检索结果-内蒙古大学图书馆

Fuzzy Q-learning approach for autonomic resource provisioning of IoT applications in fog computing environments

Journal of Ambient Intelligence and Humanized Computing 2023年第4期14卷 4237-4255页

作者： Faraji-Mehmandar, Mohammad Jabbehdari, Sam Javadi, Hamid Haj Seyyed Department of Computer Engineering Parand Branch Islamic Azad University Tehran Iran Department of Computer Engineering North Tehran Branch Islamic Azad University Tehran Iran Department of Mathematics and Computer Science Shahed University Tehran Iran

The dramatic growth of smart devices and the Internet of things (IoT) has increased the volume of exchanges and data on the web. The centralized and traditional architecture of cloud computing does not meet the demands of users and proper implementation of latency-sensitive applications due to latency and mass demands of IoT applications that have different needs compared to existing applications. As a result, edge computing has been presented for collecting and processing of data generated by these objects, which facilitates data processing with low latency and close to users at the edge of the network. Its main purpose is to bring computational resources and storage close to the end-user on the network. As far as the storage capacity of fog nodes is limited, the proper use of fog node resources significantly influences their performance. In this paper, a framework based on control MAPE-K loop has been used to obtain the optimal state in workload balance. The users’ workload forecasting model is a combination of linear regression and support vector regression methods and offers better performance compared to the conventional reactive self-assessment methods. In the planning phase, a fuzzy self-learning algorithm is used to determine the automated scale of resource provisioning policy. By comparing three criteria of load delay, cost, and amount of consumed energy in the proposed method and recent works, the proposed method has been able to balance all three criteria optimally. © 2023, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Internet of things

来源：评论

学校读者我要写书评

暂无评论

Spatio-temporal Graph-Based Generation and Detection of Adversarial False Data Injection Evasion Attacks in Smart Grids

IEEE Transactions on Artificial Intelligence

引用

IEEE Transactions on Artificial Intelligence 2024年第12期5卷 6601-6616页

作者： Takiddin, Abdulrahman Ismail, Muhammad Atat, Rachad Serpedin, Erchin Florida State University Department of Electrical and Computer Engineering FAMU-FSU College of Engineering TallahasseeFL32310 United States Tennessee Tech University Department of Computer Science CookevilleTN38505 United States Lebanese American University Department of Computer Science and Mathematics Beirut1102-2801 Lebanon Texas A&M University Department of Electrical and Computer Engineering College StationTX77843 United States

Smart power grids are vulnerable to security threats due to their cyber-physical nature. Existing data-driven detectors aim to address simple traditional false data injection attacks (FDIAs). However, adversarial false data injection evasion attacks (FDIEAs) present a more serious threat as adversaries, with different levels of knowledge about the system, inject adversarial samples to circumvent the grid's attack detection system. The robustness of state-of-the-art graph-based detectors has not been investigated against sophisticated FDIEAs. Hence, this article answers three research questions. 1) What is the impact of utilizing spatio-temporal features to craft adversarial samples and how to select attack nodes? 2) How can adversaries generate surrogate spatio-temporal data when they lack knowledge about the system topology? 3) What are the required model characteristics for a robust detection against adversarial FDIEAs? To answer the questions, we examine the robustness of several detectors against five attack cases and conclude the following: 1) Attack generation with full knowledge using spatio-temporal features leads to 5%-26% and 2%-5% higher degradation in detection rate (DR) compared to traditional FDIAs and using temporal features, respectively, whereas centrality analysis-based attack node selection leads to 3%-11% higher degradation in DR compared to a random selection;2) Stochastic geometry-based graph generation to create surrogate adversarial topologies and samples leads to 3%-13% higher degradation in DR compared to traditional FDIAs;and 3) Adopting an unsupervised spatio-temporal graph autoencoder (STGAE)-based detector enhances the DR by 5-53% compared to benchmark detectors against FDIEAs. © 2020 IEEE.

关键词： Cyber attacks

来源：评论

学校读者我要写书评

暂无评论

A Comprehensive Literature Review on AI-Assisted Multimodal Triage Systems for Health Centers 16

A Comprehensive Literature Review on AI-Assisted Multimodal ...

