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                <text>Linear and non-linear magneto-convection in couple stress fluid with non-classical heat conduction law</text>
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                <text>A theoretical examination of the thermal convection for a couple stress fluid which is electrically conductive and possessing significant thermal relaxation time with an externally applied magnetic field is carried out. Fourier's law fails when fluids are subjected to rapid heating or when it is confined and in the case of nano-devices. A frame invariant constitutive equation for heat flux is considered. The linear analysis is carried out implementing a normal mode solution and the non-linear stability of the system is analyzed using a double Fourier series. The analysis of the transfer of heat is determined in terms of the Nusselt number.   Published under licence by IOP Publishing Ltd.</text>
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                <text>Thomas M.; Pranesh S.; Sangeetha George K.</text>
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                <text>Journal of Physics: Conference Series, Vol-1850, No. 1</text>
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                <text>2021-01-01</text>
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                <text>&lt;a href="https://doi.org/10.1088/1742-6596/1850/1/012009" target="_blank" rel="noreferrer noopener"&gt;https://doi.org/10.1088/1742-6596/1850/1/012009&lt;/a&gt;
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                <text>All Open Access; Gold Open Access</text>
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                <text>ISSN: 17426588</text>
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                <text>Thomas M., Department of Mathematics, CHRIST, Deemed to Be University, Bengaluru, Karnataka, India; Pranesh S., Department of Mathematics, CHRIST, Deemed to Be University, Bengaluru, Karnataka, India; Sangeetha George K., Department of Science, Christ Academy Institute for Advanced Studies, Bengaluru, Karnataka, India</text>
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                <text>Performance Analysis of Deep Learning Algorithms for Intrusion Detection in IoT</text>
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                <text>convolution neural network; decision tree classifier; deep learning; deep neural network; IoT</text>
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                <text>Due to the wide availability of IoT devices at affordable cost and the ease of use has increased IoT devices increased usage. Due to the enormous usage of the Internet of Things (IoT) devices, the security aspects related to the data are also a significant concern in this data-driven world. Negligence of security measures from users can result in severe data falsification or data thefts. In this scenario, the Intrusion Detection System has a pivotal role in IoT security. Incorporating the deep learning techniques is an effective way to predict various attacks, either known or unknown. This paper highlights the various security threats associated with IoT, the importance of deep learning in IoT intrusion detection, and various IoT intrusion detection systems using deep learning. Comparative analysis of the different deep learning techniques was performed. The results have shown Convolution Neural Networks gave high accuracy in prediction based on various evaluation metrics.   2021 IEEE.</text>
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              <elementText elementTextId="182342">
                <text>Jose J.; Jose D.V.</text>
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                <text>ICCISc 2021 - 2021 International Conference on Communication, Control and Information Sciences, Proceedings</text>
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                <text>Institute of Electrical and Electronics Engineers Inc.</text>
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                <text>2021-01-01</text>
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                <text>&lt;a href="https://doi.org/10.1109/ICCISc52257.2021.9484979" target="_blank" rel="noreferrer noopener"&gt;https://doi.org/10.1109/ICCISc52257.2021.9484979&lt;/a&gt;
&lt;br /&gt;&lt;br /&gt;&lt;a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85113320944&amp;amp;doi=10.1109%2FICCISc52257.2021.9484979&amp;amp;partnerID=40&amp;amp;md5=5dbbd8b51b8f56da45c86c9e9a79fc53" target="_blank" rel="noreferrer noopener"&gt;https://www.scopus.com/inward/record.uri?eid=2-s2.0-85113320944&amp;amp;doi=10.1109%2fICCISc52257.2021.9484979&amp;amp;partnerID=40&amp;amp;md5=5dbbd8b51b8f56da45c86c9e9a79fc53&lt;/a&gt;</text>
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                <text>ISBN: 978-166541279-7</text>
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                <text>Jose J., CHRIST(Deemed to Be University), Dept. of Computer Science, Bangalore, India; Jose D.V., CHRIST(Deemed to Be University), Dept. of Computer Science, Bangalore, India</text>
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          <element elementId="50">
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                <text>Cryptocurrency Security and Privacy Issues: A Research Perspective</text>
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          <element elementId="49">
            <name>Subject</name>
            <description>The topic of the resource</description>
            <elementTextContainer>
              <elementText elementTextId="182326">
                <text>Bitcoin; Blockchain; cryptocurrency; Ethereum; Gold; Privacy; Security</text>
              </elementText>
            </elementTextContainer>
          </element>
          <element elementId="41">
            <name>Description</name>
            <description>An account of the resource</description>
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              <elementText elementTextId="182327">
                <text>Cryptocurrency has developed as a new mode of money exchange since it has become easier, faster and safer. The first cryptocurrency was introduced in 2009 and since then, the growth rate of cryptocurrency has been increasing drastically. As of 2020, the cryptocurrency exchange all over the world has exceeded 300%. The researchers face many challenges during their research on the various cryptocurrencies. For example, most of the high-tech companies still do not support bitcoin on mobile platforms. High-tech companies like Google and Apple are also thinking into banning the bitcoin wallet from their app stores. The work provides a review of cryptocurrency and its types, scope on the investment plans and its advantages also discussed. The growth and comparison between bitcoins and gold is also discussed. The challenges researchers face and the security issues concerning it. This review provides an overview of how the different forms of cryptocurrency are increasing from over a decade. It explains the different types and the year in which they were invented. It also gives a brief comparison with respect to bitcoin, which is one of the most used cryptocurrency. Furthermore, it gives a brief explanation on investments, and schemes for those who are new in the cryptomarket. Later emphasizes on the security issues faced by this technology. It talks about proof of work and the different data attacks the software faced and how the issues were overcome. In the end, it talks about the challenges researchers face while researching cryptocurrency.  2021 IEEE.</text>
