It combines factor analysis and multiple regression to simultaneously test measurement model and structural relationships that are specified in the model. SEM includes measuring path analysis, path modeling analysis of covariance structures and latent variable analysis. Unlike multiple regression, SEM allows for multiple dependent variables and allows variables to correlate. Structural Equation Modeling is a confirmatory factor analysis that integrates path analysis and factor analysis. SEM is more superior from multiple regression as is also took into consideration the modeling of interactions, nonlinearities, correlates independent measurement error, correlated error terms, multiple latent independent each measured by multiple indicators.
Emad A Mohammed et al , in their work big clinical data analytics would emphasize modelling of whole interacting processes in clinical settings and clinical datasets can be evolution of ultra-large-scale datasets. Arantxa Duque Barrachina et al  proposed that using Hadoop techniques large datasets can be used to identification of large dataset. K.Divya et al , for protecting the data used a progressive encryption scheme. Hong song Chen , in their research article a novel Hadoop-based biosensor Sunspot wireless network architecture, ECC digital signature algorithm, Mysql database and Hadoop HDFS cloud storage; security administrator can use it to protect and manage key data. Lidong Wang et al , in their work based on SWOT (Strengths, Weaknesses, Opportunities, Threats) analysis, Radio Frequency Identification Technology
For example, microarray technologies are used to predict a patient’s outcome. On the basis of patients’ genotypic microarray data, their survival time and risk of tumor metastasis or recurrence can be estimated. Machine learning can be used for peptide identification through mass spectroscopy. Correlation among fragment ions in a tandem mass spectrum is crucial in reducing stochastic mismatches for peptide identification by database searching. An efficient scoring algorithm that considers the correlative information in a tunable and comprehensive manner is highly desirable(Khalid
Structural data can be created utilizing certain sorts of mass spectrometers, as a rule those with various analyzers which are known as tandem (pair) mass spectrometers. This is accomplished by dividing the specimen inside the instrument and analyzing the products created. This procedure is helpful for the structural clarification of organic compounds and for peptide or oligonucleotide sequencing. There are many different types of mass spectrometers but all uses magnetic and electric fields to exert forces on charged particles produced from chemicals to be analyzed. A basic mass spectrometer consists of three parts: 1.
On one hand to analyze comprehensively the whole spectral response to retain the whole information; and on the other one to identify synthetic indices such as optimum narrow bands and new Spectral Vegetation Indices (SVIs) which are able to characterize the status of the crop and the different levels of stress (Thenkabail, 2001; Thenkabail et al., 2004; Jain et al., 2007). Thus, many approaches have been proposed for discriminating disease presence including the use of multivariate statistical analysis techniques. The proposed procedures may allow both to eliminate the redundant information and to identify synthetic indices which maximize differences among levels of stress (Broge and Leblanc, 2001; Ray et al., 2010). Classification models are divided either based on the form of the decision boundaries among classes into linear or non-linear models, or based on the multivariate probability distribution of the data into parametric or non-parametric models. The parametric methods, like Nearest Mean Classifier (NMC), are commonly used when the studied dataset represents a sample from a multivariate normally-distributed population, whereas the Non-parametric methods, such as Partial Least Squares-Discriminant Analysis (PLS-DA), Artificial Neural Networks (ANNs) and Classification and Regression Tree (CART), are used when the multivariate distribution is different from the normal (Hand,
For characterization, the bolster vector machine (SVM) system has demonstrated to have high acknowledgment precision, and been utilized more as a part of face acknowledgment. Then again, human confronts offer a comparable geometrical structure. The flexible bundle diagram coordinating (EBGM) strategy proposed by Weskit and so forth all. Exploits the facial geometry and countenances are spoken to as diagrams, with hubs situated at fiducially focuses, and edges named with 2D separation vectors. Every hub contains an arrangement of 40 complex Gabor wavelet coefficients at distinctive scales and introductions.
This is the major problem of opinion mining but the results are more accurate as the data is more authentic. There are three classification techniques used for solving this purpose i.e. Naïve Bayes classification, Support Vector Machine and Maximum Entropy. In Naive Bayes, models that assign class label to problem instances, represented as vectors of feature values, where the class labels are drawn from some finite set. Whereas Support Vector Machine(SVM) is a machine learning tool that is based on the idea of large margin data classiﬁcation and Maximum Entropy is rooted in information theory, the mem seeks to extract as much information from a measurement as is justified by the data's signal-to-noise
INTRODUCTION Integrative taxonomy is a multisource approach that takes advantage of complementarity among disciplines. Three imperatives drive integrative taxonomy. First, morphological methods fail in some cases, absolutely needing the use of other approaches. Second, even where morphology can succeed in delimiting species, other approaches can assist significantly and speed the process. Third, the use of several disciplines helps taxonomy going beyond the name of species and to understand the processes bringing them about.
In practice it is most common for approximate matching models to be used to rank retrieved doc- uments. Vector Space Models (VSMs) were introduced in 1975 [SWY75] and describe a simple model intended for information filtering, information retrieval, indexing and producing relevancy rankings. The are an algebraic model which represent documents as vectors of identifiers. Document identifiers are compared against each other and a metric is calculated for the intended purpose i.e. for filtering, retrieval, indexing or rel- evancy.