eChapters

12.1. Introduction

Artificial Neural Networks (ANNs) are computational models inspired by the way biological neural networks in the human brain process information. They consist of interconnected nodes or “neurons” arranged in layers: an input layer, one or more hidden layers, and an output layer. Each neuron in the network processes data and passes the results to the next layer. ANNs are designed to recognize patterns, learn from data, and make predictions or classifications based on that learning.

In an ANN, the connections between neurons are weighted, meaning each connection has a strength or value that determines the influence of one neuron on another. Through a process called training, the network adjusts these weights to minimize errors in its predictions. This is typically done using techniques like backpropagation, where the error is propagated backward through the network to update the weights (Abiodunet al., 2018).

ANNs are highly versatile and have been applied across various domains, including image and speech recognition, natural language processing, and decision-making systems. Their ability to model complex relationships in large datasets makes them a crucial component in the field of machine learning and artificial intelligence (Ahmed et al., 2023).

11.1. Introduction

Fuzzy Cognitive Mapping (FCM) is a powerful soft computing technique that integrates elements of fuzzy logic and cognitive mapping to model complex systems. It allows for the representation of causal relationships between variables in an interpretable and flexible manner, making it particularly valuable for analyzing and simulating real-world dynamic systems. FCM has its roots in cognitive science, systems thinking, and artificial intelligence, enabling researchers and practitioners to model uncertainties and interdependencies effectively (Tyrovolas et al., 2023). The approach is particularly advantageous when dealing with complex, ill-structured problems where traditional mathematical modeling techniques fall short. By capturing expert knowledge and combining it with computational techniques, FCM helps in making informed predictions and policy decisions.

It is widely applied across multiple domains, including environmental management for sustainability assessments (Özesmi and Özesmi, 2004), decision support systems in business and governance, healthcare for medical diagnostics and treatment planning (Apostolopoulos et al., 2024), and socio economic planning for policy analysis and forecasting (Gray et al., 2015). Furthermore, its adaptability has led to its increasing integration with artificial intelligence, machine learning, and big data analytics, making it a promising approach for tackling contemporary challenges.

10.1. Introduction

Decision-making is a fundamental aspect of human life, influencing choices in both personal and professional settings. Every individual, whether consciously or unconsciously, engages in decision-making daily, ranging from simple tasks like selecting a meal to complex strategic planning in organizations. However, decision-making becomes increasingly challenging when multiple conflicting criteria are involved. The presence of numerous factors, each with varying degrees of importance, often creates ambiguity and uncertainty, making it difficult to arrive at the most suitable option. To address these challenges, structured decision-making methodologies have been developed to provide a systematic and logical framework for evaluating alternatives.

One such widely recognized method is the Analytical Hierarchy Process (AHP), introduced by Thomas L. Saaty in the 1970s. AHP was designed to assist decision-makers in breaking down complex problems into a structured and systematic hierarchy. This approach allows for a step-by-step evaluation of different factors, making it easier to analyze their relative importance. By structuring a problem into different levels - such as goal, criteria, sub-criteria, and alternatives - AHP provides clarity in decision-making and enhances consistency in judgments. The method is particularly beneficial when dealing with decisions that involve both tangible and intangible factors, as it allows subjective preferences to be quantified systematically (Saaty, 2008).

9.1. Introduction

The human population of the world will reach nearly 10 billion by 2050, boosting agricultural order-in a situation of humble financial development (FAO, 2017). At present, approximately 37.7% of total land surface is being utilized for crop production. From employment generation to contribution to National Income, agriculture sector is contributing a significant role in economic prosperity of the developed nations and is playing an active part in the economy of the developing countries as well. The growth of agriculture has resulted in a significant increase in the per-capita income of the rural community. Thus, a greater emphasis on agricultural sector is the need of the hour. For countries, like India, the agricultural sector accounts for 18% of GDP and provides employment to 50% of the country’s workforce. Development in the agricultural sector will enrich the rural development which will lead toward rural transformation and ultimately the desired structural transformation (Mogili and Deepak, 2018; Shah et al., 2019). The role of Artificial Intelligence (AI) has been obvious in the agricultural sector recently. The sector needs the impetus from AI based technology to maximize its yield including improper soil treatment, disease and pest infestation, big data requirements, low output, and knowledge gap between farmers and technology.

