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                <text>Approach for Collision Minimization and Enhancement of Power Allocation in WSNs</text>
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                <text>Wireless sensor networks (WSNs) have attracted much more attention in recent years. Hence, nowadays, WSN is considered one of the most popular technologies in the networking field. The reason behind its increasing rate is only for its adaptability as it works through batteries which are energy efficient, and for these characteristics, it has covered a wide market worldwide. Transmission collision is one of the key reasons for the decrease in performance in WSNs which results in excessive delay and packet loss. The collision range should be minimized in order to mitigate the risk of these packet collisions. The WSNs that contribute to minimize the collision area and the statistics show that the collision area which exceeds equivalents transmission power has been significantly reduced by this technique. This proposed paper optimally reduced the power consumption and data loss through proper routing of packets and the method of congestion detection. WSNs typically require high data reliability to preserve identification and responsiveness capacity while also improving data reliability, transmission, and redundancy. Retransmission is determined by the probability of packet arrival as well as the average energy consumption.   2021 Debabrata Singh et al.</text>
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                <text>Singh D.; Bhanipati J.; Biswal A.K.; Samanta D.; Joshi S.; Shukla P.K.; Nuagah S.J.</text>
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                <text>Journal of Sensors, Vol-2021</text>
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                <text>Hindawi Limited</text>
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                <text>All Open Access; Hybrid Gold Open Access</text>
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                <text>ISSN: 1687725X</text>
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                <text>Singh D., Department of CA, ITER, SOA Deemed To Be University, Odisha, Bhubaneswar, 751030, India; Bhanipati J., Department of CSE, ITER, SOA Deemed To Be University, Odisha, Bhubaneswar, 751030, India; Biswal A.K., Department of CSE, ITER, SOA Deemed To Be University, Odisha, Bhubaneswar, 751030, India; Samanta D., Department of Computer Science, CHRIST (Deemed To Be University), Karnataka, Bengaluru, 560029, India; Joshi S., Department of Computer Engineering, SVKM'S NMIMS MPSTME Shirpur Campus, Maharashtra Savalade, 425405, India; Shukla P.K., Department of CSE, University Institute of Technology, Rajiv Gandhi Proudyogiki Vishwavidyalaya (Technological University of Madhya Pradesh), Bhopal, 462033, India; Nuagah S.J., Department of Electrical Engineering, Tamale Technical University, P.O. Box 3 E/R., Tamale, Ghana</text>
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                <text>Juice Jacking: Security Issues and Improvements in USB Technology</text>
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                <text>Authentication; Cyber-attack; Hacker; Keystroke dynamics; Malicious code; Security; USB code</text>
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                <text>For a reliable and convenient system, it is essential to build a secure system that will be protected from outer attacks and also serve the purpose of keeping the inner data safe from intruders. A juice jacking is a popular and spreading cyber-attack that allows intruders to get inside the system through the web and theive potential data from the system. For peripheral communications, Universal Serial Bus (USB) is the most commonly used standard in 5G generation computer systems. USB is not only used for communication, but also to charge gadgets. However, the transferal of data between devices using USB is prone to various security threats. It is necessary to maintain the confidentiality and sensitivity of data on the bus line to maintain integrity. Therefore, in this paper, a juice jacking attack is analyzed, using the maximum possible means through which a system can be affected using USB. Ten different malware attacks are used for experimental purposes. Various machine learning and deep learning models are used to predict malware attacks. An extensive experimental analysis reveals that the deep learning model can efficiently recognize the juice jacking attack. Finally, various techniques are discussed that can either prevent or avoid juice jacking attacks.  2022 by the authors. Licensee MDPI, Basel, Switzerland.</text>
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                <text>Singh D.; Biswal A.K.; Samanta D.; Singh D.; Lee H.-N.</text>
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                <text>Sustainability (Switzerland), Vol-14, No. 2</text>
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                <text>&lt;a href="https://doi.org/10.3390/su14020939" target="_blank" rel="noreferrer noopener"&gt;https://doi.org/10.3390/su14020939&lt;/a&gt;
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              <elementText elementTextId="112850">
                <text>All Open Access; Gold Open Access</text>
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                <text>ISSN: 20711050</text>
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                <text>Singh D., Department of Computer Application, Institute of Technical Education and Research (ITER), Siksha OAnusandhan (SOA) Deemed to be University, Odisha, Bhubaneswar, 751030, India; Biswal A.K., Department of Computer Science and Engineering, Institute of Technical Education and Research (ITER), Siksha OAnusandhan (SOA) Deemed to be University, Odisha, Bhubaneswar, 751030, India; Samanta D., Department of Computer Science, CHRIST University, Karnataka, Bangalore, 560029, India; Singh D., School of Electrical Engineering and Computer Science, Gwangju Institute of Science and Technology, Gwangju, 61005, South Korea; Lee H.-N., School of Electrical Engineering and Computer Science, Gwangju Institute of Science and Technology, Gwangju, 61005, South Korea</text>
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                <text>Early diagnosis of COVID-19 patients using deep learning-based deep forest model</text>
