检索结果-内蒙古大学图书馆

24th Asia-Pacific Network Operations and Management Symposium, APNOMS 2023

作者： Kong, Wei Du, Qianjin Cao, Huayang Li, Hu Wang, Tong Tian, Jianwen Kuang, Xiaohui National Key Laboratory of Science and Technology on Information System Security Beijing China Department of Computer Science and Technology Tsinghua University Beijing China

ISBN: (纸本)9788995004395

Deep learning (DL) introduces a novel data-driven programming paradigm, where the system logic is constructed through data training. However, this approach poses challenges in terms of system analysis and defect detection. To address this issue, recent efforts have focused on testing deep learning systems using intuitive criteria known as Neuron Coverage (NC) and its variants. These criteria measure the activation proportion of neurons in a neural network. Unfortunately, recent DL applications have largely overlooked the importance of context during testing. To address this gap, this paper first incorporates the context of the DL pipeline deployed before test execution, such as medical diagnosis and Android Malware detection. Next, we formulate structural coverage criteria to guide test suite generation based on the properties of DL pipeline in different contexts. Furthermore, we proposed a coverage-guided search algorithm to efficiently generate test suites. Experimental results highlight the effectiveness of our approach in uncovering numerous erroneous behaviors in contexts such as medical image diagnosis and Android malware detection. This approach significantly enhances the robustness of DL models by considering the structural aspects of the DL pipeline. Copyright 2023 KICS.

关键词： Deep learning

来源：评论

学校读者我要写书评

暂无评论

Forecasting and managing urban futures: machine learning models and optimization of urban expansion

引用

Asian Journal of Civil Engineering 2024年第6期25卷 4673-4682页

作者： Abid, Mohammed Talib Aljarrah, Njood Shraa, Tamer Alghananim, Haneen Marouf Department of Architecture College of Engineering Al-Nahrain University Baghdah Iraq Computer Sciences Faculty of Information Technology and Computer Sciences Ajloun National University Ajloun Jordan Faculty of Science and Information Technology Cyber Security Irbid National University Irbid Jordan Faculty of Engineering Architecture Department Ain Shams University Cairo Egypt

This paper presents a novel method for predicting and controlling urban growth by combining convolutional neural networks (CNN) with Spider Monkey Optimization (SMO) to efficiently use their combined capabilities. This strategy utilizes sophisticated machine learning algorithms to forecast urban development patterns, while optimization technologies enhance these forecasts by including sustainable urban design ideas. Integration plays a crucial role in tackling the difficulties presented by fast urbanization, including issues related to land use, resource administration, and environmental sustainability in the fields of civil and architectural engineering. The study employs large datasets to assess the efficacy of CNN models in detecting urban expansion patterns and enhances these models with optimization scenarios encompassing land use, traffic flow, electricity efficiency, water resources, and waste management, all with the goal of enhancing urban sustainability. The findings demonstrate a significant improvement in the precision of predictions and the ability to maintain positive results, highlighting the crucial role of integrating machine learning and optimization in urban planning methodologies. This method not only provides urban planners and politicians with strong and effective tools for making decisions but also creates opportunities for implementing creative and sustainable solutions for urban growth. The results provide a substantial contribution to the existing urban planning literature by demonstrating the actual use of these technologies in regulating urban expansion. This will stimulate more study in this rapidly evolving subject. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024.

关键词： Convolutional neural networks Machine learning Optimization techniques Spider monkey optimization Sustainable urban planning Urban expansion

来源：评论

学校读者我要写书评

暂无评论

LSTM-Based Recognition of Sign Language 2024

LSTM-Based Recognition of Sign Language

引用

16th International Conference on Contemporary Computing, IC3 2024

作者： Bhadouria, Aman Bindal, Parish Khare, Naivedya Singh, Deepti Verma, Ankita Department of Computer Science Engineering& Information Technology Jaypee Institute of Information Technology Uttar Pradesh Noida62 India

ISBN: (纸本)9798400709722

Sign language recognition is a critical technology for enhancing communication accessibility for individuals with hearing impairments. In this paper, we present a robust and efficient system for sign language recognition using Long Short-Term Memory networks and Mediapipe, a versatile framework for machine learning solutions. Our approach leverages Mediapipe's pre-trained hand detection and tracking models to extract key hand landmarks from video sequences in real-time. These landmarks are then fed into an LSTM network, which is adept at capturing temporal dependencies, to classify various sign language gestures. There are learning aids available for those who are deaf or have trouble speaking or hearing, but they are rarely used. Real-time image processing would be used in the proposed system to handle live sign movements. After that, classifiers would be used to discriminate between different signs, and text would appear in the translated output. The existing systems can detect movements with a significant lag because they only use image processing. Our goal in our work is to develop a cognitive system that is trustworthy and sensitive enough for people with speech and hearing problems to use it in daily activities. © 2024 Copyright held by the owner/author(s).