8.1. Introduction

Sometimes writing reference list could be so confusing since there are a lot of different rules. Each universities or even each faculty use different rule in writing reference list. The different rule of writing reference list is normal because they refer to different style guide. There are a lot of styles guide available. MLA (Modern Language Association of America) style; the Chicago Manual of Style; the Oxford Guide to Style are some examples of style guide. Difference reference of style guides chosen will result in totally different way of writing reference list. The most widely accepted style guides in social science is the APA (American Psychological Association) style. In some higher education journals (e.g. Journal of Studies in International Education, Higher Education) it is stated clearly that author must submit their paper according to APA style. Referencing in a general sense means to give credit to someone for using his or her own ideas or thoughts in a research activity. Referencing helps in gaining the originality of the ideas and thoughts used in the research activity. Failure to reference is treated as disrespect to the original author or writer and seen as a major misconduct in the area of academic research writing. Generally students made the mistake of not mentioning proper referencing at the end of their research projects, essays or any other piece of work. This may lead to cancellation of the written matter. If we do not reference properly the written matter is treated as if it is copied from somewhere. The research paper or material cannot be submitted for further research or reading purpose. We find a lot of data when we search for something.

7.1. People’s Participation in Observation, Analysis and Planning for Rural Development

Why should people participate?

There is no controversy as to whether there should be people’s participation in the development programmes in the development programmes designed to meet the basic needs of the people. The post-mortem of any unsuccessful programmes has shown that they have invariably failed when they failed to provide an adequate place and importance to people’s participation in the planning and analysis.

There is a body of philosophy and a set of assumptions behind people’s participation in rural development. These are;

i) It should be people’s programme with government’s participation;

ii) Community needs to build up their own capacity, agents have got a catalytic role;

iii) Self- imposed changes have got permanence as compared to those imposed from outsides

iv) Holistic approaches are far better than the fragmented approaches;

v) People need certain help to solve unique problems;

Statistics as a wide subject is used in almost all disciplines especially in research studies. Statistics provides scientific tools for representative data collection, appropriate data analysis and summarization of data and inferential procedure for drawing valid conclusion in the face of uncertainty. Each and every researcher should have some knowledge in Statistics and must use statistical tools in his or her research, one should know about the importance of statistical tools and how to use them in their research or survey. The quality assurance of the work must be dealt with: the statistical operations necessary to control and verify the analytical procedures as well as the resulting data making mistakes in analytical work is unavoidable. The emergence of statistical software in the twenty-first century has helped different researchers in the physical and social science to improve in the quality of research. Most renowned researchers in adopting this software in their data analysis have been able to identify the immense contribution to research findings (Adetola, 2013). Any quantitative research cannot be done effectively without Statistical Software. Moreover, it enables research data for easy presentation. It helps professionals to interact with data thereby paving way for creativity and innovation. Some are user friendly interface with drop-down tips for beginners (ATS, Ucla Edu, 2014). Advances in technology have improved all our lives (Akindutire, 2013) and has allowed experts greater freedom to come out with results within a twinkle of an eye than ever before where it takes time to finish analysis. This same technology has offer tremendous opportunity to research and keep research as a more interesting field of study. This study is significant, because it is an attempt to measure the direct impact of different statistical software on research analysis.