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              <elementText elementTextId="182328">
                <text>Varghese H.M.; Nagoree D.A.; Anshu; Jayapandian N.</text>
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              <elementText elementTextId="182329">
                <text>Proceedings of the 6th International Conference on Communication and Electronics Systems, ICCES 2021, pp. 902-907.</text>
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              <elementText elementTextId="182330">
                <text>Institute of Electrical and Electronics Engineers Inc.</text>
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              <elementText elementTextId="182331">
                <text>2021-01-01</text>
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                <text>&lt;a href="https://doi.org/10.1109/ICCES51350.2021.9489254" target="_blank" rel="noreferrer noopener"&gt;https://doi.org/10.1109/ICCES51350.2021.9489254&lt;/a&gt;
&lt;br /&gt;&lt;br /&gt;&lt;a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85113798430&amp;amp;doi=10.1109%2FICCES51350.2021.9489254&amp;amp;partnerID=40&amp;amp;md5=5347118835a680425b8e01253057da9d" target="_blank" rel="noreferrer noopener"&gt;https://www.scopus.com/inward/record.uri?eid=2-s2.0-85113798430&amp;amp;doi=10.1109%2fICCES51350.2021.9489254&amp;amp;partnerID=40&amp;amp;md5=5347118835a680425b8e01253057da9d&lt;/a&gt;</text>
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                <text>ISBN: 978-166543587-1</text>
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                <text>Online</text>
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              <elementText elementTextId="182336">
                <text>English</text>
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                <text>Varghese H.M., Christ University, Department of Cse, Bangalore, India; Nagoree D.A., Christ University, Department of Cse, Bangalore, India; Anshu, Christ University, Department of Cse, Bangalore, India; Jayapandian N., Christ University, Department of Cse, Bangalore, India</text>
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          <element elementId="50">
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                <text>Equalization of Finite-Alphabet MMSE for All-Digital Massive MU-MIMO mm-Wave Communication</text>
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            <description>The topic of the resource</description>
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              <elementText elementTextId="182312">
                <text>Antenna; MIMO; OFDM; Receiver; Transmitter</text>
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                <text>For more than twenty years, growing the performance and efficiency of wireless communications systems using antenna arrays has been an active field of study. Wireless networks with multiple-input multiple-output are also part of the current norms and are implemented around the world. Access points or BSs with comparatively few antennas are used for standard MIMO systems, and the resulting increase in spectral efficiency was relatively modest. A Multiple-Input Multiple-Output platform's capacity is researched where the transmitter outputs are processed and quantified by a set of limit quantizes through an analogue linear combining network. The linear mixing weights and cutoff levels are chosen from with a collection of possible combinations as a function of the transmitted signal. Millimetre-wave networking requires optimum data transmission to various computers on same moment network in combination with large multi-user actually massive. In order to guarantee efficient data transmission, the heavy insertion loss of wave propagation at su ch a faster speed needs proper channel estimation. A new channel estimation algorithm called Beam space Channel Estimation is suggested. From a set of possible configurations, the capacity of a massive stream from which antennas signals are handled by an analog channel as a part of the channel matrix, linear mixture weights and frequency modulation levels are selected. Probable implementations of specific analogue receiver designs for the combined network model, such as smart antenna selection, sign antennas output thresholding or linear output processing. To demonstrate the effectiveness of BEACHES in service and have FPGA implementation results, we are developing VLSI architecture. Our results show that for large MU-MIMOs, mm-wave communications with hundreds of antennas, specially made denoising can be done at maximum bandwidth and in an equipment format.  Published under licence by IOP Publishing Ltd.</text>
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                <text>Paulchamy B., Department of Electronics and Communication Engineering, Hindustan Institute of Technology, Tamil Nadu, Malumichampatti, India; Chidambaram S., Department of Electronics and Communication Engineering, Christ University, Bengaluru, Karnataka, India; Vairaprakash S., Department of Electronics and Communication Engineering, Kalasalingam Institute of Technology, Tamil Nadu, Krishnankoil, India; Duraivel A.N., Department of Electronics and Communication Engineering, King's Engineering College, Tamil Nadu, Sriperumbudur, India</text>
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                <text>Data pre-processing has taken an enhanced role with the advent of Machine learning. It is a vital element that forms the encore of the data science and business analytics process. Data pre-processing involves generating descriptive statistical summary, data cleaning, and data manipulation based on inputs gained after the initial analysis. Of late, it has been observed that data science practitioners spend 45% to 50% of their time cleaning and processing the data. Much time can be saved if the data transformation process can be automated. The COGNITO framework helps in performing the automated feature engineering and data storytelling of the dataset based on end-user discretion. The present work discusses the process and results obtained when automated feature engineering was performed on an insurance dataset using COGNITO.  2021 IEEE.</text>
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                <text>Saxena A., Christ University, Management Studies, Bangalore, India; Bhagat V.V., Christ University, Data Science, Bangalore, India; Robins B., Data Science Cleverinsight Inc., Bangalore, India</text>