关键词： Hearing aids

来源：评论

学校读者我要写书评

暂无评论

Hybrid dense matching features for cloud-based face recognition

引用

International Journal of Cloud Computing 2023年第2-4期12卷 399-423页

作者： Shreekumar, T. Karunakara, K. Faculty of Computer Science and Engineering Mangalore Institute of Technology and Engineering Mijar Moodabidre India Faculty of Information Science and Engineering Sri Siddartha Institute of Technology Sri Siddhartha Academy of Higher Education Tumakuru India

Cloud computing is a computing service done not on a local device but an internet connection to a data centre infrastructure. The cloud computing system also provides a scalability solution where cloud computing can increase the resources needed when doing larger data processing. In recent years, face bio-metric plays an important role in biometric authentication, where security is the primary concern. The two major difficulties to be addressed in face recognition are illumination and pose variation. This work proposes a cloud computing-based face recognition technique by consolidating four-patch LBP and the local landmark features to the acquired feature vector. After that, the SVM is utilised to recognise the individual. The feature set used for training and testing is also made available in the cloud. At some stage in the training phase, the kernel parameters of SVM are optimised with GWO to improve the recognition performance. This method yields a maximum recognition accuracy of 99.00% with LFW and 98.0% accuracy with YTF. Copyright © 2023 Inderscience Enterprises Ltd.

关键词： Support vector machines

来源：评论

学校读者我要写书评

暂无评论

Behaviour-diverse automatic penetration testing:a coverage-based deep reinforcement learning approach

引用

Frontiers of computer science 2025年第3期19卷 15-24页

作者： Yizhou YANG Longde CHEN Sha LIU Lanning WANG Haohuan FU Xin LIU Zuoning CHEN Zhongguancun Laboratory Beijing 100081China Zhejiang Lab Hangzhou 311121China National Research Centre of Parallel Computer Engineering and Technology Wuxi 214000China Faculty of Geographical Science Beijing Normal UniversityBeijing 100875China Department of Earth System Science Tsinghua UniversityBeijing 100084China

Reinforcement Learning(RL)is gaining importance in automating penetration testing as it reduces human effort and increases ***,given the rapidly expanding scale of modern network infrastructure,the limited testing scale and monotonous strategies of existing RLbased automated penetration testing methods make them less effective in practical *** this paper,we present CLAP(Coverage-Based Reinforcement Learning to Automate Penetration Testing),an RL penetration testing agent that provides comprehensive network security assessments with diverse adversary testing behaviours on a massive *** employs a novel neural network,namely the coverage mechanism,to address the enormous and growing action spaces in large *** also utilizes a Chebyshev decomposition critic to identify various adversary strategies and strike a balance between *** results across various scenarios demonstrate that CLAP outperforms state-of-the-art methods,by further reducing attack operations by nearly 35%.CLAP also provides enhanced training efficiency and stability and can effectively perform pen-testing over large-scale networks with up to 500 ***,the proposed agent is also able to discover pareto-dominant strategies that are both diverse and effective in achieving multiple objectives.

关键词： network security penetration testing reinforcement learning artificial intelligence

来源：评论

学校读者我要写书评

暂无评论

openGauss:An Enterprise-Grade Open-Source Database system

引用

Journal of computer science & technology 2024年第5期39卷 1007-1028页

作者： Guo-Liang Li Jiang Wang Guo Chen Department of Computer Science Tsinghua UniversityBeijing 100084China Huawei Technologies Co. LtdBeijing 100088China China Mobile Information Technology Center Beijing 100027China