引用

16th International Conference on Ambient Systems, Networks and Technologies Networks, ANT 2025 / 8th International Conference on Emerging Data and Industry 4.0, EDI40 2025

作者： Araouchi, Ziad Adda, Mehdi Mathematics Computer Science and Engineering Department University of Quebec at Rimouski QC Canada

Artificial intelligence (AI) is increasingly recognized as a transformative tool in emergency department (ED) triage. Traditional triage methods, such as the Emergency Severity Index (ESI) and the Canadian Triage and Acuity Scale (CTAS), prioritize patient care based on acuity but face challenges, including subjectivity, overcrowding, and inefficient resource allocation. AI offers enhanced predictive accuracy, optimized patient prioritization, and reduced human error. This review examines the evolution of triage systems from conventional to AI-assisted models, highlighting advancements and limitations of AI in clinical practice. Recent findings underscore AI's potential to improve diagnostic precision and streamline ED workflows. However, critical concerns include data dependency, ethical challenges, and variable performance across healthcare settings. © 2025 Elsevier B.V.. All rights reserved.

关键词： Electrotherapeutics

来源：评论

学校读者我要写书评

暂无评论

CHAmbi: A New Benchmark on Chinese Ambiguity Challenges for Large Language Models

CHAmbi: A New Benchmark on Chinese Ambiguity Challenges for ...

引用

2024 Findings of the Association for Computational Linguistics, EMNLP 2024

作者： Zhang, Qin Cai, Sihan Zhao, Jiaxu Pechenizkiy, Mykola Fang, Meng College of Computer Science and Software Engineering Shenzhen University China Department of Mathematics and Computer Science Eindhoven University of Technology Netherlands Department of Computer Science University of Liverpool United Kingdom

ISBN: (纸本)9798891761681

Ambiguity is an inherent feature of language, whose management is crucial for effective communication and collaboration. This is particularly true for Chinese, a language with extensive lexical-morphemic ambiguity. Despite the wide use of large language models (LLMs) in numerous domains and their growing proficiency in Chinese, there is a notable lack of datasets to thoroughly evaluate LLMs' ability to handle ambiguity in Chinese. To bridge this gap, we introduce the CHAmbi dataset, a specialized Chinese multi-label disambiguation dataset formatted in Natural Language Inference. It comprises 4,991 pairs of premises and hypotheses, including 824 examples featuring a wide range of ambiguities. In addition to the dataset, we develop a series of tests and conduct an extensive evaluation of pre-trained LLMs' proficiency in identifying and resolving ambiguity in the Chinese language. Our findings reveal that GPT-4 consistently delivers commendable performance across various evaluative measures, albeit with limitations in robustness. The performances of other LLMs, however, demonstrate variability in handling ambiguity-related tasks, underscoring the complexity of such tasks in the context of Chinese. The overall results highlight the challenge of ambiguity handling for current LLMs and underscore the imperative need for further enhancement in LLM capabilities for effective ambiguity resolution in the Chinese language. © 2024 Association for Computational Linguistics.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Comprehensive Literature Review on Large Language Models and Smart Monitoring Devices for Stress Management 16

Comprehensive Literature Review on Large Language Models and...

引用

16th International Conference on Ambient Systems, Networks and Technologies Networks, ANT 2025 / 8th International Conference on Emerging Data and Industry 4.0, EDI40 2025

作者： Wahab, Othman Adda, Mehdi Mathematics Computer Science and Engineering Department University of Quebec at Rimouski QC Canada

This review evaluates the complementary roles of Large Language Models (LLMs) and smart monitoring devices in stress management, aiming to identify synergies for improved mental health solutions. It explores the evolution of LLM architectures (Encoder-Decoder, Encoder-Only, Decoder-Only, and Mixture of Experts), emphasizing their strengths in personalized interventions. Simultaneously, it examines physiological monitoring technologies, ranging from traditional biometric sensors to AI-enhanced platforms, focusing on bioelectrical signals (ECG, EEG, EMG, GSR) and optical measurements (PPG, BVP, SpO2). By detailing the principles, devices, and stress monitoring applications of these modalities, the review uncovers opportunities for integration. The findings provide a foundation for future research into AI-driven, multimodal stress management systems, promoting more effective and accessible mental health support. © 2025 Elsevier B.V.. All rights reserved.

关键词： mHealth

来源：评论

学校读者我要写书评

暂无评论

Comprehensive Review of Physiological Signal-Based Emotion Recognition: Methods, Challenges, and Insights on Arousal and Valence Dimensions 16

Comprehensive Review of Physiological Signal-Based Emotion R...

