关键词： electronic nose   pear brown core   machine learning   neural network   principal component analysis  

摘要： The brown core is an internal disorder that significantly affects the palatability and economic value of Chinese pears. In this study, a framework that includes a back-propagation neural network (BPNN) and extreme learning machine (ELM) (BP-ELMNN) was proposed for the detection of brown core in the Chinese pear variety Huangguan. The odor data of pear were collected using a metal oxide semiconductor (MOS) electronic nose (E-nose). Principal component analysis was used to analyze the complexity of the odor emitted by pears with brown cores. The performances of several machine learning algorithms, i.e., radial basis function neural network (RBFNN), BPNN, and ELM, were compared with that of the BP-ELMNN. The experimental results showed that the proposed framework provided the best results for the test samples, with an accuracy of 0.9683, a macro-precision of 0.9688, a macro-recall of 0.9683, and a macro-F1 score of 0.9685. The results demonstrate that the use of machine learning algorithms for the analysis of E-nose data is a feasible and non-destructive method to detect brown core in pears.

关键词： low-frequency sensor   power transformer   machine learning   low-frequency noise   genetic algorithm  

摘要： The paper proposes a method of automatic detection of parameters of a distribution transformer (model, type, and power) from a distance, based on its low-frequency noise spectra. The spectra are registered by sensors and processed by a method based on evolutionary algorithms and machine learning. The method, as input data, uses the frequency spectra of sound pressure levels generated during operation by transformers in the real environment. The model also uses the background characteristic to take under consideration the changing working conditions of the transformers. The method searches for frequency intervals and its resolution using both a classic genetic algorithm and particle swarm optimization. The interval selection was verified using five state-of-the-art machine learning algorithms. The research was conducted on 16 different distribution transformers. As a result, a method was proposed that allows the detection of a specific transformer model, its type, and its power with an accuracy greater than 84%, 99%, and 87%, respectively. The proposed optimization process using the genetic algorithm increased the accuracy by up to 5%, at the same time reducing the input data set significantly (from 80% up to 98%). The machine learning algorithms were selected, which were proven efficient for this task.

关键词： Supervised machine learning   Wake dynamics   Turbulence  

摘要： We apply supervised machine learning techniques to a number of regression problems in fluid dynamics. Four machine learning architectures are examined in terms of their characteristics, accuracy, computational cost, and robustness for canonical flow problems. We consider the estimation of force coefficients and wakes from a limited number of sensors on the surface for flows over a cylinder and NACA0012 airfoil with a Gurney flap. The influence of the temporal density of the training data is also examined. Furthermore, we consider the use of convolutional neural network in the context of super-resolution analysis of two-dimensional cylinder wake, two-dimensional decaying isotropic turbulence, and three-dimensional turbulent channel flow. In the concluding remarks, we summarize on findings from a range of regression-type problems considered herein.

关键词： Convolutional neural network   Siamese neural network   Trademark similarity assessment   Trademark (TM) infringement   Vector space model  

摘要： The rapid development of consumer products with short life spans, along with fast, global e-commerce and e-marketing distribution of products and services requires greater due diligence to protect intangible assets such as brands and corporate logos which can easily be copied or distributed through grey channels and internet sales sites. Trademarks (TMs) are government registered intellectual property rights (IPRs) used to legally protect a companies' identities and brand equity. The rapid growth of global trademark (TM) registrations and the number of TM infringement cases pose a great challenge for TM owners to detect infringement and take action to protect TMs, consumer trust, and market share. This research develops advanced TM similarity assessment models using machine learning (ML) approaches. Litigation principles over similarity follow US TM laws which are consistent with global TM protection convention under the World Intellectual Property Organization (WIPO). This research covers the similarity analysis of TM spelling, pronunciation, and images, which are most likely to cause TM confusion among customers. The research focuses on deploying machine learning for natural language (spelling and phonetic features) and image similarity analyses. The vector space modeling algorithms are trained and verified for the similarity analysis of TM wordings in both spelling and pronunciation. The convolutional neural network and Siamese neural network models are trained and verified for TM image similarity comparison. The training and testing sets consist of 250,000 and 20,000 different image pairs respectively. This research provides a significant contribution toward implementing intelligent and automated IPR protection. The system solution supports users (companies, TM attorneys, or IP officers) to identify similar registered TMs before registering new TMs ensuring uniqueness to avoid infringement disputes. The solution also supports automatic screening of online conten

