检索结果-内蒙古大学图书馆

TSMS-InceptionNeXt:A Framework for Image-Based Combustion State Recognition in Counterflow Burners via Feature Extraction Optimization

引用

computers, Materials & Continua 2025年第6期83卷 4329-4352页

作者： Huiling Yu Xibei Jia Yongfeng Niu Yizhuo Zhang Software Engineering Department of Computer ScienceChangzhou UniversityChangzhou213146China Electrical Engineering Department of Computer ScienceChangzhou UniversityChangzhou213146China

The counterflow burner is a combustion device used for research on *** utilizing deep convolutional models to identify the combustion state of a counter flow burner through visible flame images,it facilitates the optimization of the combustion process and enhances combustion *** existing deep convolutional models,InceptionNeXt is a deep learning architecture that integrates the ideas of the Inception series and *** has garnered significant attention for its computational efficiency,remarkable model accuracy,and exceptional feature extraction ***,since this model still has limitations in the combustion state recognition task,we propose a Triple-Scale Multi-Stage InceptionNeXt(TSMS-InceptionNeXt)combustion state recognitionmethod based on feature extraction ***,to address the InceptionNeXt model’s limited ability to capture dynamic features in flame images,we introduce Triplet Attention,which applies attention to the width,height,and Red Green Blue(RGB)dimensions of the flame images to enhance its ability to model dynamic ***,to address the issue of key information loss in the Inception deep convolution layers,we propose a Similarity-based Feature Concentration(SimC)mechanism to enhance the model’s capability to concentrate on critical ***,to address the insufficient receptive field of the model,we propose a Multi-Scale Dilated Channel Parallel Integration(MDCPI)mechanism to enhance the model’s ability to extract multi-scale contextual ***,to address the issue of the model’s Multi-Layer Perceptron Head(MlpHead)neglecting channel interactions,we propose a Channel Shuffle-Guided Channel-Spatial Attention(ShuffleCS)mechanism,which integrates information from different channels to further enhance the representational power of the input *** validate the effectiveness of the method,experiments are conducted on the counterflow burner flame visible light image datase

关键词： Counterflow burner combustion state recognition InceptionNeXt dilated convolution channel shuffling

来源：评论

学校读者我要写书评

暂无评论

A Cost-Efficient Anonymous Authenticated and Key Agreement Scheme for V2I-Based Vehicular Ad-Hoc Networks

引用

IEEE Transactions on Intelligent Transportation Systems 2024年第9期25卷 12621-12630页

作者： Saleem, Muhammad Asad Li, Xiong Mahmood, Khalid Shamshad, Salman Alenazi, Mohammed J. F. Das, Ashok Kumar University of Electronic Science and Technology of China School of Computer Science and Engineering Chengdu611731 China Shenzhen Institute for Advanced Study University of Electronic Science and Technology of China Shenzhen518000 China National Yunlin University of Science and Technology Graduate School of Intelligent Data Science Yunlin Douliu64002 Taiwan The University of Lahore Department of Software Engineering Lahore54590 Pakistan King Saud University College of Computer and Information Sciences Department of Computer Engineering Riyadh11451 Saudi Arabia International Institute of Information Technology Center for Security Theory and Algorithmic Research Hyderabad500032 India

The rise of smart cities is directly connected to the increasing use of vehicles. The growing vehicle utilization has driven the emergence of Vehicular Ad-hoc Networks (VANETs), facilitating instant information exchange among vehicles. The system provides essential information regarding road conditions, traffic patterns, and more relevant data. VANETs encompass two fundamental categories of communication exchanges, namely Vehicle-to-Vehicle (V2V) and Vehicle-to-Infrastructure (V2I). V2I technology facilitates the integration of cars and transportation infrastructure, enabling effective communication between vehicles and infrastructure. Nevertheless, the potential of V2I communication has various security concerns arising from prevalent security threats. Current authentication techniques encounter challenges regarding complexity, security, and privacy considerations. We designed a hash-based lightweight and anonymous authentication scheme to address the aforementioned restrictions and enhance the effectiveness of authentication in V2I architecture. This scheme effectively combines identity, password, and bio-metric to enhance resistance against impersonation, denial of service, and privileged insider attacks. The devised scheme distinguishes itself by a comparative analysis and security proofs, highlighting its superior capability in guaranteeing secure authentication in V2I communication. The comprehensive security analysis conducted formally and informally showcases the robustness of the proposed solution against several threats. The performance evaluation results show that our scheme demonstrates a decrease in the computational cost of 51.40% approximately and a reduction in communication overhead of around 22.57%. These results establish the efficiency and scalability of the proposed scheme as a viable solution for V2I architecture. © 2000-2011 IEEE.

