检索结果-内蒙古大学图书馆

A Survey on Large Language Models for Communication, Network, and Service Management: Application Insights, Challenges, and Future Directions

引用

IEEE Communications Surveys and Tutorials 2025年

作者： Boateng, Gordon Owusu Sami, Hani Alagha, Ahmed Elmekki, Hanae Hammoud, Ahmad Mizouni, Rabeb Mourad, Azzam Otrok, Hadi Bentahar, Jamal Muhaidat, Sami Talhi, Chamseddine Dziong, Zbigniew Guizani, Mohsen Khalifa University KU6G Research Center Department of Computer Science Abu Dhabi United Arab Emirates Ecole de Technologie Superieure Department of Software and IT Engineering Montreal Canada Lebanese American University Artificial Intelligence and Cyber Systems Research Center Department of CSM Beirut Lebanon Concordia University Concordia Institute for Information Systems Engineering MontrealQC Canada Ecole de Technologie Superieure Department of Electrical Engineering Montreal Canada Department of Computer Science Abu Dhabi United Arab Emirates Khalifa University KU6G Research Center Department of Computer and Information Engineering Abu Dhabi United Arab Emirates Carleton University Department of Systems and Computer Engineering OttawaONK1S 5B6 Canada Mohamed Bin Zayed University of Artificial Intelligence Department of ML Abu Dhabi United Arab Emirates

The rapid evolution of communication networks in recent decades has intensified the need for advanced Network and Service Management (NSM) strategies to address the growing demands for efficiency, scalability, enhanced performance, and reliability of these networks. Large Language Models (LLMs) have received tremendous attention due to their unparalleled capabilities in various Natural Language Processing (NLP) tasks and generating context-aware insights, offering transformative potential for automating diverse communication NSM tasks. Contrasting existing surveys that consider a single network domain, this survey investigates the integration of LLMs across different communication network domains, including mobile networks and related technologies, vehicular networks, cloud-based networks, and fog/edge-based networks. First, the survey provides foundational knowledge of LLMs, explicitly detailing the generic transformer architecture, general-purpose and domain-specific LLMs, LLM model pre-training and fine-tuning, and their relation to communication NSM. Under a novel taxonomy of network monitoring and reporting, AI-powered network planning, network deployment and distribution, and continuous network support, we extensively categorize LLM applications for NSM tasks in each of the different network domains, exploring existing literature and their contributions thus far. Then, we identify existing challenges and open issues, as well as future research directions for LLM-driven communication NSM, emphasizing the need for scalable, adaptable, and resource-efficient solutions that align with the dynamic landscape of communication networks. We envision that this survey serves as a holistic roadmap, providing critical insights for leveraging LLMs to enhance NSM. © 1998-2012 IEEE.

关键词： Resource allocation

来源：评论

学校读者我要写书评

暂无评论

A Review of On-Device Machine Learning for Iot: An Energy

SSRN

引用

SSRN 2023年

作者： Tekin, Nazli Aris, Ahmet Acar, Abbas Uluagac, Selcuk Gungor, Vehbi Cagri Cyber-Physical Systems Security Lab Department of Electrical and Computer Engineering Florida International University MiamiFL33174 United States Department of Software Engineering Erciyes University Kayseri38280 Turkey Department of Computer Engineering Abdullah Gul University Kayseri38080 Turkey

