A neural network based human identification framework using ear images

Multicollinearity refers to the presence of collinearity between multiple variables and renders the results of statistical inference erroneous (Type II error). This is particularly important in environmental health research where multicollinearity can hinder inference. To address this, correlated variables are often excluded from the analysis, limiting the discovery of new associations. An alternative approach to address this problem is the use of principal component analysis. This method, combines and projects a group of correlated variables onto a new orthogonal space. While this resolves the multicollinearity problem, it poses another challenge in relation to interpretability of results. Standard hypothesis testing methods can be used to evaluate the association of projected predictors, called principal components, with the outcomes of interest, however, there is no established way to trace the significance of principal components back to individual variables. To address this problem, we investigated the use of sparse principal component analysis which enforces a parsimonious projection. We hypothesise that this parsimony could facilitate the interpretability of findings. To this end, we investigated the association of 20 environmental predictors with all-cause mortality adjusting for demographic, socioeconomic, physiological, and behavioural factors. The study was conducted in a cohort of 379,690 individuals in the UK. During an average follow-up of 8.05 years (3,055,166 total person-years), 14,996 deaths were observed. We used Cox regression models to estimate the hazard ratio (HR) and 95% confidence intervals (CI). The Cox models were fitted to the standardised environmental predictors (a) without any transformation (b) transformed with PCA, and (c) transformed with SPCA. The comparison of findings underlined the potential of SPCA for conducting inference in scenarios where multicollinearity can increase the risk of Type II error. Our analysis unravelled a significant association between average noise pollution and increased risk of all-cause mortality. Specifically, those in the upper deciles of noise exposure have between 5 and 10% increased risk of all-cause mortality compared to the lowest decile.

A multi-layer feed forward neural network model for accurate prediction of flue gas sulfuric acid dew points in process industries

This study aims to establish the fundamental concepts of multiple linear regression models involving qualitative predictor variables and to validate their associations using a Multilayer Feed Forward (MLFF) neural network. A simple guide is introduced to separate qualitative variables based on the number of classes, ensuring they meet the assumptions of multiple linear regression. The approach provides a basic and practical template for integrating qualitative predictors into applied linear models. Validation is carried out using the sum of square error and relative error obtained from the MLFF neural network. The low error values produced by the MLFF model highlight the effectiveness and superiority of the proposed methodology.

An intelligent system for sorting pistachio nut varieties

Research on feature vector construction and classification recognition of typical mine microseismic waveforms

Human identification has always been a topic that interested researchers around the world. Biometric methods are found to be more effective and much easier for the users than the traditional identification methods like keys, smart cards and passwords. Unlike with the traditional methods, with biometric methods the data acquisition is most of the times passive, which means the users do not take active part in data acquisition. Data acquisition can be performed using cameras, scanners or sensors. Human physiological biometrics such as face, eye and ear are good candidates for uniquely identifying an individual. However, human ear scores over face and eye because of certain advantages it has over face. The most challenging phase in human identification based on ear biometric is the segmentation of the ear image from the captured image which may contain many unwanted details. In this work, PDE based image processing techniques are used to segment out the ear image. Level Set Theory based image processing is employed to obtain the contour of the ear image. A few Level set algorithms are compared for their efficiency in segmenting test ear images.

The paper compares the use of Principal Component Analysis (PCA) to Information Gain (IG) as a feature selection method for improving the classification of Influenza-A antiviral resistance. Neural networks were used as the classification method of choice. The experiment was conducted on cDNA viral segments of Influenza-A belonging to the H1N1 strain. Sequences from each segment were further divided into Adamantane-resistant, and non-Adamantane-resistant. Accuracy, sensitivity, specificity precision and time were used as performance measures. Using PCA for feature selection increased preprocessing speeds from an average processing time of 1.5 hours to 5 minutes, as opposed to IG. The performance also stayed comparable with that of the previous results achieved using IG.

Application of principal component analysis and artificial neural networks to recognize the individual VOCs of methanol/2-propanol in a binary mixture by SAW multi-sensor array

Flash floods can demolish infrastructure and property within seconds as they are very sudden. Flash floods are the main cause of the casualties and loss of properties. Existing natural disaster prediction algorithms contains false alarms. Indefinite techniques have been applied to overcome this leading issue in many countries. A competent flood management system must have the potential and tendency to identify the flash floods and atmospheric and climatic changes on early basis with less false alarm rate. Techniques which have been designed for the flash flood investigation may be categorized into following types a. Sensors based direct measurement b. Radar images c. Satellite based X-band images. The proposed research consisted of Artificial intelligence-based decision making for multi-modal sensing (direct measurement from multi-resolution sensors). A combination of sensors like Passive infrared (PIR), water level sensor, ultrasonic sensor, temperature sensor, pressure and altimeter sensors have been integrated on a single device to investigate the flash floods. The use of most suitable pair of measurement sensors can substantially enhance the advantage of more accuracy and reliability compared to a single sensor. In recent trends Particle swarm optimization is very popular for solving stochastic global optimization problems. The data was trained and processed by modified multi-layer feed forward neural network optimized by particle swarm optimization algorithm. Hybrid Modified Particle swarm optimization has been combined with feed forward neural network for the vigorous investigation of flash floods with less false alarm rate. Simulated results showed that the proposed research algorithm Modified multi-layer feed forward neural network optimized by Particle swarm optimization for multi-modal sensing performed very well in terms of evaluation parameters compared to other existing strategies with minimum false alarm ratio. Moreover, modified multi-layer feed forward neural network optimized by article swarm optimization algorithm results have been compared with the cuckoo search, modified cuckoo search, particle swarm optimization and Multi-layer perceptron neural network configurations for the validation purpose.

