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                <text>Teaching Learning-Based Optimization with Learning EnthusiasmMechanism for Optimal Control of PV Inverters in Utility Grids for Techno-Economic Goals</text>
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                <text>Energy cost; LebTLBO; Optimal deployment; Utility-owned DG</text>
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                <text>This study presents the optimal placement and operation of distributed generation (DG) sources in a distribution system embedded with utility-owned DG sources. Cost minimization and technical improvement of the network are the key objectives of the distribution company (DisCo). With the increasing popularity for renewable energy sources, DisCos are installing their own DGs to fulfill their electricity demand partially. When DisCos are the DG owners, the technical and economic considerations overlap. A novel method is proposed in this paper based on the recent variant of the teaching learning-based optimization (TLBO) algorithm and learning enthusiasm-based TLBO (LebTLBO) to optimize locations, sizes, and operational power factors of DGs in a distribution system with DisCo-owned DGs. A multi-objective function to improve voltage stability, reduce distribution losses, and reduce energy costs has been considered for solving the problem.  2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.</text>
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                <text>Lecture Notes in Electrical Engineering, Vol-928, pp. 1-13.</text>
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                <text>ISSN: 18761100; ISBN: 978-981195481-8</text>
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                <text>John N., Department of Electrical and Electronics Engineering, School of Engineering and Technology, Christ (Deemed to Be University), Karnataka, Bangalore, 560 074, India; Janamala V., Department of Electrical and Electronics Engineering, School of Engineering and Technology, Christ (Deemed to Be University), Karnataka, Bangalore, 560 074, India; Rodrigues J., Department of Electrical and Electronics Engineering, School of Engineering and Technology, Christ (Deemed to Be University), Karnataka, Bangalore, 560 074, India</text>
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                <text>Sentiment Analysis of Stress Among the Students Amidst the Covid Pandemic Using Global Tweets</text>
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                <text>BERT; Deep learning; LSTM; Machine learning; Sentiment analysis; SVM; Twitter</text>
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                <text>Covid-19 pandemic has affected the lives of people across the globe. People belonging to all the sectors of the society have faced a lot of challenges. Strict measures like lockdown and social distancing have been imposed several times by governments throughout the world. Universities had to incorporate the online method of teaching instead of the regular offline classes to implement social distancing. Online classes were beneficial to most of the students; at the same time, there were many difficulties faced by the students due to lack of facilities to attend classes online. Students faced a lot of challenges, and a sense of anxiety was prevalent during the uncertain times of the pandemic. This research article analyzes the stress among students considering the tweets across the globe related to students stress. The algorithms considered for classification of tweets as positive or negative are support vector machine (SVM), bidirectional encoder representation from transformers (BERT), and long short-term memory (LSTM). The accuracy of the abovementioned algorithms is compared.  2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.</text>
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                <text>Jyothsna R.; Rohini V.; Paulose J.</text>
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                <text>Smart Innovation, Systems and Technologies, Vol-317, pp. 317-324.</text>
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                <text>ISSN: 21903018; ISBN: 978-981196067-3</text>
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                <text>Jyothsna R., Christ University, Bangalore, India; Rohini V., Christ University, Bangalore, India; Paulose J., Christ University, Bangalore, India</text>
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                <text>Machine Learning Methods to Identify Aggressive Behavior in Social Media</text>
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              <elementText elementTextId="176134">
                <text>Cyber-aggressive; Cyber-bullying; Machine learning</text>
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                <text>With the more usage of Internet and online social media, platforms creep with lot of cybercrimes. Texts in the online platforms and chat rooms are aggressive. In few instances, people target and humiliate them with the text. It affects victim mental health. Therefore, there is a need of detecting the abuse words in the text. In this paper, a study of machine learning methods is done to identify the aggressive behavior. Accuracy can be improved by incorporating additional features.  2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.</text>
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                <text>Pawar V.; Jose D.V.</text>
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                <text>Lecture Notes in Networks and Systems, Vol-490, pp. 507-513.</text>
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                <text>ISSN: 23673370; ISBN: 978-981194051-4</text>
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                <text>Pawar V., Assistant Professor, Department of Computer Applications, CMR Institute of Technology, Bengaluru, India, Research Scholar, Department of Computer Science, Christ Deemed to be University, Bengaluru, India; Jose D.V., Christ Deemed to be University, Bangalore, India</text>
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                <text>A Brief Review on the Role of Blockchain in Supply Chain Management</text>
