检索结果-内蒙古大学图书馆

Privacy-Preserving multi-class support vector machine Model on Medical Diagnosis

IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS 2022年第7期26卷 3342-3353页

作者： Chen, Yange Mao, Qinyu Wang, Baocang Duan, Pu Zhang, Benyu Hong, Zhiyong Xidian Univ Sch Telecommun Engn Xian 710071 Peoples R China Xuchang Univ Sch Informat Engn Xuchang 461000 Peoples R China Xidian Univ Sch Cyber Engn Xian 710071 Peoples R China Wuhan Univ Sch Elect Informat Wuhan 430072 Peoples R China Xidian Univ Sch Telecommun Engn Cryptog Res Ctr State Key Lab Integrated Serv Networks Xian 710071 Peoples R China Ant Grp Secure Collaborat Intelligence Lab Hangzhou 310000 Zhejiang Peoples R China Wuyi Univ Facil Intelligence Manufacture Yue Gang Ao Ind Big Data Collaborat Innovat Ctr Jiangmen 529020 Peoples R China

With the rapid development of machine learning in the medical cloud system, cloud-assisted medical computing provides a concrete platform for remote rapid medical diagnosis services. support vector machine (SVM), as one of the important algorithms of machine learning, has been widely used in the field of medical diagnosis for its high classification accuracy and efficiency. In some existing schemes, healthcare providers train diagnostic models with SVM algorithms and provide online diagnostic services to doctors. Doctors send the patient's case report to the diagnostic models to obtain the results and assist in clinical diagnosis. However, case report involves patients' privacy, and patients do not want their sensitive information to be leaked. Therefore, the protection of patient's privacy has become an important research direction in the field of online medical diagnosis. In this paper, we propose a privacy-preserving medical diagnosis scheme based on multi-class SVMs. The scheme is based on the distributed two trapdoors public key cryptosystem (DT-PKC) and Boneh-Goh-Nissim (BGN) cryptosystem. We design a secure computing protocol to compute the core process of the SVM classification algorithm. Our scheme can deal with both linearly separable data and nonlinear data while protecting the privacy of user data and support vectors. The results show that our scheme is secure, reliable, scalable with high accuracy.

关键词： support vector machines Medical diagnosis Kernel Medical diagnostic imaging Privacy Cryptography Cloud computing Homomorphic encryption medical diagnosis multi-class support vector machine privacy-preserving

来源：评论

学校读者我要写书评

暂无评论

E-commerce Product Review Analysis based on multi-class support vector machine 1

E-commerce Product Review Analysis based on Multi-class Supp...

引用

1st IEEE International Conference on Ambient Intelligence, Knowledge Informatics and Industrial Electronics, AIKIIE 2023

作者： Alzubaidi, Laith H. Hatem Mohammed, Ibraheem Ranjith Kumar, Gotte Sarada, B. Radha, N. The Islamic University Najaf Iraq Al Rafidain University College Department of Computer Communications Engineering Bagdad Iraq School of Computer Science & Artificial Intelligence Sr University Warangal India Malla Reddy Engineering College for Women Department of CSE-Data Science Hyderabad India Department of Cse Annapoorana Engineering Salem India

ISBN: (纸本)9798350316469

Recently, an extensive number of consumers can choose their best options from automated retailers and compare products in online stores. Sentiment analysis is frequently employed in applications meant to demonstrate and provide client service as the voice of the user. On E-commerce websites, user reviews provide helpful information about the product. Sentiment analysis of the text reviews helps forecast product sales by assessing user sentiment regarding the product. The Word Embedding Attention approach assigns additional weight to words that have a strong association with a specific class. Sentiment analysis machine learning models provide a thorough depiction of capabilities and far surpass conventional feature-based methods. The obj ective of this research is to improve sentiment analysis performance by creating a weighted ensemble with a multi-class support vector machine (MSVM) model utilising novel word embedding techniques. Because of the weighted ensemble's higher generalisation skills, MSVM produces superior results. When comparing existing methods for sentiment analysis, the Weighted ensemble with MSVM has produced higher results in terms of accuracy, precision, f-score, and recall. The Weighted ensemble with an MSVM has achieved 99.87 % accuracy, 99.39% precision, 99.03% f-score, and 99.690/0 recall in sentiment analysis when comparing existing methods such as LSTM+FL, APSO-LSTM, ABCDM, SSentiA, and Hybrid CNN+LSTM. © 2023 IEEE.