引用

16th International Conference on Ambient Systems, Networks and Technologies Networks, ANT 2025 / 8th International Conference on Emerging Data and Industry 4.0, EDI40 2025

作者： Kacimi, Yassir Adda, Mehdi Mathematics Computer Science and Engineering Department University of Quebec at Rimouski QC Canada

This review examines emotion recognition from physiological signals, focusing on processing methods and classification techniques. Key aspects include signal acquisition challenges, emotion elicitation strategies, and feature extraction methods across time, frequency, and time-frequency domains. The analysis highlights the strengths of these methods in capturing emotional information from signals such as ECG, GSR, RSP, SKT, BVP, EMG, and EOG. Feature selection and dimensionality reduction are explored regarding their role in optimizing the feature space for classification. The review also evaluates machine learning approaches and their applications in emotion recognition. This work addresses current capabilities, limitations, and emerging trends and provides a comprehensive overview of physiological signal-based emotion recognition. © 2025 Elsevier B.V.. All rights reserved.

关键词： Pulse modulation

来源：评论

学校读者我要写书评

暂无评论

Innovative Video Classification Method based on Deep Learning Approach

引用

Recent Patents on engineering 2025年第2期19卷 95-107页

作者： Hemamalini, V. Jayasutha, D. Vinothini, V.R. Devi, R. Manjula Kumar, A. Anitha, E. Department of Networking and Communications School of Computing SRM Institute of Science and Technology Chennai India Department of Computer Science and Engineering Arjun College of Technology Coimbatore Thamaraikulam India Department of Mathematics Bannari Amman Institute of Technology Sathyamangalam Erode638401 India Department of Computer Science and Design Kongu Engineering College Perundurai Erode638060 India Department of Civil Engineering University Institute of Engineering & Technology Maharshi Dayanand University Rohtak124001 India Department of Artificial Intelligence and Data Science Sri Eshwar College of Engineering Coimbatore641202 India

Background: The automated classification of videos through artificial neural networks is addressed in this work. To explore the concepts and measure the results, the data set UCF101 is used, consisting of video clips taken from YouTube to recognize actions. The study is carried out with the authors' resources to determine the feasibility of independent research in the area. Methods: This work was developed in the Python programming language using the Keras library with Tensorflow as the back-end. The objective is to develop a network that presents performance compatible with the state of the art in terms of classifying videos according to the actions taken. Results: Given the hardware limitations, there is considerable distance between the implementation possibilities in this work and what is known as the state-of-the-art. Conclusion: Throughout the work, some aspects in which this limitation influenced the development are presented, but it is shown that this realization is feasible and that obtaining expressive results is possible 98.6% accuracy is obtained in the UCF101 data set, compared to the 98 percentage points of the best result ever reported, using, however, considerably fewer resources. In addition, the importance of transfer learning in achieving expressive results as well as the different performances of each architecture are reviewed. Thus, this work may open doors to carry patent-based outcomes. © 2025 Bentham science Publishers.

关键词： Convolutional neural networks

来源：评论

学校读者我要写书评

暂无评论

Enhancing biometric authentication security through the novel integration of graph theory encryption and chaotic logistic mapping

引用

Multimedia Tools and Applications 2025年第16期84卷 16909-16943页

作者： El-Shafai, Walid El-Mesady, A. Kamal, F.M. Security Engineering Lab Computer Science Department Prince Sultan University Riyadh11586 Saudi Arabia Department of Electronics and Electrical Communications Engineering Faculty of Electronic Engineering Menoufia University Menouf32952 Egypt Department of Physics and Engineering Mathematics Faculty of Electronic Engineering Menoufia University Menouf32952 Egypt Mathematics and Engineering Physics Department Faculty of Engineering Mansoura University PO 35516 Mansoura Egypt

