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A novel radar operating mode identification approach based on variational relevance vector machine with chaotic gravitational search optimization

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TRANSACTIONS OF THE INSTITUTE OF MEASUREMENT AND CONTROL 2023年第8期45卷 1397-1410页

作者： Chen, Xinjie Wang, Wenhai Zou, Ruping Zhang, Zeyin Chen, Shichao Wang, Jun Liu, Xinggao Zhejiang Univ Control Dept State Key Lab Ind Control Technol NGICS Platform Hangzhou Peoples R China Xian Modern Control Technol Res Inst Xian Peoples R China Zhejiang Univ Math Dept Hangzhou Peoples R China Zhejiang Univ Control Dept State Key Lab Ind Control Technol NGICS Platform Hangzhou 310027 Zhejiang Peoples R China

Radar operating mode identification is one of the top priorities in the electronic countermeasure field for threat prediction. However, the inference speed and accuracy of existing identification methods are still not satisfactory for threat detection, and some of them need extra prior knowledge to extract features. This paper introduces an identification approach for radar operating mode to solve these problems. The variational relevance vector machine (VRVM) is presented as the feature extractor and the basic unit of identification. An improved chaotic gravity search algorithm (CGSA) is developed to increase the search breadth and optimize the hyperparameters of VRVM. Threshold-one-versus-one (TOVO) is further proposed to screen the identification results to form the final ensemble method called CGSA-VRVM-TOVO. Experimental results indicate the CGSA-VRVM-TOVO achieves 99.44% accuracy without prior knowledge. Compared with XGBoost, Random Forest, and LightGBM, our approach is 1.94%, 0.55%, and 1.11% higher in accuracy, respectively, and twice as fast as the fastest method of them all.

关键词： Radar operating mode identification gravity search algorithm variational relevance vector machine ensemble algorithm

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Prediction of pressure drop in solid-liquid two-phase pipe flow for deep-sea mining based on machine learning

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OCEAN ENGINEERING 2024年 304卷

作者： Wan, Chuyi Zhu, Hongbo Xiao, Shengpeng Zhou, Dai Bao, Yan Liu, Xu Han, Zhaolong Shanghai Jiao Tong Univ Sch Ocean & Civil Engn Minhang Campus Shanghai 200240 Peoples R China Shanghai Jiao Tong Univ Sichuan Res Inst Chengdu 610213 Peoples R China

In deep -sea mining, the accurate and rapid prediction of the pressure drop in a solid-liquid two-phase pipe flow (SLPF) with different parameters including particles, pipes, and flow fields, remains an issue yet to be fully resolved. In this study, an extensive investigation of the pressure drop in a slpf is conducted using machine-learning techniques. By collecting 1290 sets of data from 13 experimental papers and performing analysis and processing, we obtain a machine-learning ensemble algorithm capable of accurately predicting the pipe -pressure drop based on random forest (RF), back propagation (BP), and polynomial regression (PR) algorithms. The performance of the ensemble algorithm surpasses that of the other three algorithms, whether applied to pure substance (PS) particles or mixed particles (MP) containing PS and equivalent particles. For PS particles, the particle concentration and particle diameter -to -pipe diameter (PTP) account for the second and third weights influencing the pressure drop. Using the computational fluid dynamics (CFD)-discrete element method (DEM), this can be attributed to the significant kinetic energy loss caused by the collisions and friction between the particles and pipe wall and the excessive gravity of the particles, which influences the pressure drop.

关键词： Deep-sea mining Machine learning ensemble algorithm Pipe flow Hydraulic conveying

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Software System for Different Types of Data Classification Based on the ensemble algorithms

Software System for Different Types of Data Classification B...

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International Scientific-Technical Conference on Actual Problems of Electronics Instrument Engineering

作者： Roman I. Batygin Olga K. Alsova Novosibirsk State Technical University Novosibirsk Russia

ISBN: (纸本)9781509040704

This article describes the structure and functional content of a developed software system of different types of data classification based on the ensemble algorithms. Also the article describes the developed heterogeneous ensemble classification algorithm implemented in the software system. A distinctive feature of the algorithm is the iterative use of single (basic) classifiers on the initial training sample and inclusion in the ensemble only those classifiers whose relative error does not exceed a predetermined threshold. The software system was tested on real medical data. The accuracy of data classification using the basic classifiers and heterogeneous ensemble algorithm was compared. Test results showed the effectiveness (increase classification accuracy) of heterogeneous ensemble algorithm compared with the single classifiers.

