检索结果-内蒙古大学图书馆

第28届中国控制与决策会议

作者： Ren Rong School of Computer Science and Engineering Beifang University of Nationalities

ISBN: (纸本)9781467397155

Hyperspectral remote sensing technology is applied to many fields because of its super-multiband,high resolution and vast *** classification technology is a research hotspot *** information is not utilized fully in traditional remote sensing image classification method;so many improved algorithms are disappeared in order to enhance efficiency,accuracy and *** hyperspectral remote sensing image processing flow is ***,demerits and development tendency of classification method are clarified.

关键词： hyperspectral remote sensing classification algorithms pixel spectral feature

来源：评论

学校读者我要写书评

暂无评论

Investigation of classification algorithms for a Prototype Microwave Breast Cancer Monitor 8

Investigation of Classification Algorithms for a Prototype M...

引用

8th European Conference on Antennas and Propagation (EuCAP)

作者： Santorelli, Adam Li, Yunpeng Porter, Emily Popovic, Milica Coates, Mark McGill Univ Dept Elect Engn Montreal PQ Canada

ISBN: (纸本)9788890701849

In this paper we investigate the use of differential signals to monitor changes within the breast. Specifically, we focus on the use of machine learning classification algorithms to determine whether any malignant tissues are developing. Experimental data is obtained from a 16-element antenna array that transmits a 2 - 4 GHz broadband pulse. We implement both the Linear Discriminant Analysis and Support Vector Machine (SVM) detection algorithms to analyze the experimentally obtained data.

关键词： cancer detection classification algorithms microwave sensors

来源：评论

学校读者我要写书评

暂无评论

A Comparative Analysis of classification algorithms in Diabetic Retinopathy Screening 7

A Comparative Analysis of Classification Algorithms in Diabe...

引用

7th International Conference on Computer and Knowledge Engineering (ICCKE)

作者： Mohammadian, Saboora Karsaz, Ali Roshan, Yaser M. Khorasan Inst Higher Educ Elect Engn Dept Mashhad Iran Point Pk Univ Elect Engn Dept Pittsburgh PA 15232 USA

ISBN: (纸本)9781538608043

Automated screening of diabetic retinopathy plays an important role in diagnosis of the disease in early stages and preventing blindness in patients with diabetes. Various machine learning approaches have been studied in literature with the purpose of improving the accuracy of the screening methods. Although the performance of the machine learning algorithm depends on the application and the type of data, yet there is no comprehensive analysis of different approaches in the diabetic retinopathy screening to choose the best approach. To this end, in this study a comparative analysis of nine common classification algorithms is performed to select the most applicable approach for the specific problem of screening diabetic retinopathy patients. Individual algorithms are optimized with respect to their tunable parameters, and are compared together in terms of their accuracy, precision, recall, and F1-score. Simulation results demonstrate the difference between the performances of individual classification algorithms and can be used as a deciding factor in method selection for further research.

关键词： Diabetic retinopathy machine learning classification algorithms

来源：评论

学校读者我要写书评

暂无评论

Do we really need a segmentation step in heart sound classification algorithms? 43

Do we really need a segmentation step in heart sound classif...

引用

43rd Annual International Conference of the IEEE-Engineering-in-Medicine-and-Biology-Society (IEEE EMBC)

作者： Oliveira, Jorge Nogueira, Diogo Renna, Francesco Ferreira, Carlos Jorge, Alipio M. Coimbra, Miguel Univ Portucalense Infante D Henrique Rua Dr Antonio Bernardino de Almeida 541 P-4200072 Porto Portugal Univ Porto Fac Ciencias Inst Telecomunicacoes Rua Campo Alegre 1021-1055 P-4169007 Porto Portugal INESC TEC Rua Dr Roberto Frias P-4200465 Porto Portugal Univ Porto Fac Ciencias Porto Portugal

ISBN: (纸本)9781728111797

Cardiac auscultation is the key screening procedure to detect and identify cardiovascular diseases (CVDs). One of many steps to automatically detect CVDs using auscultation, concerns the detection and delimitation of the heart sound boundaries, a process known as segmentation. Whether to include or not a segmentation step in the signal classification pipeline is nowadays a topic of discussion. Up to our knowledge, the outcome of a segmentation algorithm has been used almost exclusively to align the different signal segments according to the heartbeat. In this paper, the need for a heartbeat alignment step is tested and evaluated over different machine learning algorithms, including deep learning solutions. From the different classifiers tested, Gate Recurrent Unit (GRU) Network and Convolutional Neural Network (CNN) algorithms are shown to be the most robust. Namely, these algorithms can detect the presence of heart murmurs even without a heartbeat alignment step. Furthermore, Support Vector Machine (SVM) and Random Forest (RF) algorithms require an explicit segmentation step to effectively detect heart sounds and murmurs, the overall performance is expected drop approximately 5% on both cases.

