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Current status and future trends on the use of innovative technologies for recovering bioactive from insects
Edible insects hold great potential as human food owing to their nutritional, economic and environmental value. Though, the negative perceptions of insects limit their intake by majority of the insects, their efficient processing and utilization in food products have steadily increased their demand in recent years. This chapter deals with the emerging and advanced extraction techniques for recovering functional and bioactive compounds from insects, considering the various factors which might influence the optimum yields. Apart from their production yields, it is of utmost significance to preserve their nutritional and sensory qualities for their effective utilization in functional food products. In this regard, various emerging technologies such as enzymatic hydrolysis, cold atmospheric pressure plasma, ultrasound-assisted extraction, high hydrostatic pressure have been explored. Mechanisms of action along with their benefits and drawbacks have been thoroughly described in the later part of the chapter which will provide insight to the readers for the selection of optimum technology for insect processing. Overall, this chapter provides the readers a comprehensive view about alternatives to conventional techniques for postprocessing of insects and optimization for case-specific technology. 2024 Elsevier Inc. All rights reserved. -
Current Trends and Future of AI in Sales and Marketing
Industry 4.0 has been fueling the growth of Artificial Intelligence owing to the expanding progress of data produced by the Internet and IoT devices and the computation of this enormous quantity of data. Artificial Intelligence has revolutionized the business world. AI technology has had a striking influence in the field of finance and accounting. As the market is becoming increasingly competitive, AI-empowered finance and accounting structures are making the business organization sturdy competitors as AI technologies and tools help in saving time and extracting deeper understandings. Today, companies use AI to predict customer behavior, serve more people, better target campaigns, forecast trends, etc. It goes without question that AI could improve the productivity of marketing teams. For example, location, age, and other essential information can lead to more personalized content. Along with big data, AI can undoubtedly provide businesses with a powerful means to engage and convert potential customers. This chapter aims to study the use of artificial intelligence in sales and marketing. The chapter also studies the role of AI in the sales and marketing field. This chapter also describes the benefits and current trends of AI applications in sales and marketing operations. This chapter includes the fundamental and theoretical base. It attempts to analyze the key practical applications of AI in the very specific work of the sales and marketing field. 2025 by Apple Academic Press, Inc. -
Current Trends in Career Decision in Youth: Opportunities and Challenges
In todays times, career is a crucial life decision. Due to its paramount importance in our lives, the youth experience diverse kinds of stresses and pressures while choosing their career in life. Contrary to earlier handful career options, current trends offer an unlimited mix of traditional as well as unconventional career roles which further create confusion and anxiety while making the final decision. Newer factors, challenges and opportunities have forced a change of frameworks and perspectives to career decision-making. In career counselling, current trends are shifting away from the traditional practice of matching skill-sets to the job. Todays career decision-making takes an inclusive approach by discussing the various internal influences, emotional management, cognitive thinking styles, coping strategies, adaptability, etc. as well as external influences of uncertainty, evolving lifestyle demands, changing economic tapestry, etc. In addition, the influences of gender, family and culture affect the nature of the goals and their acceptability. The current chapter throws light on the evolving scenario in the field of career decision-making among the youth and attempts to offer solutions to the challenges being faced by the youth. The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023. -
Curriculum for class nine CBSE poetry teaching with the integration of REBT based on NEP 2020
This study examines the effectiveness of a learner-centered poetry curriculum integrated with Rational Emotive Behavior Therapy (REBT) techniques in addressing adolescents' emotional and behavioral challenges. As found in the Checklist - Curriculum Designs by Nicolas Frichot [Frichot, N. (2024). Checklist - Curriculum designs. Research Gate, February, 0-1. https://doi.org/10.13140/RG.2.2.30357.09441], the researcher understands it is befitting to develop the curriculum for poetry teaching in class 9th based on the learner-centered approach. Aligned with NEP 2020, the curriculum was implemented by five teachers from different schools. This research evaluates the impact of this integrated approach on students' attitudes, behaviors, and the ability to overcome personal obstacles. To assess the curriculum's impact, participant teachers completed post-implementation interview questionnaires. Additionally, a survey of English teachers in Bhopal was conducted to gauge their knowledge of NEP 2020 and REBT. A focused group of five teachers from diverse backgrounds provided further insights through a Google Form questionnaire. 2025 National Association for Poetry Therapy. -
