eChapters

Introduction

Data Mining is a complex subject that describes a type of database application used to search large amounts of data for patterns using specific tools. Advanced statistical methods and tools such as classification, association rule mining and cluster analysis search large volumes of data for previously unknown relationships within the data. Data Mining can be used to decipher trends in a wide variety of fields such as computer science, the marketing and advertising of goods, services or products, artificial intelligence research, biological sciences and high-level government intelligence. Data mining is a logical process that is used to search through large amounts of information in order to find important data.

Databases

A database is a collection of information that is organized so that it can easily be accessed, managed, and updated. Databases can be classified according to types of content: bibliographic, full-text, numeric and images and by their application area, for example: accounting, music compositions, movies, banking, manufacturing, or insurance. The term database may be narrowed to specify particular aspects of organized collection of data and may refer to the logical database, to the physical database as data content in computer data storage or to many other database sub-definitions.

Abstract

Last few decades have witnessed an immense technological advancement in the education sector in India. With an advancement of technology, new gadgets are flooding the Indian market continuously bringing an immense opportunity for the education sector to improve upon the course delivery system. Higher education sector in India is adopting such new technologies in course delivery at a slow pace; however, there remains still a far way to traverse for the Indian education sector in order to achieve a perceptible and effective technology-infusion in the academic delivery arena. This paper is devoted to present an expository review on the usefulness of new gadgets, like smart phones, e-book reader as well as tablet PC, in both class-room education-delivery segments as well as in distance education sector in India.

Abstract

Fusion is a well known entity in various fields (e.g. medical imaging, weapon detection, flood monitoring etc.) that surmises information originating from several modalities. This chapter is an attempt to assort the various image fusion techniques available for human face recognition and to put a review of the advancements being made by the different researchers. The various types of fusion those have been used or may be used for face recognition have also been discussed here.

1. Introduction

With the fairly recent advent of inexpensive, rapid sequencing technologies that continue to improve sequencing efficiency and accuracy, many species of animals, plants, and microbes have annotated genomic information publicly available. It is important to use this genomic information for the identification and isolation of novel and superior alleles of important genes from gene pools of the organisms

Abstract

The study of computational toxicity prediction is very crucial and necessary, as it is quite time consuming and expensive to measure the toxicity via experimental techniques (Quan Liao et al., 2006). The use of computational approaches particularly to reduce the time and cost for the evaluation of the kinetic and toxic behaviour of any drug has been increased tremendously in recent time period (Richard Judson et al., 2004). Machine learning is a powerful tool to discover new complex combinatorial relationships in various in vitro and in vivo data information (Richard Judson et al., 2004) and has been successfully used in many diverse fields with numerous applications such as pharmacodynamics properties prediction (Czerminski et al., 2001; Livingstone et al., 2003), protein function prediction (Cai et al., 2003), medical decision making (Veropoulos, 2001), spam categorization (Drucker et al., 1999), detection of oil spills (Kubat et al., 1998), and speech recognition (Nuttakorn et al., 2001).

Introduction

The capabilities and effort to collect the geographic data has increased dramatically in the last decade to cope up with the increasing demand for the support of decision making and planning process in the state, national and global level.  From the Government sector, there is a need for spatial data to make quick responses to natural disasters, industrial accidents and environmental crises as actions will be decided based on geography. There is constant pressure to make wise decisions in a more time and cost effective and efficient manner. Accurate and current geospatial data are critical to these decisions.

1. Machine Learning

Learning denotes changes in a system that enable a system to do the same task more efficiently the next time (Simon, 1983)or Learning is constructing or modifying representations of what is being experienced (Ryszard Michalski et al., 1986). Machine learning is a scientific discipline concerned with the design and development of algorithms that allow computers to evolve behaviors based on empirical data, such as sensor data or databases. A learner can take advantage of examples (data) to capture characteristics of interest of their unknown underlying probability distribution. Data can be seen as examples that illustrate relations between observed variables. Discover new things or structure that is unknown to humanseg. data mining.A major focus of machine learning research is to automatically learn to recognize complex patterns and make intelligent decisions based on data.

Abstract
    
The amount of data being collected across a wide variety of fields today far exceeds our ability to reduce and analyze without the use of automated analysis techniques. In addition, the whole data may not be of user interest and imperfect. Therefore, it is very challenging to organize this data in formal system, which provides outputs in more relevant, useful, and structured manner. There are many techniques available to retrieve knowledge from this voluminous data. This paper discusses different rough computing based information retrieval techniques to obtain meaningful knowledge from large amount of datasets. We also discuss a real life example to show the viability of proposed research.