A linear programming problem aims to maximize or minimize a linear function subject to the linear constraints. But in many cases, the objective function may not be linear or all/some of the constraints may not be linear or both the objective and the constraints may not be linear. Such an optimization problem is called a nonlinear programming problem (NLPP). In this chapter, we discuss about NLPP’s. Nonlinear programming is the process of solving an optimization problemdefined by a system of equalities and inequalities, collectively termed constraints, over a set of unknown real variables, along with an objective function to be maximized or minimized, where some of the constraints or the objective function are nonlinear. It is the sub-field of mathematical optimization that deals with problems that are not linear. A typical non-convex problem is that of optimizing transportation costs by selection from a set of transportation methods, one or more of which exhibit economies of scale, with various connectivity and capacity constraints. An example would be petroleum product transport given a selection or combination of pipeline, rail tanker, road tanker, river barge. Owing to economic batch size the cost functions may have discontinuities in addition to smooth changes. In experimental science, some simple data analysis (such as fitting a spectrum with a sum of peaks of known location and shape but unknown magnitude) can be done with linear methods, but in general these problems, also, are nonlinear.

Multivariate analysis is essentially the statistical process of simultaneously analyzing multiple independent variables with multiple dependent (outcome or criterion) variables using matrix algebra. During the last two or three decades, multivariate statistical analysis has become increasingly popular. The theory has made great progress, and with the rapid advances in computer technology, routine applications of multivariate statistical methods are implemented in several statistical software packages. The traditional approach to the teaching of multivariate statistical analysis, as exemplified by Anderson (1958), relies heavily on advanced matrix mathematics. On the other hand, Hury and Riedwyl (1988) suggest that it is possible to understand most of the basic ideas underlying multivariate statistical analysis without a mastery of such mathematics, provided that these are conveyed with the help of real data sets. In this chapter we propose a non-mathematical data-driven approach for teaching multivariate statistical methods to students. Despite this, we are mindful of the need for students to know some basic linear algebra and univariate statistical concepts. Such basic knowledge provides students with the foundation necessary for the application of the appropriate multivariate statistical procedures and for the interpretation of results.

In statistics, sampling is concerned with the selection of a subset of individuals from within a statistical population to estimate characteristics of the whole population. Two advantages of sampling are that the cost is lower and data collection is faster than measuring the entire population. Each observation measures one or more properties (such as weight, location, colour) of observable bodies distinguished as independent objects or individuals. In survey sampling, weights can be applied to the data to adjust for the sample design, particularly stratified sampling. Results from probability theory and statistical theory are employed to guide the practice. In business and medical research, sampling is widely used for gathering information about a population. Acceptance sampling is used to determine if a production lot of material meets the governing specifications.

The sampling process comprises several stages

• Defining the population of concern

• Specifying a sampling frame, a set of items or events possible to measure

• Specifying a sampling method for selecting items or events from the frame

• Determining the sample size

• Implementing the sampling plan

• Sampling and data collecting

Statistics is a branch of science which deals with the collection, classification, description and interpretation of data obtained by conducting surveys and experiments. The essential purpose of it is to describe and draw valid inferences about numerical properties of populations” (Ferguson 1966). The experience in statistics application indicates that a single statistical method can be used in different research areas for dealing with different types of problems. Various possibilities which application of a statistical method provides in certain research areas should be considered as an adequate way of dealing with a problem of the research. However, it should be noted that statistics is not a method by which it is possible to solve all the problems in a research. Nachmias and Nachmias (2009) stated that there are basically two types of statistics which include descriptive and inferential statistics. Descriptive statistics enable the researcher to summarize and organize data in an effective and meaningful way. It involves the use of tables, charts, graphs, mean, modes, median, standard scores and correlation to treat collected data. Inferential statistics is concerned with making inferences from a unit of a population. Inferential statistics is concerned with making inferences from a unit of a population. Inferential statistics allow the researcher to make decisions or inferences by interpreting data patterns. Researchers use inferential statistics to determine whether an expected pattern designated by the theory and hypotheses is actually found in the observations. To decide whether this hypothesis is true, researchers might survey the respondents and then use descriptive statistics to make comparison between these groups and would employ inferential statistics to determine whether the differences between the groups are significant or not.

