Application of Computational Intelligence Techniques in Wireless Sensor Networks the State of the Art

A wireless sensor network may contain hundreds or even tens of thousands of inexpensive sensor devices that can communicate with their neighbors within a limited radio range. By relaying information on each other, they transmit signals to a command post anywhere within the network. Worldwide market for wireless sensor networks is rapidly growing due to a huge variety of applications it offers. In this chapter, we discuss application of computational intelligence techniques in wireless sensor networks on the coverage problem in general and area coverage in particular. After providing different types of coverage encountered in WSN, we present a possible classification of coverage algorithms. Then we dwell on area coverage which is widely studied due to its importance. We provide a survey of literature on area coverage and give an account of its state-of-the art and research directions.

Study of Basic Concepts on the Development of Protein Microarray - Gene Expression Profiling

The physical and biological activity of any organisms is mainly depended on the genetic information which stored in DNA. A process at which a gene gives rise to a phenotype is called as gene expression. Analysis of gene expression can be used to interpret the changes that occur at biological level of a stressed cell or tissue. Hybridization technology helps to study the gene expression of multiple cell at a same time. Among them microarray technology is a high- throughput technology to study the gene expression at transcription level (DNA) or translation level (Protein). Analysis the protein only can predict the accurate changes that happens in a tissue, when they are infected by a disease causing organisms. Protein microarray mainly used to identify the interactions and activities of proteins with other molecules, and to determine their function for a system at normal state and stressed state. The scope of this chapter is to outline a detail description on the fabrication, types, data analysis, and application of protein microarray technology towards gene expression profiling.

Unleashing Artificial Intelligence onto Big Data: A Review

We live in an ocean of data. Big Data is characterized by vast amounts of data sized in the order of petabytes or even exabytes. Though Big Data has great potential, Big data by itself has no value unless one can derive meaningful results from it. That is where Artificial Intelligence pitches in. Artificial Intelligence's most common application is about finding patterns in enormous quantities of data. The confluence of Big Data and Artificial Intelligence allows companies to automate and improve complex descriptive, predictive and prescriptive analytical tasks. In other words, Big Data can offer great insights with the help of Artificial Intelligence (AI). Artificial Intelligence can act as a catalyst to derive tangible value from Big data and serve as key to unlocking Big data. This review article focuses on applications of artificial intelligence to Big Data, its Limitations and issues.

BFO Optimized Automatic Load Frequency Control of a Multi-Area Power System

This chapter presents an analysis on operation of Automatic Load Frequency Control (ALFC) by developing models in SIMULINK which helps us to understand the principle behind ALFC including the challenges. The three area system is being taken into account considering several important parameters of ALFC like integral controller gains (KIi), governor speed regulation parameters (Ri), and frequency bias parameters (Bi), which are being optimized by using Bacteria Foraging Optimization Algorithm (BFOA). Simultaneous optimization of certain parameters like KIi, Ri and Bi has been done which provides not only the best dynamic response for the system but also allows us to use much higher values of Ri than used in practice. This will help the power industries for easier and cheaper realization of the governor. The performance of BFOA is also investigated through the convergence characteristics which reveal that that the Bacteria Foraging Algorithm is quite faster in optimization such that there is reduction in the computational burden and also minimal use of computer resource utilization.

Evolutionary Computing Approaches to System Identification

In this chapter, we describe an important class of engineering problem called system identification which is an essential requirement for obtaining models of system of concern that would be necessary for controlling, analyzing the systems. The system identification problem is essentially to pick up the best model out of the several candidate models. Thus, the problem of system identification or modeling building turns out to be an optimization problem. The chapter explain what are different evolutionary computing techniques used in the past and the state- of the art technologies on evolutionary computation. Then, some case studies have been included how the system identification of a number of complex systems effectively achieved by employing these evolutionary computing techniques.

Personalized Medicine in the Era of Genomics

Personalized medicine is a model that aims at customizing healthcare and tailoring medicine according to an individual`s genetic makeup. It classifies individuals that differ in their susceptibility to a particular disease or response to a particular treatment into subpopulations based on individual's unique genetic and clinical information along with environmental factors. The completion of Human Genome Project and the advent of high-throughput genome analysis tools has helped in building and strengthening this model. There lies a huge potential in the implementation of personalized medicine to significantly improve the clinical outcomes; however, its implementation into clinical practice remains slow and is a matter of concern. This chapter aims at acquainting readers with the underlying concepts and components of personalized medicine supplemented with some disease-based case studies, discussing challenges and recent advancements in the implementation of the model of personalized medicine.

Application of Uncertainty Models in Bioinformatics

This chapter provides the information related to the researches enhanced using uncertainty models in life sciences and biomedical Informatics. The main emphasis of this chapter is to present the general ideas for the time line of different uncertainty models to handle uncertain information and their applications in the various fields of biology. There are many mathematical models to handle vague data and uncertain information such as theory of probability, fuzzy set theory, rough set theory, soft set theory. Literatures from the life sciences and bioinformatics have been reviewed and provided the different experimental & theoretical results to understand the applications of uncertain models in the field of bioinformatics.

Hybrid Ensemble Learning Methods for Classification of Microarray Data

Efficient classification and feature extraction techniques pave an effective way for diagnosing cancers from microarray datasets. It has been observed that the conventional classification techniques have major limitations in discriminating the genes accurately. However, such kind of problems can be addressed by an ensemble technique to a great extent. In this paper, a hybrid RotBagg ensemble framework has been proposed to address the problem specified above. This technique is an integration of Rotation Forest and Bagging ensemble which in turn preserves the basic characteristics of ensemble architecture i.e., diversity and accuracy. Three different feature selection techniques are employed to select subsets of genes to improve the effectiveness and generalization of the RotBagg ensemble. The efficiency is validated through five microarray datasets and also compared with the results of base learners. The experimental results show that the correlation based FRFR with PCA-based RotBagg ensemble form a highly efficient classification model.

Analysis of Microarray Data using Artificial Intelligence Based Techniques

Microarray is one of the essential technologies used by the biologists to measure genome-wide expression levels of genes in a particular organism under some particular conditions or stimuli. As microarrays technologies have become more prevalent, the challenges of analyzing these data for getting better insight about biological processes have essentially increased. Due to availability of artificial intelligence based sophisticated computational techniques, such as artificial neural networks, fuzzy logic, genetic algorithms, and many other nature-inspired algorithms, it is possible to analyse microarray gene expression data in a better way. In this chapter, we present artificial intelligence based techniques for the analysis of microarray gene expression data. Further, challenges in the field and future work direction have also been suggested.

Solution of Some Differential Equation in Fuzzy Environment by Extension Principle Method and Its Application in Biomathematics

The concept of fuzzy differential equations is very important for new developments of model in various fields of science and engineering problems in uncertain environments because this theory represent a natural way to modeling dynamical system under uncertain environment. In this way we can modeled mathematical biology problem associated with differential equation in fuzzy environment and solved them. In this chapter we solve two mathematical biology models which are taken in fuzzy environment. A one species prey predator model is considered with fuzzy initial data. Whereas an insect population model are described with fuzzy initial value. The solution procedures of the fuzzy differential equation are taken as extension principle method.