varunr14/newsummary huggingface dataset:
Classifying imbalanced data in medical informatics is challenging . Motivated by this issue this study develops a classifier approach denoted as BSMAIRS . This approach combines borderline synthetic minority oversampling technique and artificial immune recognition system as global optimization searcher with the nearest neighbor algorithm used as a local classifier . Eight electronic medical datasets collected from University of California Irvine machine learning repository were used to evaluate the effectiveness and to justify the performance of the proposed BSMAIRS . Comparisons with several well known classifiers were conducted based on accuracy sensitivity specificity and G mean . Statistical results concluded that BSMAIRS can be used as an efficient method to handle imbalanced class problems . To further confirm its performance BSMAIRS was applied to real imbalanced medical data of lung cancer metastasis to the brain that were collected from National Health Insurance Research Database Taiwan . This application can function as a supplementary tool for doctors in the early diagnosis of brain metastasis from lung cancer . 


Summary: Developing a new classifier approach by combining borderline SMOTE and AIRS. Eight medical datasets from UCI machine learning repository were evaluated. Outperform well known classifiers. The proposed method can handle imbalanced class problems efficiently. Applying successfully to the prediction of lung cancer to brain metastasis.
Copper electro chemical deposition of through silicon via is a key challenge of 3D integration . This paper presents a numerical modeling of TSV filling concerning the influence of the accelerator and the suppressor . The diffusion adsorption model was used in the simulation and effects of the additives were incorporated in the model . The boundary conditions were derived from a set of experimental Tafel curves with different concentrations of additives which provided a quick and accurate way for copper ECD process prediction without complicated surface kinetic parameters fitting . The level set method was employed to track the copper and electrolyte interface . The simulation results were in good agreement with the experiments . For a given feature size the current density for superfilling could be predicted which provided a guideline for ECD process optimization . 


Summary: Influence of additives is considered by a set of experimental Tafel curves. TSV filling experiments have been carried out to validate the modeling results. Superfilling of through silicon via can be predicted with this numerical modeling.
The assessment of the state of the acrosome is a priority in artificial insemination centres since it is one of the main causes of function loss . In this work boar spermatozoa present in gray scale images acquired with a phase contrast microscope have been classified as acrosome intact or acrosome damaged after using fluorescent images for creating the ground truth . Based on shape prior criteria combined with Otsu s thresholding regional minima and watershed transform the spermatozoa heads were segmented and registered . One of the main novelties of this proposal is that unlike what previous works stated the obtained results show that the contour information of the spermatozoon head is important for improving description and classification . Other of this work novelties is that it confirms that combining different texture descriptors and contour descriptors yield the best classification rates for this problem up to date . The classification was performed with a Support Vector Machine backed by a Least Squares training algorithm and a linear kernel . Using the biggest acrosome intact damaged dataset ever created the early fusion approach followed provides a 0.9913 F Score outperforming all previous related works . 


Summary: A new early fusion approach for acrosome integrity classification is proposed. Specific segmentation based on shape priors was carried out. The biggest acrosome intact damaged dataset ever created was created and published. Acrosome contour is important for improving description and classification. The best result up to date combining shape and texture features has been obtained.
An advanced proof of concept real time plant discrimination system is presented that employs two visible laser diodes and one near infrared laser diode . The lasers sequentially illuminate the target ground area and a linear sensor array measures the intensities of the reflected laser beams . The spectral reflectance measurements are then processed by an embedded microcontroller running a discrimination algorithm based on dual Normalised Difference Vegetation Indices . Pre determined plant spectral signatures are used to define unique regions of classification for use by the discrimination algorithm . Measured aggregated NDVI values that fall within a region of classification representing an unwanted plant generate a spray control signal that activates an external spray module thus allowing for a targeted spraying operation . Dynamic outdoor evaluation of the advanced proof of concept real time plant discrimination system using three different plant species and control data determined under static laboratory conditions shows that the system can perform green from green plant detection and accomplish practical discrimination for a vehicle speed of 3km h . 