关键词： single (basic) classification algorithm ensemble algorithm heterogeneous ensemble bagging boosting random forests bootstrap - sample decision tree logistic regression neural network

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Imbalanced Data Processing Model for Software Defect Prediction

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WIRELESS PERSONAL COMMUNICATIONS 2018年第2期102卷 937-950页

作者： Zhou, Lijuan Li, Ran Zhang, Shudong Wang, Hua Capital Normal Univ Coll Informat Engn Beijing Peoples R China

In the field of software engineering, software defect prediction is the hotspot of the researches which can effectively guarantee the quality during software development. However, the problem of class imbalanced datasets will affect the accuracy of overall classification of software defect prediction, which is the key issue to be solved urgently today. In order to better solve this problem, this paper proposes a model named ASRA which combines attribute selection, sampling technologies and ensemble algorithm. The model adopts the Chi square test of attribute selection and then utilizes the combined sampling technique which includes SMOTE over-sampling and under-sampling to remove the redundant attributes and make the datasets balance. Afterwards, the model ASRA is eventually established by ensemble algorithm named Adaboost with basic classifier J48 decision tree. The data used in the experiments comes from UCI datasets. It can draw the conclusion that the effect of software defect prediction classification which using this model is improved and better than before by comparing the precision P, F-measure and AUC values from the results of the experiments.

关键词： Software defect prediction Class imbalance Attribute selection Sampling ensemble algorithm

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Prediction of the activity of Crohn's disease based on CT radiomics combined with machine learning models

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JOURNAL OF X-RAY SCIENCE AND TECHNOLOGY 2022年第6期30卷 1155-1168页

作者： Li, Tingting Liu, Yu Guo, Jiuhong Wang, Yuanjun Univ Shanghai Sci & Technol Inst Med Imaging Engn Shanghai 200093 Peoples R China Shanghai Jiao Tong Univ Shanghai Peoples Hosp 9 Dept Radiol Sch Med Shanghai 200011 Peoples R China

PURPOSE: To investigate the value of a CT-based radiomics model in identification of Crohn's disease (CD) active phase and remission phase. METHODS: CT images of 101 patients diagnosed with CD were retrospectively collected, which included 60 patients in active phase and 41 patients in remission phase. These patients were randomly divided into training group and test group at a ratio of 7 : 3. First, the lesion areas were manually delineated by the physician. Meanwhile, radiomics features were extracted from each lesion. Next, the features were selected by t-test and the least absolute shrinkage and selection operator regression algorithm. Then, several machine learning models including random forest (RF), extreme gradient boosting (XGBoost), support vector machine (SVM), logistic regression (LR) and K-nearest neighbor (KNN) algorithms were used to construct CD activity classification models respectively. Finally, the soft-voting mechanism was used to integrate algorithms with better effects to perform two classifications of data, and the receiver operating characteristic curves were applied to evaluate the diagnostic value of the models. RESULTS: Both on the training set and the test set, AUC of the five machine learning classification models reached 0.85 or more. The ensemble soft-voting classifier obtained by using the combination of SVM, LR and KNN could better distinguish active CD from CD remission. For the test set, AUC was 0.938, and accuracy, sensitivity, and specificity were 0.903, 0.911, and 0.892, respectively. CONCLUSION: This study demonstrated that the established radiomics model could objectively and effectively diagnose CD activity. The integrated approach has better diagnostic performance.

