检索结果-内蒙古大学图书馆

7th International Conference on Inventive Computation Technologies, ICICT 2024

作者： Senthilkumar, R. Yasotha, S. Manochithra, P.M. Senthil, J. Sivakumar, G. Shree Venkateswara Hi Tech Engineering College Computer Science And Engineering Tamilnadu Erode India Sri Eshwar College of Engineering Computer Science And Engineering Tamilnadu Coimbatore India Sri Krishna College of Engineering And Technology M.Tech Computer Science And Engineering Tamilnadu Coimbatore India Erode Sengunthar Engineering College Computer Science And Engineering Tamilnadu Erode India

ISBN: (纸本)9798350359299

Due to developments in technologies like Cloud Computing (CC), the Internet of Things (IoT), etc., the data volume transmitted across communication infrastructures has skyrocketed recently. In order to make network systems susceptible, attackers have increased their determinations. Improving the security of such network systems is very crucial. Using Deep Learning (DL) methods with an Intrusion Detection System (IDS) framework is put into action in this research. The Gated Recurrent Unit (GRU) as well as Support Vector Machine (SVM) are used in this study for attack detection. Therefore, the implementation of SoftMax in the GRU model's last output layer, this study introduces a linear SVM. The NSL-KDD dataset is taken into account to evaluate the proposed IDS framework's performance. In addition, when the number of features increases, current IDSs have poor test accuracy ratings when it comes to identifying new attacks. Each dataset's feature space was reduced using an XGBoost (XGB)-based feature selection approach in this research. After that, 22 detailed characteristics were selected from the NSL-KDD dataset using XGB. The results show that, the proposed IDS paradigm outperformed other approaches. © 2024 IEEE.

关键词： Support vector machines

来源：评论

学校读者我要写书评

暂无评论

Improvisation of Node Mobility Using Cluster Routing-based Group Adaptive in MANET

引用

computer Systems science & engineering 2023年第3期44卷 2619-2636页

作者： J.Shanthini P.Punitha S.Karthik Department of Computer Science&Engineering Dr.N.G.P.Institute of EngineeringCoimbatoreTamilnaduIndia Department of Computer Science&Engineering SNS College of TechnologyCoimbatoreTamilnaduIndia

In today's Internet routing infrastructure,designers have addressed scal-ing concerns in routing constrained multiobjective optimization problems examining latency and mobility concerns as a secondary *** tactical Mobile Ad-hoc Network(MANET),hubs can function based on the work plan in various social affairs and the internally connected hubs are almost having the related moving standards where the topology between one and the other are tightly coupled in steady support by considering the touchstone of hubs such as a self-sorted out,self-mending and *** in the routing process is one of the key aspects to increase MANET performance by coordinat-ing the pathways using multiple criteria and *** present a Group Adaptive Hybrid Routing Algorithm(GAHRA)for gathering portability,which pursues table-driven directing methodology in stable accumulations and on-request steering strategy for versatile *** on this aspect,the research demonstrates an adjustable framework for commuting between the table-driven approach and the on-request approach,with the objectives of enhancing the out-put of MANET routing computation in each *** analysis and replication results reveal that the proposed method is promising than a single well-known existing routing approach and is well-suited for sensitive MANET applications.

关键词： Diplomatic mobile Ad-hoc network grouping mobility interior stable hybrid routing scheme adaptive switch structure clustering communication

来源：评论

学校读者我要写书评

暂无评论

Detecting Evolving Fake News in Social Media by Leveraging Heterogeneous Deep Learning Model 2

Detecting Evolving Fake News in Social Media by Leveraging H...

引用

2nd IEEE International Conference on Advances in Information Technology, ICAIT 2024

作者： Brinda, B.M. Rajan, C. Geetha, K. Computer Science and Engineering Paavai College of Engineering Namakkal India Information Technology KSR College of Technology Thiruchengode India Computer Science and Engineering Excel Engineering College Komarapalayam India

