eChapters

Subject: Biostatistics and computer application (UNIT-I)

Write true (T) or false (F)

Q1. Write true (T) or false (F)

1. The mean and mode are always equal in case of normal distribution.

2. Pie diagram is circular diagram.

3. Skewness is the measure of asymmetry of distribution.

4. Mean sum of squares are the variances.

5. Coefficient of variation may be utilized for comparing two data of different unit.

6. Goodness fit is tested by t-test.

7. Variance is a measure of central tendency.

8. CRD can be use where experimental material is heterogenous.

9. Frequency polygon is made by joining the middle points of the top of each rectangle of histogram.

Introduction

A revolution is coming to the more than a century-old automobile industry. Electric vehicles (EVs) are viewed as a solution to a variety of problems, including reducing greenhouse gas emissions, fostering sustainable economic growth, improving air quality, and reducing the importation of fossil fuels. The demand for electric automobiles in India is rising. Incentives and programs have been launched by the federal and state governments to promote the usage of electric vehicles, and there are also regulations and standards in place. A consumer is a person or group that plans to purchase something, actually does so, or utilizes it for primarily social, family, domestic, or other non business or entrepreneurial activity-related objectives.

An asset that is an important criterion for evaluating the status of the consumer economy can be used to estimate consumer savings.

The concept of electric cars has been around for a while, but in the past ten years, it has gained a lot of attention due to the expanding carbon footprint and other environmental implications of fuel-powered cars. In contrast to an internal combustion engine, which generates power by burning a mixture of fuel and gases, an electric vehicle (EV) is propelled by an electric motor. When electricity was one of the favored sources of motor vehicle power in the late 19th century, electric vehicles (EVs) first made their appearance, providing a degree of comfort and usability that gasoline cars of the day were unable to equal.

Introduction

A payment gateway is a server that is located between the web server of the merchant and the web server of the acquiring bank. When a merchant receives an order, it is transferred to the payment gateway by the merchant server.

The payments sector in India is set to reach USD 100 trillion in volume and USD 50 trillion in transactional value by the end of 2030. The payment gateways, which are also a part of the payments sector are set to reach USD 1.21 billion in India, growing at a CAGR of 17.6% between 2024-2029. The global payment gateway landscape as of 2024 stands at USD 16.27 billion growing at a CAGR of 16.43%. Payment gateways across the world are credit card-based gateways, with the introduction of card readers in mobile phones. The Fintech and payments sector boom in India cannot be justified without mentioning UPI (Unified Payments Interface), which has encompassed payment solutions for small street vendors to large conglomerates with its user-friendly interface and QR-code-based payments. UPI is one of the major payment gateways in India, in addition to IMPS (Immediate Payment Service), RTGS (Real Time Gross Settlement), and NEFT (National Electronic Funds Transfer) and many more.

Introduction

The phrase “sustainable” has been used so much in recent years-and frequently so imprecisely—that for many people, it has become meaningless or confused, more of a rhetorical chant than a source of useful advice. Lester Brown, the founder of the World Watch Institute, defined a sustainable society as one that can meet its requirements without compromising the opportunities for future generations. This definition is the one that we come across most frequently. Brown wrote this description in the early 1980s. The Brundtland Report, a 1987 United Nations report, addressed this issue and defined sustainable development as development that satisfies present demands without jeopardising the ability of future generations to satiate their own. Despite their obvious limitations speaking simply of human wants, for example, without reference to the natural world-these are significant moral exhortations. They do serve as a helpful reminder that it is our duty to provide our kids and grandkids with an equally abundant planet as we did, but they offer no guidance on how to truly create a sustainable society. When air, water, and minerals interact with living things to fill a specific region, it is referred to as an ecosystem (Tansley, 1935). This word was first used in 1935. Before then, the main goals of land management had been to provide a single item, like food or lumber, or to conserve a single, iconic species.

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.

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).

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.

