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                <text> The study is in motivation in relation to work engagement of sales personnel in telecom industry. Motivation is the Internal and external factors that stimulate desire and energy in people to be continually interested in and committed to a job, role, or subject, and to exert persistent effort in attaining a goal.



 Workengagement is defined as a positive, fulfilling, work-related state of mind that is characterized by vigor, dedication, and absorption. Vigor is characterized by high levels of energy and mental resilience while working, the willingness to invest effort in one??s work, and persistence even in the face of difficulties; dedication by being strongly involved in one's work, and experiencing a sense of significance, enthusiasm, inspiration, pride, and challenge; and absorption by being fully concentrated and happily engrossed in one??s work, whereby time passes quickly and one has difficulties with detaching oneself from work.



 When you don't have the motivation to do your work, you will either eventually get fired or you will not likely get promoted and will stay where you are for a long time. If you are the supervisor or the owner, a lack of motivation throughout your company can create a rather unproductive workplace. This will lead to loss of sales, profits, and market share. In this case, it's important to do what it takes to create an environment where people naturally want to do their work. The importance of employee motivation shouldn't be taken lightly here. The company's survival depends on it.



 Modern organizations need energetic and dedicated employees: people who are engaged with their work. These organizations expect proactively, initiative and responsibility for personal development from their employees. Overall, engaged employees are fully involved in, and enthusiastic about their work.



 The hypotheses of the study were (a) The interaction effect between MPS and CPS does not significantly influence the personnel outcomes of sales professionals in telecom industry. (b) The interaction  effect  between  work  engagement  and  CPS  does  not  significantly  moderate  the personnel outcomes of sales professionals in telecom industry.(c) There is no significant difference in demographics in work engagement across demographics.



 The review of related literature in the area of motivation and work engagement provided the researcher valuable inputs, perspective, insights and direction in understanding these factors and designing this study. The researcher has attempted to seriously and systematically undertake the present investigation.



 There are two research models; these two models were tested using the statistical technique hierarchical regression model. These two models were framed based on the job diagnostic model of Hackman and Oldham (1974) and Job resource model of Bakker and Demerouti (2010).



 The research methodology adopted for the study was surveying of 359 sales employees in different telecom industries using structured questionnaires.



 The independent variable of this study were Motivation(core job dimensions, critical psychological states, personnel outcomes).The dependent variable is work engagement(vigor, dedication, absorption) and the demographic variables are, age, work experience, marital status, and gender.



The major findings of the study were:



1.There is a positive significant correlation between critical psychological states and motivation potential score. Critical psychological states promote high performance motivation and satisfaction at work.

2.The interaction effect between MPS and CPS significantly moderate the personnel outcomes of sales professionals in telecom industry.

3.The interaction effect between work engagement and Critical Psychological State significantly moderate the personnel outcomes of sales professionals in telecom industry.

4.Work engagement significantly influences the outcomes.



5.Age and marital status has a significant influence on work engagement.