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                <text>Blockchain; Blockchain advantages; Blockchain application in supply chain; Blockchain architecture; Blockchain intelligence; Blockchain types; Consensus</text>
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                <text>Blockchain is a proficient technology when used in combination with other intelligent technologies which gives an opportunity to an organization to rethink about improvement of their supply chain internal and external processes. It helps in improvement of transparency and provenance by removing shortfalls and building a better organizational control overall. However, blockchain faces numerous challenges, e.g., transaction speed, decentralization, scalability, interoperability, and lack of standardization that could affect its adoption across organizations. However, a greater number of research are required to overcome the governance, standardization, and technological challenges involved within. Concisely, blockchain in supply chain is still in initial phase, many improvements are needed for better adaptation of blockchain using Machine Learning, Neural Network algorithms to make optimized computation decision of blockchain framework. In this paper, we studied and discussed about blockchain and its type, consensus mechanism, blockchain in supply chain, key issues of blockchain and supply chain and intelligence in blockchain.  2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.</text>
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                <text>Sinha S.K.; Mukhopadhyay D.; Bhattacharyya S.</text>
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                <text>Potluri S., Department of CSE, Faculty of Science and Technology, IcfaiTech, ICFAI Foundation for Higher Education, Donthanpally, Shankarpalli Road, Telangana, Hyderabad, 501203, India; Satpathy S., Faculty of Emerging Technologies, Sri Sri University, Odisha, Cuttack, 751002, India; Mahapatra S., Department of Computer Application, Institute of Technical Education and Research, Siksha O Anusandhan Deemed to be University, Bhubaneswar, 751030, India; Nanjundan P., Department of Data Science, CHRIST University, Pune, Lavasa, India; Mohanty S.N., Vardhaman College of Engineering (Autonomous), Telangana, Hyderabad, 501218, India</text>
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                <text>Wilson A.B.M., School of Architecture, Christ (Deemed to be University), Kengeri, Bangalore, India; Naseer M.A., Department of Architecture and Planning, National Institute of Technology-Calicut, Kozhikode, India</text>
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                <text>The historical background of how graph theory emerged into world and gradually gained importance in different fields of study is very well stated in many books and articles. Some of the most important applications of graph theory can be seen in the field network theory. Its significance can be seen in some of the complex network systems in the field of biological system, ecological system, social systems as well as technological systems. In this paper, the basic concepts of graph theory in terms of network theory have been provided. The various network models like star network model, ring network model, and mesh network model have been presented along with their graphical representation. We have tried to establish the link between the models with the existing concepts in graph theory. Also, many application-based examples that links graph theory with network theory have been looked upon.  2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.</text>
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                <text>Lecture Notes in Networks and Systems, Vol-492, pp. 73-84.</text>
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                <text>Dahal R., Department of Mathematics, CHRIST University, Bangalore, India; Samanta D., Department of Computer Science, CHRIST University, Bangalore, India; Karuppiah M., Department of Computer Science and Engineering, SRM Institute of Science and Technology, Delhi-NCR Campus, Uttar Pradesh, Ghaziabad, 201204, India; Biswas J., Department of Computer Science, CHRIST University, Bangalore, India</text>
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                <text>Environmental Concern in TPB Model for Sustainable IT Adoption</text>
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            <description>The topic of the resource</description>
            <elementTextContainer>
              <elementText elementTextId="176218">
                <text>Environmental concern; Millennial; Structure equation modeling; Sustainable information technology; Theory of planned behavior</text>
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                <text>Rapid advancement in technology and continuous environmental degradation has attracted the attention of practitioners toward sustainable solutions. This study aims to investigate educated millennial beliefs and behavior toward sustainable IT practices. The Theory of Planned Behavior (TPB) model deployed in the study was extended through perceived environmental responsibility. A survey was conducted to examine the sustainable IT adoption behavior of millennial in the National Capital Region, Delhi India. Variance based partial least square structure equation modeling was employed to evaluate the hypothesized model. Findings of the study confirm environmental concern (ER) a precursor for attitude (ATT), perceived behavioral control (PBC), and subjective norm (SN). Further, there is a significant positive influence of ATT, PBC, and SN on the adoption intention of sustainable IT practices, followed by the effect of adoption intention on actual adoption behavior. Study disseminates valuable insights to policymakers and marketers to formulate strategies and policies to attain sustainability through sustainable IT practices.  2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.</text>
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                <text>Kumar N.; Raut R.D.; Upreti K.; Alam M.S.; Shafiuddin M.; Verma M.</text>
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                <text>Lecture Notes in Networks and Systems, Vol-550 LNNS, pp. 59-70.</text>
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              <elementText elementTextId="176225">