关键词： Attack detection   Genetic algorithm   Logistic regression   Artificial neural network   Particle swarm optimization   Feature selection  

摘要： Advances in computer networks led to the generation of much data that computer networks must be capable of transmitting. The security of this volume of data is a major challenge for companies. Intrusion detection systems is one of the solutions that researchers introduced for this challenge. This research aims to introduce a new machine learning model for intrusion detection. The proposed model includes two stages of feature selection and attack identification. The feature selection stage uses genetic algorithm and logistic regression algorithm to find a correlated subset of features. In the attack detection phase, the ANN algorithm is used. ANN is trained by particle optimization (PSO) and gravitational search (GS) algorithms. To evaluate the proposed model, two sets of NSL-KDD and KDD Cup'99 are used and results are compared with ANN based on gradient descent (GD-ANN) and decision tree, ANN based on genetic algorithm (GA-ANN) methods, ANN based on GSPSO (GSPSO-ANN), ANN based on PSO (PSO-ANN) and ANN based on GS (GS-ANN) indicate the superiority of the proposed method.

关键词： deep learning   financial distress prediction   machine learning   management factors   textual factors  

摘要： Financial distress prediction (FDP) has been widely considered as a promising approach to reducing financial losses. While financial information comprises the traditional factors involved in FDP, nonfinancial factors have also been examined in recent studies. In light of this, the purpose of this study is to explore the integrated factors and multiple models that can improve the predictive performance of FDP models. This study proposes an FDP framework to reveal the financial distress features of listed Chinese companies, incorporating financial, management, and textual factors, and evaluating the prediction performance of multiple models in different time spans. To develop this framework, this study employs the wrapper-based feature selection method to extract valuable features, and then constructs multiple single classifiers, ensemble classifiers, and deep learning models in order to predict financial distress. The experiment results indicate that management and textual factors can supplement traditional financial factors in FDP, especially textual ones. This study also discovers that integrated factors collected 4 years prior to the predicted benchmark year enable a more accurate prediction, and the ensemble classifiers and deep learning models developed can achieve satisfactory FDP performance. This study makes a novel contribution as it expands the predictive factors of financial distress and provides new findings that can have important implications for providing early warning signals of financial risk.

关键词： Magnetic resonance imaging   Lung cancer   Radiomics   Machine learning  

摘要： Objectives We develop and validate a radiomics model based on multiparametric magnetic resonance imaging (MRI) in the classification of the pulmonary lesion and identify optimal machine learning methods. Materials and methods This retrospective analysis included 201 patients (143 malignancies, 58 benign lesions). Radiomics features were extracted from multiparametric MRI, including T2-weighted imaging (T2WI), T1-weighted imaging (TIWI), and apparent diffusion coefficient (ADC) map. Three feature selection methods, including recursive feature elimination (RFE), t test, and least absolute shrinkage and selection operator (LASSO), and three classification methods, including linear discriminate analysis (LDA), support vector machine (SVM), and random forest (RF) were used to distinguish benign and malignant pulmonary lesions. Performance was compared by AUC, sensitivity, accuracy, precision, and specificity. Analysis of performance differences in three randomly drawn cross-validation sets verified the stability of the results. Results For most single MR sequences or combinations of multiple MR sequences, RFE feature selection method with SVM classifier had the best performance, followed by RFE with RF. The radiomics model based on multiple sequences showed a higher diagnostic accuracy than single sequence for every machine learning method. Using RFE with SVM, the joint model of T1WI, T2WI, and ADC showed the highest performance with AUC = 0.88 +/- 0.02 (sensitivity 83%;accuracy 82%;precision 91%;specificity 79%) in test set. Conclusion Quantitative radiomics features based on multiparametric MRI have good performance in differentiating lung malignancies and benign lesions. The machine learning method of RFE with SVM is superior to the combination of other feature selection and classifier methods.