 The  concept  of  motivation  and  work  engagement  is  gaining  importance  across  every organization. This study aims at helping Telecom organizations to build more effective policies with respect to motivation and work engagement .By offering effective policies and encouraging employees to make use of available policies and programmes the organization will in turn be increasing the employee??s level of satisfaction and also commitment towards the organization. This will help the organization retain its best people or talent.</text>
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                <text>The productivity and effectiveness of any organisation depends mainly on the performance level of the employees in the organisation. Human behaviour scientists over the years have conducted various studies and have concluded that, the performance of employees in any organisation depends largely on their motivational behaviour. Reviews of related literature confirm the role of various factors in the motivational behaviour of employees including organisational culture and career orientation of employees. The title of the present study is Motivational Behaviour of Tourism Employees in Relation to Organisational Culture and Career Orientations. The major objectives included ascertaining the relationship between motivational behaviour and organisational culture and career orientations of tourism employees and finding out whether differences in demographic variables would account for significant differences in motivational behaviour. The population of the study consisted of 323 employees of public sector, private sector and multinational companies working in travel agencies, tour operations, airlines and hotels and resorts in Bangalore. The sampling technique employed was judgment sampling. For the present study three tools namely: Motivational Analysis of Organisations- Behaviour (MAO-B) by Pareek (2003), Organisational Culture Survey by Pareek (2003) and Career orientations Inventory by Schein (1990) were used to collect data. The findings of the study show that while two aspects of organisational culture namely internal and future oriented influence the motivational behaviour of employees working in the private sector, no aspect of organisational culture has any influence on the motivational behaviour of employees working in the public sector. Further, only ambiguity tolerant aspect of organisational culture influence the motivational behaviour of employees working in multinational companies.</text>
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                <text>A P, George.</text>
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                <text>&lt;a href="http://hdl.handle.net/10603/365045" target="_blank" rel="noreferrer noopener"&gt;http://hdl.handle.net/10603/365045&lt;/a&gt;</text>
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                <text>Motivational factors leading to the limited presence of women chefs in the hotel industry of Bengaluru /</text>
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                <text>International Journal of Innovative Studies In Sociology And Humanities, Vol.3, Issue 8, pp.108-121, ISSN No: 2456-4931.</text>
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                <text>Dr. Usha Dinakaran</text>
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                <text>The diverse movie industry faces many challenges in the promotion of the product across different demographics. Movie trailer engagements provide valuable information about how the audience perceives the movie. This information can be used to predict the success of the upcoming movie before it gets released. The previous research works were mainly concentrating on Hindi language movies to predict success. The current research paper includes the success prediction of movies other than Hindi. This paper aims to analyze various Machine Learning models performance and select the best performing model to predict movie success. The developed model can efficiently classify successful and unsuccessful movies. For the current research, the data is collected from various sources through web scrapping and API calls in Sacnilk, The Movie Database (TMDB), YouTube, and Twitter. Different machine learning classification models such as Random Forest, Logistic Regression, KNN, and Gaussian Nae Bayes are tested to develop the best-performing prediction model. This research can help moviemakers to understand the popularity of the movie among the viewers and decide on an efficient promotional strategy to make the movie more successful.  2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.</text>
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                <text>Lecture Notes in Networks and Systems, Vol-290, pp. 385-393.</text>
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                <text>Emmanuvel A., CHRIST (Deemed to be University), Lavasa, Pune, India; Bhagat V., CHRIST (Deemed to be University), Lavasa, Pune, India; Jacob L., CHRIST (Deemed to be University), Lavasa, Pune, India</text>
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                <text>The fundamental tenet of consumerism revolves around the belief that the burgeoning consumption of goods is favourable for the economy. Since the dawn of the Industrial Revolution, humanity has witnessed an exponential upsurge in consumerism. It has been related both to the increase in the population size as well as an increase in our demands due to constant changes in lifestyle. Multiple sources have corroborated the fact that if this consumption behaviour continues unabated, we will soon face an acute shortage of resources of all kinds. Both consumer behaviour patterns such as addictive consumption and conspicuous consumption can be attributed to this. Amongst the solutions available, 'Demarketing' is one. It is a type of marketing when a brand wants to discourage you from buying its product. The paper is descriptive in nature and is based on secondary data which has been collected from journals, blogs, websites, magazines, books, etc. The paper intends to explore the theme of demarketing vis-vis the materialistic purchase behaviour of a modern-day consumer and green demarketing strategies that companies are adopting by way of sustainable marketing.  The Electrochemical Society</text>
