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Artificial Intelligence-Based Approaches for Anticipating Financial Market Index Trends
The stock market is an essential component of the world economy and significantly impacts how different countries handle their finances. Predicting stock prices has gained popularity recently since it can offer traders, investors, and policymakers useful information. Making informed financial decisions, lowering risk, and maximizing returns can all be facilitated by accurate stock price projections. Stock price prediction is a current research subject due to improvements in machine learning (ML) techniques, and several methodologies have been put forth in the literature. To increase the accuracy of stock price prediction, one method combines the feature extraction ability of convolutional neural networks (CNNs) with the classification strength of support vector machines (SVMs). CNNs are a subclass of neural networks that have excelled in voice and picture recognition. They can be taught to extract valuable features from the supplied data automatically. Contrarily, SVMs are a well-liked machine learning (ML) technique that has been applied for regression and classification tasks. 2024 Sachi Nandan Mohanty, Preethi Nanjundan and Tejaswini Kar. -
Promoting Net-Zero Economy for Sustainable Development: Practice-Based View
The present research investigates the utilization of various resources, including tangible assets, human expertise, and intangible assets, in a cohesive set of established procedures, which impact the development and implementation of net-zero practices. It also explores the effect on the environmental performance of SME enterprises operating in business markets. Additionally, the study explores whether digitalization plays a moderating role in this relationship. The samples of 291 were used in the study. Data were analyzed using partial least square structural equation modeling. For a sustainable net-zero economy (SNZE), it is essential for managers to acknowledge the importance of resource and capabilities management. While the management of tangible assets and human skills is vital, greater emphasis should be placed on intangible resources like organizational culture and learning. Furthermore, the capacity of small-sized enterprises (SMEs) to process and implement knowledge could prove to be instrumental in accomplishing net-zero targets. Consequently, managers should leverage Industry-4.0-based technological solutions to enhance resource and capabilities management effectively. This research pioneers an exploration into the influence of human capital and various assets (tangible and intangible), on the development and implementation of a SNZE in organizations, underpinned by empirical data. The study broadens the understanding of the practice-based view (PBV) framework in realizing SNZE, particularly within SME B2B enterprises. The Author(s), under exclusive license to Springer Nature Switzerland AG 2024. -
Customer Perspective through Artificial Intelligence: Forecasting Green Products Sustainable Development
The idea of planned behavior was developed in 1980 as a philosophy of deliberate action to interpret human behavior. The primary element of this theory is an individuals purpose, which is impacted by the attitude of expecting that the behavior will result in the desired result. This theory has helped in determining certain characteristics of an individual that includes smoking, drinking, services, and so on. The theory states that the behaviors are achieved through motivation and control. These characteristics developed are completely voluntary which can sometimes help in the betterment and improvement in any field. The name of the theory itself gives us a clarity that it is a well-planned formation of behaviors different from his or her normative and preconceived beliefs and norms. 2024 Sachi Nandan Mohanty, Preethi Nanjundan and Tejaswini Kar. -
Workforce Forecasting Using Artificial Intelligence
Workforce forecasting predicts an organizations future demand and supply of the workforce. Each organization has its strategies to manage and track the appropriate workforce. The adequate forecasting technique for the workforce involves data analysis and pattern mining from various data points. Some of the critical attributes considered for the analysis and the forecasting of the workforce requirement include the data such as demographics, economic trends, and labor market conditions; these help in calculating informed predictions about future workforce requirements [1,2]. The primary aim of workforce forecasting is to ensure that an organization has suitable employees with the appropriate skills to meet its business needs by helping organizations make informed decisions about staffing levels, employee training, and other workforce management strategies. 2024 Sachi Nandan Mohanty, Preethi Nanjundan and Tejaswini Kar. -
Strategic Management Practices for Sustainability: A Study of Micro-entrepreneurs of Wellness Industry in Mysore District