关键词： Stock price movement prediction   Machine learning methods   Persistent homology   Technical indicators  

摘要： Predicting direction of stock price movement is notably important to provide a better guidance to assist market participants in making their investment decisions. This study presents a hybrid method combining machine learning methods with persistent homology to improve the prediction performance. Three stock prices namely Kuala Lumpur Composite Index, Kuala Lumpur Stock Exchange Industrial and Kuala Lumpur Stock Exchange Technology sampled from Kuala Lumpur Stock Exchange are selected for experimental evaluation. In particular, persistent homology was applied to obtain a new and useful input vectors of invariant topological features from returns of these stock prices for further classification task using machine learning methods such as logistic regression, artificial neural network, support vector machine and random forest to predict the next day movement direction of Kuala Lumpur Composite Index. For comparative analysis, we compare the proposed method with others, where the machine learning methods are applied independently on stock returns and also on technical indicators respectively. By using the average of prediction performances and pairwise model comparison method, these two evaluation measures revealed that machine learning methods with persistent homology produced better prediction performance. Our results also demonstrated that the combination of support vector machine with persistent homology generates the best outcome. In general, a combination of machine learning methods with persistent homology is an emerging and promising alternative tool for predicting direction of stock price movement. (c) 2020 Elsevier B.V. All rights reserved.

关键词： 二手房   房价预测模型   LightGBM算法   Stacking算法  

摘要： 随着居民生活水平提升，人们对房地产市场各方面信息的关注度也水涨船高，能否精准预测房价变动、科学分析影响因素，不仅能够科学地指导居民购房，也能方便政府进行监督与管控。 随着土地资源的进一步开发利用，二手房在房地产市场占据着愈加重要的地位。作为土地资源紧缺的代表性城市，北京市二手房市场呈现出欣欣向荣的发展趋势，值得我们以其作为典型，进行深入研究。 本文以北京市普通住宅二手房为研究对象，从链家和贝壳二手网站上进行相关数据爬取，选取地铁站口数量、建筑面积、周边幼儿园数量等与区位、建筑和邻里有关的共43个特征变量，试图建立有关北京市二手房价格的相关模型并探索其影响因素。具体来说，选取单一评估模型例如XGBoost、LightGBM、随机森林等，以特征指标为自变量，二手房实际交易价格为因变量构建模型，分别对房价进行评估预测；并进一步构建融合上述模型优点的Stacking组合模型。结果表明，采取组合模型Stacking算法，能够使得样本预测达到最佳。 此外，为进一步分析特征因素对二手房价的影响，本文以成交价格为标准，将二手房分为高、中、低三个等级，结合对比模型中预测效果较好的LightGBM算法，对指标重要性进行综合性度量，分析不同价格二手房的影响指标，并以此给购房者、中介、政府等提供建设性意见。

关键词： Water supply systems   pipe breaks   statistical modelling   machine learning   causal inference  

摘要： Water supply pipes age, deteriorate and break, which puts at risk the continuous provision of safe potable water endangering the public health in cities. Risk management methods are increasingly applied to optimise the capital investment for pipe replacement and rehabilitation, taking into account the probability and hydraulic impact of pipe breaks. As part of this process, however, historic pipe break data and statistical methods should be utilised to gather causal insights for past breaks to inform operational changes and/or capital investment decisions in order to reduce future breaks. This paper presents a comparative study of statistical and machine learning methods to carry out an exploratory causal analysis for historic pipe breaks in an operational water supply network. Regression models for count data and probabilistic models have been developed. The performance of these models was assessed and enhanced with the introduction of interactions and the inclusion of different network representations.