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                <text>The Role of AI, IoT, and blockchain in mitigating the impact of COVID-19</text>
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                <text>In the wake of the global COVID-19 pandemic, humanity faced unprecedented challenges that necessitated innovative technological solutions. The Role of AI, IoT, and Blockchain in Mitigating the Impact of COVID-19 explores the transformative influence of Artificial Intelligence (AI), Internet of Things (IoT), and Blockchain technologies in combating the pandemic's effects. Key themes:  Data Precision: Accurate and reliable data are essential for tracking virus spread. The book demonstrates how AI, IoT, and Blockchain can establish digital databases that ensure data accuracy, accessibility, and real-time monitoring, addressing the challenges faced by public healthcare systems.  Innovative Applications: Chapters in this book cover a wide array of applications, from AI-driven models for COVID-19 analysis and prediction to the use of 3D printing technologies, IoT tools for virus control, and the impact of AI and IoT in healthcare. It also explores the role of social media in promoting social distancing.  Advanced AI Techniques: Readers gain insights into cutting-edge AI techniques applied to COVID-19 in areas such as treatment, diagnosis, prognosis, chest X-ray and CT analysis, pandemic prediction, and pharmaceutical research.  Industry 4.0: The book discusses Industry 4.0 technologies and their contribution to sustainable manufacturing, efficient management strategies, and their response to the challenges posed by the pandemic. Contributed by a distinguished panel of national and international researchers, with multidisciplinary backgrounds specializing in Artificial Intelligence, biomedical engineering, machine learning, and healthcare technology, public health and industrial automation. Each contribution includes derailed references to encourage scholarly research. This book serves as a valuable resource for academic and professional readers seeking to understand how modern computing technology has been harnessed to address the unique challenges posed by the COVID-19 pandemic. It offers insights into technological innovations and their potential for the betterment of society, especially in times of crisis. Readers will be introduced to computing techniques and methods to measure and monitor the impacts of medical emergencies similar to viral outbreaks and implement the necessary infection control protocols.  2023 Bentham Science Publishers. All rights reserved.</text>
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                <text>The Role of AI, IoT, and Blockchain in Mitigating the Impact of COVID-19, pp. 1-203.</text>
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                <text>Vijayalakshmi S., Department of Data Science, CHRIST (Deemed to be University), Pune Lavasa Campus - The Hub of Analytics, Maharashtra, 412112, India; Chilamkurti N., Department of Computer Science and Computer Engineering, La Trobe University, Melbourne, Australia; Savita, School of Engineering and Technology, Maharishi University of Information Technology Noida, Noida, Uttar Pradesh, India; Dhanaraj R.K., Symbiosis Institute of Computer Studies and Research (SICSR), Symbiosis International (Deemed University), Pune, India; Balusamy B., Shiv Nadar University, Delhi-National Capital Region (NCR), Greater Noida, Uttar Pradesh, 201314, India</text>
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                <text>Security Intelligence in the Age of AI: Navigating Legal and Ethical Frameworks;pp.203-221</text>
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                <text>Lakshmi S.R., Christ University, India; Rajimol K.P., Atria Institute of Technology, India; Sunidu Y.K.; Samai A., Sri Venkateshwara College of Engineering, India; Priya R.P., Sri Venkateshwara College of Engineering, India; Srija H.R., Sri Venkateshwara College of Engineering, India</text>
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                <text>Much as the financial services industry is characterised by dynamism, Artificial Intelligence (Al) is gradually transforming the Customer Relationship Management (CRM). Among the AI-related advances is the advancement of bespoke financial services that increases involvement and experience of the customers. This chapter seeks to address the following research question: how has the incorporation of Al tools influenced the CRM strategies adopted by the industry? Newer, implementing Al in CRM systems, banks can go through terabytes of customers' information looking for more about each other's preferences, tendencies and needs. This information may therefore allow for financial services, proactive customer care and consumption point marketing based on segments of the population. Information on financial organisations' successful implementation of Al-based CRM systems is provided in the chapter; in addition, challenges and ethical issues regarding the use of these technologies are also described. The findings show how the use of Al can enhance CRM in the financial services sector resulting in satisfied and loyal customers alongside more organisation productivity. As for the maximisation of CRM results, the report also suggests that financial institutions must use Al appropriately while addressing the problems associated with the latter.  2025 Lakshmi S.R., Rajimol K.P., Y.K. Sunitha, Ajatashatru Samal, Priya R.P. and Srija H.R. All rights reserved.</text>
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                <text>The Role of Artifcial Intelligence in Renewable Energy</text>
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                <text>Artifcial Intelligence; control system; General Electric; Hydrogen; powerScout; Renewable energy; Robotics</text>
