检索结果-内蒙古大学图书馆

CNN Based Features Extraction and Selection Using EPO Optimizer for Cotton Leaf Diseases Classification

Computers, Materials & Continua 2023年第9期76卷 2779-2793页

作者： Mehwish Zafar JaveriaAmin Muhammad Sharif Muhammad Almas Anjum Seifedine Kadry Jungeun Kim Department of Computer Science COMSATS University IslamabadWah CampusWah Cantt47040Pakistan Department of Computer Science University ofWahWah Cantt47040Pakistan National University of Technology(NUTECH) Islamabad44000Pakistan Department of Applied Data Science Noroff University CollegeKristiansandNorway Artificial Intelligence Research Center(AIRC) College of Engineering and Information TechnologyAjman UniversityAjmanUnited Arab Emirates Department of Electrical and Computer Engineering Lebanese American UniversityByblosLebanon Department of Software Kongju National UniversityCheonan31080Korea

Worldwide cotton is the most profitable cash *** year the production of this crop suffers because of several *** an early stage,computerized methods are used for disease detection that may reduce the loss in the production of *** several methods are proposed for the detection of cotton diseases,however,still there are limitations because of low-quality images,size,shape,variations in orientation,and complex *** to these factors,there is a need for novel methods for features extraction/selection for the accurate cotton disease *** in this research,an optimized features fusion-based model is proposed,in which two pre-trained architectures called EfficientNet-b0 and Inception-v3 are utilized to extract features,each model extracts the feature vector of length N×*** that,the extracted features are serially concatenated having a feature vector lengthN×*** prominent features are selected usingEmperor PenguinOptimizer(EPO)*** method is evaluated on two publically available datasets,such as Kaggle cotton disease dataset-I,and Kaggle *** EPO method returns the feature vector of length 1×755,and 1×824 using dataset-I,and dataset-II,*** classification is performed using 5,7,and 10 folds *** Quadratic Discriminant Analysis(QDA)classifier provides an accuracy of 98.9%on 5 fold,98.96%on 7 fold,and 99.07%on 10 fold using Kaggle cotton disease dataset-I while the Ensemble Subspace K Nearest Neighbor(KNN)provides 99.16%on 5 fold,98.99%on 7 fold,and 99.27%on 10 fold using Kaggle cotton-leaf-infection dataset-II.

关键词： Deep learning cotton disease detection features selection classification efficientnet-b0 inception-v3 quadratic discriminant analysis subspace KNN

来源：评论

学校读者我要写书评

暂无评论

Optimizing Secure Multi-User ISAC Systems With STAR-RIS: A Deep Reinforcement Learning Approach for 6G Networks

引用

IEEE Access 2025年 13卷 31472-31484页

作者： Kamal, Mian Muhammad Zain Ul Abideen, Syed Al-Khasawneh, M.A. Alabrah, Amerah Sohail Ahmed Larik, Raja Irfan Marwat, Muhammad Southeast University School of Electronic Science and Engineering Jiangning Jiangsu Nanjing211189 China Qingdao University College of Computer Science and Technology Qingdao266071 China Al-Ahliyya Amman University Hourani Center for Applied Scientific Research Amman19111 Jordan Skyline University College School of Computing University City Sharjah Sharjah United Arab Emirates King Saud University College of Computer and Information Science Department of Information Systems Riyadh11543 Saudi Arabia Ilma University Department of Computer Science Sindh Karachi75190 Pakistan University of Science and Technology Bannu Department of Software Engineering Bannu28100 Pakistan

The rapid evolution of wireless communication technologies and the increasing demand for multi-functional systems have led to the emergence of integrated sensing and communication (ISAC) as a key enabler for future 6G networks. Simultaneously transmitting and reflecting reconfigurable intelligent surfaces (STAR-RISs) have recently garnered significant attention for their ability to enhance signal coverage and improve system efficiency. This paper investigates a STAR-RIS-assisted ISAC system designed to secure communication for multiple legitimate users (LUs) while safeguarding against multiple eavesdroppers (Eves). By jointly optimizing the base station (BS) transmit beamforming, STAR-RIS transmission and reflection coefficients, and receive filters, the proposed framework aims to maximize the long-term average secrecy rate for all LUs. Constraints are imposed to ensure minimum echo signal-to-noise ratios (SNRs) for sensing and meet the achievable rate requirements of LUs. To address the inherent complexity of this non-convex problem, two deep reinforcement learning (DRL) algorithms are proposed. Numerical results demonstrate that the proposed system achieves significant improvements in secrecy rate compared to conventional RIS setups. This work provides a scalable and efficient approach for secure multi-user ISAC systems, making it highly relevant for future 6G networks, smart cities, and IoT applications. © 2025 The Authors.

