Fuzzy approaches in a low-level image processing method to partition the homogeneous regions are important challenges in image segmentation. The analysis of the fuzziness in data produces comparable or improved solutions compared with the respective crisp approaches. The novel approach proposed in this chapter has been found to enhance the functionality of the fuzzy rule base and thus enhance the

Identification of co-expressed genes is the central goal in microarray gene expression analysis. Point-symmetry-based clustering is an important unsupervised learning technique for recognising symmetrical convex- or nonconvex-shaped clusters. To enable fast clustering of large microarray data, we propose a distributed time-efficient scalable approach for point-symmetrybased K-Means algorithm. A na

Pixel classification among overlapping land cover regions in remote sensing imagery is a very challenging task. Detection of uncertainty and vagueness are always the key features for classifying mixed pixels. This paper proposes an approach for pixel classification using a hybrid approach of rough set theory and particle swarm optimization methods. Rough set theory deals with incompleteness and va

Pixel classification of mixed pixels in overlapping regions of remote sensing images is a very challenging task. Efficiency and detection of uncertainty are always the key ingredients for this task. This paper proposes an approach for pixel classification using Shannon's entropy-based fuzzy distance norm. Unsupervised clustering is used to group the objects based on some similarity or dissimilarit

Identification of cancer pathways is the central goal in the cancer gene expression data analysis. Data mining refers to the process analyzing huge data in order to find useful pattern. Data classification is the process of identifying common properties among a set of objects and grouping them into different classes. A cellular automaton is a discrete, dynamical system with simple uniformly interc

Pixel classification among overlapping land cover regions in remote sensing imagery is a challenging task. Detection of uncertainty and vagueness are always key features for classifying mixed pixels. This chapter proposes an approach for pixel classification using hybrid approach of Fuzzy C-Means and Particle Swarm Optimization methods. This new unsupervised algorithm is able to identify clusters

In rhizosphere Plant Growth Promoting Rhizobacteria (PGPR) produce N-acyl-l-homoserine lactones (AHL) as the quorum-sensing (QS) signals. AHLs can act as trans-kingdom signalling molecules between plants and rhizobacteria and that can regulate plant growth and development. The plant-beneficial PGPR Burkholderia phytofirmans PsJN promotes growth in Arabidopsis thaliana by producing 3-oxo-dodecanoyl

The inference of gene networks from gene expression data is known as "reverse engineering." Elucidating genetic networks from high-throughput microarray data in seed maturation and embryo formation in plants is crucial for storage and production of cereals for human beings. Delayed seed maturation and abnormal embryo formation during storage of cereal crops degrade the quality and quantity of food

A hybrid unsupervised learning algorithm, which is termed as Evolutionary Rough Multi-Objective Optimization (ERMOO) algorithm, is proposed in this chapter. It comprises a judicious integration of the principles of the rough sets theory with the archived multi-objective simulated annealing approach. While the concept of boundary approximations of rough sets in this implementation deals with the in

Background: The industrially important yeast Blastobotrys (Arxula) adeninivorans is an asexual hemiascomycete phylogenetically very distant from Saccharomyces cerevisiae. Its unusual metabolic flexibility allows it to use a wide range of carbon and nitrogen sources, while being thermotolerant, xerotolerant and osmotolerant. Results: The sequencing of strain LS3 revealed that the nuclear genome of

Identification of cancer subtypes is the central goal in the cancer gene expression data analysis. Modified symmetry-based clustering is an unsupervised learning technique for detecting symmetrical convex or non-convex shaped clusters. To enable fast automatic clustering of cancer tissues (samples), in this chapter, the authors propose a rough set based hybrid approach for modified symmetry-based

The paper discusses a life cycle model of wood products of Dalbergia sissoo from Saharanpur and Bijnor to calculate and trace the amount of carbon retained. A simulation model is built to trace the fate of carbon bound in the products produced in the year 2009 for a period of 200 years, until most of the carbon is released back into the atmosphere. The amount of carbon that enters the life cycle i

Remote homology detection among proteins utilizing only the unlabelled sequences is a central problem in comparative genomics. The existing cluster kernel methods based on neighborhoods and profiles and the Markov clustering algorithms are currently the most popular methods for protein family recognition. The deviation from random walks with inflation or dependency on hard threshold in similarity

In this paper we propose an automatic protein family expansion approach for recruitment of new members among the protein-coding genes in newly sequenced genomes. The criteria for adding a new member to a family depends on the structure of each individual family versus being globally uniform. The detection of a threshold in the ROC space of all sorted iterative profile sets defines the alignments s

Polyploidization is an important process in the evolution of eukaryotic genomes, but ensuing molecular mechanisms remain to be clarified. Autopolyploidization or whole-genome duplication events frequently are resolved in resulting lineages by the loss of single genes from most duplicated pairs, causing transient gene dosage imbalance and accelerating speciation through meiotic infertility. Allopol

An important approach for image classification is the clustering of pixels in the spectral domain. Fast detection of different land cover regions or clusters of arbitrarily varying shapes and sizes in satellite images presents a challenging task. In this article, an efficient scalable parallel clustering technique of multi-spectral remote sensing imagery using a recently developed point symmetry-b

Identification of cancer subtypes is the central goal in the cancer gene expression data analysis. Modified symmetry-based clustering is an unsupervised learning technique for detecting symmetrical convex or non-convex shaped clusters. To enable fast automatic clustering of cancer tissues (samples), in this chapter, the authors propose a rough set based hybrid approach for modified symmetry-based

Background: Uncovering the relationship between the conserved chromosomal segments and the functional relatedness of elements within these segments is an important question in computational genomics. We build upon the series of works on gene teams and homology teams.Results: Our primary contribution is a local sliding-window SYNS (SYNtenic teamS) algorithm that refines an existing family structure

Remote homology detction among proteins in an unsupervised approach from sequences is an important problem in computational biology. The existing neighborhood cluster kernel methods and Markov clustering algorithms are most efficient for homolog detection. Yet they deviate from random walks with inflation or similarity depending on hard thresholds. Our spectral clustering approach with new combine