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                <text>Technology is evolving at an unbelievable pace, to the extent where many of us cant keep up effectively. With increasing Artifcial Intelligence (AI) complexity, our environment will be transformed in amazing ways over the years and decades that follow. The renewable energy (RE) sector is no different. AI can observe patterns and beneft from large amounts of knowledge. Consequently, AI is able to make improvements to enhance energy production, conversion, and even delivery. These systems allow precise forecasting of, for example, weather and loads, mitigating, among countless other uses, the possibility of electrical surges. AI systems would signifcantly improve the productivity of renewable systems by automation over the next 10 years. For solar and wind energy, this will become particularly prevalent. Independent power producers would have the latitude required to deliver ever-more sustainable business models and services by integrating increased generation coupled with low-cost savings provided by automation. We are all aware of the requirements of RE, including solar power. However, how can AI help to increase the availability of RE? The demand for global energy is growing day by day, but fossil fuels cannot fulfll our future needs for energy. Because of increased energy consumption, fossil fuel carbon emissions have reached very high levels over time. RE, however, is emerging as a good replacement for fossil fuels. It is safer and also very clean in comparison to traditional sources. The RE industry has made tremendous strides during the preceding decade with developments in technology. AI and machine learning technologies can analyze data to predict the future. So, the use of AI can solve the problems and challenges of RE. In this chapter we discuss RE, its sources and challenges, and how AI can address these challenges.  The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.</text>
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                <text>Power Systems, pp. 253-269.</text>
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                <text>Vijayalakshmi S., Department of Data Science, Christ University, Maharashtra, Lavasa, India; Savita, School of Computing Science and Engineering, Galgotias University, Uttar Pradesh, Greater Noida, India; Genish T., Department of Computer Science, KPR College of Arts Science and Research, Tamil Nadu, Coimbatore, India; George J.P., Department of Data Science, Christ University, Pune, Lavasa, India</text>
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                <text>Pundhir S.K.S., Christ University, Bengaluru, India; Yadav S.K., GLA University, India; Vijay A., School of Open Learning, University of Delhi, India; Kumar S., Noida Institute of Engineering and Technology, India</text>
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                <text>Agriculture and food are very important and basic needs for humans. From ancient time human started to feed themselves with the help of grains and animal's products. Later agriculture sector revolutionized with the production of various crops. Rice, wheat and pulses are grown at large to feed the worldwide population. World population is increasing rapidly. In 2000 worldwide population was 600 crores which becomes 800 crores in 2025. To feed a large population, farmers have to yield more crops with a limited resources. Farmers were using traditional methods for a long period to grow the crops, but these traditional methods are not enough to feed the growing population. AI and data driven approaches can help a lot modern agriculture sector by analyzing the data of soil quality, weather conditions, need of nutrients, pesticides and fertilizers. By collecting a large real time data with the help of sensors, drones and AI enabled machinery, can help a lot farmers to know the exact demand of crops as per the conditions.  2026, IGI Global Scientific Publishing.</text>
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                <text>The role of artificial intelligence autonomy in higher education in India</text>
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                <text>Rethinking the Pedagogy of Sustainable Development in the AI Era;pp.307-327</text>
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                <text>Khandelwaal C., Christ University, India; Dhaundiyal P., Christ University, India; Kataria A., Christ University, India</text>
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                <text>To leverage the benefits of artificial intelligence applications for experiential learning, many higher education institutes have started using artificial intelligence by adopting many artificial intelligencedriven technologies. Some of them are chatbots, generative AI, concepts of virtual tutors, and providing students with various automated assessment tools for their own assessment, which might change the traditional teaching methodology. This chapter examines the role of artificial intelligence autonomy in higher education in India, deep-diving into AI's impact on students' learning outcomes by leveraging AI-driven technologies in education. This research will specifically have five major variables that will be examined and measured. These variables are usage intention, thought autonomy, action autonomy, sensing autonomy, and culture. This study will examine the five main dimensions of AI autonomy: usage intention, thought autonomy, action autonomy, sensing autonomy, and culture.  2025, IGI Global Scientific Publishing. All rights reserved.</text>
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                <text>The role of artificial intelligence in business model innovation</text>
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                <text>AI-Driven Business Model Innovation;pp.163-190</text>