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                <text>ECS Transactions, Vol-107, No. 1, pp. 19579-19587.</text>
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                <text>ISSN: 19386737; ISBN: 978-160768539-5</text>
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                <text>Gupta S., CHRIST (Deemed to be University), Uttar Pradesh, Ghaziabad, India; Sinha S., CHRIST (Deemed to be University), Uttar Pradesh, Ghaziabad, India; Sinha D., CHRIST (Deemed to be University), Uttar Pradesh, Ghaziabad, India</text>
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                <text>MR Brain Tumor Classification and Segmentation Via Wavelets</text>
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              <elementText elementTextId="187766">
                <text>Discrete Wavelet Transform (DWT); Fejerkorovkin Filter; Kernel Support vector Machine (KSVM); Principlecomponent Analysis (PCA); Thresholding</text>
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                <text>Timely, accurate detection of magnetic resonance (MR) images of brain is most important in the medical analysis. Many methods have already explained about the tumor classification in the literature. This paper explains the method of classifying MR brain images into normal or abnormal (affected by tumor), abnormality segments present in the image. This paper proposes DWT-discrete wavelet transform in first step to extract the image features from the given input image. To reduce the dimensions of the feature image principle component Analysis (PCA) is employed. Reduced extracted feature image is given to kernel support vector machine (KSVM) for processing. The data set has 90 brain MR images (both normal and abnormal) with seven common diseases. These images are used in KSVM process. Gaussian Radial Basis (GRB) kernel is used for the classification method proposed and yields maximum accuracy of 98% compared to linear kernel (LIN). From the analysis, compared with the existing methods GRB kernel method was effective. If this classification finds abnormal MR image with tumor then the corresponding part is separated and segmented by thresholding technique.  2018 IEEE.</text>
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                <text>Menaka Devi T.; Ramani G.; Xavier Arockiaraj S.</text>
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                <text>2018 International Conference on Wireless Communications, Signal Processing and Networking, WiSPNET 2018</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/WiSPNET.2018.8538643" target="_blank" rel="noreferrer noopener"&gt;https://doi.org/10.1109/WiSPNET.2018.8538643&lt;/a&gt;
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                <text>ISBN: 978-153863624-4</text>
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                <text>Menaka Devi T., Department of Electronics and Communication Engineering, Adhiyamann College of Engineering, Hosur, 635109, India; Ramani G., Department of Electronics and Communication Engineering, CHRIST, Faculty of Engineering, Bengaluru, 560074, India; Xavier Arockiaraj S., Department of Electronics and Communication Engineering, CHRIST, Faculty of Engineering, Bengaluru, 560074, India</text>
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                <text>MRSP-Multi Routing Systems and Parameter Explanations to Build the Path in Underwater Sensor Network</text>
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                <text>Multi-Routing System; Redundancy Reduction; Secure Transmissions; Undwerwater Network</text>
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                <text>The underwater network is currently widely used to locate moving objects beneath the sea, monitor marine security, and detect changes in the sea water. A large number of sensors, as well as a precise methodology, are necessary to detect changes in sea depth. The protocol should be revised in response to environmental and chronological changes. The sensor should have been designed with multiple knowledge to route packets in order to optimise transmissions. Because the node will choose the best route based on the circumstances, especially in an underwater network, the paper MRSP - multi routing systems and parameter validations to create the path in an underwater sensor network is discussed in the multi routing knowledge sensor operations, energy saving systems, redundancy reduction, and so on. All of these measures, combined with secure transmission with trusted neighbour selection, result in safer transmissions and more accurate path selection.   2022 IEEE.</text>
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                <text>Deepthi B.P.; Lekha J.</text>
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                <text>2022 International Conference on Trends in Quantum Computing and Emerging Business Technologies, TQCEBT 2022</text>
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                <text>Institute of Electrical and Electronics Engineers Inc.</text>
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                <text>Deepthi B.P., Sri Krishna Arts and Science College, Coimbatore, India; Lekha J., CHRIST (Deemed to Be University), Lavasa Campus, Pune, India</text>
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                <text>Mucormycosis (black fungus) ensuing COVID-19 and comorbidity meets - Magnifying global pandemic grieve and catastrophe begins</text>
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            <name>Subject</name>
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              <elementText elementTextId="196905">
