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adaboost for Improved Voice-Band Signal Classification

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Journal of Donghua University(English Edition) 2007年第2期24卷 255-259页

作者：李建彬王勇郑辉牛忠霞 National Defence Key Laboratory of Blind Processing of Signals Department of Communication Engineering Information Engineering University

A good voice-band signal classification can not only enable the safe application of speech ceding techniques, the implementation of a Digital Signal Interpolation （DSI） system, but also facilitate network administration and planning by providing accurate voice-band traffic analysis. A new method is proposed to detect and classify the presence of various voice-band signals on the General Switched Telephone Network （GSTN）. The method uses a combination of simple base classifiers through the adaboost algorithm. The conventional classification features for voice- band data classification are combined and optimized by the adaboost algorithm and spectral subtraction method. Experiments show the simpleness, effectiveness, efficiency and flexibility of the method.

关键词： voice-band adaboost spectral subtraction

Large scale classification with local diversity adaboost SVM algorithm

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Journal of Systems Engineering and Electronics 2009年第6期20卷 1344-1350页

作者： Chang Tiantian Liu Hongwei Zhou Shuisheng Dept. of Applied Mathematics Xidian Univ. Xi'an 710071 P. R. China

Local diversity adaboost support vector machine（LDAB-SVM） is proposed for large scale dataset classification *** training dataset is split into several blocks firstly, and some models based on these dataset blocks are *** order to obtain a better performance, adaboost is used in each model *** the boosting iteration step, the component learners which have higher diversity and accuracy are collected via the kernel parameters *** the local models via voting method are *** experimental study shows that LDAB-SVM can deal with large scale dataset efficiently without reducing the performance of the classifier.

关键词： ensemble learning large scale data support vector machine adaboost diversity local.

Recommendation Algorithm Based on Probabilistic Matrix Factorization with adaboost

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Computers, Materials & Continua 2020年第11期65卷 1591-1603页

作者： Hongtao Bai Xuan Li Lili He Longhai Jin Chong Wang Yu Jiang College of Computer Science and Technology Jilin UniversityChangchun130012China Symbol Computation and Knowledge Engineer of Ministry of Education Jilin UniversityChangchun130012China Department of Engineering Mechanics State Marine Technical University of St.PetersburgSt.Petersburg190008Russia

A current problem in diet recommendation systems is the matching of food preferences with nutritional requirements,taking into account individual characteristics,such as body weight with individual health conditions,such as *** dietary recommendations employ association rules,content-based collaborative filtering,and constraint-based methods,which have several *** limitations are due to the existence of a special user group and an imbalance of non-simple *** use of traditional dietary recommendation algorithm researches,we combine the adaboost classifier with probabilistic matrix *** present a personalized diet recommendation algorithm by taking advantage of probabilistic matrix factorization via adaboost.A probabilistic matrix factorization method extracts the implicit factors between individual food preferences and nutritional *** this,we can make use of those features with strong influence while discarding those with little *** incorporating these changes into our approach,we evaluated our algorithm’s *** results show that our method performed better than others at matching preferred foods with dietary requirements,benefiting user health as a *** algorithm fully considers the constraint relationship between users’attributes and nutritional characteristics of *** many complex factors in our algorithm,the recommended food result set meets both health standards and users’dietary preferences.A comparison of our algorithm with others demonstrated that our method offers high accuracy and interpretability.

关键词： Recommendation probabilistic matrix factorization adaboost characteristics correlation

Energy Theft Identification Using adaboost Ensembler in the Smart Grids

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Computers, Materials & Continua 2022年第7期72卷 2141-2158页

作者： Muhammad Irfan Nasir Ayub Faisal Althobiani Zain Ali Muhammad Idrees Saeed Ullah Saifur Rahman Abdullah Saeed Alwadie Saleh Mohammed Ghonaim Hesham Abdushkour Fahad Salem Alkahtani Samar Alqhtani Piotr Gas Electrical Engineering Department College of EngineeringNajran University Saudi ArabiaNajran61441Saudi Arabia Department of Computer Science Federal Urdu University of Science and TechnologyIslamabad44000Pakistan Faculty of Maritime Studies King Abdulaziz UniversityJeddah21589Saudi Arabia Department of Electrical Engineering HITEC UniversityTaxila47080Pakistan Department of Computer Science and Engineering University of Engineering and TechnologyNarowal CampusLahore54000Pakistan College of Computer Science and Information Systems Najran UniversityNajran61441Saudi Arabia Department of Electrical and Power Engineering AGH University of Science and Technology Mickiewicza 30 AvenueKrakow30-059Poland