Recently, there is a substantial interest in on-device Machine Learning (ML) models to provide intelligence for the Internet of Things (IoT) applications such as image classification, human activity recognition, and anomaly detection. Traditionally, ML models are deployed in the cloud or centralized servers to take advantage of their abundant computational resources. However, sharing data with the cloud and third parties degrades privacy and may cause propagation delay in the network due to a large amount of transmitted data impacting the performance of real-time applications. To this end, deploying ML models on-device (i.e., on IoT devices), in which data does not need to be transmitted, becomes imperative. However, deploying and running ML models on already resource-constrained IoT devices is challenging and requires intense energy consumption. Numerous works have been proposed in the literature to address this issue. Although there are considerable works that discuss energy-aware ML approaches for on-device implementation, there remains a gap in the literature on a comprehensive review of this subject. In this paper, we provide a review of existing studies focusing on-device ML models for IoT applications in terms of energy consumption. One of the key contributions of this study is to introduce a taxonomy to define approaches for employing energyaware on-device ML models on IoT devices in the literature. Based on our review in this paper, our key findings are provided and the open issues that can be investigated further by other researchers are discussed. We believe that this study will be a reference for practitioners and researchers who want to employ energy-aware on-device ML models for IoT applications. © 2023, The Authors. All rights reserved.

关键词： Edge computing

来源：评论

学校读者我要写书评

暂无评论

Enhancing Road Safety with Real-Time Helmet Detection and E-Challan Issuance using YOLO and OCR

Enhancing Road Safety with Real-Time Helmet Detection and E-...

引用

Cognitive Robotics and Intelligent systems (ICC - ROBINS), International Conference on

作者： Deepa S Vinoth Kumar C N S Vivek Duraivelu Balamurugan Easwaran Suguna R Aparna H Department of Computer Technology-PG Kongu Engineering College Erode Tamilnadu Department of Networking and Communications College of Engineering and Technology SRM Institute of Science and Technology Chennai Tamilnadu Department of Computer Science and Engineering B V Raju Institute of Technology Narsapur Telangana India School of IT Texila American University Zambia Africa Department of Computer Science and Business Systems Bannari Amman Institute of Technology Sathyamangalam Tamilnadu M.Sc(Software Systems) Kongu Engineering College Perundurai Erode Tamilnadu

ISBN: (数字)9798350372748

ISBN: (纸本)9798350372755

This study proposes an integrated system combining Optical Character Recognition (OCR) and YOLO (You Only Look Once) for enforcing helmet laws and improving traffic safety. A user-friendly PyQT GUI facilitates vehicle registration, creating an organized CSV database for future reference. YOLOv5, an advanced object detection algorithm, identifies key elements like motorcycles, helmets, and number plates from input images. The primary objective is detecting motorcyclists without helmets, followed by OCR extraction of text from license plates. The extracted text is compared with registered bike information in the CSV file, generating e-challans and fines for matches. Automatic email notifications ensure timely communication and compliance with traffic regulations. The system's implementation not only aids helmet enforcement but also acts as a deterrent for future violations, fostering a safer driving environment. With scalability and practicality, this approach can integrate seamlessly into existing traffic control systems, leveraging AI technologies like YOLOv5 and OCR for effective problem-solving and enhanced road safety.

关键词： YOLO Head Traffic control Road safety Real-time systems Safety Data mining

来源：评论

学校读者我要写书评

暂无评论

Comparative Analysis of the Queueing systems E2/M/1 and M/E2/1 with Shifted Distribution Laws

Comparative Analysis of the Queueing Systems E2/M/1 and M/E2...

引用

2020 IEEE International Conference on Problems of Infocommunications Science and Technology, PIC S and T 2020

作者： Bakhareva, Nadezhda Tarasov, Veniamin Povolzhskiy State University of Telecommunications and Informatics Dept. of Info. and Computer Engineering and Software and Management in Technical Systems Department Samara Russia