Abstract

Currently, computers are changing from single, isolated devices into entry points to a worldwide network of information exchange and business transactions called the World Wide Web (WWW).Current web contains billions of documents and has many administrative problems and limitations; in addition to that the web content is still accessible only to humans. For this reason, support in data, information, and knowledge exchange has become a key issue in current computer technology. The success of the WWW has made it increasingly difficult to find, access, present, and maintain the information required by a wide variety of users. In response to this problem, many new research initiatives and commercial enterprises have been set up to enrich available information with machine processable semantics.

Introduction

The role of knowledge management as the key source for competitive advantage in our organizations has become a hotly debated topic. Indeed, it is a core competency for creating competitive advantage. In this environment that knowledge management is vital, therefore, at first we should determine knowledge management situation, and then to achieve a desired point, formulate a knowledge management strategy.

Abstract

Cloud computing is becoming a technology mature enough for its use in genome research experiments. The use of large datasets, its highly demanding algorithms and the need for sudden computational resources, make large-scale sequencing experiments an attractive test-case for cloud computing. So far I have seen cloud computing demonstrated using R. However, it remains to be seen a rigorous comparison of its performance using a BLAST search and its ability to cope with ever-increasing databases and open source frameworks such as bioperl or bioconductor.

“Knowledge Management is the process by which an organization identifies, creates, manages and delivers information to enhance workforce performance. The purpose of knowledge management is to provide information collected from employees, best practices, clients, competitors and other sources in a format that employees can use to the productivity and quality of their work”

Before attempting to address the question of knowledge management, it’s probably appropriate to develop some perspective regarding this stuff called knowledge. Consider this observation made by Neil Fleming [1996] as a basis for thought relating to the following points.

Now days, in every walk of our life, we deal with a lot of information. It can be for personal use or family matters or for official or business matters. We store, process and retrieve variety of information. Information is basically the collection of various facts, records, statistics, formula or methods, which we use for our official work, for our research or for other use. When all these (information, we keep/ accumulate, for possible future use) are put in an organized manner, we find it convenient to use and easy to remember/ apply. Today, as it is rightly said, information is power; information is our knowledge-strength. It is thus important to organize and manage this knowledge that mostly depends on (and derived from) an information pool.

Introduction

Individual bits or “bytes” of “raw” biological data (e.g. the number of individual plants of a given species at a given location) do not by themselves inform the human mind. However, drawing various data together within an appropriate context yields information that may be useful (e.g. the distribution and abundance of the plant species at various points in space and time). In turn, this information helps foster the quality of knowing (e.g. whether the plant species is increasing or decreasing in distribution and abundance over space and time). Knowledge and experience blend to become wisdom—the power of applying these attributes critically or practically to make decisions. This quotation is from the Atlantic Canada Conservation Data Centre, a non-profit organization established in 1999.Thus, we are led to think about Data, Information, Knowledge, and Wisdom as we explore the capabilities and limitations of IT systems.

Many techniques are used in immunology to detect specific proteins (antigens) or antibodies in biological samples. These techniques are called immunotechniques. These methods are used for research and diagnostic purpose in many fields such as microbiology, biochemistry, endocrinology, hematology and in pathological laboratories. The techniques can be used to estimate proteins, drugs, hormones and toxic substances in a sample. Immunological techniques are highly specific, precise and reliable. These methods are simple and safe. Antigen and antibody reaction is highly specific in nature and this forms the base of the immunological methods. There are many immunotechniques which are used for the qualitative and quantitative estimation of antigens and antibodies in a sample. Some of commonly used techniques in immunology are:

One of the characteristics of the immune system is to recognize and discriminate self from the non-self antigens.  But, sometimes, there is a failure to discriminate between self and non-self antigens in the body and an autoimmune response occurs. Body starts producing antibodies against its own cells/antigens. These antibodies are called autoantibodies and this state is called autoimmunity.

The well-being of a person depends on his immune system. Any deficiency in any component of immune system may lead to defects in the normal functioning of his body and makes him more susceptible to diseases. The immunodeficiency may be either in specific components (B cells or T cells) or nonspecific component (phagocytes and complement) of the immune system.

A tumor is said to be an abnormal growth of cells to form a lump or mass of transformed cells. This growth occurs due to loss of regulatory control of cell division. The majority of cells and organs have a balance between cell renewal and cell death. A cell has a particular life span;when it dies a new cell is generated. In normal condition, this system is regulated through cell cycle and cell division but at the time of any abnormality this growth mechanism is totally disturbed and failed. These cells give rise to clone of the cells and eventually produce tumor.

Transplantation refers to the implantation of a tissue or organ from one site  to another site in an individual or from one individual to another.It involves implanting of organs or grafting of tissues to repair defects and to stimulate regeneration. Auto-transplantation or autoplasty of the skin, cartilage, bone, muscle, tendon, nerve and fatty tissue is used in plastic and cosmetic surgery. The replacement of a diseased organ by a transplant of a healthy tissue is called transplantation.