                <text>Comorbidity; COVID-19 induced Mucormycosis; Dexamethasone; Diagnosis; Hyperglycemia; Ketosis; Secondary infections</text>
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                <text>Post COVID-19, mucormycosis occurred after the SARS-CoV-2 has rampaged the human population and is a scorching problem among the pandemic globally, particularly among Asian countries. Invasive mucormycosis has been extensively reported from mild to severe COVID-19 survivors. The robust predisposing factor seems to be uncontrolled diabetes mellitus, comorbidity and immunosuppression acquired through steroid therapy. The prime susceptive reason for the increase of mucormycosis cases is elevated iron levels in the serum of the COVID survivors. A panoramic understanding of the infection has been elucidated based on clinical manifestation, genetic and non- genetic mechanisms of steroid drug administration, biochemical pathways and immune modulated receptor associations. This review lime-lights and addresses the What, Why, How and When about the COVID-19 associated mucormycosis (CAM) in a comprehensive manner with a pure intention to bring about awareness to the common public as the cases are inevitably and exponentially increasing in India and global countries as well. The article also unearthed the pathogenesis of mucormycosis and its association with the COVID-19 sequela, the plausible routes of entry, diagnosis and counter remedies to keep the infection at bay. Cohorts of case reports were analysed to spotlight the link between the pandemic COVID-19 and the nightmare-mucormycosis.  2021 Elsevier B.V.</text>
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                <text>Science of the Total Environment, Vol-805</text>
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                <text>&lt;a href="https://doi.org/10.1016/j.scitotenv.2021.150355" target="_blank" rel="noreferrer noopener"&gt;https://doi.org/10.1016/j.scitotenv.2021.150355&lt;/a&gt;
&lt;br /&gt;&lt;br /&gt;&lt;a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85115068351&amp;amp;doi=10.1016%2Fj.scitotenv.2021.150355&amp;amp;partnerID=40&amp;amp;md5=19ce5fde8b593ceb22e3822c97435a83" target="_blank" rel="noreferrer noopener"&gt;https://www.scopus.com/inward/record.uri?eid=2-s2.0-85115068351&amp;amp;doi=10.1016%2fj.scitotenv.2021.150355&amp;amp;partnerID=40&amp;amp;md5=19ce5fde8b593ceb22e3822c97435a83&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="196912">
                <text>All Open Access; Green Open Access</text>
              </elementText>
            </elementTextContainer>
          </element>
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                <text>ISSN: 489697; PubMed ID: 34818767; CODEN: STEVA</text>
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          </element>
          <element elementId="42">
            <name>Format</name>
            <description>The file format, physical medium, or dimensions of the resource</description>
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              <elementText elementTextId="196914">
                <text>Online</text>
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            <description>A language of the resource</description>
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              <elementText elementTextId="196915">
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                <text>Pushparaj K., Department of Zoology, School of Biosciences, Avinashilingam Institute for Home Science and Higher Education for Women, Coimbatore, 641 043, Tamil Nadu, India; Kuchi Bhotla H., Department of Life Science, CHRIST (Deemed to be University), Bengaluru, 560076, Karnataka, India; Arumugam V.A., Department of Human Genetics and Molecular Biology, Bharathiar University, Coimbatore, 641 046, Tamil Nadu, India; Pappusamy M., Department of Life Science, CHRIST (Deemed to be University), Bengaluru, 560076, Karnataka, India; Easwaran M., International Centre for Genetic Engineering and Biotechnology, New Delhi, 110067, India; Liu W.-C., Department of Animal Science, College of Coastal Agricultural Sciences, Guangdong Ocean University, Zhanjiang, 524088, China; Issara U., Division of Food Science and Technology Management, Faculty of Science and Technology, Rajamangala University of Technology Thanyaburi, Pathum Thani, 12110, Thailand; Rengasamy K.R.R., Green Biotechnologies Research Centre of Excellence, University of Limpopo, Private Bag X1106, Polokwane, Sovenga, 0727, South Africa; Meyyazhagan A., Department of Life Science, CHRIST (Deemed to be University), Bengaluru, 560076, Karnataka, India; Balasubramanian B., Department of Food Science and Biotechnology, College of Life Science, Sejong University, Seoul, 05006, South Korea</text>
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  <item itemId="20067" public="1" featured="0">
    <collection collectionId="16">
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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>
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              <elementTextContainer>
                <elementText elementTextId="51377">
                  <text>Conference Papers</text>
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      <name>Conference Paper</name>
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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>
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                <text>Mudhr: Malicious URL detection using heuristic rules based approach</text>
              </elementText>
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          </element>
          <element elementId="49">
            <name>Subject</name>
            <description>The topic of the resource</description>
            <elementTextContainer>
              <elementText elementTextId="176535">
                <text>Heuristic approach; Malicious URL; Phishing; Spamming; URL detection; URL feature extraction</text>
              </elementText>
            </elementTextContainer>
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            <name>Description</name>
