binus Online Learning Bina Nusantara University Computer Science Department Jakarta Indonesia Graduate School Universitas Terbuka Master of English Education Jakarta Indonesia Universiti Kebangsaan Malaysia Center for Cyber Security Faculty of Information Science and Technology Selangor Bangi43600 Malaysia Binus Online Learning Bina Nusantara University Information Systems Department Jakarta Indonesia Binus Online Learning Bina Nusantara University Manajemen Department Jakarta Indonesia Binus Graduate Program-Doctor of Computer Science Bina Nusantara University Computer Science Department Jakarta Indonesia Binus Business School Undergraduate Program Bina Nusantara University Management Department Jakarta Indonesia Universitas Terbuka Information System Department Jakarta Indonesia

ISBN: (纸本)9798350384307

This research is ongoing research into the student learning process which aims to develop artificial intelligence-based technology to calculate essay exam scores automatically, based on the textual proximity of students' answers to the answer key. This research uses an approach based on natural language processing (NLP) and text analysis. Feature extraction was carried out using the BERT model and similarity comparison using the Cosine distance method. Model evaluation was carried out using sensitivity and specificity tests. The model evaluation results show a sensitivity of 98.21%, which means this assessment model is able to detect positive cases well. © 2023 IEEE.

关键词： artificial intelligence BERT essay exam score natural language processing transformer

来源：评论

学校读者我要写书评

暂无评论

Measuring Student Satisfaction Based on Analysis of Physical Parameters in Smart Classroom

Measuring Student Satisfaction Based on Analysis of Physical...

引用

information and Education technology (ICIET), International Conference on

作者： Farhad Mortezapour Shiri Thinagaran Perumal Norwati Mustapha Raihani Mohamed Mohd Anuaruddin Bin Ahmadon Shingo Yamaguchi Faculty of Computer Science and Information Technology University Putra Malaysia Malaysia Graduate School of Sciences and Technology for Innovation Yamaguchi University Japan

ISBN: (数字)9798350371772

ISBN: (纸本)9798350371789

Several factors related to the physical environment, including temperature, environmental noise, CO2 levels, air pressure, humidity, and the lecturer's vocal delivery, significantly impact student satisfaction. Evaluating, quantifying, and enhancing these factors can significantly contribute to enhancing students' satisfaction with lecture quality. The objective of this study is to assess students' satisfaction regarding the quality of lectures through an analysis of data obtained from a variety of smart devices that record physical environmental parameters. In this study, the machine learning techniques employed encompass Decision Tree (DT), Support Vector Machine (SVM), Random Forest (RF), Adaptive Boosting (AdaBoost), K-nearest neighbors (KNN), and Stochastic Gradient Descent (SGD). Additionally, the deep learning models utilized comprise Convolutional Neural Network (CNN), Recurrent Neural Network (RNN), Gated Recurrent Unit (GRU), Bidirectional Gated Recurrent Unit (Bi-GRU), Long Short-Term Memory (LSTM), and Bidirectional Long Short-Term Memory (Bi-LSTM). The findings of this study suggest that, on the whole, deep learning models demonstrate superior performance when it comes to classifying data about this particular problem, as compared to traditional machine learning models. Notably, the CNN model emerges as the top performer among all the models assessed, achieving an impressive accuracy rate of 74.62%. it is worth mentioning that the decision tree exhibits the highest accuracy among classical machine learning models.

关键词： Deep learning Support vector machines Analytical models Adaptation models Recurrent neural networks Logic gates Data models

来源：评论

学校读者我要写书评

暂无评论

Sparse Enhancement Fuzzy Broad Learning System Based on Multiple Clustering Methods

Sparse Enhancement Fuzzy Broad Learning System Based on Mult...