关键词： Heart Support vector machines Radio frequency Machine learning algorithms Heart beat Approximation algorithms classification algorithms

来源：评论

学校读者我要写书评

暂无评论

Correctness of algebraic extensions of models of classification algorithms

引用

Cybernetics and Systems Analysis 2001年第5期37卷 777-781页

作者： Aidarkhanov, M.B. Amirgaliev, E.N. La, L.L. Institute of Problems of Informatics and Management Ministry of Education and Science Almaaty Kazakhstan

The binary representation of each classification from a subset of a space of admissible classifications is considered. A metric in a unit cube is introduced, and a correct algebra of classification algorithms is constructed. The correctness and completeness of a model of classification algorithms are proved. An example of construction of a complete model for a classification problem is considered. ©2001 Plenum Publishing Corporation.

关键词： Algebra of classification algorithms classification algorithms classification problems Correct extensions of models Correctness and completeness of models Model of classification algorithms

来源：评论

学校读者我要写书评

暂无评论

classification algorithms Analysis in the Forest Fire Detection Problem

Classification Algorithms Analysis in the Forest Fire Detect...

引用

IEEE Conference on Quality Management, Transport and Information Security, Information Technologies (IT&MQ&IS)

作者： Mikhail D. Molovtsev Irina S. Sineva Department of Mathematical Cybernetics and Information Technologies Moscow Technical University of Communications and Informatics “MTUCI” Moscow Russia

The development of information technology allows applying complex mathematical algorithms. For example, machine learning (ML) procedures are used in almost all humans life areas: smart home systems, online recommendation systems intelligent chatbots and so on. This creates a huge demand for specialists in data analysis and ML. Modern data analysis packages often do not require deep knowledge from a specialist, which allows to apply all ML algorithms without a deep understanding of their work. However, the main problem is that the data is not suitable for the algorithm and as a result, the algorithm cannot detect all the patterns or does it incorrectly. This situation can be acceptable in pet projects and is completely unacceptable in cases where the algorithm error costs a lot of money or human lives. In this paper the analysis of ML algorithms and the possibility of their application to forest fires data are done.

关键词： Fires Forestry Support vector machines classification algorithms Logistics Machine learning algorithms Machine learning

来源：评论

学校读者我要写书评

暂无评论

Comparative Analysis of classification algorithms Using Bot-IoT Dataset

Comparative Analysis of Classification Algorithms Using Bot-...

引用

2023 International Conference on Sustainable Communication Networks and Application, ICSCNA 2023

作者： Mishra, Amit Kumar Rajput, Kapil Pandey, Neeraj Kumar Pathak, Anjali Graphic Era Hill University Dept. of Computer Science & Engineering Dehradun India Department of Computer Science & Engineering Dehradun India

ISBN: (纸本)9798350313987

This study shows the comparative analysis of classification machine learning algorithms performed on the Bot-IoT dataset. The Bot-IoT dataset is an omnipresent dataset that contains network traffic data collected from Internet of Things (IoT) devices. The dataset provides useful insights into the characteristics of botnet traffic and can be used to create an environment where there are effective detection and mitigation strategies. This research analysis evaluates several well-known classification algorithms on the Bot-IoT dataset. The algorithms considered in this study include, Decision Trees (DT), Random Forest (RF), Support Vector Machine (SVM), and Naive Bayes (NB). The evaluation process consists of several steps. First, the obtained dataset is preprocessed by handling the left out values, normalizing features, and splitting it into testing and training sets. Then, each classification algorithm is trained based on the training set and the hyper parameters are fine-tuned by using the cross -validation techniques. After training, the performance of each algorithm is evaluated on the testing set using different factors such as accuracy, precision, recall and F1 score. Finally, the research results provide insights on the advantages and disadvantages of different classification algorithms for enabling botnet detection using the Bot-IoT dataset. This study compares the performance of each algorithm based on the accuracy, F1- Score, recall and other evaluation metrics, which allows to identify the most effective algorithm. Additionally, this study analyses the computational complexity and training time of each algorithm to assess their practical feasibility in realtime scenarios. The resultant observation can be valuable for researchers working on IoT security, specifically in the area of botnet detection and prevention. By understanding the performance characteristics of different classification algorithms, stakeholders can make informed decisions when selecting an

关键词： Analysis Bot-IoT classification algorithms Comparison IoT Attacks ML model

来源：评论

学校读者我要写书评

暂无评论

classification algorithms for Unbalanced High-Dimensional Data with Hyperbox Vertex Over-Sampling Iterative Support Vector Machine Approach

Classification Algorithms for Unbalanced High-Dimensional Da...