One of the major concerns for the utilities in the Smart Grid(SG)is electricity *** the implementation of smart meters,the frequency of energy usage and data collection from smart homes has increased,which makes it possible for advanced data analysis that was not previously *** this purpose,we have taken historical data of energy thieves and normal *** avoid imbalance observation,biased estimates,we applied the interpolation ***,the data unbalancing issue is resolved in this paper by Nearmiss undersampling technique and makes the data suitable for further *** proposing an improved version of Zeiler and Fergus Net(ZFNet)as a feature extraction approach,we had able to reduce the model’s time *** minimize the overfitting issues,increase the training accuracy and reduce the training loss,we have proposed an enhanced method by merging Adaptive Boosting(adaboost)classifier with Coronavirus Herd Immunity Optimizer(CHIO)and Forensic based Investigation Optimizer(FBIO).In terms of low computational complexity,minimized over-fitting problems on a large quantity of data,reduced training time and training loss and increased training accuracy,our model outperforms the benchmark *** proposed algorithms Ada-CHIO andAda-FBIO,have the low MeanAverage Percentage Error(MAPE)value of error,i.e.,6.8%and 9.5%,***,due to the stability of our model our proposed algorithms Ada-CHIO and Ada-FBIO have achieved the accuracy of 93%and 90%.Statistical analysis shows that the hypothesis we proved using statistics is authentic for the proposed technique against benchmark algorithms,which also depicts the superiority of our proposed techniques.

关键词： Smart grids and meters electricity theft detection machine learning adaboost optimization techniques

A Hybrid Intrusion Detection Method Based on Convolutional Neural Network and adaboost

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China Communications 2024年第11期21卷 180-189页

作者： Wu Zhijun Li Yuqi Yue Meng School of Safety Science and Engineering Civil Aviation University of ChinaTianjin 300300China School of Electronic Information and Automation Civil Aviation University of ChinaTianjin 300300China

To solve the problem of poor detection and limited application range of current intrusion detection methods,this paper attempts to use deep learning neural network technology to study a new type of intrusion detection ***,we proposed an intrusion detection algorithm based on convolutional neural network(CNN)and adaboost *** algorithm uses CNN to extract the characteristics of network traffic data,which is particularly suitable for the analysis of continuous and classified attack *** adaboost algorithm is used to classify network attack data that improved the detection effect of unbalanced data *** adopt the UNSW-NB15 dataset to test of this algorithm in the PyCharm *** results show that the detection rate of algorithm is99.27%and the false positive rate is lower than 0.98%.Comparative analysis shows that this algorithm has advantages over existing methods in terms of detection rate and false positive rate for small proportion of attack data.

关键词： adaboost CNN detection rate false positive rate feature extraction intrusion detection

Multi-angle Face Detection Based on DP-adaboost

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International Journal of Automation and computing 2015年第4期12卷 421-431页

作者： Ying-Ying Zheng Jun Yao School of Mechanical and Electronic Engineering and Automation Shanghai University

Although important progresses have been already made in face detection,many false faces can be found in detection results and false detection rate is influenced by some factors,such as rotation and tilt of human face,complicated background,illumination,scale,cloak and *** paper proposes a new method called DP-adaboost algorithm to detect multi-angle human face and improve the correct detection *** improved adaboost algorithm with the fusion of frontal face classifier and a profile face classifier is used to detect the multi-angle *** improved horizontal differential projection algorithm is put forward to remove those non-face images among the preliminary detection results from the improved adaboost *** results show that compared with the classical adaboost algorithm with a frontal face classifier,the textual DP-adaboost algorithm can reduce false rate significantly and improve hit rate in multi-angle face detection.

关键词： Multi-angle face detection adaboost classifier fusion improved horizontal differential projection false face.

