Preface During the past two decades the Editor was working in various organisations and was involved in information management related activities as Computer Specialist at SAARC Agricultural Information Centre (SAIC), Dhaka and as Headed Agricultural Research Information System (ARIS) of CIFA, ICAR and Biotechnology Information System (BTIS) Project on Aquaculture of the Department of Biotechnology, Ministry of Science and Technology, Govt. of India. During the work period the author was instrumental in development of Informatics based infrastructure as well as creation of Knowledge Product, popularisation, application and dissemination of Information and Communication Technology (ICT) in research in the areas agriculture and allied field. In the process a number of publications were brought out on ICT application as IT has been identified as a key component of this type of knowledge sharing, facilitating and lowering the cost of the storage, access, retrieval. Further, the 'Vision 2020' document prepared in 2002 by the Planning Commission of India to guide the country's progress 'conceives of India evolving into an information society and knowledge economy built on the edifice of information and communication technology (ICT), of which telecommunications is the springboard'. Subsequently, Government of India created 'National Knowledge Commission (NKC)' with the aim of spreading the cutting edge technologies to boost the National Economy through use of knowledge. Implementations of NKC programme through various organisations have already been initiated. These developments have prompted the author to write on the most important burning issue of the time the 'Knowledge Management'. First of all it is to be learnt how to analyse and manage the data efficiently and usefully, transfer the data into the useful information and present the knowledge which is collected from the information. It is well known that knowledge is vital in facilitating the economic progress of a country. Knowledge management is the process of capturing, distributing, and effectively using knowledge. Knowledge Management (KM) is a discipline that promotes an integrated approach to identifying, capturing, evaluating, retrieving, and sharing all of an enterprise's information assets. These assets may include databases, documents, policies, procedures, and previously un-captured expertise and experience in individual workers. According to some thought knowledge can exist in two forms: implicit knowledge, that exists only in the human mind and explicit knowledge that is recorded, such as in a book or a digital format. In addition there are three areas forming the bases for most of KM. like (1) Lessons Learned. (2) Expertise Location (3) Communities of Practice (CoPs). All these are covered in thirty two chapters in the book entitled ‘Information and Knowledge Management: Tools, Techniques and Practices’ broadly divided into five sections i) Fundamentals of Knowledge Management ii) Latest Tools and Techniques for Knowledge Management iii) Databases, Data mining, Knowledge Discovery in Biotechnology and Bioinformatics iv) Data Mining and Knowledge Discovery in Agriculture and Allied Fields v) Indigenous Knowledge Protection and IPR Issues. Initiative of 'National Knowledge Commission' as well as by CSIR, DIPA(ICAR), and ICMR in India are presented briefly. KM practices and policies undertaken by FAO, UNDP, UN, World Bank for efficient management of various International organisations are recorded. It is expected that the book will be useful because with the emergence of "Social Computing" i.e. application of KM through software tools and technology are becoming the 21st Century KM. India has found its competitive advantage in the knowledge economy. Finally, the endeavour could not have been possible without the contributions of expert professional and compilation was made from a number of secondary sources. The authors express deep sense of gratitude to all of them.

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.

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.

“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.

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.

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 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.

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.

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.

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.

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).

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 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.

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.

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.

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.

Key words : CAVD, Drug Design, Bioinformatics, CADD, Co-factor, Vaccinology, infectious diseases, Jenner, immunity, Molecular modeling, Immunoinformatics, Antigen, Antibody, Live Vaccine, Alive Vaccine, Protein-peptide, Enzyme, Tools for Vaccine design,   Vaccine – Prevention is Better than Cure A vaccine is a biological preparation that improves immunity to a particular disease. A vaccine typically contains an agent that resembles a disease-causing microorganism, and is often made from weakened or killed forms of the microbe, its toxins or one of its surface proteins. The agent stimulates the body’s immune system to recognize the agent

1. Introduction Protein–protein interactions serve as the hub of the functioning of a living cell, coordinate biological processes as well as form core for interactomics system of living organisms. Various biological processes as signal transduction, cell-cell communication, ingestion or expulsion of any macromolecule, host parasite interaction, diseases as cancer and many other metabolic networks employ protein–protein interactions. During such a process two or more proteins interact transiently with each other or form complex for a long period of time. Thus attempts have been made for years to study comprehensive mechanism of such protein–protein interactions by various branches of sciences such as biochemistry, quantum chemistry, molecular dynamics, chemical biology and signal transduction. The availability of three-dimensional (3D) structure of proteins is a prerequisite for understanding functions of protein complexes at a molecular level as well as amino acid interactions within a complex.