ISBN: (纸本)9781728191775

The objective of this work is to the output the solution in closed form for the average waiting time in a queue for queueing systems with shifted Erlangian and exponential input distributions and comparative analysis of their results with the results for conventional systems. As is known from queuing theory, the average waiting time for requirements in a queue is the main characteristic of a queuing system. To solve this problem, we used the classical method of spectral decomposition of the solution of Lindley integral equation, which allows one to obtain a solution for average the waiting time for systems under consideration in a closed form. The method of spectral decomposition of the solution of Lindley integral equation plays an important role in the theory of systems G/G/1. For the practical application of the results obtained, the well-known method of moments of probability theory is used. The introduction of the time shift parameter in the laws of input flow distribution and service time for the systems under consideration turns them into systems with a delay with a shorter waiting time. This is because the time shift operation reduces the coefficient of variation in the intervals between the receipts of the requirements and their service time, and as is known from queuing theory, the average wait time of requirements is related to these coefficients of variation by a quadratic dependence. The obtained results expand and supplement the well-known incomplete formula of the queuing theory for the average waiting time of requirements in the queue for G/G/1 systems. © 2020 IEEE.

关键词： Integral equations

来源：评论

学校读者我要写书评

暂无评论

Hierarchical Continual Learning for Domain-Knowledge Retention in Healthcare Federated Learning

引用

IEEE Transactions on Consumer Electronics 2025年

作者： Iqbal, Saeed Zhong, Xiaopin Khan, Muhammad Attique Wu, Zongze AlHammadi, Dina Abdulaziz Liu, Weixiang Choudhry, Imran Arshad Shenzhen University College of Mechatronics and Control Engineering Shenzhen518060 China Prince Mohammad Bin Fahd University Department of AI Alkhobar Saudi Arabia Princess Nourah bint Abdulrahman University College of Computer and Information Sciences Department of Information Systems Riyadh11671 Saudi Arabia University of Central Punjab Faculty of Information Technology and Computer Science Department of Software Engineering Lahore Pakistan

Internet of Medical Things (IoMT) applications encounter issues with data protection, continual adaptation, and domain-specific knowledge retention, especially in consumer-centric IoMT scenarios. We overcome these obstacles and facilitate effective knowledge retention and task adaptation in IoMT applications. This study attempts to create a unique privacy-preserving federated learning framework that combines a hierarchical learning structure with Continual Learning (CL). Despite the advancements in Federated Learning (FL), current models have trouble integrating changing datasets in real-time while protecting privacy, as well as catastrophic forgetting, which occurs when previously learned knowledge is lost when adjusting to new tasks. We present a hierarchical learning framework that makes use of three levels of models -Junior Model (JM), Consultant Model (CM), and Senior Consultant Model (SCM) -to overcome these drawbacks. Each level of the model aids in archived retention and domain-knowledge adaptation. To guarantee that the model maintains valuable information over time and adapts to new tasks with ease, our method blends domain adaptation strategies with ongoing learning approaches like knowledge distillation and elastic weight consolidation (EWC). We compare the suggested methodology with current state-of-the-art (SOTA) models on healthcare datasets for tasks like illness diagnosis and medical image categorization. According to our findings, the hierarchical continual learning model performs better than SOTA techniques in terms of accuracy, task adaptability, and privacy protection. In the healthcare industry, our study sets a new standard for privacy-preserving, continuously adaptable federated learning systems, allowing for real-time, scalable IoMT applications that can adapt dynamically to a variety of changing datasets. © 1975-2011 IEEE.

关键词： Patient treatment

来源：评论

学校读者我要写书评

暂无评论

Constrained Intelligent Frequency Control in an AC Microgrid: An Online Reinforcement Learning Based PID Tuning Approach

Constrained Intelligent Frequency Control in an AC Microgrid...

引用

IEEE General Meeting Power& Energy Society

作者： K. Nosrati A. Tepljakov E. Petlenkov V. Skiparev J. Belikov Y. Levron Department of Computer Systems Tallinn University of Technology Tallinn Estonia Department of Software Science Tallinn University of Technology Tallinn Estonia Faculty of Electrical & Computer Engineering Technion—Israel Institute of Technology Haifa Israel

