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Millets are ancient grains, following wheat, that have been a fundamental source of human sustenance. These are nutrient-rich small-seeded grains that have gained prominence and admiration globally due to their super resilience in diverse climates and significant nutritional benefits. As millets are renowned for their nutritional richness, the demand for millet-based products increases. Hence, this paper aims in identifying the growing need for innovative processing techniques that not only preserve their nutritional content but also extend their shelf life.

In traditional times, heat was the only means of cooking and processing of the foods, but the amount of damage they used to cause to the sensorial and nutritional properties was huge. Millets’ sensitivity toward heat poses a challenge, as their composition is susceptible to disruption during various heat treatments and manufacturing processes. To cater to this drawback while ensuring the prolonged shelf life and nutrient preservation, various innovative approaches such as cold plasma, infrared technology and high hydrostatic pressure (HPP) processing are being widely used. These new methodologies aim on inactivating the microorganisms that have been developed within the food, providing the unprocessed, raw and natural form of nutrients in food products.

Among these approaches, nonthermal technology has emerged as a key player that prioritizes brief treatment periods and avoids the use of high temperatures. Nonthermal techniques (cold plasma, infrared radiation, HPP processing, ultra-sonication and pulsed electric field) facilitate the conservation of millet’s nutritional integrity by minimizing the degradation of heat-sensitive nutrients like vitamins and antioxidants. Acknowledging the potential applications and processing efficiency of nonthermal techniques, the food industry has embarked on substantial investments in this technology. The present study provides an in-depth exploration of the array of nonthermal technologies used in the food industry and their effects on the physical and chemical composition of diverse millet varieties.

Nonthermal techniques, compared to conventional thermal methods, are environmentally sound processes that contribute to energy conservation. However, these conveniences are accompanied by challenges, and this review not only elucidates these challenges but also focuses on the future implications of nonthermal techniques.

The learning outcomes are as follows: Understand the concept and characteristics of Bottom of Pyramid (BoP) markets; understand the concept and characteristics of frugal innovations; understand the Design Thinking approach to product design and how it might be useful to develop frugal innovations for BoP markets.

The case details the journey of a group of students at a premier engineering college in India. The group aimed to develop and implement a social innovation that addressed a serious and important health issue – menstrual hygiene practices among urban slum dwellers in India. The case begins with how a chance visit to an NGO inspired a pair of students to take up this issue, how the project unfolded at their college, the challenges faced in their journey and, finally, an outcome that was only a partial success. It raises important questions of challenges that are specific to bottom of pyramid markets in emerging economies. The case can provide a context for discussions on approaching frugal innovations from a Design Thinking perspective.

This case can be used in social innovation courses/modules at an undergraduate or graduate level in social innovation and social entrepreneurship courses. The case is best positioned towards the beginning of the course as an overview of the process of Social Innovation, and to discuss the relevance of concepts of BoP markets and frugal innovation.

Teaching Notes are available for educators only.

CSS 7: Management Science.

The stock market generates massive databases of various financial companies that are highly volatile and complex. To forecast daily stock values of these companies, investors frequently use technical analysis or fundamental analysis. Data mining techniques coupled with fundamental and technical analysis types have the potential to give satisfactory results for stock market prediction. In the current paper, an effort is made to investigate the accuracy of stock market predictions by using the combined approach of variables from technical and fundamental analysis for the creation of a data mining predictive model.

We chose 381 companies from the National Stock Exchange of India's CNX 500 index and conducted a two-stage data analysis. The first stage is identifying key fundamental variables and constructing a portfolio based on that study. Artificial neural network (ANN), support vector machines (SVM) and decision tree J48 were used to build the models. The second stage entails applying technical analysis to forecast price movements in the companies included in the portfolios. ANN and SVM techniques were used to create predictive models for all companies in the portfolios. We also estimated returns using trading decisions based on the model's output and then compared them to buy-and-hold returns and the return of the NIFTY 50 index, which served as a benchmark.