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              </elementText>
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                <text>ISSN: 23673370; ISBN: 978-303116864-2</text>
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                <text>Kumar N., School of Business and Management, CHRIST University, Bangalore, India; Raut R.D., Department of Applied Electronics, Sant Gadge Baba University, Maharashtra, Amravati, India; Upreti K., Department of Computer Science and Engineering, Dr. Akhilesh Das Gupta Institute of Technology and Management, Delhi, India; Alam M.S., Razak Faculty of Technology and Informatics, Universiti Teknologi Malaysia, Kuala Lumpur, Malaysia; Shafiuddin M., Oman College of Management and Technology, Halban, Oman; Verma M., Delhi Technological University, Shahbad Daulatpur, Bawana Road, Delhi, India</text>
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          <element elementId="50">
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            <description>A name given to the resource</description>
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              <elementText elementTextId="176231">
                <text>ML-Based Prediction Model for Cardiovascular Disease</text>
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            <description>The topic of the resource</description>
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                <text>Cardiovascular disease (CVD); Logistic regression; Nae Bayes; Random forest</text>
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                <text>In this paper, the prediction of cardiovascular disease model based on the machine learning algorithm is implemented. In medical system applications, data mining and machine learning play an important role. Machine learning algorithms will predict heart disease or cardiovascular disease. Initially, online datasets are applied to preprocessing stage. Preprocessing stage will divide the data from baseline data. In the same way, CVD events are collected from data follow-ups. After that, data will be screened using the regression model. The regression model consists of logistic regression, support vector machine, nae Bayes, random forest, and K-nearest neighbors. Based on the techniques, the disease will be classified. Before classification, a testing procedure will be performed. At last from results, it can observe that accuracy, misclassification, and reliability will be increased in a very effective way.  2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.</text>
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                <text>Nagavelli U.; Samanta D.; Thomas B.</text>
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                <text>Lecture Notes in Networks and Systems, Vol-490, pp. 91-98.</text>
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              <elementText elementTextId="176236">
                <text>Springer Science and Business Media Deutschland GmbH</text>
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              <elementText elementTextId="176237">
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&lt;br /&gt;&lt;br /&gt;&lt;a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85138760730&amp;amp;doi=10.1007%2F978-981-19-4052-1_11&amp;amp;partnerID=40&amp;amp;md5=d149662a92dcb6a3c3e504032593a768" target="_blank" rel="noreferrer noopener"&gt;https://www.scopus.com/inward/record.uri?eid=2-s2.0-85138760730&amp;amp;doi=10.1007%2f978-981-19-4052-1_11&amp;amp;partnerID=40&amp;amp;md5=d149662a92dcb6a3c3e504032593a768&lt;/a&gt;</text>
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              <elementText elementTextId="176239">
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                <text>ISSN: 23673370; ISBN: 978-981194051-4</text>
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                <text>Online</text>
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            <description>A language of the resource</description>
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              <elementText elementTextId="176242">
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                <text>Nagavelli U., Dayananda Sagar Research Foundation, University of Mysore (UoM), Karnataka, Mysore, India; Samanta D., Dayananda Sagar Research Foundation, University of Mysore (UoM), Karnataka, Mysore, India, Department of Computer Science, CHRIST Deemed to be University, Karnataka, Bangalore, India; Thomas B., Department of Computer Science, CHRIST Deemed to be University, Karnataka, Bangalore, India</text>
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                <text>A Novel Approach for Segmenting Coronary Artery from Angiogram Videos</text>
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                <text>Lecture Notes in Networks and Systems, Vol-528, pp. 191-200.</text>
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                <text>Data Mining Approaches forHealthcare Decision Support Systems</text>
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                <text>Bayes; C4.5; Clinical decision support system; HDSS; WEKA</text>
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                <text>Data mining is a user-friendly approach to locating previously unknown or hidden information in data. The employment of data mining technologies in the healthcare system may result in the finding of relevant data. Data mining is used in healthcare medicine to construct learning models that predict a patients condition. Data mining technologies have the potential to benefit all stakeholders in the healthcare industry. For example, data mining may aid health providers in detecting theft and fraud, medical organizations in making customer service management decisions, physicians in discovering effective therapies and best practices, and customers in obtaining suitable and less expensive healthcare. Contemporary systems, due to their complexity and size, are unable to control and analyze the huge amounts of data generated by healthcare operations. Data mining is a technique and mechanism for converting a large amount of data into useful information. The fundamental purpose of this research is to look at what makes clinical data mining unique, to give an overview of existing clinical decision support systems, to identify and select the most common data mining algorithms used in modern Health and Demographic Surveillance System (HDSS), and to compare different data mining algorithms.  2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.</text>