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                <text>The effect of exothermic chemical reaction of zero-order on Bard-Darcy ferroconvection is investigated using the technique of small perturbation. The eigenvalues associated with an adiabatic lower wall are determined by employing the Galerkin method. The Darcy-Rayleigh number is computed in terms of the parameters pertaining to chemical reaction and ferromagnetic fluid. It is established that, when chemical reaction escalates, there is a considerable shift from linearity and occurrence of asymmetry in the basic temperature profiles. It is ascertained that the threshold of Bard-Darcy ferroconvection is augmented through the stresses of both mechanisms due to chemical reaction and magnetization, and the ferroconvective instability due to nonlinearity of magnetization is rather inconsequential when chemical reaction is present. It is also shown that the destabilizing feature of magnetic forces resulting from the fluid magnetization is less pronounced when chemical reaction is present.   Published under licence by IOP Publishing Ltd.</text>
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                <text>Thomas N.M., Department of Sciences and Humanities, School of Engineering and Technology, CHRIST, Deemed to Be University, Bengaluru, 560074, India; Maruthamanikandan S., Department of Mathematics, School of Engineering, Presidency University, Itgalpura, Yelahanka, Bengaluru, 560064, India</text>
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                <text>Effective ML Techniques to Predict Customer Churn</text>
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                <text>Class imbalance; Customer churn; Ensemble model; Hybrid model; Machine learning</text>
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                <text>Customer churn is one of the most challenging problems that affects revenue and growth strategy of a company. According to a recent Gartner Tech Marketing survey, 91% of C-level respondents rate customer churn as one of their top concerns. However, only 43% have invested in additional resources to support customer expansion. Hence, retaining existing customers is of paramount importance to a company's growth. Many authors in the past have presented different versions of models to predict customer churn using machine learning techniques. The aim of this paper is to study some of the most important machine learning techniques used by researchers in the recent years. The paper also summarizes the prediction techniques, datasets used and performance achieved in these studies for a deeper understanding of the domain. The analysis shows that although hybrid and ensemble methods have been widely successful in improving model performance, there is a need for well-defined guidelines on appropriate model evaluation measures. While most approaches used are quantitative in nature, there is lack of research that focuses on information-rich content in customer company interaction instances, like emails, phone calls or customer support chat records. The information presented in the paper will not only help to increase awareness in industry about emerging trends in machine learning algorithms used in churn prediction, but also help new or existing researchers position their research activity appropriately.   2021 IEEE.</text>
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                <text>The use of digital tools in the conventional architecture design thinking process which derives its basis from sketching is followed in many colleges in India. Various shortcomings due to the integration of digital tools to the manual design process have been enumerated during the past 30 years. Digital tools provide affordances different from the manual sketching design process, the effects of which can be understood by adopting a distributed cognition approach. The paper builds on design cognition research while using externalization tools in the design process. It does so by developing a theoretical framework derived from distributed cognition and an understanding of visual thinking processes from design literature. The paper utilizes the distributed cognition framework by Zhang and Norman, to arrive at resultant affordances of externalization tools in design. The same is then utilized for a protocol study which was coded for its visual thinking components and other relevant codes. The same protocol study was also coded for ideation flow analysis. The findings pointed towards compromised visual thinking and reduced ideation while utilizing digital tools in quick conceptualization.  2021 ACM.</text>
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                <text>Vriddhi V.; Sen J.; Sharma A.</text>
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                <text>ACM International Conference Proceeding Series, pp. 30-39.</text>
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                <text>Vriddhi V., School of Architecture, Christ University, India; Sen J., Department of Architecture and Regional Planning, Indian Institute of Technology Kharagpur, India; Sharma A., Department of Design, Indian Institute of Technology Delhi, India</text>
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                <text>Stock Market Trend Analysis on Indian Financial News Headlines with Natural Language Processing</text>
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                <text>Predicting the stock movement in the real-time scenario has been the most challenging and sophisticated in business. This business is affected by several factors from physical to psychological as well as rational to irrational. So far only few aspects have been taken into account while breaking down the conclusion. Implementing sentiment analysis, a subfield of Natural Language Processing (NLP), from the news, social media or financial document, investors decide whether they should invest for the company. The results have shown a significant and a feasible method for predicting the stock market trend with higher accuracy. The current research has mainly focus on finding the sentiment score from the news headlines and finding the hidden trend from it. Further the trading signals are generated based on the prevailing trend and trends are executed by the automated trading system. Using this algorithm, traders can reduce the manual intervention in the buy and sell decisions related to the stock market.  2021 IEEE.</text>
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                <text>Saxena A.; Vijay Bhagat V.; Tamang A.</text>
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                <text>Saxena A., Christ (Deemed to Be University), Bangalore, India; Vijay Bhagat V., Christ (Deemed to Be University), Bangalore, India; Tamang A., Independent Consultant, Bangalore, India</text>
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                <text>A comparative analysis of opinions and sentiments on clean India campaign and sustainability goals of 2030</text>
