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                <text>Approaches Towards A Recommendation Engine for Life Insurance Products</text>
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                <text>Clustering; Insurance; Predictive Modelling; recommendations</text>
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                <text>Recommender engines are powerful tools in today's world to overcome the problem of over choice. As the world is moving towards information overload, the life insurance industry is no more immune than any other domain. Three broad categories of life insurance plans are namely - Endowment, Term and ULIP. This paper discusses a variety of ML models that aim to classify the right fit product category for a new customer (extendable to existing customers) on a real-time life insurance company dataset. The dataset used for the modelling were of 2 kinds. The first kind contained features of customer demographics - age, location, education and occupation. The second dataset included these customer demographics as well as the bureau information of the respective customers which included multiple features describing their credit history. By the means of clustering, collaborative filtering approaches were tried on. Also, the problem was tackled using predictive modelling techniques such as Random Forest, Decision Trees and XGBoost.  2021 IEEE.</text>
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                <text>Vij A.; Preethi N.</text>
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                <text>2021 IEEE International Conference on Mobile Networks and Wireless Communications, ICMNWC 2021</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/ICMNWC52512.2021.9688436" target="_blank" rel="noreferrer noopener"&gt;https://doi.org/10.1109/ICMNWC52512.2021.9688436&lt;/a&gt;
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                <text>ISBN: 978-073814637-9</text>
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                <text>Vij A., CHRIST University, India; Preethi N., CHRIST University, India</text>
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                <text>MuLSA-Multi Linguistic Sentimental Analyzer for Kannada and Malayalam using Deep Learning</text>
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                <text>LSTM; Natural language Processing; RNN; Sentiment Analysis</text>
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                <text>Natural language Processing has been always a topic of interest in artificial intelligence. Opinion mining or Sentiment Analysis is an important application of Natural language Processing. Sentiment Analysis of text is to extract the sentiments underlined in the text. In this paper, a multi-linguistic sentimental analyzer (MuLSA), is implemented, a model that would address Malayalam, Kannada and English text. This model explores two languages in three categories of the text, its original script, transliterated script, and the combination of both along with English. Deep Learning, Recurrent Neural Network with LSTM is used as the basis for this model. The model exhibits 82% of prediction accuracy.  2021 IEEE.</text>
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                <text>Thomas M.; Ca L.</text>
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              <elementText elementTextId="183262">
                <text>Proceedings of the 2021 2nd International Conference on Communication, Computing and Industry 4.0, C2I4 2021</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/C2I454156.2021.9689301" target="_blank" rel="noreferrer noopener"&gt;https://doi.org/10.1109/C2I454156.2021.9689301&lt;/a&gt;
&lt;br /&gt;&lt;br /&gt;&lt;a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85125108165&amp;amp;doi=10.1109%2FC2I454156.2021.9689301&amp;amp;partnerID=40&amp;amp;md5=501b45006e609d7cda2949d4e91b4476" target="_blank" rel="noreferrer noopener"&gt;https://www.scopus.com/inward/record.uri?eid=2-s2.0-85125108165&amp;amp;doi=10.1109%2fC2I454156.2021.9689301&amp;amp;partnerID=40&amp;amp;md5=501b45006e609d7cda2949d4e91b4476&lt;/a&gt;</text>
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                <text>ISBN: 978-166542013-6</text>
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                <text>Thomas M., CHRIST (Deemed to Be University), Department of CSE, India; Ca L., RVITM, Visvesvaraya Technological University, India</text>
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                <text>Scrutiny In-Utero to recognize Fetal Brain MRI Anomalies</text>
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          <element elementId="49">
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            <description>The topic of the resource</description>
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                <text>corpus callosum; fetal brain abnormalities; Fetal MRI; morbidly adherent placenta (MAP); MRI(Magnetic Resonance Imaging; Posterior fossa; US (Ultrasound); utero MRI (iuMRI); ventriculomegaly</text>
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                <text>In utero MRI distinguishes fbrain irregularities high precisely compared to ultrasonography as well as gives extra medical data during the pregnancies. fMRI is medically performed to get the knowledge of the brain in conditions where the inconsistency are perceived with the help of pre-birth sonography. These are common regularly solidify ventriculomegaly, not regular of the corpus callosum, and oddities of the back fossa. Fbrain inconsistencies can cause authentic brain hurt. Therefore, it is vital to recognize them from the get-go in their course so treatment can be managed to the mother, if conceivable. The job of imaging is to decide the presence, assuming any, and the degree of brain harm in the contaminated hatchling. Even though MRI is most generally utilized as a subordinate to sonography when clinical doubt is high in the setting of a typical ultrasound or to all the more likely characterize irregularities recognized by ultrasound, MRI is regularly utilized in toxoplasmosis seroconversion to conclusively preclude brain injuries, in any event, when the ultrasound examination is viewed as ordinary. X-ray is likewise utilized sequentially all through the pregnancy to check for the improvement of brain anomalies; clinical treatment brings about the astounding clinical result if the brain is typical. Intracranial irregularities are ordinarily speculated discoveries on antenatal US that are needed for assessment which is used by MRI. This audit portrays numerous irregularities imaged as a way to direct clinicians' inappropriate determination.   2021 IEEE.</text>
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                <text>Durgadevi P.; Vijayalakshmi S.; Sabbharwal M.</text>
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                <text>Proceedings - 2021 3rd International Conference on Advances in Computing, Communication Control and Networking, ICAC3N 2021, pp. 1799-1805.</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/ICAC3N53548.2021.9725570" target="_blank" rel="noreferrer noopener"&gt;https://doi.org/10.1109/ICAC3N53548.2021.9725570&lt;/a&gt;
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                <text>ISBN: 978-166543811-7</text>
