检索结果-内蒙古大学图书馆

Caching Strategies in NDN Based Wireless Ad Hoc Network:A Survey

Computers, Materials & Continua 2024年第7期80卷 61-103页

作者： Ahmed Khalid Rana Asif Rehman Byung-Seo Kim Department of Computer Science FAST School of ComputingNational University of Computer and Emerging SciencesLahore54000Pakistan Department of Software and Communications Engineering Hongik UniversitySejong City30016Republic of Korea

Wireless Ad Hoc Networks consist of devices that are wirelessly *** Ad Hoc Networks(MANETs),Internet of Things(IoT),and Vehicular Ad Hoc Networks(VANETs)are the main domains of wireless ad hoc *** is used in wireless ad hoc *** is based on Transmission Control Protocol(TCP)/Internet Protocol(IP)network where clients and servers interact with each other with the help of IP in a pre-defined *** fetches data from a fixed *** redundancy,mobility,and location dependency are the main issues of the IP network *** these factors result in poor performance of wireless ad hoc *** main disadvantage of IP is that,it does not provide in-network ***,there is a need to move towards a new network that overcomes these *** Data Network(NDN)is a network that overcomes these *** is a project of Information-centric Network(ICN).NDN provides in-network caching which helps in fast response to user *** NDN in wireless ad hoc network provides many benefits such as caching,mobility,scalability,security,and *** considering the certainty,in this survey paper,we present a comprehensive survey on Caching Strategies in NDN-based Wireless *** cachingmechanism-based results are also *** the last,we also shed light on the challenges and future directions of this promising field to provide a clear understanding of what caching-related problems exist in NDN-based wireless ad hoc networks.

关键词： Content centric network Internet of Things mobile ad hoc network named data network vehicular ad hoc network

来源：评论

学校读者我要写书评

暂无评论

Exactly Minimax-Optimal Locally Differentially Private Sampling 38

Exactly Minimax-Optimal Locally Differentially Private Sampl...

引用

38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Park, Hyun-Young Asoodeh, Shahab Lee, Si-Hyeon School of Electrical Engineering KAIST Korea Republic of Department of Computing and Software McMaster University Canada

The sampling problem under local differential privacy has recently been studied with potential applications to generative models, but a fundamental analysis of its privacy-utility trade-off (PUT) remains incomplete. In this work, we define the fundamental PUT of private sampling in the minimax sense, using the fdivergence between original and sampling distributions as the utility measure. We characterize the exact PUT for both finite and continuous data spaces under some mild conditions on the data distributions, and propose sampling mechanisms that are universally optimal for all f-divergences. Our numerical experiments demonstrate the superiority of our mechanisms over baselines, in terms of theoretical utilities for finite data space and of empirical utilities for continuous data space. © 2024 Neural information processing systems foundation. All rights reserved.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Loan Repayment Predictive Model Using Ensemble Algorithm

Loan Repayment Predictive Model Using Ensemble Algorithm

引用

2024 IEEE SmartBlock4Africa, SmartBlock4Africa 2024

作者： Godwin Akinbayo, O. Akintola Kolawole, G. Adewole David, B. School of Computing Federal University of Technology Department of Software Engineering Ondo State Akure Nigeria

ISBN: (纸本)9798350376838

The accurate prediction of loan repayment ability is paramount in any financial industry to minimize risks and enhance decision making. This project centers on the development of a loan repayment predictive model using ensemble algorithm. The performance of the algorithm is evaluated through the following metrics: f1 score, accuracy, and precision. Five classifiers including Random Forest, AdaBoost, Gradient Boosting, Logistic Regression, and SVC were used as base classifiers for the ensemble. Ensemble-based optimization (using RSCV) is employed to get the best combination of hyperparameters. This technique performs a randomized search over specified hyperparameter values, using cross-validated performance to find the best combination to create models that make up the ensemble model. Preliminary results indicate promising performance improvements when compared to single model. Future research in this domain could further refine the model and expand its applications in the ever-evolving financial sector. © 2024 IEEE.

关键词： Decentralized finance

来源：评论

学校读者我要写书评

暂无评论

Web applications testing techniques: a systematic mapping study

引用

International Journal of Web engineering and Technology 2023年第4期17卷 372-412页

作者： Hanna, Samer Ahmad, Amro Al-Said Department of Software Engineering Faculty of Information Technology Philadelphia University Jordan School of Computing and Mathematics University of Keele United Kingdom

Due to the importance of web application testing techniques for detecting faults and assessing quality attributes, many research papers were published in this field. For this reason, it became essential to analyse, classify and summarise the research in the field. To achieve this goal, this research conducted a systematic mapping study on 98 research papers in the field of web applications testing published between 2008 and 2021. The results showed that the most commonly used web applications testing techniques in literature are model-based testing and security testing. Besides, the most commonly used models in model-based testing are finite-state machines. The most targeted vulnerability in security testing is SQL injection. Test automation is the most targeted testing goal in both model-based and security testing. For other web applications testing techniques, the main goals of testing were test automation, test coverage, and assessing security quality attributes. Copyright © 2022 Inderscience Enterprises Ltd.

