Flexible Distribution-Based Regression Models for Count Data: Application to Medical Diagnosis

Abstract

Data mining techniques have been successfully utilized in different applications of significant fields, including medical research. With the wealth of data available within the health-care systems, there is a lack of practical analysis tools to discover hidden relationships and trends in data. The complexity of medical data that is unfavorable for most models is a considerable challenge in prediction. The ability of a model to perform accurately and efficiently in disease diagnosis is extremely significant. Thus, the model must be selected to fit the data better, such that the learning from previous data is most efficient, and the diagnosis of the disease is highly accurate. This work is motivated by the limited number of regression analysis tools for multivariate counts in the literature. We propose two regression models for count data based on flexible distributions, namely, the multinomial Beta-Liouville and multinomial scaled Dirichlet, and evaluated the proposed models in the problem of disease diagnosis. The performance is evaluated based on the accuracy of the prediction which depends on the nature and complexity of the dataset. Our results show the efficiency of the two proposed regression models where the prediction performance of both models is competitive to other previously used regression models for count data and to the best results in the literature.     

A review of topic modeling methods

Topic modeling is a popular analytical tool for evaluating data. Numerous methods of topic modeling have been developed which consider many kinds of relationships and restrictions within datasets; however, these methods are not frequently employed. Instead many researchers gravitate to Latent Dirichlet Analysis, which although flexible and adaptive, is not always suited for modeling more complex data relationships. We present different topic modeling approaches capable of dealing with correlation between topics, the changes of topics over time, as well as the ability to handle short texts such as encountered in social media or sparse text data. We also briefly review the algorithms which are used to optimize and infer parameters in topic modeling, which is essential to producing meaningful results regardless of method. We believe this review will encourage more diversity when performing topic modeling and help determine what topic modeling method best suits the user needs.     

Design principles for the General Data Protection Regulation (GDPR): A formal concept analysis and its evaluation

Data and software are nowadays one and the same: for this very reason, the European Union (EU) and other governments introduce frameworks for data protection — a key example being the General Data Protection Regulation (GDPR). However, GDPR compliance is not straightforward: its text is not written by software or information engineers but rather, by lawyers and policy-makers. As a design aid to information engineers aiming for GDPR compliance, as well as an aid to software users’ understanding of the regulation, this article offers a systematic synthesis and discussion of it, distilled by the mathematical analysis method known as Formal Concept Analysis (FCA). By its principles, GDPR is synthesised as a concept lattice, that is, a formal summary of the regulation, featuring 144372 records — its uses are manifold. For example, the lattice captures so-called attribute implications, the implicit logical relations across the regulation, and their intensity. These results can be used as drivers during systems and services (re-)design, development, operation, or information systems’ refactoring towards more GDPR consistency.     

Off‐grid DoA estimation method of wideband signals

In this paper, an off‐grid direction of arrival (DoA) estimation method is proposed for wideband signals. This method is based on the sparse representation (SR) of the array covariance matrix. Similar to the time domain DoA estimation methods, the correlation function of the sources was assumed to be the same and known. A new measurement vector is obtained using the lower‐left triangular elements of the covariance matrix. The DoAs are estimated by quantizing the entire range of continuous angle space into discrete grid points. However, the exact DoAs may be located between two grid points; therefore, this estimation has errors. The accuracy of DoA estimation is improved by the minimization of the difference between the new measurement vector and its estimated values. Simulation results revealed that the proposed method can enhance the DoA estimation accuracy of wideband signals.     

A hybrid homogeneously weighted moving average control chart for process monitoring

Several modifications and enhancements to control charts in increasing the performance of small and moderate process shifts have been introduced in the quality control charting techniques. In this paper, a new hybrid control chart for monitoring process location is proposed by combining two homogeneously weighted moving average (HWMA) control charts. The hybrid homogeneously weighted moving average (HHWMA) statistic is derived using two smoothing constants λ₁ and λ₂ . The average run length (ARL) and the standard deviation of the run length (SDRL) values of the HHWMA control chart are obtained and compared with some existing control charts for monitoring small and moderate shifts in the process location. The results of study show that the HHWMA control chart outperforms the existing control charts in many situations. The application of the HHWMA chart is demonstrated using a simulated data.     