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                <text>Durgadevi P., Galgotias University, School of Computing Science and Engineering, India, Computer Science and Engineering, Galgotias College of Engineering and Technology, India; Vijayalakshmi S., Christ University, Department of Data Science, India; Sabbharwal M., Galgotias University, School of Computing Science and Engineering, India</text>
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                <text>A Comparative Study of LGMB-SVR Hybrid Machine Learning Model for Rainfall Prediction</text>
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              <elementText elementTextId="183231">
                <text>Light Gradient Boosting Model; Machine Learning; Rainfall prediction; Support Vector Regression</text>
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                <text>Weather forecasting is a critical factor in deter mining the crop production and harvest of any geographical location. Among various other factors, rainfall is a crucial determining component in the sowing and harvesting of crops. The aim and intent of this paper is to analyze various machine learning algorithms like LightGBM and SVR, and develop a hybrid model using LightGBM and SVR to accurately predict rainfall The hybrid model implements both LightGBM and SVR on a preprocessed dataset and then combines the predicted values of the results through an ensemble model which considers the average of these values based on a predefined weight. The weight of the model is determined by considering various combinations, and the result with the least error is taken into consideration for that particular dataset. The study shows that the hybrid model performed better than LightGBM and SVR individually, and produced the least root mean square error yielding a more accurate prediction of rainfall.  2021 IEEE.</text>
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                <text>Sadhana S., Department of CSE, Kalaignarkarunanidhi Institute of Technology, Coimbatore, India; Pandiarajan S., Department of CSE, Kalaignarkarunanidhi Institute of Technology, Coimbatore, India; Sivaraman E., Department of CSE, PES University, Bangalore, India; Daniel D., Department of CSE, Christ (Deemed to Be University), Bangalore, India</text>
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                <text>The fluctuating nature of wireless networks influences network performance. Estimation of channel condition is essential for many reasons. The accurate estimation and prediction help to improve the performance, like better rate adaptation in Wi-Fi, improved video streaming, reduce energy consumption, and better scheduling. There are many different approaches introduced past two decades. In this paper, we are focusing on providing a brief review of different channel estimation approaches and their importance in improving performance.   2021 IEEE.</text>
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                <text>Smera C., CHRIST(Deemed to Be University), Department of Computer Science, Bengaluru, India; Sandeep J., CHRIST(Deemed to Be University), Department of Computer Science, Bengaluru, India</text>
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                <text>In modern day of digital marketing the cloud computing is proving extremely beneficial links for businesses. Moreover, it's characteristic to access the stored data from anywhere makes it more popular among the entrepreneurs. The present paper is an exploration of the cloud computing in respect of digital marketing. The paper defines and correlates the term cloud computing, digital marketing, as well as also elaborates about benefits that can be harvested by the integration of cloud computing in digital marketing strategy.   2021 IEEE.</text>
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                <text>Vatsa M., School of Business Studies, Sharda University, Greater Noida, India; Rastogi S., CHRIST (Deemed to Be University), Department of Commerce, India; Tiwari A., CHRIST (Deemed to Be University), Department of Commerce, India; Jain S., Information Technology, SRM Institute of Science and Technology, Kattankulathur, Tamil Nadu, Chennai, 603203, India</text>
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                <text>Human Pose Estimation is one of the challenging yet broadly researched areas. Pose estimation is required in applications that include human activity detection, fall detection, motion capture in AR/VR, etc. Nevertheless, images and videos are required for every application that captures images using a standard RGB camera, without any external devices. This paper presents a real-time approach for sign language detection and recognition in videos using the Holistic pose estimation method of MediaPipe. This Holistic framework detects the movements of multiple modalities-facial expression, hand gesture and body pose, which is the best for the sign language recognition model. The experiment conducted includes five different signers, signing ten distinct words in a natural background. Two signs, 'blank' and 'sad, ' were best recognized by the model.  2021 IEEE.</text>
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                <text>Amrutha K.; Prabu P.; Paulose J.</text>
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                <text>Amrutha K., CHRIST (Deemed to Be University), Dept. of Computer Science, Bengaluru, India; Prabu P., CHRIST (Deemed to Be University), Dept. of Computer Science, Bengaluru, India; Paulose J., CHRIST (Deemed to Be University), Dept. of Computer Science, Bengaluru, India</text>
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                <text>Model Selection Procedure in Alleviating Drawbacks of the Electronic Whiteboard</text>
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          <element elementId="49">
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            <description>The topic of the resource</description>
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              <elementText elementTextId="183161">
                <text>Deep Learning; Electronic Whiteboard; Learning; Machine Learning; Transfer Learning</text>
              </elementText>
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            <description>An account of the resource</description>
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                <text>Deep learning has paved the way for critical and revolutionary applications in almost every field of life in general. Ranging from engineering to healthcare, machine learning and deep learning has left its mark as the state-of-the-art technology application which holds the epitome of a reasonable high benchmarked solution. Incorporating neural network architectures into applications has become a common part of any software development process. In this paper, we perform a comparative analysis on the different transfer learning approaches in the domain of hand-written digit recognition. We use two performance measures, loss and accuracy. We later visualize the different results for the training and validation datasets and reach to a unison conclusion. This paper aims to target the drawbacks of the electronic whiteboard with simultaneous focus on the suitable model selection procedure for the digit recognition problem.   2021 IEEE.</text>
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              <elementText elementTextId="183163">
                <text>Jha S.</text>
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            <elementTextContainer>
