检索结果-内蒙古大学图书馆

Identification and Visualization of Spatial and Temporal Trends in Textile Industry

computers, Materials & Continua 2023年第2期74卷 4165-4181页

作者： Umair Yousaf Muhammad Asif Shahbaz Ahmed Noman Tahir Azeem Irshad Akber Abid Gardezi Muhammad Shafiq Jin-Ghoo Choi Habib Hamam Department of Computer Science National Textile UniversityFaisalabad38000Pakistan Department of Computer Science and Software Engineering International Islamic UniversityIslamabad44000Pakistan Department of Computer Science COMSATS University IslamabadIslamabad45550Pakistan Department of Information and Communication Engineering Yeungnam UniversityGyeongsan38541Korea Faculty of Engineering Uni de MonctonMonctonNB E1A3E9Canada International Institute of Technology and Management Commune d’AkandaBPLibreville1989Gabon School of Electrical Engineering Department of Electrical and Electronic Eng.ScienceUniversity of JohannesburgJohannesburg2006South Africa Spectrum of Knowledge Production&Skills Development Sfax3027Tunisia

The research volume increases at the study rate,causing massive text *** to these enormous text corpora,we are drowning in data and starving for ***,recent research employed different text mining approaches to extract information from this text *** proposed approaches extract meaningful and precise phrases that effectively describe the text’s *** extracted phrases are commonly termed ***,these key phrases are employed to determine the different fields of study ***,these key phrases can also be used to determine the spatiotemporal trends in the various research *** this research,the progress of a research field can be better revealed through spatiotemporal bibliographic trend ***,an effective spatiotemporal trend extraction mechanism is required to disclose textile research trends of particular regions during a specific *** study collected a diversified dataset of textile research from 2011–2019 and different countries to determine the research *** data was collected from various open access ***,this research determined the spatiotemporal trends using quality *** research also focused on finding the research collaboration of different countries in a particular research *** research collaborations of other countries’researchers show the impact on import and export of those *** visualization approach is also incorporated to understand the results better.

关键词： Text mining spatiotemporal trend research collaboration

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

Cryptography-Inspired Federated Learning for Generative Adversarial Networks and Meta Learning 9th

Cryptography-Inspired Federated Learning for Generative Adv...

引用

19th International Conference on Advanced data Mining and Applications, ADMA 2023

作者： Zheng, Yu Song, Wei Du, Minxin S. M. Chow, Sherman Lou, Qian Zhao, Yongjun Wang, Xiuhua Department of Information Engineering Chinese University of Hong Kong N.T. Hong Kong School of Computer Science and Engineering Northeastern University Shenyang China Department of Computer Science University of Central Florida Orlando United States Shanghai China School of Cyber Science and Engineering Huazhong University of Science and Technology Wuhan China

ISBN: (纸本)9783031466632

Federated learning (FL) aims to derive a "better" global model without direct access to individuals’ training data. It is traditionally done by aggregation over individual gradients with differentially private (DP) noises. We study an FL variant as a new point in the privacy-performance space. Namely, cryptographic aggregation is over local models instead of gradients;each contributor then locally trains their model using a DP version of Adam upon the "feedback" (e.g., fake samples from GAN – generative adversarial networks) derived from the securely-aggregated global model. Intuitively, this achieves the best of both worlds – more "expressive" models are processed in the encrypted domain instead of just gradients, without DP’s shortcoming, while heavyweight cryptography is minimized (at only the first step instead of the entire process). Practically, we showcase this new FL variant over GAN and meta-learning, for securing new data and new tasks. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Generative adversarial networks

来源：评论

学校读者我要写书评

暂无评论

X-Match: A Semi-Supervised Framework for Oral Jawbones Segmentation Using Wavelet Transform for Enhanced Consistency Learning

X-Match: A Semi-Supervised Framework for Oral Jawbones Segme...

