关键词： collagen disorganization   multiphoton microscopy   second harmonic generation   autofluorescence   machine learning  

摘要： Pathogenesis of many diseases is associated with changes in the collagen spatial structure. Traditionally, the 3D structure of collagen in biological tissues is analyzed using histochemistry, immunohistochemistry, magnetic resonance imaging, and X-radiography. At present, multiphoton microscopy (MPM) is commonly used to study the structure of biological tissues. MPM has a high spatial resolution comparable to histological analysis and can be used for direct visualization of collagen spatial structure. Because of a large volume of data accumulated due to the high spatial resolution of MPM, special analytical methods should be used for identification of informative features in the images and quantitative evaluation of relationship between these features and pathological processes resulting in the destruction of collagen structure. Here, we describe current approaches and achievements in the identification of informative features in the MPM images of collagen in biological tissues, as well as the development on this basis of algorithms for computer-aided classification of collagen structures using machine learning as a type of artificial intelligence methods.

关键词： Strength   Mixture design   Predictive modeling   Machine learning   Regression analysis  

摘要： The use of statistical and machine learning approaches to predict the compressive strength of concrete based on mixture proportions, on account of its industrial importance, has received significant attention. However, previous studies have been limited to small, laboratory-produced data sets. This study presents the first analysis of a large data set (> 10,000 observations) of measured compressive strengths from actual (job-site) mixtures and their corresponding actual mixture proportions. Predictive models are applied to examine relationships between the mixture design variables and strength, and to thereby develop an estimate of the (28-day) strength. These models are also applied to a laboratory-based data set of strength measurements published by Yeh et al. (1998) and the performance of the models across both data sets is compared. Furthermore, to illustrate the value of such models beyond simply strength prediction, they are used to design optimal concrete mixtures that minimize cost and embodied CO2 impact while satisfying imposed target strengths.

关键词： Machine learning   Image and video   Pedestrian detection   Anomaly detection  

摘要： Pedestrian detection and abnormal behavior detection is the computer for a given image and video, to determine whether there are pedestrians and their behavior is normal. Pedestrian detection is the basis and premise of pedestrian tracking, behavior analysis, gait analysis, pedestrian identity recognition and so on. A good pedestrian detection algorithm can provide strong support and guarantee for the latter. The overall goal of this project is to learn different data mining methods and try to improve the detection accuracy of video crowd machine abnormal behavior. Aiming at the shortage of user behavior anomaly detection model proposed by Lane et al., a new IDS anomaly detection model is proposed. This model improves the representation of user behavior patterns and behavior profiles and adopts a new similarity assignment method. Experiments based on Unix user shell command data show that the detection model proposed in this paper has higher detection performance.

关键词： Computational fluid dynamics   Turbulent flows   Machine learning   Turbulence theory and modelling   Artificial neural networks   Aerodynamics  

摘要： In recent years, the data-driven turbulence model has attracted widespread concern in fluid mechanics. The existing approaches modify or supplement the original turbulence model by machine learning based on the experimental/numerical data, in order to augment the capability of the present turbulence models. Different from the previous researches, this paper directly reconstructs a mapping function between the turbulent eddy viscosity and the mean flow variables by neural networks and completely replaces the original partial differential equation model. On the other hand, compared with the machine learning models for the low Reynolds (Re) number flows based on direct numerical simulation data, high Reynolds number flows around airfoils present the apparent scaling effects and strong anisotropy, which induce large challenges in accuracy and generalization capability for the machine learning algorithm. We mainly concentrate on the high Reynolds number turbulent flows around the airfoils and take the results calculated by the computational fluid dynamics solver with the Spallart-Allmaras (SA) model as training data to construct a high-dimensional data-driven network model based on machine learning. The radial basis function neural network and the auxiliary optimization methods are adopted, and the individual models are built separately for the flow fields of the near-wall region, wake region, and far-field region. The training data in this paper is extracted from only three subsonic flow fields of NACA0012 airfoil. The data-driven turbulence model can be applied to various airfoils and flow states, and the predicted eddy viscosity, lift/drag coefficients, and skin friction distributions are all in good agreement with the results of the original SA model. This research demonstrates the promising prospect of machine learning methods in future studies about turbulence modeling.

