Improved AdaBoost Algorithm Research Articles

Surface depression recognition of spring plate based on feature construction and improved AdaBoost algorithm

Machine learning has introduced novel solutions for the surface quality inspection of spring plates. Hence, an improved method was proposed based on logistic regression and AdaBoost (LR-RBAdaBoost) for recognizing surface depressions in spring plates. In order to obtain the measured attributes for spring plates, feature construction methods were adopted. Then, logistic regression was used to analyze and rank the attributes of the spring plate and the filter method was used for the feature selection process. To improve the predicted performance of AdaBoost, RBAdaBoost was proposed in this paper, which is used in the issue of recognition of surface defects in spring steel plates. The effectiveness and sophistication of LR-RBAdaBoost are validated on a real spring steel plate sampling dataset. The results show that the accuracy of LR-RBAdaBoost is 0.968 and f1-score is 0.967, which can be better than the results of only using AdaBoost, random forest, and CatBoost. Furthermore, the results prove that the research has a certain reference value for the recognition of spring plate as well.

Log specification and intelligent analysis method based on oil and gas pipeline SCADA system

Abstract As the control center of the natural gas long-distance pipeline network, the SCADA system shoulders the important tasks of data collection and monitoring of the whole long-distance pipeline, gas transmission management, production scheduling, operation and maintenance coordination in production, and plays a very important role in the whole oil and gas pipeline. In this paper, firstly, the SCADA system for localized long-distance pipelines is explained in detail, including its basic structure and special solutions to problems. Secondly, the AdaBoost algorithm, which combines the MapReduce parallel computing framework, is introduced to collect and process data from the operation logs of the SCADA system and normalize the logs. Finally, to test the interaction between the improved AdaBoost algorithm and the SCADA system, a system test was conducted. The results show that the average latency of scheduling the logs of the SCADA system by AdaBoost algorithm with MapReduce parallel computing framework is only 39.82ms, the average processing speed of the log normalization file data under the multi-threaded mode of the system reaches 86.51GB/s, and the effective accuracy of the fault diagnosis is as high as 90.36%. This shows that the oil and gas pipeline SCADA system interacting with data visualization technology can process operation logs more quickly and can carry out real-time intelligent supervision of the working status and operation parameters of the whole auxiliary system, promoting the intelligent development of the oil and gas pipeline SCADA system.

An Exploration of the Practical Path of Internet Civic Education in Student Management in Colleges and Universities Based on K-means Clustering

Abstract This paper first analyzes the path of network ideological education to boost student management and constructs a student management platform that integrates network ideological education using big data technology. Secondly, K-means clustering analysis is used to mine students’ campus behaviors, and the DPC algorithm combined with the improved AdaBoost algorithm is used to detect students’ academic anomalies so as to help schools better realize student management. Finally, performance testing and application analysis were conducted to verify the effectiveness of the student management platform that integrates online ideological education. The results show that when the number of concurrent users is 5000, the CPU occupancy rate and response time of this platform are 10.34% and 0.52 s. It is able to analyze and detect abnormalities in students’ campus behaviors and academic situations and encourage universities to analyze students’ situations through visual reports. This shows that the student management platform integrating network ideological education in the era of big data can promote the level of student management in colleges and universities.

Detection and classification of DC and feeder faults in DC microgrid using new morphological operators with multi class AdaBoost algorithm