The global wellness industry is seeing a major shift in its relevance and growth post COVID-19. The fast-growing wellness industry is driven by organizations of all sizes and scope: large, medium, small and micro enterprises offering a range of services from holistic wellness offerings to focused services, such as beauty, spa, alternative therapy, gym and physical fitness. While we have heterogeneous businesses on the supply side, we have the entire global population on the demand side. Due to the size of the market and growth potential, the competition in wellness service space is intense. In such a situation, it is a challenge to register growth and sustain the same. The challenge is more pronounced for micro-entrepreneurs due to their limited resources and reach. It calls for a strategic approach to managing the businesses to endure the competition and succeed. Hence, wellness businesses are adopting Strategic Management Practices (SMPs) in greater numbers. However, not all strategies work. The purpose of this study is to analyze the impact of significant SMPs adopted by micro-entrepreneurs on business sustainability in the wellness industry. Responses of 392 microentrepreneurs from the wellness industry are recorded and analyzed for the SMPs adopted by them for the economic, environmental and social sustainability of their businesses. The study identified various strategic approaches that are implemented by micro-entrepreneurs in Mysore District and studied the impact SMPs had on sustainability factors of the wellness industry. A model is proposed to support the study. The results conclude that the application of a good amount of SMPs in the form of strategic entrepreneurship enhances the sustainability of a venture as well as the industry, aiding transformation from an unorganized to an organized sector and better regulations. 2024 by World Scientific Publishing Co. Pte. Ltd. -
Agricultural Crop-Yield Prediction: Comparative Analysis Using Machine Learning Models
Machine learning (ML) is a crucial decision-support tool for predicting agricultural crop yields, enabling choices about which crops to grow and what to do while they are in the growing season. The research on agricultural production prediction has been supported by the application of several ML techniques. We employed a comparative analysis in this study to synthesize using three ML models, including linear regression, polynomial regression, and K-nearest neighbors (KNN), and extracted the results for the prediction of yield. Crop yield depends on a variety of aspects such as temperature, pesticide usage, rainfall, and even year due to changing climatic conditions. It is in our best interest to find out the crop yield based on these factors, as it will help in advancing the farming sector. These collected data have gone through preprocessing - i.e., cleaning, to ensure that no redundant or error data is used to train the ML models. Before we train the models, the dataset is divided into training and testing to provide the performance metrics of each model we use. The experimental results on predictions indicate KNN performs slightly better in comparison with linear regression and polynomial regression models. 2024 Taylor & Francis Group, LLC. -
Demand and Supply Forecasts for Supply Chain and Retail
Demand and supply forecasts serve as the backbone of strategic decision-making in todays rapidly changing business environment, assisting organizations in optimizing inventory levels, production planning, and pricing strategies. The ability to forecast demand and supply accurately is critical for effective supply chain and retail management. This chapter provides a comprehensive overview of supply chain and retail demand and supply forecasts. It discusses various forecasting methods and techniques, as well as related concepts. In addition, the chapter emphasizes the significance of accurate forecasting in optimizing supply chain and retail operations, as well as emerging trends and future directions in demand and supply forecasting. 2024 Sachi Nandan Mohanty, Preethi Nanjundan and Tejaswini Kar. -
Farming Futures: Leveraging Machine Language for Potato Leaf Disease Forecasting and Yield Optimization
Crop yield prediction is of paramount importance in modern agriculture. It serves as a linchpin for ensuring food security, efficient resource management, risk mitigation, environmental sustainability, and socioeconomic development. Accurate predictions enable us to maintain a stable food supply, optimize resource allocation, and manage the uncertainties associated with climate and market fluctuations. By fostering sustainable farming practices, crop yield prediction also plays a crucial role in reducing environmental impact and promoting rural development. Integrating artificial intelligence (AI) and machine learning (ML) in modern agricultural practices offers the potential to revolutionize the way we produce food, making it more sustainable, efficient, and resilient. This study has demonstrated the effectiveness of convolutional neural networks (CNNs) in the classification of potato leaf disease, achieving remarkable results with a test loss of 0.0757 and a test accuracy of 0.9741. 2024 Taylor & Francis Group, LLC. -
The Evolution of Forecasting Techniques: Traditional Versus Machine Learning Methods