关键词： Coils heat exchanger   Nanofluid   Nusselt number, carbon nanotube   Artificial neural network   Sensitivity analysis  

摘要： Nowadays, nanofluids are broadly utilized for various engineering and industrial systems including heat exchangers, power plants, air-conditioning, etc. The helically coiled tube heat exchangers are of the most interesting and efficient kinds of heat exchangers. The current study has focused on proposing model to predict Nusselt number by considering Prandtl number, volumetric concentration, and helical number of helically coiled heat exchanger as input variables. The investigated heat exchanger utilizes water carbon nanofluid. To propose an accurate model, a multilayer perceptron artificial neural network (MLP-ANN), adaptive neuro-fuzzy inference system (ANFIS), and least squares support vector machine (LSSVM) models are used. 72 experimental data are utilized as input data. Results indicate that LSSVM approach has the best performance and the proposed model by this approach has R-squared value equals to 1. (C) 2018 Elsevier Ltd. All rights reserved.

关键词： deep-learning   convolution neural networks (CNNs)   artificial neural network   RapidEye   landslide mapping   mean intersection-over-union (mIOU)  

摘要： There is a growing demand for detailed and accurate landslide maps and inventories around the globe, but particularly in hazard-prone regions such as the Himalayas. Most standard mapping methods require expert knowledge, supervision and fieldwork. In this study, we use optical data from the Rapid Eye satellite and topographic factors to analyze the potential of machine learning methods, i.e., artificial neural network (ANN), support vector machines (SVM) and random forest (RF), and different deep-learning convolution neural networks (CNNs) for landslide detection. We use two training zones and one test zone to independently evaluate the performance of different methods in the highly landslide-prone Rasuwa district in Nepal. Twenty different maps are created using ANN, SVM and RF and different CNN instantiations and are compared against the results of extensive fieldwork through a mean intersection-over-union (mIOU) and other common metrics. This accuracy assessment yields the best result of 78.26% mIOU for a small window size CNN, which uses spectral information only. The additional information from a 5 m digital elevation model helps to discriminate between human settlements and landslides but does not improve the overall classification accuracy. CNNs do not automatically outperform ANN, SVM and RF, although this is sometimes claimed. Rather, the performance of CNNs strongly depends on their design, i.e., layer depth, input window sizes and training strategies. Here, we conclude that the CNN method is still in its infancy as most researchers will either use predefined parameters in solutions like Google TensorFlow or will apply different settings in a trial-and-error manner. Nevertheless, deep-learning can improve landslide mapping in the future if the effects of the different designs are better understood, enough training samples exist, and the effects of augmentation strategies to artificially increase the number of existing samples are better understood.

关键词： histotype classification   imaging mass spectrometry   machine learning   ovarian cancer  

摘要： Purpose Precise histological classification of epithelial ovarian cancer (EOC) has immanent diagnostic and therapeutic consequences, but remains challenging in histological routine. The aim of this pilot study is to examine the potential of matrix-assisted laser desorption/ionization (MALDI) imaging mass spectrometry in combination with machine learning methods to classify EOC histological subtypes from tissue microarray. Experimental design Formalin-fixed-paraffin-embedded tissue of 20 patients with ovarian clear-cell, 14 low-grade serous, 19 high-grade serous ovarian carcinomas, and 14 serous borderline tumors are analyzed using MALDI-Imaging. Classifications are computed by linear discriminant analysis (LDA), support vector machines with linear (SVM-lin) and radial basis function kernels (SVM-rbf), a neural network (NN), and a convolutional neural network (CNN). Results MALDI-Imaging and machine learning methods result in classification of EOC histotypes with mean accuracy of 80% for LDA, 80% SVM-lin, 74% SVM-rbf, 83% NN, and 85% CNN. Based on sensitivity (69-100%) and specificity (90-99%), CCN and NN are most suited to EOC classification. Conclusion and clinical relevance: The pilot study demonstrates the potential of MALDI-Imaging derived proteomic classifiers in combination with machine learning algorithms to discriminate EOC histotypes. Applications may support the development of new prognostic parameters in the assessment of EOC.

