检索结果-内蒙古大学图书馆

Efficient Cache Retrieving Scheme in VANET via Online Reinforcement Learning

IEEE Transactions on Vehicular technology 2025年第6期74卷 9506-9519页

作者： Tan, Zhenhao Chen, Bin Li, Ruidong Yang, Lei Xu, Xiaohua University of Science and Technology of China School of Computer Science and Technology Hefei230026 China Kanazawa University Institute of Science and Engineering Ishikawa920-1192 Japan South China University of Technology School of Software Engineering Guangdong510640 China

The implementation of intelligent driving technology is inseparable from reliable wireless communication and efficient cache scheduling in Vehicle Ad-hoc Networks (VANETs). The purpose of cache scheduling in VANETs is to provide efficient and fast data services to vehicles. In response to difficult to handle wireless communication interference and high requirements for communication delay and energy consumption optimization in vehicle-road cooperation, we propose a new communication model of "Retrieving-Cast"to simplify the cache scheduling process oriented by data requirements. In V2I mode, consider the uplink of all roadside units(RSUs) retrieving data from vehicles, and prioritize data retrieving based on factors such as the number, density, and urgency of vehicles within the communication coverage range of RSUs. We hope that as many high-priority RSUs as possible to complete cached data retrieving as soon as possible under wireless communication interference. To overcome the limitations of traditional heuristic algorithms with low scheduling efficiency and deep learning methods requiring a large amount of training data, we propose a double-online reinforcement learning method, which efficiently solves the cache retrieval scheduling problem. Through experiments and analysis, our method achieved better scheduling results compared to traditional approximation algorithms and single greedy Deep Q-learning methods. The designed data transformation method also addresses the issue of requiring a large amount of training data, enabling rapid computation of scheduling requests in the network at an extremely low training cost, meeting the low-latency computation requirements in VANETs. © 1967-2012 IEEE.

关键词： Reinforcement learning

来源：评论

学校读者我要写书评

暂无评论

Challenge Response Protocol using Multivariate Polynomial Cryptography

Challenge Response Protocol using Multivariate Polynomial Cr...

引用

2025 IEEE International Students' Conference on Electrical, Electronics and computer science, SCEECS 2025

作者： Jaiswal, Chandra Keshwar Sharma, Arpit Chaturvedi, Atul Allenhouse Institute of Technology Computer Science and Engineering Kanpur India

ISBN: (纸本)9798331529833

The objective of this paper is to define the meaning of a multivariate public key using two or more multivariate encrypted parties A and B which targets to improve the existing multivariate public key encryption. Two polynomials are created by a central authority for the protocol's setup phase. The client generates a public key using a secret key, then uses his secret key to respond to a challenge the server issues and submits it to the server. The response is hashed to ensure its security. Any design, including ones with physically unclonable functions, can use this multivariate polynomial cryptography scheme since it is appropriate for post-quantum computing and quantum resistant encryption. This protocol, which protects the CCA technique of post-quantum communications, validated the promise of multivariate polynomials for information security. The work makes an effort to portray polynomial implementation and polynomial contribution as security-sensitive jobs. © 2025 IEEE.

关键词： Quantum computers

来源：评论

学校读者我要写书评

暂无评论

Enhancing Mental Health Support: An LLM-Based Prompt engineering Method 8th

Enhancing Mental Health Support: An LLM-Based Prompt Enginee...

