eChapters

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.

ABSTRACT

With the rapid innovation in Information and Communication Technologies (ICTs), the libraries and information centres have gone through tremendous changes or facing transformation. In this digital era, libraries are facing heavy technological pressure to serve their users on their satisfaction level. Library and information professionals are also struggling more to implement these cutting-edge technologies for learning, research, and information for their users. Therefore, the library and information centres have now been trying to adopt and implementing various emerging or cutting-edge technologies in housekeeping activities as well as in its services. In this paper, an effort has been made to evaluate some of the cutting-edge technologies in libraries’ point of view. An overview is also made to evaluate the concept, advantages and disadvantages of cutting-edge technologies.

Keywords: ICT, Cutting-Edge technology, Emerging technology, Library services, Impact.

ABSTRACT

Recent advances in Information and Communication Technologies (ICTs) have changed the way we live, learn and interact.  They have transformed the way knowledge is produced, processed, retrieved and transferred between different stakeholders. The innovative ICT applications are dominating and driving the development scholarship and thinking in agriculture sector. The increasing availability and use of the Information and Communication Technologies offers the Extension System and all its stakeholders/partners an undeniable opportunity to redefine the way it provides information and interacts with farmers. ICTs are not just a new tool for information dissemination; it is empowering and changing the very culture of society’s information-seeking behavior. And Extension must change, too.

Cytauxzoonosis is an emerging tick-borne disease of domestic cats and wild felids caused by the protozoa of the genus Cytauxzoon. The common clinical findings in affected cats are similar to other hemoparasitic infections namely anorexia, depression, anemia, vomiting, icterus, and high fever. The first reporting of feline cytauxzoonosis with four fatal cases was done by Wagner in Missouri, USA in the year 1976.

Bobcats (Lynx rufus) are recognized as the natural wild reservoir of Cytauxzoon felis with prevalence reaching up to 100% in some enzootic areas (Zieman et al., 2017). This infection is highly fatal in domestic cats with 97% mortality without treatment (Meinkoth and Kocan, 2005). However, subclinical infection in some cats has also been evidenced i.e., they were infected with C? felis but did not display the acute clinical signs of the disease before diagnosis (Brown et al., 2010 and Rizzi et al., 2015).

Several other wild felids apart from bobcats and domestic cats have been found to be affected by fatal or nonfatal Cytauxzoon sp. infection. These include lions, jaguar, cougars, pumas, ocelots, Iberian lynx, meerkats, European wild cats and Asiatic wild cat, panthers, and tigers (Jakob and Wesemeier, 1996; Rotstein et al., 1999; Yabsley et al., 2006; Andre et al., 2009; Filoni et al., 2012 and Furtado et al., 2017).

Dashboards play a crucial role in the fields of Machine Learning and Artificial Intelligence by providing a visual and interactive interface through which complex data, model outputs, and performance metrics can be easily understood and monitored. In data-driven environments, where vast amounts of information are processed continuously, dashboards serve as an essential bridge between technical systems and human decision-making. They transform raw data and analytical results into meaningful visual representations, enabling users to interpret insights quickly and effectively. In the context of Machine Learning and Artificial Intelligence, dashboards are used at multiple stages of the workflow. During data exploration and preprocessing, dashboards help visualize trends, patterns, and anomalies in datasets, allowing data scientists to make informed decisions about feature selection and data cleaning. Once models are developed, dashboards

Some basic and most common analysis tools used in research are summary statistics. These can be analyzed in Ms-Excel through the following ways.

8.1 Summary Statistics

The following data are the yield (q/ha) of 60 varieties of paddy maintained in a research station for breeding trails:

53 ,74, 89, 65, 88, 65, 45, 70, 25, 80, 75, 85, 47, 63, 78, 44, 42, 51, 59, 86, 76, 48, 30, 38, 67, 78, 74, 68, 58, 59, 36, 76, 89, 48, 67, 69, 35, 46, 89, 75, 79, 56, 42, 41, 60, 65, 68, 69, 61, 63, 95, 94, 85, 83, 76, 71, 68, 62, 64, 62.

Calculate the summary statistics for the study of the data.

Solution: The summary statistics of the data using MS – excel can be obtained / analyzed through following steps:

Step 1: Open Excel sheet and feed the data as given in Fig. 8.1.

Step 2: Go to data menu and select Data Analysis. Sometimes this menu is not available in the data, it can be added through add in → check in, analysis tool pack → go. It will appear as the way showing in Fig. 8.1.