关键词： Crohn's disease activity radiomics machine learning ensemble algorithm

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Incorporating Feature Selection in the Improved Stacking algorithm for Online Learning Analysis and Prediction

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ENGINEERING LETTERS 2020年第4期28卷 1011-1022页

作者： Dai, Hong Wu, Wenkai Li, Jiacheng Yuan, Yangke Univ Sci & Technol Liaoning Sch Comp Sci & Software Engn Anshan Peoples R China Liaoning Tech Univ Sch Mapping & Geog Sci Fuxin Peoples R China

Online learning is becoming a common learning method in the field of education. The correct classification of online learners plays a vital role in solving the key issues such as low pass rates and high dropout rates. In this paper, we propose a improved ensemble algorithm for classifying learners, which integrates feature selection and the improved Stacking algorithm (Stacking-PMLR). One feature selection algorithm is Mean Decrease Impurity algorithm based on Random Forest. It is used to investigate the learning behavior factors which contribute to class of learner. It is also used to select the most frequent features and to reduce the dimensions.. Analyzing learners' behavior features by the feature selection algorithm, we know that a number of chapters, interaction days, interactions times and video viewed times are the most important factors. Learners' behavior features from feature selection are used as the attribute input of Stacking-PMLR for classifying learners. After that, we use the multilevel improved ensemble algorithm Stacking-PMLR to classify learners. We improve the Stacking algorithms in terms of its hierarchical structure, data features representation, combination strategy and classification algorithm according to its own characteristics. We use the improved Stacking algorithm to construct the classification model. In addition, fifteen real world different type datasets in UCI machine learning repository are applied. The experimental results show that the improved Stacking algorithm has better performance in accuracy, precision and F-1. It also shows the feasibility of the Stacking-PMLR. Finally, we use feature selection and the Stacking-PMLR algorithm to classify the public dataset of the edX online learning platform. The experimental results show that the performance of Stacking-PMLR is better. It shows the practical value of the Stacking-PMLR in online learning prediction.

关键词： Online learning ensemble algorithm feature selection stacking algorithm

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SLB - SMOTE logistic blending hybrid machine learning model for chronic polycystic ovary syndrome prediction with correlated feature selection

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INFORMATICS FOR HEALTH & SOCIAL CARE 2024年第3-4期49卷 190-211页

作者： Vairachilai, S. Anuhya, Devarakonda Tirkey, Anjeleen Raja, S. P. VIT Bhopal Univ Sch Comp Sci & Engn Kothrikalan 466114 Madhya Pradesh India Vellore Inst Technol Sch Comp Sci & Engn Vellore India

ObjectiveIn this study, we aimed to develop a machine learning (ML) model for predicting Polycystic Ovary Syndrome (PCOS) based on demographic, clinical, and biochemical *** collected data from Kaggle, which included information on age, body mass index, menstrual cycle length, follicle-stimulating hormone, hair growth, and more. Using this data, we trained several traditional ML and ensemble algorithms to predict *** the traditional ML algorithms, Logistic Regression emerged as the best, boasting the highest accuracy of 0.91 and an AUC of 0.90. In ensemble algorithms, the Blending algorithm outperformed other ensemble methods, also achieving an accuracy of 0.91 and an AUC of 0.90, with a balanced precision and recall of *** of the ResearchThese results establish Logistic Regression and the Blending algorithm as optimal choices for accurate and reliable PCOS prediction, demonstrating strong discriminative power and the ability to correctly classify PCOS cases.

关键词： Polycystic ovary syndrome machine learning ensemble algorithm menstrual irregularity health informatics

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Novel Partitioned Time-Stepping algorithms for Fast Computation of Random Interface-Coupled Problems with Uncertain Parameters

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Numerical Mathematics(Theory,Methods and Applications) 2024年第1期17卷 145-180页

作者： Yizhong Sun Jiangshan Wang Haibiao Zheng School of Mathematical Sciences East China Normal UniversityShanghaiChina School of Mathematical Sciences Ministry of Education Key Laboratory of Mathematics and Engineering ApplicationsShanghai Key Laboratory of PMMPEast China Normal UniversityShanghaiChina

The simulation of multi-domain,multi-physics mathematical models with uncertain parameters can be quite demanding in terms of algorithm design and com-putation *** main objective in this paper is to examine a physical interface coupling between two random dissipative systems with uncertain *** to the complexity and uncertainty inherent in such interface-coupled problems,un-certain diffusion coefﬁcients or friction parameters often arise,leading to consid-ering random *** employ Monte Carlo methods to produce independent and identically distributed deterministic heat-heat model samples to address ran-dom systems,and adroitly integrate the ensemble idea to facilitate the fast calcu-lation of these *** achieve unconditional stability,we introduce the scalar auxiliary variable(SAV)method to overcome the time constraints of the ensemble implicit-explicit ***,for a more accurate and stable scheme,the ensemble data-passing algorithm is raised,which is unconditionally stable and convergent without any auxiliary *** algorithms employ the same co-efﬁcient matrix for multiple linear systems and enable easy parallelization,which can signiﬁcantly reduce the computational ***,numerical experiments are conducted to support the theoretical results and showcase the unique features of the proposed algorithms.

