eChapters

I. INTRODUCTION

The Websites are soaked in information, much of it with varying degrees of copyright protection. In fact, the reality is that almost everything on the Net is protected by copyright law. E-business Websites are a composition of materials, often consisting of words, graphics, audio, and video that are expressed to the consumer as information content. The owners and Website developers carefully select the content to sell the company’s product or service. The subject matter expressed in the site is an electronic publication of this content. Since, designing, producing, and maintaining a sophisticated Website is very expensive, protecting content ownership is extremely important. Electronic commerce will continue to be highly competitive. As Websites become more and more interactive with consumers, their creation, design, and maintenance place enormous demands on innovative marketing techniques that should be legally protected.

Never before has it been so easy to violate a copyright owner’s exclusive right to copy the material. Everyone with a computer and an Internet connection creates his own Web pages and thus become a publisher. Hence the rules that once applied to only a few companies bind million of people now. Imagine having paid a Website developer hundreds of thousands of rupees to launch a killer application and acquiring millions of Rupees from a venture capitalist firm, only to discover that another e—business is using your online material. Or consider a scenario where your Website is linked to an e-business that displayed copyrighted material without the owner’s consent. Could your company be liable under a theory of copyright infringement? This paper highlights the scenario when contents of your Website are exploited by others without your permission or knowledge. The discussion is centered on copyright issues involved in the practices of linking, inlining and framing technologies which are normally being used on the Internet.

Cotton is often called as “white gold”. True to its name cotton supports the largest agro based industries in India and cotton industry ranks first in the agro based industries and engages 4 to 5 million people in cotton growing. The industrial sector employs 1.0 million people. At present there are 3660 ginning and 1570 textile mills in India. India earns foreign exchange to a tune of Rs. 2500 million through exports of cotton textiles and products. Cotton is cultivated for ‘lint’ which is used for preparing threads, yarns and medical cotton mixed with synthetic fibre and for various other purposes.

2.1 Introduction
As time passes, the customers are used to the practice of having short life for the products, which they buy and use. This is because of the globalization of business and growing global competition all over the world. The companies engaged in manufacturing and selling the products which are used at large volume by households and partly by industries, introduce new models to attract customers and to have more market share. This in turn has impacts on customer buying and retention decisions of those products. Though the companies introduce new models, they are expected to keep the prices of the products at affordable ranges so that there is always a larger customer segment to buy those products. This is like egg or chicken situation, because companies introduce many new models to out beat competition and in turn customers expect improved versions of those models in shorter time intervals. This forces companies to continuously engage themselves to come out with new models or improved models which have convergence of technologies to give maximal customer satisfaction. So, creativity in product design becomes an essential requirement of the product development teams of the companies.
2.2 Product Design
Product design aims to include the following tasks to solve the problems of the users or address expected needs of customers in a given market. These tasks are human intensive.
i. Understanding end-users’ requirements
ii. Imagining alternate product ideas to address their needs

This chapter provides an in-depth exploration of advanced techniques and conceptual frameworks—here termed “Weather Intelligence”—and their potential applications in precision agriculture. Although the term is not yet standardized, it serves as an exploratory framework for integrating advanced forecasting, diverse datasets, and data analytics to capture both natural and anthropogenic influences on weather patterns. The discussion spans crop and livestock production as well as allied sectors such as fisheries, aquaculture, plantations, horticulture, and specialized areas like flower farming. By integrating real-time data from sensors, remote sensing platforms, and historical records, these exploratory systems aim to empower farmers and food producers to optimize irrigation, planting, harvesting, and overall resource management. Agriculture has always depended on weather, yet the rapid pace of climate change and the need for sustainable practices have driven a technological revolution in farm management. In today’s digital age, advanced forecasting techniques and data analytics are converging into a new conceptual framework we call “Weather Intelligence.” Although this term is exploratory and not yet widely adopted, it encapsulates the vision of integrating high-resolution satellite imagery, IoT devices, and machine learning algorithms to forecast local microclimatic conditions with unprecedented accuracy. These forecasts enable proactive adjustments to critical operations—such as irrigation scheduling, planting, and harvesting, thereby mitigating weather-induced risks and enhancing productivity. Moreover, it is increasingly evident that weather patterns are not only shaped by natural phenomena but also by human-induced risks such as wars, conflicts, and environmental abuse. Factors like landfills, water and sea pollution, toxic dumping, and unsustainable industrial practices are altering microclimates around the world. This complex interplay of natural and anthropogenic forces calls for the incorporation of a broader array of variables and larger datasets into forecasting systems. In this context, embracing Weather Intelligence as a dedicated field of study and application in agricultural and allied sciences becomes not only desirable but essential. This chapter aims to detail the evolution and current state of these integrated approaches, discuss their potential applications across various agricultural sectors, and outline strategies for precision decision making. While much of the content is exploratory in nature, it provides a conceptual framework for future research