关键词： E-commerece multi-class support vector machine Natural language processing Sentiment analysis Weighted ensemble Word embedding attention

来源：评论

学校读者我要写书评

暂无评论

Feature selection with kernelized multi-class support vector machine

引用

PATTERN RECOGNITION 2021年 117卷 107988-107988页

作者： Guo, Yinan Zhang, Zirui Tang, Fengzhen China Univ Min & Technol Sch Informat & Control Engn Xuzhou 221008 Jiangsu Peoples R China Chinese Acad Sci Shenyang Inst Automat 114 Nanta St Shenyang 110016 Liaoning Peoples R China Chinese Acad Sci Inst Robot & Intelligent Mfg Shenyang 110016 Peoples R China China Univ Min & Technol Beijing Sch Electromech & Informat Engn Beijing 100083 Peoples R China

Feature selection is an important procedure in machine learning because it can reduce the complexity of the final learning model and simplify the interpretation. In this paper, we propose a novel non-linear feature selection method that targets multi-class classification problems in the framework of support vector machines. The proposed method is achieved using a kernelized multi-class support vector machine with a fast version of recursive feature elimination. The proposed method selects features that work well for all classes, as the involved classifier simultaneously constructs multiple decision functions that separates each class from the others. We formulate the classifier as a large optimisation problem, and iteratively solve one decision function at a time, leading to a lower computational time complexity than when solving the large optimisation problem directly. The coefficients of the classifier are then used as a ranking criterion in the accelerated recursive feature elimination by adding batch elimination and a rechecking process. Experimental results on several datasets demonstrate the superior performance of the proposed feature selection method. (c) 2021 Elsevier Ltd. All rights reserved.

关键词： Feature selection multi-class support vector machine Kernel machine Recursive feature elimination

来源：评论

学校读者我要写书评

暂无评论

Pashto Characters Recognition Using multi-class Enabled support vector machine

引用

Computers, Materials & Continua 2021年第6期67卷 2831-2844页

作者： Sulaiman Khan Shah Nazir Habib Ullah Khan Anwar Hussain Department of Computer Science University of SwabiSwabiPakistan Department of Accounting and Information Systems College of Business and EconomicsQatar UniversityDohaQatar

During the last two decades signicant work has been reported in the eld of cursive language’s recognition especially,in the Arabic,the Urdu and the Persian *** unavailability of such work in the Pashto language is because of:the absence of a standard database and of signicant research work that ultimately acts as a big barrier for the research *** slight change in the Pashto characters’shape is an additional challenge for *** paper presents an efcient OCR system for the handwritten Pashto characters based on multi-class enabled support vector machine using manifold feature extraction *** feature extraction techniques include,tools such as zoning feature extractor,discrete cosine transform,discrete wavelet transform,and Gabor lters and histogram of oriented gradients.A hybrid feature map is developed by combining the manifold feature *** research work is performed by developing a medium-sized dataset of handwritten Pashto characters that encapsulate 200 handwritten samples for each 44 characters in the Pashto *** results are generated for the proposed model based on a manifold and hybrid feature *** overall accuracy rates of 63.30%,65.13%,68.55%,68.28%,67.02%and 83%are generated based on a zoning technique,HoGs,Gabor lter,DCT,DWT and hybrid feature maps *** of the proposed model is also tested by comparing its results with a convolution neural network *** convolution neural network-based model generated an accuracy rate of 81.02%smaller than the multi-class support vector *** highest accuracy rate of 83%for the multi-class SVM model based on a hybrid feature map reects the applicability of the proposed model.

关键词： Pashto multi-class support vector machine handwritten characters database zoning and histogram of oriented gradients

来源：评论

学校读者我要写书评

暂无评论

Human age classification using appearance and facial skin ageing features with multi-class support vector machine

引用

INTERNATIONAL JOURNAL OF BIOMETRICS 2019年第1期11卷 22-34页

作者： Jagtap, Jayant Kokare, Manesh Symbiosis Int Deemed Univ Symbiosis Inst Technol Dept Elect & Telecommun Engn Pune 412115 Maharashtra India Shri Guru Gobind Singhji Inst Engn & Technol Dept Elect & Telecommun Engn Vishnupuri 431606 Nanded India