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                <text>Pramanik S.; Galety M.G.; Samanta D.; Joseph N.P.</text>
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                <text>Lecture Notes in Networks and Systems, Vol-490, pp. 721-733.</text>
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                <text>Pramanik S., Department of CSE, Haldia Institute of Technology, West Bengal, Haldia, India; Galety M.G., College of Information Technology and Computer Science, Catholic University in Erbil, Erbil, Iraq; Samanta D., Department of Computer Science, CHRIST University, Karnataka, Bangalore, India; Joseph N.P., Department of Computer Science, CHRIST University, Karnataka, Bangalore, India</text>
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                <text>Growth of cerium oxide nanorods by hydrothermal method and electrochromic properties of CeO2/WO3 hybrid thin films for smart window applications</text>
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              <elementText elementTextId="176274">
                <text>Cerium oxide nanorods; Electrochromic; Nanocomposite; Smart window; Thin film</text>
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                <text>Innovative electrochromogenic nanomaterials such as composite materials and also hybrid films can improve electrochromic performance, because of their potential application qualities in electronic, low-power screens, automotive anti-reflect mirrors, and smart windows. In this study, we used a hydrothermal method to used grow the CeO2 nanorods both with and without HCl added to the solution. And also, DC magnetron sputtering was used to deposit the tungsten oxide films on the cerium oxide nanorods. The surface plasmon effect changes with the size of CeO2 Nanorods, and this phenomenon influences electrochromic outcomes. The electrochromic characteristics of CeO2/WO3 nanostructures on FTO-coated glass are examined in the visible spectrum to use a 0.5 M concentration of H2SO4 as such electrolyte. At 600 nm, these structures produce significant optical modulation (50 %) and coloring efficiency (11.60 cm2/C at 700 nm).  2022</text>
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                <text>G V A.R.; Naveen Kumar K.; Shaik H.; Shetty H.D.; Imran Jafri R.; Abdul Sattar S.; Kamath K.; Doreswamy B.H.</text>
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                <text>Materials Today: Proceedings, Vol-80, pp. 833-839.</text>
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                <text>ISSN: 22147853</text>
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                <text>G V A.R., Department of Physics, Nitte Meenakshi Institute of Technology, VTU, Karnataka, Belgaum, 590018, India, Research Center, Department of Physics, SJB Institute of Technology, VTU, Karnataka, Belgaum, 560060, India; Naveen Kumar K., Department of Physics, Nitte Meenakshi Institute of Technology, VTU, Karnataka, Belgaum, 590018, India, Center for Nanomaterials and MEMS, Nitte Meenakshi Institute of Technology, Yelahanka, Bengaluru, 560064, India; Shaik H., Department of Physics, Nitte Meenakshi Institute of Technology, VTU, Karnataka, Belgaum, 590018, India, Center for Nanomaterials and MEMS, Nitte Meenakshi Institute of Technology, Yelahanka, Bengaluru, 560064, India; Shetty H.D., Department of Physics, Nitte Meenakshi Institute of Technology, VTU, Karnataka, Belgaum, 590018, India; Imran Jafri R., Department of Physics and Electronics, Christ University, Hosur Road, Bengaluru, 560029, India; Abdul Sattar S., Department of Physics, Nitte Meenakshi Institute of Technology, VTU, Karnataka, Belgaum, 590018, India; Kamath K., Department of Physics, Nitte Meenakshi Institute of Technology, VTU, Karnataka, Belgaum, 590018, India; Doreswamy B.H., Research Center, Department of Physics, SJB Institute of Technology, VTU, Karnataka, Belgaum, 560060, India</text>
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                <text>Search Engine Optimization for Digital Marketing to Raise the Rank, Traffic, and Usability of the Website</text>
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                <text>Digital marketing; Optimization; PPC; SEM; SEO</text>
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                <text>According to the Content Marketing Institute, 93% of online experiences start with search. That is the explanation search. Thats why search promoting is a crucial procedure for all organizations to improve and develop their organizations. At that time the marketers and the clients who paid for advertisements started analyzing SEO and SEM. Web crawler promoting expands the perceivability of sites through SEO or through paid publicizing with the plan of expanding traffic to the site. SEM eludes to all advertising exercises that utilization web index innovation for promoting purposes. These incorporate SEO, paid postings and advertisements, and other web crawler related administrations and capacities that will expand reach and introduction of the site, bringing about more prominent traffic.  2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.</text>
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                <text>Singh D.; Panigrahi A.; Das A.; Krishnan A.; Samanta D.; Bhattacharya A.; Dutta S.</text>
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                <text>Smart Innovation, Systems and Technologies, Vol-316, pp. 217-226.</text>
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                <text>Singh D., Deptartmant of CA, ITER, SOA Deemed to be University, Odisha, Bhubaneswar, India; Panigrahi A., Digital Marketing Associate, Hydrabad, Mediamint, India; Das A., Digital Marketing Associate, Hydrabad, Mediamint, India; Krishnan A., Digital Marketing Associate, Hydrabad, Mediamint, India; Samanta D., CHRIST (Deemed to Be University), Karnataka, Bengaluru, India; Bhattacharya A., Institute of Engineering Management, Kolkata, India; Dutta S., Institute of Engineering Management, Kolkata, India</text>