Variable output power in isolated microgrids (MGs) threatens frequency stability and may even degrade power quality. In response, intelligent control methods have been developed and applied to frequency deviation control systems with excellent results. Nevertheless, a potential problem is that the application of such advanced techniques with a large search space is not enough to deal with highly dynamic environment and real-time operations of MGs. In this light, the present study introduces a flexible artificial neural network (ANN)-based frequency deviation control solution in a constrained structure that operates as follows. First, the stable controller parameter space of the PID-based AC microgrid is derived by using the stability boundary locus method. Then, the controller parameters are tuned and updated online by searching for an optimal combination of the coefficients with consideration of output variations sensed by a constrained ANN in the derived reduced parameter space. To accomplish this step, a reinforcement learning technique is applied to train the ANN-based tuners. The performance of the proposed technique has been verified under a given scenario to demonstrate how the reduced parameter space should facilitate the optimization procedure.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Designing and simulating IoT environments by using a model-driven approach*

Designing and simulating IoT environments by using a model-d...

引用

Iberian Conference on Information systems and Technologies (CISTI)

作者： José A. Barriga Pedro J. Clemente Department of Computer and Telematic Systems Engineering Quercus Software Engineering Group Universidad de Extremadura C&#x00E1 ceres Spain

Developing, deploying and testing IoT projects require high investments on devices, fog nodes, cloud nodes, analytic nodes, hardware and software. However, in order to decrease the cost associated to develop and test the IoT system it can be previously simulated. In this regard, designing IoT simulation environments has been tackled focusing on low level aspects such as networks, motes and so on more than focusing on the high level concepts related to IoT environments. Model-driven development aims to develop the software systems from domain models which capture at high level the domain concepts and relationships, generating from them the software artefacts by using code-generators. In this paper, a model-driven development approach is applied to define, generate code and deploy IoT systems simulations. The IoT simulation environment generated from each model includes the sensors, actuators, fog nodes, cloud nodes and analytical characteristics. Additionally, a case study, focused on an Industrial IoT environment is presented to show the simulation expressiveness.

关键词： Analytical models Codes Focusing computer architecture Sensor phenomena and characterization software systems systems simulation

来源：评论

学校读者我要写书评

暂无评论

Using transformers and Bi-LSTM with sentence embeddings for prediction of openness human personality trait

引用

PeerJ computer Science 2025年 11卷 1-42页

作者： Naz, Anam Khan, Hikmat Ullah Alsahfi, Tariq Alhajlah, Mousa Alshemaimri, Bader Daud, Ali Department of Information Technology University of Sargodha Punjab Sargodha Pakistan Department of Information Systems and Technology College of Computer Science and Engineering University of Jeddah Jeddah Saudi Arabia Applied of Computer Science College King Saud University Riyadh Saudi Arabia Software Engineering Department College of Computing and Information Sciences King Saud University Riyadh Saudi Arabia Faculty of Resilience Rabdan Academy Abu Dhabi United Arab Emirates

Understanding human personality traits is significant as it helps in decision making related to consumers’ behavior, career counselling, team building and top candidates’ selection for recruitment. Among various traits, openness is essential as it shows both diverse aspects of sensitive nature or intuitive nature. The individuals having a sensing nature tends to be more practical and prefer to focus on concrete information whereas the users having intuitive trait type is characterized by a focus on abstract ideas, creative thinking and future-oriented perspectives. In this research work, we aim to explore diverse natural language processing (NLP) based features and apply state of the art deep learning algorithms for openness trait prediction. Using standard Myers-Briggs Type Indicator (MBTI) dataset, we propose the use of the latest deep features of sentence embeddings which captures contextual semantics of the content to be used with deep learning models. For comparison, we explore textual features of Frequency-Inverse Document (TF-IDF) and parts of speech (POS) tagging with machine learning models and deep features of word2vec and global vectors for word representation (GloVe) with deep learning models. The comprehensive empirical analysis reveals that TF-IDF used with gradient boosting achieves high accuracy of 90% whereas, the deep feature of sentence embeddings when used and with deep model bidirectional long short-term memory (Bi-LSTM) achieves 90.5% accuracy. The best results have been achieved using the latest Transformer-based DistilBERT, which achieves the highest accuracy of 92% outperforming the existing studies in relevant literature. Subjects Human-computer Interaction, Artificial Intelligence, Data Mining and Machine Learning, Natural Language and Speech © 2025 xxxx

