关键词： Algorithms   Applied mathematics   Artificial intelligence   Biology and life sciences   Body temperature   Computer and information sciences   Dementia   Health care   Health care facilities   Hospitals   Machine learning   Machine learning algorithms   Mathematics   Medicine and health sciences   Mental health and psychiatry   Neurology   Physical sciences   Physiological parameters   Physiological processes   Physiology   Research and analysis methods   Research Article   Simulation and modeling   Sleep   Sleep disorders   Urinary tract infections   Urology  

摘要： Dementia is a neurological and cognitive condition that affects millions of people around the world. At any given time in the United Kingdom, 1 in 4 hospital beds are occupied by a person with dementia, while about 22% of these hospital admissions are due to preventable causes. In this paper we discuss using Internet of Things (IoT) technologies and in-home sensory devices in combination with machine learning techniques to monitor health and well-being of people with dementia. This will allow us to provide more effective and preventative care and reduce preventable hospital admissions. One of the unique aspects of this work is combining environmental data with physiological data collected via low cost in-home sensory devices to extract actionable information regarding the health and well-being of people with dementia in their own home environment. We have worked with clinicians to design our machine learning algorithms where we focused on developing solutions for real-world settings. In our solutions, we avoid generating too many alerts/alarms to prevent increasing the monitoring and support workload. We have designed an algorithm to detect Urinary Tract Infections (UTI) which is one of the top five reasons of hospital admissions for people with dementia (around 9% of hospital admissions for people with dementia in the UK). To develop the UTI detection algorithm, we have used a Non-negative Matrix Factorisation (NMF) technique to extract latent factors from raw observation and use them for clustering and identifying the possible UTI cases. In addition, we have designed an algorithm for detecting changes in activity patterns to identify early symptoms of cognitive decline or health decline in order to provide personalised and preventative care services. For this purpose, we have used an Isolation Forest (iForest) technique to create a holistic view of the daily activity patterns. This paper describes the algorithms and discusses the evaluation of the work using a lar

关键词： Survey   algorithmic trading   statistics   machine learning   high frequency trading   hardware implementation  

摘要： Automated trading, which is also known as algorithmic trading, is a method of using a predesigned computer program to submit a large number of trading orders to an exchange. It is substantially a real-time decision-making system which is under the scope of Enterprise Information System (EIS). With the rapid development of telecommunication and computer technology, the mechanisms underlying automated trading systems have become increasingly diversified. Considerable effort has been exerted by both academia and trading firms towards mining potential factors that may generate significantly higher profits. In this paper, we review studies on trading systems built using various methods and empirically evaluate the methods by grouping them into three types: technical analyses, textual analyses and high-frequency trading. Then, we evaluate the advantages and disadvantages of each method and assess their future prospects.

关键词： Heavy haul train   Intelligent operation   Machine learning   Air brake   Imbalanced data  

摘要： Compared with high speed trains and metro subways, heavy haul train operations are more challenging for their complex dynamic characteristics and complicated running environments. When running on a continuous and steep descent, the air brake operation strategy has become the most important issue for the safety and efficiency of heavy haul train transportation. Due to the difficulty in modeling the train's dynamics and pneumatic brake system precisely, this paper addresses the intelligent driving problem for heavy haul train based on the fusion of expert knowledge and machine learning methodologies. By considering the characteristics of manual driving on steep descent, the pneumatic brake operation problem is formulated as a multi-class classification model. To overcome the negative influences of data imbalances, EasyEnsemble for the multi-class with a KNN based Denoising (EMKD) algorithm is introduced to determine the feasible Air Pressure Reduction (APR) and the exact time for exerting and releasing the air brake. This approach utilizes the EasyEnsemble.M algorithm to moderate the class imbalanced datasets and takes advantage of the AdaBoost.M1 algorithm to ensemble weak classifiers. Specifically, the K-nearest neighbor based Denoising (KD) algorithm is elaborated to remove the possible noise data from the minority dataset. Additionally, expert knowledge is obtained by abstracting the experience of sophisticated drivers and technical specifications, which are employed as operating constraints to regulate the output of the EMKD algorithm. The operational safety in terms of the in-train forces and punctuality of the proposed algorithm are validated by a number of experiments under the real running circumstances of Shuohuang heavy haul railway. (C) 2018 Elsevier B.V. All rights reserved.