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                <text>Human are blessed with natural intelligence. Artificial Intelligence can help human minds to make a best usage of machines to handle huge amount of data with accuracy and precision. AI has a widespread application in 21st century. Opinion mining is an application of artificial intelligence. The opinions expressed in social media can be extracted using python which can be used as an input for various machine learning algorithms to identify many patterns which can help policy makers to make effective policies. Clean India Campaign started in India with a set of goals to be achieved. Sustainability goals of 2030 given by United Nations puts light on many important aspects which need immediate attention in the next 9 years. Current pandemic Covid-19 has also triggered the necessity behind putting immediate attention for a better tomorrow. Without proper awareness programs, brainstorming knowledge cultivation, orienting minds towards the "what-why-where"aspects of sustainable growth in each sphere of life, aligning industrial development and digital era towards sustainable industrial development in digital era, sustainable economy, sustainable care of each natural resource; it is not easy to accomplish the sustainability goals of 2030 given by United Nations.This work emphasizes on the case study conducted as an initiative to motivate future policy makers to be aware of the different dimension of 2030 United Nations Agenda and the clean India campaign to take initiatives as a professional through the skills learned focusing on India. Realizing Individual social Responsibility can make a big difference in the planning and implementation of the goals and missions. Swachch Bharat Abhiyan (Clean India Campaign) started Swachch Bharat Mission-Urban (SBM-U) with a few objectives to make India Clean.This work has proposed two phases for analyzing opinions. This research have provided a methodology to apply AI to improve the opinion mining. The conventional opinion analysis is limited by reachability but the automated opinion analysis can be scaled up using artificial intelligence based applications. The uniqueness of the work lies in its focus on 'one-three verticals' in phase 1 of the methodology. Many prominent regions of India are considered as a part of the study. It helps us to provide a clearer picture across different regions of India. It also provide an avenue to list tasks to be done for each region and a set of ways which could be adopted by the future professionals and current stakeholders of higher education institute. Phase 2 focusses on more number of opinions collected from across the globe through digital platforms.   2021 Author(s).</text>
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                <text>Goswami M.; Sarkar D.</text>
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                <text>AIP Conference Proceedings, Vol-2396</text>
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              <elementText elementTextId="182191">
                <text>American Institute of Physics Inc.</text>
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                <text>&lt;a href="https://doi.org/10.1063/5.0066545" target="_blank" rel="noreferrer noopener"&gt;https://doi.org/10.1063/5.0066545&lt;/a&gt;
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              <elementText elementTextId="182194">
                <text>All Open Access; Bronze Open Access</text>
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                <text>ISSN: 0094243X; ISBN: 978-073544130-9</text>
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                <text>Goswami M., Christ (Deemed to Be University), Bangalore, India; Sarkar D., Amity University, Kolkata, India</text>
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                <text>Enhancing the confidentiality of text embedding using image steganography in spatial domain</text>
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                <text>Different color components; Least significant bit (LSB); One time pad (OTP); Stego-image</text>
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                <text>Rapid growth in technological development, the use of the internet has grown many folds. Along with it, the sharing of privacy information in networks creates ownership issues. In order to create a high level of security for sharing private information, the concept of steganography is introduced along with encryption based invisible watermarking techniques. The proposed system hides the encrypted private messages by using onetime pad which follows the concept LSB algorithm in spatial domain. The system combines steganography and encryption for enhancing the confidentiality of the intended messages. At first, the private information of the user is encrypted by using the onetime pad algorithm. Then the encrypted text is hidden the Least Significant Bit (LSB) of the different components of the color image in such a way that as to minimize the perceived loss of quality of the cover image. The beneficiary of the message is able to retrieve the hidden back and from the stego-image and extract the cipher text and find the plaintext from using the onetime pad algorithm. The proposed algorithm will be tested and analysed against three different hiding positions of color image components.  2021 American Institute of Physics Inc.. All rights reserved.</text>
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                <text>Karthikeyan N.; Kousalya K.; Krishnamoorthy N.; Jayapandian N.</text>
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                <text>AIP Conference Proceedings, Vol-2387</text>
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                <text>&lt;a href="https://doi.org/10.1063/5.0068807" target="_blank" rel="noreferrer noopener"&gt;https://doi.org/10.1063/5.0068807&lt;/a&gt;
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                <text>Karthikeyan N., Department of Computer Science and Engineering, Government College of Engineering, Tamil Nadu, Sengipatti, Thanjavur, 613001, India; Kousalya K., Department of Computer Science and Engineering, Kongu Engineering College, Tamil Nadu, Perundurai, Erode, 638060, India; Krishnamoorthy N., Department of Computer Science and Engineering, Kongu Engineering College, Tamil Nadu, Perundurai, Erode, 638060, India; Jayapandian N., Department of Computer Science and Engineering, School of Engineering and Technology, CHRIST (Deemed to be University), Karnataka, Bangalore, 560029, India</text>
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                <text>In Tamil Nadu, the state government has imposed a ban on plastic bags two years ago. This has created a major impact of the day to day life of common people. Though it has positive effect on the environment, the common public had different perception as a consumer. This paper aimed at studying the consumer attitude towards the ban on plastic bags. A descriptive research design adopted to address the various dimension of consumer perception towards the ban on plastic ban. A sample size of 400 respondents was selected on the basis of systematic random sampling technique to collect data through structured questionnaire. For conducting the survey, consumers of retail shops in urban and rural places were chosen as target respondents. The collected data were analyzed with the help of statistical tools such as ANOVA, t-Test, Correlation, Linear Regression and Structural equation modelling and the interpretation reported. The result revealed that only 34 percentage of respondent were aware the environmental impact of plastic bags. About 71 percentage of consumers reported that they have faced difficulties in their day to day life due to plastic ban.  2021 American Institute of Physics Inc.. All rights reserved.</text>