Forecasting is used effectively and efficiently to support decision-making for the future. Over time, several methods have been created to conduct forecasting. Finding a forecasting technique with the ability to provide the best estimate of the system being modeled has always been a challenge. The selection and comparison criteria for forecasting methodologies can be organized in a variety of ways. Accurate forecasting has a great demand for various fields like weather prediction, economic condition, business forecasting, demand and supply forecasts and many more. When deciding whether to utilize a certain model to predict future events, accuracy is very important. In every field, machine learning (ML) algorithms are being used to forecast future events. These algorithms can handle more complex data and make predictions that are more accurate. Based on the least values of forecasting errors, forecasters create a model to determine the best strategy for prediction. For centuries, forecasting has been used to assist individuals in making future-related decisions. In the past, forecasts were based on intuition and experience, but as technology has advanced, so have forecasting methods. Currently, advanced ML models and methods for data analysis are used to provide forecasts. To forecast the future, these models incorporate a range of inputs, including historical data, present trends, and economic indicators. Forecasting is a vital tool for businesses to employ when making future plans. It is used in a wide range of industries, from finance to weather prediction. 2024 Sachi Nandan Mohanty, Preethi Nanjundan and Tejaswini Kar. -
Mediating Effect of Digital Literacy Between Attitude Towards AI and Job Insecurity Among HR Professionals
As businesses continue to incorporate technologies that use AI into a variety of business processes, the connection between employee attitudes towards AI and job insecurity has attracted some attention. However, a critical aspect that has not been covered in the existing literature is the potential mediating role of digital literacy in shaping this relationship. This study investigates the interplay between attitudes towards AI, job insecurity, and digital literacy among HR employees through an online survey. Utilizing established scales, including Attitudes Towards AI (ATAI), Job Insecurity, and Digital Literacy, significant results reveal a substantial mediated relationship. Finding also states a significant impact of attitudes towards AI on job insecurity. Acceptance AI attitude indirectly reduce job insecurity through heightened digital literacy. Also, the pivotal role of digital literacy as a mediator, emphasizing its importance in alleviating job insecurity concerns amidst AI integration. These findings offer practical insights for organizations seeking to foster employee confidence in AI-rich workplaces. The Author(s), under exclusive license to Springer Nature Switzerland AG 2024. -
Public-Private Partnership Ventures: A Diasporic Initiative in India Through Social Remittances and Philanthropic Work
Diaspora networks across the globe think of their homeland, which makes them continuously assist various projects that have public-private partnership ventures. Many of the members of the Indian diaspora had difficult times during their childhood in their native country. The conditions were not favorable within India prior to Indian Independence for acquiring desired skills in entrepreneurial activities. However, they managed to sail through the rough sea and reach the western coast in great adversity. The journey itself was a training for the early diaspora that resulted in learning the skills needed for setting up their business in the adopted countries. Indian diaspora from various developed countries have learned a great deal about the local culture, new skills in business partnership, consultancy services, research and innovations in technology that helped them to prosper in the adopted land and implement those ideas in their home country as a part of social remittances and giving back to the society. Developed countries have exhibited the feasibility of sustainable development through social entrepreneurship. Compulsory community service that trains people in new skill development also educates them in preserving the environment in which they reside. The public-private partnership model, which is in practice in developed countries, has become the agent of creation of social entrepreneurship with accountability toward the society. Diaspora communities that send social remittances to their home countries not only in the form of money but also ideas, identities and behaviors help set up public- private models of undertakings that would ensure sustainable growth in the long run. Philanthropic work is reckoned in the Public-Private Partnership (PPP) model that we see in various parts of India, especially in states like Punjab (Sikh diaspora), Kerala (Malayali diaspora) and Gujarat (Gujarati diaspora). In this background, this chapter tries to examine the Indian diasporas schemes in India on the model of public-private partnership that they had either set up or observed in their adopted countries. This chapter also looks into how far diaspora remittances in totality help mitigate the existing problems in Indian villages, create new job opportunities for the local population, manage skill development centers and educate the masses in preserving the environment that would help in sustainable development through social entrepreneurship. 2024 by World Scientific Publishing Co. Pte. Ltd. -
Socio-economic Development and Value Creation Through Corporate Social Responsibility: A Case Study of Bosch India Foundation