Human age classification via face images is not only difficult for human being but also challenging for a machine. But, because of potential applications in the field of computer vision, this topic has attracted attention of many researchers. In this paper, a novel two stage age classification framework based on appearance and facial skin ageing features with multi-class support vector machine (M-SVM) is proposed to classify the face images into seven age groups. Appearance features consist of shape features such as, geometric ratios and face angle and facial skin textural features extracted by using local Gabor binary pattern histogram (LGBPH). Facial skin ageing features consist of facial skin textural features and wrinkle analysis. The proposed age classification framework is trained and tested with face images collected from FG-NET ageing database and PAL face database and achieved greatly improved age classification accuracy of 94.45%.

关键词： appearance features facial skin ageing features local Gabor binary patterns histogram LGBPH wrinkle analysis age classification framework multi-class support vector machine M-SVM

来源：评论

学校读者我要写书评

暂无评论

Arabian horse identification based on whale optimised multi-class support vector machine

引用

INTERNATIONAL JOURNAL OF COMPUTER APPLICATIONS IN TECHNOLOGY 2020年第1-2期63卷 83-92页

作者： Taha, Ayat Darwish, Ashraf Hassanien, Aboul Ella ElKholy, Ahmed Al Azhar Univ Fac Sci Cairo Egypt Helwan Univ Fac Sci Cairo Egypt Cairo Univ Fac Comp & Informat Cairo Egypt

In this study, a biometric identification approach for Arabian horse identification is proposed based on the optimised multi-class support vector machine (MCSVM). The identification approach is performed in three phases: feature extraction, classification, and optimisation. The feature extraction phase uses Histogram of Oriented Gradient (HOG) to extract features vectors from muzzle print images of the Arabian horses and then stored in the database with its labels. The second phase is the classification phase which uses MCSVM for training and testing classification. Finally in the optimised MCSVM phase, three different swarms, Particle Swarm Optimisation (PSO), Grey Wolf Algorithm (GWA) and Whale Optimisation (WO), are used to optimise MCSVM parameters to enhance the identification accuracy of the Arabian horse. The results obtained show that the MCSVM achieves accuracy of 93.2% and increases to 97.4% with WO algorithm which achieves the best accuracy compared to PSO and GWA.

关键词： Arabian horse identification histogram of oriented gradient multi-class support vector machine whale optimisation algorithm

来源：评论

学校读者我要写书评

暂无评论

Cavitation intensity monitoring in an axial flow pump based on vibration signals using multi-class support vector machine

引用

PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART C-JOURNAL OF MECHANICAL ENGINEERING SCIENCE 2018年第17期232卷 3013-3026页

作者： Shervani-Tabar, Mohammad Taghi Ettefagh, Mir Mohammad Lotfan, Saeed Safarzadeh, Hamed Univ Tabriz Dept Mech Engn Ctr CFD Studies Heat Engines Cavitat Flow & Petr Tabriz Iran Univ Tabriz Dept Mech Engn Res Lab Vibrat & Modal Anal POB *** Tabriz Iran

The cavitation phenomenon, which is rampant in axial flow pumps, should be avoided due to its undesirable effects on the pump's performance. Therefore, in this study the cavitation performance of an axial flow pump is monitored based on vibration signals. For this purpose, experimental vibration data is collected for five different levels of cavitation. Time-domain features are extracted based on statistical behavior of the measured signals. Considering the nonlinear and high-frequency nature of the cavitation noise in the signal, the second set of features including both time- and frequency-domain parameters are obtained based on statistical behavior of the first intrinsic mode function, via empirical mode decomposition combined with Hilbert Huang transform. Compensation distance evaluation technique is applied to pick the appropriate features. multi-class support vector machine is trained for classification of the various levels of cavitation intensity. The results of testing the support vector machine algorithm show that the developed methodology can monitor the pump's cavitation intensity in onsite operation with high accuracy.

