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2010 2nd International Conference on Communication systems, Networks and Applications, ICCSNA 2010

作者： Li, Jinlai Lu, Xiangqing Gao, Guohong College of Computer and Information Technology Nanyang Normal University Nanyang China School of Information Engineer Henan Institute of Science and Technology Xinxiang China

ISBN: (纸本)9781424474769

We use immune agent system for reference to realize immunity-based security architecture in this paper, and give an example to analyze how to keep the network from attack. Detection Agent in our architecture, regarded as a T-cell, is responsible for identifying invaders and organizing other agents to beat back. Other normal nodes in network can be regarded as B-cell, when necessary, they can produce a large number of antibodies which are activated Counterattack Agents to clear the antigen by interacting directly with the invader. ©2010 IEEE.

关键词： Network security

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Optimal Deep Reinforcement Learning for Intrusion Detection in UAVs

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computers, Materials & Continua 2022年第2期70卷 2639-2653页

作者： V.Praveena A.Vijayaraj P.Chinnasamy Ihsan Ali Roobaea Alroobaea Saleh Yahya Alyahyan Muhammad Ahsan Raza Department of Computer Science and Engineering Dr.N.G.P Institute of TechnologyCoimbatore641048India Department of Information Technology Vignan’s Foundation for ScienceTechnology&ResearchGuntur522213India Department of Information Technology Sri Shakthi Institute of Engineering and TechnologyCoimbatore641062India Department of Computer System and Technology Faculty of Computer Science and Information TechnologyUniversity of MalayaKuala Lumpur50603Malaysia Department of Computer Science College of Computers and Information TechnologyTaif UniversityTaif21944Saudi Arabia Department of Computer Science Community College in DwadmiShaqra University11961Saudi Arabia Department of Information Technology Bahauddin Zakariya UniversityMultan60000Pakistan

In recent years,progressive developments have been observed in recent technologies and the production cost has been continuously *** such scenario,Internet of Things(IoT)network which is comprised of a set of Unmanned Aerial Vehicles(UAV),has received more attention from civilian tomilitary *** network security poses a serious challenge to UAV networks whereas the intrusion detection system(IDS)is found to be an effective process to secure the UAV *** IDSs are not adequate to handle the latest computer networks that possess maximumbandwidth and data *** order to improve the detection performance and reduce the false alarms generated by IDS,several researchers have employed Machine Learning(ML)and Deep Learning(DL)algorithms to address the intrusion detection *** this view,the current research article presents a deep reinforcement learning technique,optimized by BlackWidow Optimization(DRL-BWO)algorithm,for UAV *** addition,DRL involves an improved reinforcement learning-based Deep Belief Network(DBN)for intrusion *** parameter optimization of DRL technique,BWO algorithm is *** helps in improving the intrusion detection performance of UAV *** extensive set of experimental analysis was performed to highlight the supremacy of the proposed *** the simulation values,it is evident that the proposed method is appropriate as it attained high precision,recall,F-measure,and accuracy values such as 0.985,0.993,0.988,and 0.989 respectively.

关键词： Intrusion detection UAV networks reinforcement learning deep learning parameter optimization

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Generative API usage code recommendation with parameter concretization

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Science China(information Sciences) 2019年第9期62卷 51-72页

作者： Chi CHEN Xin PENG Jun SUN Zhenchang XING Xin WANG Yifan ZHAO Hairui ZHANG Wenyun ZHAO School of Computer Science Fudan University Shanghai Key Laboratory of Data Science Fudan University Shanghai Institute of Intelligent Electronics & Systems Pillar of Information System Technology and Design Singapore University of Technology and Design Research School of Computer Science Australian National University

Many programming languages and development frameworks have extensive libraries(e.g., JDK and Android libraries) that ease the task of software engineering if used effectively. With numerous library classes and sometimes intricate API(application programming interface) usage constraints, programmers often have difficulty remembering the library APIs and/or using them correctly. This study addresses this problem by developing an engine called DeepAPIRec, which automatically recommends the API usage *** to the existing proposals, our approach distinguishes itself in two ways. First, it is based on a tree-based long short-term memory(LSTM) neural network inspired by recent developments in the machinelearning community. A tree-based LSTM neural network allows us to model and reason about variable-length,preceding and succeeding code contexts, and to make precise predictions. Second, we apply data-flow analysis to generate concrete parameters for the API usage code, which not only allows us to generate complete code recommendations but also improves the accuracy of the learning results according to the tree-based LSTM neural network. Our approach has been implemented for supporting Java programs. Our experimental studies on the JDK library show that at statement-level recommendations, DeepAPIRec can achieve a top-1 accuracy of about 37% and a top-5 accuracy of about 64%, which are significantly better than the existing approaches. Our user study further confirms that DeepAPIRec can help developers to complete a segment of code faster and more accurately as compared to IntelliJ IDEA.