ISBN: (纸本)9798350383867

A recent study revealed that fake articles spread considerably faster than factual content on Twitter, specifically at a 70% higher rate of retweets and reaching the first 1,500 users six times faster. This highlights the urgent need for scalable automated detection solutions to combat the pervasive threat of fake news online. While machine learning methods often rely solely on semantic text analysis, evolving fake news campaigns mimic genuine news, employing tactics like bot networks and sophisticated language imitation. This renders text-based meth- ods increasingly vulnerable. To address this challenge,we propose a heterogeneous deep learning model that incorporates richer information beyond text, specifically dissemination structure features from social networks. These features capture aspects like user interactions, sharing patterns, and network topology, providing valuable insights into the spread of information. Our model leverages the sentiment and affects the enriched Alt dataset for text analysis. Instead of using a popular language model called BERT, we explore alternative pre-trained language models like the Sentence Transformer, which focuses on capturing con- textual sentence embeddings, or the Universal Language Model (ULMFiT), which excels at understanding semantic relationships within text. Additionally, we consider spaCy, a versatile NLP library offering similar functionalities for text processing and feature extraction. While The Quint Dataset Challenge Dataset lacks sentiment annotations, it could be a potential alternative to Alt depending on research goals. This network-aware approach achieves an impressive 95.27% accuracy on The Boom Live dataset and demonstrates generalizability to The Quint dataset with 92.1% accuracy, highlighting its effectiveness in combatting evolving fake news tactics. © 2024 IEEE.

关键词： Latent semantic analysis

来源：评论

学校读者我要写书评

暂无评论

PSO-DBNet for Peak-to-Average Power Ratio Reduction Using Deep Belief Network

引用

computer Systems science & engineering 2023年第5期45卷 1483-1493页

作者： A.Jameer Basha M.Ramya Devi S.Lokesh P.Sivaranjani D.Mansoor Hussain Venkat Padhy Department of Computer Science and Engineering Hindusthan Institute of TechnologyCoimbatore641032India Department of Computer Science and Engineering Hindusthan College of Engineering and TechnologyCoimbatore641032India Department of Electronics and Communication Engineering Kongu Engineering CollegeErode638060India Department of Computer Science and Engineering Sri Krishna College of Engineering and TechnologyCoimbatore641008India School of Computing Science&Engineering VIT Bhopal UniversityBhopal466114India

Data transmission through a wireless network has faced various signal problems in the past *** orthogonal frequency division multiplexing(OFDM)technique is widely accepted in multiple data transfer patterns at various frequency bands.A recent wireless communication network uses OFDM in longterm evolution(LTE)and 5G,among *** main problem faced by 5G wireless OFDM is distortion of transmission signals in the *** transmission loss is called peak-to-average power ratio(PAPR).This wireless signal distortion can be reduced using various *** study uses machine learning-based algorithm to solve the problem of PAPR in 5G wireless *** transmit sequence(PTS)helps in the fast transfer of data in wireless *** is merged with deep belief neural network(DBNet)for the efficient processing of signals in wireless 5G *** indicates that the proposed system outperforms other existing ***,PAPR reduction in OFDM by DBNet is optimized with the help of an evolutionary algorithm called particle swarm ***,the specified design supports in improving the proposed PAPR reduction architecture.

关键词： 5G wireless network orthogonal frequency division multiplexing signal distortion peak to average power ratio partial transmit sequence deep belief network

来源：评论

学校读者我要写书评

暂无评论

Enabling Technology Integrated Learning for Autistic Children Using Augmented Reality Based Cognitive Rehabilitation

引用

SN computer science 2024年第1期5卷 151页

作者： Abdullah, A. Sheik Karthikeyan, J. Gomathi, V. Parkavi, R. Rajarajeswari, P. School of Computer Science and Engineering Vellore Institute of Technology Chennai India Department of Computer Science and Engineering National Engineering College Kovilpatti India Department of Information Technology Thiagarajar College of Engineering Madurai India

Augmented reality (AR)-based rehabilitation is an excellent teaching tool (ICT tool) for simulating daily activities for autistic children. Children with autism may become more mentally capable with this type of AR-based rehabilitation. It is intended to observe the children's performance in terms of concentration, attention, and identification. The observation has been done through placards as a target image to display the 3D objects on a mobile phone or tablet. In this project, observations are made for 21 autism children in the age group of 7–14, out of whom 17 are boys and 5 are girls. Those 21 children are given practice identifying 15 different objects in an augmented reality environment. Their performance was initially evaluated using conventional instructional techniques. The majority of the kids were having more difficulty identifying things during that observation. Then, with an Augmented Reality environment, the identical observation has been made once more. Using a mobile device or tablet, the 3D objects from the provided placard photos are produced in an augmented reality environment with animation and voice in the languages of English and Tamil. Children with autism are able to recognize and also grasp the behaviors of those objects while viewing them in 3D. Their efforts are measured using a two-point scale (0, 1, 2). The pre-assessment and post-assessment reports for the above observations are tabulated. All the observations are made in the presence of the special education teacher (therapist). However, the children observed in this project fall into three different categories: mild, moderate, and severe. In the Mild category, statistical significance is evident with p values of 0.002 in pre-assessment and 0.014 in post-assessment. Likewise, in the Moderate category, where p values are 0.023 in pre-assessment and 0.033 in post-assessment, significance is observed, as all p values fall below the chosen significance level of 0.05. This leads to rejecti

