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                <text>Kavalli, Kiran; Hebbar, Gurumoorthy S.; Rout, Amruta</text>
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                <text>Application of Fuzzy-NSGA-II for achieving maximum biodiesel yield from waste cooking oil</text>
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                <text>Environmental Science and Pollution Research;Volume;32;Issue;52;pp.29487-29501</text>
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                <text>Kavalli K., Mechanical and Automobile Engineering Department, School of Engineering &amp;amp; Technology, CHRIST University, Karnataka, Bengaluru, 560029, India; Hebbar G.S., Mechanical and Automobile Engineering Department, School of Engineering &amp;amp; Technology, CHRIST University, Karnataka, Bengaluru, 560029, India; Rout A., Mechanical and Automobile Engineering Department, School of Engineering &amp;amp; Technology, CHRIST University, Karnataka, Bengaluru, 560029, India</text>
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                <text>The increasing demand for renewable energy and efficient waste management has highlighted the need for innovative biodiesel production techniques. This study optimises biodiesel production from waste cooking oil (WCO) using fuzzy modelling and non-dominated sorting genetic algorithm-II (NSGA-II). The optimisation process focuses on key input parameters: methanol quantity, reaction temperature, reaction time, and catalyst concentration, which were normalised and represented using linguistic variables. Fuzzy logic was employed to predict biodiesel yield, expressed in terms of linguistic variables, and defuzzified to yield crisp output values. The developed model achieved a high R2 value of 96.34%, demonstrating a strong correlation between input variables and biodiesel yield. The NSGA-II algorithm was utilised for multi-objective optimisation, determining the optimal conditions for biodiesel production: 150ml of methanol, a reaction temperature of 62C, a reaction time of 63min, and a catalyst concentration of 7.5g. These parameters resulted in a maximum biodiesel yield of 97.36%. The Box-Behnken experimental design validated the models efficiency, achieving a yield of 96.88%. This study emphasises the practical implications of optimised biodiesel production, such as reducing environmental pollution by recycling WCO and minimising reliance on fossil fuels. The optimised process meets ASTM standards and exhibits scalability potential for industrial-level production with minor modifications. The models robustness makes it suitable for integration into intelligent manufacturing systems, ensuring consistent biodiesel quality and yield through automated monitoring and control mechanisms. Despite its success, challenges such as feedstock variability and initial setup costs must be addressed. Future studies should focus on adaptive models and energy-efficient processing technologies to enhance scalability and sustainability. This research demonstrates a significant step towards sustainable biofuel production, combining waste management with renewable energy generation.  The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2025.</text>
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                <text>Green supply chain concept is used to reduce environmental degradation and emissions of air, water, and waste by incorporating green practices into business operations. Growing impacts of global warming, climate change, waste, and air pollution problems have prompted experts all over the world to think more environment friendly and find the best possible approach for "Green" solutions. green supply chain management is one of the factors that motivates organizations to be more sustainable. This study focuses on the green supply chain management in the auto parts industry in South Indian. Data from three green initiatives: recyclable packaging, green warehouse management and milk run approach for logistics is taken and compared with nongreen approaches. It is found that there is significant reduction in costs by adopting the green approaches. With environmental issues growing all the time, green supply chain deserves to be a long-term community concern in developing countries.  2023 by IGI Global. All rights reserved.</text>
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                <text>Saha S.K.; Rashmi S.; Pujari S.K.</text>
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                <text>Developing Skills and Competencies for Digital and Green Transitions, pp. 281-300.</text>
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                <text>Saha S.K., Christ University, India; Rashmi S., Christ University, India; Pujari S.K., Christ University, India</text>
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                <text>Application of hydrogel in paddy field for soil moisture retention and yield optimization</text>