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                <text>Balancing thoughts and memories of our life is indeed the most critical part of the human brain.Thus, its stability and sustenance are also important for smooth functioning.The changes in the structure can lead to disorders such as dementia and one such type of condition is known as Alzheimers disease.Multi modal neuroimaging like magnetic resonance imaging (MRI) and positron emission tomography (PET) is used for the early diagnosis of Alzheimers disease (AD) by providing complementary information.Different modalities like PET and MRI data were acquired from the same subject, there exists markable materiality between MRI and PET data.Mild cognitive impairment (MCI) is the initial stage with few symptoms of AD.To recognise the subjects which are capable of converting from MCI to AD is to be analysed for further treatments.In this research, specific convolutional neural networks (CNN) which are designed for classifications like VGG-16 and VGG-19 deep learning architectures were used to check the accuracy of cognitively normal (CN) versus MCI, CN versus AD and MCI to AD conversion using MRI data.The proposed research is analysed and tested using MRI data from Alzheimers disease neuroimaging initiative (ADNI).  2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.</text>
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                <text>Smart Innovation, Systems and Technologies, Vol-312, pp. 199-207.</text>
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                <text>Antony F., Department of Computer Science, Christ (Deemed to be University), Karnataka, Bangalore, India; Anita H.B., Department of Computer Science, Christ (Deemed to be University), Karnataka, Bangalore, India; George J.A., Department of Life Sciences, Christ (Deemed to be University), Karnataka, Bangalore, India</text>
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                <text>A Review of Various Line Segmentation Techniques Used in Handwritten Character Recognition</text>
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                <text>Segmentation is a very critical stage in the character recognition process as the performance of any character recognition system depends heavily on the accuracy of segmentation. Although segmentation is a well-researched area, segmentation of handwritten text is still difficult owing to several factors like skewed and overlapping lines, the presence of touching, broken and degraded characters, and variations in writing styles. Therefore, researchers in this area are working continuously to develop new techniques for the efficient segmentation and recognition of characters. In the character recognition process, segmentation can be implemented at the line, word, and character level. Text line segmentation is the first step in the text/character recognition process. The line segmentation methods used in the character recognition of handwritten documents are presented in this paper. The various levels of segmentation which include line, word, and character segmentation are discussed with a focus on line segmentation.  2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.</text>
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                <text>Joseph S.; George J.</text>
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                <text>Lecture Notes in Networks and Systems, Vol-400, pp. 353-365.</text>
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                <text>ISSN: 23673370; ISBN: 978-981190094-5</text>
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                <text>Joseph S., CHRIST (Deemed to be University), Bengaluru, India, Carmel College of Arts, Science and Commerce for Women, Goa, India; George J., CHRIST (Deemed to be University), Bengaluru, India</text>
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                <text>Customized SEIR Mathematical Model to Predict the trends of Vaccination for Spread of COVID-19</text>
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                <text>The uncertainty in life plans, restrictions on physical classrooms, loss of jobs, large number of infections and deaths due to COVID-19 are some significant causes of concern for the public as well as Governments all over the globe. Moreover, the exponential increase in the number of infected people in a short time is responsible for the collapse of the health industry during the pandemic caused by COVID-19. The health experts recommended that the quick and early diagnosis followed by treatment of patients in isolation is a way to minimize its spread and save lives. The objective of this research is to propose a customized SEIR model to predict the trends of vaccination in the USA. The experimental results prove that the Moderna vaccine reports the efficacy of 93%, which is higher than the Pfizer and Johnson and Johnson vaccines.  2022 ACM.</text>
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                <text>Alluhaidan A.S.; Poonia R.C.; Prabu P.; Alluhaidan M.S.</text>
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                <text>ACM International Conference Proceeding Series, pp. 77-80.</text>
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                <text>Association for Computing Machinery</text>
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                <text>&lt;a href="https://doi.org/10.1145/3589437.3589450" target="_blank" rel="noreferrer noopener"&gt;https://doi.org/10.1145/3589437.3589450&lt;/a&gt;
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                <text>ISBN: 978-145039763-6</text>
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                <text>Alluhaidan A.S., Department of Information Systems, College of Computer and Information Science, Princess Nourah Bint Abdulrahman University, Riyadh, Saudi Arabia; Poonia R.C., Department of Computer Science, CHRIST (Deemed to Be University), Karnataka, Bangalore, India; Prabu P., Department of Computer Science, CHRIST (Deemed to Be University), Karnataka, Bangalore, India; Alluhaidan M.S., Ministry of Defense, Riyadh, Saudi Arabia</text>
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                <text>Social Characteristics and Its Relationship with Intent to Stay-with Reference to Financial Sectors</text>