关键词： Vehicular ad hoc networks

来源：评论

学校读者我要写书评

暂无评论

Graph-Based Feature Learning for Cross-Project software Defect Prediction

引用

computers, Materials & Continua 2023年第10期77卷 161-180页

作者： Ahmed Abdu Zhengjun Zhai Hakim A.Abdo Redhwan Algabri Sungon Lee School of Software Northwestern Polytechnical UniversityXi’anChina School of Computer Science Northwestern Polytechnical UniversityXi’anChina Department of Computer Science Hodeidah UniversityPO Box 3114Al-HudaydahYemen Research Institute of Engineering and Technology Hanyang UniversityAnsanKorea Department of Robotics Hanyang UniversityAnsanKorea

Cross-project software defect prediction(CPDP)aims to enhance defect prediction in target projects with limited or no historical data by leveraging information from related source *** existing CPDP approaches rely on static metrics or dynamic syntactic features,which have shown limited effectiveness in CPDP due to their inability to capture higher-level system properties,such as complex design patterns,relationships between multiple functions,and dependencies in different software projects,that are important for *** paper introduces a novel approach,a graph-based feature learning model for CPDP(GB-CPDP),that utilizes NetworkX to extract features and learn representations of program entities from control flow graphs(CFGs)and data dependency graphs(DDGs).These graphs capture the structural and data dependencies within the source *** proposed approach employs Node2Vec to transform CFGs and DDGs into numerical vectors and leverages Long Short-Term Memory(LSTM)networks to learn predictive *** process involves graph construction,feature learning through graph embedding and LSTM,and defect *** evaluation using nine open-source Java projects from the PROMISE dataset demonstrates that GB-CPDP outperforms state-of-the-art CPDP methods in terms of F1-measure and Area Under the Curve(AUC).The results showcase the effectiveness of GB-CPDP in improving the performance of cross-project defect prediction.

关键词： Cross-project defect prediction graphs features deep learning graph embedding

来源：评论

学校读者我要写书评

暂无评论

Object Tracking-Based “Follow-Me” Unmanned Aerial Vehicle (UAV) System

引用

computer Systems science & engineering 2022年第6期41卷 875-890页

作者： Olubukola D.Adekola Onyedikachi Kenny Udekwu Oluwatobi Tolulope Saliu Damilola Williams Dada Stephen O.Maitanmi Victor Odumuyiwa Olujimi Alao Monday Eze Funmilayo Abibat Kasali Ayokunle Omotunde Department of Software Engineering Babcock UniversityIlisan-RemoOgun StateNigeria Department of Computer Science Babcock UniversityIlisan-RemoOgun StateNigeria Department of Computer Science University of LagosAkokaLagos StateNigeria Department of Computer Science and Mathematics Mountain Top UniversityOgun StateNigeria

The applications of information technology (IT) tools and techniqueshave, over the years, simplified complex problem solving procedures. But thepower of automation is inhibited by the technicality in manning advanced equipment. To this end, tools deliberately combating this inhibition and advancing technological growth are the Unmanned Aerial Vehicles (UAVs). UAVs are rapidlytaking over major industries such as logistics, security, and *** others, this is a very efficient way of carrying out missions unconventional to humans. An application area of this technology is the local film industrywhich is not producing quality movies primarily due to the lack of technicalknow-how in utilizing these systems. This study therefore aim to devise an autonomous object tracking UAV system that would eliminate the complex procedureinvolved in stabilizing an aerial camera (aerial bot) midair and promote the creation of quality aerial video shooting. The study adopted Unified Modeling Language (UML) tools in modeling the system’s functionality. The traditionalServer-Client model architecture was adopted. The OpenCV library employedproved highly efficient in aiding the tracking procedure. The system provided ausable web controller which provides easy interaction between the pilot and thedrone. Conclusively, investments in UAVs would enhance creation of quality graphic contents.

关键词： Artificial intelligence cinematography unmanned aerial vehicles drones object tracking

来源：评论

学校读者我要写书评

暂无评论

Self-Care Assessment for Daily Living Using Machine Learning Mechanism

引用

computers, Materials & Continua 2022年第7期72卷 1747-1764页

作者： Mouazma Batool Yazeed Yasin Ghadi Suliman A.Alsuhibany Tamara al Shloul Ahmad Jalal Jeongmin Park Department of Computer Science Air UniversityIslamabad44000Pakistan Department of Computer Science and Software Engineering Al Ain UniversityAl Ain15551UAE Department of Computer Science College of ComputerQassim UniversityBuraydah51452Saudi Arabia Department of Humanities and Social Science Al Ain UniversityAl Ain15551UAE Department of Computer Engineering Korea Polytechnic University237 Sangidaehak-ro Siheung-siGyeonggi-do15073Korea