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                <text>Sai Charan Reddy C., Christ University, India; Sharma S., Christ University, India</text>
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                <text>Businesses are fundamentally changing because of AI, allowing for a greater focus on creativity. The chapter introduces advancements in AI like machine learning, predictive analytics, and natural language processing that help boost customer service, operational efficiencies and asset monetization opportunities. Key considerations will include personalized customer engagements, process efficiencies, optimizing resources and planning to set up new business models-subscription- based ser- vices/platform economies, etc. Emerging technologies like artificial intelligence have broader implications in e- commerce, finance, manufacturing-showing how firms use AI strategies to gain competitive advantage and foster business growth. In addition, the chapter addresses ethical considerations like bias, privacy as well as workforce implications and their role in ensuring responsible AI practices. AI can improve flexibility, realizing the highest level of value and maintaining competitive advantage.  2025, IGI Global Scientific Publishing. All rights reserved.</text>
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                <text>Swain K.P., Department of ETC, Trident Academy of Technology, Odisha, Bhubaneswar, India; Mohapatra S.K., Department of CSE (AI &amp;amp; ML), Dayananda Sagar University School of Engineering, Karnataka, Bengaluru, India; Nayak S.R., School of Computer Engineering, KIIT Deemed to Be University, Odisha, Bhubaneswar, India; Poonia R.C., Department of CSE, CHRIST University, Delhi NCR, Ghaziabad, India</text>
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                <text>The?? use of Artificial Intelligence (AI) is changing the whole car landscape. Indeed, AI is the main driving force behind the most innovative electric and autonomous vehicles. The technology is making transportation environmentally friendly, intelligent, and cost-effective. The chapter demonstrates the role of AI in self-driving cars and electric vehicles through various examples, such as autonomous driving, battery performance, charging systems, predictive maintenance, safety, efficiency, and fleet management. AI is the reason that cars can now drive themselves, whereby it is the technology that enables navigation, object detection, and systems like Tesla Autopilot. Besides, it is heavily involved in battery management as it lowers the battery life through usage, overheating, and prolongs the battery life. The chapter also talks about AI technology that supports smart EV charging, allowing integration of renewable sources and even making charging more comfortable and hassle-free. Predictive maintenance is yet another significant area where the AI system is monitoring the health of the car, the earliest detection of the faults, and extending the lifespan of EV components. Implementation of AI in safety vehicles is a great advancement in this industry. In conjunction with this technology, AI-based safety systems, like driver assistance, hazard detection, and emergency response, provide safety to the cars. Moreover, the technology enhances energy efficiency, range prediction, and real-time vehicle performance. The chapter concludes with a discussion about the coming of reflection in the AI realm of environmental sustainability, intelligent fleet management, and challenges of the future. In summary, this article accentuates how AI is rewiring the future of electric and self-driving vehicles and why its role is key for researchers, industry professionals, and ??policymakers.  2026</text>
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                <text>Sardana K., Christ University, India; Singh T., Christ University, India</text>
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                <text>With accuracy in customer insights, personalization at its best is made possible, and all of this is automated within the creation of content itself. AI helps brands sift through huge datasets, foretell consumer preferences, and even adjust campaigns in real time to increase the importance and impact of marketing initiatives. Brands use tools such as chatbots and recommendation engines to connect with the customers through targeted content for higher customer satisfaction and loyalty and eventually influence consumer decisions in purchasing. Another area is programmatic advertising and DCO, in which AI maximizes ad efficiency and reach. Ethical challenges in data privacy, bias, and transparency form the essence of these aspects and require brands to strike a balance between innovation and responsible AI use in maintaining consumer trust.  2025, IGI Global Scientific Publishing. All rights reserved.</text>
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                <text>Shingate, Kranti; Grover, Veena; Khanna, Shivi; Gupta, Gaurav; Chintale, Pradeep; Nerella, HarshaVardhan; Vanipriya, C.H.; Gochhait, Saikat</text>
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                <text>The Role of Artificial Intelligence in Simulating, Automating, and Analyzing Business Operations</text>
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                <text>Lecture Notes in Networks and Systems;Volume;1196 LNNS;pp.51-61</text>
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                <text>Shingate K., Symbiosis Institute of Digital and Telecom Management, constituent of Symbiosis International Deemed University, Pune, India; Grover V., NIET, Greater Noida, India; Khanna S., Christ University, Bangalore, India; Gupta G., Shoolini University, Himachal Pradesh, Solan, India; Chintale P., Borough, United States; Nerella H., Cleveland, United States; Vanipriya C.H., Sir M Visvesvaraya Institute of Technology, Bangalore, India; Gochhait S., Symbiosis Institute of Digital and Telecom Management, constituent of Symbiosis International Deemed University, Pune, India</text>