DC microgrids with energy storage systems based on photovoltaic (PV) and wind energy are gaining popularity as a means to offer users with reliable supply in either a stand-alone or grid connected mode. However, because DC and AC side faults have similar current–voltage profiles, developing a viable safety strategy for the proposed integrated DC microgrid is difficult. Traditional protection techniques based on pre-defined thresholds are unable to discriminate between DC and AC side faults, and so fail to offer independent control actions in both circumstances. In this context, new morphological operators with improved AdaBoost algorithm is proposed for detecting and classifying the AC and DC side faults in the proposed DC microgrid. To explore this, current signals are captured at the DC bus of the proposed integrated DC microgrid. The captured signals comprise background noise which is eliminated by dilation erosion difference operator (DEDO) and opening closing difference (OCDO) operators. The two operators work together to meet the accurate fault detection to avoid nuisance tripping by multiscale operation of structuring element (SE). For effective outcomes the multiple scales are optimized by sparse kurtosis (SK) index. The optimized scales are passes through target features to retrieve the data. The acquired data is sent into the multi-class AdaBoost approach, which recognizes faults by modifying the distribution of data and iteratively adjusting the weight of each instance. The proposed system's efficacy is tested using the MATLAB/Simulink platform under various operating situations such as load variation, irradiation and fault resistance changes. The proposed algorithm's superiority is demonstrated by comparing it to existing approaches using confusion matrix (CM) parameters.

An improved AdaBoost algorithm for identification of lung cancer based on electronic nose

The research developed an improved intelligent enhancement learning algorithm based on AdaBoost, that can be applied for lung cancer breath detection by the electronic nose (eNose). First, collected the breath signals from volunteers by eNose, including healthy individuals and people who had lung cancer. Additionally, the signals' features were extracted and optimized. Then, multi sub-classifiers were obtained, and their coefficients were derived from the training error. To improve generalization performance, K-fold cross-validation was used when constructing each sub-classifier. The prediction results of a sub-classifier on the test set were then achieved by the voting method. Thus, an improved AdaBoost classifier would be built through heterogeneous integration. The results shows that the average precision of the improved algorithm classifier for distinguishing between people with lung cancer and healthy individuals could reach 98.47%, with 98.33% sensitivity and 97% specificity. And in 100 independent and randomized tests, the coefficient of variation of the classifier's performance hardly exceeded 4%. Compared with other integrated algorithms, the generalization and stability of the improved algorithm classifier are more superior. It is clear that the improved AdaBoost algorithm may help screen out lung cancer more comprehensively. Additionally, it will significantly advance the use of eNose in the early identification of lung cancer.

Temperature Drift Compensation Method for Tunable F-P Filter Based on Improved AdaBoost Algorithm

Temperature Drift Compensation Method for Tunable F-P Filter Based on Improved AdaBoost Algorithm

An Improved AdaBoost Algorithm for Highly Imbalanced Datasets in the Co-Authorship Recommendation Problem

An Improved AdaBoost Algorithm for Highly Imbalanced Datasets in the Co-Authorship Recommendation Problem

Financial Early Warning Model for Listed Companies Based on the Smart Sensor Data Network

This paper uses intelligent sensors to build a data network, collects information about listed enterprises in all aspects, performs frequency statistics and semantic analysis based on the financial domain lexicon on information related to listed enterprises, and introduces big data variables into a nonlinear support vector machine early warning model of enterprises combined with financial indicators. This paper introduces online reviews as big data indicators based on financial early warning theories and methods. Suitable financial indicators and big data indicators are selected for the financial early warning model to filter the available indicators. The prediction results of only financial indicators are compared with the prediction results of incorporating big data indicators. Quantify the comment information related to enterprises on the platform through sentiment classification and statistics on the number of comment information posted. The cost-sensitive support vector machine is used as the base classifier of the improved AdaBoost algorithm to build a dynamic imbalance warning model. To address the problems of large complexity and computation of unbalanced big data classification, high reliance on a priori knowledge, and classification performance to be improved, the classification and detection method of the intelligent sensor data network is proposed. By introducing migration learning, the problem of knowledge acquisition and training efficiency of high-dimensional complex data feature extraction in a big data environment is effectively solved, and the network performance is optimized by a conjugate gradient descent algorithm. Through simulation experiments, the prediction accuracy of the model for positive class samples is significantly improved after the nonequilibrium processing, the recall rate of the network model reaches 84.15%, and the prediction accuracy of the network model under different time steps reaches more than 90%. The experiment proves that the model can send out financial alert information more quickly and efficiently and accurately when a financial crisis may occur, relative to the traditional financial forecasting methods.