1.1. Introduction

Research in simple terms refers to search for knowledge. It is a scientific and systematic search for information on a particular topic or issue. It is also known as the art of scientific investigation. Several social scientists have defined research in different ways.

In the Encyclopaedia of Social Sciences, D. Slesinger and M. Stephenson (1930) defined research as “the manipulation of things, concepts or symbols for the purpose of generalizing to extend, correct or verify knowledge, whether that knowledge aids in the construction of theory or in the practice of an art”. According to Redman and Mory (1923), research is a “systematized effort to gain new knowledge”. It is an academic activity and therefore the term should be used in a technical sense. According to Clifford Woody (Kothari, 1988), research comprises “defining and redefining problems, formulating hypotheses or suggested solutions; collecting, organizing and evaluating data; making deductions and reaching conclusions; and finally, carefully testing the conclusions to determine whether they fit the formulated hypotheses”

Introduction

The rapid growth and socio-economic upliftment of a developing country are directly related to the intensity of industrial growth of that country (Bastian et al., 2019; Kniivila, n.d). The emergence of new enterprises is the main source of a healthy and growing industrial ecosystem. They represent 99% of businesses in developing countries and account for two-thirds of employment creation (Secretary-General and UNCTAD, 2022). Enterprises hence create employment, drive inclusive growth, promote innovation, and improve socio cultural conditions along with addressing sustainable development goals- No poverty(SDG-1), Industry, innovation and infrastructure(SDG-9), Reduce inequality(SDG-10)of the UN’s 2030 agenda (Forbes, 2022; Secretary-General and UNCTAD, 2022). Their innovative outputs in the form of goods and services, address local as well as global requirements of the customer market and bring them on the path of sustainability (Szopik-Depczynska et al.,2018; Rivera et al.,2020). The entrepreneurs have a long vision of success, engaging their primary activities with No poverty (SDG-1), Zero hunger(SDG-2), Gender equality(SDGs-5), Decent work and economic growth(SDG-8), Industry, innovation and infrastructure(SDG-9), Reduce inequality(SDG-10), Responsible consumption and production(SDG-12), Climate action(SDG-13), Partnership for goals(SDG-17) and addressing challenges of society and the environment in return for optimal economic profit (Alagpuria,2021; Lammers et al.,2022; Secretary-General and UNCTAD,2022; Vig,2021; Del-Aguila Arcentales et al.,2022; Sadiq et al.,2022).

Introduction

Chemical exposure in the workplace is a prevalent concern across various industries, including manufacturing, agriculture, healthcare, and construction. These exposures can range from acute to chronic and involve many substances, from heavy metals and solvents to pesticides and industrial chemicals. While the physical health effects of chemical exposure are well-documented, the psychological impacts are less understood but equally significant. Psychological stress is a response to perceived threats or challenges characterized by physical, emotional, and cognitive symptoms. Theories such as the Transactional Model of Stress and Coping by Lazarus and Folkman emphasize the role of individual perception and coping mechanisms in stress experiences.

The modern industrial landscape is characterized by ubiquitous chemicals, ranging from benign substances to potentially hazardous compounds. As industries strive for higher productivity and efficiency, the workforce is frequently exposed to various chemicals. This exposure is not without consequences; while the physical health effects of chemical exposure have been extensively studied, the psychological impacts are less understood. Emerging research indicates that chemical exposure can significantly contribute to psychological stress, affecting worker health, productivity, and overall well being.