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

Micro, small, and medium enterprises (MSMEs) dominate the Indonesian economy. These enterprises account for around 99 percent of all businesses in Indonesia and employ more than 95 percent of the total workforce across the country. They should be the primary engines of economic growth, although they generate only approximately 56 percent of the overall gross domestic product (GDP) of Indonesia. Around 80 percent of MSMEs are in the trading sector involved in buying and selling existing products/services). Within the MSME group, micro and small enterprises (MSEs), including those in the manufacturing industry, called micro and small industries (MSIs), i.e. business units with up to a maximum of 500 million Indonesian rupiah (IDR), are the ones that receive the most attention from the Indonesian government because they account for almost 99 percent of total MSMEs. So, compared to medium and large industries (MLIs), MSIs are the largest job creators in the country. These enterprises also have high resilience in various previous crises, including the COVID-19 pandemic. Their high resilience has played a role as a cushion for the economy because of their ability to survive periods of pressure and grow back faster and higher after pressure.

Introduction

The value of data privacy in the linked world of today cannot be emphasised. As digital technologies proliferate and personal data is collected, stored, and analysed on a large scale, people and organisations are more susceptible to data exploitation and privacy violations. In the modern era, privacy of data are vital because of following reasons:

1. Safety Regarding Personal Information: Fundamental People’s freedom to manage their personal data protected by data privacy. In a time when everyone has a digital footprint, maintaining one’s liberty and dignity requires protecting private information such as financial records, health records, and internet activity

2. Trust Management is Critical for Organisations and Companies: Maintaining data privacy improves reputation and brand loyalty by building trust with stakeholders, clients, and consumers. On the other hand, privacy violations and data breaches can seriously harm an organization’s reputation by undermining people’s confidence in its ability to protect sensitive data.

3. Compliance with Laws and Regulations: Strict data privacy laws have been passed by governments all over the world to uphold people’s rights and make businesses responsible for handling personal information. Adherence to regulatory mandates similar to the General Data Protection Regulation (GDPR) in the EU and the California Consumer Privacy Act (CCPA) in the US not only necessary from a legal standpoint but also morally required for moral business practices.

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.

Introduction

A Brief Overview of Industry 4.0

It is widely agreed that foundational technologies such as 5G, IoT, Robotics, Nano-Tech, Brain-Computer Interface (BCI), Neural Networks, Quantum Computing, Cost-efficient and low-emission Transportation, and Sustainable Power have coalesced to give birth to industry 4.0.

Industry 4.0, sometimes also designated as the fourth wave of Industrial Revolution, is all about transforming traditional manufacturing and related industrial practices through various digital technologies. It represents a paradigm shift characterized by the amalgamation of multiple technologies such as computing, the Internet of Things (IoT), convergence, artificial intelligence (AI), big data, and automation into a unified protocol for application in the industrial processes.

At its core, Industry 4.0 emphasizes the interconnectedness and intelligent communication between machines, products, and people. It revolutionizes production and operational methods by enabling machines to operate semi autonomously and make decentralized decisions. This allows for increased f lexibility, efficiency, and customization within manufacturing processes.

Key elements of Industry 4.0 include the digitization of manufacturing, smart factories, and the use of data-driven technologies to optimize production. It incorporates concepts such as predictive maintenance, where machines predict their own maintenance needs, and the Industrial Internet of Things (IIoT), allowing devices and systems to communicate, analyze, and optimize processes in real-time.

Introduction

In recent years, the convergence of digital technologies has ushered in a new era of industrial transformation known as Industry 4.0, characterized by the fusion of physical, digital, and biological systems. While much attention has been focused on its implications for urban industries and global supply chains, the potential of Industry 4.0 to reshape rural economies remains relatively unexplored. This paper seeks to address this gap by examining how Industry 4.0 technologies can optimize supply chains in rural contexts, with a focus on the unique challenges and opportunities present in rural India.

Rural economies play a crucial role in the socio-economic fabric of nations, particularly in countries like India, where a significant portion of the population resides in rural areas. According to the World Bank, over 65% of India’s population lives in rural regions, where agriculture remains the primary source of livelihood for millions (World Bank, 2020). However, rural communities in India face a myriad of challenges, including inadequate infrastructure, limited access to markets, and low levels of technological adoption. These challenges not only impede economic growth but also contribute to disparities in income, education, and healthcare between rural and urban areas.