Abstract Protein-protein interactions play a major role in most cellular processes. Many essential cellular processes such as signal transduction, transport, cellular motion and most regulatory mechanisms are mediated by protein-protein interactions. The study of protein-protein interactions is of great importance in determining the biological function and impact of proteins. The experimental techniques remain expensive, time-consuming and labor-intense. Computational methods play an important role at all the stages of determining protein-protein interactions. In this chapter, we describe the different types of protein-protein interactions, computational methods, currently available databases and tools, protein-protein docking and as well as protein-protein interactions as drug targets.

Abstract The field of bioinformatics emerged as a tool to facilitate biological discoveries more than 10 years ago. Structure prediction is fundamentally different from the inverse problem of protein design. Protein structure prediction is one of the most important goals pursued by bioinformatics and theoretical chemistry; it is highly important in medicine and biotechnology. Structure prediction is fundamentally different from the inverse problem of protein design. While the genome of many organism have been sequenced over the last few year, transforming such raw sequence data into knowledge remains a hard task. A great number of prediction program for genes have been developed hat try to address this problem, which consists of locating the genes along a genome.

The term bioinformatics was coined by Paulien Hogeweg in 1979 for the study of informatics processes in biotic systems. It was primarily used in genetic and genomic studies, particularly in those areas of genomics involving large-scale DNA sequencing. Bioinformatics involves application of information technology to store, organise and analyse the vast amount of biological data available in the form of sequences and structures of proteins (the building blocks of organisms) and nucleic acids (the information carrier). There are umpteen ways to define bioinformatics, mainly owing to its rapid evolutions.

Establishment of databases and data warehouse; knowledge discovery from database/data warehouse (KDD) and development of the mechanism of dissemination of knowledge on information networks as per requirements of user groups is gaining importance now a days. Since there is a large number of data collection agencies and equally diverse resources for which the information is collected, it is easy to visualize the heterogeneity of information. The problem is compounded by the fact that there are no common standards that are applied in data collection. Designing data warehouses to integrate the collected information poses a formidable challenge to any data warehouses architect. In order to use the information for planning and decision-making level, data have to be integrated and aggregated properly.

Abstract Crop cultivation is a complex process and involves a set of activities like land preparation, planting, pest control, irrigation, nutrient management, harvesting, marketing, etc. Entire cycle of crop production requires multitude of information by the farmers. Despite many efforts over the years to disseminate and transfer agriculture knowledge to the stakeholders, large amounts of expertise and knowledge are still out of reach to most of them. Agriculture knowledge may be contained in the corporate database, or it may reside undocumented inside the brain of the researchers or even stored in locations unknown to the majority of the people in the organization. Large sections of the farming community, particularly the rural folk, do not have access to the huge knowledge base acquired by agricultural universities, extension-centers and businesses. In this respect the main challenge is to find this knowledge and apply it to the decision making process involved in agriculture development.

1. Introduction An expert system is defined as “a computer program designed to model the problem solving ability of a human expert” (Durkin, 1994). It is also defined as “a system that uses human knowledge captured in a computer to solve problems that ordinarily require human expertise”. An intelligent computer program that uses knowledge and inference procedures to solve problems that was difficult enough to acquire significant human expertise for their solutions. It is a computer application that solves complicated problems that would require extensive human expertise. To do so, it simulates the human reasoning process by applying specific knowledge and interfaces. Expert system also uses human knowledge to solve problems that normally

Introduction ICT may be defined as technologies that facilitate communication and the processing and transmission of information by electronic means. ICT is an acronym that stands for Information Communications Technology and refers to technologies that facilitate communication and the processing of information by electronic means and includes everything from radio and television to telephones (fixed and mobile), computers and the internet. Information and communication has become an increasingly powerful tool for improving the delivery of basic services and enhancing local development opportunities. Recent years have seen increases in the use of ICT in almost all spheres of rural life. Especially, the rural communities require information among others on supply of inputs, innovations, improved technologies, credit, market prices and competitors. Since the concepts, methods and applications involved in ICT are constantly evolving on an almost daily basis. It’s difficult to keep up with the changes - they happen so fast.

Evolution of Computer technology and advent of database concept have provided enormous opportunities to the  scientist, researchers and managers of diverse field  to create, manipulate integrate, link and analyse their data in order to generate information and knowledge. The research projects involving exploration, survey and collection of data, generally create database to maintain and analyze the data.  Almost in every sphere of life and industry this database concept/technology is used in one form or other.  Scientists and researchers of life sciences realized the importance of the database and used this technology to develop databases of different types/groups of living organisms. The database technology has eased the difficulty in designing, developing and maintaining the complex information systems.

