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Exploring Consumer Choices and Shopping Patterns: Examining Influences on Consumer Choices
This chapter explores the dynamic world of consumer behaviour and buying patterns, focusing on the psychological, social, cultural, and economic factors that shape decisions. It examines how consumers manage their preferences and choices in various market situations, highlighting trends like sustainable consumption, loyalty-driven purchases, and impulsive buying. The chapter also investigates the impact of the digital revolution, including social media and e-commerce, on consumer engagement and purchasing habits. By addressing elements such as peer influence, brand perception, and decision-making processes, it emphasises the importance of understanding consumer diversity in demographics, culture, and lifestyle. Combining theoretical frameworks, real-world examples, and data-driven insights, this chapter provides businesses and researchers with a foundation for predicting demands and creating effective marketing strategies. 2025 by IGI Global Scientific Publishing. All rights reserved. -
Student Performance Prediction in Learners Centric Approach with Machine Learning
Predicting student performance helps educators locate students who are at risk and tailor appropriate and timely interventions for those students. This research proposes a learner-centered machine learning framework to integrate demographic, academic and behavioral features in order to predict student grade performance. The dataset consists of 2392 students and 15 attributes including age, gender, parental education, study time, absences, and extracurricular activities. Four supervised learning models - Logistic Regression, Decision Tree, Random Forest and Support Vector Machines (SVM) were trained and measured using 70:30 stratified split. The performance of the model was evaluated using accuracy, precision, recall, and F1-score metrics. Among these, Decision Tree classifier achieved the highest accuracy (92.48%) which was followed by Random Forest (88.31%), SVM (83.51%) and Logistic Regression (75.16%). The results show that such factors as study time, absences, and parental involvement were the most predictive. The proposed learner-centered approach shows that the combination of contextual, behavioral, and academic data can greatly increase the predictive accuracy and the interpretability of the data, facilitating early risk detection and intervention in education. The Authors, published by EDP Sciences. -
Green accounting and its application: A study on reporting practices of environmental accounting in India
Green Accounting is an important device for understanding the role of business ventures in the economy towards environmental security and welfare. It is a well-known term for environment and natural resources accounting. Many companies all over the world have initiated the practices of making environmental disclosures in their annual reports. However, these practices are still largely voluntary in nature. The objective of this research paper is to study the environment-related disclosures of companies taken from Nifty 50 based on the summary of Global Reporting Standards. Content Analysis, both sector-wise and keyword-wise is used on the annual reports of 29 sample companies using MAXQDA software. A high count of the formulated keywords is observed in some relevant sectors of Energy, Cement and Metals. 2022 Inderscience Enterprises Ltd. -
ResFruitGrader: Leveraging Residual Networks for Advanced Fruit Quality Grading Systems
The rising agricultural industrys requirement for effective sorting and grading procedures has increased the demand for automated and precise fruit quality assessment in recent years. This study aims to attain high classification accuracy by investigating the use of Convolutional Neural Networks for fruit quality identification. As customers place a higher value on fresh and wholesome options, the agriculture and food industries must meet rising demands for premium produce. Fruit quality must be guaranteed since it directly affects consumer happiness and the profitability of the sector. Preprocessing methods, CNN model creation, training, and evaluation utilizing cutting-edge deep learning techniques comprise the methodology applied in our study. The research demonstrates the CNN-based methods stability and dependability in identifying a range of quality attributes, such as fruit imperfections, size, color, and maturity. The suggested CNN architecture performs remarkably well, recognizing fruit quality parameters with a 99.5% accuracy rate by utilizing a collection of various fruit photos. A promising path for improving efficiency and accuracy in fruit quality assessment within the agricultural industry is provided by the researchs insights into the transferability and scalability of the developed model for practical applications in automated fruit sorting systems. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025. -
Text-Based Sentimental Analysis to Understand User Experience Using Machine Learning Approaches
