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Seventh International Conference on Measuring Technology and Mechatronics Automation (ICMTMA 2015)

作者： Liao Shaowen Zhang Jianqing Chen Yong Hexi Univ Informat Technol & Media Inst Zhangye 734000 Gansu Peoples R China Artillery Training Base Gen Staff Xuanhua 075100 Hebei Peoples R China

ISBN: (纸本)9781467371438

adaboost algorithm is a kind of very important feature classification machine learning algorithm, But if difficult samples exist in the training samples, With the iterative Number increasing, this easily leads to degeneration Phenomenon, and reduces the generalization ability of the classifier. In view of the face detection under complex background degeneration appeared problem, This article Proposes LWE-adaboost algorithm which can limit weight expansion, the experimental results indicate that the LWE-adaboost algorithm can restrain the recurrence of degeneration Phenomenon well.

关键词： Face detection adaboost algorithm Integral image Weak classifier Strong classifier Cascaded classifier

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Miner Face Detection is Based on Improved adaboost algorithm

Miner Face Detection is Based on Improved AdaBoost Algorithm

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2nd International Conference on Computer Science and Network Technology (ICCSNT)

作者： Jiang, Chao Han, Gu-yong Tian, Lei Lu, Song Huang, Wei-xing Air Force Serv Coll Xuzhou Jiangsu Peoples R China

ISBN: (纸本)9781467329644;9781467329637

This article connects with Coal mine video monitoring image be impacted for special environment, which be vulnerable to mineral dust in coal mines, light, as well as miner's safety helmet for the realization of face detection in real-time and accuracy, I will study on face identification and analysis on the characters of behavior in the follow-up work for getting a good foundation, which will be in intelligent Coal mine video monitoring. This article simulates rectangle Haar-like character and Extended Haar-like character of the adaboost algorithm about face detection in real-time and accuracy, is based on OpenCV, also describes briefly the rectangular Haar-like characteristic model and about computational algorithm and faster algorithm of the characteristic value, analysis detailedly extended Haar-like character model and the characteristic value of computational algorithm-integral image. Experimental resulted show that extended Haar-like characteristic model can be implemented more quickly and more accurately in the miners' face detection, as well as real-time.

关键词： adaboost algorithm Face detection Machine vision Monitoring image

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Using adaboost algorithm Along with Artificial Neural Networks for Efficient Human Emotion Recognition From Speech

Using Adaboost Algorithm Along with Artificial Neural Networ...

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IEEE International Conference on Control, Automation, Robotics and Embedded Systems (CARE)

作者： Bhalla, Jasdeep Singh Aggarwal, Anmol Bharati Vidyapeeths Coll Engn Dept Comp Sci New Delhi India Bharati Vidyapeeths Coll Engn Dept Informat Technol New Delhi India

ISBN: (纸本)9781467361538

Emotion Recognition from speech has evolved itself as the most significant research area in the field of affective computing. In this paper, two emotional speech datasets, have been analyzed, based on gender distinction (male and female speech). This paper introduces a new approach of speech-emotion recognition based on the use of adaboost classification algorithm. Artificial neural network has been implemented for pattern classification and recognition. English is used as the basic language for the testing of the method. We have recognized the emotions into four different groups happy, normal, sad and anger by using adaboost algorithm and ANN. The output for the two datasets are evaluated and analyzed.

关键词： Speech Recognition adaboost algorithm Artificial Neural Networks Emotion Recognition adaboost Classification

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Application of adaboost algorithm in Enterprise Financial Risk Analysis Model 1

Application of Adaboost Algorithm in Enterprise Financial Ri...

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

作者： Zhang, Donghong Li, Hanjun Wenhua College Department of Accounting Department of Financial Management Hubei Wuhan China

ISBN: (纸本)9798350316469

adaboost (Adaptive Boosting) algorithm is an ensemble learning method, which combines multiple weak classifiers to build a strong classifier, and improves the performance of the model iteratively. It is widely used in various fields, including enterprise financial risk analysis models. Therefore, this article used the adaboost algorithm to study the financial risk analysis model, with the aim of improving the accuracy of risk prediction. This paper mainly used the method of experiment and algorithm comparison to compare the performance of adaboost algorithm, decision tree algorithm and Logistic regression model. The experimental data showed that the recall rate of high-risk categories was 0.91, which means that the success rate of the adaboost model in predicting high-risk companies was 91%;the specificity was 0.902, which was 90.2% of the predicted risk-free enterprises in the model. The F1 value reached 0.818, indicating that the model performs well in predicting risk categories. © 2023 IEEE.