Summary: We present an advanced real time green from green plant discrimination system. Uniform power multi spot beams are used to illuminate the target vegetation. Spectral reflectance measurements are captured using a line scan sensor. Three laser diodes are used to compute dual NDVI discrimination parameters. Reliable plant detection and discrimination is attained for a 3km h vehicle speed.
In several empirical applications analyzing customer by product choice data it may be relevant to partition individuals having similar purchase behavior in homogeneous segments . Moreover should individual and or product specific covariates be available their potential effects on the probability to choose certain products may be also investigated . A model for joint clustering of statistical units and variables is proposed in a mixture modeling framework and an appropriate EM type algorithm for ML parameter estimation is presented . The model can be easily linked with similar proposals appeared in various contexts such as co clustering of gene expression data clustering of words and documents in web mining data analysis . 


Summary: A two level finite mixture model for clustering customers and products is proposed. Clusters of products nested in segments of customers are determined. Customer product features influence the allocation to segments clusters. An appropriate EM algorithm is presented for the special case of purchase counts. The application shows high purchase rate segments linked with clusters of products.
Introduction Paroxysmal versus persistent atrial fibrillation can be distinguished based on differences in the spectral parameters of fractionated atrial electrograms . Maximization of these differences would improve characterization of the arrhythmogenic substrate . A novel spectral estimator has been shown previously to provide greater distinction in AF spectral parameters as compared with the Fourier transform estimator . Herein it is described how the differences in NSE spectral parameters can be further improved . Method In 10 persistent and 9 paroxysmal AF patients undergoing electrophysiologic study fractionated electrograms were acquired from the distal bipolar ablation electrode . A total of 204 electrograms were recorded from the pulmonary vein antra and from the anterior and posterior left atrial free wall . The following spectral parameters were measured the dominant frequency which reflects local activation rate the DF amplitude and the mean spectral profile which represents background electrical activity . To optimize differences in parameters between paroxysmal versus persistent AF patients the NSE was varied by selectively removing subharmonics using a threshold . The threshold was altered in steps to determine the optimal subharmonics removal . Results At the optimal threshold level mean differences in persistent versus paroxysmal AF spectral parameters were DA 0.371mV DF 0.737Hz and MP 0.096mV . When subharmonics were not removed the differences were substantially less DA 0.301mV DF 0.699Hz and MP 0.063mV . Conclusions NSE optimization produces greater spectral parameter difference between persistent versus paroxysmal AF data . Quantifying spectral parameter differences can be assistive in characterizing the arrhythmogenic substrate . 


Summary: A novel spectral estimator is used to discern paroxysmal versus persistent atrial fibrillation fractionated electrograms. The estimator is improved by selectively imparting antisymmetry to basis vectors used to construct the power spectrum. This preprocessing step leads to improved differentiation of spectral parameters in paroxysmal versus persistent atrial fibrillation fractionated electrograms. The improvement is potentially useful to better discern regions of atrial fibrillation substrate that should be targeted for catheter ablation. This method has been shown to provide better discernment of atrial fibrillation type as compared with the Fourier transform estimator.
In this paper we propose a class of flexible weight functions for use in comparison of two cumulative incidence functions . The proposed weights allow the users to focus their comparison on an early or a late time period post treatment or to treat all time points with equal emphasis . These weight functions can be used to compare two cumulative incidence functions via their risk difference their relative risk or their odds ratio . The proposed method has been implemented in the R CIFsmry package which is readily available for download and is easy to use as illustrated in the example . 


Summary: The proposed class of flexible weight functions allows emphasis on different time periods when comparing cumulative incidence functions between two groups. The comparisons include the risk difference the relative risk and the odds ratio. A large improvement in power was shown when an early weight function was used to compare two cumulative incidence functions with an early difference. The improvement in power under a late difference scenario was more moderate. The companion R package CIFsmry is available for download at CRAN.
This paper presents an integrated system for the automatic analysis of mammograms to assist radiologists in confirming their diagnosis in mammography screening . The proposed automated confirmatory system can process a digitalized mammogram online and generates a high quality filtered segmentation of an image for biological interpretation and a texture feature based diagnosis . We use a serial of image pre processing and segmentation techniques including 2D median filtering seeded region growing algorithm image contrast enhancement to remove noise delete radiopaque artifacts and eliminate the projection of the pectoral muscle from a digitalized mammogram . We also develop an entire image texture feature based classification method by combining a Rough set approach to extract five fundamental texture features from images and then an Artificial Neural Network technique to classify a mammogram as normal indicating the presence of a benign lump or representing a malignant tumor . Here 222 random images from the Mammographic Image Analysis Society database are used for the offline ACS training . Once the system is tuned and trained it is ready for the automated use for the analysis and diagnosis of new mammograms . To test the trained system a separate set of 100 random images from the MIAS and another set of 100 random images from the independent BancoWeb database are selected . The proposed ACS is shown to be successful in confirming diagnosis of mammograms from the two independent databases . 


