Empirical investigation of multi-tier ensembles for the detection of cardiac autonomic neuropathy using subsets of the Ewing Features

This article is devoted to an empirical investigation of per- formance of several new large multi-tier ensembles for the detection of cardiac autonomic neuropathy (CAN) in diabetes patients using subsets of the Ewing features. We used new data collected by the diabetes screening research initiative (DiScRi) project, which is more than ten times larger than the data set originally used by Ewing in the investigation of CAN. The results show that new multi-tier ensembles achieved better performance compared with the outcomes published in the literature previously. The best accuracy 97.74% of the detection of CAN has been achieved by the novel multi-tier combination of AdaBoost and Bagging, where AdaBoost is used at the top tier and Bagging is used at the middle tier, for the set consisting of the following four Ewing features: the deep breathing heart rate change, the Valsalva manoeuvre heart rate change, the hand grip blood pressure change and the lying to standing blood pressure change.

Variance-covariance based weighing for neural network ensembles

Neural network (NN) is a popular artificial intelligence technique for solving complicated problems due to their inherent capabilities. However generalization in NN can be harmed by a number of factors including parameter's initialization, inappropriate network topology and setting parameters of the training process itself. Forecast combinations of NN models have the potential for improved generalization and lower training time. A weighted averaging based on Variance-Covariance method that assigns greater weight to the forecasts producing lower error, instead of equal weights is practiced in this paper. While implementing the method, combination of forecasts is done with all candidate models in one experiment and with the best selected models in another experiment. It is observed during the empirical analysis that forecasting accuracy is improved by combining the best individual NN models. Another finding of this study is that reducing the number of NN models increases the diversity and, hence, accuracy.

Personal ICT Ensembles and Ubiquitous Information Systems Environments: Key Issues and Research Implications

Personal information and communication technologies (ICTs) have become commonplace. Today many people own, or have access to, a range of different computing and communication devices, information technologies, and services, which they incorporate into their everyday routines. Increasingly, these technologies impact the way that individuals work, socialize, and play. Workers are bringing their personal ICTs to the office, and organizations are tailoring their computing environments toward ubiquitous integration with personal ICTs. These developments are opening up new ways of working, but they also create new challenges for organizations in accommodating this “nonaffiliated” use as part of their information systems environments. In this article we propose a framework for analyzing the composition and impact of personal ICT ensembles. The framework is positioned as pre-theory that invites further development and empirical testing. We illustrate how the proposed framework could be applied to consider personal ICT use across the work/home context. Several implications stemming from the notion of a personal ICT ensemble are highlighted, including practical considerations for nonaffiliated use in organizations. We conclude with suggestions for further development of the proposed framework.

Empirical investigation of decision tree ensembles for monitoring cardiac complications of diabetes

Cardiac complications of diabetes require continuous monitoring since they may lead to increased morbidity or sudden death of patients. In order to monitor clinical complications of diabetes using wearable sensors, a small set of features have to be identified and effective algorithms for their processing need to be investigated. This article focuses on detecting and monitoring cardiac autonomic neuropathy (CAN) in diabetes patients. The authors investigate and compare the effectiveness of classifiers based on the following decision trees: ADTree, J48, NBTree, RandomTree, REPTree, and SimpleCart. The authors perform a thorough study comparing these decision trees as well as several decision tree ensembles created by applying the following ensemble methods: AdaBoost, Bagging, Dagging, Decorate, Grading, MultiBoost, Stacking, and two multi-level combinations of AdaBoost and MultiBoost with Bagging for the processing of data from diabetes patients for pervasive health monitoring of CAN. This paper concentrates on the particular task of applying decision tree ensembles for the detection and monitoring of cardiac autonomic neuropathy using these features. Experimental outcomes presented here show that the authors' application of the decision tree ensembles for the detection and monitoring of CAN in diabetes patients achieved better performance parameters compared with the results obtained previously in the literature.