In recent years, it is mandatory for profitable organizations in India to work toward Corporate Social Responsibility (CSR). Many thinkers in the industry have appreciated the move of the Indian Government by mandating profitable businesses to take responsibility for society by sharing certain portions of the profit made by these organizations. This study focuses on various initiatives taken by BOSCH India Foundation (BIF) for socio-economic development and value creation through its CSR activities. The primary data are collected by conducting interviews with the seniorlevel managers working in the CSR department of the Bidadi plant. The data are also collected by visiting the field of action, discussing with various stakeholders and observing their initiatives. The secondary data are collected from published sources and official records of the company. This case study shows that BOSCH India Foundation is focusing on the development of the villages in Bidadi. Their CSR initiatives focus on education, agriculture and livestock development, health and hygiene, environment, women empowerment, youth development and access to potable water. This study analyzes the economic and social impacts it has created in the society. The case provides new insight for researchers and students about the CSR approaches and best practices which can be a model for companies working on CSR projects. 2024 by World Scientific Publishing Co. Pte. Ltd. -
Involvement of Social Enterprises in Promoting Sustainable Agricultural Practices: The Case of Uravu and Buffalo Back
Social enterprises are not-for-profit or for-profit organizations that work for the development of the community in different ways and are sustainable through their products or services. One such initiative of social enterprises is supporting and promoting agriculture and agricultural products. This chapter focuses on two social enterprises, Uravu and Buffalo Back, which work with farm products and their role in promoting sustainable agriculture practices. The primary data are collected through personal interviews with top-level managers, and secondary data are collected from websites and other published documents. This study looks at the concept of sustainability in terms of finitude, fragility and fairness. These two case studies explain how social enterprises promote the development of agriculture. The former organization ensures the communitys livelihood through farming support, upgrading local knowledge, technologies, skill development and marketing their commodities. The latter focuses on promoting farmers to focus on sustainable organic farming techniques and selling their products to customers. This study can help future entrepreneurs understand different models they can use to develop the agricultural sector through their social actions. 2024 by World Scientific Publishing Co. Pte. Ltd. -
The Intellectual Structure of Application of Artificial Intelligence in Forecasting Methods: A Literature Review using Bibliometric Thematic Analysis
Crude oil is a valuable asset class which forms the nucleus of the energy core of the transport sector for any country. According to report [1], crude oil helps in meeting 93% of energy needs for the transportation sector globally. It has been projected across various forums that crude oil along with coal and natural gas is going to satisfy world energy needs for the forthcoming years. Consequently, it has been observed that fluctuations in crude oil prices tilt the economies of scale across the world. 2024 Sachi Nandan Mohanty, Preethi Nanjundan and Tejaswini Kar. -
Business and Society: A Symbiotic Relationship
Business can be a for-profit, not-for-profit or hybrid organization. But all these businesses focus on the satisfaction of their stakeholders. Although many businesses adopt a limited perspective of their stakeholders, focusing primarily on the interests of their investors, customers and, in some cases, their employees, it is a fact that the long-term sustainability of any business will depend on its contributions to the society. The long-term objective of all businesses is to serve and support the society and contribute to the socioeconomic development of their people. Therefore, this chapter presents a comprehensive review of the relationship between business and society, with special reference to the three main types of businesses: commercial businesses, social enterprises and non-governmental organizations. As in the case of biological systems, the relationship between business and society may be characterized predominantly by one of the three types of symbiotic relationships: mutualism, commensalism and parasitism. However, the successful co-existence of business and society, in the long run, would depend on the degree of mutualism in their relationship. 2024 by World Scientific Publishing Co. Pte. Ltd. -
Blockchain Using Wireless Technology
In today's dynamic digital landscape, blockchain technology emerges as a pioneering force with the potential to redefine industries and transform the way we conduct business, share information, and establish trust. This chapter explores the foundational concepts of blockchain technology, its versatile applications, and the profound impacts it can have on various sectors. While blockchain holds immense promise, challenges like scalability, energy consumption, and regulatory frameworks must be addressed. Decentralized apps and smart contracts introduce new vulnerabilities that demand vigilant management. The integration of blockchain with wireless technology expands opportunities and streamlines processes. Wireless connectivity enhances accessibility, reach, and interaction with blockchain applications, benefiting finance, supply chain, and healthcare sectors. Real-time data sharing and reduced infrastructure reliance boost productivity. Environmental concerns, including blockchain's energy consumption and e-waste from wireless devices, need mitigation. In conclusion, the fusion of blockchain and wireless technology offers tremendous potential but demands a delicate balance between technological progress and environmental stewardship. Addressing reliability, security, scalability, and environmental impacts through innovative solutions and ethical practices is vital for a connected and sustainable future. 2024 CRC Press. -
