A Unified Approach to Encoding and Classification Using Bimodal Projection-Based Features

In general the objective of accurately encoding the input data and the objective of extracting good features to facilitate classification are not consistent with each other. As a result, good encoding methods may not be effective mechanisms for classification. In this paper, an earlier proposed unsupervised feature extraction mechanism for pattern classification has been extended to obtain an invertible map. The method of bimodal projection-based features was inspired by the general class of methods called projection pursuit. The principle of projection pursuit concentrates on projections that discriminate between clusters and not faithful representations. The basic feature map obtained by the method of bimodal projections has been extended to overcome this. The extended feature map is an embedding of the input space in the feature space. As a result, the inverse map exists and hence the representation of the input space in the feature space is exact. This map can be naturally expressed as a feedforward neural network.

Structural Alignment based Kernels for Protein Structure Classification

Structural alignments are the most widely used tools for comparing proteins with low sequence similarity. The main contribution of this paper is to derive various kernels on proteins from structural alignments, which do not use sequence information. Central to the kernels is a novel alignment algorithm which matches substructures of fixed size using spectral graph matching techniques. We derive positive semi-definite kernels which capture the notion of similarity between substructures. Using these as base more sophisticated kernels on protein structures are proposed. To empirically evaluate the kernels we used a 40% sequence non-redundant structures from 15 different SCOP superfamilies. The kernels when used with SVMs show competitive performance with CE, a state of the art structure comparison program.

Contolling Alkali Induced Heave in soil with fly ash

The properties of the soils can change drastically due to the presence of contaminants leading to several geotechnical failures founded on them. One important pollutant that can have considerable effect is the alkali released from varies industries. It is known that alkali solutions can increase the swelling of soil containing both expansive and non-expansive minerals.Many attempts to control this alkali-induced heave in soils through chemical agents were not successful. With a view to study the use of fly ash to stabilize alkali contaminated soil, the behavior of soils containing 25% and 50% of fly ash has been studied in the presence of 2N-alkali solution. Results of volume change behavior of non-expansive soil containing kaolinite clay mineral in the presence of fly ash showed that it is effective to control the alkali induced swelling in the soil. The effectiveness increases with an increase in the percentage of fly ash in soils. Detailed X-ray diffraction and SEM studies showed that the mineralogical changes that occur in soil due to alkali interaction are inhibited in the presence of fly ash.

Three-Dimensional Site Characterization Model Using Artificial Neural Networks

This research is designed to develop a new technique for site characterization in a three-dimensional domain. Site characterization is a fundamental task in geotechnical engineering practice, as well as a very challenging process, with the ultimate goal of estimating soil properties based on limited tests at any half-space subsurface point in a site.In this research, the sandy site at the Texas A&M University's National Geotechnical Experimentation Site is selected as an example to develop the new technique for site characterization, which is based on Artificial Neural Networks (ANN) technology. In this study, a sequential approach is used to demonstrate the applicability of ANN to site characterization. To verify its robustness, the proposed new technique is compared with other commonly used approaches for site characterization. In addition, an artificial site is created, wherein soil property values at any half-space point are assumed, and thus the predicted values can compare directly with their corresponding actual values, as a means of validation. Since the three-dimensional model has the capability of estimating the soil property at any location in a site, it could have many potential applications, especially in such case, wherein the soil properties within a zone are of interest rather than at a single point. Examples of soil properties of zonal interest include soil type classification and liquefaction potential evaluation. In this regard, the present study also addresses this type of applications based on a site located in Taiwan, which experienced liquefaction during the 1999 Chi-Chi, Taiwan, Earthquake.

Clustering Based Large Margin Classification: A Scalable Approach using SOCP Formulation

This paper presents a novel Second Order Cone Programming (SOCP) formulation for large scale binary classification tasks. Assuming that the class conditional densities are mixture distributions, where each component of the mixture has a spherical covariance, the second order statistics of the components can be estimated efficiently using clustering algorithms like BIRCH. For each cluster, the second order moments are used to derive a second order cone constraint via a Chebyshev-Cantelli inequality. This constraint ensures that any data point in the cluster is classified correctly with a high probability. This leads to a large margin SOCP formulation whose size depends on the number of clusters rather than the number of training data points. Hence, the proposed formulation scales well for large datasets when compared to the state-of-the-art classifiers, Support Vector Machines (SVMs). Experiments on real world and synthetic datasets show that the proposed algorithm outperforms SVM solvers in terms of training time and achieves similar accuracies.

Investigating Diproline Segments in Proteins: Occurrences, Conformation and Classification

The covalent linkage between the side-chain and the backbone nitrogen atom of proline leads to the formation of the five-membered pyrrolidine ring and hence restriction of the backbone torsional angle phi to values of -60 degrees +/- 30 degrees for the L-proline. Diproline segments constitute a chain fragment with considerably reduced conformational choices. In the current study, the conformational states for the diproline segment ((L)Pro-(L)Pro) found in proteins has been investigated with an emphasis on the cis and trans states for the Pro-Pro peptide bond. The occurrence of diproline segments in turns and other secondary structures has been studied and compared to that of Xaa-Pro-Yaa segments in proteins which gives us a better understanding on the restriction imposed on other residues by the diproline segment and the single proline residue. The study indicates that P(II)-P(II) and P(II)-alpha are the most favorable conformational states for the diproline segment. The analysis on Xaa-Pro-Yaa sequences reveals that the XaaPro peptide bond exists preferably as the trans conformer rather than the cis conformer. The present study may lead to a better understanding of the behavior of proline occurring in diproline segments which can facilitate various designed diproline-based synthetic templates for biological and structural studies. (C) 2011 Wiley Periodicals, Inc. Biopolymers 97: 54-64, 2012.

Multi-spectral satellite image classification using Glowworm Swarm Optimization

This paper investigates a new Glowworm Swarm Optimization (GSO) clustering algorithm for hierarchical splitting and merging of automatic multi-spectral satellite image classification (land cover mapping problem). Amongst the multiple benefits and uses of remote sensing, one of the most important has been its use in solving the problem of land cover mapping. Image classification forms the core of the solution to the land cover mapping problem. No single classifier can prove to classify all the basic land cover classes of an urban region in a satisfactory manner. In unsupervised classification methods, the automatic generation of clusters to classify a huge database is not exploited to their full potential. The proposed methodology searches for the best possible number of clusters and its center using Glowworm Swarm Optimization (GSO). Using these clusters, we classify by merging based on parametric method (k-means technique). The performance of the proposed unsupervised classification technique is evaluated for Landsat 7 thematic mapper image. Results are evaluated in terms of the classification efficiency - individual, average and overall.