关键词： Mapping

来源：评论

学校读者我要写书评

暂无评论

Automatic Multi-Class Non-Functional software Requirements Classification Using Machine Learning Algorithms

Automatic Multi-Class Non-Functional Software Requirements C...

引用

2024 International Research Conference on Smart computing and Systems engineering, SCSE 2024

作者： Saroth, M.A.F. Wijerathne, P.M.A.K. Kumara, B.T.G.S. Sabaragamuwa University of Sri Lanka Faculty of Computing Department of Computing and Information Systems Sri Lanka Sabaragamuwa University of Sri Lanka Faculty of Computing Department of Software Engineering Sri Lanka

ISBN: (纸本)9798350375688

A successful software development process depends on software requirements. Those requirements are often classified into two categories: functional requirements (FR) and non-functional requirements (NFR). In software development, NFRs play a crucial role, but they are often forgotten by the developers. Therefore, it causes adverse consequences. As a result, an NFR classification method is required to assist software developers in classifying NFR from requirement documents. The majority of research in this area focuses on categorizing requirements as FR or NFR. However, NFR can be divided into more than two categories. As a result, a multi-label categorization strategy was required. This study presented an approach for categorizing NFR based on product, process, and external factors. To categorize the NFR, the authors developed four machine learning-based algorithms entitled Logistic Regression (LR), Support Vector Machine (SVM), Decision Tree (DT), and K Nearest Neighbor Classifier (KNN) classifier, as well as one deep learning method named Long Short-Term Memory (LSTM) model. Ultimately, the authors examine the models and conclude that the LSTM model which gained an accuracy of 99.69% is appropriate for this type of classification. © 2024 IEEE.

关键词： Long short-term memory

来源：评论

学校读者我要写书评

暂无评论

SeerahGPT: Retrieval Augmented Generation based Large Language Model 18

SeerahGPT: Retrieval Augmented Generation based Large Langua...

引用

18th International Conference on Open Source Systems and Technologies, ICOSST 2024

作者： Obaid, Surayya Bawany, Narmeen Zakaria Center for Computing Research Department of Computer Science and Software Engineering Jinnah Univesity for Women Karachi Pakistan

ISBN: (纸本)9798331508692

Large Language Models(LLMs) have become widely recognized in recent years for their exceptional performance in language generation capabilities. As a result, an unprecedented rise is seen in its use cases in various domains specifically involving Natural Language Processing(NLP). These models however perform suboptimally when exploited in the field of studies where authenticity of the generated content is a critical aspect. One such domain is usage of LLM for exploration of the life of Prophet Muhammad S.A.W(commonly referred to as Seerah). It is of utmost significance to ensure the authenticity and reliability in the sources used and reported by the LLM due to the sensitive nature of the domain. The contemporary LLMs, however, lack the explainability in their response due to their inherent black-box nature. In our study, we have presented a novel LLM named SeerahGPT that addresses this challenge with the help of retrieval-augmented generation (RAG). This technique enables the model to utilize both parametric and nonparametric memories for generating response of queries. Our model, built on the Llama-2-7b architecture, employs Sentence Transformer embedding to effectively retrieve relevant information. The model's capabilities are augmented by integrating it with a corpus having Islamic texts such as the Quranic translation and Hadith collections, and historical accounts. The model's performance is benchmarked against its base model using both quantitative and qualitative metrics. The comparative analysis with Llama-2-7b revealed that SeerahGPT incorporation with external knowledge sources, provided more authentic and verifiable responses, despite the others exhibiting greater fluency. Performance metrics such as BLEU, ROUGE, and METEOR indicated SeerahGPT's better accuracy and contextual handling. This study paves way for analysis of such sensitive domains in more efficient way that can be utilized in other complex domains such as Islamic theology and Fiqh or legal

关键词： Natural language processing systems

来源：评论

学校读者我要写书评

暂无评论

Federated Learning for Enhanced Intrusion Detection in Smart City Environments 18

Federated Learning for Enhanced Intrusion Detection in Smart...