引用

2021 International Conference on Robotics Automation and Intelligent Control, ICRAIC 2021

作者： Wang, Jing Lyu, Shubin Duan, Junwei Lin, Zhengchun Faculty of Computer Science Guangdong Polytechnic Normal University Guangzhou China School of Information Science and Technology Jinan University Guangzhou China

The fuzzy broad learning system (FBLS) is a novel, neuro-fuzzy model. Different from other neuro-fuzzy models with low efficiency, FBLS can obtain better performance using less computation time. However, the clustering-based fuzzy rule generation approach makes the performance of FBLS limited. Meanwhile, it is unknown how the enhancement layers from FBLS contribute to the model performance, which hinders the further extension of the model structure. To solve these problems, we propose a sparse enhancement fuzzy broad learning system (SEFBLS). It uses only a sparse set of enhancement nodes to replace the original enhancement node groups. To obtain a better representation, the designed principal component-based sparse autoencoder is used for feature reconstruction and information removal. In addition, to explore the optimal model structure and performance, multiple clustering methods (fuzzy and non-fuzzy) are used to improve SEFBLS. The results on 10 UCI classification datasets show that the proposed SEFBLS obtains competitive accuracy using fewer fuzzy rules. © 2022 Institute of Physics Publishing. All rights reserved.

关键词： Learning systems

来源：评论

学校读者我要写书评

暂无评论

Recognition of Student Engagement and Affective States Using ConvNeXtlarge and Ensemble GRU in E-Learning

Recognition of Student Engagement and Affective States Using...

引用

information and Education technology (ICIET), International Conference on

ISBN: (数字)9798350371772

ISBN: (纸本)9798350371789

The advent of online learning has revolutionized the educational landscape, significantly enhancing global access to and affordability of education. However, a critical challenge in this domain is the efficacy of online classes, particularly in the context of the feedback loop between instructors and students. A pivotal aspect of this challenge is the assessment of student engagement and other affective states in online learning environments, a task that is inherently complex due to its nuanced nature. In this paper, we introduce a novel spatio-temporal hybrid deep learning model that combines the strengths of ConvNeXtLarge with an ensemble of gated recurrent unit (GRU and Bi-GRU) models to detect and classify affective states of students from video data. This model adeptly handles the multi-label, multi-class nature of the problem, employing four parallel fully connected (Dense) layers for effective classification. Our model's performance is evaluated using the DAiSEE dataset, a publicly available resource that captures a range of student affective states in an online learning environment. The proposed model demonstrates promising results in identifying four key affective states of boredom, engagement, confusion, and frustration, with accuracy rates of 54.89%, 56.46%, 70.18%, and 79.32% respectively. These findings underscore the efficacy of our model in accurately gauging student affective states, thereby offering significant insights into the potential of spatio-temporal hybrid networks in this realm. The study demonstrates the feasibility of employing such models to enhance the quality of online education.

关键词： Deep learning Feedback loop Electronic learning Instruments Education Logic gates Feature extraction

来源：评论

学校读者我要写书评

暂无评论

Optimization and Computing Model of Fish Resource Supply Chain Distribution Network 5

Optimization and Computing Model of Fish Resource Supply Cha...

引用

5th International Conference on Computing and Applied Informatics, ICCAI 2020

作者： Nurdin Zarlis, M. Tulus Efendi, S. Graduate Program of Computer Science Faculty of Computer Science and Information Technology Universitas Sumatera Utara Medan Indonesia Department of Computer Science Faculty of Computer Science and Information Technology Universitas Sumatera Utara Medan Indonesia Department of Mathematics Fakultas Matematika Dan Ilmu Pengetahuan Alam Universitas Sumatera Utara Medan Indonesia

Aceh is a province that is rich in fishery resource potential. Fish resources become one of the leading commodities, therefore we need a supply chain optimization model. Optimization is one of the technologies widely used in supply chain network management. Fish resource supply chain becomes very important because it is caused by several factors, including the diversity of types of products from fish resources and their short expiry. The steps used in this research is literature review, determine parameters and decision variables, formulate the objective function and constraint function, modeling and model implementation. The purpose of this research is to get an optimization model in planning and managing the fish resource supply chain by using mixed integer linear programming, so that the resulting model can solve problems in the limitations of fish resources in certain areas, can distribute all consumer demand quickly. This new model can minimize transportation costs to suppliers, minimize transportation costs from suppliers to distribution centers and minimize transportation costs from distribution centers to consumers, and minimize product inventory costs to suppliers and to distribution centers. © Published under licence by IOP Publishing Ltd.

关键词： Supply chains

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：