Mixed-pixels classification in land-cover regions is a challenging task in remote sensing imagery. To classify mixed-pixels, vagueness is always the main characteristic by handling uncertainty. We propose a hybrid approach for pixel classification using Rough sets and Cellular automata models to solve this problem. Multiple belongingness and vagueness among data can be handled efficiently using Ro

Pixel classification of watershed satellite image is a challenging task in remote sensing. Uses of Particle Swarm Optimisation and Cellular Automata are significant methods in watershed image segmentation. This paper proposes a method of pixel classification using a new hybrid Particle Swarm Optimization-Cellular Automata approach. The proposed unsupervised method identifies clusters using 2-Dimen

Image segmentation among overlapping land cover areas in satellite images is a very crucial task. Detection of belongingness is the important problem for classifying mixed pixels. This paper proposes an approach for pixel classification using a hybrid approach of Fuzzy C-Means and Cellular automata methods. This new unsupervised method is able to detect clusters using 2-Dimensional Cellular Automa

In this paper a novel digital phase lock loop (DPLL) is introduced with Teager energy operator (TEO) incorporated discrete energy separation algorithm DESA2. The output of the DESA2 is controlled using genetic algorithm (GA) optimized fuzzy controller. The input and output membership functions are optimized using GA along with the input and output controller gains of the fuzzy controller. This kin

Image segmentation among overlapping land cover areas in satellite images is a very crucial task. Detection of belongingness is the important problem for classifying mixed pixels. This paper proposes an approach for pixel classification using a hybrid approach of Fuzzy C-Means and Cellular automata methods. This new unsupervised method is able to detect clusters using 2-Dimensional Cellular Automa

Kernel based learning algorithms are sensitive to the choice of appropriate kernel function and parameter setting. Classification accuracies yielded by the kernel based classifiers may show variation depending on the choice of the kernel and its associated parameters. Suggesting an efficient kernel function and effective setting of kernel parameters are thus important problems for kernel based cla

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