            <description>An account of the resource</description>
            <elementTextContainer>
              <elementText elementTextId="176536">
                <text>Technology advancement helps the people in numerous ways such as it supports business development, banking, education, entertainment etc. Especially time critical and money related activities, people are fully really on internet and web applications. It saves valuable time and money. Despite of the benefits, it also gives wide space for the attackers to focus more victims. Malicious URL based attacks are most common and more dangerous attacks now a day which steals the credentials and sensitive data from the victims and perform malicious activities in the victim's space. Phishing, Spamming, drive by download are the example of such attacks and are preformed through malicious URL. Plenty of approaches are available to detect the malicious URL. That are grouped under three categories such as Blacklist based, Heuristic based and Machine Learning based approaches. Among the three, heuristic approach is better than the blacklist approach in term of better generalizing the malicious URL and gives equally accurate prediction with machine learning approach. This paper presents recent works in the field of malicious URL detection and novel technique to detect malicious URL based on the most important features derived from URL.  2022 Author(s).</text>
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          <element elementId="39">
            <name>Creator</name>
            <description>An entity primarily responsible for making the resource</description>
            <elementTextContainer>
              <elementText elementTextId="176537">
                <text>Raja A.S.; Pradeepa G.; Arulkumar N.</text>
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            <name>Source</name>
            <description>A related resource from which the described resource is derived</description>
            <elementTextContainer>
              <elementText elementTextId="176538">
                <text>AIP Conference Proceedings, Vol-2393</text>
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            <name>Publisher</name>
            <description>An entity responsible for making the resource available</description>
            <elementTextContainer>
              <elementText elementTextId="176539">
                <text>American Institute of Physics 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>
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              <elementText elementTextId="176540">
                <text>2022-01-01</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/10.1063/5.0074077" target="_blank" rel="noreferrer noopener"&gt;https://doi.org/10.1063/5.0074077&lt;/a&gt;
&lt;br /&gt;&lt;br /&gt;&lt;a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85131153186&amp;amp;doi=10.1063%2F5.0074077&amp;amp;partnerID=40&amp;amp;md5=28c4ffa38baf30312328584a7e77d893" target="_blank" rel="noreferrer noopener"&gt;https://www.scopus.com/inward/record.uri?eid=2-s2.0-85131153186&amp;amp;doi=10.1063%2f5.0074077&amp;amp;partnerID=40&amp;amp;md5=28c4ffa38baf30312328584a7e77d893&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="176542">
                <text>Restricted Access</text>
              </elementText>
            </elementTextContainer>
          </element>
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            <name>Relation</name>
            <description>A related resource</description>
            <elementTextContainer>
              <elementText elementTextId="176543">
                <text>ISSN: 0094243X; ISBN: 978-073544198-9</text>
              </elementText>
            </elementTextContainer>
          </element>
          <element elementId="42">
            <name>Format</name>
            <description>The file format, physical medium, or dimensions of the resource</description>
            <elementTextContainer>
              <elementText elementTextId="176544">
                <text>Online</text>
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            </elementTextContainer>
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            <name>Language</name>
            <description>A language of the resource</description>
            <elementTextContainer>
              <elementText elementTextId="176545">
                <text>English</text>
              </elementText>
            </elementTextContainer>
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          <element elementId="51">
            <name>Type</name>
            <description>The nature or genre of the resource</description>
            <elementTextContainer>
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                <text>Conference paper</text>
              </elementText>
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            <name>Coverage</name>
            <description>The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant</description>
            <elementTextContainer>
              <elementText elementTextId="176547">
                <text>Raja A.S., IT Department, University of Technology and Applied Science-Shinas, Sultanate of Oman, India; Pradeepa G., Department of Computer Science, Vels Institute of Science, Technology &amp;amp; Advanced Studies, Chennai, India; Arulkumar N., Department of Computer Science, CHRIST, Deemed to Be University, Bangalore, India</text>
              </elementText>
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  <item itemId="14113" public="1" featured="0">
    <collection collectionId="5">
      <elementSetContainer>
        <elementSet elementSetId="1">
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          <description>The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/.</description>
          <elementContainer>
            <element elementId="50">
              <name>Title</name>
              <description>A name given to the resource</description>
              <elementTextContainer>
                <elementText elementTextId="64">
