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Assessment of male millennial digital purchase intent with regard to online fashion
There has been a tremendous growth in the number of people opting for online purchases in recent years especially among the tech savvy millennials not just in tier 1 city by also in tier 2 and 3 cities of Karnataka, Reasons for such a massive growth can be a result of several benefits offered by online shopping such as convenience, time-saving, reduces time and cost of travelling and avoiding traffic chaos in metro cities and so on. Also, we can observe from previous studies that online shopping is widely preferred by females when compared to males in India and also male millennials are reluctant to opt for online purchases (Chaudhary et al.2022). Thus, there exists a need to find out the factors affecting digital purchase intent among male millennials with regard to online fashion Purchases. This study aims to assess the validity and reliability of the measurement instrument, assess the issues and challenges faced by male millennials and mediating effect of e-satisfaction and e-experience. 2024, IGI Global. All rights reserved. -
ASSESSMENTS IN TEACHING INTERNSHIP
Teaching internship assessment is a blurred area in teacher education, as it involves multiple items to assess and require multiple measurement tools. The present chapter attempts to mention various teaching internship assessment practices prevailing in teacher education programmes across the globe. It also discusses a few established training assessment frameworks, assessment standards of a few countries, new experiments assessing internships as per the review of literature, value added assessment models, overall internship effectiveness, assessment after teacher training programme, and current teaching internship assessment practices of a few countries. While exploring the assessment, the chapter also details various components considered by teacher education institutes of assessment in teaching internships. The chapter provides a birds eye view of teaching internship assessment and helps the stakeholders to note, reflect, and create an indigenous effective assessment method for teaching internships. 2023 selection and editorial matter G.S. Prakasha and Anthony Kenneth; individual chapters, the contributors. -
Atman's awakening: Bhagavad Gita's Path to Moksha through Karma Yoga and Atmabodha
Indian psychology is characterized by its diverse and rich traditions that have evolved over several centuries. This chapter tries to fulfill four objectives: 1) To provide a brief overview of the concept of self in Bhagavad Gita; 2) to give a brief overview of the two frameworks for moksha given in the Bhagavad Gita with the help of empirical evidence of current research; 3) to propose a conceptual model using Triguna Framework and Trimarg Framework; and 4) to provide the implications of the proposed model. The chapter begins with an explanation of the Indian philosophical understanding of self from the lens of Bhagavad Gita. In the second section, an effort has been made to compare and contrast the two frameworks given in Bhagavad Gita for Moksha. The last section introduces a conceptual model to enhance sattva guna and reduce the rajas and tamas gunas to attain atmabodha that can have positive psychological implications in modern times. 2024, IGI Global. All rights reserved. -
Attention to Economic Factors and Its Response to Foreign Portfolio Investment: An Evidence from Indian Capital Market
Stock market consists of a variety of investors. Among these, Foreign Portfolio Investors (FPIs) is a key investment influx. These investments can change or fluctuate due to several macroeconomic factors which can cause a shift in the dynamics of the markets in India. This paper examines the factors influencing for foreign portfolio investment in long run as well as short run. The sample comprises of 120 monthly observations on Foreign Portfolio Investment (FPIs) and Macro economic variables such as Oil prices (OP), Gross Domestic Product (GDP), Interest Rate (IR), Exchange rate of Indian Rupee with USD (ER), Inflation (CPI), Nifty Index (NSEI), 10year Bond Prices (BP) and Index of Industrial production (IIP) over a period of 10years, spanning from January 2013 to November 2022. The study employed Autoregressive Distributed Lag model (ARDL) to establish the long run association with error correction models. The result indicates that there is long run association between the Foreign Portfolio Investment and macro-economic variables. Among this, NSEI, IIP and ER played a significant role to determine FPI investments in the long run, whereas in the short run, FPI was impacted by ER and NSEI significantly. The Author(s), under exclusive license to Springer Nature Switzerland AG 2024. -
Augmented Reality-Enabled IoT Devices for Wireless Communication
[No abstract available] -
Autoimmune diseases and an approach to type 1 diabetes analysis using PSO, K-means, and silhouette values
An estimated 50 million Americans suffer from autoimmune diseases, as per the report from AARDA (American Autoimmune Related Diseases Association). More than 30 million people suffer in India from type 1 diabetes. More than $100 billion is spent on healthcare for autoimmune diseases in America, more than for cancer healthcare. Host genes and environmental factors control autoimmune diseases, and typically they do not have any specific cure. This paper proposes an artificial intelligence-based framework for the initial prediction of autoimmune diseases. This work attempts to identify characteristics of autoimmune diseases, and it lists the commonly occurring autoimmune diseases, the organs attacked by them, and the different stages involved. It also seeks to identify ways to prioritize the severity of the patient's disease, for providing treatments based on the severity, with the goal of reducing the pressure on the healthcare sector. Type 1 diabetes is an autoimmune disease and identifying the risk associated with diabetes and other related health problems could help to improve health worldwide. This work proposes a framework while exploring autoimmune disease prediction using machine learning techniques. The autoimmune disease considered is type 1 diabetes. The usage of machine learning techniques can help to enhance patient care and early prediction. This research is an attempt to explore the possibilities and also to propose a framework for early prediction of type 1 diabetes. Clustering is performed using K-means and PSO K-means. Validation of the clusters is carried out using silhouette coefficient. 2024 Elsevier Inc. All rights are reserved including those for text and data mining AI training and similar technologies. -