引用

18th International Conference on Open Source Systems and Technologies, ICOSST 2024

作者： Hamid, Soomaiya Bawany, Narmeen Zakaria Jinnah University for Women Center for Computing Research Department of Computer Science and Software Engineering Karachi Pakistan

ISBN: (纸本)9798331508692

The integration of IoT devices in smart cities enhances urban infrastructure, services, and governance but also introduces significant cybersecurity challenges. Traditional centralized Intrusion Detection Systems (IDS) face several issues, including data privacy concerns and high-power consumption due to centralized data processing. These challenges increase the risks of unauthorized access, data breaches, and privacy violations, undermining user trust and compliance with privacy regulations. Additionally, the centralization of data and processing leads to higher power consumption, making these systems less sustainable for widespread deployment in smart cities. This research addresses these issues by proposing a Federated Learning (FL)based intrusion detection framework for smart cities. FL enables collaborative and privacy-preserving model training across distributed IoT devices, mitigating the need to share sensitive data centrally. By aggregating local model updates, FL ensures data privacy and distributes the computational workload, significantly reducing power consumption compared to traditional centralized systems. The proposed model leverages advanced AI techniques and is trained using the IoTID20 dataset. The Flower framework, utilizing the FedAvg algorithm, facilitates the federated learning process. Our experimental results demonstrate that the global model achieves 98% accuracy, with individual clients achieving accuracies of around 85% to 98%. This approach provides continuous learning mechanisms, anomaly detection, and ensemble learning capabilities, enhancing the resilience of federated intrusion detection systems against emerging threats and adversarial attacks. This research systematically investigates the application of federated learning for intrusion detection in smart city networks, addressing key challenges and advancing the state-of-the-art in decentralized cybersecurity solutions. The proposed framework offers a robust, scalable, and privacyco

关键词： Federated learning

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

Design of a Secured and Trusted E-Voting System Using Blockchain Technology

Design of a Secured and Trusted E-Voting System Using Blockc...

引用

2024 IEEE SmartBlock4Africa, SmartBlock4Africa 2024

作者： Elegba Jeremiah, O. Agbelusi, O. Olutayo Victor, A. Akintola Kolawole, G. School of Computing The Federal University of Technology Department of Software Engineering Akure Nigeria School of Computing The Federal University of Technology Department of Computer Science Akure Nigeria

ISBN: (纸本)9798350376838

The state of election in Nigeria is worrisome. Electoral malpractices have been a major challenge to the Nigerian government in recent times. Government has made frantic efforts to tackle these electoral challenges but the recent event in year 2023 presidential election is worrisome suggests that the solution is not near. Recently, the percentage of eligible voters who vote on election days is declining. Voters turnout went up from 52.3% in 1999 - the first general election since 1993 - to 69% in 2003. But, it has been on the decline nearly ever since - 57.5% in 2007, 53.7% in 2011, 43.7% in 2015 and 34.8% in 2019. In 2023, it is 28.63%. With the way things are going, if urgent action is not taken, this could signal the end of democracy in Nigeria. This research presents the design and implementation of a trusted and secured e-voting based on blockchain technology that will increase voters confidence and trust. The system utilizes *** for the frontend and Solidity on the Polygon blockchain for the backend, ensuring transparency, tamper-resistance, and heightened security. This innovative approach addresses trust and reliability concerns in traditional voting systems, offering a modernized and trustworthy electoral process. © 2024 IEEE.

关键词： Blockchain

来源：评论

学校读者我要写书评

暂无评论

Classification of Privacy Preserved Medical Data with Fractional Tuna Sailfish Optimization Based Deep Residual Network in Cloud

引用

Annals of Data Science 2024年 1-26页

作者： Shikalgar, Shabanam K. Kumar, N. V. S. Pavan Singh, Gavendra Rashid, Faizur Sharad Institute of Technology College of Engineering Yadrav India Koneru Lakshmiah Education Foundation Vijayawada India Department of Software Engineering College of Computing and Informatics Haramaya University Dire Dawa138 Ethiopia College of Computing and Informatics Haramaya University Dire Dawa138 Ethiopia

Nowadays, with the growth of emerging technologies, increased attention has been paid to the classification of privacy-preserved medical data and development of various privacy-preserving models for the promotion of online medical pre-diagnosis systems. Medical data is highly sensitive and it is essential to ensure privacy of medical records from third-party users to increase service quality, satisfy patients and earn trust. The classification of medical preserved data is helpful to build a clinical decision system by classifying patients based on their disease and symptoms. In this article, a hybrid optimization-based deep learning model named Fractional Tuna Sailfish Optimization–Deep Residual Network (FractionalTSFO-DRN) is designed to precisely classify the privacy preserved medical data. A privacy utility coefficient matrix is used to ensure the privacy of medical data by generating a key matrix using Tuna Sailfish Optimization (TSFO) algorithmic technique. The privacy-preserved medical data is allowed for the classification process using DRN and the introduced Fractional TSFO is used to optimize and enhance the classification in DRN. The assessment followed by using heart disease prediction databases proved that the employed classification technique recorded an accuracy of 94.67%, a True Positive Rate of 93.56%, and a True Negative Rate of 89.68% respectively. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.

关键词： Classification (of information)

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：