Introduction “Quality” of fish refers to the aesthetic appearance and freshness or degree of spoilage which the fish has undergone or involves safety aspects such as being free from harmful bacteria, parasites or chemicals. “Quality’’ is a term which must be defined in association with an individual product type. For example, it is often thought that the best quality fish is the one which is consumed within the first few hours of post mortem. However, very fresh fish which are in rigor mortis will be of hard texture and difficult to fillet and skin are often unsuitable for smoking or drying. Thus, for the processor, a slightly older fish which have passed through the rigor process are more desirable.

Introduction Northeastern Region of India consists of eight States, viz., Arunachal Pradesh, Assam, Manipur, Meghalaya, Mizoram, Nagaland, Tripura and Sikkim.  The northeastern states are located between geographical ordinates, 21° to 29°30' N and 89°46' to 97°30' E. The region, surrounded almost on all sides by Bhutan, China, Myanmar and Bangladesh, which can be broadly divided into two characteristic zones viz., Mountainous and Plain. Although distinguished in the altitude differences, both the mountain and plain are closely interrelated in terms of natural resource sharing and economic flow. According to the physiographic characters this region is divided into five physiographic units, the Assam Himalayas, the Brahmaputra valley, the Shillong plateau, the Barak valley and the Southeastern hill region. Except the Brahmaputra and Barak valleys in Assam and the Imphal valley in Manipur, the terrain is hilly and mountainous, only making 65% of the northeast an undulating land with elevations ranging from 200 to 900 m above MSL

1. Introduction Antheraea assamensis is lepidopteron insects of Assam which is commercially utilized for the heritage golden silk “muga”. Rearing of muga silkworm is one of the most significant traditional handloom industries restricted only to the North Eastern Region of India (N.E. India) and plays a major role in the rural economy.  Considering the ecological conditions, food plant distribution, presence of eco-types and species of diverse nature in co-existence, it is speculated that this region is a possible home of origin of Antharaea, from where radiations would have occurred (Neog et. al., 2010). Despite the fact that N.E. India is the original home of muga, earlier studies have shown that the population is declining due to depletion in genetic variability (www.cdfd.org.in/wildsilkbase/info_moths.php).

What is Indigenous Knowledge? Indigenous knowledge (IK) is the local knowledge which is considered as the social capital of the poor. It is their main asset to invest in the struggle for survival, to produce food, to provide for shelter and to achieve control of their own lives. Indigenous knowledge is a cumulative body of knowledge, practices and manifestations maintained and developed by people with long histories of interaction with their natural environment. It forms the basis for local-level decision-making in agriculture, health, natural resource management and other activities, especially for the poor, and provides problem-solving strategies for communities (World Bank, 1998). IK is a tacit knowledge that is not easily codifiable and unique to a particular culture and society.

Introduction Indigenous peoples established distinct systems of knowledge, innovation and customary practices from their centuries old interactions with the local biodiversity and environment, relating to the uses and management of local natural resources. Today, this knowledge is looked as an important resource for research and development in different areas such as pharmaceuticals, agriculture, veterinary, jewelry, fashion and cosmetic products. This has led to rampant exploitation of such knowledge disregarding the customary practices and rules of indigenous peoples holding and preserving such knowledge as well as the biodiversity and natural resources. As a consequence, the traditional knowledge is under threat of being misappropriated or lost.

Introduction   Intellectual property is a cluster of legally recognized rights associated with innovation and creativity, the works of the mind, as against physical products, land and other tangible resources. Even though it is intangible, intellectual property (IP) is often recognized as personal property, to be sold and traded like other forms of property. Under intellectual property law, owners are granted certain exclusive rights to various markets, machines, musical, literary, and artistic works; discoveries and inventions; and applications. But national laws normally don’t define ‘intellectual property’ as a single property right. Instead, ‘intellectual property’ is used as a convenient general term describing a range of distinct legal rights, relating to literary, artistic and scientific works; performances of performing artists; phonograms, and broadcasts; inventions in all fields of human endeavour; scientific discoveries; industrial designs; trademarks, service marks, and commercial names and designations; protection against unfair competition and all other rights resulting from intellectual activity in the industrial, scientific, literary or artistic fields.

No society can ever desire to achieve a long-term goal of sustainable development unless it builds upon its own knowledge, traditions, ethical foundations and technological endowments. The knowledge of the people particularly in drought and flood prone regions, hill areas, tribal areas and other high risk environments, has to become a building block of new technological improvements. Knowledge happens when our expectations are belied, modified and sometimes confirmed. Because the asymmetry in our expectations is influenced by our access to resources, institutions, technology and cultural platforms, we get different kinds of knowledge, even when we confront similar situations Gupta (2005). In any community living close to nature, asymmetry in knowledge generation and its utilization is inevitable. The experts exist not just in our profession, but in every profession.