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                <text>CNN; COVID-19; deep forest; deep learning; ResNet101; testing</text>
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                <text>Coronavirus disease-19 (COVID-19) has rapidly spread all over the world. It is found that the low sensitivity of reverse transcription-polymerase chain reaction (RT-PCR) examinations during the early stage of COVID-19 disease. Thus, efficient models are desirable for early-stage testing of COVID-19 infected patients. Chest X-ray (CXR) images of COVID-19 infected patients have shown some bilateral changes. In this paper, deep transfer learning and a deep forest-based model are proposed to diagnose COVID-19 infection from CXR images. Initially, features of X-ray images are extracted using the well-known deep transfer learning model (i.e., ResNet101), which does not require tuning many parameters compared to the deep convolutional neural network (CNN). After that, the deep forest model is utilised to predict COVID-19 infected patients. The deep forest is based upon ensemble learning and requires a small number of hyper-parameters. Additionally, the proposed model is trained on a multi-class dataset that contains four different classes as COVID-19 (+), pneumonia, tuberculosis, and healthy patients. The comparisons are drawn among the proposed deep transfer learning and deep forest-based models, the competitive models. The obtained results show that the proposed model effectively diagnoses COVID-19 infection with an accuracy of 99.4%.  2022 Informa UK Limited, trading as Taylor &amp;amp; Francis Group.</text>
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                <text>Singh D.; Kumar V.; Kaur M.; Kumari R.</text>
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                <text>Journal of Experimental and Theoretical Artificial Intelligence, Vol-35, No. 3, pp. 365-375.</text>
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                <text>Taylor and Francis Ltd.</text>
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                <text>&lt;a href="https://doi.org/10.1080/0952813X.2021.2021300" target="_blank" rel="noreferrer noopener"&gt;https://doi.org/10.1080/0952813X.2021.2021300&lt;/a&gt;
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                <text>Singh D., School of Electrical Engineering and Computer Science, Gwangju Institute of Science and Technology, Gwangju, South Korea; Kumar V., Department of Computer Science &amp;amp; Engineering, National Institute of Technology Hamirpur, Hamirpur, India; Kaur M., School of Electrical Engineering and Computer Science, Gwangju Institute of Science and Technology, Gwangju, South Korea; Kumari R., Department of Computer Science, Christ (Deemed to Be University), Bangalore, India</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>Deep learning techniques have become increasingly common in biometrics over the last decade. However, due to a lack of large ear datasets, deep learning models in ear biometrics are limited. To address this drawback, researchers use transfer learning based on various pre-trained models. Conventional machine learning algorithms using traditional feature extraction techniques produce low recognition results for the unconstrained ear dataset AWE. In this paper, an ear recognition model based on the ResNet-50 pretrained architecture outperforms traditional methods in terms of recognition accuracy in AWE dataset. A new feature level fusion of ResNet50 and GLBP feature is also experimented to improve the recognition accuracy compared to traditional features.  2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.</text>
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                <text>Lecture Notes in Electrical Engineering, Vol-828, pp. 545-551.</text>
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                <text>Singh D., Christ (Deemed to be) University, Pune, India; Raju G., Department of Computer Science and Engineering Christ (Deemed to be) University, Bengaluru, India</text>
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                <text>Applicability of Search Engine Optimization for WordPress (WP) Website</text>
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                <text>AdWords; Information retrieval; P-Purchase; Quality Score; SERP</text>
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                <text>91 percent of online experiences begin with a search, according to the Content Marketing Institute. That is the hunt for an explanation. As a result, search marketing is a critical practice for any businesses looking to grow and improve. Marketers and clients that paid for adverts began researching SEO and SEM at that time. This pursuit plans to give knowledge into the paid and unpaid procedures of search engine marketing (SEM) and what falls under its umbrella including search engine optimization (SEO) and pay per click (PPC). So in this exploration work, we feel the most ideal approach to utilize a web search tool SEM, is such a method of Internet showcasing that incorporates the utilization of web crawler result pages to advance business sites. SEM was earlier used as a protective gadget for anything to be done with the online search marketing field and it was girdled along with SEO.  2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.</text>
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                <text>Singh D.; Shivamurthaiah M.; Samanta D.; Bhattacharya A.; Dutta S.</text>
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                <text>Smart Innovation, Systems and Technologies, Vol-316, pp. 227-237.</text>
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                <text>ISSN: 21903018; ISBN: 978-981195402-3</text>
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                <text>Singh D., Deptartment of CA, ITER, SOA Deemed to be University, Odisha, Bhubaneswar, India; Shivamurthaiah M., Department of Computer Science and Engineering, Brindavan College of Engineering, Bengaluru, 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>Impact of Using Partial Gait Energy Images for Human Recognition by Gait Analysis</text>