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                <text>Agricultural sustainability is essential to enhance food and water security, particularly in the context of climate change. To ensure food security and to protect water resources, agricultural and irrigation practices need to be amended with innovative technology that conserves water and increases productivity. In the recent past, applications of hydrogels in agriculture have received substantial attention among researchers as well as among farmers. Paddy is the core crop for the vast newlineparts of the world. The present study elaborates on various aspects of hydrogels such as classifications, ideal properties for agricultural application, analysis of soil characteristic changes for pre and post crop newlineseason, irrigation water quality analysis for crop season. BPT 5204 and NDLR 07 varities of paddy had been experimented in this study. newlineVerification of hydrogel degradation was conducted using Fourier transform infrared (ft-ir) spectroscopy. The experimental methods for determining hydrogel properties were given specific attention to properties such as swelling, retention, slow release, and degradation which are vital for agricultural sustainability. Hydrogel experiments have demonstrate significant improvement in water consumption, water use newlineefficiency, crop growth and yield parameters. The reduction in water footprint in major crops such as paddy and wheat through hydrogel might establish a shift towards sustainable irrigation practices if adopted on a large scale. Integrating innovative solutions with environmental-friendly newlinehydrogels in the coming decades will contribute to the pursuit of achieving newlinesustainable development goals. The application of hydrogel as soil conditioners was identified as a possible solution. to increase water use efficiency in irrigation and optimization of crop yield. The study points towards developing a framework for the evaluation of the suitability of hydrogel for agricultural applications when get scaled up to regional level.</text>
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                <text>Mani, K Shibu.</text>
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      <name>Article</name>
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            <name>Creator</name>
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              <elementText elementTextId="227030">
                <text>Shrotriya, Shobhit; Nizar, Banu P. K.; Kulkarni, Avi; Kumar, Vinod G.</text>
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          <element elementId="50">
            <name>Title</name>
            <description>A name given to the resource</description>
            <elementTextContainer>
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                <text>Application of Large Language Models for Data-Driven Analytics in Oncology: Insights and Evidence Generation from Real-World Imaging Data</text>
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            <name>Source</name>
            <description>A related resource from which the described resource is derived</description>
            <elementTextContainer>
              <elementText elementTextId="227033">
                <text>International Journal of Image, Graphics and Signal Processing;Volume;17;Issue;6;pp.38-59</text>
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            <name>Identifier</name>
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                <text>&lt;a href="https://doi.org/10.5815/ijigsp.2025.06.03" target="_blank" rel="noreferrer noopener"&gt;https://doi.org/10.5815/ijigsp.2025.06.03&lt;/a&gt; &lt;br /&gt;&lt;br /&gt;&lt;a href="https://www.scopus.com/pages/publications/105025258237?origin=resultslist" target="_blank" rel="noreferrer noopener"&gt;https://www.scopus.com/pages/publications/105025258237?origin=resultslist&lt;/a&gt;</text>
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            <description>The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant</description>
            <elementTextContainer>
              <elementText elementTextId="227035">
                <text>Shrotriya S., Department of Computer Science, CHRIST (Deemed to be University), Bangalore, 560029, India, Accenture, India; Nizar B.P.K., Department of Computer Science, CHRIST (Deemed to be University), Bangalore Central Campus, Hosur Road, Near Dairy Circle, Bangalore, 560029, India; Kulkarni A., ThoughtSphere, 99 S Almaden Blvd, San Jose, 95113, CA, United States; Kumar V.G., Accenture, Prestige Technopolis, 8/1 Dr M. H. Marigowda Road, Adugodi Rd, Bangalore, 560029, India</text>
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                <text>Breast cancer is one of the most common and serious types of cancer. It can affect people of all ages and genders around the world. The increasing incidence of breast cancer, coupled with its complexity, has placed a significant burden on healthcare systems and patients alike. Traditional diagnostic methods, while effective, often face limitations in early detection and accurate prognosis, which are critical for improving patient outcomes. In recent years, artificial intelligence (AI) and machine learning (ML) are changing the way we solve problems and make decisions in the field of medical diagnostics, enhancing the ability to detect, diagnose and predict breast cancer. However, there are still challenges, such as the need for large and diverse datasets to train these models, making AI tools work smoothly in hospitals, and addressing ethical concerns in healthcare. This paper looks at how AI and ML are used in breast cancer care, especially in analyzing real-world medical data like images, histopathology, and other datasets such as doctor notes &amp;amp; discharge summaries, to identify patterns that may be unnoticeable to medical experts. Large Language Models (LLMs) using embeddings, are highlighted for their capacity to improve the accuracy of image related interpretations, potentially detect early-stage tumours, and predict disease progression and treatment responses. Real-world medical datasets have been collected and analysed using different models. A publicly available Convolutional