Enhancing predictive accuracy of cardiac autonomic neuropathy using blood biochemistry features and iterative multitier ensembles

Blood biochemistry attributes form an important class of tests, routinely collected several times per year for many patients with diabetes. The objective of this study is to investigate the role of blood biochemistry for improving the predictive accuracy of the diagnosis of cardiac autonomic neuropathy (CAN) progression. Blood biochemistry contributes to CAN, and so it is a causative factor that can provide additional power for the diagnosis of CAN especially in the absence of a complete set of Ewing tests. We introduce automated iterative multitier ensembles (AIME) and investigate their performance in comparison to base classifiers and standard ensemble classifiers for blood biochemistry attributes. AIME incorporate diverse ensembles into several tiers simultaneously and combine them into one automatically generated integrated system so that one ensemble acts as an integral part of another ensemble. We carried out extensive experimental analysis using large datasets from the diabetes screening research initiative (DiScRi) project. The results of our experiments show that several blood biochemistry attributes can be used to supplement the Ewing battery for the detection of CAN in situations where one or more of the Ewing tests cannot be completed because of the individual difficulties faced by each patient in performing the tests. The results show that AIME provide higher accuracy as a multitier CAN classification paradigm. The best predictive accuracy of 99.57% has been obtained by the AIME combining decorate on top tier with bagging on middle tier based on random forest. Practitioners can use these findings to increase the accuracy of CAN diagnosis.

Gold nanoelectrode ensembles for direct trace electroanalysis of iodide

A procedure for the standardization of ensembles of gold nanodisk electrodes (NEE) of 30 nm diameter is presented, which is based on the analytical comparison between experimental cyclic voltammograms (CV) obtained at the NEEs in diluted solutions of redox probes and CV patterns obtained by digital simulation. Possible origins of defects sometimes found in NEEs are discussed. Selected NEEs are then employed for the study of the electrochemical oxidation of iodide in acidic solutions. CV patterns display typical quasi-reversible behavior which involves associated chemical reactions between adsorbed and solution species. The main CV characteristics at the NEE compare with those observed at millimeter sized gold disk electrodes (Au-macro), apart a slight shift in E1/2 values and slightly higher peak to peak separation at the NEE. The detection limit (DC) at NEEs is 0.3 mu M, which is more than one order of magnitude lower than DL at the Au-macro (4 RM). The mechanism of the electrochemical oxidation of iodide at NEEs is discussed. Finally, NEEs are applied to the direct determination of iodide at micromolar concentration levels in real samples, namely in some ophthalmic drugs and iodized table salt. (c) 2006 Elsevier B.V. All rights reserved.

Electrical properties of individual and small ensembles of InAs/InP nanostructures

We investigate electrical properties of InAs/InP semiconductor nanostructures by conductive atomic force microscopy (C-AFM) and current measurements at low temperatures in processed devices. Different conductances and threshold voltages for current onset were observed for each type of nanostructure. In particular, the extremity of the wire could be compared to a dot with similar dimensions. The processed devices were used in order to access the in-plane conductance of an assembly of a reduced number of nanostructures. Here, fluctuations on I-V curves at low temperatures (<40 K) were observed. At these low temperatures and for a suitable range of applied voltages, random telegraph noise (RTN) in the current was observed for devices with dots. These fluctuations can be associated to electrons trapped in dots, as suggested by numerical simulations. A crossover from a semiconductor-like to a metallic transport behavior is also observed for similar parameters. © 2006 WILEY-VCH Verlag GmbH & Co. KGaA.

Molecular Dynamics simulations of track formation at different ensembles

A series of Molecular Dynamics simulations of thermal spikes has been run in zircon. For two different ensembles: microcanonical one and a combination of microcanonical one acting on the simulation core with Langevin one on the side walls of simulation. Depending on the used ensemble, different track-formation threshold energies were found. When the combined ensemble is carried out, the total energy of the simulations varies with the temperature which can influence how annealing fission-track models should deal with the lattice recovery. A fission-track annealing model is tested with the simulation results. © 2012 Elsevier Ltd. All rights reserved.

Asymptotic integral kernel for ensembles of random normal matrices with radial potentials

The method of steepest descent is used to study the integral kernel of a family of normal random matrix ensembles with eigenvalue distribution P-N (z(1), ... , z(N)) = Z(N)(-1)e(-N)Sigma(N)(i=1) V-alpha(z(i)) Pi(1 <= i<j <= N) vertical bar z(i) - z(j)vertical bar(2), where V-alpha(z) = vertical bar z vertical bar(alpha), z epsilon C and alpha epsilon inverted left perpendicular0, infinity inverted right perpendicular. Asymptotic formulas with error estimate on sectors are obtained. A corollary of these expansions is a scaling limit for the n-point function in terms of the integral kernel for the classical Segal-Bargmann space. (C) 2012 American Institute of Physics. [http://dx.doi.org/10.1063/1.3688293]