Curvature Ductility of Reinforced Masonry Walls and Reinforced Concrete Walls
Research conducted in this work proposes an equation to evaluate and compares the curvature ductility of reinforced masonry (RM) and reinforced concrete (RC) walls. The curvature ductilities are measured at varying levels of axial stresses for walls for aspect ratio (l/h) of 0.5, 1.0 and 1.5. The percentage of reinforcement is increased from 0.25% (minimum reinforcement for RC walls as per IS-13920) to 1.00%. The curvature ductilities are evaluated by plotting flexural strength (M) versus curvature (?) for the walls. The stressstrain curves of masonry, concrete and reinforcing steel are all adopted from existing literature. The compressive strength of masonry and concrete has been chosen as 10MPa and 25MPa, respectively. The yield strength of the steel is fixed as 415MPa. The height and thickness of the wall are 3000 and 230mm, respectively, and the length of the wall is varied to obtain different aspect ratios. Results obtained from this paper imply due to increase curvature ductility, RM walls provide a better alternative for the construction of structural walls compared to RC walls in regions of significant seismicity. 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Curvit: An open-source Python package to generate light curves from UVIT data
Curvit is an open-source Python package that facilitates the creation of light curves from the data collected by the Ultra-Violet Imaging Telescope (UVIT) onboard AstroSat, Indias first multi-wavelength astronomical satellite. The input to Curvit is the calibrated events list generated by the UVIT-Payload Operation Center (UVIT-POC) and made available to the principal investigators through the Indian Space Science Data Center. The features of Curvit include: (i) automatically detecting sources and generating light curves for all the detected sources and (ii) custom generation of light curve for any particular source of interest. We present here the capabilities of Curvit and demonstrate its usability on the UVIT observations of the intermediate polar FO Aqr as an example. Curvit is publicly available on GitHub at https://github.com/prajwel/curvit. 2021, Indian Academy of Sciences. -
Custodial deaths: A legacy of shame
The Sivagangai case highlights the need for urgent structural reform in Tamil Nadus police culture. -
Customary complications and screening techniques of early pregnancy
Complications arising during pregnancy are one of the significant public health issues around the world. Being the critical aspect for the growth and development in every human's life, it is necessary to assess any pathological disturbance mainly concerning the well-being of the fetus and mother throughout pregnancy. Few women face health downline while pregnancy, whereas few faces before getting pregnant; both the conditions can lead to complications in maternal and fetal health. Usually, pregnancy-related complications disappear as soon as the baby is delivered or shortly after that. Few complications include premature delivery, abruption of the placenta, preeclampsia, and diabetes. At the same time, gestation and these complications reoccur in successive pregnancies at a higher rate. They can also make the women prone to lifelong medical complications such as metabolic diseases or cardiovascular disease later in life. Most of the complications are mild without progression, but it harms both the mother and baby when they progress. Hence, it is important to diagnose early the danger signs and provide antenatal care to elevate the chances of proper health in both the mother and infant throughout the pregnancy and afterwards. This chapter focuses on the complications faced in the early phase of pregnancies and the screening methods utilized to diagnose them early, along with long-term effects in mothers due to some complications. The Author(s), under exclusive license to Springer Nature Switzerland AG 2023. -
Customer Acceptance of Sustainable Fintech Innovation: A Study from an Indian Customer Perspective