干扰条件下的“北斗”信号解扩重构DOA估计

在干扰条件下，卫星导航抗干扰波束形成算法往往需要卫星信号波达方向（Direction-of-Arrival，DOA）的先验信息。但当存在低信噪比信号或主动干扰源时，常规的DOA估计算法性能急剧下降甚至失效。针对此问题，提出了一种被干扰信号压制的低信噪比“北斗”信号的DOA估计算法。该算法首先通过对接收信号进行子空间投影抑制干扰信号，然后对抑制干扰后的信号进行解扩重构处理，最后通过多重信号分类算法完成对“北斗”信号的DOA估计。仿真结果表明，在干扰信号干信比80 dB条件下，“北斗”信号DOA估计误差在5°以内，为下一步进行波束形成计算提供了高精度的入射角信息。     

Revisiting rock classification to estimate rock mass properties

This paper presents the results of ongoing research carried out by the author exploring methods to provide a more robust estimate of rock mass properties specifically for use in tunnel design. Data from various large-scale rock mass failures are introduced, including coal pillars. The damage-initiation,spalling-limit approach is compared to the coal pillar database. New comparisons of estimating the geological strength index(GSI) and relationships to estimate the Hoeke Brown failure criterion parameters, mb, s and a, are presented.     

Comparison of co-current and counter-current flow in a bifunctional reactor containing ammonia synthesis and 2-butanol dehydrogenation to MEK

In this study, a multi-tubular thermally coupled packed bed reactor in which simultaneous production of ammonia and methyl ethyl ketone (MEK) takes place is simulated. The simulation results are presented in two co-current and counter-current flow modes. Based on this new configuration, the released heat from the ammonia synthesis reaction as an extremely exothermic reaction in the inner tube is employed to supply the required heat for the endothermic 2-butanol dehydrogenation reaction in the outer tube. On the other hand, MEK and hydrogen are produced by the dehydrogenation reaction of 2-butanol in the endothermic side, and the produced hydrogen is used to supply a part of the ammonia synthesis feed in the exothermic side. Thus, 30.72% and 31.88% of the required hydrogen for the ammonia synthesis are provided by the dehydrogenation reaction in the co-current and counter-current configurations, respectively. Also, according to the thermal coupling, the required cooler and furnace for the ammonia synthesis and 2-butanol dehydrogenation conventional plants are eliminated, respectively. As a result, operational costs, energy consumption and furnace emissions are considerably decreased. Finally, a sensitivity analysis and optimization are applied to study the effect of the main process parameters variation on the system performance and obtain the minimum hydrogen make-up flow rate, respectively.     

基于残差决策的自动飞行控制系统远程实时故障诊断

针对自动飞行控制系统结构复杂、关联部件众多,发生故障时诊断时间长,从而影响飞机运行效率的问题,提出一种基于飞机通信寻址报告系统(ACARS)的远程实时故障诊断方案。首先,分析自动飞行控制系统的故障特点,设计搭建检测滤波器;然后,利用ACARS数据链实时发送的自动飞行控制系统的关键信息进行相关部件的残差计算,并根据残差决策算法进行故障诊断及定位;最后,针对不同故障部件残差间的差异大、决策门限无法统一的缺点,提出基于二次差值的残差决策改进算法,减缓了检测对象的整体变化趋势,降低了随机噪声和干扰的影响,避免了将瞬态故障诊断为系统故障的情况。实验仿真结果表明,基于二次差值的改进残差决策算法避免了多决策门限的复杂性,在采样时间为0.1 s的情况下,故障检测所需时间大约为2 s,故障检测时间大幅降低,有效故障检测率大于90%。