The escalating reliance on biometric systems for identity verification underscores the imperative for robust data protection mechanisms. Biometric authentication, leveraging unique biological and behavioral characteristics, offers unparalleled precision in individual identification. However, the integrity and confidentiality of biometric data remain paramount concerns, given its susceptibility to compromise. This research delineates the development and implementation of an innovative framework for cancellable biometrics, focusing on facial and fingerprint recognition. This study introduces a novel cancellable biometrics framework that integrates graph theory encryption with three-dimensional chaotic logistic mapping. The methodology encompasses a multifaceted approach: initially employing graph theory for the secure and efficient encryption of biometric data, subsequently enhanced by the complexity and unpredictability of three-dimensional chaotic logistic mapping. This dual-layered strategy ensures the robustness of the encryption, thereby significantly elevating the security of biometric data against unauthorized access and potential compromise. Thus, the resulting cancellable biometrics, characterized by the ability to transform biometric data into an adjustable representation, addresses critical challenges in biometric security. It allows for the revocation and reissuance of biometric credentials, thereby safeguarding the original biometric characteristics of individuals. This feature not only enhances user privacy and data security but also introduces a dynamic aspect to biometric authentication, facilitating adaptability across diverse systems and applications. Preliminary evaluations of the proposed framework demonstrate a marked improvement in the security of face and fingerprint recognition systems. Through the application of graph theory encryption, coupled with three-dimensional chaotic logistic mapping, our framework mitigates the risks associated with t

关键词： Cryptography

来源：评论

学校读者我要写书评

暂无评论

MOPSO-driven optimization for sustainable retrofitting: balancing time, cost, and environmental impacts

引用

Asian Journal of Civil engineering 2025年第5期26卷 2165-2178页

作者： Singh, Rekha Sangeeta, Viswanadham Manjunath, T.C. Singh, Prachi Bishoyi, Sasanka Sekhar Pati, Aditya Kumar Department of Civil Engineering Raj Kumar Goel Institute of Technology Ghaziabad India Computer Science and Engineering GITAM Deemed to Be University Visakhapatnam India Department of Computer Science & Engineering Rajarajeswari College of Engineering Bengaluru India Department of Civil Engineering Madhav Institute of Technology and Science Gwalior India Department of Mathematics NIST University Berhampur India Department of Mathematics Centurion University of Technology and Management Gajapati R. Sitapur India

Retrofitting projects play a critical role in enhancing the sustainability of existing structures, yet balancing time, cost, and environmental impact remains a significant challenge for decision-makers. This study introduces a Multi-Objective Particle Swarm Optimization (MOPSO) approach to achieve optimal trade-offs among these competing objectives. By leveraging MOPSO’s capability to explore Pareto-efficient solutions, the research provides a robust framework for sustainable decision-making in retrofitting projects. The model evaluates project scenarios based on key metrics such as time efficiency, cost minimization, and reduced carbon emissions. Through the application of MOPSO, the study generates a range of viable solutions, enabling project managers to make informed decisions tailored to specific sustainability goals. A case study is conducted to validate the model’s effectiveness, comparing its performance with conventional optimization techniques. The results demonstrate that MOPSO excels in balancing multiple objectives, delivering superior outcomes in sustainability metrics. This research contributes to the advancement of sustainable construction practices by offering a practical tool for optimizing retrofitting projects in alignment with environmental and economic priorities. The findings provide valuable insights for practitioners seeking to integrate sustainability into decision-making processes, addressing the pressing need for environmentally conscious infrastructure development. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2025.

关键词： MOPSO Multi Objective optimization Sustainable construction practices Sustainable retrofitting Time-cost-environmental trade-off

来源：评论

学校读者我要写书评

暂无评论

Harnessing Large Language Models for Precision Topic Extraction and Technology Patent Nomination: A GPT-centric Methodology 16

Harnessing Large Language Models for Precision Topic Extract...

引用

16th International Conference on Ambient Systems, Networks and Technologies Networks, ANT 2025 / 8th International Conference on Emerging Data and Industry 4.0, EDI40 2025

作者： Nkolongo, Franck Tshibanda Echchakoui, Said Mehdi, Adda Department of Mathematics Computer Science and Engineering University of Quebec at Rimouski Canada Canada

Topic modeling is widely used for analyzing large textual datasets, particularly in technology patent nomination. Traditional methods, such as Latent Dirichlet Allocation (LDA) and Non-Negative Matrix Factorization (NMF), often struggle with semantic comprehension, scalability, and adaptability. This study investigates Large Language Models (LLMs) to overcome these limitations. Leveraging their advanced language understanding, GPT models generate coherent and contextually relevant topics, outperforming conventional methods. They also excel in identifying emerging trends, enhancing patent nomination processes. Our findings demonstrate the potential of GPT-based approaches to streamline patent analysis and accelerate technological discovery. © 2025 Elsevier B.V.. All rights reserved.

关键词： Non-negative matrix factorization

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：