引用

8th World Conference on Smart Trends in systems Security and Sustainability, WorldS4 2024

作者： Kaysar, Md Nadim Shiramatsu, Shun Computer Science & Engineering Nagoya Institute of Technology Nagoya Japan

ISBN: (纸本)9789819793266

Mental health disorders are conditions of the mind that cause changes in emotion, thought, and behavior. It can be connected with distress and problems functioning in social, professional, or family activities. This study tries to address this issue and improve the user’s mental health situation by using the proposed LLM-based Agent system. Moreover, this research study tries to determine the user’s current situation through text-based conversation and provide suggestions to improve his mental health problems. Therefore, we developed this system by using LLM-based nCoT (nested Chain of Thought) prompt engineering. In the result section, we sequentially obtained the following: a patient goal achievement score (Average: 5.42), patient Satisfaction score (Mean: 5.57), system response Correctness score (Mean: 6.09), and system Engagement Score (Mean: 6.16). In addition, we analyzed the based CTRS and we obtained all item’s averages (Mean: 5.70), and the standard deviation (Mean: 0.91). Moreover, a system usability scale framework was used for the analysis of the usability (Mean Score: 3.60) of our proposed system. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Chatbot CoT Dialogue system LLM Mental Health nCoT

来源：评论

学校读者我要写书评

暂无评论

Falcon Optimization Algorithm-Based Energy Efficient Communication Protocol for Cluster-Based Vehicular Networks

引用

computers, Materials & Continua 2024年第3期78卷 4243-4262页

作者： Youseef Alotaibi B.Rajasekar R.Jayalakshmi Surendran Rajendran Department of Software Engineering College of ComputingUmm Al-Qura UniversityMakkah21955Saudi Arabia Department of Electronics and Communication Engineering Sathyabama Institute of Science and TechnologyChennai600119India Department of Computer Science and Engineering Panimalar Engineering CollegeChennai600123India Department of Computer Science and Engineering Saveetha School of EngineeringSaveetha Institute of Medical and Technical ScienceChennai602105India

Rapid development in Information technology(IT)has allowed several novel application regions like large outdoor vehicular networks for Vehicle-to-Vehicle(V2V)*** networks give a safe and more effective driving experience by presenting time-sensitive and location-aware *** communication occurs directly between V2V and Base Station(BS)units such as the Road Side Unit(RSU),named as a Vehicle to Infrastructure(V2I).However,the frequent topology alterations in VANETs generate several problems with data transmission as the vehicle velocity differs with ***,the scheme of an effectual routing protocol for reliable and stable communications is *** research demonstrates that clustering is an intelligent method for effectual routing in a mobile ***,this article presents a Falcon Optimization Algorithm-based Energy Efficient Communication Protocol for Cluster-based Routing(FOA-EECPCR)technique in *** FOA-EECPCR technique intends to group the vehicles and determine the shortest route in the *** accomplish this,the FOA-EECPCR technique initially clusters the vehicles using FOA with fitness functions comprising energy,distance,and trust *** the routing process,the Sparrow Search Algorithm(SSA)is derived with a fitness function that encompasses two variables,namely,energy and distance.A series of experiments have been conducted to exhibit the enhanced performance of the FOA-EECPCR *** experimental outcomes demonstrate the enhanced performance of the FOA-EECPCR approach over other current methods.

关键词： Vehicular networks communication protocol clustering falcon optimization algorithm routing

来源：评论

学校读者我要写书评

暂无评论

The Power of Virtual Reality-Next-Gen Radiology Training 15

The Power of Virtual Reality-Next-Gen Radiology Training

引用

15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Ahmed, Alisha Rajarajeswari, S. Mallegowda, M. Subash, N. Ramaiah Institute of Technology Computer Science Engineering India

ISBN: (纸本)9798350370249

Advancements in medical education are continuously evolving, with virtual reality (VR) emerging as a groundbreaking technology reshaping the landscape of radiology training. This abstract presents a paradigm shift in radiology education through the creation of immersive VR simulations tailored for medical students and professionals. Our VR simulation leverages state-of-the-art graphics, interactive modules, and realistic patient scenarios to provide a dynamic learning environment. Users can navigate through virtual clinics, interact with lifelike patients, and practice diagnostic procedures in a risk-free yet lifelike setting. The incorporation of haptic feedback and spatial audio enhances realism, fostering deeper engagement and skill acquisition. Furthermore, our VR simulation integrates artificial intelligence algorithms for personalized learning pathways, adapting to individual learning styles and providing real-time feedback. By harnessing the power of VR, this simulation not only enhances diagnostic skills but also promotes critical thinking, teamwork, and decision-making under pressure. This abstract explores the transformative impact of VR simulations in revolutionizing radiology education, paving the way for a new era of experiential learning in medical training. © 2024 IEEE.