The results show that the returns of both the portfolios are higher than the benchmark buy-and-hold strategy return. It can be concluded that data mining techniques give better results, irrespective of the type of stock, and have the ability to make up for poor stocks. The comparison of returns of portfolios with the return of NIFTY as a benchmark also indicates that both the portfolios are generating higher returns as compared to the return generated by NIFTY.

As stock prices are influenced by both technical and fundamental indicators, the current paper explored the combined effect of technical analysis and fundamental analysis variables for Indian stock market prediction. Further, the results obtained by individual analysis have also been compared. The proposed method under study can also be utilized to determine whether to hold stocks for the long or short term using trend-based research.

This paper aim to find optimal cluster head and minimize energy wastage in WSNs. Wireless sensor networks (WSNs) have low power sensor nodes that quickly lose energy. Energy efficiency is most important factor in WSNs, as they incorporate limited sized batteries that would not be recharged or replaced. The energy possessed by the sensor nodes must be optimally used so as to increase the lifespan. The research is proposing hybrid artificial bee colony and glowworm swarm optimization [Hybrid artificial bee colony and glowworm swarm optimization (HABC-GSO)] algorithm to select the cluster heads. Previous research has considered fitness-based glowworm swarm with Fruitfly (FGF) algorithm, but existing research was limited to maximizing network lifetime and energy efficiency.

The proposed HABC-GSO algorithm selects global optima and improves convergence ratio. It also performs optimal cluster head selection by balancing between exploitation and exploration phases. The simulation is performed in MATLAB.

The HABC-GSO performance is evaluated with existing algorithms such as particle swarm optimization, GSO, Cuckoo Search, Group Search Ant Lion with Levy Flight, Fruitfly Optimization algorithm and grasshopper optimization algorithm, a new FGF in the terms of alive nodes, normalized energy, cluster head distance and delay.

This research work is original.

Introduction: The insurance industry has unprecedented growth, and the demand for insurance has outgrown in the recent past due to the prevailing pandemic. The companies have a large base of the data set at their disposal, and companies must appropriately handle these data to come out with valuable solutions. Data mining enables insurance companies to gain an insightful approach to map strategies and gain competitive advantage, thus strengthening the profits that will allow them to identify the effectiveness of back-propagation neural network (BPNN) and support vector machines (SVMs) for the companies considered under study. Data mining techniques are the data-driven extraction techniques of information from large data repositories, thus discovering useful patterns from the voluminous data (Weiss & Indurkya, 1998).

Purpose: The present study is performed to investigate the comparative performance of BPNNs and SVMs for the selected Indian insurance companies.

Methodology: The study is conducted by extracting daily data of Indian insurance companies listed on the CNX 500. The data were then transformed into technical indicators for predictive model building using BPNN and SVMs. The daily data of the selected insurance companies for four years, that is, 1 April 2017 to 21 March 2021, were used for this. The data were further transformed into 90 data sets for different periods by categorising them into biannual, annual, and two-year collective data sets. Additionally, the comparison was made for the models generated with the help of BPNNs and SVMs for the six Indian insurance companies selected under this study.

Findings: The findings of the study exhibited that the predictive performance of the BPNN and SVM models are significantly different from each other for SBI data, General Insurance Corporation of India (GICRE) data, HDFC data, New India Assurance Company Ltd. (NIACL) data, and ICICIPRULI data at a 5% level of significance.

Introduction: The insurance industry is one of the lucrative sectors of the economy. However, it is volatile because of the large chunk of data generated by the transactions taking place daily. However, every bit of it is responsible for creating market trends for stock investors to predict the returns. The specialised data mining techniques act as a solution for decision-making, reducing uncertainty in decision-making.

Purpose: There are limited studies that have examined the efficiency and effectiveness of data mining techniques across the companies in the insurance industry to date. To enable the companies to take exact benefit of data mining techniques in insurance, the present study will focus on investigating the efficiency of artificial neural network (ANN) and support vector machine SVM across insurance companies of CNX 500.

