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Government is trying but consumers are not buying: A barrier analysis for electric vehicle sales in India
It is a harsh fact that the introduction of various government schemes to push electric vehicle (EV) utilisation does not seem to appeal to the consumers. There are a few barriers that prevent consumers from purchasing EVs. Thus, in the present study, we have tried to identify and analyse the prominent barriers to the adoption of EVs by scrutinising the existing literature and defining new barriers. From the literature review, 35 barriers have been initially identified in the context of the Indian market. The study uses the Decision-Making Trial and Evaluation Laboratory (DEMATEL) approach for its analysis. The prominence and causal relationship analyses indicate that familial factors that hinder the decision to buy an EV and unclear government policies regarding EVs were the primary concerns with regard to these vehicles getting traction in the market. The results of this study illustrate the causal relationships amongst the identified barriers. On the other hand, the study reveals that the pricing of the EVs is not a major issue and that the consumers are more concerned about the availability of maintenance support post purchasing of the vehicles. 2021 Institution of Chemical Engineers -
Consumption Behaviour of Poor Consumers: A Bibliometric and Content Analysis
The poor people segment has gained considerable scholarly attention, particularly after eight scholars established it as an untapped market. This study attempts to understand the nine consumption behaviours of a poor consumer. Therefore, we collate 384 scholarly papers indexed in the Scopus database, published during the period 19752020. Both evaluative and bibliometric relationship techniques, namely, Biblioshiny and VOSviewer, are utilized to understand the domains progress in the past 45 years. Besides this, content analysis is conducted via hand search to identify the most utilized research methods, popularly applied theories and most explored contexts. Results show that research in this field has grown exponentially, particularly after 2007, with research focusing on low-income consumers, bottom-of-pyramid consumers and mobile money users. The findings indicate this domain demonstrates skewed development, and there is a huge room for future studies. 2022 Fortune Institute of International Business. -
APPLICATIONS OF ARTIFICIAL INTELLIGENCE IN BUSINESS AND FINANCE 5.0
AI has evolved as a burgeoning technology in many industries, including the financial and banking sectors, which are facing greater challenges in data management, identity theft, and fraud as transactions and other company processes shift online and gain popularity. As systems using deep learning technology are able to detect data patterns and spot suspicious activity and probable fraud, AI can advance many financial and business activities. This book, Applications of Artificial Intelligence in Business and Finance 5.0, provides a valuable overview of how artificial intelligence (AI) applications are transforming global businesses and financial organizations, looking at the newest artificial intelligence-based solutions for e-commerce, corporate management, finance, banking and trading, and more. Chapters look at using artificial intelligence and machine learning techniques to forecast and assess different financial risks such as liquidity risk, volatility risk, and credit risk. The book also describes the use of natural language processing and text mining paired with machine learning models to assist in guiding sophisticated investors and corporate managers in financial decision making. Other topics include cryptocurrency in emerging markets; the role of artificial intelligence in making a positive impact on sustainable development; the use of fintech for micro, small and medium enterprises; the role of AI in financial education; the application of artificial intelligence in cyber security; and more. With a cross-disciplinary theme, this volume will be helpful to those in the corporate world, including professionals in business, finance, the e-commerce, economic sociology, political science, public administration, mass media and communications, information systems, development studies, among others. 2025 by Apple Academic Press, Inc. -
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. -
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. -
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. -
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. -
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”. -
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. -
Transforming healthcare engagement in the medtech industry through digital marketing
[No abstract available] -
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. -
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. -
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. -
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. -
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. -
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. -
Wound Healing, Cell Viability and Antimicrobial Potency of Mucus from Pangasianodon hypophthalmus