关键词： Deep reinforcement learning

来源：评论

学校读者我要写书评

暂无评论

Density Transformer for Unsupervised Time Series Anomaly Detection in Cloud Computing

Density Transformer for Unsupervised Time Series Anomaly Det...

引用

International Joint Conference on Neural Networks (IJCNN)

作者： Bin Yang Zian Wang Zelan Zhu Ying Xing Yanbing Bai Lanshan Zhang Yaya Wei China Unicom Research Institute Beijing University of Posts and Telecommunications Center for Applied Statistics and School of Statistics Renmin University of China Yunnan Key Laboratory of Software Engineering Omni-Channel Operations Center China Telecom Group’s

ISBN: (数字)9798350359312

ISBN: (纸本)9798350359329

Unsupervised anomaly detection in cloud computing is crucial for system security and efficiency. However, the challenges posed by large data volumes, low anomaly rates, and diverse anomaly patterns in time series within cloud computing scenarios make it difficult for previous methods to obtain consistent and reliable representations for distinguishing anomalies. To avoid the degradation of model representation ability caused by abnormal sparsity, we propose the Density Transformer, a novel reconstruction-based explicit association modeling model that can amplify the non-trivial correlation of abnormal points with adjacent time points. Specifically, we express the density association by calculating the kernel density estimate at each time point, and the series association by calculating the self-attention at each time point. Then, the model uses an adversarial training strategy to produce a more significant difference in "association discrepancy" between normal points and abnormal points, thereby ensuring robust results in anomaly detection. Our model has been rigorously evaluated on a comprehensive collection of 6 publicly available real-world datasets, and the Density Transformer can achieve up to 46% improvement in F1-score compared to existing methods.

关键词： Training Cloud computing Computational modeling Time series analysis Reconstruction algorithms Transformers Security

来源：评论

学校读者我要写书评

暂无评论

Clustered Single-Board Devices with Docker Container Big Stream Processing Architecture

引用

Computers, Materials & Continua 2022年第12期73卷 5349-5365页

作者： N.Penchalaiah Abeer S.Al-Humaimeedy Mashael Maashi J.Chinna Babu Osamah Ibrahim Khalaf Theyazn H.H.Aldhyani Department of CSE AITSRajampet516126Andhra PradeshIndia Software Engineering Department College of Computer and Information SciencesKing Saud UniversityP.O.Box 103786Riyadh11616Saudi Arabia Software Engineering Department King Saud UniversityRiyadh11543Saudi Arabia Department of Electronics and Communication Engineering Annamacharya Institute of Technology and SciencesRajampet516126Andhra PradeshIndia Al-Nahrain University Al-Nahrain Nanorenewable Energy Research CenterBaghdad10072Iraq Applied College in Abqaiq King Faisal UniversityP.O.Box 400Al-Ahsa31982Saudi Arabia

The expanding amounts of information created by Internet of Things(IoT)devices places a strain on cloud computing,which is often used for data analysis and *** paper investigates a different approach based on edge cloud applications,which involves data filtering and processing before being delivered to a backup cloud *** Paper suggest designing and implementing a low cost,low power cluster of Single Board Computers(SBC)for this purpose,reducing the amount of data that must be transmitted elsewhere,using Big Data ideas and *** Apache Hadoop and Spark Cluster that was used to run a test application was containerized and deployed using a Raspberry Pi cluster and *** obtain system data and analyze the setup’s performance a Prometheusbased stack monitoring and alerting solution in the cloud based market is *** Paper assesses the system’s complexity and demonstrates how containerization can improve fault tolerance and maintenance ease,allowing the suggested solution to be used in *** evaluation of the overall performance is presented to highlight the capabilities and limitations of the suggested architecture,taking into consideration the suggested solution’s resource use in respect to device restrictions.

关键词： Big data edge cloud cluster architecture performance engineering Raspberry pi dockers warm container technology data streaming

来源：评论

学校读者我要写书评

暂无评论

Efficient Privacy-Preserving ML for IoT: Cluster-Based Split Federated Learning Scheme for Non-IID Data 7

Efficient Privacy-Preserving ML for IoT: Cluster-Based Split...