              <elementText elementTextId="183164">
                <text>Proceedings - 2021 3rd International Conference on Advances in Computing, Communication Control and Networking, ICAC3N 2021, pp. 319-322.</text>
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              <elementText elementTextId="183165">
                <text>Institute of Electrical and Electronics Engineers Inc.</text>
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                <text>&lt;a href="https://doi.org/10.1109/ICAC3N53548.2021.9725365" target="_blank" rel="noreferrer noopener"&gt;https://doi.org/10.1109/ICAC3N53548.2021.9725365&lt;/a&gt;
&lt;br /&gt;&lt;br /&gt;&lt;a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85126950039&amp;amp;doi=10.1109%2FICAC3N53548.2021.9725365&amp;amp;partnerID=40&amp;amp;md5=188fcf6cef14359fc2cd6d8370de48d8" target="_blank" rel="noreferrer noopener"&gt;https://www.scopus.com/inward/record.uri?eid=2-s2.0-85126950039&amp;amp;doi=10.1109%2fICAC3N53548.2021.9725365&amp;amp;partnerID=40&amp;amp;md5=188fcf6cef14359fc2cd6d8370de48d8&lt;/a&gt;</text>
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            <elementTextContainer>
              <elementText elementTextId="183168">
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                <text>ISBN: 978-166543811-7</text>
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            <description>The file format, physical medium, or dimensions of the resource</description>
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            <elementTextContainer>
              <elementText elementTextId="183173">
                <text>Jha S., Christ University, School of Science, New Delhi, India</text>
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            </elementTextContainer>
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          <element elementId="50">
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            <description>A name given to the resource</description>
            <elementTextContainer>
              <elementText elementTextId="183146">
                <text>An Approach for Detecting Frauds in E-Commerce Transactions using Machine Learning Techniques</text>
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          <element elementId="49">
            <name>Subject</name>
            <description>The topic of the resource</description>
            <elementTextContainer>
              <elementText elementTextId="183147">
                <text>Credit card; Decision Tree(DT); Fraud detection; Frauds; K-Nearest Neighbor; Logistic regression; Random Forest(RF); Supervised machine learning algorithm; Support Vector Machine(SVM)</text>
              </elementText>
            </elementTextContainer>
          </element>
          <element elementId="41">
            <name>Description</name>
            <description>An account of the resource</description>
            <elementTextContainer>
              <elementText elementTextId="183148">
                <text>This paper is primarily focused on E-commerce fraud detection using machine learning techniques. There are many different ways to detect E-commerce fraud using machine learning approach. In this work, comparison study is conducted between various available machine learning algorithms to detect the online frauds. During the comparative study, focus is underlined on comparison of all the algorithms to identify the fraud transactions. When compared to other algorithms, such as support vector machine, Decision Tree, K-nearest neighbour and Random Forest, it has been observed that Logistic regression gives better result among all machine learning algorithms.   2021 IEEE.</text>
              </elementText>
            </elementTextContainer>
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              <elementText elementTextId="183149">
                <text>Abhirami K.; Pani A.K.; Manohar M.; Kumar P.</text>
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            <elementTextContainer>
              <elementText elementTextId="183150">
                <text>Proceedings - 2nd International Conference on Smart Electronics and Communication, ICOSEC 2021, pp. 826-831.</text>
              </elementText>
            </elementTextContainer>
          </element>
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            <name>Publisher</name>
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              <elementText elementTextId="183151">
                <text>Institute of Electrical and Electronics Engineers Inc.</text>
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          </element>
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              <elementText elementTextId="183152">
                <text>2021-01-01</text>
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                <text>&lt;a href="https://doi.org/10.1109/ICOSEC51865.2021.9591720" target="_blank" rel="noreferrer noopener"&gt;https://doi.org/10.1109/ICOSEC51865.2021.9591720&lt;/a&gt;
&lt;br /&gt;&lt;br /&gt;&lt;a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85123181008&amp;amp;doi=10.1109%2FICOSEC51865.2021.9591720&amp;amp;partnerID=40&amp;amp;md5=482e6b5af6f82f2708ea42139331d2d4" target="_blank" rel="noreferrer noopener"&gt;https://www.scopus.com/inward/record.uri?eid=2-s2.0-85123181008&amp;amp;doi=10.1109%2fICOSEC51865.2021.9591720&amp;amp;partnerID=40&amp;amp;md5=482e6b5af6f82f2708ea42139331d2d4&lt;/a&gt;</text>
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            <elementTextContainer>
              <elementText elementTextId="183154">
                <text>Restricted Access</text>
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              <elementText elementTextId="183155">
                <text>ISBN: 978-166543368-6</text>
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            <name>Format</name>
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              <elementText elementTextId="183156">
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              <elementText elementTextId="183157">
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              <elementText elementTextId="183159">
                <text>Abhirami K., School of Engineering and Technology Christ(Deemed To Be University), Department of Computer Science and Engineering, Bangalore, India; Pani A.K., School of Engineering and Technology Christ(Deemed To Be University), Department of Computer Science and Engineering, Bangalore, India; Manohar M., School of Engineering and Technology Christ(Deemed To Be University), Department of Computer Science and Engineering, Bangalore, India; Kumar P., Motihari College of Engineering, Department of Computer Science and Engineering, Bihar, Motihari, India</text>
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          <element elementId="50">
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            <description>A name given to the resource</description>
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              <elementText elementTextId="183132">
                <text>Impact of Machine Learning Algorithms in Intrusion Detection Systems for Internet of Things</text>
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          <element elementId="49">
            <name>Subject</name>
            <description>The topic of the resource</description>