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                <text>Gait analysis is a behavioral biometric that classifies human, based on how they walk and other variables involved in the forward movement. In this study, we have attempted to comprehend the significance of the upper portion of the body in gait analysis for human recognition. The data for this study came from the CASIA dataset, which was donated by the Chinese Academy of Sciences Institute of Automation. We began by extracting the gait energy image (GEI) from the dataset and employing principal component analysis to minimize the dimensionality (PCA). For classification, random forest, support vector machine (SVM), and convolution neural network (CNN) algorithms are implemented to recognize the human subjects. This paper provides experimental results to show the accuracy attained when classification is done on GEI of full-body images is higher than the accuracy attained when classification is done on GEI of the lower portion of the body only. It also shows the significance of the GEI of the upper portion of the body.  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-947, pp. 175-185.</text>
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                <text>ISSN: 18761100; ISBN: 978-981195935-6</text>
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                <text>Singh D., Christ (Deemed to be) University, Pune, India; Thomas K.T., Christ (Deemed to be) University, Pune, India</text>
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                <text>CNN-RNN based Hybrid Machine Learning Model to Predict the Currency Exchange Rate: USD to INR</text>
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              <elementText elementTextId="179369">
                <text>CNN; DNN; FLANN; LSTM; RNN</text>
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                <text>Foreign currency exchange plays an imperative part in the global business and in monetary market. It is also an opportunity for many traders as an investment option and the advance knowledge of fluctuation helps the investors making right decision on time. However, due to its volatile nature, prediction of foreign currency exchange is a challenging task. This paper implements two models based on machine learning, namely Recurrent Neural Networks (RNN) and a Hybrid model of Convolutional Neural Networks (CNN) with RNN known as CNN-RNN to assess the accuracy in predicting the conversion rate of US Dollar (USD) to Indian Rupees (INR). The data set used to verify and validate the models is the daily currency exchange rate (USD to INR) available in public domain. The experimental results show that the simple RNN model performs slightly better than the hybrid model in this particular case. Though the accuracy of the hybrid model is very high in terms of error calculation still the single RNN model is the better performer. This does not straight away reject the hybrid model rather needs more experimental analysis with changing architecture and data set.  2022 IEEE.</text>
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                <text>Singh G.; Sarangi P.K.; Rani L.; Sharma K.; Sinha S.; Sahoo A.K.; Rath B.P.</text>
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                <text>2022 2nd International Conference on Advance Computing and Innovative Technologies in Engineering, ICACITE 2022, pp. 1668-1672.</text>
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                <text>Institute of Electrical and Electronics Engineers Inc.</text>
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                <text>&lt;a href="https://doi.org/10.1109/ICACITE53722.2022.9823844" target="_blank" rel="noreferrer noopener"&gt;https://doi.org/10.1109/ICACITE53722.2022.9823844&lt;/a&gt;
&lt;br /&gt;&lt;br /&gt;&lt;a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85135450011&amp;amp;doi=10.1109%2FICACITE53722.2022.9823844&amp;amp;partnerID=40&amp;amp;md5=0430a53b1d8def6ab8a7c56ca034948f" target="_blank" rel="noreferrer noopener"&gt;https://www.scopus.com/inward/record.uri?eid=2-s2.0-85135450011&amp;amp;doi=10.1109%2fICACITE53722.2022.9823844&amp;amp;partnerID=40&amp;amp;md5=0430a53b1d8def6ab8a7c56ca034948f&lt;/a&gt;</text>
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                <text>ISBN: 978-166543789-9</text>
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                <text>Singh G., Chitkara University Institute of Engineering and Technology, Chitkara University Punjab, Department of Computer Science and Engineering, India; Sarangi P.K., Chitkara University Institute of Engineering and Technology, Chitkara University Punjab, Department of Computer Science and Engineering, India; Rani L., Chitkara University Institute of Engineering and Technology, Chitkara University Punjab, Department of Computer Science and Engineering, India; Sharma K., Chitkara University Institute of Engineering and Technology, Chitkara University Punjab, Department of Computer Science and Engineering, India; Sinha S., Christ University, Department of Computer Science and Engineering, Bangalore, India; Sahoo A.K., Graphic Era Hill University, Department of Computer Science and Engineering, Dehradun, India; Rath B.P., Cognizant Technology Solutions Ltd., Department of Computer Science and Engineering, Bangalore, India</text>
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          <element elementId="50">
            <name>Title</name>
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                <text>Proficient randomized response model based on blank card strategy to estimate the sensitive parameter under negative binomial distribution</text>
              </elementText>
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            <name>Subject</name>
            <description>The topic of the resource</description>
            <elementTextContainer>
              <elementText elementTextId="105823">
                <text>Negative binomial distribution; Privacy protection; Randomized response model; Sensitive attribute</text>
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            <name>Description</name>
            <description>An account of the resource</description>
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                <text>This paper has great potential for estimating population proportion who possess stigmatized character by using Negative binomial distribution as a randomization device. The properties of the proposed estimation procedure have been examined. Measure of privacy protection for the proposed randomization device has been also quantified. Empirical studies are performed to support the theoretical results, which show the dominance of the proposed estimator over its competitors. Results are analysed and suitable recommendations are put forward for survey practitioners whenever they deal with sensitive characteristics.  2021</text>