Financial technology, or FinTech, aims to simplify and enhance financial services. FinTech is frequently referred to as financial technology. The customers day-today financial operations, processes, and existence are all significantly impacted by the financial transactions that they engage in. Although there has been a notable rise in the literacy rate among Indian consumers in the last 20 years, financial literacy seems to still be in its nascent phase. Hence, the research investigates the determinants that impact the adoption of FinTech services, which include a range of societal, ecological, and environmental advantages. This research presents a comprehensive model that expands on the TAM model by including reported relief as a mediator and perceived ease of use as an independent variable. The study also takes into account consumer acceptance as the dependent variable. Through quantitative approaches, a total of 253 responses have been collected from Indian citizens from online chatrooms and financial education sites. A Google Form link has been shared with the students and participants in these online chatrooms. After some time had passed, the quantitative data were evaluated using SmartPLS by employing the approach of partial least squares. The research findings indicate that consumers perception of the usefulness and alleviation provided by sustainable fintech services greatly influence their overall adoption of these services. The research conducted a moderation analysis utilizing the PLS-MGA technique to determine whether generation, Gen Z or Gen Y, had a significant moderating impact and shown more willingness to use FinTech services. Eventually, the research offered practical recommendations for managers to provide suitable, dependable, and enduring services to clients at a fair price that aligns with their needs and eventually enhances their psychological well-being. 2025 Scrivener Publishing LLC. -
Customer Behavior Analysis Using Unsupervised Clustering and Profiling: A Machine Learning Approach
Now-a-days, client conduct models are reliably established on information mining of client information, and each model is supposed to answer one solicitation at one point on schedule. Anticipating client conduct is a problematic and irksome task. Thus, making client conduct models requires the right strategy and approach. Right when an estimate model has been fabricated, it is challenging to restrict it for the motivations driving the advertiser, to pick the very thing displaying moves to make for every client or for the party of clients. Notwithstanding the multifaceted nature of this arrangement, most client models are completely fundamental. As the need might arise, most client conduct investigation models ignore such endless proper factors that the gauges they make are overall not altogether strong. This paper plans to encourage a connection rule mining model to expect client conduct using a typical electronic retail store for data combination and concentrate critical examples from the client conduct data. In this undertaking, a solo grouping of information on the customer's records from a regular food item company's data set will be played out. Customer segmentation is the act of clustering customers into bunches that reflect likenesses among customers in each group. Customers are separated into sections to advance the meaning of every customer to the business. To change items as indicated by unmistakable requirements and practices of the customers. It additionally assists the business with obliging the worries of various kinds of customers. Customers were clustered using a technique known as agglomerative clustering, which is a type of hierarchical clustering. Agglomerative clustering is a method for clustering data in a hierarchical order. It entails merging cases until you reach the appropriate number of clusters. The number of clusters to be produced is determined using the Elbow Method. 2022 IEEE. -
Customer churn behaviour prediction in telecommunication using classification algorithms and modelling
The cost of obtaining a high-quality client is usually five times more than the cost of keeping an existing customer. This is why it is very important that businesses keep their customers at home. To retain and improve their customers' satisfaction, researchers in various fields such as marketing, information technology, and business intelligence studied various ways to deliver the best possible services. Despite the good performance of the work done before, there is still a considerable gap in their prediction of the churners. In most cases, the training dataset is too large, and the high dimensionality of it causes the classification algorithms to fail. In the present paper, an attempt was made to estimate customer churn with greater accuracy in the membership of cellular wireless services using a call details records dataset consisting of 3333 clients having 21 attributes each. With the advancement of Machine Learning (ML) and artificial intelligence, most popular approaches such as logistic regression, CART, and C5 algorithms have been used with and without using the data balancing technique SMOTE. The performance evaluation of these predictive models is done using the model accuracy, confusion matrix, AUC value, ROC curve, and Cohen's Kappa statistics. The study results indicate that the C5 algorithm could estimate customer churn with an accuracy of more than 92% for both balanced and imbalanced datasets. 2025 Author(s). -
Customer Evaluation of Internet Banking Services: Analysing South Indian Bank's Digital Banking Experience