关键词： Radiology

来源：评论

学校读者我要写书评

暂无评论

A Scalable and Secure Facial Recognition Attendance System Leveraging CNN Algorithms for Real-Time Accuracy and Efficiency

A Scalable and Secure Facial Recognition Attendance System L...

引用

2025 IEEE International Students' Conference on Electrical, Electronics and computer science, SCEECS 2025

作者： Kumar, Aakash Sharma, Jiya Renjith, P.N. Vellore Institute of Technology School of Computer Science and Engineering India

ISBN: (纸本)9798331529833

The facial recognition attendance system represents a modern approach to attendance tracking, designed to improve efficiency, accuracy, and security in various organizational settings. Advanced biometric technologies are leveraged to capture and encode facial features, creating unique digital identifiers for each individual. The methodology employs cutting-edge facial recognition algorithms powered by convolutional neural networks (CNNs) with a 99.5% recognition accuracy rate. These algorithms extract and encode facial vectors with sub-millisecond latency, which are then compared in real-time against a secure, encrypted database of preregistered individuals. Upon a successful match, the system automatically records the attendance, ensuring an accurate and efficient process. With an intuitive web interface built on ReactJS and a backend supported by *** and MongoDB, the system provides seamless access to attendance records and administrative functionalities. The entire attendance recording process is optimized for speed, typically completing within two seconds per individual. Additionally, advanced anti-spoofing measures and integration support with existing HR management systems redefine traditional attendance management practices with a scalable, robust, and secure solution. (Abstract) © 2025 IEEE.

关键词： Efficiency

来源：评论

学校读者我要写书评

暂无评论

Efficient Text Compression Algorithms: Principles, Performance, and Applications 5

Efficient Text Compression Algorithms: Principles, Performan...

引用

5th International Conference on Intelligent Communication Technologies and Virtual Mobile Networks, ICICV 2024

作者： Sriman, B. Stephi, Adlin Annie Silviya, S.H. Sangeetharani, M. Shreya, B. Sivakumar, S. Rajalakshmi Institute of Technology Department of Computer Science and Engineering Chennai India Sree Sastha Institute of Engineering and Technology Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350385649

Textual data is a fundamental element of human communication and information exchange, playing a pivotal role in a wide array of applications across various domains. However, the digital age has ushered in an era of unprecedented data growth, resulting in the need for efficient storage, transmission, and processing of vast amount of text. Lossless text compression techniques emerge as a vital solution to this challenge, offering the ability to compactly represent textual content without compromising any information. This research study explores the lossless text compression algorithms by investigating the underlying principles, performance, and applications. © 2024 IEEE.

关键词： Digital storage

来源：评论

学校读者我要写书评

暂无评论

WGSDMM+GA: A genetic algorithm-based service clustering methodology assimilating dirichlet multinomial mixture model with word embedding

引用

Future Generation computer systems 2023年第1期145卷 254-266页

作者： Agarwal, Neha Sikka, Geeta Awasthi, Lalit Kumar Computer Science and Engineering Department Indian Institute of Information Technology Karnataka Raichur584135 India Department of Computer Science and Engineering National Institute of Technology Delhi Delhi110036 India 246174 India