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              <elementText elementTextId="183133">
                <text>Internet of Things; Intrusion Detection System; Machine Learning</text>
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            <description>An account of the resource</description>
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              <elementText elementTextId="183134">
                <text>The importance of security aspects is increased recently due to the enormous usage of IoT devices. Securing the system from all sorts of vulnerabilities is inevitable to use IoT applications. Intrusion detection systems are power mechanism which provides this service. The introduction of artificial intelligence into intrusion detection systems can further enhance its power. This paper is an attempt to understand the impact of machine learning algorithms in attack detection. Using the UNSW-NB 15 dataset, the impact of different machine learning algorithms is assessed.   2021 IEEE.</text>
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              <elementText elementTextId="183135">
                <text>Jose J.; Jose D.V.; Rao K.S.; Janz J.</text>
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              <elementText elementTextId="183136">
                <text>10th International Conference on Advances in Computing and Communications, ICACC 2021</text>
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            <elementTextContainer>
              <elementText elementTextId="183137">
                <text>Institute of Electrical and Electronics Engineers Inc.</text>
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                <text>&lt;a href="https://doi.org/10.1109/ICACC-202152719.2021.9708404" target="_blank" rel="noreferrer noopener"&gt;https://doi.org/10.1109/ICACC-202152719.2021.9708404&lt;/a&gt;
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            <description>Information about rights held in and over the resource</description>
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                <text>ISBN: 978-166543919-0</text>
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                <text>Jose J., CHRIST University, Department of Computer Science, Bangalore, India; Jose D.V., CHRIST University, Department of Computer Science, Bangalore, India; Rao K.S., CHRIST University, Department of Computer Science, Bangalore, India; Janz J., CHRIST University, Department of Computer Science, Bangalore, India</text>
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                <text>A Two-Pass Hybrid Mean and Median Framework for Eliminating Impulse Noise From a Grayscale Image</text>
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            <name>Subject</name>
            <description>The topic of the resource</description>
            <elementTextContainer>
              <elementText elementTextId="183119">
                <text>Image Noise Cancellation; Image processing; Image Restoration; Salt and Pepper Noise; Visual quality</text>
              </elementText>
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            <name>Description</name>
            <description>An account of the resource</description>
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                <text>In a digital era, Image recuperation plays a vital role in the area of digital image processing. Image instauration offers more visualization on the quality of the image thereby eliminating noise. Elimination of Gaussian and impulse noise is a challenging problem in the area of image restoration. Rigorous research is pursued to restore salt-and-pepper (SAP) noise utilizing spatial filters. Mean and Median are two contributing spatial filters for eliminating impulse noise. This paper applies a two-pass hybrid mean and median framework on a corrupted grayscale image to replace salt and pepper noise. The hybrid framework is effectively restoring the image by abstracting the low, medium, and high-density impulse noise. The efficacy of the recommended strategy is evaluated by quantifying the peak signal to noise ratio and structural similarity index metric. The result obtained when compared with recent recuperation strategies outperforms to remove noise from grayscale images.  2021 IEEE</text>
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            <elementTextContainer>
              <elementText elementTextId="183121">
                <text>Shiju Thomas M.Y.; Krishna A.V.N.</text>
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              <elementText elementTextId="183122">
                <text>ACCESS 2021 - Proceedings of 2021 2nd International Conference on Advances in Computing, Communication, Embedded and Secure Systems, pp. 206-210.</text>
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              <elementText elementTextId="183123">
                <text>Institute of Electrical and Electronics Engineers 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/10.1109/ACCESS51619.2021.9563285" target="_blank" rel="noreferrer noopener"&gt;https://doi.org/10.1109/ACCESS51619.2021.9563285&lt;/a&gt;
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            <description>Information about rights held in and over the resource</description>
            <elementTextContainer>
              <elementText elementTextId="183126">
                <text>Restricted Access</text>
              </elementText>
            </elementTextContainer>
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            <description>A related resource</description>
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              <elementText elementTextId="183127">
                <text>ISBN: 978-172817136-4</text>
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            <name>Format</name>
            <description>The file format, physical medium, or dimensions of the resource</description>
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              <elementText elementTextId="183128">
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            <description>A language of the resource</description>
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              <elementText elementTextId="183129">
                <text>English</text>
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            <description>The nature or genre of the resource</description>
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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="183131">
                <text>Shiju Thomas M.Y., Department of Computer Science and Engineering, School of Engineering and Technology Christ (Deemed to be) University, Karnataka, Bangalore, India; Krishna A.V.N., Department of Computer Science and Engineering, School of Engineering and Technology Christ (Deemed to be) University, Karnataka, Bangalore, India</text>
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          <elementContainer>
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              <name>Title</name>
              <description>A name given to the resource</description>
              <elementTextContainer>
                <elementText elementTextId="51377">
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      <description>Faculty Publications- Conference Papers</description>
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        <elementContainer>
          <element elementId="50">
            <name>Title</name>