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                <text>Umasankar M.; Padmavathy S.; Radhikaashree M.; Pavithra V.</text>
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                <text>AIP Conference Proceedings, Vol-2387</text>
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              <elementText elementTextId="182167">
                <text>All Open Access; Bronze Open Access</text>
              </elementText>
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                <text>ISSN: 0094243X; ISBN: 978-073544146-0</text>
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                <text>Umasankar M., Department of Professional Studies, Christ (Deemed to be University); Padmavathy S., Department of Management Studies, Kongu Engineering College; Radhikaashree M., Dr.MGR Educational and Research Institute; Pavithra V., Department of Management Studies, Kongu Engineering College</text>
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                <text>A comparative study of the impact of thermal indices on Indian coral ecosystem</text>
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          <element elementId="49">
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            <description>The topic of the resource</description>
            <elementTextContainer>
              <elementText elementTextId="182146">
                <text>Climate Change; Ecosystems; India Seas</text>
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            <description>An account of the resource</description>
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                <text>Coral reefs have been the diversified ecosystem in the planet. Advantages are opportunities in tourism, coastal protection and fisheries production. Corals, as key ingredient is sourced got drug manufacturing. Its distribution is evident in locations of where sea water temperature ranges between 16C to 30C. Their presence is &amp;gt;0.2% of ocean area and supports &amp;gt;25% of marine species. India has five reef formations. Globally, last two decades have seen an increase in reporting reef deterioration. The reason significantly attributed to be climate change, apart other challenges such as pollution, sedimentation, oil spillage, etc. Such events lead to widespread mortality of corals. Mortality during bleaching events are inevitable and varied; depends on intensity of such events. The primary reason is due to significant rise in average sea surface temperature (SST). Recovery takes time after such events, and it becomes worse with recurring events. The reefs of Indian seas have reported events of severe bleaching during 1998, 2010 and 2016. IPCC reviews show mass bleaching will be prominent in future due to elevated SST. This work tries to compare the HS values of a few regions. The data collected is from 2001 to 2017. A few significant observations are drawn which could further help us to extend the work to take help from Artificial Intelligence to make predictions for the future. This study uses the indices derived out of SST to look at relative risk faced by Indian reefs. The need for comprehensive and localized actions will be discussed.   2021 Author(s).</text>
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              <elementText elementTextId="182148">
                <text>Ali H.; Goswami M.</text>
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            <elementTextContainer>
              <elementText elementTextId="182149">
                <text>AIP Conference Proceedings, Vol-2396</text>
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              <elementText elementTextId="182150">
                <text>American Institute of Physics Inc.</text>
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                <text>2021-01-01</text>
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                <text>&lt;a href="https://doi.org/10.1063/5.0066549" target="_blank" rel="noreferrer noopener"&gt;https://doi.org/10.1063/5.0066549&lt;/a&gt;
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            <elementTextContainer>
              <elementText elementTextId="182153">
                <text>All Open Access; Bronze Open Access</text>
              </elementText>
            </elementTextContainer>
          </element>
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                <text>ISSN: 0094243X; ISBN: 978-073544130-9</text>
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            <description>The file format, physical medium, or dimensions of the resource</description>
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                <text>Online</text>
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            <description>A language of the resource</description>
            <elementTextContainer>
              <elementText elementTextId="182156">
                <text>English</text>
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          </element>
          <element elementId="51">
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            <description>The nature or genre of the resource</description>
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                <text>Ali H., Christ (Deemed to Be University), Karnataka, Bangalore, India; Goswami M., Christ (Deemed to Be University), Karnataka, Bangalore, India</text>
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                <text>Insights into Artificial Neural Network techniques, and its Application in Steganography</text>
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            </elementTextContainer>
          </element>
          <element elementId="49">
            <name>Subject</name>
            <description>The topic of the resource</description>
            <elementTextContainer>
              <elementText elementTextId="182132">
                <text>Convolution Neural Networks; Deep Steganography; Feed forward; LSTM; Optimization Algorithms; Recurrent Neural network</text>
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                <text>Deep Steganography is a data concealment technology that uses artificial intelligence (AI) to automate the process of hiding and extracting information through layers of training. It enables for the automated generation of a cover depending on the concealed message. Previously, the technique depended on the existing cover to hide data, which limited the number of Steganographic characteristics available. Artificial intelligence and deep learning techniques have been used to steganography recently and the results are satisfactory. Although neural networks have demonstrated their ability to imitate human talents, it is still too early to draw comparisons between people and them. To improve their capabilities, neural networks are being employed in a number of disciplines, including steganography. Recurrent Neural Networks (RNN) is a widely used technology that automatically creates Stego-text regardless of payload volume. The features are extracted using a convolution neural network (CNN) based on the image. Perceptron, Multi-Layer Perceptron (MLP), Feed Forward Neural Network, Long Short Term Memory (LSTM) networks, and others are examples of this. In this research, we looked at all of the neural network approaches for Steganographic purposes in depth. This article also discusses the problems that each technology faces, as well as potential solutions.  2021 Institute of Physics Publishing. All rights reserved.</text>
