Search results for: Venkateswaran A. R

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R</title> <meta name="description" content="Search results for: Venkateswaran A. R"> <meta name="keywords" content="Venkateswaran A. 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R"> <input type="submit" class="btn_search" value="Search"> </div> </div> </form> </div> </div> <div class="row mt-3"> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Commenced</strong> in January 2007</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Frequency:</strong> Monthly</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Edition:</strong> International</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Paper Count:</strong> 5</div> </div> </div> </div> <h1 class="mt-3 mb-3 text-center" style="font-size:1.6rem;">Search results for: Venkateswaran A. R</h1> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">5</span> Effect of Water Hardness and Free Residual Chlorine on Black Tea Brew</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=P.%20Murugesan">P. Murugesan</a>, <a href="https://publications.waset.org/abstracts/search?q=G.%20Venkateswaran"> G. Venkateswaran</a>, <a href="https://publications.waset.org/abstracts/search?q=V.%20A.%20Shanmuga%20Selvan"> V. A. Shanmuga Selvan</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Water used for brewing tea plays a major role in the quality of tea. Water with higher hardness gives very dark coloured brew. This study was conducted to determine the effect of water hardness and free residual chlorine on the quality of black tea liquor. Theaflavin (TF) and Thearubigin (TR) levels are lower in comparison with the tea brewed in distilled water. At the same time, there is an increase in High Polymerized Substance (HPS) and Total Liquor Colour (TLC). While water with higher hardness has a negative impact on tea brew, water with high concentration of free residual chlorine did not affect the quality of tea brew. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Theaflavin" title="Theaflavin">Theaflavin</a>, <a href="https://publications.waset.org/abstracts/search?q=Thearubigin" title=" Thearubigin"> Thearubigin</a>, <a href="https://publications.waset.org/abstracts/search?q=high%20polymerised%20substance" title=" high polymerised substance"> high polymerised substance</a>, <a href="https://publications.waset.org/abstracts/search?q=total%20liquor%20colour" title=" total liquor colour"> total liquor colour</a>, <a href="https://publications.waset.org/abstracts/search?q=hardness" title=" hardness"> hardness</a>, <a href="https://publications.waset.org/abstracts/search?q=residual%20chlorine" title=" residual chlorine"> residual chlorine</a> </p> <a href="https://publications.waset.org/abstracts/53009/effect-of-water-hardness-and-free-residual-chlorine-on-black-tea-brew" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/53009.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">257</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">4</span> An Analytical Study on the Effect of Chronic Liver Disease Severity and Etiology on Lipid Profiles</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Thinakar%20Mani%20Balusamy">Thinakar Mani Balusamy</a>, <a href="https://publications.waset.org/abstracts/search?q=Venkateswaran%20A.%20R."> Venkateswaran A. R.</a>, <a href="https://publications.waset.org/abstracts/search?q=Bharat%20Narasimhan"> Bharat Narasimhan</a>, <a href="https://publications.waset.org/abstracts/search?q=Ratnakar%20Kini%20S."> Ratnakar Kini S.</a>, <a href="https://publications.waset.org/abstracts/search?q=Kani%20Sheikh%20M."> Kani Sheikh M.</a>, <a href="https://publications.waset.org/abstracts/search?q=Prem%20Kumar%20K."> Prem Kumar K.</a>, <a href="https://publications.waset.org/abstracts/search?q=Pugazhendi%20Thangavelu"> Pugazhendi Thangavelu</a>, <a href="https://publications.waset.org/abstracts/search?q=Arun%20Murugan"> Arun Murugan</a>, <a href="https://publications.waset.org/abstracts/search?q=Sibi%20Thooran%20Karmegam"> Sibi Thooran Karmegam</a>, <a href="https://publications.waset.org/abstracts/search?q=Radhakrishnan%20N."> Radhakrishnan N.</a>, <a href="https://publications.waset.org/abstracts/search?q=Mohammed%20Noufal"> Mohammed Noufal</a>, <a href="https://publications.waset.org/abstracts/search?q=Amit%20Soni"> Amit Soni</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Background and Aims: The liver is integral to lipid metabolism, and a compromise in its function leads to perturbations in these pathways. In this study, we hope to determine the correlation between CLD severity and its effect on lipid parameters. We also look at the etiology-specific effects on lipid levels. Materials and Methods: This is a retrospective cross-sectional analysis of 250 patients with cirrhosis compared to 250 healthy age and sex-matched controls. Severity assessment of CLD using MELD and Child-Pugh scores was performed and etiological details collected. A questionnaire was used to obtain patient demographic details and lastly, a fasting lipid profile (Total, LDL, HDL cholesterol, Triglycerides and VLDL) was obtained. Results: All components of the lipid profile declined linearly with increasing severity of CLD as determined by MELD and Child-Pugh scores. Lipid levels were clearly lower in CLD patients as compared to healthy controls. Interestingly, preliminary analysis indicated that CLD of different etiologies had differential effects on Lipid profiles. This aspect is under further analysis. Conclusion: All components of the lipid profile were definitely lower in CLD patients as compared to controls and demonstrated an inverse correlation with increasing severity. The utilization of this parameter as a prognosticating aid requires further study. Additionally, preliminary analysis indicates that various CLD etiologies appear to have specific effects on the lipid profile – a finding under further analysis. