eChapters

Vector data analysis is performed to convert raw input vector data to output that provides new and meaningful information which ultimately supports decision making and disclose hidden patterns. There are various techniques to analyse vector data like buffering, intersect, union, and so on. Some of the most commonly used techniques with their R-code examples are explained in this chapter.

First of all, you need to import all the packages which are required for performing this exercise. These packages include rgdal and sp for importing and re-projecting vector data as well as raster and rgeos for performing vector analysis operations.

Q.1 In random effect model, the treatments are taken as a random sample of treatments drawn from a large group of T treatments.

Q.2 In varietal trials for plant breeding or genetics, the appropriate statistical model is not the random effect model.

The calculation of raster data statistics is very essential before performing some geoprocessing operations. Raster data statistics involve knowing the distribution of pixel values in a band, comparing the pixel values of two bands, extracting values of all bands for a particular geographical location or point, and computing statistics like mean, median, standard deviation, etc. at pixel level across all bands of raster dataset.

In R, codes to perform raster data statistics are present in raster package. The visualisation of raster data statistics can be done by rasterVis package. So, we need to import these packages in R in order to perform this exercise.

Introduction

The finance sector is undergoing a profound transformation driven by technological advancements, with artificial intelligence (AI) and blockchain technology at the lead of innovation. AI, characterized by its capacity to analyze vast datasets, recognize patterns, and take data-driven decisions has revolutionized various industries, including finance. On the other hand blockchain technology, initially popularized by cryptocurrencies like Bitcoin has arose as a disruptive force in finance, offering decentralized, transparent and secure transactional systems.

The integration of AI and blockchain technology holds immense promise for the finance sector, offering new avenues for efficiency, transparency, and risk management. By combining the analytical power of AI with the security and transparency of blockchain, financial institutions can streamline operations improve customer experiences, and mitigate risks more effectively. However realizing the full potential of this integration requires a nuanced understanding of its complexities, challenges, and opportunities.

A. Agronomic Considerations for Good Experimentation

For laying out of any experiment (Short / long term) at on-station and on farmers field, some of the agronomic considerations are very important for better results using experimental designs. Therefore, these are discussed here in this chapter.

1. Cropping History

The crop performance and its yield is affected by the crop grown and management followed during previous years. Therefore, for meaningful interpretation and factual reflection on yield trend due to the treatment proposed to be evaluated, the details of crop grown during previous 2 years/ seasons with details of management followed especially input applied needs to be recorded.

Introduction

AI plays a key role in management effectiveness and adaptation, especially in small and medium enterprises (SMEs) facing evolving markets (Lemos S. I. C., et al., 2022). Designing AI-based management frameworks enhances companies’ economic activities through data analysis and informed decision making (Emine, Kalender., 2023). Academic research shows a growing interest in AI in management, with a focus on effective adoption practices and potential areas for development (Jbid, Arsenyan., Anke, Piepenbrink. 2023). Artificial intelligence (AI) significantly influences decision-making processes in management by enhancing rationality, efficiency, and fairness. AI, through machine learning and big data analysis, reduces managerial bounded rationality, enabling a shift from satisficing to optimizing decision making modes (Shtepa, S., et. al., 2023). In human resource management AI-enabled interview analysis improves candidate selection by uncovering hidden patterns and mitigating biases, leading to more objective decisions (Rajneesh, Panwar. 2023). Additionally, by increasing productivity, efficacy and creativity, the incorporation of AI into business strategy transforms decision-making; nonetheless, ethical considerations and human oversight are essential for responsible implementation (Gonesh, C., et. al., 2023).

At country level, there is major paradiam shift from cropping systems towards farming system research in XI Plan period. The mandate of Project Directorate for Cropping Systems Research, Modipuram stands changed with focus on Farming Systems and re-named as Project Directorate for Farming Systems Research. w.e.f. 1.4.2009. The All India Co-ordinated Research Project on Cropping Systems with its 69 on-station and on-farm centres representing different agro-climatic zones of the country also stands re-named as AICRP on Integrated Farming Systems. As such, the much needed methodologies and procedures for planning, monitoring and interpretation of farming systems related data are not readily available. Therefore, some methodologies and procedures which can be adopted to execute Farming Systems Research for drawing meaningful conclusions are discussed here.

Introduction

Global economies have witnessed very deepened financial integration in past decade and have more room for leverage. However, these integrations have also witnessed various turmoil in the economy in the form of Russian- Brazil debt crisis, Asian financial crisis, Dot com bubble, Argentina debt crisis, Global financial crisis and many more since 1990s. Due to the existence of globalization, these crises have impacted not only a particular economy but has extended its scope.

The previous studies have found the credit growth and asset prices as a leading indicator. Some studies also showed that the foreign exchange reserve, broad money supply and trade as variables were also statistically significant. These studies have used either the Signal Extraction Approach used by Kaminsky and Reinhart (1999) which used a threshold value as a limit for multiple variables beyond which a crisis signal is given. Bucharest Stock Exchange (BSE)in Romania (Brezinski and Stephen, 2011) lost its attractiveness among foreign investors. However, there are not enough resources which focuses on Asian economies or India. The emergence of novel technologies, including artificial intelligence (AI), blockchain, cloud computing, big data analytics, machine learning (ML), internet of things (IoT), and 3-D printing, commonly known as Industrial Revolution 4.0 (IR4.0), offers developing nations like India both significant opportunities and challenges. IR4.0 technologies, sometimes referred to as the “digital revolution,” have numerous uses in the industries of services, industry, and agriculture.