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              <elementText elementTextId="176344">
                <text>Finance and banking sectors; India; Intent to stay; Social characteristics</text>
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                <text>One of the challenging tasks of the HR management of the organization is to design the job in such a way that facilitates a good work culture/atmosphere for the employees to ensure their stay in the organization. The present study analyzed the role of social characteristics of the job with their intention to leave among the employees working in the finance sector. Primary data were collected from 250 employees working at all levels of management in the finance and banking sector in Indias southwest region through the Convenience sampling method. Morgeson and Humphrey (2006) developed the work design questionnaire which was adopted and used for data collection. Hierarchical multiple regression used for applied for data analysis. The results show that social characteristics cannot predict intent to stay. Also, age and gender do not have a significant role as mediating factors to social characteristics and intent to stay.  2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.</text>
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              <elementText elementTextId="176346">
                <text>George G.; Pullot L.; Sivakumar S.; Thomas P.</text>
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                <text>Lecture Notes in Networks and Systems, Vol-495 LNNS, pp. 729-735.</text>
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                <text>Springer Science and Business Media Deutschland GmbH</text>
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                <text>2023-01-01</text>
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                <text>&lt;a href="https://doi.org/10.1007/978-3-031-08954-1_62" target="_blank" rel="noreferrer noopener"&gt;https://doi.org/10.1007/978-3-031-08954-1_62&lt;/a&gt;
&lt;br /&gt;&lt;br /&gt;&lt;a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85135040998&amp;amp;doi=10.1007%2F978-3-031-08954-1_62&amp;amp;partnerID=40&amp;amp;md5=9921bb9179cb8b4f7c58cad45657f791" target="_blank" rel="noreferrer noopener"&gt;https://www.scopus.com/inward/record.uri?eid=2-s2.0-85135040998&amp;amp;doi=10.1007%2f978-3-031-08954-1_62&amp;amp;partnerID=40&amp;amp;md5=9921bb9179cb8b4f7c58cad45657f791&lt;/a&gt;</text>
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            <name>Rights</name>
            <description>Information about rights held in and over the resource</description>
            <elementTextContainer>
              <elementText elementTextId="176351">
                <text>Restricted Access</text>
              </elementText>
            </elementTextContainer>
          </element>
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            <name>Relation</name>
            <description>A related resource</description>
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                <text>ISSN: 23673370; ISBN: 978-303108953-4</text>
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            <name>Format</name>
            <description>The file format, physical medium, or dimensions of the resource</description>
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              <elementText elementTextId="176353">
                <text>Online</text>
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            <description>A language of the resource</description>
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              <elementText elementTextId="176354">
                <text>English</text>
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            <name>Type</name>
            <description>The nature or genre of the resource</description>
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                <text>Conference paper</text>
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            <description>The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant</description>
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              <elementText elementTextId="176356">
                <text>George G., Department of Commerce, Christ (Deemed to Be University), Bangalore, India; Pullot L., Department of Commerce, Christ (Deemed to Be University), Bangalore, India; Sivakumar S., Department of Commerce, Kristu Jayanti College Autonomous, Bangalore, India; Thomas P., Department of Commerce, Kristu Jayanti College Autonomous, Bangalore, India</text>
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              <name>Title</name>
              <description>A name given to the resource</description>
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                <elementText elementTextId="51377">
                  <text>Conference Papers</text>
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          <element elementId="50">
            <name>Title</name>
            <description>A name given to the resource</description>
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                <text>An Efficient Underwater Image Restoration Model for Digital Image Processing</text>
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          <element elementId="49">
            <name>Subject</name>
            <description>The topic of the resource</description>
            <elementTextContainer>
              <elementText elementTextId="176358">
                <text>Atmospheric light; Dehazing; Digital image processing; Improved bright channel prior; Transmittance image; Underwater image restoration</text>
              </elementText>
            </elementTextContainer>
          </element>
          <element elementId="41">
            <name>Description</name>
            <description>An account of the resource</description>
            <elementTextContainer>