Role of Artificial Intelligence in Neuroimaging for Cognitive Research
Artificial intelligence (AI)-based solutions are used in most of our daily activities. AI has been adapted and it has found various applications. Cognitive research is one area where AI has been applied to understand the hidden patterns in the data. Neuroimaging techniques investigate the neural basis of cognitive processes like perception, attention, memory, language, reasoning, decision-making, and problem-solving. The irregularities in the cognitive process lead to cognitive disabilities and diseases. Neuroimaging techniques, including magnetic resonance imaging (MRI), functional MRI (fMRI), electroencephalography (EEG), and positron emission tomography (PET), along with other data-gathering techniques, are studied to identify cognitive disorders. The imaging techniques generate large amounts of complex data. AI methods, including machine learning (ML), deep learning (DL), natural language processing (NLP), and computer vision, are applied and used to analyse and interpret the data generated by various imagining techniques. Numerous techniques have been designed, developed, and proposed to handle the neuroimaging data for cognitive research with the help of AI techniques. AI techniques include ML algorithms like decision trees, random forest, support vector machine (SVM), principal component analysis (PCA), and DL algorithms, including convolution neural networks (CNNs), long short-term memory (LSTM), and generative adversarial networks (GANs). Recent advancements in the field of neuroimages use AI techniques to preprocess, process, and analyse the data generated by various neuroimaging modalities. This chapter provides an in-depth analysis and summary of various AI techniques for processing neuroimages for cognitive disorders. 2024 selection and editorial matter, Anitha S. Pillai and Bindu Menon; individual chapters, the contributors. -
A Comparative Analysis of Traditional and Machine Learning Forecasting Techniques
Forecasting is the process of making predictions or estimates about future events or conditions based on historical data, trends, and patterns. It involves analyzing past data and using statistical or other quantitative methods to project future outcomes, such as sales figures, market trends, weather patterns, or financial performance. Forecasting can be used in a wide range of fields, including economics, finance, business, weather forecasting, and sports. The accuracy of a forecast depends on the quality of the data, the methods used, and the assumptions made about the future. 2024 Sachi Nandan Mohanty, Preethi Nanjundan and Tejaswini Kar. -
Practical Benefits of Using AI for More Accurate Forecasting in Mental Health Care
Artificial Intelligence (AI) is the general term for being able to make computers do things that require human-like intelligence. AI is the novel idea of the computer pioneers like Alan Turning and John von Neumann in the 1940s. Their novel intuition towards making machines think is the key start for this AI technology evolution. As shown in Fig. 1, the first milestone of AI happened in the year 1956 when it was proved by a group of researchers that a machine could solve any problem with the use of an unlimited amount of memory. Here they named this program General Problem Solver (GPS). 2024 Sachi Nandan Mohanty, Preethi Nanjundan and Tejaswini Kar. -
Integration of Mobile Edge Computing in Wireless Technology
This chapter delves into the potential for Mobile Edge Computing (MEC) to revolutionize wireless networks through its incorporation in wireless technologies. The authors give a thorough introduction to MEC, including its components, design, and the reasoning behind combining it with wireless networks. This chapter provides a foundational understanding of technologies for wireless communication, focusing on the establishment and improvement of 4G, 5G, and Wi-Fi networks. Different deployment strategies and supporting technologies for MEC integration with mobile networks are explored to demonstrate the adaptability and scalability of this approach. Improved connection, lower latency, and higher bandwidth utilization are just some of the benefits and obstacles of MEC integration that are demonstrated using practical scenarios and applications. This chapter also discusses techniques for optimizing performance and managing resources, as well as security and privacy concerns unique to wireless networks that make use of MEC. In this article, we explore the continuing standardization efforts and industry activities that are pushing MEC usage in wireless networks. Finally, the authors describe the unanswered questions and potential future developments in MEC-enabled wireless networks. This chapter presents a thorough analysis of MEC's incorporation into wireless technology, revealing how this development has the potential to revolutionize mobile communications and open up fresh avenues for developing useful services and applications. 2024 CRC Press.