Improved AdaBoost algorithm using misclassified samples oriented feature selection and weighted non-negative matrix factorization

To improve the classification performance of existing adaptive boosting (AdaBoost) based algorithms effectively, an improved AdaBoost algorithm based on misclassified samples oriented feature selection and weighted non-negative matrix factorization (WNMF) is proposed in this paper. Firstly, in order to consider the effects of sample weights, a misclassified samples oriented feature selection (called MOFS) is proposed to select the most discriminative features which occur in the samples with high weights. Secondly, the explicit features and the part-based features of the training samples are both considered, and the WNMF algorithm is introduced and combined with MOFS to reduce the dimension of the training sample set. Finally, the concept of misclassification degree is introduced and a fine grained sample weight updating method is proposed to distinguish the samples with different misclassification degrees. Numerical experiments show that the proposed MOFS method achieves higher accuracy compared to traditional feature selection methods, and the proposed MOFS and WNMF based AdaBoost method obtains significant improvement on classification accuracy when comparing with typical existing AdaBoost based algorithms using different classifiers.

Lead–Acid Battery SOC Prediction Using Improved AdaBoost Algorithm

Research on the state of charge (SOC) prediction of lead–acid batteries is of great importance to the use and management of batteries. Due to this reason, this paper proposes a method for predicting the SOC of lead–acid batteries based on the improved AdaBoost model. By using the online sequence extreme learning machine (OSELM) as its weak learning machine, this model can achieve incremental learning of the model, which has a high computational efficiency, and does not require repeated training of old samples. Through improvement of the AdaBoost algorithm, the local prediction accuracy of the algorithm for the sample is enhanced, the scores of the proposed model in the maximum absolute error (AEmax) and maximum absolute percent error (APEmax) indicators are 6.8% and 8.8% lower, and the accuracy of the model is further improved. According to the verification with experimental data, when there are a large number of prediction samples, the improved AdaBoost model can reduce the prediction accuracy indicators of mean absolute percent error (MAPE), mean absolute error (MAE), and mean square error (MSE) to 75.4%, 58.3, and 84.2%, respectively. Compared with various other prediction methods in the prediction accuracy of battery SOC, the prediction accuracy indicators MAE, MSE, MAPE, AEmax, and APEmax of the model proposed in this paper are all optimal, which proves the validity and adaptive ability of the model.

Prediction of Dental Implants Using Machine Learning Algorithms

It has been claimed that artificial intelligence (AI) has transformative potential for the healthcare sector by enabling increased productivity and creative methods of delivering healthcare services. Recently, there has been a major shift to artificial intelligence by businesses, government, and private sectors in general and the health sector in particular. Many studies have proven that artificial intelligence is contributing greatly to the health sector by discovering diseases and determining the best treatments for patients. Dentistry requires new innovative methods that serve both the patient and the service provider in obtaining the best and appropriate medical services. Artificial intelligence has the ability to develop the field of dentistry through early diagnosis and prediction of dental implant cases. This research develops a set of four machine learning algorithms to predict when a patient might need dental implants. These models are the Bayesian network, random forest, AdaBoost algorithm, and improved AdaBoost algorithm. This work shows that the developed algorithms can predict when a patient needs dental implants. Also, we believe that this proposal will advise managers and decision-makers in targeting patients with particular diagnoses. Analysis of the obtained results indicates good performance of the developed machine learning. As a result of this research, we note that the proposed improved AdaBoost algorithm increases the level of prediction accuracy and gives significantly higher performance than the other studied methods with the accuracy for the improved AdaBoost algorithm reaching 91.7%.