关键词： adaboost algorithm Analysis Model Enterprise Finance Risk Factor

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The Research of Face Detection Method Based on adaboost algorithm and Skin Color Segmentation

The Research of Face Detection Method Based on Adaboost Algo...

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IEEE International Conference on Imaging Systems and Techniques (IST) / IEEE International School on Imaging

作者： Liu Chang Beihang Univ Sch Instrumentat Sci & Optoelect Engn Dept Measurement Control & Informat Technol Beijing 100191 Peoples R China

ISBN: (纸本)9781538616208

As a key pre-processing of face recognition, face detection is an important foundation of some intelligent applications such as video retrieval and human-computer interaction. The performance of the adaboost algorithm is remarkable in the face detection methods, but there are still gaps. Concerning the high false positive rate of adaboost algorithm, the skin color segmentation method based on Gaussian model is introduced, which is used as the front and back processing of adaboost algorithm, respectively. Experiments show that the using of these two methods can significantly reduce the false positive rate in the detection of real-time video stream, while the skin color segmentation as a pre-processing can increase the detection speed by more than 20%, which can help achieve a stable and efficient real-time face detection system.

关键词： Face detection adaboost algorithm skin color segmentation real-time video streaming

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Islanding detection of synchronous distributed generation resources using adaboost algorithm

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INTERNATIONAL TRANSACTIONS ON ELECTRICAL ENERGY SYSTEMS 2016年第8期26卷 1604-1624页

作者： Chavoshi, Seyyed Ali Noroozian, Reza Amiri, Ali Univ Zanjan Dept Elect & Comp Engn Fac Engn Univ Blvd Zanjan Iran

This paper presents a novel approach for intelligent islanding detection in a multi-machine distribution network using the Adaptive Boosting (adaboost) algorithm. The adaboost algorithm is an iterative procedure that improves the accuracy of classification models by combining multiple base classifiers. In this paper, two different case studies are specified so as to assess the performance of the adaboost in islanding detection. In order to train the adaboost algorithm, various scenarios are implemented in the case studies. A number of electrical parameters including frequency and voltage are utilized as a feature vector to describe the designed scenarios. In order to determine the accuracy of adaboost in detecting the islanding phenomenon, the proposed method is subsequently tested in further scenarios. Response time and non-detection zone of the suggested method are also evaluated in the case studies as the two other main criteria. The results indicate that this method can successfully detect islanding operation in a variety of operating states in the networks under study. All the simulations presented here are produced in the Simulink toolbox of MATLAB. Copyright (C) 2015 John Wiley & Sons, Ltd.

关键词： adaboost algorithm distributed generation resources islanding detection synchronous generator

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Brillouin Frequency Shift Extraction Based on adaboost algorithm

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SENSORS 2022年第9期22卷 3354页

作者： Zheng, Huan Xiao, Feng Sun, Shijie Qin, Yali Zhejiang Univ Technol Inst Fiber Opt Commun & Informat Engn Coll Informat Engn Hangzhou 310023 Peoples R China

The Brillouin Optical Time-Domain Analyzer assisted by the adaboost algorithm for Brillouin frequency shift (BFS) extraction is proposed and experimentally demonstrated. The Brillouin gain spectrum classification under different BFS is realized by iteratively updating the weak classifier in the form of a decision tree, forming several base classifiers and combining them into a strong classifier. Based on the pseudo-Voigt curve training set with noise, the performance of the adaboost algorithm is studied, and the influence of different signal-to-noise ratio (SNR), frequency range, and frequency step is also studied. Results show that the performance of BFS extraction decreases with the decrease in SNR, the reduction in frequency range, and the increase in frequency step.