Data cleaning is one of the most important parts of machine learning. It plays an important role in building a machine learning model. Data quality is a significant aspect to train the ML model. Inaccurate data can have an impact on results. Data quality problems can occur anywhere in information systems. A technique that helps to convert improper data into meaningful data. Machine Learning is datadriven. With the data cleaning techniques, your Machine Learning model will perform better. So, it is important to process data before use. Without quality data, it is unwise to expect a correct output. Data cleaning refers to identifying and correcting errors in the dataset that may negatively impact a predictive model. It is used to refer to all kinds of tasks and activities to detect and repair errors in the data. These problems can be solved by using various data cleaning techniques. The process of fixing or removing incorrect, corrupted, incorrectly formatted, duplicate, or incomplete data within a dataset. When combining multiple data

Reliable artificial intelligence depends on accurate, timely and multidimensional livestock data. This chapter examines the transition from manual record-keeping to sensor-based systems that capture physiological, behavioural, production and environmental information. Data sources include wearable devices, IoT monitoring networks, computer-vision systems and environmental sensors. Their applications include monitoring health, welfare, movement, feeding and productivity. The chapter also considers the integration of heterogeneous datasets and persistent challenges involving missing values, noise, interoperability, scalability and system reliability. By connecting data generation with intelligent analysis, it emphasizes that robust acquisition protocols and dependable infrastructure are essential for developing effective, evidence-based and responsive livestock-management systems.

Now we have to learn how to enter numbers and text, how to edit any erroneous entry and then how to do simple calculations. Open empty excel spread sheet by clicking on the start menu in the window tool bars, click on Programs and then click on the Excel menu item.

6.1 Entering Numbers

Numeric entries are constant, can be integers, decimal fractions, integer fractions and scientific nations which can be entered by typing the number in the active cell [cell which can be made active by clicking the mouse on the cell.

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.

Livestock farms generate large quantities of physiological, environmental, behavioural and production data through sensors, IoT devices and management records. Raw information must be processed before it can support dependable analysis. This chapter explains data cleaning, missing-value treatment, noise reduction, anomaly detection, integration, normalization, transformation and dimensionality reduction. It also discusses feature extraction, categorical encoding, variable transformation and temporal-feature analysis. Machine learning, AutoML, Python, R and other analytical platforms are introduced as tools for preparing livestock datasets. Applications include disease prediction, yield improvement and movement monitoring. The chapter concludes by examining scalability, data quality, privacy and ethical challenges in intelligent livestock analytics.

Healthcare artificial intelligence depends on the quality, fairness and reliability of the data used for model development. This chapter examines the characteristics of healthcare data and explains how incomplete, inconsistent or unrepresentative datasets can produce inaccurate and discriminatory clinical outcomes. It identifies sampling, demographic, measurement, historical, institutional and annotation biases and discusses strategies for mitigating them at the data, algorithm and evaluation stages. Model robustness across different populations and clinical environments is also considered. Particular attention is given to explainable AI, human oversight, continuous auditing and interdisciplinary collaboration as essential components of trustworthy, equitable and clinically dependable healthcare systems.

2.1. INTRODUCTION

Many biological variables do not meet the assumptions of parametric statistical tests as they are not normally distributed, the variances are not homogeneous, or both. Objects in nature commonly exhibit one of three distributions:

Financial data visualization is the process of turning financial information into visual formats like charts, graphs, and dashboards. It helps finance teams interpret large datasets quickly, spot patterns or anomalies, and make betterinformed business decisions. Data visualization in finance is a craft of transforming complex financial numbers into accessible visual forms. Visual formats such as charts, graphs, dashboards, maps, and interactive displays serve as lenses through which data becomes visible and understandable. Finance practitioners navigate risk profiles, asset performance, expense trends, cash flows, and portfolio behavior more effectively when raw inputs become visual stories. Creation of visual representations hinges on careful selection of chart types, scaling, color, and annotation. Communicating precise meanings through visuals demands an understanding of data relationships, temporal changes, distributions, and comparisons.

15.1 Data Visualization

Data visualization is the graphical representation of information and data. By using different visual elements like charts, graphs and maps. Data visualization tool provides easy way to see the understand the data and related trend/pattern.

1. There are multiple tools available for data visualization using which we can perform data visualization.

2. In python we can use a library called “matplot” for data visualization.

3. Python based plotting library “matplotlib” enables us to produce 2D as well as 3D graphics i.e. charts/graphs.

4. Pyplot interface of matplotlib is used for representing 2D graphs.

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.

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.

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

Biological databases that are composed of a large collection of digital nucleic acid sequences, protein sequences, or other polymer sequences stored on a computer, which is made available to the research community worldwide using web servers. The vast amount of information generated in molecular biology is analyzed, curated, tabulated, and arranged in databases. There are different types of biological databases, based on their contents. Besides, the database management system, a program to manage the databases, can be of two types: distributed DBMS and relational DBMS. This chapter briefly narrates the importance and purpose of biological databases.

Deep learning has significantly advanced automated plant phenotyping and disease detection by enabling computers to identify complex patterns in large volumes of plant imagery and sensor data. This chapter examines the fundamentals and applications of deep learning architectures in plant science. It discusses convolutional neural networks for image-based plant analysis and advanced architectures including recurrent neural networks, transformers and hybrid models. Data acquisition through imaging and sensor technologies is also considered. The chapter highlights how deep learning can support automated identification of plant characteristics, disease symptoms and crop health conditions. Challenges related to datasets, environmental variability, computational requirements and model generalization are examined alongside emerging research opportunities.