Data Analysis is turning into a driving force in every industry. It is a process in which data is analyzed in multiple ways to come to certain conclusions for the given situation. Sentiment analysis can be said to be a sub-section of data analysis where analysis is carried out on the emotions and opinions of the text. Social media has a plethora of sentiment data in various forms such as tweets, updates on the status, and so forth. Sentiment analysis on the huge volume of data can help in identifying the opinions of the general mass.The primary goal is to find the opinion of customers on the services of the Bangalore airport and to enhance the nature of these services according to the feedback provided. In this paper, we aim to measure customer opinion on services provided by Bangalore Airport through sentiment. Data is collected by a python-based scraper. The tweets are processed to determine whether they are of positive or negative opinion. These opinions are then analyzed to determine the factors which cause the negative opinions and the airport staff are alerted about the same. Various algorithms were used as part of the experimental analysis. LSTM produces more accuracy compared with existing approaches. 2023 IEEE. -
Performance Analysis of User Behavior Pattern Mining Using Web Log Database for User Identification
User behavior analytics is a progressive research domain. Understanding the users behavior patterns and identifying their behavior patterns will provide solutions to many issues like identity theft and user authentication. So many research works are done in analyzing the frequent access patterns of the users by pre-processing access logs and applying various algorithms to understand the frequent access behavior of the user. From the literature, it founds that the frequent user access pattern identification needs improvement on prediction accuracy and the minimal false positives. To accomplish these, three different approaches were proposed to overcome the existing issues and intended to reduce false positives and improve the frequent pattern mining accuracy based on web access logs. Proposed methods were found to be good while compared with the existing works. 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Research aligned analysis on web access behavioral pattern mining for user identification
Human activity understanding includes activity recognition and activity pattern discovery. Monitoring human activity and finding abnormality in their activities used by many field like medical applications, security systems etc. Basically it helps and support in decision making systems. Mining user activity from web logs can helps in finding hidden information about the user access pattern which reveals the web access behaviour of the users. Clustering and Classification techniques are used for web user identification. Clustering is the task of grouping similar patterns for web user identification. Classification is the process of classifying web patterns for user identification. In this paper we have implemented the existing works and discussed the results here to find the limitations. In existing methods, many data mining techniques were introduced for web user behaviour identification. But, the user identification accuracy was not improved and time consumption was not reduced. Our objective is to study the existing work and explore the possibility to improve the identification accuracy and reduce the time consumption using machine learning and deep learning techniques. BEIESP. -
Toward precision agriculture in Cyber-Physical Agricultural System
Agriculture 4.0 or Agri 4.0 is a newly developed system that consists of various digital technologies adapted from Industry 4.0 based on smart automation. Agriculture 4.0 is a subset of Industry 4.0 aimed at sustainable precision agriculture (PA) and increasing agricultural efficiency using digital technologies and the Internet of Things. The cyber-physical system (CPS) is the seamless integration of digital and physical domains and when CPS is applied in agriculture, it is termed cyber-physical agricultural system (CPAS). The application of CPS in carrying out PA with sustainable management of resources is termed Agri 4.0. Research papers are reviewed to understand the bigger picture behind various details of digital technologies and CPS with a focus on agriculture 4.0 and to determine its applications, challenges, and developments in the field. It is apparent that most of the small and marginal farms in remote areas are not able to use this technology due to a lack of knowledge and resources. It is the need of the hour to support these farmers by making favorable policies and appropriating budgets such that it will lead to more profitable and sustained PA and in the process contribute to the social and economic upliftment of farmers of India. 2024 Elsevier Inc. All rights reserved. -
TAMIL- NLP: Roles and Impact of Machine Learning and Deep Learning with Natural Language Processing for Tamil