Response to Cavalli-Sforza Interview: [Human Biology 82(3):245-266 (June 2010)

This paper presents neural network-based dimension reduction of texture features in content-based image retrieval. In particular, we highlight the usefulness of hetero-associative neural networks to this task, and also propose a scheme to combine the hetero-associative and auto-associative functions. A multichannel Gabor-filtering approach is used to derive 30-dimensional texture features from a set of homogeneous texture images. Multi-layer feedforward neural networks are then trained to reduce the number of feature dimensions. Our results show that the methods lead to a reduction of up to 30% while keeping or even improving the performance of similarity ranking. This has the benefit of alleviating the ill-effects of the high dimensionality of features in current image indexing methods and resulting in significant speeding up retrieval rates. Results using principal component analysis are also provided for comparison.

This overview of process fault diagnosis concentrates on steady-state processes, continuous dynamic processes and batch processes. In steady-state processes, the classic linear model for process fault diagnosis based on the use of principal component analysis is discussed in some detail, followed by extensions of this model to nonlinear steady-state (non)Gaussian processes. These extensions include higher-order statistical models, such as based on the use of independent components, the use of principal curves and surfaces as well as neural networks as nonlinear extensions of principal component analysis. Likewise, innovations and applications of kernel methods are among other considered, including kernel principal component analysis, kernel partial least squares, kernel independent component analysis as well as multiple kernel learning variants of some of these approaches. Continuous dynamic processes are considered in terms of manifold models, adaptive methods and phase space methods, where the application of process diagnostics, such as correlation dimension and recurrence quantitative analyses, has been proposed. The multitude of recent developments in batch processing are similarly reviewed in terms of the multiway principal component model, extended to multiphase and multiblock models. These developments are considered in the broad framework outlined in Chap. 1.

Aims: We show the use of principal component analysis (PCA) and Fourier decomposition (FD) method as tools for variable star diagnostics and compare their relative performance in studying the changes in the light curve structures of pulsating Cepheids and in the classification of variable stars. Methods: We have calculated the Fourier parameters of 17,606 light curves of a variety of variables, e.g., RR Lyraes, Cepheids, Mira Variables and extrinsic variables for our analysis. We have also performed PCA on the same database of light curves. The inputs to the PCA are the 100 values of the magnitudes for each of these 17,606 light curves in the database interpolated between phase 0 to 1. Unlike some previous studies, Fourier coefficients are not used as input to the PCA. Results: We show that in general, the first few principal components (PCs) are enough to reconstruct the original light curves compared to FD method where 2 to 3 times more number of parameters are required to satisfactorily reconstruct the light curves. The computation of required number of Fourier parameters on the average needs 20 times more CPU time than the computation of required number of PCs. Therefore, PCA does have some advantage over the FD method in analysing the variable stars in a larger database. However in some cases, particularly in finding the resonances in Fundamental mode (FU) Cepheids, the PCA results show no distinct advantages over the FD method. We also demonstrate that the PCA technique can be used to classify variables into different variability classes in an automated, unsupervised way, a feature that has immense potential for larger databases of the future.

The aim of this study was to explore the use of principal component analysis (PCA) in understanding multivariate relationships in soccer training load data. Training load data were collected from 20 professional male soccer players during a 28-week in-season period. Twelve training load variables (total distance, PlayerLoadTM, low-speed running distance, moderate-speed running distance, high-speed running distance, sprint distance, moderate-speed running efforts, high-speed running efforts, sprint efforts, accelerations, decelerations, and changes of direction) were collected during training sessions, with correlation analysis revealing high intercorrelation between most variables (r = 0.04-0.98). Principal component analysis was performed on datasets containing all players and on individual players. On the whole dataset, two principal components were retained explaining a total of 81% of data variance. The first component comprised variables associated with distances in speed zones and the second component changes of direction. Whilst some individual variation existed among players, distances in speed zones were loaded on the first component and inertial movement analysis variables, such as accelerations, decelerations, and changes of direction, were loaded on the second component. These findings evidence the strong relationships between several common training load variables and highlight the risk of data redundancy. By selecting variables from each component, practitioners can reduce the number of variables reported whilst retaining as much of the variation in data as possible.

Use of principal components analysis for displaying variation between pyrograms of micro-organisms