2.1 What is a frequency distribution?

A frequency distribution shows the frequencies of occurrence of the observations in a data set. Often the distribution of the observed data is called an empirical frequency distribution, in contrast to the theoretical probability distribution determined from a mathematical model.

The class interval is a term used in statistics when we are given a continuous series. Class means a group of numbers in which items are placed such as 0-10, 10-20, 20-30, etc. Class interval refers to the numerical width of any class in a particular distribution.

2.2 Relative frequency distributions

Although creating a frequency distribution is a useful way of describing a set of observations, it is difficult to compare two or more frequency distributions if the total number of observations in each distribution is different. A way of overcoming this difficulty is to calculate the proportion or percentage of observations in each class or category. These are called relative frequencies and each is obtained by dividing the frequency for that category by the total number of observations (column 3 of Table 2.1). The sum of the relative frequencies of all the categories is unity (or 100%) apart from rounding errors.

1. Introduction

Have you ever dreamt of exploring an entirely different realm? Envision a place where an alternate version of yourself can freely roam and pursue various activities without the constraints of reality. In this realm, you can engage in work, socialize, acquire possessions, conduct transactions, embark on novel experiences, and revel in unprecedented adventures. Now, imagine that major technology companies are actively developing this virtual world to make it a tangible part of your life. The once fantastical concept of the Metaverse is now poised to become the next major innovation(Jung et al., 2024). It is widely acknowledged by scholars and researchers that the Metaverse is an idea of technology that attracts itself as a three-dimensional monotony of the Internet. Moreover, as noted earlier, definitions of the metaverse in existing literature and among professionals reflect the integration of physical objects in virtual space as a consumable description of the metaverse great role(Dwivedi et al., 2022). The key to any Metaverse technology is their user experience. Analysis of Metaverse applications shows that most current applications are stand-alone or centralized. These applications provide different user experiences, based on the cardinal technology and interface devices used to communicate within virtual environments.2

Introduction

The technology behind Internet search engines is advancing quickly, and this has an impact on how individuals find the information they need online as well as how search engines improve their algorithms (N. Zafar et al. 2022). Search engines are playing a prime role in generating leads for businesses (Olbrich and Schultz, 2014), Because of this, search engines are regarded as the most recent online communication channels for dynamic marketing (Olbrich and Schultz, 2014). To examine how academic institutions (in higher education) use the web to facilitate student relations and identify how prospective students are using the Internet to research academic institutions, Will et al. conducted a study that involved the analysis of more than 3000 university websites. The web continues to be the primary research resource for prospective students, counselors, and parents. The research was undertaken (Sampath Kumar and Kumar, 2013) to determine whether or not academics utilized search engines to access scholarly content from the internet. 285 (95%) of the 300 respondents used search engines to find academic content, compared to just 5% who didn’t. To access the Ed-tech companies that are playing a role in academia, comparable assessment is crucial.

Correlation coefficient

We measure the degree of association between two variables by calculating Pearson’s product moment correlation coefficient, usually just called the correlation coefficient.

It was developed by Karl Pearson from a related idea introduced by Francis Galton in the 1880s, and for which the mathematical formula was derived and published by Auguste Bravais in 1844. In 1896, Karl Pearson published his first rigorous treatment of correlation and regression in the Philosophical Transactions of the Royal Society of London.

The linear correlation coefficient (sometimes called Pearson's Correlation Coefficient), commonly denoted, is a measure of the strength of the linear relationship between two variables.

• We say that we have perfect correlation if all the points lie on the line; in this case, the value of the correlation coefficient takes one of its extreme values, either +1 or −1 (Figure 7a and 7b).

• We have positive correlation if the sign of the correlation coefficient is positive; then there is a direct relationship between the two variables so that as one variable increases in value, the other variable increases (Figure 7a) or there is a tendency for it to do so.

• We have negative correlation if the sign of the correlation coefficient is negative; then there is an inverse relationship between the two variables so that as one variable increases in value, the other variable decreases (Figure 7b) or there is a tendency for it to do so.

• We have no linear association (i.e. the variables are uncorrelated) if the correlation coefficient is zero; then there is a random scatter of points with no indication of a linear relation between the variables (Figure 7c.). Note that a non-linear relationship between the variables can also give a correlation coefficient of zero.

A probability distribution is a statistical function that describes all the possible values and likelihoods that a random variable can take within a given range. This range will be restricted between the minimum and maximum possible values.

The probability distribution shows how the set of all possible mutually exclusive events are distributed, and can be presented as an equation, a chart or a table.

A variable which can take different values with given probabilities is called a random variable.

There are numerous probability distributions which may be distinguished by whether the random variable is discrete, taking only a finite set of possible values, or continuous, taking an infinite set of possible values in a range of values.