Automated neurological brain disease detection in magnetic resonance imaging using deep learning approaches
A neurological type of brain disease called multiple sclerosis (MS) impairs how well the nervous system is able to function efficiently and causes people to experience visual, sensory, and problems with movement. Multiple methods of detection have been proposed so far for diagnosing MS; among them, magnetic resonance imaging (MRI) has drawn a lot of interest from healthcare providers. The ability to quickly diagnose lesions related to MS depends on a fundamental understanding of the anatomy and workings of the brain that MRI technology provides doctors. Using an MRI for diagnosing MS is tedious, time-consuming, and prone to human error. In the present investigation, lesion activity involves preprocessing and segmentation of the MS images from two time points using deep learning approaches. 2024 by IGI Global. All rights reserved. -
Bacterial Pigments as Antimicrobial Agents
In this chapter, we discuss various bacterial derived secondary metabolites pigments which has antimicrobial properties. Though these metabolites were identified more than several decades ago, attention into their bioactivities has emerged in the last few decades. Their increasing acceptance is an outcome of their cost-effectiveness, biodegradability, noncarcinogenic property, and eco-friendly characteristics. This chapter has made an attempt to take an in-depth observation into the current bacterial derived pigments and their bioactivity against various microorganisms. 2024 selection and editorial matter, Mohammed Kuddus, Poonam Singh, Raveendran Sindhu and Rachana Singh; individual chapters, the contributors. -
Bacteriocins as Biotechnological Tools in Food and Pharmaceuticals: Applications and Future Prospects
The World Health Organization (WHO) and FAO have defined probiotics as non- pathogenic living organisms that greatly benefit host cells and have several positive outcomes at the level of gut. The intake of probiotics at an adequate amount confers good health and many times is used for several treatments (Hill et al. 2014; Gibson et al. 2017). Not only the microorganisms as a whole, but the proteins or peptides secreted by these species have tremendous applications in food spoilage, pharmaceuticals, antibiotic development, and much more. Thus, antimicrobial peptides from bacteria have drawn more attention for their wide range of applications. 2023 selection and editorial matter, Arti Gupta and Ram Prasad; individual chapters, the contributors. -
Barriers hindering digital transformation in SMEs
The chapter aims to find interdependencies between barriers that hinder adoption of digital transformation technologies in small and medium firms. Barriers were identified using an extensive literature review and finalized after consulting an expert panel. Next, a pairwise questionnaire was developed, and responses from essential stakeholders working with small and medium firms were collected. Data were analyzed using the DEMATEL technique. Salient challenges for implementing digital transformation technologies were identified, and the cause-and-effect relationship between the barriers was established. Lack of proper digital vision and strategy was identified as the most critical barrier that hinders adoption of digital transformation technologies in small and medium firms. Digital technologies help to improve the efficiency of the firms and improve resource utilization by facilitating timely and accurate decision making. Hence, overcoming the identified challenges in transformation will improve the operations of the production system and organizational process. 2024, IGI Global. All rights reserved. -
Behavioral Bias as an Instrumental Factor in Investment Decision-An Empirical Analysis
Investment decisions are always complex in nature. Investment assets are volatile in nature there are less volatile, medium volatile and high volatile investment assets in the financial market. In the current study how, the behavioral biases of the investors affecting their investment decisions in the less volatile asset classes is examined using an extensive survey method among the IT professionals in the Bangalore city. The relationship between the demographic variables and behavioral biases is tested. Also, a detailed study is conducted to examine the risk-taking behavior of the investors in the less volatile assets. There are basically three type of investors on the basis of their risk-taking behavior i.e. Risk seeking, Risk Neutral and Risk averse investors. Current study reveals that investors in the less volatile asset classes are very much cautious about the risk factor and therefore they are risk averse in nature. The Author(s), under exclusive license to Springer Nature Switzerland AG. 2024. -
BERT-Based Secure and Smart Management System for Processing Software Development Requirements from Security Perspective