关键词： code recommendation API deep learning data dependency parameter concretization

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Deep Learning and Machine Learning Algorithms to Predict Lung Cancer 2

Deep Learning and Machine Learning Algorithms to Predict Lun...

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2nd International Conference on Emerging Trends in information technology and engineering, ic-ETITE 2024

作者： Gowthami, K. Kamalakannan, J. School of Computer Science Engineering and Information System Vellore Institute of Technology Vellore India

ISBN: (纸本)9798350328202

Lung cancer is the most deadly illness for patients, and it is an incurable condition. Patient survival rates are greatly increased by early cancer prognosis and identification. The Computed Tomography (CT) scan is an important imaging tool for a lung cancer diagnosis. On the other hand, a manual CT scan inspection is laborious and prone to mistakes. Lung cancer can be impulsively recognized by utilizing an image processing technique of computer-Aided Diagnosis (CAD). In recent years, various researchers identified various ways for accurate prediction by Using techniques for deep learning (DL) and machine learning (ML). This research represents a number of methodologies like Lung cancer disease prediction analysis makes use of Artificial Neural Network (ANN), Logistic Regression (LR), Support Vector Machine (SVM), K-means clustering, Convolutional Neural Network (CNN) Recurrent Neural Network (RNN) and Long Short-Term Memory (LSTM). © 2024 IEEE.

关键词： Diseases

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Handover procedure using probability based mobility patterns in 3G-WLAN interworking environment

Handover procedure using probability based mobility patterns...

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2012 International Conference on Recent Advances in Computing and Software systems, RACSS 2012

作者： Gupta, Vishal Swaroop, K Shanthi Department of Computer Science and Information System Birla Institute of Technology and Science Pilani India

ISBN: (纸本)9781467302548

With the projected increase of 3G network traffic in near future, telecom operators are looking for the alternative means of satisfying the data needs of mobile subscribers without scaling the existing 3G network infrastructure. Over the last decade, IEEE 802.11 network (better called as Wi-Fi network) has emerged as one of the most competitive technology for it. It can function with the macro cellular network, as an adjunct network, to provide an alternative path with the best performance capability. On the other hand, several studies have reported the discovery of fundamental statistical features of human mobility. Studies show that human trajectories show a high degree of temporal and spatial regularity, thus following simple reproducible patterns. This paper proposes a probability based approach to find the mobility patterns of users and then use the information to effectively decrease the handover latency and increase throughput. © 2012 IEEE.

关键词： Wi-Fi

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Agent Modeling of User Preferences Based on Fuzzy Classified ANNs in Automated Negotiation

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Journal of Donghua University(English Edition) 2011年第1期28卷 45-48页

作者：顾铁军汤兵勇马溪骏李毅 Glorious Sun School of Business and Management Donghua University Institute of Computer Network System Hefei University of Technology Information Security System Testing Lab The Third Research Institute of Ministry of Public Security

In agent-based automated negotiation research area,a key problem is how to make software agent more adaptable to represent user preferences or suggestions,so that agent can take further proposals that reflect user requirements to implement ecommerce activities like automated *** difficulty lies in the uncertainty of user preferences that include uncertain description and contents,non-linear and dynamic *** this paper,fuzzy language was used to describe the uncertainty and combine with multiple classified artificial neural networks(ANNs) for self-adaptive learning of user *** refinement learning results of various negotiation contracts' satisfaction degrees in the extent of fuzzy classification can be *** to unclassified computation,the experimental results illustrate that the learning ability and effectiveness of agents have been improved.

关键词： agent automated negotiation user modeling artificial neural network（ANN） fuzzy classification

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Anomaly Detection in Microservice systems Using Autoencoders 4

Anomaly Detection in Microservice Systems Using Autoencoders

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4th International Conference on Advancements in Computing, ICAC 2022

作者： de Silva, Manul Daniel, Sam Kumarapeli, Manith Mahadura, Sashika Rupasinghe, Lakmal Liyanapathirana, Chethana Computer System Engineering Sri Lanka Institute of Information Technology Malabe Sri Lanka

ISBN: (纸本)9798350398090

The adaptation of microservice architecture has increased massively during the last few years with the emergence of the cloud. Containers have become a common choice for microservices architecture instead of VMs (Virtual Machines) due to their portability and optimized resource usage characteristics. Along with the containers, container-orchestration platforms are also becoming an integral part of microservice-based systems, considering the flexibility and scalability offered by the container-orchestration media. With the virtualized implementation and the dynamic attribute of modern microservice architecture, it has been a cumbersome task to implement a proper observability mechanism to detect abnormal behaviour using conventional monitoring tools, which are most suitable for static infrastructures. We present a system that will collect required data with the understanding of the dynamic attribute of the system and identify anomalies with efficient data analysis methods. © 2022 IEEE.