                  <text>Articles</text>
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              </elementTextContainer>
            </element>
          </elementContainer>
        </elementSet>
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      <name>Article</name>
      <description>Faculty Publications -Articles</description>
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    <elementSetContainer>
      <elementSet elementSetId="1">
        <name>Dublin Core</name>
        <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>
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                <text>Mulberry Leaves (Morus Rubra)-Derived Blue-Emissive Carbon Dots Fed to Silkworms to Produce Augmented Silk Applicable for the Ratiometric Detection of Dopamine</text>
              </elementText>
            </elementTextContainer>
          </element>
          <element elementId="49">
            <name>Subject</name>
            <description>The topic of the resource</description>
            <elementTextContainer>
              <elementText elementTextId="94450">
                <text>carbon dots; dopamine detection; mulberry leaves; silk fibres; silkworm silk</text>
              </elementText>
            </elementTextContainer>
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          <element elementId="41">
            <name>Description</name>
            <description>An account of the resource</description>
            <elementTextContainer>
              <elementText elementTextId="94451">
                <text>Silk fibers (SF) reeled from silkworms are constituted by natural proteins, and their characteristic structural features render them applicable as materials for textiles and packaging. Modification of SF with functional materials can facilitate their applications in additional areas. In this work, the preparation of functional SF embedded with carbon dots (CD) is reported through the direct feeding of a CD-modified diet to silkworms. Fluorescent and mechanically robust SFare obtained from silkworms (Bombyx mori) that are fed on CDs synthesized from the Morus rubra variant of mulberry leaves (MB-CDs). MB-CDs are introduced to silkworms from the third instar by spraying them on the silkworm feed, the mulberry leaves. MB-CDs are synthesized hydrothermally without adding surface passivating agents and are observed to have a quantum yield of 22%. With sizes of ?4nm, MB-CDs exhibited blue fluorescence, and they can be used as efficient fluorophores to detect Dopamine (DA) up to the limit of 4.39nM. The nanostructures and physical characteristics of SF weren't altered when the SF are infused with MB-CDs. Also, a novel DA sensing application based on fluorescence with the MB-CD incorporated SF is demonstrated.  2023 Wiley-VCH GmbH.</text>
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            <name>Creator</name>
            <description>An entity primarily responsible for making the resource</description>
            <elementTextContainer>
              <elementText elementTextId="94452">
                <text>John V.L.; Nayana A.R.; Keerthi T.R.; Athira Krishnan K.A.; Sasidharan B.C.P.; Vinod T.P.</text>
              </elementText>
            </elementTextContainer>
          </element>
          <element elementId="48">
            <name>Source</name>
            <description>A related resource from which the described resource is derived</description>
            <elementTextContainer>
              <elementText elementTextId="94453">
                <text>Macromolecular Bioscience, Vol-23, No. 9</text>
              </elementText>
            </elementTextContainer>
          </element>
          <element elementId="45">
            <name>Publisher</name>
            <description>An entity responsible for making the resource available</description>
            <elementTextContainer>
              <elementText elementTextId="94454">
                <text>John Wiley and Sons Inc</text>
              </elementText>
            </elementTextContainer>
          </element>
          <element elementId="40">
            <name>Date</name>
            <description>A point or period of time associated with an event in the lifecycle of the resource</description>
            <elementTextContainer>
              <elementText elementTextId="94455">
                <text>2023-01-01</text>
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            </elementTextContainer>
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          <element elementId="43">
            <name>Identifier</name>
            <description>An unambiguous reference to the resource within a given context</description>
            <elementTextContainer>
              <elementText elementTextId="94456">
                <text>&lt;a href="https://doi.org/10.1002/mabi.202300081" target="_blank" rel="noreferrer noopener"&gt;https://doi.org/10.1002/mabi.202300081&lt;/a&gt;
&lt;br /&gt;&lt;br /&gt;&lt;a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85158094384&amp;amp;doi=10.1002%2Fmabi.202300081&amp;amp;partnerID=40&amp;amp;md5=71f3916f642a58c0a5398241c0e6b60a" target="_blank" rel="noreferrer noopener"&gt;https://www.scopus.com/inward/record.uri?eid=2-s2.0-85158094384&amp;amp;doi=10.1002%2fmabi.202300081&amp;amp;partnerID=40&amp;amp;md5=71f3916f642a58c0a5398241c0e6b60a&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="94457">
                <text>Restricted Access</text>
              </elementText>
            </elementTextContainer>
          </element>
          <element elementId="46">
            <name>Relation</name>
            <description>A related resource</description>
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                <text>ISSN: 16165187; PubMed ID: 37097218; CODEN: MBAIB</text>