Summary: This paper presents an integrated system for the automatic analysis of mammograms to assist radiologists in confirming their diagnosis in mammography screening. The proposed system consists of four off line stages Image pre processing and segmentation Feature extraction Selection of fundamental features using Rough Set theory Training of an Artificial Neural Network. Once the system is trained and tuned is ready for its on line use. Here the system is successfully tested on two independent databases.
Mechanical stimuli play a significant role in the process of long bone development as evidenced by clinical observations and in vivo studies . Up to now approaches to understand stimuli characteristics have been limited to the first stages of epiphyseal development . Furthermore growth plate mechanical behavior has not been widely studied . In order to better understand mechanical influences on bone growth we used Carter and Wong biomechanical approximation to analyze growth plate mechanical behavior and explore stress patterns for different morphological stages of the growth plate . To the best of our knowledge this work is the first attempt to study stress distribution on growth plate during different possible stages of bone development from gestation to adolescence . Stress distribution analysis on the epiphysis and growth plate was performed using axisymmetric finite element analysis in a simplified generic epiphyseal geometry using a linear elastic model as the first approximation . We took into account different growth plate locations morphologies and widths as well as different epiphyseal developmental stages . We found stress distribution during bone development established osteogenic index patterns that seem to influence locally epiphyseal structures growth and coincide with growth plate histological arrangement . 


Summary: Growth plate characteristics affect SOC development. Mechanical environment within growth plate varies according to epiphyseal ossification state. Mechanical stimuli may affect similarly growth plate and epiphyseal ossification.
The prediction of the number of clusters in a dataset in particular microarrays is a fundamental task in biological data analysis usually performed via validation measures . Unfortunately it has received very little attention and in fact there is a growing need for software tools libraries dedicated to it . Here we present ValWorkBench a software library consisting of eleven well known validation measures together with novel heuristic approximations for some of them . The main objective of this paper is to provide the interested researcher with the full software documentation of an open source cluster validation platform having the main features of being easily extendible in a homogeneous way and of offering software components that can be readily re used . Consequently the focus of the presentation is on the architecture of the library since it provides an essential map that can be used to access the full software documentation which is available at the supplementary material website . The mentioned main features of ValWorkBench are also discussed and exemplified with emphasis on software abstraction design and re usability . A comparison with existing cluster validation software libraries mainly in terms of the mentioned features is also offered . It suggests that ValWorkBench is a much needed contribution to the microarray software development algorithm engineering community . For completeness it is important to mention that previous accurate algorithmic experimental analysis of the relative merits of each of the implemented measures carried out specifically on microarray data gives useful insights on the effectiveness of ValWorkBench for cluster validation to researchers in the microarray community interested in its use for the mentioned task . 


Summary: Cluster validation for microarray data analysis is an essential task in bioinformatics and biomedicine that is not receiving enough attention. The state of the art does not offer software systems able to help the development of much needed new measures. We propose ValWorkBench and describe its internal modules and classes in order to provide the much needed software platform for the development and testing of new validation measures as well as clustering algorithms. From the programmer point of view no other platform in the same area offers what ValWorkBench provides. From the user point of view the measures contained in it have been the object of rigorous scientific studies i.e. in BMC Bioinformatics 9 2008 462 Algorithms for Molecular Biology 6 2011 1 Natural Computing pages 2014 in press .
Analyzing the acceleration photoplethysmogram is becoming increasingly important for diagnosis . However processing an APG signal is challenging especially if the goal is to detect its small components . Accurate detection of c d and e waves is an important first step for any clinical analysis of APG signals . In this paper a novel algorithm that can detect c d and e waves simultaneously in APG signals of healthy subjects that have low amplitude waves contain fast rhythm heart beats and suffer from non stationary effects was developed . The performance of the proposed method was tested on 27 records collected during rest resulting in 97.39 sensitivity and 99.82 positive predictivity . 