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                <text>Digital image processing (DIP) is showing a massive growth intodays trending world particularly, in the field of biological research. Underwater image analysis plays a vital role, where the images are easily prone to attenuation and haziness. Capturing underwater images has always been a challenging job due to dispersion and scattering of light inside water on a high scale. Several image enhancement and restoration methodologies are currently available to address these issues, where hazing and color diffusion are viewed as a common phenomenon in it. Such procedures normally includes two basic methodologies in it, namely dehazing and contrast or color enhancement, which improves the overall output of the degraded image. However, the quality and processing time of the images can still be enhanced with additional techniques incorporated to it. This work is intended toward proposing one such channel called improvised bright channel prior for dehazing the underwater images. The technique further improves on the existing methodologies by estimating the atmospheric light and refining the transmittance of the image along with image restoration. The experimental results show that the improvised bright channel prior methodology is found to perform better in dehazing underwater images with a balanced intensity in terms of dark and white patches obtained from it. When comparing and contrasting the processing time of the proposed methodology with the existing techniques, it is found that improvised bright channel prior performs better. Also, the quality of the dehazed underwater image obtained from the proposed channel is found to be effective when compared with the existing channels.  2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.</text>
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            <name>Creator</name>
            <description>An entity primarily responsible for making the resource</description>
            <elementTextContainer>
              <elementText elementTextId="176360">
                <text>Jacob J.J.; Savitha S.; Mukherjee D.</text>
              </elementText>
            </elementTextContainer>
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          <element elementId="48">
            <name>Source</name>
            <description>A related resource from which the described resource is derived</description>
            <elementTextContainer>
              <elementText elementTextId="176361">
                <text>Lecture Notes in Networks and Systems, Vol-401, pp. 95-107.</text>
              </elementText>
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            <name>Publisher</name>
            <description>An entity responsible for making the resource available</description>
            <elementTextContainer>
              <elementText elementTextId="176362">
                <text>Springer Science and Business Media Deutschland GmbH</text>
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            <name>Date</name>
            <description>A point or period of time associated with an event in the lifecycle of the resource</description>
            <elementTextContainer>
              <elementText elementTextId="176363">
                <text>2023-01-01</text>
              </elementText>
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            <name>Identifier</name>
            <description>An unambiguous reference to the resource within a given context</description>
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              <elementText elementTextId="176364">
                <text>&lt;a href="https://doi.org/10.1007/978-981-19-0098-3_11" target="_blank" rel="noreferrer noopener"&gt;https://doi.org/10.1007/978-981-19-0098-3_11&lt;/a&gt;
&lt;br /&gt;&lt;br /&gt;&lt;a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85132969781&amp;amp;doi=10.1007%2F978-981-19-0098-3_11&amp;amp;partnerID=40&amp;amp;md5=18a83089832a958416cd18e031f334d9" target="_blank" rel="noreferrer noopener"&gt;https://www.scopus.com/inward/record.uri?eid=2-s2.0-85132969781&amp;amp;doi=10.1007%2f978-981-19-0098-3_11&amp;amp;partnerID=40&amp;amp;md5=18a83089832a958416cd18e031f334d9&lt;/a&gt;</text>
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          <element elementId="47">
            <name>Rights</name>
            <description>Information about rights held in and over the resource</description>
            <elementTextContainer>
              <elementText elementTextId="176365">
                <text>Restricted Access</text>
              </elementText>
            </elementTextContainer>
          </element>
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            <description>A related resource</description>
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              <elementText elementTextId="176366">
                <text>ISSN: 23673370; ISBN: 978-981190097-6</text>
              </elementText>
            </elementTextContainer>
          </element>
          <element elementId="42">
            <name>Format</name>
            <description>The file format, physical medium, or dimensions of the resource</description>
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              <elementText elementTextId="176367">
                <text>Online</text>
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            <description>A language of the resource</description>
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              <elementText elementTextId="176368">
                <text>English</text>
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            <description>The nature or genre of the resource</description>
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            </elementTextContainer>
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            <description>The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant</description>
            <elementTextContainer>
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                <text>Jacob J.J., Department of Computer Science and Engineering, Christ University, Karnataka, Bangalore, India; Savitha S., Department of Computer Science and Engineering, Christ University, Karnataka, Bangalore, India; Mukherjee D., Department of Computer Science and Engineering, Christ University, Karnataka, Bangalore, India</text>
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      <elementSetContainer>
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          <description>The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/.</description>
          <elementContainer>