关键词： Augmented reality (AR) Autism ICT tools Observation Rehabilitation Teaching methodology

来源：评论

学校读者我要写书评

暂无评论

Adaptive Marine Predator Optimization Algorithm(AOMA)-Deep Supervised Learning Classification(DSLC)based IDS framework for MANET security

引用

Intelligent and Converged Networks 2024年第1期5卷 1-18页

作者： M.Sahaya Sheela A.Gnana Soundari Aditya Mudigonda C.Kalpana K.Suresh K.Somasundaram Yousef Farhaoui Department of Electronics and Communication Engineering Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and TechnologyChennai 600069India Department of Computer Science and Engineering Saveetha School of EngineeringSIMATSChennai 602105India JNIAS School of Planning and Architecture Hyderabad 500034India Department of Computer Science and Engineering NPR College of Engineering and TechnologyNatham Dindigul 624401India Department of Computer Science and Engineering PSNA College of Engineering and TechnologyPoolangulathupatti 620009India Department of Computer Science and Engineering Sri Muthukumaran Institute of TechnologyChennai 600069India Department of Computer Science Moulay Ismail UniversityMeknes 5003Morocco

Due to the dynamic nature and node mobility,assuring the security of Mobile Ad-hoc Networks(MANET)is one of the difficult and challenging tasks *** MANET,the Intrusion Detection System(IDS)is crucial because it aids in the identification and detection of malicious attacks that impair the network’s regular *** machine learning and deep learning methodologies are used for this purpose in the conventional works to ensure increased security of ***,it still has significant flaws,including increased algorithmic complexity,lower system performance,and a higher rate of ***,the goal of this paper is to create an intelligent IDS framework for significantly enhancing MANET security through the use of deep learning ***,the min-max normalization model is applied to preprocess the given cyber-attack datasets for normalizing the attributes or fields,which increases the overall intrusion detection performance of ***,a novel Adaptive Marine Predator Optimization Algorithm(AOMA)is implemented to choose the optimal features for improving the speed and intrusion detection performance of ***,the Deep Supervise Learning Classification(DSLC)mechanism is utilized to predict and categorize the type of intrusion based on proper learning and training *** evaluation,the performance and results of the proposed AOMA-DSLC based IDS methodology is validated and compared using various performance measures and benchmarking datasets.

关键词： Intrusion Detection System(IDS) Security Mobile Ad-hoc Network(MANET) min-max normalization Adaptive Marine Predator Optimization Algorithm(AOMA) Deep Supervise Learning Classification(DSLC)

来源：评论

学校读者我要写书评

暂无评论

Intelligent Digital Envelope for Distributed Cloud-Based Big Data Security

引用

computer Systems science & engineering 2023年第7期46卷 951-960页

作者： S.Prince Chelladurai T.Rajagopalan Department of Computer Science and Engineering University College of EngineeringVillupuramKakupppam605103India Department of Science and Humanities University of College of EngineeringAriyalurKavanur621705India

Cloud computing offers numerous web-based *** adoption of many Cloud applications has been hindered by concerns about data security and *** service providers’access to private information raises more security *** addition,Cloud computing is incompatible with several industries,including finance and ***-key cryptography is frequently cited as a significant advancement in *** contrast,the Digital Envelope that will be used combines symmetric and asymmetric methods to secure sensitive *** study aims to design a Digital Envelope for distributed Cloud-based large data security using public-key *** strategic design,the hybrid Envelope model adequately supports enterprises delivering routine customer services via independent multi-sourced *** the Cloud service provider and the consumer benefit from the proposed scheme since it results in more resilient and secure *** suggested approach employs a secret version of the distributed equation to ensure the highest level of security and confidentiality for large amounts of *** on the proposed scheme,a Digital Envelope application is developed which prohibits Cloud service providers from directly accessing insufficient or encrypted data.

关键词： Digital Envelope Cloud computing big data encryption decryption

来源：评论

学校读者我要写书评

暂无评论

Deep learning based video surveillance for predicting vehicle density in real time scenario

引用

Journal of Ambient Intelligence and Humanized Computing 2023年第4期14卷 4371-4383页

作者： Priyanka, G. Senthil Kumar, J. Veena, S.T. Department of Computer Science and Engineering Mepco Schlenk Engineering College Sivakasi India Department of Electronics and Communication Engineering Mepco Schlenk Engineering College Sivakasi India