Abstract

This paper portray the generation of spectral signature library for semi arid area crops using hyperspectral data and the scope of the application of the generated digital library for the classification of the satellite imagery of similar regions.

Hypersprctral data have narrow bands which help to provide additional information related to observed features. In case of this agriculture study crop related information are gathered using hyperspectral sensors The variation in the observations are due to physic-chemical characteristics such as biochemical composition, physical structure, water content and plant ecophysical status. Using the hand held spectroradiometer the reflectance is captured for the bajra, moong, moth crops from the Rajasthan, India’s semi arid region. The observations have been taken for different dates considering the phenology of the crops. Hyperion data from EO-1 satellite have been used to implement the classification operation based on the spectral signature library of bajra. It is observed that the image with the crops can be classified according to the phenology difference.

ABSTRACT

The concept and nature of intellectual property have undergone a paradigm shift during the last few decades. The importance of effective IPR governance has attracted the attention of its stakeholders. Hence, this paper focuses on the role of IPR governance and management practices for improving the quality of relationships among stakeholders besides documenting current and future trends of IPR governance and management.

INTELLECTUAL PROPERTY RIGHTS – AN INTRODUCTION

Intellectual property rights refer to the privileges granted by the government to any individual or organization for creative prowess. In recent years, such rights have gained immense importance. The laws in existence for protection and enforcement of these rights are meant to foster innovation and for regulating the use of inventions.

Introduction

Resources are used unscientifically commonly. For example applying feed somewhere in the pond results in loss, but by using technology, we can know when to apply where to apply, and how to apply which leads to efficient management of resources. Monitoring should be continuous which is not possible and very difficult for humans, but with technology, it could be monitored and the status could be known even from home which is communicated through mobile, which is sent by sensors installed. The data are also easily maintained. When technology is used, resources are efficiently utilized, since everything has a demand and that can be satisfied. So resources can easily be maintained which reduces wastage. Field management is a very skilled activity and it needs years of experience and onsite exposure which needs skilled labour, in such cases technologies aids with better methodologies. When the resources are applied at the right time, at the right amount, the growth becomes normal which increases the produce quality. After a product is produced, it has to be sent to a series of channels such as transport, market etc. and a technology should backup and monitor everything. The produce after being produced should be sold and many apps can be downloaded to improve the marketing. The whole world is changing within a period of time. So one should stay updated to impress the livelihood and the wide knowledge on technology helps to lead a better life with easy management practices. Thus data driven technology interventions are needed for efficient management of resources, monitoring of things anytime & anywhere, reduction of wastage of resources, reduction of dependence on trained and skilled manpower, enhanced product quality, better integration, better marketability, to stay updated with latest knowledge and access to other knowledge source for better upgrades.

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

Agriculture has long been at the mercy of the weather, and in today’s datadriven world, harnessing meteorological information is more critical than ever. Weather intelligence—the process of collecting, analyzing, and disseminating weather data—supports decision-making in diverse fields such as aviation, transportation, emergency management, and especially agriculture (Fig 3.1). This chapter explores how advanced data acquisition methods and robust database management techniques underpin agricultural weather intelligence. It covers the variety of data sources available, details strategies for integrating and ensuring the quality of vast datasets, and examines the forecasting techniques that transform raw data into actionable insights for precision forecasting and decision making in food production. Fig. 3.1. Weather Data Acquisition System In this chapter, we not only discuss the technological means by which weather data is collected—from satellites, ground sensors, and IoT networks—but also dive into the structure of weather databases. These repositories house surface weather observations, radar and satellite imagery, upper air measurements, and outputs from weather models. Further, we outline the different forecast types and forecasting methodologies that are essential to interpreting this data, ranging from nowcasting for immediate weather conditions to seasonal forecasting that supports long-term planning. Finally, the role of big data analytics in supporting precision forecasting and optimizing decision-making in agricultural production is examined, highlighting the integration of advanced techniques like numerical

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.