关键词： Scalar auxiliary variable ensemble algorithm random interface-coupled problems implicit-explicit partitioned method data-passing partitioned method

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Acoustic feature-based emotion recognition and curing using ensemble learning and CNN

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APPLIED SOFT COMPUTING 2024年 166卷

作者： Anand, Raghav, V Md, Abdul Quadir Sakthivel, G. Padmavathy, T., V Mohan, Senthilkumar Damasevicius, Robertas Vellore Inst Technol Sch Comp Sci Engn Chennai 600127 India Vellore Inst Technol Sch Informat Technol & Engn Vellore 632014 India Kaunas Univ Technol Dept Software Engn Kaunas Lithuania

Emotion recognition and understanding plays a crucial role in various domains, including healthcare, human- computer interaction, and mental well-being. In this context, this paper proposes a methodology for recognizing and curing emotions using acoustic features and machine learning algorithms. The approach involves extracting acoustic features from the signals using diverse signal processing techniques. These features are then utilized as inputs for machine learning and deep learning algorithms, including the Random Forest classifier, XG Boost classifier, Convolutional Neural Network (CNN), and an ensemble algorithm. The ensemble algorithm combines Random Forest and XG Boost as base classifiers, with the Na & iuml;ve Bayes algorithm serving as the meta classifier. We also propose a novel model that generates personalized curing strategies for individuals based on emotion recognition, so they can keep their emotional state positive. With the help of an ensemble learning model the proposed model achieved an emotion recognition accuracy of 92 % by combining three publicly available datasets containing emotional speech recordings. In the neutral and positive emotion classifications, the Receiver Operating Characteristic curve (ROC) had a 98 % accuracy rate while negative emotion classifications had a 91 % true positive rate. The effectiveness of the proposed curing methodology model has also been demonstrated by conducting experiments on a group of individuals and comparing the results with a state-of-theart Generative Pre-Trained Transformer-3 (GPT-3) and ChatGPT, it was inferred that 89.35 % of the test group preferred the responses of the proposed curing model, over the GPT models The results of our experiments show that our proposed methodology can significantly boost the emotional state of an individual, thereby highlighting its potential for use in clinical settings.

关键词： Emotion recognition Acoustic features Signal processing Random forest classifier XG Boost classifier Convolutional neural network ensemble algorithm

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ensemble methods with outliers for phonocardiogram classification

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PHYSIOLOGICAL MEASUREMENT 2017年第8期38卷 1631-1644页

作者： Homsi, Masun Nabhan Warrick, Philip Univ Simon Bolivar Caracas Venezuela PeriGen Inc Montreal PQ Canada

Objective: Heart sound classification and analysis play an important role in the early diagnosis and prevention of cardiovascular disease. To this end, this paper introduces a novel method for automatic classification of normal and abnormal heart sound recordings. Approach: Signals are first preprocessed to extract a total of 131 features in the time, frequency, wavelet and statistical domains from the entire signal and from the timings of the states. Outlier signals are then detected and separated from those with a standard range using an interquartile range algorithm. After that, feature extreme values are given special consideration, and finally features are reduced to the most significant ones using a feature reduction technique. In the classification stage, the selected features either for standard or outlier signals are fed separately into an ensemble of 20 two-step classifiers for the classification task. The first step of the classifier is represented by a nested set of ensemble algorithms which was cross-validated on the training dataset provided by PhysioNet Challenge 2016, while the second one uses a voting rule of the class label. Main results: The results show that this method is able to recognize heart sound recordings efficiently, achieving an overall score of 96.30% for standard signals and 90.18% for outlier signals on a cross-validated experiment using the available training data. Significance: The approach of our proposed method helped reduce overfitting and improved classification performance, achieving an overall score on the hidden test set of 80.1% ( 79.6% sensitivity and 80.6% specificity).

关键词： phonocardiogram outlier detection and classification ensemble algorithm random forest LogitBoost cost-sensitive classifier combination rule

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