Video surveillance systems are often used for traffic monitoring and to characterize traffic load. However, most of the surveillance videos are low frame rated and extracting the right motion feature from them is a challenging task. Deep learning is a growing technology that extracts each object’s feature without human intervention. This paper proposes two different Convolutional Neural Networks (CNN) named Multi Scale Convolutional Neural networks (MSCNN) and Single Scale Convolutional Neural networks (SSCNN) to extract the features and prediction of traffic density. The experimental evaluation of the proposed models was carried out on the UCSD traffic dataset. Our method exploits the benefits of the redundant nature of traffic scenes that are captured from a top-down perspective showing vehicles on a road encircled by background objects. Though a variety of objects is present in each frame, our proposed method attempts to acquire information only about specific object features which helps to predict traffic density better in real-time scenarios. Based on the extracted features, each traffic scene is categorized into three different classes low, medium, and high. Also, the performance of the proposed architecture exhibits 12% more accuracy compared with the existing deep neural networks. Furthermore, in comparison with the MAE score, the proposed system is able to reduce the error from 1 to 0.3 than the existing models. In addition, the training and testing phases of the proposed approach consume considerably less time and optimized usage of CPU resources. © 2023, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Convolution

来源：评论

学校读者我要写书评

暂无评论

Fraud Detection in Banking using the Kaggle Credit Card Dataset and XGBoost Model 5

Fraud Detection in Banking using the Kaggle Credit Card Data...

引用

5th International Conference on IoT Based Control Networks and Intelligent Systems, ICICNIS 2024

作者： Ango, Rithika Masih, Raj Kumar Reddy, C Kishor Kumar Shuaib, Mohammed Monika Singh, T. Alam, Shadab Stanley College of Engineering & Technology for Women Department of Computer Science and Engineering Hyderabad India Jazan University College of Engineering and Computer Science Department of Computer Science Jazan Saudi Arabia

ISBN: (纸本)9798331518097

The threat posed by credit card fraud, and by extension, online banking, continues to grow with the convenience brought forth by online banking services. Many financial institutions and customers stand at great risk because of this reality. The purpose of the paper is to enhance the security of banking systems through advanced technologies - machine learning. The research employed the well-known dataset from Kaggle Credit Card, which contains numerous records of transactions labeled as either fraud or not, thus presenting an aspect similar to real life imbalanced data. The main focus in the present work is the XGBoost model which involves one of the ensemble learning methods, which is characterized by a high level of precision and fast execution times. As a result of hyperparameter optimization and feature engineering, the paper also works on the performance of the algorithm improving its ability to detect fraudulent transactions in a sea of normal transactions. The results obtained from the experiments show that the XGBoost model is not only proven to be more accurate than earlier approaches to fraud detection, but also greatly reduces the number of false positives. This study not only contributes to the existing literature on detecting financial fraud but also represents the ways in which finance can benefit from machine learning to design secure systems within banking operations. The metrics in which the model suggested outperformed the available approaches are, the accuracy is 98.60%, error rate is 2.10% while its precision reached 95.40, recall of 92.30, F1 score 93.20 and AUC-ROC 99.70. This study not only contributes to the existing literature on detecting financial fraud but also represents the ways in which finance can benefit from machine learning to design secure systems within banking operations. © 2024 IEEE.

关键词： Banking

来源：评论

学校读者我要写书评

暂无评论

Optimized Visual Recognition Through a Deep Convolutional Neural Network With Hierarchical Modular Organization

引用

IEEE Access 2024年 12卷 95517-95528页

作者： Clinton Sitepu, Ade Liu, Chuan-Ming National Taipei University of Technology College of Electrical Engineering and Computer Science Taipei10608 Taiwan National Taipei University of Technology Department of Computer Science and Information Engineering Taipei10608 Taiwan

Artificial Intelligence, including machine learning and deep convolutional neural networks (DCNNs), relies on complex algorithms and neural networks to process and analyze data. DCNNs for visual recognition often require access to high-performance hardware, such as GPUs and cloud-based computing resources, to perform tasks efficiently. Visual recognition requires DCNNs with numerous layers. Fully connected layers in DCNNs are often the most computationally intensive. These layers connect every neuron in one layer to every neuron in the next layer, resulting in a large number of parameters to compute. To mitigate redundancy and make DCNNs more efficient, this article implements and demonstrates the concept to identifying and removing redundant or low-impact connections from fully connected layers using convolution neural network with hierarchical modular organization. The modularity of the DCNNs is built based on the cluster hierarchy of the similar image. These clusters are created based on a novel similarity metric, which measures how closely related images are to each other. The architecture uses multiple smaller DCNNs, referred to as modules, designed to progressively classify images into super clusters according to their similarity. Experimental results using popular image datasets show that the proposed DCNNs model to optimized number of operations by 49% to 99% and keeps its performance comparable. © 2024 The Authors.

关键词： Convolution

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：