Reading information in your mother tongue gives the feeling of enjoying juice of fruit. Researchers are working on regional languages to provide convenient and perfect automated tools to convert the content of knowledge from other languages. There exist many challenges based on the grammar of language. One of the classic regional languages, Tamil which is rich in Morphology, contains more processing challenges. The Natural Language Processing (NLP) technique along with Machine Learning (ML) and Deep Learning (DL) algorithms have been used to overcome those challenges. The accuracy of work is depending on the corpus provided to train the model. Among the reviewed papers using Support Vector Machine (SVM) of ML produced higher accuracy then other ML techniques. As DL techniques for NLP are booming one the researchers are working with different DL algorithms. Most of the NLP with Review Discussion in this paper will direct the researchers doing NLP in Tamil language to move further and to choose the right Machine Learning and Deep Learning algorithm to come out with accurate outcomes. 2023 IEEE. -
Transforming healthcare engagement in the medtech industry through digital marketing
[No abstract available] -
Non-orthogonal multiple access wireless systems using deep learning
In 5G networks, non-orthogonal multiple access (NOMA) increases spectral efficiency and user capacity greatly by letting multiple users share the same time, frequency, and code resources. Wireless communication systems stand to benefit significantly from deep learning owing to its ability to model intricate patterns. This chapter centers around deep learning-NOMA integration with special attention given to areas like channel estimation, interference management, and dynamic resource allocation. Using advanced deep learning frameworks such as convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and deep reinforcement learning (DRL), this chapter demonstrates how NOMA system performance can be optimized to meet the stringent requirements of 5G and beyond networks. Moreover, this chapter also discusses the challenges associated with implementing deep learning in NOMA including computational complexity and data requirements, alongside future trends like federated learning and edge computing among others. The integration of these technologies promises improved network efficiency, reduced latency, and enhanced user experience, thereby making NOMA a fundamental technology in wireless communication evolution. 2025 selection and editorial matter, Mariyam Ouaissa, Mariya Ouaissa, Hanane Lamaazi, Khadija Slimani, Ihtiram Raza Khan, and B. Sundaravadivazhagan. -
Election Forecasting with Machine Learning and Sentiment Analysis: Karnataka 2023
Data science is rapidly transforming the political sphere, enabling more informed and data- driven electoral processes. The ensemble machine model which is made up of Random Forest Classifier, Gradient Boosting Classifier, and Voting Classifier, introduced in this paper makes use of machine learning methods and sentiment analysis to correctly forecast the results of the Karnataka state elections in 2023. Election features such as winning party, runner- up party, district name, winning margin, and voting turnout are used to evaluate the effectiveness of different machine learning paradigms. Similarly, it also makes use of sentiment analysis through party tweet and public reactions for further breaking down reliance upon past elections data alone. This study demonstrates that using both past historical records and current public opinion yields precise predictions about how electable leaders are. This reduces reliance on a historical dataset. The experimented results shows that, how machine learning and sentiment analysis can predict election results and provide useful data for election decision making. We compared various machine learning models in this study, including logistic regression, Grid SearchCV, XGBoost, Gradient Boosting Classifier, and ensemble model. With an accuracy of 85%, we demonstrated that our ensemble model outperformed machine models such as XGBoost and Gradient Boosting Classifier. It also offers a novel method for predictive analysis. 2023 IEEE. -
Predicting Liver Injury Risk from Chemical Properties and Drug Label Information Using Machine Learning Models
This research aims to create a drug-induced liver injury (DILI) severity prediction system based on machine learning to aid healthcare professionals in safety assessment. FDA's Liver Toxicity Knowledge Base supplied a drug dataset of 1042 drugs, and later, after pre-processing and API data extraction, each drug was defined by 16 chemical features such as molecular descriptors and pharmacokinetic properties. To improve uniformity and get quality input for training, data preparation involved correcting missing values, encoding categorical values, and normalising numerical data. Various machine learning models were trained and evaluated to forecast the levels of DILI severity, i.e., Random Forest, Gradient Boosting, and XGBoost. The importance of features was approximated for identifying the predictors that impacted the most. The best overall performance was recorded for XGBoost, and it had 81% accuracy when it was evaluated. Its acceptable discrimination was established for mild, moderate, and severe cases. The aptness of being applied to the medical sector is demonstrated by drastically lowering the principal misclassifications, especially from mild to severe. The application of machine learning in improving medicine safety assessment and reducing risks associated with pharmaceutical development is illustrated here. 2025 IEEE. -
Positive ageing: self-compassion as a mediator between forgiveness and psychological well-being in older adults
