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The attraction of online shopping has raised the demand for customized image searches, mainly in the fashion industry. Daily updates in this industry increase the size of the clothing database at a rapid rate. Hence, it is crucial to design an efficient and fast image retrieval system owing to the short-listing of images depending upon various parameters such as color, pattern, material used, style, etc.

This manuscript introduces an improved algorithm for the retrieval of images. The inherited quality of images is first enhanced through intensity modification and morphological operations achieved with the help of a light adjustment algorithm, followed by the speeded up robust feature (SURF) extraction and convolutional neural networks (CNN).

The results are validated under three performance parameters (precision, recall and accuracy) on a DeepFashion dataset. The proposed approach helps to extract the most relevant images from a larger dataset based on scores conferred by multiple cloth features to meet the demands of real-world applications. The efficiency of the proposed work is deduced from its effectiveness in comparison to existing works, as measured by performance parameters including precision, recall and F1 score. Further, it is also evaluated against other recent techniques on the basis of performance metrics.

The presented work is particularly advantageous in the fashion industry for creating precise categorization and retrieving visually appealing photographs from a diverse library based on different designs, patterns and fashion trends. The proposed approach is quite better than the other existing ML/DL-based approaches for image retrieval and classification. This further reflects a significant improvement in customized image retrieval in the field of the fashion industry.

The All India Institute of Medical Sciences (AIIMS) is a premier medical institution in India that has significantly contributed to cancer research. This study aims to highlight the cancer research productivity of AIIMS by assessing the impact and reach of the institution’s research output.

The study was based on 2,078 published papers on cancer of AIIMS indexed in the Web of Science (WoS) database from 1989 to 2021. A combination of tools such as Microsoft Excel, Biblioshiny, BibExcel and VOSviewer was used to evaluate and visualize the selected data.

The analysis revealed the interdisciplinary nature of research outputs, which have collaborative contributions from various fields such as oncology, pathology, radiology and surgery. The most productive research area within cancer was found to be breast cancer. In terms of international collaboration, the analysis revealed that AIIMS has a strong presence in the global cancer research community, with collaborations with researchers from more than 50 countries.

This study has some limitations. First, the study is limited to using only the WoS Core Collection database. Other databases, such as Scopus and PubMed, were excluded. Second, there is ambiguity in author names and nonuniformity in the institutions’ names, which can significantly affect the study’s outcomes.

Identifying research productivity in cancer at AIIMS aids resource allocation, collaboration and strategic planning, enhancing India’s overall cancer research impact and patient outcomes.

To the best of the authors’ knowledge, this study is to use scientometric indicators to evaluate AIIMS’s research productivity with particular reference to cancer for the first time. This detailed analysis provides a deeper understanding of AIIMS’s contribution to cancer research and its potential implications.

Introduction: In today’s world of increasing competition, diminishing product differentiation, higher customer expectations, easy product replacements and lowering brand loyalty, organisations are evolving new marketing strategies for economic, societal and sustainability. Cause-related marketing (hereafter referred to as CRM), a strategic sustainable philanthropic practice, is the upcoming form of CSR. CRM plays an instrumental role in achieving self-brand connection and brand loyalty.

Purpose: To explore, integrate and interconnect concepts of CRM and self-brand connection to get more insights into the imperative role of CRM strategy in developing self-brand connections that can lead to brand loyalty in the most sustainable way. For this, CRM and self-brand connection, as proposed by societal marketing and branding literature, were explored. This chapter is a propositional inventory where the researcher has explored the antecedents of CRM strategy and its role in developing brand loyalty through self-brand connection.

Methodology: This chapter is centred upon the existing literature on sustainability, CRM and branding to understand better the relationships between dimensions and consequences of CRM and its interlinkage with brand loyalty.

Findings: The literature recommends that selected dimensions: Cause-brand fit, product type, altruistic motivation and brand credibility determine the effectiveness of CRM strategy. It also establishes the profound impact of attitude towards brand, brand perception and brand distinctiveness on self-brand connection. A theoretical framework based on the existing literature represents an amalgamated groundwork for developing effective, sustainable CRM strategies in conjunction with the self-brand connection. The proposed framework is distinct as no study conjoins the abovementioned concepts and aims to comprehend whether this integration is brand loyalty.