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                <text>John V.L., Department of Chemistry, CHRIST (Deemed to be University), Bangalore, 560029, India; Nayana A.R., School of Biosciences, Mahatma Gandhi University, P.D Hills (P.O), Kerala, Kottayam, 686560, India; Keerthi T.R., School of Biosciences, Mahatma Gandhi University, P.D Hills (P.O), Kerala, Kottayam, 686560, India; Athira Krishnan K.A., Centre for Neuroscience, Cochin University of Science and Technology, Kerala, Kochi, 682022, India; Sasidharan B.C.P., Centre for Neuroscience, Cochin University of Science and Technology, Kerala, Kochi, 682022, India; Vinod T.P., Department of Chemistry, CHRIST (Deemed to be University), Bangalore, 560029, India</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., CHRIST (Deemed to Be University), Department of CSE, India; Ca L., RVITM, Visvesvaraya Technological University, India</text>
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                <text>Multi Parameterized Modified Local Binary Pattern for Lung Cancer Detection by Deep Learning Methods  </text>
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                <text>Computer Science and Engineering</text>
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                <text>The research work is focusing on developing a classification model for Lung Cancer detection by integrating the image features with Modified Local Binary Pattern (MLBP), Modified Principal Component Analysis (MPCA), newlinesymptoms and Risk factors using Deep Learning methods and converting the image features into three dimensional (3D) images. The aim of this research is to identify the malignant and normal tumours from the Computer newlineTomography (CT) images with improved accuracy. The 2D CT images of Lung Cancer patients have been preprocessed with Median and Gabor filtering methods and watershed segmentation. The CT images are also newlineprocessed with the Zero Component Analysis (ZCA) whitening and Modified Local Binary Pattern. The processed image is used in the research for classification. The Lung Cancer dataset in the research are collected from newlinevarious medical colleges. The dataset contain CT images with Lung Cancer and without Lung Cancer. The research is conducted by integrating the selected Image features, Risk factor and symptoms of Lung Cancer of the newlinesame patients. The Integration using feature selections is carried out with Modified Principal Component Analysis. The Modified Principal Component Analysis is used in the research to reduce the time complexity. The results are evaluated with Gini coefficient, Confusion Matrix parameters and ROC newlinecurve. Two Dimensional (2D) CT images are converted into a Three Dimensional (3D) image for the clarity and the visibility of Lung Cancer nodules. The conversion from 2D to 3D has been using combining two methods, the orthogonality and visualization of 4D rotation. This enabled to find the location of the Lung Cancer from different angle and with different viewpoints. The 3D image shows the location of the Lung Cancer by Four Dimensional (4D) visualization and 3D rotation, thus giving clarity to the newlineexisting 2D images.</text>
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                <text>Dominic, Denny</text>
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                <text>K, Balachandran.</text>
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                <text>&lt;a href="http://hdl.handle.net/10603/478099" target="_blank" rel="noreferrer noopener"&gt;http://hdl.handle.net/10603/478099&lt;/a&gt;</text>
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                <text>Multi-Agent Deep Reinforcement Learning for Hybrid Motion Planning in Dynamic Environments</text>
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                <text>Autonomous agents; Distributed algorithms; Dynamic obstacles; Multi-agent systems; Trajectory optimization</text>
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                <text>This research presents a novel approach to addressing the challenges of gesture forecasting in impenetrable and dynamic atmospheres by integrating a hybrid algorithm within a multi-agent system framework. Traditional methods such as Force-based motion planning (FMP) &amp;amp; deep reinforcement learning (RL) often struggle to handle complex scenarios involving multiple autonomous agents due to their inherent limitations. To overcome these challenges, we propose a hybrid algorithm that seamlessly combines the strengths of RL and FMP while leveraging the coordination capabilities of a multi-agent system. By integrating this hybrid algorithm into a multi-agent framework, we demonstrate its effectiveness in enabling multiple agents to navigate densely populated environments with dynamic obstacles. Through extensive simulation studies, we illustrate the superior performance of our approach compared to traditional methods, achieving higher success rates and improved efficiency in scenarios involving simultaneous motion planning for multiple agents. A hybrid motion planning algorithm is also introduced in this very research. Performance Comparison of Hybrid Algorithm, Deep RL, and FMP are also discussed in the result section. This research paves the way for the development of robust and scalable solutions for motion planning in real-world applications such as collaborative robotics, autonomous vehicle fleets, and intelligent transportation systems.   2024 IEEE.</text>
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                <text>Chopra J.; Avhad P.; Ekatpure S.R.; Rallapalli M.; Kumari S.; Upreti K.</text>
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                <text>2024 International Conference on Emerging Trends in Networks and Computer Communications, ETNCC 2024 - Proceedings, pp. 235-241.</text>
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                <text>Institute of Electrical and Electronics Engineers Inc.</text>
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                <text>ISBN: 979-835035326-6</text>
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                <text>Chopra J., SEAS, GWU, Washington, DC, United States; Avhad P., Computer Engineering, Veermata Jijabai Technological Inst., Mumbai, India; Ekatpure S.R., Industrial Engineering, Kulicke and Soffa, PA, United States; Rallapalli M., CSE - AI, JAIN University, Bengaluru, India; Kumari S., HSBC, Maharashtra, India; Upreti K., Computer Science Engineering, CHRIST University, Delhi Ncr, India</text>