We have built openGauss,an enterprise-grade open-source database *** has fulfilled its design goal of high performance,high availability,high security,and high *** high performance,it leverages NUMA(non-uniform memory access)-aware data access among multiple cores to enable efficient concurrent transaction processing,and symmetric multi-processing to make use of parallel processing resources ***,memory-optimized tables(MOTs)are designed to put everything in *** high availability,a three-tier pooling architecture that shares storage among the master and standby instances is proposed to achieve availability at 99.99%,containing both a distributed memory service(DMS)and a distributed storage service(DSS).For high security,it is a fully encrypted database with safe storage features,efficient complex querying,and *** high intelligence,an AI-based optimizer in the kernel and a self-driving platform named DBMind are demonstrated to achieve better performance and greater *** has served over 150 enterprises and institutions since its release in *** share the lessons we learned from its development and operation,and our customers.

关键词： database system open-source high performance storage engine

来源：评论

学校读者我要写书评

暂无评论

Obstacle Detection Using Arduino Board and Bluetooth Control 8th

Obstacle Detection Using Arduino Board and Bluetooth Control

引用

8th International Conference on information and Communication technology for Competitive Strategies, ICTCS 2023

作者： Pavitha, N. Dardige, Rohit Patil, Vaibhav Pawar, Ameya Shah, Bhavesh Department of Computer Engineering Faculty Science and Technology Vishwakarma University Pune India

ISBN: (纸本)9789819994885

An intelligent robotic vehicle with an ultrasonic sensor that can avoid obstacles in its path is the research idea. This sensor recognizes obstructions, permitting the vehicle to perform activities like halting, turning right or left, and going in reverse. The essential objective is to make a robot vehicle that can work independently or under client direction. The vehicle responds to avoid collision when the ultrasonic sensor detects an obstruction. The vehicle's development incorporates an Arduino UNO microcontroller, ultrasonic sensor, engines, and other electric parts. The microcontroller, which serves as the vehicle's brain, receives information about objects in the vehicle's path from the ultrasonic sensor. After that, the microcontroller issues commands to a servo motor that is connected to the wheels. This ensures that the vehicle comes to a stop in order to avoid colliding with anything. Moreover, the vehicle's usefulness incorporates Bluetooth availability for control. Bluetooth allows users to control the vehicle from a distance and even use voice commands to do so. The joining of a Bluetooth module empowers these control choices, upgrading the vehicle's flexibility and connection prospects. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Bluetooth

来源：评论

学校读者我要写书评

暂无评论

Robust Machine Learning Technique to Classify COVID-19 Using Fusion of Texture and Vesselness of X-Ray Images

引用

computer Modeling in Engineering & sciences 2024年第2期138卷 1981-2004页

作者： Shaik Mahaboob Basha Victor Hugo Cde Albuquerque Samia Allaoua Chelloug Mohamed Abd Elaziz Shaik Hashmitha Mohisin Suhail Parvaze Pathan Department of Electronics and Communication Engineering N.B.K.R.Institute of Science&TechnologyVidyanagarAndhra Pradesh524413India Department of Teleinformatics Engineering Federal University of CearáFortaleza60455-970Brazil Information Technology Department College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh84428Saudi Arabia Department of Mathematics Faculty of ScienceZagazig UniversityZagazig44519Egypt Faculty of Computer Science and Engineering Galala UniversitySuez435611Egypt Artificial Intelligence Research Center(AIRC) College of Engineering and Information TechnologyAjman UniversityAjmanUnited Arab Emirates Department of Electrical and Computer Engineering Lebanese American UniversityByblos13-5053Lebanon Department of Avionics Indian Institute of Space Science and TechnologyThiruvananthapuramKerala695547India Department of Veterinary Clinical Sciences University of MinnesotaSt.PaulMinnesota55108USA

Manual investigation of chest radiography(CXR)images by physicians is crucial for effective decision-making in COVID-19 ***,the high demand during the pandemic necessitates auxiliary help through image analysis and machine learning *** study presents a multi-threshold-based segmentation technique to probe high pixel intensity regions in CXR images of various pathologies,including normal *** information is extracted using gray co-occurrence matrix(GLCM)-based features,while vessel-like features are obtained using Frangi,Sato,and Meijering *** learning models employing Decision Tree(DT)and RandomForest(RF)approaches are designed to categorize CXR images into common lung infections,lung opacity(LO),COVID-19,and viral pneumonia(VP).The results demonstrate that the fusion of texture and vesselbased features provides an effective ML model for aiding *** ML model validation using performance measures,including an accuracy of approximately 91.8%with an RF-based classifier,supports the usefulness of the feature set and classifier model in categorizing the four different ***,the study investigates the importance of the devised features in identifying the underlying pathology and incorporates histogrambased *** analysis reveals varying natural pixel distributions in CXR images belonging to the normal,COVID-19,LO,and VP groups,motivating the incorporation of additional features such as mean,standard deviation,skewness,and percentile based on the filtered ***,the study achieves a considerable improvement in categorizing COVID-19 from LO,with a true positive rate of 97%,further substantiating the effectiveness of the methodology implemented.