This research intends to assess the valuation of Christ University's customers on internet banking services provided by South Indian Bank. The research objectives are to evaluate the impact of efficiency, security, ease of use, reliability and social influence on customer satisfaction in the context of internet banking. Consumers of Christ University were selected to answer in the survey to give their view and experience toward the internet banking services offered by the South Indian Bank. The strategies used in data analysis incorporated regression analysis to realize correlation between such factors and extent of customer satisfaction. The research offers huge information on how it is possible to enhance the levels of customer satisfaction specifically in internet banking services of South Indian Bank. Therefore, the study underpins an importance of enhancing digital banking platforms to fulfil the need and expectation of customers. The study is also beneficial for other financial institution with the aim at improving the level of customer satisfaction and ability to retain the clients in the realm of digital banking. 2025 IEEE. -
Customer Lifetime Value Prediction: An In-Depth Exploration with Regression, Regularization and Hyperparameter Tuning
In today's dynamic business environment, companies have been strategically shifting towards a customer-centric approach from their traditional product-centric focus. The main goal of this paper is to estimate customer lifetime value of 5,000 customers in the retail industry. This research follows a step-by-step approach to construct a multiple regression machine learning model. The model used in the study is based on the nine features to predict the customer life time value. First basic train-test split model is developed, which predicted 74% of variation in the customer lifetime value. This necessitates to improve the model performance, hence to address the multicollinearity problem lasso regularization is used. After lasso regularization , final model is trained with hyperparameter turning for further model performance improvement. The results show significant improvements in predicting customer lifetime value with the final model. This study suggests that the machine learning regression models can help to businesses to better understand how much value they can generate from individual customer.This deep understanding about customers helps retail businesses to align their customer engagement strategies to create a positive impact on the profitability and maximizing overall value offered to the customers. 2024 IEEE. -
Customer perspective on a curated gift-box service: A study in Sikkim, India
Due to the proliferation of choices and brands, accessibility to information, and new communication mediums, consumer behavior, particularly decision-making processes, has been altered by the spending power of various segments. In the Indian environment, although product appearance has been identified as a significant factor in influencing customer behavior, its effect on decision making when combined with other factors such as cost, features, and intrinsic psychological factors has not been studied thoroughly. This study aims to highlight consumers' perspective on a curated gift-box service in Sikkim. Focusing on gifting during special occasions, impulse buying, and self-gift opportunities, this study stands on the possibility that there is a need for such service in the market. 2023, IGI Global. -
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. -
Customer preferences to select a restaurant through smart phone applications: An exploratory study
The increasing number of Smart Phone Applications (SPA) user and fast growing restaurant industry proves the great potential of using SPA as business marketing opportunity in Malaysia. The constant growth in mobile technology has created a prospect for the restaurant industry to use SPA as a restaurant promotion tool. The growing attention of use of SPA among the Malaysian customer, marketing research remains understudied in the field of SPA based restaurant promotion activities. The aim of this study is to explore the increase in customer acceptance to use SPA based restaurant promotion and to identify the customer preference to use SPA to select the restaurant. Thus, this paper mainly focuses on restaurant information on product and promotion as antecedents of customer acceptance of smart phone apps by underpinning the Unified theory of acceptance and use of technology (UTAUT) model. A conceptual model and hypotheses are tested with a sample of 116 students from a private university at Selangor district, Malaysia. The findings indicate that there is a positive relationship to increase customer acceptance level through SPA based restaurant product information and also strong relationship with the restaurant promotion information. It also indicates that customer acceptance of SPA through experience and satisfaction has a positive significant effect on customer preference to select a restaurant. Based on the results, this paper rounds off with conclusion, recommendations for future marketing research and provides a new marketing strategy to formulate among the restaurant business sector. 2015 American Scientific Publishers. All rights reserved. -
Customer Relationship Management (CRM) Systems: Impact of AI on Post-COVID-19 Student Enrolment in Keralas Higher Education Institutions