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              <elementText elementTextId="105825">
                <text>Singh G.; Singh C.</text>
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              <elementText elementTextId="105826">
                <text>Ain Shams Engineering Journal, Vol-13, No. 5</text>
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              <elementText elementTextId="105827">
                <text>Ain Shams University</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>&lt;a href="https://doi.org/10.1016/j.asej.2021.10.006" target="_blank" rel="noreferrer noopener"&gt;https://doi.org/10.1016/j.asej.2021.10.006&lt;/a&gt;
&lt;br /&gt;&lt;br /&gt;&lt;a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85126059731&amp;amp;doi=10.1016%2Fj.asej.2021.10.006&amp;amp;partnerID=40&amp;amp;md5=553c14311b1d1409011c5af584b9ccb1" target="_blank" rel="noreferrer noopener"&gt;https://www.scopus.com/inward/record.uri?eid=2-s2.0-85126059731&amp;amp;doi=10.1016%2fj.asej.2021.10.006&amp;amp;partnerID=40&amp;amp;md5=553c14311b1d1409011c5af584b9ccb1&lt;/a&gt;</text>
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              <elementText elementTextId="105831">
                <text>ISSN: 20904479</text>
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            <name>Format</name>
            <description>The file format, physical medium, or dimensions of the resource</description>
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            <description>A language of the resource</description>
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                <text>English</text>
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          </element>
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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="105835">
                <text>Singh G., Department of Management Studies, IIT(ISM), Dhanbad, India; Singh C., Department of Statistics, CHRIST(Deemed to be University), Karnataka, Bengaluru, India</text>
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      <name>Article</name>
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        <elementContainer>
          <element elementId="50">
            <name>Title</name>
            <description>A name given to the resource</description>
            <elementTextContainer>
              <elementText elementTextId="108841">
                <text>An Improved Alternative Method of Imputation for Missing Data in Survey Sampling</text>
              </elementText>
            </elementTextContainer>
          </element>
          <element elementId="49">
            <name>Subject</name>
            <description>The topic of the resource</description>
            <elementTextContainer>
              <elementText elementTextId="108842">
                <text>bias; imputation; mean squared error (MSE); percent relative efficiency (PRE); Population mean</text>
              </elementText>
            </elementTextContainer>
          </element>
          <element elementId="41">
            <name>Description</name>
            <description>An account of the resource</description>
            <elementTextContainer>
              <elementText elementTextId="108843">
                <text>In the present paper, a new and improved method of ratio type imputation and corresponding point estimator to estimate the finite population mean is proposed in case of missing data problem. It has been shown that this estimator utilizes the readily available auxiliary information efficiently and gives better results than the ratio and mean methods of imputation; furthermore, its efficiency is also compared with the regression method of imputation and some other imputation methods, discussed in this article, using four real data sets. A simulation study is carried out to verify theoretical outcomes, and suitable recommendations are made.  2022 NSP Natural Sciences Publishing Cor.</text>
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              <elementText elementTextId="108844">
                <text>Singh G.N.; Jaiswal A.K.; Singh C.; Usman M.</text>
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            <name>Source</name>
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            <elementTextContainer>
              <elementText elementTextId="108845">
                <text>Journal of Statistics Applications and Probability, Vol-11, No. 2, pp. 535-543.</text>
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              <elementText elementTextId="108846">
                <text>Natural Sciences Publishing</text>
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                <text>&lt;a href="https://doi.org/10.18576/jsap/110214" target="_blank" rel="noreferrer noopener"&gt;https://doi.org/10.18576/jsap/110214&lt;/a&gt;
&lt;br /&gt;&lt;br /&gt;&lt;a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85127098817&amp;amp;doi=10.18576%2Fjsap%2F110214&amp;amp;partnerID=40&amp;amp;md5=ce39c25679078f273d2a8f42b798a1b5" target="_blank" rel="noreferrer noopener"&gt;https://www.scopus.com/inward/record.uri?eid=2-s2.0-85127098817&amp;amp;doi=10.18576%2fjsap%2f110214&amp;amp;partnerID=40&amp;amp;md5=ce39c25679078f273d2a8f42b798a1b5&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="108849">
                <text>Restricted Access</text>
              </elementText>
            </elementTextContainer>
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              <elementText elementTextId="108850">
                <text>ISSN: 20908423</text>
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            <name>Format</name>
            <description>The file format, physical medium, or dimensions of the resource</description>
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                <text>Online</text>
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                <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>Article</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="108854">
                <text>Singh G.N., Indian Institute of Technology (Indian School of Mines), Dhanbad, 826004, India; Jaiswal A.K., Indian Institute of Technology (Indian School of Mines), Dhanbad, 826004, India; Singh C., Department of Statistics, CHRIST (Deemed to be University), Karnataka, Bengaluru, India; Usman M., Indian Institute of Technology (Indian School of Mines), Dhanbad, 826004, India</text>