An Efficient AdaBoost Algorithm with the Multiple Thresholds Classification

Adaptive boost (AdaBoost) is a prominent example of an ensemble learning algorithm that combines weak classifiers into strong classifiers through weighted majority voting rules. AdaBoost’s weak classifier, with threshold classification, tries to find the best threshold in one of the data dimensions, dividing the data into two categories-1 and 1. However, in some cases, this Weak Learning algorithm is not accurate enough, showing poor generalization performance and a tendency to over-fit. To solve these challenges, we first propose a new Weak Learning algorithm that classifies examples based on multiple thresholds, rather than only one, to improve its accuracy. Second, in this paper, we make changes to the weight allocation scheme of the Weak Learning algorithm based on the AdaBoost algorithm to use potential values of other dimensions in the classification process, while the theoretical identification is provided to show its generality. Finally, comparative experiments between the two algorithms on 18 datasets on UCI show that our improved AdaBoost algorithm has a better generalization effect in the test set during the training iteration.

A Study on the Design and Implementation of an Improved AdaBoost Optimization Mathematical Algorithm Based on Recognition of Packaging Bottles

In this paper, a special design system is developed based on the design of the packaging bottle to achieve the effective acquisition of the image of the cross-section of the packaging bottle to be measured under the condition of limited space size, avoiding the distortion of the object to be measured. At the same time, the image of the area where the target packaging bottle is located is segmented, and the curve features are quickly determined and effectively matched with the template library to realize the recognition of the shape features of the bottle. In this paper, the design of the packaging bottle is first designed by mechanism design and 3D modeling, followed by rapid prototyping methods such as 3D printing, and the prototype is made for functional verification. Finally, the transmission speed and stability of the design system for packaging bottle recognition are improved through structural analysis and optimization methods. To realize the intelligent control of the packaging bottle transmission and identification system, the hardware control circuit is designed and the relevant intelligent control program is prepared based on the embedded system so that the packaging bottles in the transmission process can be quickly and accurately positioned and identified. An improved AdaBoost algorithm is proposed for packaging bottle detection. In the process of algorithm learning, the Haar features are too large and time-consuming, and the training sample is cropped to remove the sample edge pixels, which effectively reduces the number of features, thus reducing the computation. The proposed optical flow method is used to obtain the motion region in the video image as the region of interest, and the canny operator is used in the region of interest for edge detection, and the region of interest is filtered by the edge energy to exclude the noninterest region. Finally, the AdaBoost algorithm is used to detect the region of interest, which reduces the detection area and decreases the detection time. The improved AdaBoost algorithm has a high accuracy improvement over the traditional AdaBoost algorithm for the recognition of various packaging bottles with relatively suitable training set samples, and the system recognition time has reached the requirements of industrial recognition.

Research on the Robot’s Intelligent Inspection, Its Target Detection Method

Intelligent inspection robot has the characteristics of programmable, and can be applied to various inspection environments. In general, studies show the present trend is to replace humans with an inspection robot thereby reducing the risks and improving the inspection efficiency. The intelligent inspection robot is based on intelligent technology and has programmability. In this paper, based on the research of intelligent inspection robot technology, we analyze inspection techniques, their algorithms, functions, characteristics and other important parameters. The research mainly focuses on two things: the target detection, methods and improved Adaboost algorithm to improve the accuracy of target detection; the Camshift algorithm which is improved to complete tracking design, timely data acquisition, timely problem discovery and timely solution. The target detection and target tracking are studied and their algorithms are analyzed. We present that a tracking algorithm based on improved Camshift deals with the problems which exist in traditional Camshift algorithm. In addition, we present Meanshift algorithm improves the Camshift algorithm for the whole-process tracking, automation and intelligence level, and efficient tracking. Next, combined with relevant technologies and techniques, the algorithm is improved to complete the target detection design and tracking design, and to solve the problems of inaccurate target detection and untimely detection.

A novel webpage layout aesthetic evaluation model for quantifying webpage layout design

It is widely accepted that webpage layout is important for website aesthetics. However, knowledge is limited about which kind of webpage layout is aesthetic. In this paper, we conduct a large-scale analysis of 13,017 real webpages with different aesthetic levels. The results demonstrate that there are significant differences between aesthetic webpages and non-aesthetic webpages in several aspects of layout design, such as the position of objects and the average number of objects. In order to identify whether the web layout is aesthetic, we propose a novel Webpage Layout Aesthetic Evaluation model (WLAE) based on an improved Adaboost algorithm to automatically predict the aesthetics of a webpage layout. Experimental results show that our WLAE model is significantly better than other existing methods. Based on the feature analysis of 13,017 real-world webpages and the contribution of each feature to webpage aesthetic prediction, we have summarized 4 webpage design principles for webpage designers. Besides, a large number of real webpages collected by our team can be used for further research on webpage layout aesthetic design and/or related research.