Software requirements management is the first and essential stage for software development practices, from all perspectives, including the security of software systems. Work here focuses on enabling software requirements managers with all the information to help build streamlined software requirements. The focus is on ensuring security which is addressed in the requirements management phase rather than leaving it late in the software development phases. The approach is proposed to combine useful knowledge sources like customer conversation, industry best practices, and knowledge hidden within the software development processes. The financial domain and agile models of development are considered as the focus area for the study. Bidirectional encoder representation from transformers (BERT) is used in the proposed architecture to utilize its language understanding capabilities. Knowledge graph capabilities are explored to bind together the knowledge around industry sources for security practices and vulnerabilities. These information sources are being used to ensure that the requirements management team is updated with critical information. The architecture proposed is validated in light of the financial domain that is scoped for this proposal. Transfer learning is also explored to manage and reduce the need for expensive learning expected by these machine learning and deep learning models. This work will pave the way to integrate software requirements management practices with the data science practices leveraging the information available in the software development ecosystem for better requirements management. 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Beyond boundaries: Role of artificial intelligence and ChatGPT in transforming higher education
The goal of the proposed chapter is to give readers a thorough understanding of the complex effects of ChatGPT on higher education. It will cover the short-and long-term benefits that ChatGPT offers, as well as limitations that may affect both educators and learners. The chapter will also highlight a wide range of ethical issues and challenges that arise while using ChatGPT. Research in this area is very limited and the literature review reveals that there are benefits as well as limitations of using ChatGPT in the domain of higher education but the fact is that it is going to grow further which makes it an urgent need for the policymakers and stakeholders to explore and understand how ChatGPT should be integrated into higher education to deliver more value to the educators and learners. The proposed chapter will cover the evolution of ChatGPT, its growing popularity and impact, benefits it offers, the associated disadvantages and the road ahead. 2024, IGI Global. -
Beyond Humour: How Memes Shape Brand Associations and Drive Purchase Choices
Memes are the perfect marketing tools a brand can use while promoting their products or services. In this ever-changing consumer preferences memes are the convenient marketing tools that a consumer pays attention, the usage of memes has become a completely modern approach for brands to seek the attention of consumers. The study examines the impact of internet meme that spreads through social media which catches the consumer attention and improves the intention of purchasing products and also learn about the brands. A structured questionnaire and convenience sampling technique are designed to collect data from frequent internet users who are active in social media from Gen-z and have at least a little knowledge on meme marketing, and responses yielded were 353. This paper gives a general study of meme marketing and if the consumer brand relatability and purchase decisions are affected by meme marketing. The findings state that there is a relationship between branding memes and consumer brand relatability and using the memes in social handles affect the consumer behaviour however there is a discomfort among consumers when brands solely use memes for marketing purposes. Also the study found that there is no significance between Gender and meme motivation into buying products. Thus the study contributes to understand the consumer behaviour, purchase intention and likeliness towards the brands. In addition, the authors contribute to the finding the significance of meme in daily life of a consumer and what type of memes would pursue consumers more towards the brand. The Author(s), under exclusive license to Springer Nature Switzerland AG 2024. -
Beyond Teacher Quality: Understanding the Moderating Role of Infrastructure in Student Learning Outcomes in Secondary Education
Education is an essential resource for individuals and societies, and it plays a significant role in shaping the future of any nation. Depriving a generation of young children of their basic right to quality education can easily be regarded as the highest form of injustice in a society. Bihar, which was once the epitome of education and knowledge across the world, is now counted among the states with the lowest literacy rates and the poorest educational infrastructure. While a list of reasons can be enumerated behind this downfall, including historic and social reasons, it is prudent to act on those that we can effectively alter and improve upon, such as infrastructure and teaching quality. The quality of education provided to students is influenced by various factors, such as infrastructure, teacher quality, and student-teacher relationships. This study explores the moderating effect of infrastructure on the relationship between teacher quality and student outcome in secondary education in Bihar, mapping an intriguing contrast with Kerala, the state with the highest literacy rate in India. With the help of a simple moderation analysis and drawing on the resource dependency theory, our findings indicate that the moderation effect of infrastructure on student outcome is stronger in Bihar than in Kerala. This study highlights the urgent need to prioritise consolidating and enhancing the quality of education in schools in Bihar rather than adding up a number of concrete blocks. 2024 Patliputra School of Economics. -