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            <element elementId="50">
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      <name>Article</name>
      <description>Faculty Publications -Articles</description>
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          <element elementId="50">
            <name>Title</name>
            <description>A name given to the resource</description>
            <elementTextContainer>
              <elementText elementTextId="123749">
                <text>Randomized response model to alter the nuisance effect of non-response due to stigmatized issues in survey sampling</text>
              </elementText>
            </elementTextContainer>
          </element>
          <element elementId="49">
            <name>Subject</name>
            <description>The topic of the resource</description>
            <elementTextContainer>
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                <text>Poisson distribution; probability proportional to size (PPS) sampling; Randomized response model; rare sensitive attribute; unrelated attribute</text>
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                <text>The present study deals with the estimation procedures of the mean number of persons bearing a rare sensitive attribute in the clustered population under two-stage sampling scheme. The resultant estimators have been suggested using two-stage randomized response model when a rare unrelated attribute is assumed to be known as well as unknown. The properties of resultant estimators are studied where the first-stage samples are drawn using the probability proportional to the size with replacement sampling scheme. The estimation procedures have been further extended for the stratified clustered population. The empirical studies are performed for the validation of the suggested estimation procedures. Recommendations have been made to survey practitioners for their real-life applications.  2020 Informa UK Limited, trading as Taylor &amp;amp; Francis Group.</text>
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                <text>Journal of Statistical Computation and Simulation, Vol-90, No. 13, pp. 2395-2420.</text>
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                <text>Singh G.N., Department of Mathematics &amp;amp; Computing, Indian Institute of Technology (Indian School of Mines), Dhanbad, India; Singh C., Department of Mathematics &amp;amp; Computing, Indian Institute of Technology (Indian School of Mines), Dhanbad, India; Suman S., Department of Economics, Christ (Deemed to be University) NCR Campus, Uttar Pradesh, India</text>
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                <text>Voluntary cybersecurity risk disclosures and firms characteristics: the moderating role of the knowledge-intensive industry</text>
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                <text>Cybersecurity; Cybersecurity risk disclosure; Firm-characteristics; Knowledge-intensive; Voluntary risk disclosure</text>
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                <text>Purpose: This study examines voluntary cybersecurity risk disclosures (VCRD) by listed Indian companies. It also investigates how it relates to firm-specific characteristics such as size, leverage, profitability, liquidity, beta, market growth and industry. Design/methodology/approach: The extent of VCRD was measured by assessing the cumulative occurrence of cybersecurity risk keywords in the annual report of 100 listed Indian non-financial companies. Keyword extraction and occurrence counts were performed using Python software. A multiple regression analysis was applied to predict the characteristics of VCRD. Findings: The results showed that the theoretical frameworks underpinned by agency and signalling theories continued to provide a valid explanation of VCRD by Indian companies. Specifically, the findings emphasized the importance of firm size, leverage, and beta as significant VCRD determinants. Additionally, the study found that knowledge-intensive industries had a favourable impact on the extent of VCRD. Research limitations/implications: This study is relevant because it informs company management, regulators and investors about the nature and characteristics of companies that satisfy stakeholder demands to prevent cyber breaches. Originality/value: Understanding disclosure characteristics is crucial from policy and regulatory perspectives. Studies on cybersecurity disclosures are related to developed economies such as the United States of America and Canada. This is the first study to explore this issue in a developing nation, in general, and in India, in particular, where cybersecurity risk disclosure has yet to be recognized.  2025, Harmandeep Singh.</text>
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                <text>Asian Journal of Accounting Research</text>
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                <text>&lt;a href="https://doi.org/10.1108/AJAR-12-2023-0413" target="_blank" rel="noreferrer noopener"&gt;https://doi.org/10.1108/AJAR-12-2023-0413&lt;/a&gt;
&lt;br /&gt;&lt;br /&gt;&lt;a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85215929608&amp;amp;doi=10.1108%2FAJAR-12-2023-0413&amp;amp;partnerID=40&amp;amp;md5=c0a78af60d01dd78b1973f4b180423ae" target="_blank" rel="noreferrer noopener"&gt;https://www.scopus.com/inward/record.uri?eid=2-s2.0-85215929608&amp;amp;doi=10.1108%2fAJAR-12-2023-0413&amp;amp;partnerID=40&amp;amp;md5=c0a78af60d01dd78b1973f4b180423ae&lt;/a&gt;</text>
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                <text>ISSN: 24599700</text>
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                <text>Singh H., School of Commerce, Finance and Accountancy, Christ University, Bangalore, India</text>
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                <text>Is corporate reputation associated with voluntary cybersecurity risk reporting?</text>
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          <element elementId="49">
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              <elementText elementTextId="85316">
                <text>Cybersecurity; Cybersecurity risk disclosure; Risk disclosure; Voluntary disclosure</text>