Purpose: Positive aging aims to promote the physical health and psychological well-being of older adults for them to age successfully. Under the domain of positive aging, this study aims to explore the mediating role of self-compassion between forgiveness and psychological well-being in older adults. Design/methodology/approach: It was based on a quantitative research design, with a sample of 250 individuals within the age group of 6075 years. Data was collected using Self-compassion Scale (2003), Heartland Forgiveness Scale (2005) and Psychological Well-being Scale. Analysis was performed using Pearsons correlation, linear regression, followed by the generalised linear model of mediation. Findings: The results revealed a significant (p ? 0.001), high and positive correlation between self-compassion and forgiveness (r = 0.821), forgiveness and psychological well-being (r = 0.852) and self-compassion and psychological well-being (r = 0.802). Linear regression suggested that self-compassion and forgiveness are significant (p ? 0.001) predictors of psychological well-being, causing a variance of 75.6%. Mediation revealed significant (p ? 0.001) direct, indirect and total effect between the variables, showing that self-compassion partially mediates the relationship between forgiveness and psychological well-being. Research limitations/implications: The findings provide valuable insights on how fostering self-compassion along with forgiveness can improve psychological well-being among the elderly, however, research on additional variables, drawing comparisons between gender, economic status and clinical populations can be further explored. Nevertheless, this study can be used to develop interventions and therapeutic techniques to enhance self-compassion and forgiveness to improve psychological well-being among older adults. Originality/value: As per the best knowledge of the researcher, this work is original as it is a primary research and no data has been collected of a similar nature from the participants. 2024, Emerald Publishing Limited. -
The role of consumer ethnocentrism in purchase decisions
Consumer ethnocentrism means consumers have propensity to give preference to products made in their country of residence and it has a major impact on consumer behaviour. This chapter examines the causes of consumer ethnocentrism with focus on patriotism, animosity, consumer affinity and other factors. This paper also analyses the moderating roles of product necessity and product knowledge on ethnocentric tendencies. Theoretical implications for marketers are explored and possible solutions for the consumer ethnocentric issue that marketers might employ segmentation, positioning, and communication strategies. This chapter found that consumer ethnocentrism is an important variable that global marketers need to consider when managing brands and entering international markets. 2025, IGI Global Scientific Publishing. -
Piracy in fashion business and protection for the creativity of designers: A comparative study /
The fashion business is one of the fastest-paced industries today. Beyond the simple act of selling clothing, a company's ability to create and capitalize on a distinctive brand is a crucial factor in achieving sustained success in this industry. When a style or brand becomes well-known, many others copy it mindlessly, which causes a huge loss for the people who made the original products. We also encounter fake products from well-known brands very often, which not only ruin the fashion industry but also pose a serious risk to the economy. Fashion nowadays thus goes beyond only clothing and accessories. The substantial expansion of the fashion industry is significantly influenced by intellectual property. “Intellectual property law may be used to safeguard the originality of a wide variety of creations, including those in the fashion industry. The types of intellectual property and their applicability to the fashion industry have only been touched on briefly. The purpose of this dissertation is to educate the reader about current fashion trends, hotly debated problems, and the importance of intellectual property rights in the fashion business”. -
Comparative Study of Product Liability and Data Confidentiality in Case of Intermediaries with Special Reference to India and The European Union
Technology has played a major role in human development. The advent and invention of wheel and fire changed the coverage of human society. On a similar note in 90 s a technology called internet was developed and it changed all rules of the game. This technology removed all hindrances of place and time. It created faceless market place wherein; consumer not only have huge choices and varieties but also, they can create goods and services on their own. This was the origin of Electronic Business and it gave birth to new breed of middleman / intermediaries to facilitate it. These intermediaries are application provider, ISP, network service provider etc. The mantras of success were wide choices and data. But this mantra created a new legal challenge of data handling and liability for defects in goods and services. Researcher has studied and analysed all dimensions of intermediaries newlineand how they handled the two new legal challenge