Introduction

Digitalization is transforming the digital economy, offering opportunities for entrepreneurs and businesses, and transforming production and trade. The rapid advancement of technology and information systems has led to significant changes in both individuals and companies, particularly in the realm of digitalization in commerce (BarNir et al., 2003). Digitalization uses digital technologies to revolutionize business models, simplify transactions, and enhance value-producing opportunities in processes like customer service, shipping, buying, and selling (Zhang et al., 2013). All over the world, businesses are transitioning from manual to e-commerce for benefits such as efficiency, productivity, security, competitiveness, cost reduction, marketing, promotion, and information distribution (Daniel & Wilson, 2002; Gefen, 2000). E-commerce, a key feature, is reshaping global production and trade (Koh et al., 2017; Scalco, 2017). Moreover, the e-commerce industry has seen a substantial transition over the last few decades, due to rapid technical improvements and shifting customer behaviors.

Introduction

The advent of Industry 4.0 marks a pivotal era of transformation in the industrial sector, characterized by a fusion of technologies blurring the lines between the physical, digital, and biological spheres. This paper aims to delve into the bibliometric analysis of Industry 4.0, offering a comprehensive overview of the current state of research, predominant themes, and emerging trends within this revolutionary domain. By systematically mapping out the scholarly landscape, this study will not only highlight the intellectual contours that shape Industry 4.0 but also provide insights into the collaborative networks and influential regions propelling this field forward. Through this bibliometric scrutiny, we endeavour to discern the patterns of publication, citation trajectories, and the evolution of topics over time, thereby furnishing stakeholders with a strategic vantage point from which to foresee future directions and opportunities in Industry 4.0. This introduction sets the stage for a nuanced exploration of the intricate tapestry of research that constitutes the backbone of Industry 4.0, laying the groundwork for understanding its impact on the industrial paradigm and its potential to drive innovation, efficiency, and sustainability in the Fourth Industrial Revolution.

Introduction

The Fourth Industrial Revolution or Industry 4.0 is the aggregation of advanced digital technologies into manufacturing and production processes. These technologies include Cyber-Physical Systems (CPS), the Internet of Things (IoT), big data, cloud computing, artificial intelligence (AI), and autonomous systems, which collectively facilitate smart manufacturing and interconnected industrial operations. When examining Industry 4.0’s wider ramifications, contradictions or fascinating facts surface. Its impact is widespread in society and the economy, even if it mostly concentrates on manufacturing. It may be seen in the service industry, urban development, and agriculture, among other areas (Ane & Yasmin, 2019; Kraus et al., 2022). Furthermore, the phrase encompasses not only the development of technology but also the evolution of organizational leadership and the significance of security and privacy in the digital era (Beattie & Wyer, 2019; ONIK et al., 2019). Overall, Industry 4.0 represents a transformative era where digital technologies are harnessed to create intelligent, interconnected systems that revolutionize traditional industrial practices.

Introduction

Artificial intelligence is an effective technology that makes it possible for computers and other devices to simulate human intellectual abilities and ability to solve problems. Comprehensive study on artificial intelligence may be traced back to Alan Turing’s development of the renowned Turing Test in the 1950s, which examined whether machines are capable of thinking. (Turing, 1950), and John McCarthy came up with the phrase “artificial intelligence” in 1955 when he organised the 1956 Dartmouth Summer AI Research Project. They suggest that “making a machine behave in ways that would be called smart if a human were doing so” is an issue with AI (McCarthy et al., 1955). Compared to humans, artificial intelligence can analyse data, both organised and unstructured, substantially faster and more accurately.

Introduction

The term "artificial intelligence" was originally coined in 1956 at a conference hosted by Dartmouth College. While the aspirations and dreams of the attendees were sky-high, the reality was sobering. The thought of citizens in space or self-driving cars within a decade was ancient science fiction. The building blocks of the required computing infrastructure, as well as AI, notably deep learning and statistical machine learning methods, were not even in the distant future. The aim was to create an artificial brain for a machine, where the "brain" is the so-called "expert system", a software program that can integrate a large knowledge base and use it to solve problems in the same manner that a human expert could. The intellectual merits and achievements of a few AI systems designed solely to narrowly excel in a specific problem became the actual problem of the nascent field.

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.