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                <text>Purpose: This study investigated the effect of voluntary cybersecurity risk reporting (VCRR) on corporate reputation. By examining the association between VCRR and corporate reputation, this study aims to provide exploratory evidence of how cybersecurity risk is sensitive to a companys image and reputation. Design/methodology/approach: An automated content analysis of VCRR by 95 Bombay Stock Exchange-listed companies was undertaken using Python code. Signaling and legitimacy theories were adopted to interpret the findings, establishing whether VCRR was related to corporate reputation. Findings: The results confirm that VCRR improves the corporate reputation in the financial market. The results also confirm the signalling and legitimacy theory that a company can manage reputational risks through higher voluntary risk disclosure. Practical implications: The corporations managers can gain insights from the studys findings and proactively address cybersecurity risks through strategic disclosure and management practices. In addition, organizations can recognize that investors value transparency and establish a positive reputation for those who communicate openly. Social implications: A significant association between VCRR and corporate reputation implies that such disclosures enhance trust and transparency in the business sector and induce security and accountability among investors engaging with the company. Originality/value: To the best of the authors knowledge, this study is the first that empirically investigates this issue and adds to the international literature a new explanatory variable, corporate reputation, to explain VCRR practices.  2024, Emerald Publishing Limited.</text>
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                <text>Meditari Accountancy Research</text>
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&lt;br /&gt;&lt;br /&gt;&lt;a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85208616682&amp;amp;doi=10.1108%2FMEDAR-02-2024-2329&amp;amp;partnerID=40&amp;amp;md5=30a74ae79f44886234403e49e6ff005a" target="_blank" rel="noreferrer noopener"&gt;https://www.scopus.com/inward/record.uri?eid=2-s2.0-85208616682&amp;amp;doi=10.1108%2fMEDAR-02-2024-2329&amp;amp;partnerID=40&amp;amp;md5=30a74ae79f44886234403e49e6ff005a&lt;/a&gt;</text>
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                <text>ISSN: 2049372X</text>
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                <text>Singh H., Department of Commerce, Christ University, Bangalore, India</text>
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                <text>Drivers of Mandatory and Non-Mandatory Internet Corporate Reporting in Public and Private Sector Indian Companies</text>
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              <elementText elementTextId="103431">
                <text>disclosure; Internet corporate reporting; regulation; SEBI</text>
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                <text>The papers objective was to measure the drivers of mandatory and non-mandatory internet corporate reporting by public and private sector companies following the internet disclosure compliance of listing and obligation requirement of SEBI under Clause 46. Several drivers, namely firm size, profitability, leverage, liquidity, board size, independence of board, and CEO duality, were used to measure the effectiveness of mandatory and non-mandatory disclosure. A multiple regression model was applied to test the present papers hypotheses. The results of multiple regression revealed that the firms size was exceptionally important for both sectors. In contrast, public sector disclosure was largely impacted by leverage, liquidity, board size, and board independence. In comparison, the private sector disclosure scores were mainly impacted by leverage and board size, although there is no relationship between ICR and firm profitability and CEO duality. In performing separate multivariate regression between the two sectors, many disparities emerged. This disparity showed that public and private sector corporations had quite different firm and governance characteristics of the disclosure. As the first exploratory research to assess the mandate internet disclosure of public and private sector companies in India, it is very informational, specifically for those working on Indian companies regulation, compliance, and research.  2022, Associated Management Consultants Pvt. Ltd. All rights reserved.</text>
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                <text>Singh H.</text>
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                <text>Prabandhan: Indian Journal of Management, Vol-15, No. 12, pp. 39-53.</text>
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                <text>Associated Management Consultants Pvt. Ltd.</text>
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                <text>&lt;a href="https://doi.org/10.17010/pijom/2022/v15i12/172600" target="_blank" rel="noreferrer noopener"&gt;https://doi.org/10.17010/pijom/2022/v15i12/172600&lt;/a&gt;
&lt;br /&gt;&lt;br /&gt;&lt;a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85144122501&amp;amp;doi=10.17010%2Fpijom%2F2022%2Fv15i12%2F172600&amp;amp;partnerID=40&amp;amp;md5=352178e1aae6d3613df6367bcadc359c" target="_blank" rel="noreferrer noopener"&gt;https://www.scopus.com/inward/record.uri?eid=2-s2.0-85144122501&amp;amp;doi=10.17010%2fpijom%2f2022%2fv15i12%2f172600&amp;amp;partnerID=40&amp;amp;md5=352178e1aae6d3613df6367bcadc359c&lt;/a&gt;</text>
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                <text>Attitude; Blockchain Technology; Technology Acceptance Model; Traceability; Transparency</text>
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                <text>The concept of fashion has been coupled with technology, where technology has become the protagonist. The transparency between an organization and a customer works as a catalyst, and the customer has taken a more mainstream role. With blockchain technology, companies can reconnect with customers and customers can track the journey of a product from its raw materials to the finished goods. The primary focus of the study is on services and data collected from the following sectors, namely fashion, apparel, and online platforms. The authors main goals are (1) to illustrate an overview of how big data is transforming the service industry, especially the fashion and design sector, and (2) to present various mechanisms adopted in the service industry. The study aims to investigate a model that fits through EXT-TAM and uses additional attributes of blockchain technology with a special reference to fashion apparel. The findings of this study depict a model, where PEOU, PU, and attitude are the major constructs and present a win-win scenario for both the customer and the organization.  2022 Authors. All rights reserved.</text>