关键词： Containers

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Image Enhancement via Associated Perturbation Removal and Texture Reconstruction Learning

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IEEE/CAA Journal of Automatica Sinica 2024年第11期11卷 2253-2269页

作者： Kui Jiang Ruoxi Wang Yi Xiao Junjun Jiang Xin Xu Tao Lu IEEE the School of Computer Science and Technology Harbin Institute of Technology Zhengzhou Research Institute Harbin Institute of Technology the School of Artificial Intelligence Jianghan University the School of Geodesy and Geomatics Wuhan University the Hubei Province Key Laboratory of Intelligent Information Processing and Real-Time Industrial System Wuhan University of Science and Technology the School of Computer Science and Engineering and also with the Hubei Province Key Laboratory of Intelligent Robot Wuhan Institute of Technology

Degradation under challenging conditions such as rain, haze, and low light not only diminishes content visibility, but also results in additional degradation side effects, including detail occlusion and color distortion. However, current technologies have barely explored the correlation between perturbation removal and background restoration, consequently struggling to generate high-naturalness content in challenging scenarios. In this paper, we rethink the image enhancement task from the perspective of joint optimization: Perturbation removal and texture reconstruction. To this end, we advise an efficient yet effective image enhancement model, termed the perturbation-guided texture reconstruction network(PerTeRNet). It contains two subnetworks designed for the perturbation elimination and texture reconstruction tasks, respectively. To facilitate texture recovery,we develop a novel perturbation-guided texture enhancement module(PerTEM) to connect these two tasks, where informative background features are extracted from the input with the guidance of predicted perturbation priors. To alleviate the learning burden and computational cost, we suggest performing perturbation removal in a sub-space and exploiting super-resolution to infer high-frequency background details. Our PerTeRNet has demonstrated significant superiority over typical methods in both quantitative and qualitative measures, as evidenced by extensive experimental results on popular image enhancement and joint detection tasks. The source code is available at https://***/kuijiang94/PerTeRNet.

关键词： Association learning attention mechanism image enhancement perturbation modeling

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Automated object recognition in high-resolution optical remote sensing imagery

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National Science Review 2023年第6期10卷 38-41页

作者： Yazhou Yao Tao Chen Hanbo Bi Xinhao Cai Gensheng Pei Guoye Yang Zhiyuan Yan Xian Sun Xing Xu Hai Zhang School of Computer Science and Engineering Nanjing University of Science and Technology Aerospace Information Research Institute Chinese Academy of Sciences School of Electronic Electrical and Communication Engineering University of Chinese Academy of Sciences Key Laboratory of Network Information System Technology (NIST) Aerospace Information Research Institute Chinese Academy of Sciences Department of Computer Science and Technology Tsinghua University School of Computer Science and Engineering University of Electronic Science and Technology of China Pazhou Laboratory (Huangpu) School of Mathematics Northwest University

INTRODUCTION With the rapid development of remote sensing technology,high-quality remote sensing images have become widely *** automated object detection and recognition of these images,which aims to automatically locate objects of interest in remote sensing images and distinguish their specific categories,is an important fundamental task in the *** provides an effective means for geospatial object monitoring in many social applications,such as intelligent transportation,urban planning,environmental monitoring and homeland security.

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Macro liveness graph and liveness of ω-independent unbounded nets

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Science China(information Sciences) 2015年第3期58卷 132-141页

作者： WANG ShouGuang GAN MengDi ZHOU MengChu School of Information and Electronic Engineering Zhejiang Gongshang University Key Laboratory of Embedded System and Service Computing Ministry of EducationTongji University Department of Electrical and Computer Engineering New Jersey Institute of Technology

Liveness is a basic property of a system and the liveness issue of unbounded Petri nets remains one of the most difficult problems in this *** work proposes a novel method to decide the liveness of a class of unbounded generalized Petri nets calledω-independent unbounded nets,breaking the existing limits to one-place-unbounded *** algorithm to construct a macro liveness graph(MLG)is developed and a critical condition based on MLG deciding the liveness ofω-independent unbounded nets is *** are provided to demonstrate its effectiveness.

关键词： discrete event system(DES) Petri nets complex systems liveness property analysis

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