引用

Chinese Control and Decision Conference, CCDC

作者： Chao Wang Jun Zhou Haoqian Huang Hanyi Shen College of Energy and Electrical Engineering Hohai University Nanjing China

ISBN: (数字)9781728158556

ISBN: (纸本)9781728158563

This paper is written for addressing the bisectional classification problem for big and high-dimensional data sets with unbalanced characteristics distribution, whenever the support vector machine (SVM) algorithm is used. As is well known in conventional SVM, calculating large-scale covariance matrices and biased support vectors due to unbalanced characteristics are thorny issues that severely affect accuracy and efficiency of SVM classification algorithm in general. To surmount these problems, a class of modified SVM classification algorithm (hyperbox vertex over-sampling iterative SVM: HVS-ISVM) based on hyperbox vertex over-sampling data compensating and active iterative learning are innovatively proposed. The algorithm procedures are explained briefly and the main features are summarized, which are conducive to implementing and revealing deeper data indices. Numerical simulation results show that the proposed HVS-ISVM algorithm have higher calculation speed and better classification accuracy, and the obtained iterative support vectors and their corresponding hyperplanes are also robust against numerical noise due to over-sampling.

关键词： Support vector machine classification classification algorithms Entropy Interpolation Iterative algorithms Standards

来源：评论

学校读者我要写书评

暂无评论

classification algorithms on a large continuous random dataset using rapid miner tool

Classification algorithms on a large continuous random datas...

引用

International Conference on Electronics and Communication Systems (ICECS)

作者： Pooja Sharma Divakar Singh Anju Singh Department of Computer Science and Engineering Bhopal BU India Department of Computer Science and information technology Bhopal UTD-BU India

classification is widely used technique in the data mining domain, where scalability and efficiency are the immediate problems in classification algorithms for large databases. Now a day's large amount of data is generated, that need to be analyse, and pattern have to be extracted from that to get some knowledge. classification is a supervised machine learning task which builds a model from labelled training data. The model is used for determining the class; there are many types of classification algorithms such as tree-based algorithms (C4.5 decision tree, j48 decision tree etc.), naive Bayes and many more. These classification algorithms have their own pros and cons, depending on many factors such as the characteristics of the data. We can measure the classification performance by using several metrics, such as accuracy, precision, classification error and kappa on the testing data. We have used a random dataset in a rapid miner tool for the classification. Stratified sampling is used in different classifier such as J48, C4.5 and naïve Bayes. We analysed the result of the classifier using the randomly generated dataset and without random dataset.

关键词： classification algorithms Decision trees Data mining Accuracy Machine learning algorithms Training Data models

来源：评论

学校读者我要写书评

暂无评论

Evaluating Various Feature Extraction Methods and classification algorithms for Music Genres classification 27

Evaluating Various Feature Extraction Methods and Classifica...

引用

27th International Computer Conference, Computer Society of Iran, CSICC 2022

作者： Bakhtyari, Mohammadreza Davoudi, Saeedeh Mirzaei, Sayeh College Of Engineering University Of Tehran Department Of Engineering Science Tehran Iran

ISBN: (纸本)9781665480277

Thousands of songs are released monthly through each of the music streaming services. This amount of data requires careful data management and analysis. Toward this aim, Music Genres classification (MGC), one of the most prominent research fields of Music Information Retrieval (MIR), has been introduced. While the end-to-end deep learning approach has been successful in many classification tasks, the two-steps approach is more efficient in some practical applications. The first step is to extract appropriate features by a feature extraction technique. Then, they will serve as an input for the classification model in the next step. In this study, we intend to use a two-step approach. We examine the harmonic-percussion source separation (HPSS), the Mel-spectrogram and the modulation spectrogram for the feature extraction stage, and different versions of an inception block for nonlinear features. In this paper, we develop several models by combining various feature extraction methods and classification algorithms and assess the impact of each presence. The model based on the attention mechanism and a convolutional-recurrent architecture with the input of Melspectrogram has shown the best accuracy compared to others. © 2022 IEEE.

关键词： Deep learning Source separation Computational modeling Modulation Computer architecture Feature extraction classification algorithms

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：