关键词： Chest radiography(CXR)image COVID-19 classifier machine learning random forest texture analysis

来源：评论

学校读者我要写书评

暂无评论

A Multi-Objective Clustered Input Oriented Salp Swarm Algorithm in Cloud Computing

引用

computers, Materials & Continua 2024年第12期81卷 4659-4690页

作者： Juliet A.Murali Brindha T. Department of Computer Science and Engineering Noorul Islam Centre for Higher EducationKumaracoil629180India Department of Information Technology Noorul Islam Centre for Higher EducationKumaracoil629180India

Infrastructure as a Service(IaaS)in cloud computing enables flexible resource distribution over the Internet,but achieving optimal scheduling remains a *** resource allocation in cloud-based environments,particularly within the IaaS model,poses persistent *** methods often struggle with slow opti-mization,imbalanced workload distribution,and inefficient use of available *** limitations result in longer processing times,increased operational expenses,and inadequate resource deployment,particularly under fluctuating *** overcome these issues,a novel Clustered Input-Oriented Salp Swarm Algorithm(CIOSSA)is *** approach combines two distinct strategies:Task Splitting Agglomerative Clustering(TSAC)with an Input Oriented Salp Swarm Algorithm(IOSSA),which prioritizes tasks based on urgency,and a refined multi-leader model that accelerates optimization processes,enhancing both speed and *** continuously assessing system capacity before task distribution,the model ensures that assets are deployed effectively and costs are *** dual-leader technique expands the potential solution space,leading to substantial gains in processing speed,cost-effectiveness,asset efficiency,and system throughput,as demonstrated by comprehensive *** a result,the suggested model performs better than existing approaches in terms of makespan,resource utilisation,throughput,and convergence speed,demonstrating that CIOSSA is scalable,reliable,and appropriate for the dynamic settings found in cloud computing.

关键词： Cloud computing clustering resource allocation scheduling swam algorithms optimization common with in the subject discipline

来源：评论

学校读者我要写书评

暂无评论

An Enhanced RNN-LSTM Model for Fundus Image Classification to Diagnose Glaucoma

引用

SN computer science 2024年第5期5卷 514页

作者： Veena, H.N. Patil, Kavita K. Vanajakshi, P. Ambore, Anilkumar Gowda, Naveen Chandra Department of Computer Science and Engineering SJB Institute of Technology Bengaluru India Department of Information Science and Engineering Global Academy of Technology Bengaluru India Department of Information Science and Engineering Vivekananda Institute of Technology Bengaluru India School of CSE REVA University Bengaluru India School of Computer Science and Engineering REVA University Bengaluru India

In the current medical implications, one of the leading ocular diseases is Glaucoma which majorly damage the Optic Nerve Head (ONH) of the eye retina. The intraocular pressure of the eye leads to glaucoma, which may lead to complete or partial vision loss. Regular screening and early detection is the only solution to avoid further vision loss. Due to the laborious and manual procedure in diagnosis, an automatic system is needed to diagnose glaucoma. This paper presents a novel method using deep learning-based RNNLSTM classification model to develop an automatic approach to predict and classify the images to be as healthy or glaucomatous. The RNN and LSTM with dense, dropout, and batch normalization layers are used for training and testing the proposed prediction model. The RNN model is used for training the model and to overcome the problems that occur during training, the LSTM model is applied to increase the performance of the model. The proposed model achieved an accuracy of 97.4%, specificity of 97.9% and sensitivity of 97%in classifying the images. We have made use of the DRISHTI-GS database for training and testing the proposed model. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： Deep learning Glaucoma Long short-term memory (LSTM) Recurrent neural network (RNN)

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：