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                <text>Multi-atlas Graph Convolutional Networks and Convolutional Recurrent Neural Networks-Based Ensemble Learning for Classification of Autism Spectrum Disorders</text>
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                <text>Autism spectrum disorder; Automatic diagnosis; Deep learning; Ensemble learning; Graph convolution network</text>
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                <text>Kaushik R., Amity Institute of Information Technology, Amity University Rajasthan, Rajasthan, Jaipur, 303002, India; Singh V., Department of Computer Science Engineering, Manipal University Jaipur, Rajasthan, Jaipur, 302002, India; Kumari R., Department of Computer Science, CHRIST (Deemed to be University), Karnataka, Bangalore, 560029, India</text>
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                <text>Aromatic heterocycles constitute the most diverse family of organic compounds. Moreover, aromatic heterocycles are widely used for the synthesis of dyes and polymeric materials of high value. The development of selective reactions that utilize easily available and abundant precursors for the efficient synthesis of heterocyclic compounds is a long-standing goal of chemical research. Despite the centrality of its role in a number of important research areas, including medicinal chemistry, total synthesis, and materials science, a general, selective, step-economical, and step-efficient synthesis of heterocycles is still needed. newlinePyrano[2,3-c]pyrazole derivatives have been synthesised by a one-pot multicomponent condensation of different aldehydes, dialdehydes, and ketones with malononitrile, ethyl acetoacetate, hydrazine hydrate (or phenylhydrazine) in the presence of magnetic nano-[CoFe2O4] catalyst under ultrasonic irradiation. The catalyst can be retrieved using an external magnet and used repeatedly. A practical, scalable method for obtaining various pyranopyrazoles has been demonstrated. The extraordinary catalytic role of the various catalyst has been discovered in the processes, which reveals a possible character of enhancing reaction rates and stabilising the intermediates during the course of the reactions.</text>
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                <text>Multiple Criteria Decision Making has been one of the powerful and structured approach in solving real world problems in the past. The aim is to determine the best alternative based on multiple criteria. It has shown a remarkable performance in the field of education. In order to gain insights into the existing body of research in this area, a bibliometric analysis was conducted. The study is conducted to provide a comprehensive analysis since 2000 in the field of application of MCDM in the various domains of education. The publication information was accessed from Scopus Database on 1 December 2023 and the bibliometric analysis has been done through Vosviewer, R package bibliometrics and Tableau. Initially 5185 documents were found which were reduced to 1706 after multi layered screening criteria. The analysis is performed to find the relevant documents, most valuable researchers, the major countries where the research in this area is exhaustively conducted. After extensive research it is observed that researchers belonging to China are highly involved in the domain taken for study. Also, research conducted in China is highly cited which shows its quality of work. Further, it is observed that mostly fuzzy analysis techniques are widely used for MCDM. The collaborative work done by Arunodaya Raj Mishra and Rani Pratibha research work is remarkable and highly recommended to conduct the research in the considered domain in the research paper. The conducted bibliometric analysis provides an overview of the scope and global trends of MCDM in shaping the education sector. This would help the researchers to explore the most relevant study, analysis and finding the research gaps as per their research needs.  The Author(s) under exclusive licence to The Society for Reliability Engineering, Quality and Operations Management (SREQOM), India and The Division of Operation and Maintenance, Lulea University of Technology, Sweden 2024.</text>
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                <text>Srivastava S.; Tripathi A.; Arora N.</text>
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                <text>International Journal of System Assurance Engineering and Management</text>
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                <text>Springer</text>
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                <text>2024-01-01</text>
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                <text>&lt;a href="https://doi.org/10.1007/s13198-024-02332-9" target="_blank" rel="noreferrer noopener"&gt;https://doi.org/10.1007/s13198-024-02332-9&lt;/a&gt;
&lt;br /&gt;&lt;br /&gt;&lt;a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85192940949&amp;amp;doi=10.1007%2Fs13198-024-02332-9&amp;amp;partnerID=40&amp;amp;md5=dc14152990c99a330488ad1dfe4d012c" target="_blank" rel="noreferrer noopener"&gt;https://www.scopus.com/inward/record.uri?eid=2-s2.0-85192940949&amp;amp;doi=10.1007%2fs13198-024-02332-9&amp;amp;partnerID=40&amp;amp;md5=dc14152990c99a330488ad1dfe4d012c&lt;/a&gt;</text>
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                <text>ISSN: 9756809</text>
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                <text>Srivastava S., Christ University, Bengaluru, India; Tripathi A., Department of Data Science and Engineering, Manipal University Jaipur, Jaipur, India; Arora N., Department of Computer Science, Kalindi College, University of Delhi, Delhi, India</text>
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