Neural Network (CNN) and a custom-built Large Language Model (LLM) with embeddings were tested. The Generative AI model achieved 98.44% accuracy, significantly higher than the traditional AI model's 61.72%. Future research can explore how Generative AI can help classify patients based on risk levels. This could lead to personalized treatment plans, reducing unnecessary treatments and improving patients' quality of life. Given the research is primarily focussed on breast cancer, there is an attempt to showcase that by harnessing the power of AI and ML, there is potential to significantly reduce the global burden of breast cancer, offering new avenues for early detection, accurate diagnosis, and tailored therapeutic strategies. Continued research and collaboration among oncologists, data scientists, and policymakers are essential to fully realize the benefits of AI in the fight against breast cancer, ultimately leading to better patient outcomes and a decrease in breast cancer-related mortality.  2025, Modern Education and Computer Science Press. All rights reserved.</text>
              </elementText>
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            <name>Subject</name>
            <description>The topic of the resource</description>
            <elementTextContainer>
              <elementText elementTextId="227037">
                <text>Artificial Intelligence; Breast Cancer; Embeddings; Generative AI; Large Language Models; Machine Learning</text>
              </elementText>
            </elementTextContainer>
          </element>
          <element elementId="45">
            <name>Publisher</name>
            <description>An entity responsible for making the resource available</description>
            <elementTextContainer>
              <elementText elementTextId="227038">
                <text>Modern Education and Computer Science Press</text>
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              <elementText elementTextId="227039">
                <text>ISSN: 20749074;</text>
              </elementText>
            </elementTextContainer>
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            <description>A language of the resource</description>
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              <elementText elementTextId="227040">
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            <name>Type</name>
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                <text>Article</text>
              </elementText>
            </elementTextContainer>
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          <element elementId="47">
            <name>Rights</name>
            <description>Information about rights held in and over the resource</description>
            <elementTextContainer>
              <elementText elementTextId="227042">
                <text>All Open Access; Gold Open Access</text>
              </elementText>
            </elementTextContainer>
          </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="227043">
                <text>online</text>
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              <description>A name given to the resource</description>
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            <name>Title</name>
            <description>A name given to the resource</description>
            <elementTextContainer>
              <elementText elementTextId="178711">
                <text>Application of LSTM Model for Western Music Composition</text>
              </elementText>
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          <element elementId="49">
            <name>Subject</name>
            <description>The topic of the resource</description>
            <elementTextContainer>
              <elementText elementTextId="178712">
                <text>LSTM; Machine Learning; Neural Networks; Western Music Composition</text>
              </elementText>
            </elementTextContainer>
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          <element elementId="41">
            <name>Description</name>
            <description>An account of the resource</description>
            <elementTextContainer>
              <elementText elementTextId="178713">
                <text>Music is one of the innate creative expressions of human beings. Music composition approaches have always been a focal point of music-based research and there has been an increasing interest in Artificial Intelligence (AI) based music composition methods in recent times. Developing an accurate algorithm and neural network architecture is imperative to the success of an AI-based approach to music composition. The present work explores the composition of western music through neural network using a Long Short-Term Memory (LSTM) algorithm. Compositions from seminal western composers such as J.S. Bach, W.A. Mozart, L.V. Beethoven, and F. Chopin were used as the dataset to train the neural network. Seven compositions were generated by the LSTM model and these outputs were presented to a group of thirty volunteers between 18-24 years of age. They were surveyed to identify the music piece as composed by a human or AI and how interesting they found the melodies of each piece. It was found that the LSTM model generated compositions that were thought to be made by a human and create melodies of interest from the perception of the volunteers. It is expected that through this study, more AI-based composition approaches can be developed which encompass more and more of the musical phenomenon.   2022 IEEE.</text>
              </elementText>