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                <text>Business processes have been transformed with the advent of artificial intelligence. However, to efficiently utilize the technology and to close the gap, we reviewed the literature to find these solutions in this work. We ensured that styles worked because they allowed for extensions and replication. In these studies, we correlated patterns that assisted with task automation and helped analysts create, expand, or re-engineer business processes with the confidence to make judgments. The authors used various AI methods, including swarm intelligence, Bayesian networks, and K-means. Our analysis gives data on the approaches and issues being dealt with and indicates potential future directions. Processes for predictive business future planning and activity prediction are examples of monitoring jobs that are becoming less significant as new technologies allow for the intelligent automation of company processes. Deep learning models are used in recent work on this subject to encapsulate historical event information without further processing. The data context, which includes the dependence of conditions and particular traits, might also have an impact on the anticipated data, even though it was not taken into account in earlier research. We present a novel encoding strategy for state data, encompassing non-existent, multi-character private, and regular event states. We present the transformer and LSTM deep learning models, two new deep learning models, and two popular deep learning models.  The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.</text>
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                <text>Artificial intelligence,; Automation analysis; Business operations; Learning models; Simulation</text>
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                <text>Springer Science and Business Media Deutschland GmbH</text>
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                <text>ISSN: 23673370; ISBN: 978-981979525-3;</text>
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                <text>Arockiasamy, J. Charles; Hoque, Mohammed Shamsul; A, Sangeeth Kumar; Kumar, J. Sathish; Kotte, Rupesh</text>
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                <text>The role of audiobooks in developing English listening proficiency: A study of undergraduate learner in Tamil Nadu</text>
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              <elementText elementTextId="226837">
                <text>International Journal of English Language and Literature Studies;Volume;14;Issue;4;pp.566-578</text>
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                <text>&lt;a href="https://doi.org/10.55493/5019.v14i4.5716" target="_blank" rel="noreferrer noopener"&gt;https://doi.org/10.55493/5019.v14i4.5716&lt;/a&gt; &lt;br /&gt;&lt;br /&gt;&lt;a href="https://www.scopus.com/pages/publications/105033120630?origin=resultslist" target="_blank" rel="noreferrer noopener"&gt;https://www.scopus.com/pages/publications/105033120630?origin=resultslist&lt;/a&gt;</text>
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                <text>Arockiasamy J.C., Department of English, St. Josephs College, Tiruchirappalli, Tamilnadu, 620002, India; Hoque M.S., Daffodil International University, Ashulia, Dhaka, Bangladesh; A S.K., Department of English, St. Josephs College, Tiruchirappalli, Tamilnadu, 620002, India; Kumar J.S., Department of English, Vel Tech Rangarajan Dr. Sagunthala R&amp;amp;D Institute of Science and Technology, Tamil Nadu, Chennai, 600062, India; Kotte R., Christ University, Bengaluru, India</text>
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                <text>Listening is one of the inevitable yet often overlooked skills in the process of learning a second language, especially for Indian undergraduate students learning English as a Second Language (ESL). Even after years of studying English in school, many students still struggle to understand spoken English. This occurs because students do not get sufficient exposure to real English as spoken in daily life, and they often feel nervous or uncomfortable in classroom settings. Various apps and tools are used to facilitate language learning, but there remains limited understanding of how audiobooks specifically contribute to enhancing listening skills. This study examines the effectiveness of audiobooks in developing listening skills through a twelve-week program involving 66 undergraduate students from Tamil Nadu. Students participated in a pre-test before the intervention and a post-test after completing the twelve-week audiobook listening program, which involved daily sessions of one hour. They listened to audiobooks with teacher support initially and then independently. The results indicated a significant improvement in listening scores, with an average increase of 25%. Additionally, students reported feeling less anxious about listening, better retention of new vocabulary, and improved ability to follow spoken English. The findings suggest that audiobooks are a vital component of language learning, providing autonomous, low-pressure, and long-term benefits in listening development. Consequently, audiobooks enhance confidence and foster consistent listening skills among ESL learners. The study recommends integrating audiobooks into regular classroom activities to support language acquisition. Contribution/Originality: This study contributes to the existing literature by validating audiobooks as effective ESL tools. It uses a new estimation methodology through SPSS analysis, originates a formula linking Krashens Affective Filter with audiobook learning, and is one of the few studies on Tamil Nadu undergraduates. The paper contributes quantitative and emotional factors, documents progress, and finds that audiobooks enhance proficiency.  2025 AESS Publications. All Rights Reserved.</text>