引用

2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Weng, Zhengkai Zheng, Songwei Yu, Chunyan Zhu, Danhong Yu, Hao Jia, Linghui Zhang, Dong Lin, Honglei Fuzhou University College of Computer and Data Science Fuzhou China Fujian Medical University Department of Prosthodontics School and Hospital of Stomatology Fuzhou China Fuzhou University Zhicheng College Fuzhou China

ISBN: (纸本)9798350386226

Determining the occlusal position in CBCT images is a critical step in the digital virtual articulator treatment of Anterior Disc Displacement with Reduction (ADDwR). Current oral segmentation methods primarily rely on supervised learning, which typically requires a large dataset. However, acquiring oral datasets is complex and challenging, making semi-supervised learning more suitable. Current semi-supervised segmentation methods have several limitations. The perturbations used in consistency-based semi-supervised methods are often manually designed, which can introduce negative biases detrimental to training. Furthermore, semi-supervised learning often faces an empirical mismatch between labeled and unlabeled data. When these two data types are handled independently or without alignment, significant information derived from labeled data may not be fully utilized. We propose a novel semi-supervised framework X-Match for oral jawbones segmentation. The X-Match utilizes wavelet transforms to extract low-frequency and high-frequency information for consistency training, reducing the learning bias caused by manual perturbations. Furthermore, it combines labeled with unlabeled data bidirectionally during training, allowing unlabeled data to acquire comprehensive shared features from labeled data. Experimental results demonstrate that our method outperforms baselines and achieves superior performance in oral jawbones dataset. © 2024 IEEE.

关键词： Semantic Segmentation

来源：评论

学校读者我要写书评

暂无评论

Boosting Performance and QoS for Concurrent GPU B+trees by Combining-Based Synchronization 23

Boosting Performance and QoS for Concurrent GPU B+trees by C...

引用

28th ACM SIGPLAN Annual Symposium on Principles and Practice of Parallel programming, PPoPP 2023

作者： Zhang, Weihua Zhao, Chuanlei Peng, Lu Lin, Yuzhe Zhang, Fengzhe Lu, Yunping School of Computer Science Fudan University China Institute of Big Data Fudan University China State Key Laboratory of Mathematical Engineering and Advanced Computing China Parallel Processing Institute Fudan University China Department of Computer Science Tulane University United States

ISBN: (纸本)9798400700156

Concurrent B+trees have been widely used in many systems. With the scale of data requests increasing exponentially, the systems are facing tremendous performance pressure. GPU has shown its potential to accelerate concurrent B+trees performance. When many concurrent requests are processed, the conflicts should be detected and resolved. Prior methods guarantee the correctness of concurrent GPU B+trees through lock-based or software transactional memory (STM)-based approaches. However, these methods complicate the request processing logic, increase the number of memory accesses and bring execution path divergence. They lead to performance degradation and variance in response time increasing. Moreover, previous methods do not guarantee linearizability among concurrent requests. In this paper, we design a combined-based concurrency control framework, called Eirene, for GPU B+tree to reduce the overhead of conflict detection and resolution. First, a combining-based synchronization method is designed to combine and issue requests. It combines the requests with the same key, constructs their dependence, decides the issued request, and determines their return values. Since only one request for each key is issued, key conflicts are eliminated. Then, an optimistic STM method is used to reduce structure conflicts. The query and the update requests are partitioned into different kernels. For the update kernels, STM is involved only when the number of the retry reaches a threshold. Finally, a locality-aware warp reorganization optimization is proposed to improve memory behavior and reduce conflicts by exploiting the locality among requests. Evaluations on an NVIDIA A100 GPU show that Eirene is efficient (a throughput of 2.4 billion per second) and can guarantee linearizability. Compared to the state-of-the-art GPU B+tree, it can achieve a speedup of 7.43X and reduce the response time variance from 36% to 5%. © 2023 ACM.

关键词： Graphics processing unit

来源：评论

学校读者我要写书评

暂无评论

Applications of Topological data Analysis with Morphology Operators on Hypergraphs

Applications of Topological Data Analysis with Morphology Op...