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          <element elementId="39">
            <name>Creator</name>
            <description>An entity primarily responsible for making the resource</description>
            <elementTextContainer>
              <elementText elementTextId="178714">
                <text>Cyriac S.; Ashvinee; Kim Y.W.; Tavis R.L.</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="178715">
                <text>International Conference on ICT Convergence, Vol-2022-October, pp. 136-141.</text>
              </elementText>
            </elementTextContainer>
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          <element elementId="45">
            <name>Publisher</name>
            <description>An entity responsible for making the resource available</description>
            <elementTextContainer>
              <elementText elementTextId="178716">
                <text>IEEE Computer Society</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="178717">
                <text>2022-01-01</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="178718">
                <text>&lt;a href="https://doi.org/10.1109/ICTC55196.2022.9952571" target="_blank" rel="noreferrer noopener"&gt;https://doi.org/10.1109/ICTC55196.2022.9952571&lt;/a&gt;
&lt;br /&gt;&lt;br /&gt;&lt;a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85143256620&amp;amp;doi=10.1109%2FICTC55196.2022.9952571&amp;amp;partnerID=40&amp;amp;md5=405544f1dc26f67726c3dc4b258b440e" target="_blank" rel="noreferrer noopener"&gt;https://www.scopus.com/inward/record.uri?eid=2-s2.0-85143256620&amp;amp;doi=10.1109%2fICTC55196.2022.9952571&amp;amp;partnerID=40&amp;amp;md5=405544f1dc26f67726c3dc4b258b440e&lt;/a&gt;</text>
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            </elementTextContainer>
          </element>
          <element elementId="47">
            <name>Rights</name>
            <description>Information about rights held in and over the resource</description>
            <elementTextContainer>
              <elementText elementTextId="178719">
                <text>Restricted Access</text>
              </elementText>
            </elementTextContainer>
          </element>
          <element elementId="46">
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            <description>A related resource</description>
            <elementTextContainer>
              <elementText elementTextId="178720">
                <text>ISSN: 21621233; ISBN: 978-166549939-2</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="178721">
                <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="178722">
                <text>English</text>
              </elementText>
            </elementTextContainer>
          </element>
          <element elementId="51">
            <name>Type</name>
            <description>The nature or genre of the resource</description>
            <elementTextContainer>
              <elementText elementTextId="178723">
                <text>Conference paper</text>
              </elementText>
            </elementTextContainer>
          </element>
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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="178724">
                <text>Cyriac S., Christ (Deemed to Be University), Centre for Digital Innovation, Bengaluru, India; Ashvinee, Christ (Deemed to Be University), Centre for Digital Innovation, Bengaluru, India; Kim Y.W., Christ (Deemed to Be University), Centre for Digital Innovation, Bengaluru, India; Tavis R.L., Christ (Deemed to Be University), Ashton-Bell Department of Western Music, Bengaluru, India</text>
              </elementText>
            </elementTextContainer>
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  <item itemId="18732" public="1" featured="0">
    <collection collectionId="15">
      <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>
                <elementText elementTextId="44496">
                  <text>Book Chapter</text>
                </elementText>
              </elementTextContainer>
            </element>
          </elementContainer>
        </elementSet>
      </elementSetContainer>
    </collection>
    <itemType itemTypeId="27">
      <name>Book Chapter</name>
      <description>Faculty Publications- Book Chapter</description>
    </itemType>
    <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>
              <elementText elementTextId="157966">
                <text>Application of machine intelligence-based knowledge graphs for software engineering</text>
              </elementText>
            </elementTextContainer>
          </element>
          <element elementId="41">
            <name>Description</name>
            <description>An account of the resource</description>
            <elementTextContainer>
              <elementText elementTextId="157967">
                <text>This chapter focuses on knowledge graphs application in software engineering. It starts with a general exploration of artificial intelligence for software engineering and then funnels down to the area where knowledge graphs can be a good fit. The focus is to put together work done in this area and call out key learning and future aspirations. The knowledge management system's architecture, specific application of the knowledge graph in software engineering like automation of test case creation and aspiring to build a continuous learning system are explored. Understanding the semantics of the knowledge, developing an intelligent development environment, defect prediction with network analysis, and clustering of the graph data are exciting explorations.  2021, IGI Global.</text>
