A Banking Platform to Leverage Data Driven Marketing with Machine Learning

Payment data is one of the most valuable assets that retail banks can leverage as the major competitive advantage with respect to new entrants such as Fintech companies or giant internet companies. In marketing, the value behind data relates to the power of encoding customer preferences: the better you know your customer, the better your marketing strategy. In this paper, we present a B2B2C lead generation application based on payment transaction data within the online banking system. In this approach, the bank is an intermediary between its private customers and merchants. The bank uses its competence in Machine Learning driven marketing to build a lead generation application that helps merchants run data driven campaigns through the banking channels to reach retail customers. The bank’s retail customers trade the utility hidden in its payment transaction data for special offers and discounts offered by merchants. During the entire process banks protects the privacy of the retail customer.     

A Hybrid Method Using HAVOK Analysis and Machine Learning for Predicting Chaotic Time Series

The prediction of chaotic time series systems has remained a challenging problem in recent decades. A hybrid method using Hankel Alternative View Of Koopman (HAVOK) analysis and machine learning (HAVOK-ML) is developed to predict chaotic time series. HAVOK-ML simulates the time series by reconstructing a closed linear model so as to achieve the purpose of prediction. It decomposes chaotic dynamics into intermittently forced linear systems by HAVOK analysis and estimates the external intermittently forcing term using machine learning. The prediction performance evaluations confirm that the proposed method has superior forecasting skills compared with existing prediction methods.     

An Exploratory Study on the Complexity and Machine Learning Predictability of Stock Market Data

This paper shows if and how the predictability and complexity of stock market data changed over the last half-century and what influence the M1 money supply has. We use three different machine learning algorithms, i.e., a stochastic gradient descent linear regression, a lasso regression, and an XGBoost tree regression, to test the predictability of two stock market indices, the Dow Jones Industrial Average and the NASDAQ (National Association of Securities Dealers Automated Quotations) Composite. In addition, all data under study are discussed in the context of a variety of measures of signal complexity. The results of this complexity analysis are then linked with the machine learning results to discover trends and correlations between predictability and complexity. Our results show a decrease in predictability and an increase in complexity for more recent years. We find a correlation between approximate entropy, sample entropy, and the predictability of the employed machine learning algorithms on the data under study. This link between the predictability of machine learning algorithms and the mentioned entropy measures has not been shown before. It should be considered when analyzing and predicting complex time series data, e.g., stock market data, to e.g., identify regions of increased predictability.     

基于MobileNet-SSD的红外人脸检测算法

针对"可见光"+"热红外"融合的双光人脸检测算法对硬件依赖性高的劣势.本文提出一种基于MobileNet-SSD的红外人脸检测算法,该算法可以直接检测出红外图像中的人脸区域,对硬件的依赖性较低.同时实验结果表明该算法的检测精度和实时性均有所提高,可以直接运用到"疫情"期间的智能体温监控系统中.     

基于条件生成式对抗网络的油藏单井产量预测模型

为克服机器学习方法在油藏单井产量预测中的过拟合问题,提高油田生产中的产量预测精度,提出一种基于条件生成式对抗网络(CGAN)的油藏单井产量预测模型。该模型使用长短期记忆、全连接等基础神经网络,构建生成和判别网络模型。生成网络模型以产量影响因素为条件输入,生成预测产量数据,利用对数损失函数评价预测数据与真实数据之间的偏差,通过条件生成式对抗网络的博弈训练,并结合贝叶斯超参数优化算法,优化模型结构,综合提高模型的泛化能力。基于Eclipse数值模拟软件建立同一井网条件下不同地质和生产条件下的油藏单井产量数据库,以地质与生产条件等产量影响因素作为模型的条件输入,进行油藏单井产量预测。结果表明：与全连接神经网络(FCNN)、随机森林(RF)以及长短期记忆神经网络(LSTM)模型的预测结果相比,CGAN模型在测试集上的平均绝对百分比误差分别提升了2.59%、 0.81%以及1.72%,并且过拟合比最小(1.027)。说明CGAN降低了机器学习产量预测模型的过拟合程度,提高了模型的泛化能力与预测精度,验证了所提算法的优越性,对指导油田高效开发和保障我国能源战略安全具有重要意义。     

基于自主管理机制的网格监测系统故障诊断与恢复

为了使网格监测系统能够自主地处理自身的故障、应对复杂多变的网格环境,基于自主组织、管理机制和自主计算技术中的自修复、自愈等思想,R-Net监测系统(RNMS)对监测系统故障的探测、诊断、处理和恢复进行了研究,并把重点放在节点组的故障恢复上.实现了网格监测系统故障诊断与恢复机制,使得监测系统能够无需管理人员的参与、自主地处理一些常见的故障,降低了系统维护的复杂性和成本,增加了系统的易用性和可靠性.     

基于三维动态激光成像技术的在线学习模型研究

针对传统在线学习模型平均累计错误率较高的问题,提出基于三维动态激光成像技术的在线学习模型研究.分析图像数据维度,利用三维空间域当中,各像素点之间的联系,计算图像空间相关程度,采用零树结构,实现在线学习数据集训练,选取特征样本集,利用核函数,计算概率密度,获取深度信息,完成基于三维动态激光成像技术的在线学习模型的建立.设...     

穿墙雷达多维参数人体姿态识别方法

现有的人体姿态识别方案大多数是从单一的角度来考察人体的姿态特征,但是仅采用距离像很难体现人体关节的位置信息,仅提取微多普勒特征有时会覆盖掉径向速度不明显的特征.为此,本文首先利用慢时间-距离像和慢时间-微多普勒谱图构建出人体姿态的三维张量数据集,扩展了人体姿态的特征维度,然后采用改进型瓶颈残差模块构成的神经网络提高了人...     

Machine Learning Approaches for Metalloproteins

Metalloproteins are a family of proteins characterized by metal ion binding, whereby the presence of these ions confers key catalytic and ligand-binding properties. Due to their ubiquity among biological systems, researchers have made immense efforts to predict the structural and functional roles of metalloproteins. Ultimately, having a comprehensive understanding of metalloproteins will lead to tangible applications, such as designing potent inhibitors in drug discovery. Recently, there has been an acceleration in the number of studies applying machine learning to predict metalloprotein properties, primarily driven by the advent of more sophisticated machine learning algorithms. This review covers how machine learning tools have consolidated and expanded our comprehension of various aspects of metalloproteins (structure, function, stability, ligand-binding interactions, and inhibitors). Future avenues of exploration are also discussed.