Method: For predictive models, various technical indicators were considered independent variables, and change in return, i.e. increase and decrease, was deemed a dependent variable. The indicators were transformed from daily raw data of insurance company’s stock values spanning four years. We formed 90 data sets of varied periods for building the model – specifically six months, one year, two years, and four years for selected six insurance companies.

Findings: The study’s findings revealed that ANN performed best for the ICICIPRULI data model in terms of hit ratio. Whereas the performance of SVM was observed to be the best for the ICICIGI data model. In the case of pairwise comparison among the six selected Indian insurance companies from CNX 500, the extracted data evaluated and concluded that there were eight significantly different pairs based on hit ratio in the case of ANN models and nine significantly different pairs based on hit ratio for SVM models.

This study aims to provide a systematic review about the Internet of Things (IoT) and its impacts on happiness. It intends to serve as a platform for further research as it is sparse in in-depth analysis.

This systematic review initially observed 2,501 literary articles through the ScienceDirect and WorldCat search engines before narrowing it down to 72 articles based on subject matter relevance in the abstract and keywords. Accounting for duplicates between search engines, the count was reduced to 66 articles. To finally narrow down all the literature used in this systematic review, 66 articles were given a critical readthrough. The count was finally reduced to 53 total articles used in this systematic review.

This paper necessitates the claim that IoT will likely impact many aspects of our everyday lives. Through the literature observed, it was found that IoT will have some significant and positive impacts on people's welfare and lives. The unprecedented nature of IoTs impacts on society should warrant further research moving forward.

While the literature presented in this systematic review shows that IoT can positively impact the perceived or explicit happiness of people, the amount of literature found to supplement this argument is still on the lower end. They also necessitate the need for both greater depth and variety in this field of research.

Since technology is already a pervasive element of most people’s contemporary lives, it stands to reason that the most important factors to consider will be in how we might benefit from IoT or, more notably, how IoT can enhance our levels of happiness. A significant implication is its ability to reduce the gap in happiness levels between urban and rural areas.

Currently, the literature directly tackling the quantification of IoTs perceived influence on happiness has yet to be truly discussed broadly. This systematic review serves as a starting point for further discussion in the subject matter. In addition, this paper may lead to a better understanding of the IoT technology and how we can best advance and adapt it to the benefits of the society.

This paper aims to assess the impact of research in the field of scientometrics by using the altmetrics (social media metrics) approach.

This is an applied study which uses scientometric and altmetrics methods. The research population consists of the studies and their citations published in the two core journals (Scientometrics and Journal of Informetrics) in a period of five years (included 1,738 papers and 11,504 citations). Collecting and extracting the studies directly was carried from Springer and ScienceDirect databases. The Altmetric Explorer, a service provided by Altmetric.com, was used to collect data on studies from various sources (www.altmetric.com/). The research studies with the altmetric scores were identified (included 830 papers). The altmetric scores represent the quantity and quality of attention that the study has received on social media. The association between altmetric scores and citation indicators was investigated by using correlation tests.

The findings indicated a significant, positive and weak statistical relationship between the number of citations of the studies published in the field of scientometrics and the altmetric scores of these studies, as well as the number of readers of these studies in the two social networks (Mendeley and Citeulike) with the number of their citations. In this study, there was no statistically significant relationship between the number of citations of the studies and the number of readers on Twitter. In sum, the above findings suggest that some social networks and their indices can be representations of the impact of scientific papers, similar citations. However, owing to the weakness of the correlation coefficients, the replacement of these two categories of indicators is not recommended, but it is possible to use the altmetrics indicators as complementary scientometrics indicators in evaluating the impact of research.

Investigating the impact of research on social media can reflect the social impact of research and can also be useful for libraries, universities, and research organizations in planning, budgeting, and resource allocation processes.