            <description>A name given to the resource</description>
            <elementTextContainer>
              <elementText elementTextId="183104">
                <text>Oppositional Glowworm Swarm based Vector Quantization Technique for Image Compression in Fiber Optic Communication</text>
              </elementText>
            </elementTextContainer>
          </element>
          <element elementId="49">
            <name>Subject</name>
            <description>The topic of the resource</description>
            <elementTextContainer>
              <elementText elementTextId="183105">
                <text>Image compression; LBG; Optic fiber communication; Optical networks; Vector quantization</text>
              </elementText>
            </elementTextContainer>
          </element>
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            <name>Description</name>
            <description>An account of the resource</description>
            <elementTextContainer>
              <elementText elementTextId="183106">
                <text>In recent times, fiber optic communication networks have become commonly applied for commercial as well as military applications. Fiber optic networks have gained popularity owing to the high data rate. At the same time, the generation of huge quantity of data at a faster rate poses a major challenge in the storing and transmission process. To resolve this issue, data compression approaches have been presented to reduce the quantity of transmitted data and thereby minimizes bandwidth utilization and memory. Vector quantization (VQ) is a commonly employed image compression technique and Linde Buzo Gray (LBG) is used to construct an optimum codebook to compress images. With this motivation, this paper presents a new oppositional glowworm swarm optimization based LBG (OGSO-LBG) technique for image compression in fiber optic communication. The OGSO algorithm involves the integration of oppositional based learning (OBL) concept into the GSO algorithm to boost its convergence rate. The OGSO-LBG algorithm produces the codebook at a faster rate with minimal computation complexity. In order to highlight the enhanced compression performance of the OGSO-LBG technique, a series of experiments were carried out and the results are examined under different dimensions.  2021 IEEE</text>
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            <name>Creator</name>
            <description>An entity primarily responsible for making the resource</description>
            <elementTextContainer>
              <elementText elementTextId="183107">
                <text>Alapatt B.P.; Philip F.M.; Jims A.</text>
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            </elementTextContainer>
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            <name>Source</name>
            <description>A related resource from which the described resource is derived</description>
            <elementTextContainer>
              <elementText elementTextId="183108">
                <text>ACCESS 2021 - Proceedings of 2021 2nd International Conference on Advances in Computing, Communication, Embedded and Secure Systems, pp. 198-205.</text>
              </elementText>
            </elementTextContainer>
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            <name>Publisher</name>
            <description>An entity responsible for making the resource available</description>
            <elementTextContainer>
              <elementText elementTextId="183109">
                <text>Institute of Electrical and Electronics Engineers Inc.</text>
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            </elementTextContainer>
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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="183110">
                <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="183111">
                <text>&lt;a href="https://doi.org/10.1109/ACCESS51619.2021.9563298" target="_blank" rel="noreferrer noopener"&gt;https://doi.org/10.1109/ACCESS51619.2021.9563298&lt;/a&gt;
&lt;br /&gt;&lt;br /&gt;&lt;a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85126188156&amp;amp;doi=10.1109%2FACCESS51619.2021.9563298&amp;amp;partnerID=40&amp;amp;md5=3867d91bdca0611558286a5987293e33" target="_blank" rel="noreferrer noopener"&gt;https://www.scopus.com/inward/record.uri?eid=2-s2.0-85126188156&amp;amp;doi=10.1109%2fACCESS51619.2021.9563298&amp;amp;partnerID=40&amp;amp;md5=3867d91bdca0611558286a5987293e33&lt;/a&gt;</text>
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          </element>
          <element elementId="47">
            <name>Rights</name>
            <description>Information about rights held in and over the resource</description>
            <elementTextContainer>
              <elementText elementTextId="183112">
                <text>Restricted Access</text>
              </elementText>
            </elementTextContainer>
          </element>
          <element elementId="46">
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            <description>A related resource</description>
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              <elementText elementTextId="183113">
                <text>ISBN: 978-172817136-4</text>
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            </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="183114">
                <text>Online</text>
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            </elementTextContainer>
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          <element elementId="44">
            <name>Language</name>
            <description>A language of the resource</description>
            <elementTextContainer>
              <elementText elementTextId="183115">
                <text>English</text>
              </elementText>
            </elementTextContainer>
          </element>
          <element elementId="51">
            <name>Type</name>
            <description>The nature or genre of the resource</description>
            <elementTextContainer>
              <elementText elementTextId="183116">
                <text>Conference paper</text>
              </elementText>
            </elementTextContainer>
          </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>
            <elementTextContainer>
              <elementText elementTextId="183117">
                <text>Alapatt B.P., Computer Science Department, CHRIST (Deemed to be University) Delhi NCR, India; Philip F.M., CS and IT Department, JAIN (Deemed-to-be University), Kochi, India; Jims A., CS and IT Department, JAIN (Deemed-to-be University), Kochi, India</text>
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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>
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              </elementTextContainer>
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      <name>Conference Paper</name>
      <description>Faculty Publications- Conference Papers</description>
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    <elementSetContainer>
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        <description>The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/.</description>
        <elementContainer>
          <element elementId="50">
            <name>Title</name>
            <description>A name given to the resource</description>
            <elementTextContainer>
              <elementText elementTextId="183090">