Aerolysin Nanopore Identification of Single Nucleotides Using the adaboost Model

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Journal of Analysis and Testing 2019年第2期3卷 134-139页

作者： Xue-Jie Sui Meng-Yin Li Yi-Lun Ying Bing-Yong Yan Hui-Feng Wang Jia-Le Zhou Zhen Gu Yi-Tao Long School of Information Science and Engineering East China University of Science and TechnologyShanghai 200237China School of Chemistry and Molecular Engineering East China University of Science and TechnologyShanghai 200237China

Nanopores employ the ionic current from the single molecule blockage to identify the structure,conformation,chemical groups and charges of a single *** the tremendous development in designing sensitive pore-forming materials,at some extent,the analyte with the single group difference still exhibits similar residual current or duration *** serious overlap in the statistical results of residual current and duration time brings the difficulties in the nanopore discrimination of each single molecules from the *** this paper,we present the adaboost-based machine learning model to identify the multiple analyte with single group difference in the mixed blockages.A set of feature vectors which is obtained from Hidden Markov Model(HMM)is used to train the adaboost *** employing the aerolysin sensing of 5ʹ-AAAA-3ʹ(AA3)and 5ʹ-GAAA-3ʹ(GA3)as the model system,our results show that adaboost model increases the identification accu-racy from~0.293 to above ***,five sets of mixed blockages of AA3 and GA3 further validate the average accuracy of training and validation,which are 0.997 and 0.989,*** proposed methods improve the capacity of wild-type biological nanopore in efficiently identify the single nucleotide difference without designing of protein and optimizing of the experimental ***,the adaboost-based machine learning approach could promote the nanopore practical application such as genetic and epigenetic detection.

关键词： Nanopore single nucleotide discrimination Single molecule analysis adaboost Hidden Markov model

Real-Time Multi-View Face Detection and Pose Estimation Based on Cost-Sensitive adaboost

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Tsinghua Science and Technology 2005年第2期10卷 152-157页

作者：马勇丁晓青 Department of Electronic Engineering Tsinghua University Beijing 100084 China

Locating multi-view faces in images with a complex background remains a challenging problem. In this paper, an integrated method for real-time multi-view face detection and pose estimation is presented. A simple-to-complex and coarse-to-fine view-based detector architecture has been designed to detect multi- view faces and estimate their poses efficiently. Both the pose estimators and the view-based face/nonface detectors are trained by a cost-sensitive adaboost algorithm to improve the generalization ability. Experi- mental results show that the proposed multi-view face detector, which can be constructed easily, gives more robust face detection and pose estimation and has a faster real-time detection speed compared with other conventional methods.

关键词： face detection pose estimation multi-view face detection adaboost

Monocular vision and calculation of regular three-dimensional target pose based on Otsu and Haar-feature adaboost classifier

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International Journal of Agricultural and Biological Engineering 2020年第5期13卷 171-180页

作者： Yuanhong Li Hongjun Wang Weiliang Zhou Zehao Xue Department of Engineering South China Agricultural UniversityGuangzhou 510642China Southern Key Laboratory of Agricultural Equipment Machinery South China Agricultural UniversityGuangzhou 510642China Department of Biological and Agricultural Engineering Texas A&M UniversityCollege StationTX77843USA

Using machine vision to identify and sort scattered regular targets is an urgent problem to be solved in automated production *** study proposed a three-dimensional(3D)recognition method combining monocular vision and machine learning *** to the color characteristics of the targets,to convert the original color picture into YCbCr mode and use the 2D Otsu algorithm to perform gray level image segmentation on the Cb *** the Haar-feature training was carried *** comparison of feature training and Haar method for Hough transform showed that the recognized time of Haar-feature adaboost trainer reached 31.00 ms,while its false recognized rate was 3.91%.The strong classifier was formed by weight combination,and the Hough contour transformation algorithm was set to correct the normal vector between plane coordinate and camera coordinate *** monocular vision system ensured that the field of camera view had not obstructed while the dots were being *** was measured and calculated angles between targets and the horizontal plane which coordinate points of the identified plane *** testing results were compared with the Otsu and adaboost trainer where the prediction and training set have an error of no more than 0.25 *** correct rate can reach 95%.It shows that the Otsu and Haar-feature based on adaboost algorithm is feasible within a certain error ranges and meet the engineering requirements for solving the poses of automated regular three-dimensional targets.

关键词： Otsu Haar-feature adaboost 3D position target pose monocular vision error analysis