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=CLD" title="CLD">CLD</a>, <a href="https://publications.waset.org/abstracts/search?q=cholesterol" title=" cholesterol"> cholesterol</a>, <a href="https://publications.waset.org/abstracts/search?q=HDL" title=" HDL"> HDL</a>, <a href="https://publications.waset.org/abstracts/search?q=LDL" title=" LDL"> LDL</a>, <a href="https://publications.waset.org/abstracts/search?q=lipid%20profile" title=" lipid profile"> lipid profile</a>, <a href="https://publications.waset.org/abstracts/search?q=triglycerides" title=" triglycerides"> triglycerides</a>, <a href="https://publications.waset.org/abstracts/search?q=VLDL" title=" VLDL"> VLDL</a> </p> <a href="https://publications.waset.org/abstracts/81615/an-analytical-study-on-the-effect-of-chronic-liver-disease-severity-and-etiology-on-lipid-profiles" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/81615.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">220</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3</span> Role of Endotherapy vs Surgery in the Management of Traumatic Pancreatic Injury: A Tertiary Center Experience</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Thinakar%20Mani%20Balusamy">Thinakar Mani Balusamy</a>, <a href="https://publications.waset.org/abstracts/search?q=Ratnakar%20S.%20Kini"> Ratnakar S. Kini</a>, <a href="https://publications.waset.org/abstracts/search?q=Bharat%20Narasimhan"> Bharat Narasimhan</a>, <a href="https://publications.waset.org/abstracts/search?q=Venkateswaran%20A.%20R"> Venkateswaran A. R</a>, <a href="https://publications.waset.org/abstracts/search?q=Pugazhendi%20Thangavelu"> Pugazhendi Thangavelu</a>, <a href="https://publications.waset.org/abstracts/search?q=Mohammed%20Ali"> Mohammed Ali</a>, <a href="https://publications.waset.org/abstracts/search?q=Prem%20Kumar%20%20K."> Prem Kumar K.</a>, <a href="https://publications.waset.org/abstracts/search?q=Kani%20Sheikh%20M."> Kani Sheikh M.</a>, <a href="https://publications.waset.org/abstracts/search?q=Sibi%20Thooran%20Karmegam"> Sibi Thooran Karmegam</a>, <a href="https://publications.waset.org/abstracts/search?q=Radhakrishnan%20N."> Radhakrishnan N.</a>, <a href="https://publications.waset.org/abstracts/search?q=Mohammed%20Noufal"> Mohammed Noufal</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Introduction: Pancreatic injury remains a complicated condition requiring an individualized case by case approach to management. In this study, we aim to analyze the varied presentations and treatment outcomes of traumatic pancreatic injury in a tertiary care center. Methods: All consecutive patients hospitalized at our center with traumatic pancreatic injury between 2013 and 2017 were included. The American Association for Surgery of Trauma (AAST) classification was used to stratify patients into five grades of severity. Outcome parameters were then analyzed based on the treatment modality employed. Results: Of the 35 patients analyzed, 26 had an underlying blunt trauma with the remaining nine presenting due to penetrating injury. Overall in-hospital mortality was 28%. 19 of these patients underwent exploratory laparotomy with the remaining 16 managed nonoperatively. Nine patients had a severe injury ( > grade 3) – of which four underwent endotherapy, three had stents placed and one underwent an endoscopic pseudocyst drainage. Among those managed nonoperatively, three underwent a radiological drainage procedure. Conclusion: Mortality rates were clearly higher in patients managed operatively. This is likely a result of significantly higher degrees of major associated non-pancreatic injuries and not just a reflection of surgical morbidity. Despite this, surgical management remains the mainstay of therapy, especially in higher grades of pancreatic injury. However we would like to emphasize that endoscopic intervention definitely remains the preferred treatment modality when the clinical setting permits. This is especially applicable in cases of main pancreatic duct injury with ascites as well as pseudocysts. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=endotherapy" title="endotherapy">endotherapy</a>, <a href="https://publications.waset.org/abstracts/search?q=non-operative%20management" title=" non-operative management"> non-operative management</a>, <a href="https://publications.waset.org/abstracts/search?q=surgery" title=" surgery"> surgery</a>, <a href="https://publications.waset.org/abstracts/search?q=traumatic%20pancreatic%20injury" title=" traumatic pancreatic injury"> traumatic pancreatic injury</a> </p> <a href="https://publications.waset.org/abstracts/81489/role-of-endotherapy-vs-surgery-in-the-management-of-traumatic-pancreatic-injury-a-tertiary-center-experience" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/81489.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">207</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">2</span> Multivariate Analysis on Water Quality Attributes Using Master-Slave Neural Network Model</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=A.%20Clementking">A. Clementking</a>, <a href="https://publications.waset.org/abstracts/search?q=C.