Nowadays,activities of daily living(ADL)recognition system has been considered an important field of computer *** and optical sensors are widely used to assess the daily living activities in healthy people and people with certain *** conventional ADL utilizes RGB optical sensors but an RGB-D camera with features of identifying depth(distance information)and visual cues has greatly enhanced the performance of activity *** this paper,an RGB-D-based ADL recognition system has been ***,human silhouette has been extracted from the noisy background of RGB and depth images to track human movement in a *** on these silhouettes,full body features and point based features have been extracted which are further optimized with probability based incremental learning(PBIL)***,random forest classifier has been used to classify activities into different *** n-fold crossvalidation scheme has been used to measure the viability of the proposed model on the RGBD-AC benchmark dataset and has achieved an accuracy of 92.71%over other state-of-the-art methodologies.

关键词： Angular geometric features decision tree classifier human activity recognition probability based incremental learning ridge detection

来源：评论

学校读者我要写书评

暂无评论

Improving multi-class opinion classification in social networks by combining Fuzzy SVM and CNN 28th

Improving multi-class opinion classification in social netwo...

引用

28th International Conference on Knowledge Based and Intelligent information and engineering Systems, KES 2024

作者： Ghezaiel Hammouda, Nadia Toujani, Radhia College of Computer Science and Software Engineering University of Hail Hail Saudi Arabia ESTMOD Department Higher Institute of management University of Tunis Tunisia

Machine learning techniques, particularly fuzzy Support Vector Machines (fuzzy SVM), have demonstrated effectiveness in sentiment classification within social networks. However, the evolving complexity of social networks content and the necessity to handle diverse opinions prompt ongoing advancements in sentiment analysis methodologies. In this study, we propose an innovative approach by integrating a Convolutional Neural Network (CNN) model with fuzzy SVM. Our key contribution lies in the fusion of these two methodologies, capitalizing on the strengths of fuzzy SVM for opinion mining and CNN for extracting intricate features. Additionally, we extend this method to enable multi-class opinion classification, allowing for a finer understanding of nuances in expressed sentiments. Through evaluation on benchmark datasets, we highlight a notable improvement in performance compared to baseline methods. The results underscore the enhanced generalization capabilities and increased accuracy in opinion classification offered by our hybrid model, rendering it a promising asset for decision-making applications based on multi-opinion analysis. © 2024 The Authors.

关键词： Convolutional neural networks

来源：评论

学校读者我要写书评

暂无评论

Analysis of Techniques for Detection and Removal of Zero-Day Attacks (ZDA) 2nd

Analysis of Techniques for Detection and Removal of Zero-Day...

引用

2nd International Conference on Ubiquitous Security, UbiSec 2022

作者： Hamid, Khalid Iqbal, Muhammad Waseem Aqeel, Muhammad Liu, Xiangyong Arif, Muhammad Department of Software Engineering The Superior University Lahore54000 Pakistan Department of Computer Science The Superior University Lahore54000 Pakistan School of Computer Science and Cyber Engineering Guangzhou University Guangzhou510006 China

ISBN: (纸本)9789819902712

Zero-day attacks (ZDAs) are previously unknown flaws and errors in operating systems, networks, and general-purpose software. ZDAs are the cause to open security breach holes for external users or hackers for illegal operations before patches. ZDAs are different from viruses, worms and Trojans. They are very difficult to detect and remove because they are unknown to users and even programmers. Conventional defense systems are useless against such types of attacks. This paper investigates the zero-day attacks, their vulnerabilities, the intensity of these attacks and the reasons in a real-time environment. Different techniques are compared to investigate ZDAs. The hybrid technique has been used to check the behavior of data traffic coming from outside the world in the virtual environment for zero-day attacks. Static check tools (MD5 Checksum, AV-Suite, and SOPHOS) and behavioral check tools (SANDBOX) have been used for the verification and validation of results. The second comparatively best technique is Machine Learning-Based Technique which showed the best results. The results show how to choose the best solution against zero-day attacks in the real-time environment of the software. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Zero-day attack

来源：评论

学校读者我要写书评

暂无评论

Classification of Unbalanced Data Based on Bayesian Optimal Neural Network Model

Journal of Network Intelligence

引用

Journal of Network Intelligence 2024年第3期9卷 1427-1440页

作者： Ye, Li-Zhu Alkawaz, Mohammed Hazim Johar, Md Gapar Md School of Information Technology and Engineering Guangzhou College of Commerce Guangzhou511363 China School of Graduate Studies Management and Science University 40100 Shah Alam Selangor Malaysia Department of Computer Science College of Education for Pure Science University of Mosul Mosul41002 Iraq Software Engineering and Digital Innovation Center Management and Science University Shah Alam40100 Malaysia

