Data harmonisation for information fusion in digital healthcare: A state-of-the-art systematic review,meta-analysis and future research directions

Removing the bias and variance of multicentre data has always been a challenge in large scale digital healthcare studies, which requires the ability to integrate clinical features extracted from data acquired by different scanners and protocols to improve stability and robustness. Previous studies have described various computational approaches to fuse single modality multicentre datasets. However, these surveys rarely focused on evaluation metrics and lacked a checklist for computational data harmonisation studies. In this systematic review, we summarise the computational data harmonisation approaches for multi-modality data in the digital healthcare field, including harmonisation strategies and evaluation metrics based on different theories. In addition, a comprehensive checklist that summarises common practices for data harmonisation studies is proposed to guide researchers to report their research findings more effectively. Last but not least, flowcharts presenting possible ways for methodology and metric selection are proposed and the limitations of different methods have been surveyed for future research.     

Student cluster competition: ParConnect reproducibility task report

Reproducibility is a desirable characteristic of any experimental work in computer science. In this paper, we reproduce the results in the paper, “A parallel connectivity algorithm for de Bruijn graphs in metagenomic applications”, for verifying their claims on the speed and scalability of their algorithm. We first state the claims made in the original paper. Then, we describe our hardware and software setup used for the reproducibility task, followed by the steps taken to setup the proposed algorithm and profiling software. Lastly, we present the results obtained from our experiments and show that we are able to reproduce most of the claims made in the original paper.     

Analysis of the impact of space vector modulation techniques on the operation of ultrahigh speed induction machines

The paper investigates the impact of different space vector modulation (SVM) techniques on the operation of ultrahigh speed induction machines (USIMs), where the m_f frequency ratio is low owing to the necessarily high fundamental frequency f₁. Three different sampling techniques, the Regular Sampled, Naturally Sampled and the Oversampled are studied by simulation. It is found that the SVM is proned to generate DC current in the stator windings. Furthermore subharmonic flux and current components with considerable amplitudes can also be generated in USIM. In the paper it is revealed that both the DC and the subharmonic components could lead to extremely large additional losses in the USIM enhanced by its special parameters.     

Multi-stage modeling using fuzzy multi-criteria feature selection to improve survival prediction of ICU septic shock patients

In many binary medical classification problems, the cost of misclassifying one category is higher than the other, and in these applications it is desirable to employ a classifier with selective sensitivity or specificity. This work explores the utility of a fuzzy multi–criteria function for performance evaluation during knowledge–based medical classification and prediction. The method presented here uses fuzzy optimization to combine the sensitivity, specificity, and accuracy of classification as goals in a single objective function. This approach is used to assign flexible goals, which can be used to maximize the outcome in terms of each one of the goals. The proposed approach significantly increases the sensitivity and the specificity while maintaining or increasing accuracy. The versatility of the method is further exploited in a multi-model approach, using individual structures of multi-objective optimization of sensitivity and specificity separately, and then combining their outcomes through a decision-making module. Among various medical benefits derived from applying this technique, the divergent feature sets selected by high sensitivity and specificity models lend insight into factors more integrally connected to what causes risk of death for patients.     

基于Aprior & FP-growth算法的研究

关联规则挖掘在数据挖掘中占有极其重要的地位,Apriori算法和FP-growth算法是当前关联规则中两大主要频繁项集发现算法.研究了这两种算法的基本思想,指出了算法各自的优缺点并通过具体的实例说明发现频繁项集的方法,最后通过实验对算法进行了性能上的比较.     

数据挖掘中关联规则Apriori算法

深入研究关联规则算法, 针对Apriori算法瓶颈问题提出了一种改进算法, 该算法在构建向量矩阵的基础上, 只需要扫描一次事务数据库, 通过优化连接和剪枝, 提高了算法的运行效率. 研究和实验表明, 改进后的算法在大规模的事务数据库中, 较Apriori算法有明显的优势.     

统计分析及关联挖掘在大学生心理健康中的应用

为深入了解影响大学生心理健康的主要因素以及心理症状之间的关系,以某高校2011级的学生心理测试数据为基础,采用统计分析和关联规则挖掘两种方法,从性别、学生干部、独生子女、来源地、家庭结构、家庭月收入等方面进行了分析研究,根据研究结果为高校开展大学生心理健康教育的规划、决策提供依据．     

Adaptive Routing on the New Switch Chip for IBM SP Systems

The IBM RS/6000 SP is one of the most successful commercially available multicomputers. SP owes its success partially to the scalable, high bandwidth, low latency network. This paper describes the architecture of Switch2 switch chip, the recently developed third generation switching element which future IBM RS/6000 SP systems may be based on. Switch2 offers significant enhancements over the existing SP switch chips by incorporating advances in both VLSI technology and interconnection network research. One of the major new features of Switch2 is the incorporation of adaptive routing support into it. We describe the adaptive source routing architecture of the Switch2 chip which is a unique feature of this chip. The performance of the adaptive source routing and oblivious routing for a wide range of system characteristics and traffic patterns is evaluated. It is shown that adaptive source routing outperforms or performs comparably with oblivious routing. We propose two novel algorithms for generating adaptive routes specifications required for enabling the usage of adaptive source routing. A comparison between the cost of these two algorithms and the performance improvement obtained from using these algorithms are discussed. We also propose different output selection functions to be used in switching elements for implementing the adaptive routing. We evaluate and compare the performance of these selection functions and discover that the best selection functions for BMINs are not dependent on the traffic pattern, message size, or system size.     

DPD: An intelligent parallel hybrid algorithm for economic load dispatch problems with various practical constraints

The Economic Load Dispatch (ELD) problem has attracted much attention in the field of electric power system. This paper proposes a novel parallel hybrid optimization methodology aimed at solving ELD problem with various generator constraints. The proposed approach combines the Differential Evolution (DE) and Particle Swarm Optimization (PSO). Initially the whole population (in increasing order of fitness) is divided into three groups - Inferior Group, Mid Group and Superior Group. DE is employed in the inferior and superior groups, whereas PSO is used in the mid-group. The proposed method is called DPD as it uses DE-PSO-DE on a population in parallel manner. Two strategies namely Elitism (to retain the best obtained values so far) and Non-redundant search (to improve the solution quality) have been employed in DPD cycle. Moreover, the suitable mutation strategy for both DEs used in DPD is investigated over a set of 8 popular mutation strategies. Combination of 8 mutation strategies generated 64 different variants of DPD. Top 4 DPDs are investigated through IEEE CEC 2006 functions. Based on the performance analysis, best DPD is reported and further used in solving four different typical test systems of ELD problem. Numerical and graphical results indicate the efficiency, convergence characteristic and robustness of proposed DPD.