Summary: A robust algorithm to detect c d and e waves in the acceleration photoplethysmogram signals. To our knowledge this is the first algorithm published for this purpose. The accurate detection of c d and e waves will enable researchers to monitor serious abnormalities and health problems.
A new algorithm is proposed for OLS estimation of linear models with multiple high dimensional category variables . It is a generalization of the within transformation to arbitrary number of category variables . The approach unlike other fast methods for solving such problems provides a covariance matrix for the remaining coefficients . The article also sets out a method for solving the resulting sparse system and the new scheme is shown by some examples to be comparable in computational efficiency to other fast methods . The method is also useful for transforming away groups of pure control dummies . A parallelized implementation of the proposed method has been made available as an R package lfe on CRAN . 


Summary: We generalize the linear within estimator to two or more category variables. We describe a general procedure for projecting out dummy encoded category variables. Parameters for dummy variables can optionally be estimated.
Identification of intrinsic characteristics and structure of high dimensional data is an important task for financial analysis . This paper presents a kernel entropy manifold learning algorithm which employs the information metric to measure the relationships between two financial data points and yields a reasonable low dimensional representation of high dimensional financial data . The proposed algorithm can also be used to describe the characteristics of a financial system by deriving the dynamical properties of the original data space . The experiment shows that the proposed algorithm can not only improve the accuracy of financial early warning but also provide objective criteria for explaining and predicting the stock market volatility . 


Summary: A kernel entropy manifold learning algorithm for financial data MLFD MLFD employs the information metric to measure the relationships between two financial data points. MLFD yields reasonable and accurate low dimensional embedding of the original financial data set. The accuracy of the financial early warning is improved by MLFD.
This study was performed to evaluate the influences of the myocardial bridges on the plaque initializations and progression in the coronary arteries . The wall structure is changed due to the plaque presence which could be the reason for multiple heart malfunctions . Using simplified parametric finite element model of the coronary artery having myocardial bridge and analyzing different mechanical parameters from blood circulation through the artery we investigated the prediction of the best position for plaque progression . We chose six patients from the angiography records and used data from DICOM images to generate FE models with our software tools for FE preprocessing solving and post processing . We found a good correlation between real positions of the plaque and the ones that we predicted to develop at the proximal part of the myocardial bridges with wall shear stress oscillatory shear index and residence time . This computer model could be additional predictive tool for everyday clinical examination of the patient with myocardial bridge . 


Summary: We evaluated the influences of the myocardial bridges on the plaque localization in the coronary arteries. We investigated the prediction of the best position for plaque progression using WSS OSI and residence time. A good correlation between positions of the plaque at the proximal part of MB with WSS OSI and residence time. This computer model could be additional predictive tool for everyday clinical examination.
The lifetime of subjects in reliability and survival analysis in the presence of several causes of failure has attracted attention in the literature . Most studies have simplified the computations by assuming that the causes are independent though this does not hold . Dependent competing risks under progressively hybrid censoring condition using a Marshall Olkin bivariate Weibull distribution is investigated . Maximum likelihood and approximated maximum likelihood estimators are developed for estimating the unknown parameters . Asymptotic distributions of the maximum likelihood estimators are used to construct approximate confidence intervals using the observed Fisher information matrix . Based on a simulation and real applications it is illustrated that when a parametric distributional assumption is nearly true a close approximation could be achieved by deliberately censoring the number of subjects and the study duration using Type II progressively hybrid censoring which might help to save time and money in research studies . 


Summary: Dependent competing risks are investigated. Type II progressively hybrid censoring is considered to save time and costs. Marshall Olkin bivariate Weibull distribution is applied. Simulation study has shown the performances of estimators under the Type II PHCS. Two illustrative examples based on real datasets are provided.