              </elementText>
            </elementTextContainer>
          </element>
          <element elementId="39">
            <name>Creator</name>
            <description>An entity primarily responsible for making the resource</description>
            <elementTextContainer>
              <elementText elementTextId="157968">
                <text>Althar R.R.; Samanta D.</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="157969">
                <text>Methodologies and Applications of Computational Statistics for Machine Intelligence, pp. 186-202.</text>
              </elementText>
            </elementTextContainer>
          </element>
          <element elementId="45">
            <name>Publisher</name>
            <description>An entity responsible for making the resource available</description>
            <elementTextContainer>
              <elementText elementTextId="157970">
                <text>IGI Global</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="157971">
                <text>2021-01-01</text>
              </elementText>
            </elementTextContainer>
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          <element elementId="43">
            <name>Identifier</name>
            <description>An unambiguous reference to the resource within a given context</description>
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              <elementText elementTextId="157972">
                <text>&lt;a href="https://doi.org/10.4018/978-1-7998-7701-1.ch010" target="_blank" rel="noreferrer noopener"&gt;https://doi.org/10.4018/978-1-7998-7701-1.ch010&lt;/a&gt;
&lt;br /&gt;&lt;br /&gt;&lt;a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85124792951&amp;amp;doi=10.4018%2F978-1-7998-7701-1.ch010&amp;amp;partnerID=40&amp;amp;md5=6d06593a7697903a3c01240f9ceaa7df" target="_blank" rel="noreferrer noopener"&gt;https://www.scopus.com/inward/record.uri?eid=2-s2.0-85124792951&amp;amp;doi=10.4018%2f978-1-7998-7701-1.ch010&amp;amp;partnerID=40&amp;amp;md5=6d06593a7697903a3c01240f9ceaa7df&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="157973">
                <text>Restricted Access</text>
              </elementText>
            </elementTextContainer>
          </element>
          <element elementId="46">
            <name>Relation</name>
            <description>A related resource</description>
            <elementTextContainer>
              <elementText elementTextId="157974">
                <text>ISBN: 978-179987703-5; 978-179987701-1</text>
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            </elementTextContainer>
          </element>
          <element elementId="42">
            <name>Format</name>
            <description>The file format, physical medium, or dimensions of the resource</description>
            <elementTextContainer>
              <elementText elementTextId="157975">
                <text>Online</text>
              </elementText>
            </elementTextContainer>
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          <element elementId="44">
            <name>Language</name>
            <description>A language of the resource</description>
            <elementTextContainer>
              <elementText elementTextId="157976">
                <text>English</text>
              </elementText>
            </elementTextContainer>
          </element>
          <element elementId="51">
            <name>Type</name>
            <description>The nature or genre of the resource</description>
            <elementTextContainer>
              <elementText elementTextId="157977">
                <text>Book chapter</text>
              </elementText>
            </elementTextContainer>
          </element>
          <element elementId="38">
            <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="157978">
                <text>Althar R.R., Christ (Deemed to be University), QMS, First American India, Bangalore, India; Samanta D., Department of Computer Science, CHRIST (Deemed to be University), Bangalore, India</text>
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            </elementTextContainer>
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  <item itemId="20520" public="1" featured="0">
    <collection collectionId="16">
      <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="51377">
                  <text>Conference Papers</text>
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              </elementTextContainer>
            </element>
          </elementContainer>
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      </elementSetContainer>
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    <itemType itemTypeId="28">
      <name>Conference Paper</name>
      <description>Faculty Publications- Conference Papers</description>
    </itemType>
    <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>
              <elementText elementTextId="182810">
                <text>Application of Machine Learning in Customer Churn Prediction</text>
              </elementText>
            </elementTextContainer>
          </element>
          <element elementId="49">
            <name>Subject</name>
            <description>The topic of the resource</description>
            <elementTextContainer>
              <elementText elementTextId="182811">
                <text>Customer churn; Customer relationship management; Data mining; Machine learning</text>
              </elementText>
            </elementTextContainer>
          </element>
          <element elementId="41">