                <text>Enhanced Energy Efficient Routing for Wireless Sensor Network Using Extended Power Efficient Gathering in Sensor Information Systems (E-PEGASIS) Protocol</text>
              </elementText>
            </elementTextContainer>
          </element>
          <element elementId="49">
            <name>Subject</name>
            <description>The topic of the resource</description>
            <elementTextContainer>
              <elementText elementTextId="183091">
                <text>E-PEGASIS; Energy efficient routing; LEACH; PEGASIS; Wireless Sensor Networks</text>
              </elementText>
            </elementTextContainer>
          </element>
          <element elementId="41">
            <name>Description</name>
            <description>An account of the resource</description>
            <elementTextContainer>
              <elementText elementTextId="183092">
                <text>Recent technological advancement in wireless communication and sensors made Wireless Sensor Networks (WSNs) as one of the demanding platforms in the current scenario. In WSN, tiny sensor nodes are collecting and monitoring the biological data or physical data or environmental data and transmitted to the base station (BS) through gateway routers. These data can be accessed anywhere and anytime. Usually sensor nodes have restrained battery power which creates the rigorous lifetime duration issues in WSN. Sensor nodes can communicate with each other using various routing protocols. Data transmission devours more amounts of energy and power. So, energy preservation is an important factor in WSN. There are plenty of researches going on in designing less energy consuming protocols for data transmission which helps to increase the lifetime of WSN. In this paper we have proposed Extended Power Efficient Gathering in Sensor Information Systems (E-PEGASIS) protocol for enhanced energy efficient data transmission based on PEGASIS protocol. In this proposed method average distance between the sensor nodes are considered as the criterion for chaining and fix the outermost node's radio range value the base station. Later it chains the related nodes available in the radio range. Consequently, the chained node checks their distance with the next nearest end node to go on with the chaining procedure which will enhance the performance of data transmission between the sensor node and the base station. The simulation of the proposed work shows that lifetime of the network is increased when comparing to the LEACH and PEGASIS protocol.  2021 The Authors. Published by Elsevier B.V.</text>
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              <elementText elementTextId="183093">
                <text>Sadhana S.; Sivaraman E.; Daniel D.</text>
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            <description>A related resource from which the described resource is derived</description>
            <elementTextContainer>
              <elementText elementTextId="183094">
                <text>Procedia Computer Science, Vol-194, pp. 89-101.</text>
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            </elementTextContainer>
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          <element elementId="45">
            <name>Publisher</name>
            <description>An entity responsible for making the resource available</description>
            <elementTextContainer>
              <elementText elementTextId="183095">
                <text>Elsevier B.V.</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>
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              <elementText elementTextId="183096">
                <text>2021-01-01</text>
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            <description>An unambiguous reference to the resource within a given context</description>
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                <text>&lt;a href="https://doi.org/10.1016/j.procs.2021.10.062" target="_blank" rel="noreferrer noopener"&gt;https://doi.org/10.1016/j.procs.2021.10.062&lt;/a&gt;
&lt;br /&gt;&lt;br /&gt;&lt;a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85121778596&amp;amp;doi=10.1016%2Fj.procs.2021.10.062&amp;amp;partnerID=40&amp;amp;md5=f2da3288aea63661266b0c1720e426a7" target="_blank" rel="noreferrer noopener"&gt;https://www.scopus.com/inward/record.uri?eid=2-s2.0-85121778596&amp;amp;doi=10.1016%2fj.procs.2021.10.062&amp;amp;partnerID=40&amp;amp;md5=f2da3288aea63661266b0c1720e426a7&lt;/a&gt;</text>
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            <name>Rights</name>
            <description>Information about rights held in and over the resource</description>
            <elementTextContainer>
              <elementText elementTextId="183098">
                <text>All Open Access; Gold Open Access</text>
              </elementText>
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            <description>A related resource</description>
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              <elementText elementTextId="183099">
                <text>ISSN: 18770509</text>
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            <name>Format</name>
            <description>The file format, physical medium, or dimensions of the resource</description>
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              <elementText elementTextId="183100">
                <text>Online</text>
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            <description>A language of the resource</description>
            <elementTextContainer>
              <elementText elementTextId="183101">
                <text>English</text>
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            <description>The nature or genre of the resource</description>
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                <text>Sadhana S., Department of Computer Science and Engineering, KalaignarKarunanidhi Institute of Technology, Coimbatore, India; Sivaraman E., Department of Computer Science and Engineering, PES University, Bangalore, India; Daniel D., Department of Computer Science and Engineering, Christ University, Bangalore, India</text>
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                <text>License Platform Detection is a computer technology that enables us to identify digital images on the platform automatically. Different operations are covered in this system, such as imaging, number pad locations, alphanumeric character truncation and OCR. The final objective of the system is to construct and create efficient image processing procedures and techniques to position a licensing platter on the Open Computer View Library picture. It was used and implemented the K-NN algorithm and python programming language. The technology can be used in different industries such as security, highway speed detection, lighting violations, manuscript documents, automatic charging system, etc. Auto plate recognition is an integrated technology which identifies the auto licence plate. Auto plate auto recognition. Multiple applications include complex safety systems, public spaces, parking and urban traffic control. Automatic Vehicle License Plate Recognition (AVLPR) has undesirable aspects because of many effects, such as light and speed. This work presents an alternative technique to leverage free software for the implementation of AVLPR systems including Python and the Open Computer Vision (openCV).   2021 IEEE.</text>