This study investigates the influence of artificial intelligence (AI) on student enrollment in higher educational institutions in Kerala, with a particular emphasis on the mediating role of customer relationship management (CRM) systems. Employing a descriptive research design, a self-prepared questionnaire was administered to a purposive sample of 200 students, encompassing various educational stages. The findings reveal a significant positive correlation between AI usage and student enrollment, with AI usage accounting for 37.3% of the variance in enrollment outcomes. Furthermore, the analysis indicates that CRM systems partially mediate the relationship between AI and student enrollment, enhancing the effectiveness of AI-driven strategies in the recruitment process. With high reliability demonstrated in the research instrument (Cronbach alpha = 0.827), the study underscores the necessity of integrating AI and CRM technologies to optimize enrollment strategies in the evolving educational landscape. 2025 by IGI Global Scientific Publishing. All rights reserved. -
Customer Segmentation and Future Purchase Prediction using RFM measures
Winning in the E-Commerce business race at a competitive age like this requires proper usage of Customer data. Using that database and grouping it in similar segments in terms of spending expenditure, observation time, sex, and location so that every customer falls in a segment of characteristics. This mechanism is called Customer Segmentation. In the modern era of highly compatible technological advancements, Machine Learning Algorithms are being vastly used to bring solutions to these difficult yet essential services. In the field of research methods like simple clustering based on purchase behaviour, buyer targeting or automated customer promotion mechanism by dividing into two major categories, have been worked on. However, ensemble algorithms have come handy where different clustering algorithms are combined to deliver best segmentation. Lately combination techniques like clustering and classification mechanism have also delivered good results where, not only segmentation is done but also classification of existing and new customers are possible into the clusters. Depending on that an effective customer relationship management can really benefit the company to a huge extent. Unlike other studies where clustering was performed directly on RFM table, a different approach was taken in this study where, one dimensional clustering was done individually on Recency, Frequency, Monetary columns, then an overall score was calculated and customers were classified into three segments. However, for a new customer depending on his purchase behaviour he/she also can be classified into any of the categories. 2022 IEEE. -
Customer Segmentation in the Field of Marketing
The motive of this work is to classify and categorize customers depending on their familiar traits/characteristics so as to enable a company or a firm to adequately market their products to each category more attractively and competently. It is imperative for a firm to educate themselves with each and every detail about the customer, such as age group, sexuality, social class, purchase pattern etc as it paves way for customer segmentation. Businesses may utilize segmentation to make better use of their marketing resources, get a competitive advantage over competitors, and, most importantly, display a deeper understanding of their consumers' requirements and desires. Customer segmentation, when combined with customer targeting and positioning, creates the foundation for strategic marketing. A manager can find new marketing possibilities and create or adjust the product to satisfy the demands of potential clients using the notion of strategic marketing. The product's quality level determines its position in the market's overall offering. It's a crucial aspect in selecting which market segment a collection will target. The commercial world has gotten more competitive over time, as enterprises like these have to fulfil their consumers' demands and aspirations, attract new customers, and enhance their bottom lines. In this research, I have put the spotlight on the information used by firms for the purpose of customer segmentation in the most valuable manner. In addition to that, I have portrayed different models of customer segmentation and the benefits reaped by a business in implementing them. 2022 IEEE. -
Customers response to online food delivery services during COVID-19 outbreak using binary logistic regression
This study aims to empirically measure the distinctive characteristics of customers who did and did not order food through Online Food Delivery services (OFDs) during the COVID-19 outbreak in India. Data are collected from 462 OFDs customers. Binary logistic regression is used to examine the respondents characteristics, such as age, patronage frequency before the lockdown, affective and instrumental beliefs, product involvement and the perceived threat, to examine the significant differences between the two categories of OFDs customers. The binary logistic regression concludes that respondents exhibiting high-perceived threat, less product involvement, less perceived benefit on OFDs and less frequency of online food orders are less likely to order food through OFDs. This study provides specific guidelines to create crisis management strategies. 2020 John Wiley & Sons Ltd