引用

7th Cyber Security in Networking Conference, CSNet 2023

作者： Arafeh, Mohamad Wazzeh, Mohamad Ould-Slimane, Hakima Talhi, Chamseddine Mourad, Azzam Otrok, Hadi Department of Software and It Engineering Montreal Canada Department of Mathematics and Computer Science Canada New York University Division of Science Abu Dhabi United Arab Emirates Cyber Security Systems and Applied Ai Research Center Lebanese American University Department of Csm Beirut Lebanon Khalifa University Department of Eecs Abu Dhabi United Arab Emirates

ISBN: (纸本)9798350342871

This paper proposes a scheme addressing the challenges of integrating privacy-preserving distributed machine learning in the Internet of Things (IoT) context while improving the efficiency of the learning process and accelerating the convergence of the model. Specifically, it addresses the presence of non-independent and identically distributed (Non-IID) data and device heterogeneity, which occurs during parallel training and are common characteristics of IoT environments. In this context, SplitFed is a privacy-preserving technique that combines Federated Learning (FL) and Split Learning (SL) to enable fast and privacy-preserving model training on IoT devices. Through parallelism, FL allows fast and parallel model training. Furthermore, SL enables weak devices to train models through model splitting. However, SplitFed suffers from the increasing convergence time due to the FL parallelism introducing Non-IID and stragglers issues. In this paper, we aim to address the aforementioned problems by proposing a clustering parallelization scheme. As such, similar clients are combined based on their weight divergence and considered a single FL unit addressing the Non-IID problem. Moreover, to improve time efficiency and overcome the stragglers' presence, we offer a second clustering stage that separates clients by efficiency and uses FedAsync for model aggregation. © 2023 IEEE.

关键词： Internet of things

来源：评论

学校读者我要写书评

暂无评论

Comparative Analysis of Machine Learning Models for Solar Power Generation Prediction

Comparative Analysis of Machine Learning Models for Solar Po...

引用

International Conference on Smart Computing & Communications (ICSCC)

作者： Goodness Chinaza Orish Samuel Nii Tackie Devrim Kayali Kezban Alpan Lida Ebrahimi Vafaei Kamil Dimililer Department of Electrical and Electronic Engineering Near East University Nicosia North Cyprus Department of Electrical and Electronic Engineering Near East University Research Center for Science Technology and Engineering Nicosia North Cyprus Department of Software Engineering Near East University Research Center for Science Technology and Engineering Nicosia North Cyprus Department of Alternative Energy Resources Technology Near East University Research Center for Science Technology and Engineering Nicosia North Cyprus Department of Electrical and Electronic Engineering Applied Artificial Intelligence Research Centre (AAIRC) Research Center for Science Technology and Engineering Near East University Nicosia North Cyprus

ISBN: (数字)9798350363104

ISBN: (纸本)9798350363111

When constructing renewable energy facilities, it is important to predict the amount of solar electricity generated precisely. To make these precise solar power output forecasts, varying weather patterns should be considered. In this study, the effectiveness of three well-known machine learning methods: K nearest neighbour, Random Forest, and support vector regression was examined and assessed. According to the obtained R2 values, Random Forest performed better compared to the other techniques with 98% on training data and 88.7% on testing data. This shows that when compared to SVR and KNN, the generalization of Random Forest is better when capturing the connection between the input and output data.

关键词： Radio frequency Training Accuracy Electricity Measurement uncertainty Training data Weather forecasting

来源：评论

学校读者我要写书评

暂无评论

Usability Evaluation of Kids' Learning Apps 2

Usability Evaluation of Kids' Learning Apps

引用

2nd International Conference on Business Analytics for Technology and Security, ICBATS 2023

作者： Ibrahim, Amer Al-Rajab, Murad Hamid, Khalid Aqeel, Muhamamd Muneer, Salman Parveen, Mehvish Saleem, Maryam American University in the Emirates College of Computer and Information Technology Dubai United Arab Emirates Abu Dhabi University Computer Scienc and IT College of Engineering United Arab Emirates Superior University Department of Computer Science Lahore Pakistan Superior University Department of Software Engineering Lahore Pakistan NCBA&E School of Computer Science Lahore Pakistan Applied Science Private University Applied Science Research Center Amman11937 Jordan NCBAE School of Computer Science Lahore Pakistan

ISBN: (纸本)9798350335644

The main objective of learning applications is to use modern electronic technologies to impart knowledge, communicate knowledge, and Direct learning models with their learning activities in a timely and effective manner. The preponderance of the apps were designed to up-skill the kids in the fundamentals of letters and numbers. Generally, they were grounded and practice skills in nature, based on a lack of abilities in critical thinking, favoring parrot-fashion skills, and contributing to a deep conceptual comprehension of more specific concepts are unable. By using appropriate evaluation methodologies, the purposed study observes essential characteristics and aspects that affect the usability of interfaces. An evaluation of the app's features will figure out to ensure user contentment. When a kid interacts with a learning interface, the ease of use provided by the interface is validated. Because a large number of the apps were designed to train kids' fundamentals of alphabetic and numeric. The focus of this research is to see whether self-proclaimed learning apps for kids are built according to developmentally appropriate standards to help kids in formal and informal learning settings develop socially, emotionally, and academically. © 2023 IEEE.