Introduction

In the dynamic realm of contemporary work environments, the psychological contract has emerged as a cornerstone concept, influencing the fabric of employee-employer relationships and organizational dynamics. While traditional employment agreements delineate formal terms and conditions, the psychological contract encapsulates the implicit understanding between individuals and their employers regarding mutual expectations, obligations, and entitlements. This symbiotic agreement, rooted in perceptions and beliefs rather than written documentation, shapes the essence of the employment relationship, impacting employee engagement, organizational commitment, and overall workplace satisfaction.

The significance of the psychological contract has become increasingly pronounced in recent years, propelled by a confluence of factors such as rapid technological advancements, demographic shifts, and evolving socio economic trends. In this era of unprecedented change, the nature of work itself has undergone a metamorphosis, with traditional notions of employment giving way to flexible arrangements, remote work options, and gig economy platforms. Consequently, the traditional employer-employee dynamic has evolved, necessitating a deeper understanding of the implicit expectations and reciprocal obligations that underpin modern work relationships.

Introduction

The 21st century has witnessed unprecedented changes in the global job mar ket, driven by technological innovation, globalization, and evolving industry needs. As traditional job roles become obsolete and new ones emerge, skill development has become paramount for both individuals and organizations. This paper explores the concept, importance, key elements, methodologied and challenges in skill development.

Objectives of the study

This study carried on the basis of following objectives

1. To study the concept of Skill Development.

2. To know the Importance of Skill Development.

3. To know Key elements of Skill Development.

4. To know Methodologies of Skill Development.

5. To know Challenges in Skill Development.

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

In recent years, there has been an exponential increase in the volume of datasets generated across various application domains. However, a prevalent issue within these datasets is class imbalance, wherein the distribution of samples is biased towards the major class, resulting in an extremely skewed dataset. Number of samples in the major class far exceeds those in the minor class, sometimes with an extreme ratio like 1/1000 or even higher. While traditional machine learning models trained on such imbalanced datasets often yield high accuracy for the overall dataset and the major class samples, their ability to correctly predict class labels for minor class samples is signicantly compromised. In many real-world scenarios, the minor class samples hold greater signicance and importance compared to the major class samples. Therefore, researchers are increasingly focusing on addressing the challenges posed by class imbalanced datasets and exploring solutions to improve the performance of machine learning models in handling such imbalances

Introduction

Stocks from different firms can be traded and listed on the stock market. In addition, the stock market evaluates listed firms based on their worth, effectiveness, profitability, and capability. Anybody can trade shares on the stock market, which is an open global market. GDP is the total amount of profit produced in a country over a given period. To develop products and services in a country to a plausible degree, it is helpful to measure the monetary policy of completed goods and services that are acquired for a single end consumer. It comprises all earnings generated inside a country’s borders. The GDP of any nation may be used to determine the size and state of that nation’s economy. GDP is a measure of a nation’s total production and performance and is used as a gauge of economic health. Strong GDP growth frequently signals more consumer spending, corporate profitability, and company expansion, all of which are positive indicators of optimistic mood in the stock market. On the other hand, a slow GDP growth rate might undermine investor confidence and cause stock values to decline.

Changing Dynamics in HM

HM has transformed significantly; a lot because of changes in consumer behaviour, technological advancement, economic developments, regulatory changes, and growing consciousness of sustainability (Samaddar & Mondal, 2024). These changing dynamics have pushed customer’s expectations toward better and more personalized service (Law et al., 2024). Initially, the focus was on basic services and manual processes, but since then, the HM industry has embraced smart technologies like mobile apps, AI, IoT, and big data analytics. These developments have revolutionized the way customers interact; from merely checking in with their mobile phones, and controlling their room settings, to house-keeping and service delivery, thanks to AI-powered customization and automation. Using cloud services for managing properties and improving cyber security helps keep operations smooth and keeps data safe. Efforts to use energy and water more wisely show the industry's move towards being more sustainable. As the HM continues to evolve, industry stakeholders eye towards more personalized, efficient, and sustainable guest experiences. This would reflect the ongoing commitment to excellence and adaptation efforts to strive and evolve in the dynamic global landscape.

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.