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              <elementText elementTextId="226841">
                <text>Audiobook; Digital technology; Emotional factors; Less anxious; Less anxious; Listening skill; Low pressure; Second language acquisition; Spoken English</text>
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                <text>Asian Economic and Social Society</text>
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                <text>ISSN: 23069910;</text>
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              <elementText elementTextId="226846">
                <text>All Open Access; Gold Open Access</text>
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      <name>Book Chapter</name>
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              <elementText elementTextId="290629">
                <text>Vashist, Dev Dutt; Sharma, Manjari; Pandey, Pallavi</text>
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          <element elementId="50">
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                <text>The role of behavioral finance in shaping economic decision-making: Insights from AI and big data integration</text>
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              <elementText elementTextId="290632">
                <text>Understanding Human Decision-Making in Economic Models;pp.27-64</text>
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              <elementText elementTextId="290633">
                <text>&lt;a href="https://doi.org/10.4018/979-8-3693-8166-3.ch002" target="_blank" rel="noreferrer noopener"&gt;https://doi.org/10.4018/979-8-3693-8166-3.ch002&lt;/a&gt; &lt;br /&gt;&lt;br /&gt;&lt;a href="https://www.scopus.com/pages/publications/105007398160?origin=resultslist" target="_blank" rel="noreferrer noopener"&gt;https://www.scopus.com/pages/publications/105007398160?origin=resultslist&lt;/a&gt;</text>
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                <text>Vashist D.D., Christ University, India; Sharma M., Christ University, India; Pandey P., Christ University, India</text>
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                <text>This chapter examines how behavioral biases and institutional factors influence investment decisions, bridging gaps in behavioral finance literature. It explores cognitive biases like overconfidence, loss aversion, and herd behavior alongside institutional elements such as regulations and market stability. A mixed-methods approach combines survey data from 320 investors and 30 interviews. Findings show a strong link between biases and decisions, with overconfidence and loss aversion as key drivers. Regulatory transparency reduces bias impact, while uncertainty amplifies emotional reactions. The study highlights the need for clear policies, financial education, and future research on AI's role in mitigating biases.  2025, IGI Global Scientific Publishing.</text>
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                <text>ISBN: 979-836938168-7; 979-836938166-3;</text>
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                <text>Big data refers to enormous amounts of structured and unstructured data that arise daily. All the finance sector records transactions, market data, social media sentiments, customer behavior, and macroeconomic indicators. The big three dimensions of big data that are commonly called the "3 Vs" include Volume, Velocity, and Variety. It means the data came from sources such as social media, IoT devices, transactions, and sensors. This process involves large amounts of data hence requiring scalable storages, complex processing tools, and resources for effective data management and analytics. Velocity means the rate or speed at which data is originated and processed. Lastly, Variety denotes the different kinds of data, which include the structured ones such as databases and the unstructured kinds, such as images, video, social media post, and audio. Collectively, the 3 Vs capture both the richness and the potentiality of big data, demanding the need for innovation in terms of technologies and strategies to leverage this value.  2026, IGI Global Scientific Publishing. All rights reserved.</text>
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                <text>The role of big data in predicting consumer behavior</text>
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                <text>Consumer behavior prediction is a significant task, and it is a prerequisite for marketing activities. Regardless of the product type/market type, predicting consumer behavior plays a vital role in determining the target market. The activities involved in identifying a target market include the tasks of analyzing the offerings, conducting market research, identifying market segments to create consumer profiles, and assessing the competition. In order to complete all four tasks mentioned above, it needs to have comprehensive and precise data/dataset in hand. It also means that the data/fact is the primary source of predicting consumer behavior. In today's digital world, sources of source (data) are multifold. During the process of data collection, if the repository is accepting data from such sources, then all five "V" (Volume, Velocity, Variety, Veracity, and Value) of data should be considered. The role of big data in predicting consumer behavior is inevitable. Machine learning models shall be deployed to analyze data from big data. In this chapter, benchmark datasets, and machine learning, are used to demonstrate the usage of artificial intelligence in analyzing, forecasting, and predicting consumer behavior. Before concluding the chapter, the performance of algorithms is evaluated and compared to find the most suitable models for predicting consumer behavior. Benchmark datasets are used in this chapter to represent the role of big data in predicting consumer behavior.  2023 Nova Science Publishers, Inc. All rights reserved.</text>