关键词： Discrete wavelet transform (DWT)   Electroencephalogram (EEG)   Epileptic seizure   Genetic algorithm (GA)   Particle swarm optimization (PSO)   Support vector machines (SVMs)  

摘要： The aim of this study is to establish a hybrid model for epileptic seizure detection with genetic algorithm (GA) and particle swarm optimization (PSO) to determine the optimum parameters of support vector machines (SVMs) for classification of EEG data. SVMs are one of the robust machine learning techniques and have been extensively used in many application areas. The kernel parameter's setting for SVMs in training process effects the classification accuracy. We used GA- and PSO-based approach to optimize the SVM parameters. Compared to the GA algorithm, the PSO-based approach significantly improves the classification accuracy. It is shown that the proposed Hybrid SVM can reach a classification accuracy of up to 99.38% for the EEG datasets. Hence, the proposed Hybrid SVM is an efficient tool for neuroscientists to detect epileptic seizure in EEG.

关键词： Counseling psychology   Computer science  

摘要： Motivational Interviewing (MI) is an evidence-based communication technique to increase intrinsic motivation and self-efficacy for behavior change. This goal is achieved through the exploration of the patient's own desires, ability, reasons, need for and commitment to the targeted behavior change. However, communication science approaches to understanding the efficacy of MI are inherently limited by traditional qualitative coding methods which is a time-consuming and resource-intensive process. Thus, an efficient method is required to automate the coding process which will accelerate the pace of communication research in behavioral science. The specific provider behaviors responsible for the elicitation of change talk, are also less clear and may vary by treatment context. Therefore, new design objective and perspective are necessary to understand which provider behaviors and in which contexts lead to patient change talk. In this dissertation, we deal with two types of clinical conversation, one that involves a face to face dialogue between patient and counselor and another one which involves an email-based conversation between patient and an ecoach. First, we leverage eight supervised machine learning models to automatically annotate counseling sessions with 37 African American adolescents with obesity and their caregivers. We examine the performance of classifiers using lexical, contextual, and semantic features, to predict the behavioral codes in the previously coded data. Second, understanding motivational interviewing mechanisms of effect, we focus on deep learning and probabilistic models and analyze the sequencing of patient-provider communication. The goal of these experiments is to identify the communication behaviors leading to the elicitation of client change talk, a marker of success in MI, and counter change talk, a marker of unsuccessful communication. Two approaches, recurrent neural networks and Markov models, were tested. As a continuation of our se

关键词： 量化投资   因子分析   机器学习方法   择时策略  

摘要： 决策树算法、随机森林和支持向量机模型是机器学习的细化分支领域,多年来深受各个学者、券商专业团队的喜爱,将其应用在量化投资领域。在此背景下,因子的构建和对于股票走势的判断,是这一领域专家学者不断追寻的话题。本文则希望通过研究和借鉴各个已有的文章的指标体系,结合相关机器学习算法,在此基础上进行创新和突破。本文把选股过程根据基本面指标和技术面指标的不同性质,将选股过程划分为两个阶段,以基本面指标判断上市公司长期状态和技术面指标判断买卖时机选择的理念进行本文投资组合的研究工作。首先,本文在第一章节阐述选题的背景与意义、相关文献综述、研究对象与研究工具;第二章节基于双因素模型的投资组合构造,围绕文中双因素模型投资组合策略构建思路进行展开,对本文提出的双因素模型进行具体介绍;第三章节内容围绕本文提出的双因素模型策略的第一个因素(阶段)即基本面因素进行实证研究;第四章节是对上节内容的优化和提升,在上节选出目标上市公司的基础上加入技术面因素(阶段),利用机器学习方法提升选股策略的效果;第五章节,对本文的研究内容结合具体实证情况进行总结。其次,本文将因子分析方法和决策树、随机森林、支持向量机方法分别应用在基本面选股和技术面选股(择时)两个阶段。结果显示:根据动态支持向量机模型实施后的交易策略能够获得较中证800指数3.29倍的期间累积收益和较全指消费指数3倍的期间累积收益,对应的期间累积收益率为106%。较之前仅由基本面选股,半年期间买入持有的等份额投资组合23.5%的累积收益率有大幅度的提升,前者的收益率相当于后者的4.51倍。除此之外,经过技术指标建模构造的投资组合在资金风险方面也较未经优化的模型有更好的表现。最后,本文基于2018年上半年度股票行情数据,从基本面角度在主要消费行业进行股票标的筛选,运用技术面指标对经由筛选后得出的股票标的涨跌情况进行逐日预测并根据预测结果进行相应的买卖操作,从而构建在期间的投资组合。对于完善选股投资策略的多样性有重要指导意义。