引用

International Conference on Mathematics of Intelligent Computing and data science, ICMICDS 2022

作者： Ramkumar, P.B. Dhanya, P.M. Department of Mathematics Rajagiri School of Engineering and Technology Kerala Kakkanad India Department of Computer Science and Business Systems Rajagiri School of Engineering and Technology Kerala Kakkanad India

ISBN: (纸本)9789819615049

A hypergraph is a type of graph, H=(V,E) where V is the set of nodes and E is the set of hyperedges;where one edge consists of may nodes. Morphological operators are nonlinear operators which can be applied on images, graphs, hypergraphs etc. Dilation and erosion are morphological operators and other operators like opening, closing can be obtained by repeated application of these operators. Topological space consists of a set X together with topology τ defined on X, which satisfies the axioms namely Nullset, arbitrary union of opensets and finite intersection of opensets. Such a topological space S=(X,T) is compatible with a hypergraph H if X=VH∪EH where VH and EH are the vertex set and edge set respectively. The morphological operators induces several topology on hypergraph with its compatible topological space S=(X,T). Neighborhood of a node is defined in many ways which can be considered as open sets in the corresponding topological space. The purpose of this paper is to apply topological properties on hypergraphs using morphological operators. Separation axiom and closure function are illustrated using hypergraph structure. The work is extended to crime analysis, text processing and patient disease analysis. Topological feature extraction is done on Barcodes created from crime hypergraphs. This gives a better visualization of crime analysis. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Graph theory

来源：评论

学校读者我要写书评

暂无评论

Sustainable Agriculture-Based Climate Change Training Models using Remote Hyperspectral Image with Machine Learning Model

引用

Remote Sensing in Earth Systems sciences 2024年第4期7卷 261-270页

作者： Durairaj, M. Rubenraju, Kasapaka Krishna, B. V. Rama Yajid, Mohd Shukri Ab Jackson, Beulah Babu, Jampani Satish Padmavathi, Kodali Lakshmi Department of ECE Saveetha School of Engineering SIMATS Chennai India Department of Data Science & amp IT Malla Reddy University Telangana Hyderabad 500100 India Department of CSE Aditya College of Engineering and Technology Andhra PradeshSurampalem India Management and Science University Selangor Shah Alam Malaysia Department of ECE Vel Tech Rangarajan Dr. Sagunthala R & amp D Institute of Science and Technology Chennai India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Vaddeswaram A.P. Guntur 522302 India Department of Artificial Intelligence and Data Science Lakireddy Bali Reddy College of Engineering Andhra Pradesh India

In order to help farmers and crop managers better understand the elements influencing crop status and growth, hyperspectral and multispectral data processing methods have shown to be beneficial. Utilising advanced computational methods via machine learning is one strategy that has been in use recently. This method can forecast satellite image data based on the circumstances of mapping different types of land and vegetation in the field. This research proposes novel technique in sustainable agriculture-based climate change detection using hyperspectral image analysis with machine learning model. Here, the hyperspectral image of agricultural field is collected as input and processed for smoothening with normalisation. The proposed image analysis model is carried out in two stages which is feature extraction and classification. In stage 1, the feature extraction of processed input hyperspectral image is carried out using multilayer Bayesian encoder vector model (MBEV). The second stage of this proposed model is to classify the extracted image using deep convolutional belief neural networks (DCBNN). The experimental analysis has been carried out for various agriculture-based hyperspectral image datasets in terms of training accuracy, sensitivity, specificity, and AUC. The experimental findings demonstrate that, when compared to other ways, the suggested strategy performed exceptionally well. Proposed technique attained training accuracy of 97%, AUC of 85%, sensitivity of 96%, and specificity of 93%. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024.

关键词： Classification Climate change Hyperspectral image analysis Machine learning Sustainable agriculture

来源：评论

学校读者我要写书评

暂无评论

Dynamic Trust Management in IoT Networks using Reputation-Based Systems

Dynamic Trust Management in IoT Networks using Reputation-Ba...

引用

2024 International Conference on Distributed Systems, computer Networks and Cybersecurity, ICDSCNC 2024

作者： Naidu, D J Samatha Jain, Upma Shruthi, S. Rao, K. B. V. Brahma Iqbal, Syed Mohammad Uzair Natrayan, L. Annamacharya Pg College of Computer Studies Department of Master of Computer Applications India Graphic Era Deemed to Be University Department of Computer Science & Engineering Dehradun India Bms Institute of Technology and Management Department of Artificial Intelligence and Machine Learning Bengaluru India Koneru Lakshmaiah Education Foundation Department of Computer Science and Engineering India Symbiosis Law School Department of Law Pune India Simats Saveetha School of Engineering Department of Mechanical Engineering India