A discrete random variable with only two possible values is called a binary variable, e.g. pregnant or not pregnant, diseased or healthy.

Probability distributions for discrete random variables The probability distribution for a discrete random variable y is the table, graph or formula that assigns the probability P(y) for each possible value of the variable y.

Introduction

Industry 4.0 is essentially the digital revolution of artificial intelligence and subsequent production processes utilizing contemporary smart technology. Cyber-physical systems, or intelligent machines, are the cornerstone of this revolution (Scoop, 2018). The key features of Industry 4.0 include the integration of IoT, increasing automation technology, and enhanced communication and self-counseling techniques (Tay et al., 2018). According to Schwab (2017), new developments in artificial intelligence (AI), robotics, nanotechnology, biotechnology, the Internet of Things (IoT), decentralized systems, 3D printing, fifth-generation communication technologies, quantum computing, and autonomous cars will all be part of this revolution. Early in the 2010s, Germany saw the emergence of Industry 4.0 as a result of government initiatives to advance manufacturing computerization as part of its high-tech strategy project. The concept of Industry 4.0 has been implemented in a variety of contexts and rebranded by various industries or businesses as needed.

Introduction Data analytics is a systematic analysis of data or statistics. It is also the process of collecting, cleaning, transforming, interpreting, modelling and visualization the data. Such transformed data has to be further investigated to retrieve helpful information that may help conclude and support decision making. This study presents revising selected two relative valuation models (PE Model, PB Model) by replacing the most non-significant variables used in the model with the critical variable that is the potential cause for improving the model’s performance. The objective of relative valuation is to value assets based on how similar assets are priced in the market. This paper measures the valuation accuracy using Root Mean Squared Errors and refine the relative valuation models using the machine learning algorithm for predicting the value of a stock. The following steps helped to revise and refine the PE Model, PB model (Relative valuation models) to predict the value of Sensex stocks.

Introduction

India has shaped its economic growth and transformed through information technology and digitalisation/ The Digital India initiative has tremendously improved its economic development. India’s efforts to revolutionize its information technology have taken the road ahead among the developed countries/ The contribution in the digital payment system has shifted in the banking and financial services sector. The emergence of technology upgradation through the internet has gained momentum in shifting towards internet and mobile banking technologies. The advent of technology has revolutionized the banking system through the evolution of financial technology with sustainable technological architecture through artificial intelligence which has enhanced ease of access for customers in the banking and financial sector. Artificial intelligence revolutionized various industries especially in the banking sector which has enhanced the way for interact the customers. The AI technology helps in reducing the cost for the Financial Sector. The Banks are welcoming the new age technology to serve betterment of the modern banking services.

5.1 Introduction

In random sampling it is assumed in prior that the population is divisible into a finite number of distinct and identifiable units and these units are known as sampling units. The smallest unit into which the population can be divide, is called an elementary unit (element) of the population. A group of such units is known as cluster. When such cluster is treated as sampling unit, then the sampling procedure adopted is known as cluster sampling. If the entire area containing the population under study is divided into smaller area segments and each element in the population belongs to one and only one such area segment, the sampling is sometimes known as area sampling.

Generally, identification and location of an element requires considerable time. H

6.1 Introduction

So far we have considered the methods of sampling in which the successive units (elements or clusters) were selected with the help of random numbers. Now, we shall consider a new method of sample selection in which only the first unit of the sample is selected with the help of random number, the rest being selected automatically according to a predetermined pattern. The method is known as systematic sampling. It is operationally more convenient than simple random sampling and which at the same time ensures for each unit, equal probability ofinclusion in the sample. The systematic sampling can also be termed as systematic random sampling because of the first unit of the sample being chosen randomly. The pattern usually followed in selecting a systematic sample is a simple pattern involving regular spacing of units.

Computational intelligence is enabling agriculture to evolve into a data-driven, adaptive and increasingly autonomous system. This chapter introduces its concepts, scope and role in smart agriculture by presenting farms as interconnected cyber-physical-biological environments. It discusses artificial intelligence, data analytics and decision-support systems and explains the progression from traditional farming to precision and smart agriculture. IoT, remote sensing, cloud platforms and digital agricultural ecosystems are examined as enabling technologies. The chapter also considers socioeconomic, environmental and scalability challenges. It establishes the conceptual and architectural foundation required to understand how computational intelligence can improve productivity, sustainability, resilience and decision-making in next-generation agricultural systems.

Early and accurate crop-disease diagnosis is essential for preventing production losses and reducing unnecessary pesticide use. Manual diagnosis can be slow, subjective and difficult to apply across large agricultural areas. This chapter introduces computational approaches using artificial neural networks, fuzzy logic, support vector machines, deep learning and hybrid optimization models. It discusses image acquisition, preprocessing, feature extraction, pattern recognition and classification of disease symptoms from leaves, crop canopies and sensor data. Integration with precision-agriculture systems, mobile advisory applications and real-time monitoring enables timely intervention. Challenges involving data quality, field variability, model interpretation and large-scale deployment are examined through practical examples and case studies.