Acute and chronic wounds are the major cause of death according to World Health Organization (WHO), in which, antimicrobial resistance is considered to be a major plight. In this regard, our study is aimed at developing an antimicrobial agent using the mucus of Pangasianodon hypophthalmus against the clinically resistant microbial pathogens and to evaluate the cell cytotoxicity and cell viability followed by an in vitro wound healing analysis. The evaluation of antimicrobial activity was performed through well diffusion method and micro dilution method. The cell cytotoxicity and cell viability were assessed using MTT assay. The cell migration and in vitro wound healing was performed using scratch assay. The acidic extracts of mucus showed antimicrobial activity against the eight different selected bacterial strains while the organic extract showed against seven bacterial strains. L929 showed a cell viability of 102.96% at a concentration of 75 g/mL and did not show cell toxicity effect up to the concentration of 300 g/mL. In the in vitro wound healing analysis, the cell migration rate was 99.27% in the treated cells while, the untreated showed only 94.68%. The current research work clearly shows that the mucus of P. hypophthalmus possesses antimicrobial activity and wound healing potency. Furthermore, gene expression analysis and in vivo trials have to be performed for a thorough understanding of the actual cellular mechanism of wound healing. The Author(s) 2024. -
A Systematic Review of Fish-Based Biomaterial on Wound Healing and Anti-Inflammatory Processes
Objective: To conduct a systematic literature review to study the effects of fish-based biomaterials on wound healing in both in vivo and in vitro animal models. Approach: This review covers the study reported in different articles between 2016 and August 2022 concentrating mainly on the cytotoxicity evaluation of different fish-based biomaterials on inflammation, reepithelialization and wound healing. Significance: This review shows considerable amount of research work carried out with fish-based biomaterials and collagen for treating burn wounds. Surprisingly there are only a few commercial products developed so far in this particular regard for surgical purpose and therefore, there is a way out and need for developing medical support product from fish-based biomaterials to treat and cure wounds. Recent Advances: Three-dimensional skin bioprinting technique is a large-scale solution for severe burn wounds that requires collagen as a raw material for printing, wherein fish collagen can be used in place of bovine and porcine, as it is biocompatible, promotes cell proliferation, adhesion, and migration, and degrades enzymatically. In the recent times, there are a few fish-based surgical products that have been formulated by Kerecis in United States. Critical Issues: The different fish-based biomaterial products are all mere supplements taken in orally as food or supplements till date and there is no proper proven medications that has been formulated so far in the field of wound healing and inflammation based on fish biomaterials except the surgical products that can be finger counted. Future Directions: Fish-based biomaterials are known for the medicinal properties that are used throughout the world and further investigations should be carried out to understand the actual physiochemical properties of its derivatives for the discovery of novel products and drugs. Copyright 2024 by Mary Ann Liebert, Inc. -
Malicious node detection using heterogeneous cluster based secure routing protocol (HCBS) in wireless adhoc sensor networks
In wireless, every device can moves anywhere without any infrastructure also the information can be maintained constantly for routing the traffic. The open issues of wireless Adhoc network the attacks which are chosen the forwarding attack that is dropped by malicious node to corrupt the network performance then the information integrity exposure. Aim of the problem that existing methods in Adhoc network for malicious node detection which cannot assure the traceability of the node as well as the fairness of node detection. In this paper, the proposed heterogeneous cluster based secure routing scheme provides trust based secure network for detection of attacks such as wormhole and black hole caused by malicious nodes presence in wireless Adhoc network. The simulation result shows that the proposed model is detect the malicious nodes effectively in wireless Adhoc networks. The malicious node detection efficiency can be achieved 96% also energy consumption also 10% better than existing method. 2020, Springer-Verlag GmbH Germany, part of Springer Nature. -
Design and validation of the digital well-being scale
As the reliance on digital products and services continues to increase, there arises the need to measure and understand how the use of digital devices affects our well-being. In order to do so, the researchers attempted to create and validate an instrument. The items for the instrument were identified through an extensive review of literature, followed by a brainstorming session. The statements were then validated by a panel of experts, post which the instrument was administered, and the data was collected and analyzed for reliability and validity. The final instrument returned a Cronbachs alpha score of 0.921, indicating high reliability. The validity of the instrument was also established through a confirmatory factor analysis. 2023, University of Bologna. All rights reserved.