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                <text>Gurunath R.; Samanta D.</text>
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              <elementText elementTextId="182135">
                <text>Journal of Physics: Conference Series, Vol-2089, No. 1</text>
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              <elementText elementTextId="182136">
                <text>IOP Publishing Ltd</text>
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                <text>2021-01-01</text>
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                <text>&lt;a href="https://doi.org/10.1088/1742-6596/2089/1/012043" target="_blank" rel="noreferrer noopener"&gt;https://doi.org/10.1088/1742-6596/2089/1/012043&lt;/a&gt;
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            <description>Information about rights held in and over the resource</description>
            <elementTextContainer>
              <elementText elementTextId="182139">
                <text>All Open Access; Gold Open Access</text>
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                <text>ISSN: 17426588</text>
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                <text>English</text>
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                <text>Gurunath R., Department of Computer Science, CHRIST University, Bangalore, India; Samanta D., Department of Computer Science, CHRIST University, Bangalore, India</text>
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            <description>A name given to the resource</description>
            <elementTextContainer>
              <elementText elementTextId="182117">
                <text>Energy saving, waste management, and pollution free steps for university campuses</text>
              </elementText>
            </elementTextContainer>
          </element>
          <element elementId="49">
            <name>Subject</name>
            <description>The topic of the resource</description>
            <elementTextContainer>
              <elementText elementTextId="182118">
                <text>Efficacy; SDG-implementation training; UN-SDG; University campus</text>
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            </elementTextContainer>
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          <element elementId="41">
            <name>Description</name>
            <description>An account of the resource</description>
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                <text>Global warming is a worldwide concern and the documents related to the need for sustainable measures seen in academic and non-academic literature. In a highly populated country like India, these are more severe worries. Multiple established educational institutions across India have taken significant steps in educating their students on sustainable development goals (SDG). Currently there is a need to assess the extent of effect such training has on student populations of such institutes. Present study attempted to assess the efficacy of SDG-implementation training programmes in a reputed private university, through student assessment of student behaviors outside the institute and in their personal life. Using semi-structured in-depth interview methods, interviewed eight students of Undergraduate and Postgraduate programmes. These students were active participants of community service programmes arranged by the university within a sustainable development model. Data were analyzed using reflexive thematic analysis methods. Emerged themes from data analysis indicate a positive change in their worldview and significant modifications in their personal behavior towards sustainability because of being part of such programmes. Educating others through practice and increased socio-environmental awareness were also major themes. Current study contributes in assessing efficacy of sustainability programmes in educational institutions. This study also suggests few recommendations for increasing competence of the same.   2021 Author(s).</text>
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            <name>Creator</name>
            <description>An entity primarily responsible for making the resource</description>
            <elementTextContainer>
              <elementText elementTextId="182120">
                <text>Prakasha G.S.; Chakraborty R.; Kenneth A.</text>
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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="182121">
                <text>AIP Conference Proceedings, Vol-2396</text>
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            </elementTextContainer>
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            <name>Publisher</name>
            <description>An entity responsible for making the resource available</description>
            <elementTextContainer>
              <elementText elementTextId="182122">
                <text>American Institute of Physics Inc.</text>
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            <description>A point or period of time associated with an event in the lifecycle of the resource</description>
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                <text>2021-01-01</text>
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                <text>&lt;a href="https://doi.org/10.1063/5.0066325" target="_blank" rel="noreferrer noopener"&gt;https://doi.org/10.1063/5.0066325&lt;/a&gt;
&lt;br /&gt;&lt;br /&gt;&lt;a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85116727587&amp;amp;doi=10.1063%2F5.0066325&amp;amp;partnerID=40&amp;amp;md5=a950c001e84ed09275aafb8644d0c510" target="_blank" rel="noreferrer noopener"&gt;https://www.scopus.com/inward/record.uri?eid=2-s2.0-85116727587&amp;amp;doi=10.1063%2f5.0066325&amp;amp;partnerID=40&amp;amp;md5=a950c001e84ed09275aafb8644d0c510&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="182125">
                <text>All Open Access; Bronze Open Access</text>
              </elementText>
            </elementTextContainer>
          </element>
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            <description>A related resource</description>
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              <elementText elementTextId="182126">
                <text>ISSN: 0094243X; ISBN: 978-073544130-9</text>
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          </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="182127">
                <text>Online</text>
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            <name>Language</name>
            <description>A language of the resource</description>
            <elementTextContainer>
              <elementText elementTextId="182128">