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                <text>Singh H., Symbiosis International (Deemed), India; Jain G., Keele University, United Kingdom; Kumar N., CHRIST University, India; Hashimy L., Universitat Automa de Barcelona, Spain</text>
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                <text>Depth analysis of object in image; Masked RCNN; Relative distance of object in images</text>
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                <text>Getting distance of an object from a single 2D image has always been a task. Due to various reasons, it was difficult to compare from images whether an object is closer or farther from camera. In this paper, we propose an idea to compare multiple images taken from same focal length cameras and specifying the distance of an object in those images with respect to each other. Our dataset contains images of palm of hand with particular distance from camera, and the output difference can specify in which image the palm is closer to camera as compared to others and vice versa. For this model, we are using Mask RCNN to recognize the object; in our case, it has been trained to identify palm, and then giving the output of masked RCNN to a depth identifier model to specify the distance of the palm from the camera. Directly using depth identifier model cannot give correct output as distance of background from camera results in different value for distance of targeted object in different images. So, we will be using mask RCNN to specify which part of image depth model should find distance from the camera. In the final step, we take the output of the depth model and take the mean of the output generated by it and compare the means of various images to specify relative distance from each other.  2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.</text>
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                <text>Lecture Notes in Electrical Engineering, Vol-806, pp. 81-88.</text>
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                <text>Purpose: This study aims to investigate the awareness of extensible business reporting language (XBRL) and the perception of chartered accountants of India concerning the inhibitors of XBRL adoption, namely, environmental, organizational and innovation factors developed by Troshani and Rao (2007) from Rogers innovation diffusion theory. In addition, the analysis also investigated the relationship between the perception of issues regarding XBRL adoption and individual characteristics (training, age, gender and professional experience). Design/methodology/approach: A Web-based questionnaire was circulated through e-mail to chartered accountants registered with the Institute of Chartered Accountants India (ICAI) and 233 chartered accountants responded to the questionnaire. The data was analyzed using reliability statistics and multivariate regression analyses. Findings: The results indicate that accountants perceived that environmental, organizational and innovational factors were challenging in adopting XBRL. Interestingly, training and experience were significant factors in explaining respondents perceptions. Practical implications: From a practical panorama, the significance of issues implies that associations such as XBRL International, XBRL India, ICAI and the Ministry of Corporate Affairs should collectively take the appropriate steps to sustain and ameliorate the reliability and adoption of XBRL. Social implications: The results can motivate ICAI/Institute of Company Secretaries of India (ICSI) courses to teach academic content about XBRL. Originality/value: The present study differs from previous research because it examines the inhibitors in adopting XBRL, namely, environmental, organizational and innovation factors, in an empirical setting. Moreover, to the best of the authors knowledge, this is the first study to analyze the influence of individual factors on accountants perceptions about inhibitors of XBRL adoption.  2021, Emerald Publishing Limited.</text>
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                <text>Singh H.; Singh A.</text>
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                <text>Accounting Research Journal, Vol-35, No. 5, pp. 598-615.</text>
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                <text>Emerald Group Holdings Ltd.</text>
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                <text>&lt;a href="https://doi.org/10.1108/ARJ-05-2021-0144" target="_blank" rel="noreferrer noopener"&gt;https://doi.org/10.1108/ARJ-05-2021-0144&lt;/a&gt;
&lt;br /&gt;&lt;br /&gt;&lt;a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85121378684&amp;amp;doi=10.1108%2FARJ-05-2021-0144&amp;amp;partnerID=40&amp;amp;md5=a956fc1a721799345234bb5d480137db" target="_blank" rel="noreferrer noopener"&gt;https://www.scopus.com/inward/record.uri?eid=2-s2.0-85121378684&amp;amp;doi=10.1108%2fARJ-05-2021-0144&amp;amp;partnerID=40&amp;amp;md5=a956fc1a721799345234bb5d480137db&lt;/a&gt;</text>
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                <text>ISSN: 10309616</text>
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                <text>Singh H., School of Commerce, Finance and Accountancy, Christ (Deemed to be University), Bangalore, India; Singh A., Department of Commerce and Business Management, Guru Nanak Dev University, Rc, Gurdaspur, India</text>
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                <text>Faculty acceptance of virtual teaching platforms for online teaching: Moderating role of resistance to change</text>
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                <text>COVID-19; online teaching; synchronous learning; technology acceptance model; videoconferencing</text>