关键词： distributed fiber sensing Brillouin scattering Brillouin frequency shift adaboost algorithm

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Reliability Analysis of Complex Structures Under Multi-Failure Mode Utilizing an Adaptive adaboost algorithm

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APPLIED SCIENCES-BASEL 2024年第22期14卷

作者： Zhang, Feng Qiao, Zijie Tian, Yuxiang Wu, Mingying Xu, Xiayu Northwestern Polytech Univ Sch Mech Civil Engn & Architecture Xian 710129 Peoples R China

A reliability analysis can become intricate when addressing issues related to nonlinear implicit models of complex structures. To improve the accuracy and efficiency of such reliability analyses, this paper presents a surrogate model based on an adaptive adaboost algorithm. This model employs an adaptive method to determine the optimal training sample set, ensuring it is as evenly distributed as possible on both sides of the failure curve and fully contains the information it represents. Subsequently, with the integration and iterative characteristics of the adaboost algorithm, a simple binary classifier is iteratively applied to build a high-precision alternative model for complex structural fault diagnosis to cope with multiple failure modes. Then, the Monte Carlo simulation technique is employed to meticulously assess the failure probability. The accuracy and stability of the proposed method's iterative convergence process are validated through three numerical examples. The findings of the study illuminate that the proposed method is not only remarkably precise but also exceptionally efficient, capable of addressing the challenges related to the reliability evaluation of complex structures under multi-failure mode. The method proposed in this paper enhances the application of mechanical structures and facilitates the utilization of complex mechanical designs.

关键词： adaboost algorithm adaptive iteration complex structure multiple-failure mode failure probability

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A Face Detection Method Based on Skin Color Model and Improved adaboost algorithm

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TRAITEMENT DU SIGNAL 2020年第6期37卷 929-937页

作者： Yang, Xiaoying Liang, Nannan Zhou, Wei Lu, Hongmei Suzhou Univ Sch Informat Engn Suzhou 234000 Peoples R China

This paper integrates skin color model and improved adaboost into a face detection method for high-resolution images with complex backgrounds. Firstly, the skin color areas were detected in a multi-color space. Each image was subject to adaptive brightness compensation, and converted into the YCbCr space, and a skin color model was established to solve face similarity. After eliminating the background interference by morphological method, the skin color areas were segmented to obtain the candidate face areas. Next, the inertia weight control factors and random search factor were introduced to optimize the global search ability of particle swami optimization (PSO). The improved PSO was adopted to optimize the initial connection weights and output thresholds of the neural network. After that, a strong adaboost classifier was designed based on optimized weak BPNN classifiers, and the weight distribution strategy of adaboost was further improved. Finally, the improved adaboost was employed to detect the final face areas among the candidate areas. Simulation results show that our face detection method achieved high detection rate at a fast speed, and lowered false detection rate and missed detection rate.

关键词： face detection image processing skin color model adaboost algorithm

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CNVABNN: An adaboost algorithm and neural networks-based detection of copy number variations from NGS data

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COMPUTATIONAL BIOLOGY AND CHEMISTRY 2022年 99卷 107720页

作者： Wang, Xuan Li, Junqing Huang, Tihao Liaocheng Univ Sch Comp Sci & Technol Liaocheng Peoples R China HengXing Univ Sch Informat Engn Qingdao Peoples R China

Copy number variation (CNV) is a non-negligible structural variation on the genome. And next-generation sequencing (NGS) technology is widely used to detect CNVs due to the feature of high throughput and low cost on the whole genome. Based on the original MFCNV method, this paper proposes an improved CNV detection method, which is called CNVABNN. In comparison to the MFCNV method, CNVABNN has three ad-vantages: (1) It adds detectable categories, and refines the categories of loss into hemi_loss and homo_loss. (2) It utilizes the idea of integrated learning. The adaboost algorithm is used as the core framework and neural net-works are used as weak classifiers, then CNVABNN combines all of the weak classifiers into a strong classifier. The overall performance of CNV detection is improved by using the strong classifier. (3) The detection is opti-mized by predicting CNVs twice through neural networks and voting mechanisms. To evaluate the performance of CNVABNN, six existing detection methods are used for comparison. The experimental results show that CNVABNN achieves better results in terms of precision, sensitivity, and F1-score for both simulated and real samples.

关键词： Copy number variation adaboost algorithm Next -generation sequencing Neural network Read depth

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