关键词： Vibration signal cavitation monitoring axial flow pump empirical mode decomposition compensation distance evaluation multi-class support vector machine

来源：评论

学校读者我要写书评

暂无评论

classification of EMI discharge sources using time-frequency features and multi-class support vector machine

引用

ELECTRIC POWER SYSTEMS RESEARCH 2018年第PartA期163卷 261-269页

作者： Mitiche, Imene Morison, Gordon Nesbitt, Alan Hughes-Narborough, Michael Stewart, Brian G. Boreham, Philip Glasgow Caledonian Univ Sch Engn & Built Environm Glasgow G4 0BA Lanark Scotland Univ Strathclyde Inst Energy & Environm 204 George St Glasgow G1 1XW Lanark Scotland Innovat Ctr Online Syst 7 Townsend Business Pk Bere Regis BH20 7LA England

This paper introduces the first application of feature extraction and machine learning to electromagnetic interference (EMI) signals for discharge sources classification in high voltage power generating plants. This work presents an investigation on signals that represent different discharge sources, which are measured using EMI techniques from operating electrical machines within power plant. The analysis involves time frequency image calculation of EMI signals using general linear chirplet analysis (GLCT) which reveals both time and frequency varying characteristics. Histograms of uniform local binary patterns (LBP) are implemented as a feature reduction and extraction technique for the classification of discharge sources using multi-class support vector machine (MCSVM). The novelty that this paper introduces is the combination of GLCT and LBP applications to develop a new feature extraction algorithm applied to EMI signals classification. The proposed algorithm is demonstrated to be successful with excellent classification accuracy being achieved. For the first time, this work transfers expert's knowledge on EMI faults to an intelligent system which could potentially be exploited to develop an automatic condition monitoring system.

关键词： EMI Partial discharge GLCT Uniform LBP multi-class support vector machine classification accuracy Intelligent system Experts system

来源：评论

学校读者我要写书评

暂无评论

Cooperative Spectrum Sensing in Cognitive Radio Networks Using multi-class support vector machine Algorithms 9

Cooperative Spectrum Sensing in Cognitive Radio Networks Usi...

引用

9th International Conference on Signal Processing and Communication Systems (ICSPCS)

作者： Awe, Olusegun Peter Lambotharan, Sangarapillai Univ Loughborough Sch Elect Elect & Syst Engn Adv Signal Proc Grp Loughborough LE11 3TU Leics England

ISBN: (纸本)9781467381185

This paper addresses the problem of spectrum sensing in cognitive radio networks under multiple primary users condition using multi-class support vector machine (SVM) algorithms. First, we formulated the spectrum sensing problem under multiple primary users scenario as a multiple class signal detection problem where each class is comprised of one or more sub-classes and generalized expressions for the possible classes are provided. Next, we investigate the performance of energy based features and the error correcting output codes (ECOC) based multi-class SVM algorithms for solving the multi-class spectrum sensing problem using two different coding strategies. The performance of the proposed detector is quantified in terms of receiver operating characteristics curves and classification accuracy. Simulation results show that the proposed detector is robust to joint spatio-temporal detection of spectrum holes in cognitive radio networks.

关键词： Cognitive radio spectrum sensing machine learning multi-class support vector machine multiple primary users

来源：评论

学校读者我要写书评

暂无评论

Phase partition and identification based on kernel entropy component analysis and multi-class support vector machines-fireworks algorithm for multi-phase batch process fault diagnosis

引用

TRANSACTIONS OF THE INSTITUTE OF MEASUREMENT AND CONTROL 2020年第12期42卷 2324-2337页

作者： Zhang, Min Wang, Ruiqi Cai, Zhenyu Cheng, Wenming Southwest Jiaotong Univ Sch Mech Engn Chengdu 61003 Peoples R China Technol & Equipment Rail Transit Operat & Mainten Chengdu Peoples R China

For the characteristics of nonlinear and multi-phase in the batch process, a self-adaptive multi-phase batch process fault diagnosis method is proposed in this paper. Firstly, kernel entropy component analysis (KECA) method is used to achieve multi-phase partition adaptively, which makes the process data mapped into the high-dimensional feature space and then constructs the core entropy and the angular structure similarity. Then a multi-phase KECA failure monitoring model is developed by using the angular structure similarity as the statistic, which is based on the partitioned phases and the effective failure features by the KECA feature extraction method. A multi-phase batch process fault diagnosis method, which applies the multi-class support vector machines (MSVM) and fireworks algorithm (FWA), is proposed to recognize each sub-phase fault diagnosis automatically. The effectiveness and advantages of the proposed multi-phase fault diagnosis method are illustrated with a case study on a fed-batch penicillin fermentation process.

关键词： Batch process multi-phase fault diagnosis kernel entropy component analysis fireworks algorithm multi-class support vector machine

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：