Advancement of web 2.0 results in the expeditious growth of services in repositories and service portals, which raises the demand for service management. With the clusters of services, different processes like discovery, selection, ranking, etc., can be expedited efficiently. K-Means clustering is highly recommended for service clustering but it can be stuck in the local optimum values. Developers generally use short text to describe the functionality of Web services. For effective service clustering, it is required that relevant features of services are mapped in vector space based on semantic relations. Gibbs Sampling algorithm for Dirichlet Multinomial Mixture model (GSDMM) model is able to provide good performance on short text because it works on the assumption that there will be one topic related to one document. In this paper, a novel technique for web service clustering, i.e., WGSDMM+GA, is proposed in which relevant features, semantic relations, and their mappings in vector space are analyzed based on an assimilated approach of Word2Vec and GSDMM models. As K-means clustering cannot provide effective results due to local optima problem, GA-based clustering is applied in which fitness function is based on Manhattan distance. The proposed model is applied to five real-time datasets, and the performance is evaluated based on standard evaluation measures. Experimental results illustrate that WGSDMM+GA yields better results, and the accuracy score is increased by 23%, 17%,17%, 21%, and 25% on five real-time datasets. © 2023 Elsevier B.V.

关键词： Genetic algorithms

来源：评论

学校读者我要写书评

暂无评论

Soundtrack Success: Unveiling Song Popularity Patterns Using Machine Learning Implementation

引用

SN computer science 2024年第3期5卷 278页

作者： Arora, Shruti Rani, Rinkle Department of Computer Science and Engineering Thapar Institute of Engineering and Technology Punjab Patiala India

Music streaming services are getting increasingly popular as a result of the prevalent need for web and smart gadgets. Melophiles are drawn toward a range of musical genres and create a unique digital footprint. The emotional reactions of the music listeners may cause physiological changes as well as neurological benefits. However, any musician should avoid being influenced by an algorithmic recommendation presented in the research. The most prominent aspect for any musician is to stay true to their own musical style and to be novel, honest, and sincere. The main objective of this research work is to provide precise music predictions to the artists before they launch their music in the market. This work also aims to improve the accuracy of the prediction models using some feature-engineering-based techniques to manipulate the Spotify dataset and predict whether the song will be a hit or not. The proposed work leverages state-of-the-art machine learning models, including Linear Regression, Lasso Regression, Ridge Regression, Elastic Net, Random Forest, GBM, and neural networks to explore the predictive factors influencing the inclusion of a particular music track in the featured Spotify Hit-50 list. Furthermore, it offers descriptive statistical analysis of the features that can help in popularity prediction. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd 2024.

关键词： Hit song science audio features Music information retrieval Music streaming data Regression

来源：评论

学校读者我要写书评

暂无评论

Deep Learning-Based Histopathological Analysis for Colon Cancer Diagnosis: A Comparative Study of CNN and Transformer Models with Image Preprocessing Techniques 23th

Deep Learning-Based Histopathological Analysis for Colon Can...

引用

23rd International Conference on Intelligent systems Design and Applications, ISDA 2023

作者： Karthik, R. Thalanki, Vaibhav Yadav, Preyash Centre for Cyber Physical Systems Vellore Institute of Technology Chennai India School of Computer Science and Engineering Vellore Institute of Technology Chennai India

ISBN: (纸本)9783031648120

Colon cancer is a serious life-threatening type of cancer that affects the colon and rectum. Manual diagnosis of this disease is time-consuming and la-bor-intensive, requiring pathologists to carefully examine multiple tissue sections and slides per patient. Deep learning techniques offer promising solutions to these challenges. This research investigates the performance of various deep learning architectures, including ResNet50, InceptionResnetV2, NasNet, EfficientNetB0, DenseNet169, ViT, and MobileViT, in histopathology. Four stain normalization methods (Vahadane, Macenko, Reinhard, and ITK) and four image-denoising techniques (Wavelet-based denoising, Gaussian Filtering, Bilateral Filtering, and Median Filtering) were employed to ad-dress staining and noise issues. An ensemble is developed integrating a CNN and a transformer. CNNs excel at extracting local features from images through the use of convolutional layers and pooling operations, while the transformer can capture long-range dependencies. This allows the ensemble to capture intricate local patterns while also considering the relationships be-tween objects, regions, or features that may span the entire image. A quantitative comparative study on the ‘EBHI’ dataset was conducted, resulting in the development of an ensemble model achieving an accuracy of up to 92.02%. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Image denoising

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：