ISBN: (纸本)9798350375442

The Internet of Things (IoT) enables intelligent data exchange across many kinds of interconnected devices in a diverse and unpredictable physical setting. The Internet of Things (IoT) network used in this study is a cluster-based sensor network that includes a base station and an application server that offers various services in a cloud setting. In this setting, assaults may occur in both device-to-device and user-to-device communication. For pervasive devices with limited resources, the use of conventional encryption techniques to address this problem is not practical. In addition, the majority of current trust-based security solutions either address the user level or the devices level. Therefore, this study aims to tackle environment-specific security concerns at the device and user levels in light of the environment's inherent dynamic and dispersed nature. This research explored the usage of a Trust Based Security Framework (TBSF) and Reputation Based Systems in the Internet of Things (IoT) to accurately describe the actions of devices and users. Fuzzy logic was used to calculate both the device trust and the user trust. A trust-based access control system, secure cluster head selection, and safe routing were all built using these trust values. At first, the security of device-to-device communication was addressed by the design and effective implementation of the Trust Aware Device Access Control System (TADACS). © 2024 IEEE.

关键词： Access control

来源：评论

学校读者我要写书评

暂无评论

Machine-Learning Based Optimal Design of a Wireless Power Transfer Coil for Battery-Powered Tram 11

Machine-Learning Based Optimal Design of a Wireless Power Tr...

引用

11th International Conference on Power Electronics - ECCE Asia, ICPE 2023-ECCE Asia

作者： Noh, Eunchong So, Junhyuk Lee, Seung-Hwan University of Seoul School of Electrical and Computer Engineering Seoul Korea Republic of Postech Department of Computer Science and Engineering Pohang Korea Republic of

ISBN: (纸本)9788957083505

This paper proposes an optimal design method for a wireless power transfer (WPT) coil using a machine learning regression model. The proposed method was applied to design a WPT coil for a tram wireless power charging system. Designing a wireless power transmission coil with ferrite is challenging since it is difficult to calculate self-inductance, coupling coefficient, and winding resistance with an analytical model. This paper proposes a method for optimal design by inferring coil parameters using a machine learning regression model based on finite element analysis (FEA) simulation data. The proposed design methodology achieves optimal efficiency for coupling coefficients and shapes under given design conditions by using parameter estimation with a regression model and NSGA-II. This methodology searches not only for optimal shapes but also for optimal coupling coefficients, resulting in high coil-coil efficiency. The proposed WPT coil design methodology was used to design a coil for a 200kW 60kHz WPT system for a battery-powered tram, achieving a coil-coil efficiency of up to 97%. © 2023 The Korean Institute of Power Electronics.

关键词： Efficiency

来源：评论

学校读者我要写书评

暂无评论

Enhancing the Accuracy for Predicting Nutritious Food Recommendations for Patients using Ensemble Random Forest over Artificial Neural Network 2

Enhancing the Accuracy for Predicting Nutritious Food Recomm...

引用

2nd IEEE International Conference on Trends in Quantum Computing and Emerging Business Technologies, TQCEBT 2024

作者： Mukund, J. Akilandeswari, A. Department of Computer Science and Engineering Saveetha School of Engineering Saveetha University Saveetha Institute of Medical and Technical Sciences Chennai India

ISBN: (数字)9798350384277

ISBN: (纸本)9798350384277

This study uses Innovative Ensemble Random Forest (ERF) and Artificial Neural Network to propose healthy diet to patients based on their health. data collection and model training are required. This study uses Innovative Ensemble Random Forest (ERF) and Artificial Neural Network (ANN) data mining approaches on two groups. Each group has 10 samples for 20 total. Sample size is calculated using 0.95 G power. The parameters are CI 95, alpha 0.05, and beta 0.002. ERF and ANN should be used to train and assess healthy meal recommendation systems. The Innovative Ensemble Random Forest (ERF) approach outperforms the Artificial Neural Network algorithm with a 95.72 % accuracy and a statistical significance of 0.008 (p © 2024 IEEE.

关键词： Training Quantum computing Artificial neural networks data collection Market research data models data mining

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：