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                <text>Under this new normal world scenario, online teaching has been essential rather than a choice in continuing learning activities. During the COVID-19 period, virtual teaching platforms played an important role in the success of online teaching in various higher educational institutions. Thus, the current study attempted to predict faculty adoption of online platforms by introducing a set of essential drivers for engaging in online teaching. Following the theory of reasoned action, the study broadened the technology acceptance model variables and security and trust as extrinsic determinants and included resistance to change as moderators to invigorate the research model. Data were collected through an online survey with a sample size of 418 Indian respondents. Our results posit that perceived ease of use, usefulness, security and trust positively influence the faculty's intentions to adopt online platforms. In addition, the study also reported that positive intention leads to the actual use of virtual platforms. Furthermore, the research found the moderating role of the resistance to change dimension in the association of intention and actual use of virtual teaching platforms. The findings provide both theoretical and practical applications of educational technology. Implications for practice or policy  The first step for accepting virtual teaching platforms is to help faculty to reduce their resistance for effective online teaching.  Higher education institutions should have a policy promising faculty that online teaching using virtual teaching platforms will offer a safer and more trustworthy environment.  Higher education institutions should undertake intense organisational renewal and implement bottom-up processes for synchronous learning.  Regulators could frame a policy including virtual teaching platforms to provide interactive professional development opportunities.  Articles published in the Australasian Journal of Educational Technology (AJET) are available under Creative Commons Attribution Non-Commercial No Derivatives Licence (CC BY-NC-ND 4.0). Authors retain copyright in their work and grant AJET right of first publication under CC BY-NC-ND 4.0.</text>
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              <elementText elementTextId="99372">
                <text>Singh H.; Singh P.; Sharma D.</text>
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                <text>Australasian Journal of Educational Technology, Vol-39, No. 6, pp. 33-50.</text>
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              <elementText elementTextId="99374">
                <text>Australasian Society for Computers in Learning in Tertiary Education (ASCILITE)</text>
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                <text>2023-01-01</text>
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                <text>&lt;a href="https://doi.org/10.14742/ajet.7529" target="_blank" rel="noreferrer noopener"&gt;https://doi.org/10.14742/ajet.7529&lt;/a&gt;
&lt;br /&gt;&lt;br /&gt;&lt;a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85184273923&amp;amp;doi=10.14742%2Fajet.7529&amp;amp;partnerID=40&amp;amp;md5=13f30a3d9c177240306d94f4c370c3fc" target="_blank" rel="noreferrer noopener"&gt;https://www.scopus.com/inward/record.uri?eid=2-s2.0-85184273923&amp;amp;doi=10.14742%2fajet.7529&amp;amp;partnerID=40&amp;amp;md5=13f30a3d9c177240306d94f4c370c3fc&lt;/a&gt;</text>
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              <elementText elementTextId="99377">
                <text>All Open Access; Gold Open Access</text>
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                <text>ISSN: 14495554</text>
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                <text>Singh H., Christ (Deemed to be University), Bangalore, India; Singh P., Akal University, Bathinda, India; Sharma D., Kristu Jayanti College, Bangalore, India</text>
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      <name>Book Chapter</name>
      <description>Faculty Publications- Book Chapter</description>
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      <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>
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              <elementText elementTextId="144923">
                <text>Bookstagrammers vs. BookTubers: A comparative study on readers' preferred social media book influencer</text>
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                <text>As the Internet has become a part of many people's daily lives, it has led to the growth of a reading culture influenced by book bloggers on different social media platforms. This chapter identifies two social media platforms that the readers utilize to share about the books they have read. While readers have found their reading space on social media platforms, some have become book influencers. This chapter identifies two categories of prominent literary influencers i.e., Bookstagrammers and BookTubers. Since the readers follow book influencers to learn about the latest books and to read their reviews before making their purchase decision. This chapter aims to compare and analyse the prominent categories of book influencers focusing on knowing more about the preferred book influencers from the readers' point of view.  2024, IGI Global. All rights reserved.</text>
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                <text>Singh H.; Singh S.K.</text>
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                <text>Marketing Innovation Strategies and Consumer Behavior, pp. 325-347.</text>
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                <text>IGI Global</text>
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                <text>2024-01-01</text>
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                <text>&lt;a href="https://doi.org/10.4018/979-8-3693-4195-7.ch014" target="_blank" rel="noreferrer noopener"&gt;https://doi.org/10.4018/979-8-3693-4195-7.ch014&lt;/a&gt;
&lt;br /&gt;&lt;br /&gt;&lt;a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85192433068&amp;amp;doi=10.4018%2F979-8-3693-4195-7.ch014&amp;amp;partnerID=40&amp;amp;md5=e5cc450c6aa85481146548d4d2684751" target="_blank" rel="noreferrer noopener"&gt;https://www.scopus.com/inward/record.uri?eid=2-s2.0-85192433068&amp;amp;doi=10.4018%2f979-8-3693-4195-7.ch014&amp;amp;partnerID=40&amp;amp;md5=e5cc450c6aa85481146548d4d2684751&lt;/a&gt;</text>
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                <text>ISBN: 979-836934196-4; 979-836934195-7</text>
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                <text>Singh H., Christ University, India; Singh S.K., Christ University, India</text>
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