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                <text>&lt;a href="https://doi.org/10.1109/ICECA52323.2021.9676015" target="_blank" rel="noreferrer noopener"&gt;https://doi.org/10.1109/ICECA52323.2021.9676015&lt;/a&gt;
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                <text>ISBN: 978-166543524-6</text>
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                <text>Samantaray M., Udayanath (Auto) College of Sc. Tech., Dept. of ITM, Odisha, India; Biswal A.K., Siksha 'o' Anusandhan University, Dept.of CSE, Odisha, India; Singh D., Siksha 'o' Anusandhan University, Dept.of CSE, Odisha, India; Samanta D., Siksha 'o' Anusandhan University, Dept. of CA, Odisha, India; Karuppiah M., SRM Institute of Science and Technology, Department of CSE, Ghaziabad, India; Joseph N.P., CHRIST University, Department of Computer Science, Bangalore, India</text>
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                <text>An Automated Deep Learning Model for Detecting Sarcastic Comments</text>
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          <element elementId="49">
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                <text>deep learning; interpretability; machine learning; sarcasm detection; self-attention; social media analysis; social media data</text>
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                <text>The concept of Natural Language Processing is immensely vast with a wide range of fields in which ideas can be explored and innovations can be developed. An algorithm based on deep learning is used to detect sarcasm in text in this paper. It is usually only possible to detect sarcasm through speech and very rarely through text. 1.3 million comments from Reddit were analyzed, of which half were sarcastic and half were not, and then various deep learning models were applied, such as standard neural networks, CNNs, and LSTM RNNs. The best performing model was LSTM-RNNs, followed by CNNs, and standard neural networks came last. With textual data, it is much harder to understand whether the other person is being sarcastic or not, it can only be understood by listening to their tone of voice or looking at their behaviour. The purpose of this paper is to demonstrate how to detect sarcasm in textual data using deep learning models.  2021 IEEE.</text>
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              <elementText elementTextId="183065">
                <text>Jose J.; Preethi N.</text>
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                <text>2021 IEEE International Conference on Mobile Networks and Wireless Communications, ICMNWC 2021</text>
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              <elementText elementTextId="183067">
                <text>Institute of Electrical and Electronics Engineers Inc.</text>
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                <text>&lt;a href="https://doi.org/10.1109/ICMNWC52512.2021.9688410" target="_blank" rel="noreferrer noopener"&gt;https://doi.org/10.1109/ICMNWC52512.2021.9688410&lt;/a&gt;
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                <text>ISBN: 978-073814637-9</text>
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                <text>Jose J., CHRIST University, Department of Data Science, Maharashtra, Pune, India; Preethi N., CHRIST University, Department of Data Science, Maharashtra, Pune, India</text>
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          <element elementId="50">
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            <description>A name given to the resource</description>
            <elementTextContainer>
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                <text>An AI Approach to Pose-based Sports Activity Classification</text>
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          </element>
          <element elementId="49">
            <name>Subject</name>
            <description>The topic of the resource</description>
            <elementTextContainer>
              <elementText elementTextId="183049">
                <text>Activity; Classification; Keypoints; Pose estimation; Sports; Tennis</text>
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            </elementTextContainer>
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                <text>Artificial intelligence systems have permeated into all spheres of our life-impacting everything from our food habits to our sleep patterns. One untouched area where such intelligent systems are still in their infancy is sports. There has not been enough indulgence of AI techniques in sports, and most of the works are carried on manually by coaching staff and human appointees. We believe that intelligent systems can make coaching staff's work easier and produce findings that the human eye can often overlook. Here, we have proposed an intelligent system to analyze the beautiful game of tennis. With the use of computer vision architecture Detectron2 and activity-based pose estimation and subsequent classification, it can identify an action from a tennis shot (activity). It can produce a performance score for the player based on pose and movement like forehand and backhand. It can also be used to understand and evaluate the strengths and weaknesses of the player. The proposed approach provides a piece of valuable information for a player's performance and activity detection to be used for better coaching. The study achieves a classification accuracy of 98.60% and outperforms other SOTA CNN models.  2021 IEEE</text>
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          <element elementId="39">
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            <elementTextContainer>
              <elementText elementTextId="183051">
                <text>Chatterjee R.; Roy S.; Islam S.K.H.; Samanta D.</text>
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              <elementText elementTextId="183052">
                <text>Proceedings of the 8th International Conference on Signal Processing and Integrated Networks, SPIN 2021, pp. 156-161.</text>
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              <elementText elementTextId="183053">
                <text>Institute of Electrical and Electronics Engineers Inc.</text>
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              <elementText elementTextId="183054">
                <text>2021-01-01</text>
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                <text>&lt;a href="https://doi.org/10.1109/SPIN52536.2021.9565996" target="_blank" rel="noreferrer noopener"&gt;https://doi.org/10.1109/SPIN52536.2021.9565996&lt;/a&gt;
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                <text>ISBN: 978-166543564-2</text>
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              <elementText elementTextId="183059">
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              <elementText elementTextId="183061">
                <text>Chatterjee R., School of Computer Engineering, KIIT Deemed to be University, Odisha, Bhubaneswar, 751024, India; Roy S., School of Computer Engineering, KIIT Deemed to be University, Odisha, Bhubaneswar, 751024, India; Islam S.K.H., Department of Computer Science and Engineering, Indian Institute of Information Technology Kalyani, West Bengal, Kalyani, 741235, India; Samanta D., Department of Computer Science, CHRIST Deemed to be University, Hosur Road, Karnataka, Bengaluru, 560029, India</text>