            <element elementId="50">
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              <description>A name given to the resource</description>
              <elementTextContainer>
                <elementText elementTextId="51377">
                  <text>Conference Papers</text>
                </elementText>
              </elementTextContainer>
            </element>
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    <itemType itemTypeId="28">
      <name>Conference Paper</name>
      <description>Faculty Publications- Conference Papers</description>
    </itemType>
    <elementSetContainer>
      <elementSet elementSetId="1">
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        <description>The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/.</description>
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          <element elementId="50">
            <name>Title</name>
            <description>A name given to the resource</description>
            <elementTextContainer>
              <elementText elementTextId="176371">
                <text>Digital Platforms and Techniques for Marketing in the Era of Information Technology</text>
              </elementText>
            </elementTextContainer>
          </element>
          <element elementId="49">
            <name>Subject</name>
            <description>The topic of the resource</description>
            <elementTextContainer>
              <elementText elementTextId="176372">
                <text>Digital marketing; Digital platforms; Digital tools</text>
              </elementText>
            </elementTextContainer>
          </element>
          <element elementId="41">
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            <description>An account of the resource</description>
            <elementTextContainer>
              <elementText elementTextId="176373">
                <text>Digital marketing is the promotion of a product or service through at least one form of electronic media. This form of marketing is distinct from traditional marketing, but it uses some of the ideologies of traditional marketing. This research article examines the various technologies and platforms used in digital marketing that allow any organization or business to do this form of marketing and study what works for them and what does not. The article also explores the recent advancements in digital marketing due to the increase in users and the vast amount of data collected from these users. The two main advancements analyzed and discussed in this paper are machine learning (ML) and artificial intelligence (AI) tools.  2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.</text>
              </elementText>
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          <element elementId="39">
            <name>Creator</name>
            <description>An entity primarily responsible for making the resource</description>
            <elementTextContainer>
              <elementText elementTextId="176374">
                <text>Sharma R.; Siby A.</text>
              </elementText>
            </elementTextContainer>
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          <element elementId="48">
            <name>Source</name>
            <description>A related resource from which the described resource is derived</description>
            <elementTextContainer>
              <elementText elementTextId="176375">
                <text>Lecture Notes in Networks and Systems, Vol-400, pp. 669-677.</text>
              </elementText>
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            <name>Publisher</name>
            <description>An entity responsible for making the resource available</description>
            <elementTextContainer>
              <elementText elementTextId="176376">
                <text>Springer Science and Business Media Deutschland GmbH</text>
              </elementText>
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            <description>A point or period of time associated with an event in the lifecycle of the resource</description>
            <elementTextContainer>
              <elementText elementTextId="176377">
                <text>2023-01-01</text>
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                <text>&lt;a href="https://doi.org/10.1007/978-981-19-0095-2_64" target="_blank" rel="noreferrer noopener"&gt;https://doi.org/10.1007/978-981-19-0095-2_64&lt;/a&gt;
&lt;br /&gt;&lt;br /&gt;&lt;a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85134325616&amp;amp;doi=10.1007%2F978-981-19-0095-2_64&amp;amp;partnerID=40&amp;amp;md5=f790068283e47c6c5d009716fe31efc9" target="_blank" rel="noreferrer noopener"&gt;https://www.scopus.com/inward/record.uri?eid=2-s2.0-85134325616&amp;amp;doi=10.1007%2f978-981-19-0095-2_64&amp;amp;partnerID=40&amp;amp;md5=f790068283e47c6c5d009716fe31efc9&lt;/a&gt;</text>
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            <description>Information about rights held in and over the resource</description>
            <elementTextContainer>
              <elementText elementTextId="176379">
                <text>Restricted Access</text>
              </elementText>
            </elementTextContainer>
          </element>
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            <description>A related resource</description>
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                <text>ISSN: 23673370; ISBN: 978-981190094-5</text>
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            <name>Format</name>
            <description>The file format, physical medium, or dimensions of the resource</description>
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              <elementText elementTextId="176381">
                <text>Online</text>
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            <description>A language of the resource</description>
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              <elementText elementTextId="176382">
                <text>English</text>
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                <text>Sharma R., CHRIST (Deemed to Be University), Bengaluru, India; Siby A., CHRIST (Deemed to Be University), Bengaluru, India</text>
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