关键词： Deep learning

来源：评论

学校读者我要写书评

暂无评论

Examining the Use of Non-fungible Tokens (NFTs) as a Trading Mechanism for the Metaverse 1

引用

29th European systems, software and Services Process Improvement, EuroSPI 2022

作者： Yilmaz, Murat Hacaloğlu, Tuna Clarke, Paul Department of Computer Engineering Engineering Faculty Gazi University Ankara Turkey Metaverse Research and Development Laboratory Gazi University Ankara Turkey Department of Information Systems Engineering Atilim University Ankara Turkey School of Computing Dublin City University Dublin Ireland Lero the Science Foundation Ireland Research Centre for Software Limerick Ireland

ISBN: (数字)9783031155598

ISBN: (纸本)9783031155581

The notion of a metaverse seems hard to define but encourages the impression that it can be considered as a new virtual metaphysical landscape that somehow goes beyond our geographical locations and understanding (i.e., independent of time and space). Based on virtual reality, augmented reality, and blockchain, it is envisioned as an independent but extended world that is planned to be a digital virtuality entrenched not only in our old habits such as gaming and entertainment but also in virtual asset trade. In particular, trading is a pillar of the virtual economy, and auction houses will be crucial for Metaverse trading. This exploratory study examines the possibility of using an auction environment to improve the trading capabilities in a virtual universe. We investigate the cases of creating a virtual auction house with the potential of social trading of virtual assets with crypto coins and bartering. To this end, we built a virtual auction house and tested it initially using a set of scenarios. Our preliminary findings suggest that creating a virtual trading environment would be beneficial as an environment for buying and selling virtual assets and exploring their consequences. © 2022, Springer Nature Switzerland AG.

关键词： Augmented reality

来源：评论

学校读者我要写书评

暂无评论

Enhanced Student Stress Prediction in Higher Education Using a TriBranch CNN Framework 3

Enhanced Student Stress Prediction in Higher Education Using...

引用

3rd International Conference on Integrated Circuits and Communication systems, ICICACS 2025

作者： Kunwar, Fateh Bahadur Srivastava, Satyajee Dhivya, R. Gulati, Monika Amudha, K. Raj, D. Antony Arul Geetanjali Institute of Technical Studies Department of Computer Science and Engineering Udaipur India School of Engineering and Technology Manav Rachna International Institute of Research and Studies Department of Computer Science and Engineering Faridabad India M.Kumarasamy College of Engineering Department of Information Technology Karur India Mullana Ambala India Kongunadu College of Engineering and Technology Department of Electronics and Communication Engineering Trichy India Psg College of Arts & Science Department of Software Systems Coimbatore India

ISBN: (纸本)9798331508456

Students' stress levels have a significant impact on their academic performance in higher education, thus it's important to identify signs of stress early on and intervene to improve academic performance. Machine learning has evolved into a powerful tool for stress prediction, allowing for proactive support of at-risk youngsters. In order to better anticipate college students' stress levels, this study presents an enhanced TriBranch CNN framework. In order to provide the best possible performance, the technique includes data preparation, feature extraction, and model training. The dimensionality and redundancy are reduced using Principal Component Analysis (PCA). After being trained and tested against other models, the Fully Conditional Temporal Bayesian Network (Fully CTBN) outperformed them all with an accuracy of 93.29%, beating both traditional CNN and FCN approaches. These findings demonstrate that the Fully CTBN approach is useful for identifying students who may be struggling academically and providing schools with the information they need to implement data-driven interventions that boost student well-being and performance. © 2025 IEEE.

关键词： Students

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：