关键词： Learning systems

来源：评论

学校读者我要写书评

暂无评论

Multi-phase-field simulation of austenite peritectic solidification based on a ferrite grain

引用

Chinese Physics B 2021年第1期30卷 572-577页

作者： Chao Yang Jing Wang Junsheng Wang Yu Liu Guomin Han Haifeng Song Houbing Huang School of Materials Science and Engineering Beijing Institute of TechnologyBeijing 100081China Advanced Research Institute of Multidisciplinary Science Beijing Institute of TechnologyBeijing 100081China Laboratory of Computational Physics Institute of Applied Physics and Computational MathematicsBeijing 100088China Software Center for High Performance Numerical Simulation China Academy of Engineering PhysicsBeijing 100088China

A multi-phase-field model is implemented to investigate the peritectic solidification of Fe-C alloy. The nucleation mode of austenite is based on the local driving force, and two different thicknesses of the primary austenite on the surface of the ferrite equiaxed crystal grain are used as the initial conditions. The simulation shows the multiple interactions of ferrite, austenite, and liquid phases, and the effects of carbon diffusion, which presents the non-equilibrium dynamic process during Fe-C peritectic solidification at the mesoscopic scale. This work not only reveals the influence of the austenite nucleation position, but also clarifies the formation mechanism of liquid phase channels and molten pools. Therefore, the present study contributes to the understanding of the micro-morphology and micro-segregation evolution mechanisms of Fe-C alloy during peritectic solidification.

关键词： multi-phase-field simulation morphology evolution peritectic solidification carbon diffusion Fe-C alloy

来源：评论

学校读者我要写书评

暂无评论

Self-Supervised Entity Alignment Based on Multi-Modal Contrastive Learning

引用

IEEE/CAA Journal of Automatica Sinica 2022年第11期9卷 2031-2033页

作者： Bo Liu Ruoyi Song Yuejia Xiang Junbo Du Weijian Ruan Jinhui Hu the School of Software Engineering Xi’an Jiaotong UniversityXi’an 710000 the China Electronics Technology Group Corporation(CETC)Key Laboratory of Smart City Model Simulation and Intelligent Technology the Smart City Research Institute of CETC and National Center for Applied Mathematics Shenzhen(NCAMS)Shenzhen 518000China the Chinese University of Hong Kong Shenzhen 518000China Tencent Shenzhen 518000China the CETC Key Laboratory of Smart City Model Simulation and Intelligent Technology the Smart City Research Institute of CETC and National Center for Applied Mathematics Shenzhen(NCAMS)Shenzhen 518000China the Shenzhen Institute of Advanced Technology Chinese Academy of SciencesShenzhen 518055

Dear Editor,This letter proposes an unsupervised entity alignment method,which realizes integration of multiple multi-modal knowledge graphs *** recent years,Large-scale multi-modal knowledge graphs(LMKGs),containing text and image,have been widely applied in numerous knowledge-driven topics,such as question answering,entity linking,information extraction,reasoning and recommendation.

关键词： modal linking letter

来源：评论

学校读者我要写书评

暂无评论

Robust Network Optimization by Deep Generative Models and Stochastic Optimization

引用

IEEE Transactions on Wireless Communications 2025年

作者： Zhang, Shutao Xue, Ye Tang, Zhiwei Wang, Hao Shen, Chao Shi, Qingjiang Chang, Tsung-Hui The Chinese University of Hon g Kong School of Science and Engineering Shenzhen China Shenzhen Research Institute of Big Data Shenzhen China The Chinese University of Hon g Kong School of Data Science Shenzhen China Shenzhen Research Institute of Big Data Shenzhen International Center for Industrial and Applied Mathematics China Guangdong Provincial Engineering Technology Research Center for Intelligent Industrial Digital Twin and Optimization Shenzhen518172 China Tongji University School of Software Engineering Shanghai China

Wireless network optimization is essential for improving the network performance in mobile communications. However, due to the stochastic nature of wireless networks, existing schemes based on analytical models and deterministic optimization are less reliable. To this end, we design a framework for robust network optimization based on deep generative models and stochastic optimization. Inspired by the powerful diffusion process, we propose a deep generative simulator to capture the statistical distribution of the network performance. By sampling from the deep generative simulator, we can alleviate the inherent uncertainty related to the network performance and devise an innovative expectation-quantile-based stochastic objective function. The inner expectation is designed for the temporal statistics, while the outer quantile is developed for the spatial statistics. This designated two-tier objective function is capable of mitigating temporal fluctuations and ensuring satisfactory network performance across most geographical grids, thereby achieving robustness. To solve this stochastic optimization problem, a smooth zeroth-order approach is introduced by taking advantage of the unique structure of quantile functions. Through theoretical performance analysis and simulation experiments with real-world datasets, we demonstrate the superiority of our approach over other baseline schemes, highlighting its practical utility in robust network optimization. © 2002-2012 IEEE.

关键词： Generative diffusion model quantile function robust approach wireless network optimization

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：