Introduction

The term analytics and data analytics are used inter-changeably. When referring to analytics, it means “analysis of data” or “what can be learnt from an analysis of data”. In an industry like fishing industry, the researchers have been slow to adopt the tools used to portray accurate representations of data. For example, a dashboard with historical data from the past ten or fifteen years displayed on a digital map can enable the researchers to decide the places where fishing can be good. This map can help in locating the points fished during specific seasons. This will enable the researchers to filter the data on various parameters like area, year, season etc. or on any other specific parameter which can give valuable information. Similarly, this type of dashboard can affect the decision making process while fishing, like one can find the monetary value associated with each trip during a specific season or compare the values associated with two different trips to gauge the cost-benefits.

Data Analytics helps in taking most informed decisions in the fishing industry if one of the goals is to increase income while ensuring sustainability. It helps in deciding whether to reduce overhead costs or to increase expenditure to avoid overhead costs in future.

Fisheries sector undergoes continuous changes with introduction of newer technologies evolved through research and development institutions. Statistics per se deals with generation of data, data management, data analysis and information generation from data. There are varied disciplines of fisheries viz., Aquaculture, Fisheries Resource Management, Fish Genetics, Fish Biotechnology, Aquatic Health, Nutrition, Environment, Fish Physiology, and Post-harvest Technology, wherein research on practical aspects is undertaken which affect production and sustainability.

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.

6.1 Introduction
Our journey through remote sensing has, thus far, focused on the remarkable capabilities of various platforms and sensors to observe Earth’s atmosphere and surface. From the localized, high-resolution insights provided by groundbased instruments like Wind Profilers and SODARs, to the expansive, global coverage offered by an array of satellite-borne imagers, sounders, radars, and lidars, we’ve seen how a continuous stream of invaluable meteorological data is acquired.
However, the mere act of collecting this data, no matter how sophisticated the technology or comprehensive the observations, represents only the initial phase of its lifecycle. The true power and utility of remote sensing data are unlocked only when it can be efficiently organized, securely preserved, and readily available to the diverse community of meteorologists, climate scientists, researchers, and operational forecasters who rely on it daily. This necessitates a profound understanding of the principles and practices governing data formats, data storage, and data access.
The sheer volume, velocity, and variety of remote sensing data pose significant challenges. Petabytes of information are generated annually, requiring robust infrastructure and standardized methodologies to ensure its integrity, interoperability, and long-term usability. Without effective strategies for managing this deluge of information, the potential of remote sensing to enhance weather prediction, advance climate science, and inform critical decision-making would remain largely untapped. 

Abstract

Droughts are normal recurring climatic phenomena that vary in space, time, and intensity. The spatial and temporal variability and multiple impacts of droughts provide challenges for mapping and monitoring on regional scales. Large historical datasets are required in drought studies to understand the complex inter-relationship between the climatic and meteorological data. Extraction of valuable information from such large data archives demands an automated and efficient way. Data mining is answer to above problem as it has the potential to search for hidden pattern and identify the relationship between the datasets. For the current research, rainfall data from 1951-2006 was used to compute Standardized Precipitation Index (SPI) and NOAA-AVHRR NDVI images for the period 1981-2006 was used to calculate Vegetation Condition Index (VCI). SPI and VCI were used as input parameters in the Apriori Algorithm for generating the association rules. Interesting rules were developed identifying the relationship between user-specified target episodes and meteorological events. Therefore, spatial and temporal pattern of drought was achieved with 88% confidence.

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.

Introduction

The first step of geostatistical analysis in R is the creation of dataset which is acceptable in R environment by using the data collected from field survey or laboratory experiments. Different type of data is collected during experiments e.g. sand content, silt content, organic carbon content, geographical location of data points, soil type, land use type etc. Here, we will discuss how these data are converted into a acceptable dataset format for R environment and how we manage these data. In R this task is performed in two steps. First, a data structure is created to hold the data and second is the entering or exporting the data into data structure.