%20Jothi%20Venkateswaran"> C. Jothi Venkateswaran</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Mathematical and computational functionalities such as descriptive mining, optimization, and predictions are espoused to resolve natural resource planning. The water quality prediction and its attributes influence determinations are adopted optimization techniques. The water properties are tainted while merging water resource one with another. This work aimed to predict influencing water resource distribution connectivity in accordance to water quality and sediment using an innovative proposed master-slave neural network back-propagation model. The experiment results are arrived through collecting water quality attributes, computation of water quality index, design and development of neural network model to determine water quality and sediment, master–slave back propagation neural network back-propagation model to determine variations on water quality and sediment attributes between the water resources and the recommendation for connectivity. The homogeneous and parallel biochemical reactions are influences water quality and sediment while distributing water from one location to another. Therefore, an innovative master-slave neural network model [M (9:9:2)::S(9:9:2)] designed and developed to predict the attribute variations. The result of training dataset given as an input to master model and its maximum weights are assigned as an input to the slave model to predict the water quality. The developed master-slave model is predicted physicochemical attributes weight variations for 85 % to 90% of water quality as a target values.The sediment level variations also predicated from 0.01 to 0.05% of each water quality percentage. The model produced the significant variations on physiochemical attribute weights. According to the predicated experimental weight variation on training data set, effective recommendations are made to connect different resources. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=master-slave%20back%20propagation%20neural%20network%20model%28MSBPNNM%29" title="master-slave back propagation neural network model(MSBPNNM)">master-slave back propagation neural network model(MSBPNNM)</a>, <a href="https://publications.waset.org/abstracts/search?q=water%20quality%20analysis" title=" water quality analysis"> water quality analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=multivariate%20analysis" title=" multivariate analysis"> multivariate analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=environmental%20mining" title=" environmental mining"> environmental mining</a> </p> <a href="https://publications.waset.org/abstracts/31405/multivariate-analysis-on-water-quality-attributes-using-master-slave-neural-network-model" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/31405.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">477</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1</span> Uplift Segmentation Approach for Targeting Customers in a Churn Prediction Model</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Shivahari%20Revathi%20Venkateswaran">Shivahari Revathi Venkateswaran</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Segmenting customers plays a significant role in churn prediction. It helps the marketing team with proactive and reactive customer retention. For the reactive retention, the retention team reaches out to customers who already showed intent to disconnect by giving some special offers. When coming to proactive retention, the marketing team uses churn prediction model, which ranks each customer from rank 1 to 100, where 1 being more risk to churn/disconnect (high ranks have high propensity to churn). The churn prediction model is built by using XGBoost model. However, with the churn rank, the marketing team can only reach out to the customers based on their individual ranks. To profile different groups of customers and to frame different marketing strategies for targeted groups of customers are not possible with the churn ranks. For this, the customers must be grouped in different segments based on their profiles, like demographics and other non-controllable attributes. This helps the marketing team to frame different offer groups for the targeted audience and prevent them from disconnecting (proactive retention). For segmentation, machine learning approaches like k-mean clustering will not form unique customer segments that have customers with same attributes. This paper finds an alternate approach to find all the combination of unique segments that can be formed from the user attributes and then finds the segments who have uplift (churn rate higher than the baseline churn rate). For this, search algorithms like fast search and recursive search are used. Further, for each segment, all customers can be targeted using individual churn ranks from the churn prediction model. Finally, a UI (User Interface) is developed for the marketing team to interactively search for the meaningful segments that are formed and target the right set of audience for future marketing campaigns and prevent them from disconnecting. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=churn%20prediction%20modeling" title="churn prediction modeling">churn prediction modeling</a>, <a href="https://publications.waset.org/abstracts/search?q=XGBoost%20model" title=" XGBoost model"> XGBoost model</a>, <a href="https://publications.waset.org/abstracts/search?q=uplift%20segments" title=" uplift segments"> uplift segments</a>, <a href="https://publications.waset.org/abstracts/search?q=proactive%20marketing" title=" proactive marketing"> proactive marketing</a>, <a href="https://publications.waset.org/abstracts/search?q=search%20algorithms" title=" search algorithms"> search algorithms</a>, <a href="https://publications.waset.org/abstracts/search?q=retention" title=" retention"> retention</a>, <a href="https://publications.waset.org/abstracts/search?q=k-mean%20clustering" title=" k-mean clustering"> k-mean clustering</a> </p> <a href="https://publications.waset.org/abstracts/171364/uplift-segmentation-approach-for-targeting-customers-in-a-churn-prediction-model" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/171364.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">71</span> </span> </div> </div> </div> </main> <footer> <div id="infolinks" class="pt-3 pb-2"> <div class="container"> <div style="background-color:#f5f5f5;" class="p-3"> <div class="row"> <div class="col-md-2"> <ul class="list-unstyled"> About <li><a href="https://waset.org/page/support">About Us</a></li> <li><a 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