关键词： 数据挖掘   网络异常检测   入侵检测技术  

摘要： 随着信息化革命进程的不断深入,社会民众的信息交流方式呈现出明显的变化,网络不安全因素也逐渐增多,因此,做好网络异常信息的检测工作,显得十分重要。本文基于数据挖掘和机器学习方法,在对入侵检测技术的定义和方法进行综合阐述的基础上,论述了数据挖掘方法的定义和方法,以期为相关人士提供借鉴和参考。

关键词： 大于胎龄儿   风险预测模型   机器学习   XGBoost  

摘要： 目的:尝试应用机器学习方法建立大于胎龄儿的风险预测模型。方法:回顾分析2013年1月1日至2014年12月31日于在浙江大学医学院附属妇产科医院规范产检并分娩的12683例孕妇的临床资料,包括孕妇年龄、身高、孕前体质量、孕期增重和各项化验结果及新生儿体重,运用Logistic回归和XGBoost两种机器学习算法构建大于胎龄儿的预测模型,评估这两种模型对大于胎龄儿预测的准确性。结果:XGBoost模型的受试者操作曲线的曲线下面积较大(0.92),Logistic回归模型较小(0.72),两者比较差异有统计学意义(P<0.05)。XGBoost模型的约登指数(0.63)、灵敏度(0.83)、特异度(0.80)均优于Logistic回归模型(0.34、0.65、0.68)(P均<0.05)。Logistic回归模型及XGBoost模型的阳性预测值均较低,分别为0.15、0.27。结论:机器学习方法(尤其是XGBoost)构建的模型预测大于胎龄儿的综合性能较强,具有潜在应用价值,值得进一步研究。

关键词： 脂肪肝   主成分分析   决策树   神经网络   支持向量机   贝叶斯网络   随机森林  

摘要： 目的:实现和比较5种常见的机器学习算法在脂肪肝分类预测研究中的应用。方法:通过主成分分析对数据的体检指标进行降维,然后应用决策树、神经网络、支持向量机、贝叶斯网络和随机森林算法,分别构建脂肪肝分类预测模型,对1956例体检数据进行脂肪肝的分类预测。结果:决策树分类模型在脂肪肝分类预测上的准确率最高,达到70.14%,其次是支持向量机和神经网络模型,处于68%左右的水平。结论:本文所研究的典型算法在脂肪肝分类的预测上具有较为可靠分类预测能力,但决策树模型在应用小样本数据上表现出了优势;同时,还发现臀围(HIP)和甘油三酯(TG)可能与脂肪肝分类关联密切。

关键词： 信用风险   刚性数据   机器学习方法   神经网络  

摘要： 大数据、人工智能技术的运用为企业信用风险预测提供了新的视角。本文通过爬虫程序抓取全市场约9000家企业的司法诉讼信息、招聘信息、法人和核心团队工商变更记录等无法粉饰的刚性数据,并采用机器学习等人工智能方法,开发出违约概率预测模型,可对企业违约概率做出前瞻性预测,从而实现对企业信用风险更早的预警。

摘要： Highlights from the article: F29investigating supervised machine learning methods for prediction of schizophrenia in uk biobank B Background: b Supervised machine learning encompasses a range of methods for learning patterns from labelled data. This study has developed and tested a machine learning framework for analysis of UK Biobank data and investigated several approaches using genetic variants for prediction.

关键词： 乳腺癌   Logistic回归   随机森林   敏感度  

摘要： 收集乳腺癌的预后状况,通过Logistic回归模型、决策树和随机森林算法,分析比较不同变量对乳腺癌预后的敏感性,并比较特征变量的重要性。使用Logistic回归的多变量分析显示,年龄、N-stage、乳腺癌内分泌治疗、化疗手段、多灶性病灶、化疗与预后有关。随机森林分析的结果得到了各个因素指标的重要性评分,有助于正确选择高风险人群,有利于建立分类系统。通过该结果找到危险因素,建立较高准确率的预测模型,预测乳腺癌患者的生存能力,并可作为制定医疗决策的参考。根据患者的特征制定针对患者不同特征的个体化术后随访及辅助治疗策略,争取最大程度上预防乳腺癌的术后复发。

关键词： 机器学习   消费金融   反欺诈   进展综述  

摘要： 消费金融机构向个人提供信贷额度或资金,具有客群下沉、 流程便捷等特点.相比其他传统金融业务,欺诈在消费金融行业更为严重,是妨碍行业发展的关键痛点.本文以构建欺诈侦测模型面临的五大关键挑战为线索,概述了机器学习方法在消费金融反欺诈模型上的应用进展.