Purpose of This Chapter: This chapter examines the gender-based skewness witnessed in terms of women-led unicorns, as well as, in the field of entrepreneurship in general in India. India has been witnessing a booming startup landscape lately, with the country producing several new unicorns. Competing internationally, India comes third in world rankings regarding the number of unicorns made.

Design / Methodology / Approach: The methodology adopted in this chapter is case-based analysis of individuals with the help of secondary data available in the public domain. The authors employ comparative analysis methodology keeping two major parameters of interest as the verticals that form the basis of the comparative analysis.

Findings: The special provisions in place that are especially meant for women entrepreneurs in order to help them scale up their business and target higher profits have loopholes in them and as a result, a very low number of women-led businesses have been able to mark their presence in the unicorn club.

Research Limitations / Implications: A lesser number of women entrepreneurs in the unicorn club, so making generalizations has not been possible.

Practical Implications: The chapter gives a better understanding of the dynamics of the entrepreneurship arena in India with respect to women entrepreneurs who are doing significant work on the basis of scale of operation and profits.

Originality: This is an original chapter which has not been presented or published before. This chapter can be of immense value to anyone interested in India’s current entrepreneurial scenario, and useful to policymakers, researchers, and academicians.

Research in the area of behavioral finance has demonstrated that investors exhibit irrational behavior while making investment decisions. Investor behavior usually deviates from logic and reason, and consequently, investors exhibit various behavioral biases which impact their investment decisions. The purpose of this paper is to rank the behavioral biases influencing the investment decision making of individual equity investors from the state of Punjab, India. This research would provide valuable insight into the different behavioral biases to investors and other participants of the capital market and help them in improving investment decisions.

The research is conducted on the individual equity investors of Punjab, India. Fuzzy analytic hierarchy process was applied to rank the factors influencing the decision making of individual equity investors of Punjab. The primary factors considered for the study are overconfidence bias, representative bias, anchoring bias, availability bias, regret aversion bias, loss aversion bias, mental accounting bias and herding bias.

The three most influential criteria were herding bias, loss aversion bias and overconfidence bias. The five most influential sub-criteria were “I readily sell shares that have increased in value (C61),” “News about the company (Newspapers, TV and magazines) affects my investment decision (C84),” “I invest each element of my investment portfolio separately (C71)” and “I usually hold loosing stock for long time, expecting trend reversal (C52).”

Although sample survey conducted in the present study was based on a limited sample selected from a particular area that truly represented the total population, it is considered as the limitation of this study.

The outcome of this research provides investors with a better understanding of behavioral biases that influence their decision making. This study provides them a guideline on different behavioral biases that they should consider while making investment decisions.

The research model is based on the available literature on behavioral finance and the research results and findings would add value to the existing knowledge base.

This study aims to investigate the possibilities of cryptocurrencies as hedges and diversifiers in the Indian stock market before and during financial crisis due to the pandemic and the Russia–Ukraine war.

Researchers have used daily data on cryptocurrencies and Indian stock prices from March 10, 2015 to August 26, 2022. The researchers have used the dynamic conditional correlations (DCC)-GARCH model to determine the volatility spillover and dynamic correlation between stocks and digital currencies. Further, researchers have explored hedge ratio, portfolio weight and hedging effectiveness using the estimates of the DCC-GARCH model.

The findings indicate a negative conditional correlation between equities and cryptocurrencies before the crisis and a positive conditional correlation except for Tether during the crisis. Which implies that cryptocurrencies serve as a hedging asset in the stock market before a crisis but are not more than a diversifier during the crisis, except for Tether. Notably, Tether serves as a safe haven during times of crisis. Finally, the study suggests that Bitcoin, Ethereum, Binance Coin and Ripple are the most effective diversifiers for Indian stocks during the crisis.

This study makes several contributions to the existing literature. First, it compares the hedge and diversification roles of cryptocurrencies in the Indian stock market before and during crisis. Second, the study findings provide insights on risk hedging and can serve as a guide for investors. Third, it may help rational investors avoid underestimating risk while constructing portfolios, particularly in times of financial turmoil.