A novel R-package graphic user interface for the analysis of metabonomic profiles

Background Analysis of the plethora of metabolites found in the NMR spectra of biological fluids or tissues requires data complexity to be simplified. We present a graphical user interface (GUI) for NMR-based metabonomic analysis. The "Metabonomic Package" has been developed for metabonomics research as open-source software and uses the R statistical libraries. /Results The package offers the following options: Raw 1-dimensional spectra processing: phase, baseline correction and normalization. Importing processed spectra. Including/excluding spectral ranges, optional binning and bucketing, detection and alignment of peaks. Sorting of metabolites based on their ability to discriminate, metabolite selection, and outlier identification. Multivariate unsupervised analysis: principal components analysis (PCA). Multivariate supervised analysis: partial least squares (PLS), linear discriminant analysis (LDA), k-nearest neighbor classification. Neural networks. Visualization and overlapping of spectra. Plot values of the chemical shift position for different samples. Furthermore, the "Metabonomic" GUI includes a console to enable other kinds of analyses and to take advantage of all R statistical tools. /Conclusion We made complex multivariate analysis user-friendly for both experienced and novice users, which could help to expand the use of NMR-based metabonomics

Exploiting Evolution for an Adaptive Drift-Robust Classifier in Chemical Sensing

Gas chemical sensors are strongly affected by drift, i.e., changes in sensors' response with time, that may turn statistical models commonly used for classification completely useless after a period of time. This paper presents a new classifier that embeds an adaptive stage able to reduce drift effects. The proposed system exploits a state-of-the-art evolutionary strategy to iteratively tweak the coefficients of a linear transformation able to transparently transform raw measures in order to mitigate the negative effects of the drift. The system operates continuously. The optimal correction strategy is learnt without a-priori models or other hypothesis on the behavior of physical-chemical sensors. Experimental results demonstrate the efficacy of the approach on a real problem

Weighted k-Nearest-Neighbor Techniques and Ordinal Classification

In the field of statistical discrimination k-nearest neighbor classification is a well-known, easy and successful method. In this paper we present an extended version of this technique, where the distances of the nearest neighbors can be taken into account. In this sense there is a close connection to LOESS, a local regression technique. In addition we show possibilities to use nearest neighbor for classification in the case of an ordinal class structure. Empirical studies show the advantages of the new techniques

A Review on Facial Expression Recognition Techniques

Facial expression is in the topic of active research over the past few decades. Recognition and extracting various emotions and validating those emotions from the facial expression become very important in human computer interaction. Interpreting such human expression remains and much of the research is required about the way they relate to human affect. Apart from H-I interfaces other applications include awareness system, medical diagnosis, surveillance, law enforcement, automated tutoring system and many more. In the recent year different technique have been put forward for developing automated facial expression recognition system. This paper present quick survey on some of the facial expression recognition techniques. A comparative study is carried out using various feature extraction techniques. We define taxonomy of the field and cover all the steps from face detection to facial expression classification

Using Imagery of Vegetation and Rooftops to Predict Solar Roof Candidacy

Generally, the present disclosure is directed to predicting whether a property can be conducive to solar energy system installation with minimal adjustment to peripheral vegetation. In particular, in some implementations, the systems and methods of the present disclosure can include or otherwise leverage one or more machine-learned models to predict whether a property can be conducive to solar energy installation with minimal adjustment to peripheral vegetation based on imagery and/or publicly available or user-submitted information about the property or area surrounding the property

A Method For Early Detection Of Cardiac Arrhythmias

According to the WHO (World Health Organization), the beginning of specific cardiovascular illnesses is the leading cause of death worldwide. Cardiac arrhythmias, in particular, can develop into cardiovascular diseases like heart disease, so it's essential to figure out how to make an early diagnosis to stop the arrhythmia from developing into a condition that, in more advanced stages, wouldn't respond well to treatments. It was possible to extract typical heart electrophysiology patterns like the QRS complex, the P.R. segment, the Q.R. segment, the R.S. segment, and the S.T. segment by characterizing the signals picked up by external ambulatory monitors and using the T.W. (Wavelet Transform) for this type of signal analysis. Digital filters were used in the filtering process, and the signal was then described, facilitating easier differentiation through a support vector machine-based classification method established by comparing the outcomes from the various methodologies. The research showed that it is possible to create an automatic tool for detecting cardiac issues as a decision support tool for sending patients for examination by a specialist doctor using the proposed model