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                <text>Siva Balan R.V., Department of Computer Science, CHRIST (Deemed to be University), Bangalore, Karnataka, India; Kalpana P., Department of Computer Science, CHRIST (Deemed to be University), Bangalore, Karnataka, India</text>
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                <text>Patent Number: 202241037972, Appliccant: Dr. Shariq Mohammed.&#13;
The role of block chain technology in banking and financial markets Abstract Blockchain technology may be used to send our information more securely. This means that our information is interconnected like a chain of blocks. The main reason for the creation of this blockchain is information security. Yes, in the current age of the Internet, it is a question of whether one's data is protected. This blockchain technology is designed to send data and crypto currency securely and directly without intermediaries.</text>
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                <text>The study aimed to investigate the role of non-financial measures in predicting corporate financial distress in the Indian industrial sector. The proportion of independent directors on the board and the proportion of the promoters share in the ownership structure of the business were the non-financial measures that were analysed, along with ten financial measures. For this, sample data consisted of 82 companies that had filed for bankruptcy under the Insolvency and Bankruptcy Code (IBC). An equal number of matching financially sound companies also constituted the sample. Therefore, the total sample size was 164 companies. Data for five years immediately preceding the bankruptcy filing was collected for the sample companies. The data of 120 companies evenly drawn from the two groups of companies were used for developing the model and the remaining data were used for validating the developed model. Two binary logistic regression models were developed, M1 and M2, where M1 was formulated with both financial and non-financial variables, and M2 only had financial variables as predictors. The diagnostic ability of the model was tested with the aid of the receiver operating curve (ROC), area under the curve (AUC), sensitivity, specificity and annual accuracy. The results of the study show that inclusion of the two non-financial variables improved the efficacy of the financial distress prediction model. This study made a unique attempt to provide empirical evidence on the role played by non-financial variables in improving the efficiency of corporate distress prediction models.  2021 by the authors.</text>
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                <text>Shetty S.H., Department of Commerce, CHRIST (Deemed to Be University), Bengaluru, 560029, India; Vincent T.N., Department of Commerce, CHRIST (Deemed to Be University), Bengaluru, 560029, India</text>
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                <text>The Indian construction industry is in boom in terms of the growth and development taking place in the field of infrastructure and the service sector and subsequently many public owned design, architecture, construction and engineering companies were set up in India. The current study emphasises on the role of business development management consultants in the overall performance of construction companies and the existing review of literature focuses on the various factors which affects the overall business performance of the construction companies which includes different variables considering all the existing studies this study identified the gap in terms of intervention of BDMC in the construction field. The methodology adopted for the study is quantitative exploratory research and the results of the study was very positive that the promoters believe that the business development management consultants contribute a lot in the overall performance of the construction company with their expertise in the field and professional way of dealing with the other stake holders of the company helps them to perform very effectively and efficiently. This study helped in getting the practical exposure to the working of construction companies and understanding the role of business development management consultants.  The Author(s) 2019.</text>
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                <text>Kiran G., Welingkar Institute of Management Development and Research, India; Chotai N., Christ University, India; Appasab L.V., Department of MBA, VTU-Belgaum, India; Gopalakrishna M., Christ University, India; Nero A., Welingkar Institute of Management Development and Research, India</text>
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                <text>This chapter critically explores the role of ChatGPT and AI in higher education, examining their effects, challenges, and contributions to teaching and learning. It reviews studies highlighting ChatGPT's ability to personalize education, enhance student engagement, and boost research. Yet, it also addresses AI-related challenges like misinformation and dependency issues. The chapter recommends a balanced AI integration, focusing on ethical use, bridging the digital divide, and promoting continuous learning for educators and students. Concluding with future perspectives, it emphasizes AI's role in enriching education while cautioning about its careful application. The chapter offers an insightful analysis of AI's intricate role in higher education and strategies for its responsible integration.  2024, IGI Global.</text>
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                <text>Sharma M., Christ University, India; Gupta S., Christ University, India</text>