Distributed Power Trading System Based on Blockchain Technology

The power trading system has the characteristics of nonlinearity, dynamics, and complexity. Part of the business data in the trading system needs to be exposed to numerous external business systems. The traditional centralized power trading model has some problems, such as low data security and trust crisis of regulators. Blockchain technology provides prominent ideas for solving these problems. Firstly, the improved AdaBoost algorithm is used to predict the supply and demand gap of power trading nodes. Secondly, based on the fact that the information on the blockchain is only open to the power supply side, a two-stage game model of asymmetric information between the power supply side and the power user is constructed to capture the price competition behavior between them in order to find the Nash equilibrium price in two stages. Finally, the US PJM market electricity market data are used to carry out an example analysis to verify the effectiveness of the algorithm and model.

Real-time Monitoring of Fluidized Bed Agglomerating based on Improved Adaboost Algorithm

Absctract: In order to to detect the polymer agglomeration in fluidized bed reactor (FBR), a method of real-time monitoring of agglomeration in fluidized bed polyolefin reactor based on voiceprint feature recognition is developed. First, the acoustic emission detection technology is applied to collect the acoustic signal generated by the polymer collision on the inner wall of FBR. Then, the voiceprint features of the collected acoustic signal are extracted with the Mel Frequency Cepstrum Coefficients (MFCC) and the Linear Prediction Cepstrum Coefficients (LPCC). To classify the extracted voiceprint features, an improved Adaboost algorithm is proposed to establish the real-time agglomeration classification model. Due to the introduction of cost factor and Gini index decision-making calculation to the Adaboost algorithm, the proposed improved Adaboost algorithm can classify unbalanced small samples with better accuracy and F-score index compared with the traditional Adaboost algorithm. The experiment results in a fluidized bed pilot plant have verified the effectiveness and feasibility of the proposed method.

Improved AdaBoost algorithm using group degree and membership degree based noise detection and dynamic feature selection

Improved AdaBoost algorithm using group degree and membership degree based noise detection and dynamic feature selection

Research on anomaly data mining method of new energy field stations based on improved Adaboost algorithm

Traditional anomalous data mining methods require a lot of prior knowledge, which leads to low data mining integrity and efficiency. For this reason, a new energy field abnormal data mining method based on improved Adaboost algorithm is proposed. After pre-processing the new energy field data, the algorithm is improved by introducing dynamic weight parameters to address the shortcomings of the Adaboost algorithm. After calculating the degree of data anomaly using the direct push belief machine, the neural network is used to reduce the error value of the Adaboost algorithm, and finally the output of the Adaboost algorithm is used to realize abnormal data mining. The simulation experiment proves that the researched abnormal data mining method has high data integrity and high efficiency.

Prediction of special OD passenger flow based on transfer learning

The prediction of railway passenger flow has always been an important topic in the field of railway operation. Due to the limitation of the number of allocated tickets, some special OD(Origin to Destination) data are sometimes inaccurate. It will obviously affect the final prediction accuracy if it is directly used as a sample. However, it is very difficult to restore the biased data to the true value. In this paper, we choose case-based transfer learning to remove the unqualified samples from the target domain and replace them with the corresponding samples in the source domain and form the final training sample data set. At the same time, we choose the improved AdaBoost algorithm, the sample weight is adjusted by the error size, and the model is retrained to get the final model. The results show that the prediction accuracy of transfer learning based on improved AdaBoost algorithm is better than that of traditional ensemble learning, ARIMA model and multiple regression model. It also provides a new theoretical view for special OD passenger flow prediction in the future.