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                <text>A Comparative Study on Indian Sign Language Representation</text>
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              <elementText elementTextId="183035">
                <text>continuous sign language; Indian sign language; isolated sign language; sign language recognition system</text>
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                <text>Communication among people can happen with the help of verbal or nonverbal language. Nonverbal communication is shared only among the hearing and speech impaired and is not common among others. Non-verbal communication is also different for different countries around the world. A solution to remove the gap between verbal and non-verbal communicators is to create an automated language translation model that can effortlessly convert sign language to text or audio. This area has been under research for a long time, but an economical and robust system that can efficiently convert signs into speech still does not exist. This paper focuses on different approaches that were put forward to turn Indian sign language into audio signals. The Sign Language Recognition (SLR) system is classified as isolated and continuous sign language models based on its input.  2021 IEEE.</text>
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                <text>Amrutha K., CHRIST(Deemed to Be University), Dept. of Computer Science, Bangalore, India; Prabu P., CHRIST(Deemed to Be University), Dept. of Computer Science, Bangalore, India</text>
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                <text>Machine Learning-based Currency Information Retrieval for Aiding the Visually Impaired People</text>
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                <text>Blurriness detection; Classification; Counterfeit currency detection; Currency recognition; Deep Learning</text>
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                <text>Paper currency is one of the most in-demand and long-established payment modes across the globe. People suffering from visual disabilities often face difficulties while handling paper currencies. Over the years, assisting technology has been rekindling itself to serve the aged and disabled person more aptly. Image processing methods and other sophisticated technologies, like Artificial Intelligence, Deep Learning, etc., can be employed to identify banknotes and fetch other valuable pieces of information from them. This paper proposes a framework that focuses on an integrated approach to retrieving data from the paper currency's uploaded image. The current version of the framework focuses on identifying the authenticity of the paper currency and classifying it according to its value. This work is an initiative to help visually impaired people to use paper currencies without assistance from other individuals and support them in living independently.   2021 IEEE.</text>
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                <text>Chatterjee K.; Obaidat M.S.; Samanta D.; Hafizul Islam S.K.; Joseph N.P.</text>
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                <text>Proceedings of the International Conference on Computer, Information, and Telecommunication Systems, CITS 2021</text>
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                <text>Institute of Electrical and Electronics Engineers Inc.</text>
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                <text>ISBN: 978-166544913-7</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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                <text>Chatterjee K., CHRIST (Deemed to Be) University, Department of Computer Science, Karnataka, Bangalore, India; Obaidat M.S., University of Sharjah, College of Computing and Informatics, Sharjah, 27272, United Arab Emirates; Samanta D., CHRIST (Deemed to Be) University, Department of Computer Science, Karnataka, Bangalore, India; Hafizul Islam S.K., Indian Institute of Information Technology, Department of CSE, West Bengal, Kalyani, 741235, India; Joseph N.P., CHRIST (Deemed to Be) University, Department of Computer Science, Karnataka, Bangalore, India</text>
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                <text>Cloudsim exploration: A knowledge framework for cloud computing researchers</text>
              </elementText>
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          <element elementId="49">
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            <description>The topic of the resource</description>
            <elementTextContainer>
              <elementText elementTextId="183007">
                <text>Cloud infrastructure; CloudSim; Infrastructure intelligence; Learning path; PACT; Research setup; Scheduling algorithms</text>
              </elementText>
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                <text>This paper aims to help find solutions for questions an early researcher may have to set up experiments in their development environment. Simultaneously, while identifying the steps required for experimenting, the authors narrowed on an experimenting toolkit for Cloud Computing as an area of their study. Because of such simulators, the cloud computing environment itself is available easily at the comfort of ones desktop resources instead of visiting an actual physical data center to collect trace and log files as data sets for real workloads. This paper acts as an experience sharing to naive researchers who are interested in how to go about to start cloud computing setups. A new framework called Cloud Computing Simulation Environment (CCSE) is presented with inspiration from Procure Apply Consider and Transform (PACT) model to ease the learning process. The literature survey in this paper shares the path taken by researchers for understanding the architecture, technology, and tools required to set up a resilient test environment. This path also depicts the introduced framework CCSE. The parameters found out of the experiments were Virtual Machines (VMs), Cloudlets, Host, and Cores. The appropriate combination of the values of the parameters would be horizontal scaling of VMs. Increasing VMs does not influence the average execution time after a specific limit on the number of VMs allocated. Nevertheless, in vertical scaling, appropriate combinations of the cores and hosts yield better execution times. Thereby maintaining the optimal number of hosts is an ultimate saving of resources in case of VM allocations.  The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021.</text>
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                <text>Sankaran L.; Subramanian S.J.; Subramanian E-Mail: S.J.</text>
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                <text>Lecture Notes in Networks and Systems, Vol-187, pp. 107-122.</text>
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                <text>ISSN: 23673370; ISBN: 978-981336172-0</text>
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                <text>Sankaran L., CHRIST (Deemed to be University), Karnataka, Bengaluru, 560029, India; Subramanian S.J., CHRIST (Deemed to be University), Karnataka, Bengaluru, 560029, India; Subramanian E-Mail: S.J.</text>
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