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                <text>The role of classroom engagement on academic grit, intolerance to uncertainty and well-being among school students during the second wave of the COVID-19 pandemic in India</text>
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              <elementText elementTextId="97313">
                <text>academic grit; India; intolerance to uncertainty; mediation; pandemic; student engagement; well-being</text>
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                <text>The forced changes and disruptions in educational systems and learning experiences due to the pandemic has impacted students' mental health and well-being. The present study aims to understand the effects of the determinants of well-being on students in India during the second wave (April to August 2021) of the COVID-19 pandemic. The determinants of well-being in this study areacademic grit, intolerance to uncertainty and students' engagement in an online learning environment. In this study, well-being is characterized as students' confidence and satisfaction in an online learning and pandemic environment. The data collected from 1174 students (1219 years) from various states, using standardized tools, were analyzed to find out about the mediating effect of students' engagement on the relationship between academic grit and well-being, and between intolerance to uncertainty and well-being. Further, the model fit analysis of the determinants of well-being is explored.The paper reports that students' classroom engagement does mediate in the path of academic grit and well-being, and in the path of intolerance to uncertainty and well-being. It also evidence the model fit of the influence of the determinants of well-being on that of school students during the second wave of the COVID-19 pandemic. The study also draws implications and suggestions for educators using the current model of students' well-being.  2022 Wiley Periodicals LLC.</text>
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                <text>Kareem J.; Thomas S.; Kumar P. A.; Neelakantan M.</text>
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                <text>Psychology in the Schools, Vol-60, No. 5, pp. 1594-1608.</text>
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                <text>&lt;a href="https://doi.org/10.1002/pits.22758" target="_blank" rel="noreferrer noopener"&gt;https://doi.org/10.1002/pits.22758&lt;/a&gt;
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              <elementText elementTextId="97320">
                <text>All Open Access; Bronze Open Access; Green Open Access</text>
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                <text>ISSN: 333085</text>
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                <text>Kareem J., School of Education, Christ University, Bangalore, India; Thomas S., Department of Psychology, Christ University, Bangalore, India; Kumar P. A., Department of Psychology, Christ University, Bangalore, India; Neelakantan M., Department of Psychology, Christ University, Bangalore, India</text>
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                <text>The Role of Cognitive Appraisal in Informed Decision-Making among Social Entrepreneurs: A Thematic Analysis</text>
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          <element elementId="49">
            <name>Subject</name>
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              <elementText elementTextId="99314">
                <text>cognitive appraisal; component process model; social entrepreneurs; Social entrepreneurship; thematic analysis</text>
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              <elementText elementTextId="99315">
                <text>Social entrepreneurship (SE) is gaining momentum by providing innovative solutions to economic, social, and environmental problems by generating jobs and social inclusion. However, it involves different challenges that may lead to a negative appraisal. This study aimed to explore cognitive appraisal processes social entrepreneurs use to make informed decisions in their entrepreneurial journey. Interviews were conducted with 13 Indian social entrepreneurs, and the data were subjected to thematic analysis. The main themes were; appropriateness, implications, coping potential, and normative significance. The study proposes a cognitive model for the appraisal of SE. The study is important for aspiring social entrepreneurs to understand the evaluation components of appraisal to decide how appropriate SE is as a career for them.  2023 Informa UK Limited, trading as Taylor &amp;amp; Francis Group.</text>
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                <text>Viswanath P.; Reddy S.</text>
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                <text>Social entrepreneurship (SE) is gaining momentum by providing innovative solutions to economic, social, and environmental problems by generating jobs and social inclusion. However, it involves different challenges that may lead to a negative appraisal. This study aimed to explore cognitive appraisal processes social entrepreneurs use to make informed decisions in their entrepreneurial journey. Interviews were conducted with 13 Indian social entrepreneurs, and the data were subjected to thematic analysis. The main themes were; appropriateness, implications, coping potential, and normative significance. The study proposes a cognitive model for the appraisal of SE. The study is important for aspiring social entrepreneurs to understand the evaluation components of appraisal to decide how appropriate SE is as a career for them.  2023 Informa UK Limited, trading as Taylor &amp;amp; Francis Group.</text>
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