Aiming at the unbalanced problem of big data classification, this paper proposes a classification method of unbalanced data on the ground of Bayesian optimal neural network model. The method firstly uses Optimized Graph Convolutional Network (OGCN) network to carry out message passing mechanism in data graph, designs neural network model with self-attention pooling and mean maximization pooling, and then uses Bayes optimization to update the posterior probability of optimization function by constantly updating the probability model and evaluating the objective function. Effectively solve the problem of speed and optimal solution of hyperparameter optimization in the neural network model. Secondly, the model is used for data set training to obtain the weight of the entire network. Secondly, the unbalanced data is mixed sampling to reduce the number of negative samples. Finally, the capability of the model was verified on the CiteSeer dataset. Experimental results showed that the accuracy rate, accuracy rate and recall rate of the proposed algorithm were 0.974, 0.982 and 0.973, respectively, which effectively improved the accuracy rate, accuracy rate and recall rate of unbalanced data classification compared with other algorithms. © 2024, Taiwan Ubiquitous Information CO LTD. All rights reserved.

关键词： Neural network models

来源：评论

学校读者我要写书评

暂无评论

Anomaly-based network intrusion detection using Transfer learning 2

Anomaly-based network intrusion detection using Transfer lea...

引用

2nd International Conference on Multidisciplinary engineering and Applied science, ICMEAS 2023

作者： Ola-Obaado, Similoluwa Suleiman, Muhammad Aliyu Nile University of Nigeria Computer Science Department Abuja Nigeria Nile University of Nigeria Software Engineering Department Abuja Nigeria

ISBN: (纸本)9798350358834

The rapid development of computer networks and network applications, along with the present global increase in hacking and computer network attacks, has increased the demand for stronger intrusion detection and prevention solutions. The intrusion detection system (IDS) is crucial in detecting irregularities and attacks on the network, which has expanded in size and pervasiveness. In this paper, we develop an intrusion detection system based on deep learning and leverage a recent security dataset, CSE-CIC-IDS2018, to implement a more realistic IDS. We proposed an approach for network intrusion detection using Transfer learning to address both the binary and multiclass classification of network attacks. Network features from the dataset are transformed into grayscale images, the images are used as input to the ResNet50 deep learning model. The dataset is imbalanced, therefore, to increase the generalization of model, the imbalance ratio is reduced by using a synthetic data generation model called Synthetic Minority Oversampling Technique (SMOTE). Experimental results show that our model achieves good performance for both binary and multiclass classification with accuracy of 92 percent for each case. The proposed approach provides a new method for intrusion detection using transfer learning and SMOTE for correcting data imbalance. © 2023 IEEE.

关键词： Intrusion detection

来源：评论

学校读者我要写书评

暂无评论

K-Banhatti Sombor Invariants of Certain computer Networks

引用

computers, Materials & Continua 2022年第10期73卷 15-31页

作者： Khalid Hamid Muhammad Waseem Iqbal Abaid Ur Rehman Virk Muhammad Usman Ashraf Ahmed Mohammed Alghamdi Adel A.Bahaddad Khalid Ali Almarhabi Department of Computer Science Superior UniversityLahore54000Pakistan Department of Software Engineering Superior UniversityLahore54000Pakistan Department of Mathematics University of SialkotSialkot51013Pakistan Department of Computer Science GC Women UniversitySialkot53310Pakistan Department of Software Engineering College of Computer Science and EngineeringUniversity of Jeddah21493Saudi Arabia Department of Information System Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia Department of Computer Science College of Computing in Al-QunfudahUmm Al-Qura UniversityMakkah24381Saudi Arabia

Any number that can be uniquely determined by a graph is called a graph *** the last twenty years’countless mathematical graph invariants have been characterized and utilized for correlation ***,no reliable examination has been embraced to decide,how much these invariants are related with a network graph or molecular *** this paper,it will discuss three different variants of bridge networks with good potential of prediction in the field of computer science,mathematics,chemistry,pharmacy,informatics and biology in context with physical and chemical structures and networks,because k-banhatti sombor invariants are freshly presented and have numerous prediction qualities for different variants of bridge graphs or *** study solved the topology of a bridge graph/networks of three different types with two invariants KBanhatti Sombor Indices and its reduced *** deduced results can be used for the modeling of computer networks like Local area network(LAN),Metropolitan area network(MAN),and Wide area network(WAN),backbone of internet and other networks/structures of computers,power generation,bio-informatics and chemical compounds synthesis.

关键词： Bridge networks invariants k-banhatti sombor indices maple network graph molecular graph

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：