                <text>English</text>
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            </elementTextContainer>
          </element>
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            <name>Type</name>
            <description>The nature or genre of the resource</description>
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              <elementText elementTextId="182129">
                <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>
            <elementTextContainer>
              <elementText elementTextId="182130">
                <text>Prakasha G.S., School of Education, Christ University, Bangalore, India; Chakraborty R., Department of Psychology, Christ University, Bangalore, India; Kenneth A., School of Education, Christ University, Bangalore, India</text>
              </elementText>
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            <element elementId="50">
              <name>Title</name>
              <description>A name given to the resource</description>
              <elementTextContainer>
                <elementText elementTextId="51377">
                  <text>Conference Papers</text>
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              </elementTextContainer>
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      <name>Conference Paper</name>
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          <element elementId="50">
            <name>Title</name>
            <description>A name given to the resource</description>
            <elementTextContainer>
              <elementText elementTextId="182103">
                <text>Machine Learning Approach for the Prediction of Consumer Food Price Index</text>
              </elementText>
            </elementTextContainer>
          </element>
          <element elementId="49">
            <name>Subject</name>
            <description>The topic of the resource</description>
            <elementTextContainer>
              <elementText elementTextId="182104">
                <text>ANN; CFPI; forecasting; MAPE</text>
              </elementText>
            </elementTextContainer>
          </element>
          <element elementId="41">
            <name>Description</name>
            <description>An account of the resource</description>
            <elementTextContainer>
              <elementText elementTextId="182105">
                <text>The price of food and food related items are dynamic. A measure change in the price affects the buying behaviour of the consumer and monetary policies by the Government. The Consumer Food Price Index (CFPI) reflects the variations in food prices during a certain period. In India, the CFPI is released monthly by the Central Statistical Organization. It also reflects the inflation and helps the Government to take corrective measures in time. In this paper we have applied the machine learning approach in forecasting the consumer food price index in India. In specific, this work has focused on the applicability of Artificial Neural Network (ANN) models with back propagation learning in predicting the future values of CFPI. The monthly data for rural, urban and combined from the period 2013 to 2021 have been used to train and validate the models. The Mean Absolute Percentage Error (MAPE) values have been used to validate the accuracy of the models. The experimental results show that a simple ANN model with back propagation algorithm is highly capable in forecasting the future values of CFPI.  2021 IEEE.</text>
              </elementText>
            </elementTextContainer>
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          <element elementId="39">
            <name>Creator</name>
            <description>An entity primarily responsible for making the resource</description>
            <elementTextContainer>
              <elementText elementTextId="182106">
                <text>Sarangi P.K.; Sinha D.; Sinha S.; Mittal N.</text>
              </elementText>
            </elementTextContainer>
          </element>
          <element elementId="48">
            <name>Source</name>
            <description>A related resource from which the described resource is derived</description>
            <elementTextContainer>
              <elementText elementTextId="182107">
                <text>2021 9th International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions), ICRITO 2021</text>
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            </elementTextContainer>
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            <name>Publisher</name>
            <description>An entity responsible for making the resource available</description>
            <elementTextContainer>
              <elementText elementTextId="182108">
                <text>Institute of Electrical and Electronics Engineers Inc.</text>
              </elementText>
            </elementTextContainer>
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          <element elementId="40">
            <name>Date</name>
            <description>A point or period of time associated with an event in the lifecycle of the resource</description>
            <elementTextContainer>
              <elementText elementTextId="182109">
                <text>2021-01-01</text>
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            </elementTextContainer>
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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="182110">
                <text>&lt;a href="https://doi.org/10.1109/ICRITO51393.2021.9596527" target="_blank" rel="noreferrer noopener"&gt;https://doi.org/10.1109/ICRITO51393.2021.9596527&lt;/a&gt;
&lt;br /&gt;&lt;br /&gt;&lt;a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85125916788&amp;amp;doi=10.1109%2FICRITO51393.2021.9596527&amp;amp;partnerID=40&amp;amp;md5=e134879a1b111bb09c0783bdfba33866" target="_blank" rel="noreferrer noopener"&gt;https://www.scopus.com/inward/record.uri?eid=2-s2.0-85125916788&amp;amp;doi=10.1109%2fICRITO51393.2021.9596527&amp;amp;partnerID=40&amp;amp;md5=e134879a1b111bb09c0783bdfba33866&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="182111">
                <text>Restricted Access</text>
              </elementText>
            </elementTextContainer>
          </element>
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            <description>A related resource</description>
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              <elementText elementTextId="182112">
                <text>ISBN: 978-166541703-7</text>
              </elementText>
            </elementTextContainer>
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          <element elementId="42">
            <name>Format</name>
            <description>The file format, physical medium, or dimensions of the resource</description>
            <elementTextContainer>
              <elementText elementTextId="182113">
                <text>Online</text>
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            </elementTextContainer>
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            <name>Language</name>
            <description>A language of the resource</description>
            <elementTextContainer>