of data confidentiality and newlineproduct liability. In addition, researcher has examined the legal framework of India and compared it with legal framework of European Union and finally concluded on the coverage and effectiveness of Indian legal structure and what India learn and implement from European Union. This thesis mainly focusing on generic business model used by intermediaries. Issues like IPR, industry specific domain like financial systems and medical domain are excluded. Researcher followed the doctrinal research methodology to understand the evolution of intermediaries, product liability, data confidentiality in India by various primary resources like the Indian Laws i.e., Consumer newlineProtection Act, 2019, Indian Contract Act, 1872, Information Technology Act, 2000 and other various statutes. This thesis compares Indian legal framework with European Union and test the hypothesis of coverage and effectiveness of Indian legal structure with European Union. -
An Innovative Method for Housing Price Prediction using Least Square - SVM
The House Price Prediction is often employed to forecast housing market shifts. Individual house prices cannot be predicted using HPI alone due to the substantial correlation between housing price and other characteristics like location, area, and population. While several articles have used conventional machine learning methods to predict housing prices, these methods tend to focus on the market as a whole rather than on the performance of individual models. In addition, good data pretreatment methods are intended to be established to boost the precision of machine learning algorithms. The data is normalized and put to use. Features are selected using the correlation coefficient, and LSSVM is employed for model training. The proposed approach outperforms other models such as CNN and SVM. 2023 IEEE. -
Positive ageing: self-compassion as a mediator between forgiveness and psychological well-being in older adults
Purpose: Positive aging aims to promote the physical health and psychological well-being of older adults for them to age successfully. Under the domain of positive aging, this study aims to explore the mediating role of self-compassion between forgiveness and psychological well-being in older adults. Design/methodology/approach: It was based on a quantitative research design, with a sample of 250 individuals within the age group of 6075 years. Data was collected using Self-compassion Scale (2003), Heartland Forgiveness Scale (2005) and Psychological Well-being Scale. Analysis was performed using Pearsons correlation, linear regression, followed by the generalised linear model of mediation. Findings: The results revealed a significant (p ? 0.001), high and positive correlation between self-compassion and forgiveness (r = 0.821), forgiveness and psychological well-being (r = 0.852) and self-compassion and psychological well-being (r = 0.802). Linear regression suggested that self-compassion and forgiveness are significant (p ? 0.001) predictors of psychological well-being, causing a variance of 75.6%. Mediation revealed significant (p ? 0.001) direct, indirect and total effect between the variables, showing that self-compassion partially mediates the relationship between forgiveness and psychological well-being. Research limitations/implications: The findings provide valuable insights on how fostering self-compassion along with forgiveness can improve psychological well-being among the elderly, however, research on additional variables, drawing comparisons between gender, economic status and clinical populations can be further explored. Nevertheless, this study can be used to develop interventions and therapeutic techniques to enhance self-compassion and forgiveness to improve psychological well-being among older adults. Originality/value: As per the best knowledge of the researcher, this work is original as it is a primary research and no data has been collected of a similar nature from the participants. 2024, Emerald Publishing Limited. -
Use Cases of Intelligent Manufacturing
In the sphere of manufacturing, the adoption of Intelligent Manufacturing (IM) has become crucial for maintaining competitiveness and efficiency. This abstract chapter explores the paradigms and progression of IM, tracing the evolution from Industry 1.0 to Industry 5.0. It highlights the significance of digital manufacturing and the Internet of Things in enhancing connectivity. The chapter demonstrates the importance of IM through its diverse applications, including supply chain management, logistics, and warehouse automation. The authors examine the transformative influence of artificial intelligence on order management, predictive maintenance, and the creation of virtual twins. Real-world examples, such as GEs utilization of AI to accelerate product design and Toyotas partnership with Invisible AI for optimizing production quality control, illustrate the practical advantages of IM in driving innovation and operational efficiency. This chapter provides a comprehensive overview of the various aspects of IM, highlighting its current and future impact on the manufacturing sector. 2025 selection and editorial matter, Alka Chaudhary, Vandana Sharma, and Ahmed Alkhayyat individual chapters, the contributors.