Beyond the borders: Fashion influencers shaping global trends
Social media has emerged as a powerful platform that revolutionized the way the fashion industry operates. Fashion influencers have significantly impacted global consumer trends and brand perceptions with their large followings and engaging content. This chapter investigated the ways in which fashion influencers leverage social media platforms to shape consumer behavior, promote sustainable fashion practices, and bridge cultural boundaries to create a more inclusive fashion community across borders. This chapter has contributed to a better understanding of fashion beyond borders, social media influencers' role in transforming the fashion landscape, and their potential to influence positive change in the industry by conducting a comprehensive literature review. This chapter explores social media influencers' multifaceted roles in redefining fashion globally. 2024, IGI Global. All rights reserved. -
Bibliometric Analysis of AI Research in Sustainable Smart Cities
Smart cities have the potential to improve city-wide governance, environmental sustainability, sustainable transportation, and economic growth. Urban areas may find these advantages useful in their pursuit of SDG-11 objectives. A key component of smart city architecture is the addition of artificial intelligence (AI) and other smart technology into urban areas. The Artificial Neural Network (ANN) is a major machine learning approach. A number of review studies have already been published, reflecting the substantial interest in artificial neural networks (ANN) for smart city applications. In the past, researchers have shown an interest in studying structural monitoring applications, transportation systems, cybersecurity, and the Internet of Things (IoT). But knowledge about how ANN can help Smart Cities achieve SDG-11 is limited. This paper provides a systematic bibliometric analysis of present research trends on artificial neural networks for smart cities, with an emphasis on SDG-11. The research employed a keyword-based search to obtain 131 papers for content analysis and 743 papers for descriptive analysis. Both the amount of interest in the topic and the tendency for related topics to cluster have increased exponentially, according to the findings. Urbanization, Transportation, and Eco-friendly were identified as the main topics of this study. Specifically, this evaluation focuses on particular SDG-11 issues and provides insights on research trends and thematic importance. 2025 Saravanan Krishnan, A. Jose Anand and Raghvendra Kumar. -
Bibliometric analysis of the impact of blockchain technology on the tourism industry
The tourism sector is one of the world's fastest-expanding industries. Because of the benefits, it provides to individuals and organizations, the tourism sector has attracted a lot of attention throughout the years. But because of its poor and obsolete data management techniques, this industry is in desperate need of reform. Blockchain technology is one method for managing and exploring data relevant to the tourism industry. This study used bibliometric methods to analyze the impact of blockchain technology on the tourism sector from 2017 to 2022. The publications were extracted from the dimensions database, and the VOS viewer software was used to visualize research patterns. The findings provided valuable information on the publication year, authors, author's country, author's organizational affiliations, publishing journals, etc. Based on the findings of this analysis, researchers may be able to design their studies better and add more insights into their empirical studies. 2024 Srinesh Thakur, Anvita Electronics, 16-11-762, Vijetha Golden Empire, Hyderabad. -
Big data analytics lifecycle
Big data analysis is the process of looking through and gleaning important insights from enormous, intricate datasets that are too diverse and massive to be processed via conventional data processing techniques. To find patterns, trends, correlations, and other important information entails gathering, storing, managing, and analyzing massive amounts of data. Datasets that exhibit the three Vs-volume, velocity, and variety-are referred to as "big data. " The vast amount of data produced from numerous sources, including social media, sensors, devices, transactions, and more, is referred to as volume. The rate at which data is generated and must be processed in real-time or very close to real-time is referred to as velocity. Data that is different in its sorts and formats, such as structured, semi-structured, and unstructured data, is referred to as being varied. 2024, IGI Global. All rights reserved. -
Big Data Analytics: A Trading Strategy of NSE Stocks Using Bollinger Bands Analysis
The availability of huge distributed computing power using frameworks like Hadoop and Spark has facilitated algorithmic trading employing technical analysis of Big Data. We used the conventional Bollinger Bands set at two standard deviations based on a band of moving average over 20 minute-by-minute price values. The Nifty 50, a portfolio of blue chip companies, is a stock index of National Stock Exchange (NSE) of India reflecting the overall market sentiment. In this work, we analyze the intraday trading strategy employing the concept of Bollinger Bands to identify stocks that generates maximum profit. We have also examined the profits generated over one trading year. The tick-by-tick stock market data has been sourced from the NSE and was purchased by Amrita School of Business. The tick-by-tick data being typically Big Data was converted to a minute data on a distributed Spark platform prior to the analysis. 2019, Springer Nature Singapore Pte Ltd.