              <elementText elementTextId="182114">
                <text>English</text>
              </elementText>
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          </element>
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            <name>Type</name>
            <description>The nature or genre of the resource</description>
            <elementTextContainer>
              <elementText elementTextId="182115">
                <text>Conference paper</text>
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            </elementTextContainer>
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            <name>Coverage</name>
            <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>
              <elementText elementTextId="182116">
                <text>Sarangi P.K., Chitkara University Institute of Engineering and Technology, Chitkara University, Punjab, India; Sinha D., CHRIST (Deemed to be University), India; Sinha S., CHRIST (Deemed to be University), India; Mittal N., Amity University, Noida, Uttar Pradesh, India</text>
              </elementText>
            </elementTextContainer>
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          <elementContainer>
            <element elementId="50">
              <name>Title</name>
              <description>A name given to the resource</description>
              <elementTextContainer>
                <elementText elementTextId="51377">
                  <text>Conference Papers</text>
                </elementText>
              </elementTextContainer>
            </element>
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      <name>Conference Paper</name>
      <description>Faculty Publications- Conference Papers</description>
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          <element elementId="50">
            <name>Title</name>
            <description>A name given to the resource</description>
            <elementTextContainer>
              <elementText elementTextId="182089">
                <text>Consumer Characteristics and Consumption Patterns of Soft Drinks</text>
              </elementText>
            </elementTextContainer>
          </element>
          <element elementId="49">
            <name>Subject</name>
            <description>The topic of the resource</description>
            <elementTextContainer>
              <elementText elementTextId="182090">
                <text>Age; Consumption patterns; Educational qualifications; Gender; Marital status; Soft drinks</text>
              </elementText>
            </elementTextContainer>
          </element>
          <element elementId="41">
            <name>Description</name>
            <description>An account of the resource</description>
            <elementTextContainer>
              <elementText elementTextId="182091">
                <text>A soft drink is generally treated as very common product aimed at a very casual consumption. Normally, not much of attention is paid to this product, which has almost become 'commoditized'. But, a deeper and more careful observation would reveal that soft drinks are strong demographic descriptors of their consumers. Key insights into the characteristics and consumption patterns of consumers can be obtained through an incisive study of the soft drinks market. This research paper makes a concerted effort at unearthing the demographic details and consumption contours of the soft drink users in Kanpur, Agra, Varanasi, Allahabad, and Lucknow - the five representative cities of Uttar Pradesh, the most populous state of India. It has been conclusively established through this research that the residents of these five cities - which are demographically similar in nature - exhibit varying consumption patterns when it comes to soft drinks. It was also found that demographic variables like age, gender, educational qualification, income, and marital status do not significantly impact the consumption of soft drinks, whereas employment status is a key influencer of the same.  2021 IEEE.</text>
              </elementText>
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            <name>Creator</name>
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            <elementTextContainer>
              <elementText elementTextId="182092">
                <text>Sinha S.; Sinha D.; Mittal N.</text>
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          </element>
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            <elementTextContainer>
              <elementText elementTextId="182093">
                <text>2021 9th International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions), ICRITO 2021</text>
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            </elementTextContainer>
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            <name>Publisher</name>
            <description>An entity responsible for making the resource available</description>
            <elementTextContainer>
              <elementText elementTextId="182094">
                <text>Institute of Electrical and Electronics Engineers Inc.</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="182095">
                <text>2021-01-01</text>
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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="182096">
                <text>&lt;a href="https://doi.org/10.1109/ICRITO51393.2021.9596207" target="_blank" rel="noreferrer noopener"&gt;https://doi.org/10.1109/ICRITO51393.2021.9596207&lt;/a&gt;
&lt;br /&gt;&lt;br /&gt;&lt;a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85125938791&amp;amp;doi=10.1109%2FICRITO51393.2021.9596207&amp;amp;partnerID=40&amp;amp;md5=000cad5452b763365763ecc3ce70276c" target="_blank" rel="noreferrer noopener"&gt;https://www.scopus.com/inward/record.uri?eid=2-s2.0-85125938791&amp;amp;doi=10.1109%2fICRITO51393.2021.9596207&amp;amp;partnerID=40&amp;amp;md5=000cad5452b763365763ecc3ce70276c&lt;/a&gt;</text>
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            <name>Rights</name>
            <description>Information about rights held in and over the resource</description>
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              <elementText elementTextId="182097">
                <text>Restricted Access</text>
              </elementText>
            </elementTextContainer>
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                <text>ISBN: 978-166541703-7</text>
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            <description>The file format, physical medium, or dimensions of the resource</description>
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            <name>Language</name>
            <description>A language of the resource</description>
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              <elementText elementTextId="182100">
                <text>English</text>
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                <text>Sinha S., CHRIST (Deemed to be University), India, AIIT-Amity University, Noida; Sinha D., CHRIST (Deemed to be University), India, AIIT-Amity University, Noida; Mittal N., CHRIST (Deemed to be University), India, AIIT-Amity University, Noida</text>
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