            <name>Description</name>
            <description>An account of the resource</description>
            <elementTextContainer>
              <elementText elementTextId="182812">
                <text>Retaining customers is the central component of a company's growth strategy. It is evident that several industries are experiencing a surge in customer churn due to the global pandemic. As a result, customer retention that lies at the core of customer relationship management, has become the foundation for every industry to plan for future growth. By reducing customer churn, a company can maximize its profit. Studies suggest that significant advancements are made in the field of customer churn prediction in domains like telecom, banking, e-commerce and energy sector. The focus of the paper is to present a detailed review of the various machine learning techniques applied to address churn. Fifty-five papers related to churn classification published between 2004 and 2020 are collected and analyzed. The reviewed papers are categorized into five main themes. These themes are feature selection techniques, methods to handle class imbalance, experimentation with machine learning algorithms, hybrid models and ensemble models respectively. Finally, few suggestions are presented as direction for future research.  2021 IEEE.</text>
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          <element elementId="39">
            <name>Creator</name>
            <description>An entity primarily responsible for making the resource</description>
            <elementTextContainer>
              <elementText elementTextId="182813">
                <text>De S.; Prabu P.; Paulose J.</text>
              </elementText>
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          </element>
          <element elementId="48">
            <name>Source</name>
            <description>A related resource from which the described resource is derived</description>
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                <text>For years, human resource development and management (HRDM) has used behavioral assessments to gauge employee potential. However, advancements in cognitive behavioral neuroscience (CBN) have opened up new possibilities for understanding how the human mind works. This chapter explores the practical applications of neuroscience methods like EEG, ERP, MRI, and fMRI, as well as neurofeedback and biofeedback, in talent identification, leadership development, and employee well-being. Importantly, these insights can be directly applied in HRDM practices, leading to more effective talent management, leadership development, and improved employee well-being. While recognizing the ethical considerations involved with these technologies, the chapter presents a compelling vision for a future where HRDM practices are informed by a deeper understanding of the brain, enabling the workforce to reach its full potential.  2024 by IGI Global. All rights reserved.</text>
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                <text>Patangia B., Christ University, India; Rithani J.M., Indian Institute of Management, Bangalore, India; Alfonso T., Christ University, India</text>
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                <text>Application of normal wiggly dual hesitant fuzzy sets to site selection for hydrogen underground storage</text>
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                <text>Dual hesitant fuzzy set; Entropy measure; Hesitant entropy measure; MCDM; NWDHFV; Storage of hydrogen</text>
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                <text>The hesitant fuzzy set is a mathematical tool to express multiple values in decision making. If they could not give a resolution, it is important to give priority and importance to a number of different values. Here, we propose normal wiggly dual hesitant fuzzy set (NWDHFS), as an extension of normal wiggly hesitant fuzzy set. We define a new score function of normal wiggly dual hesitant fuzzy information. The NWDHFS can express deep ideas of membership and non-membership information. In this work, we use hesitant fuzzy set to expose the deepest ideas hidden in the thought-level of the decision makers. We show that the NWDHFS can handle the hesitant fuzzy information. It expresses the deeper ideas of hesitant fuzzy set. An illustration is provided to demonstrate the practicality and effectiveness to the application of site selection of the underground storage of hydrogen. We are compelled to look for alternating fuels to suits changing weather conditions and increasing number of vehicles. This alternative fuel is necessary to control global warming and to be economically viable. Based on this, hydrogen gas is selected as a good alternative fuel. The most important statement is the saving of the selected hydrogen gas. Thus, when saving hydrogen fuel, a safe storage space must be selected. Here, we use the MCDM ideas by use of proposed NWDHFV method is to select the appropriate hydrogen underground storage location.  2019 Hydrogen Energy Publications LLC</text>
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                <text>Narayanamoorthy S.; Ramya L.; Baleanu D.; Kureethara J.V.; Annapoorani V.</text>
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                <text>International Journal of Hydrogen Energy, Vol-44, No. 54, pp. 28874-28892.</text>
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                <text>ISSN: 3603199; CODEN: IJHED</text>
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                <text>Narayanamoorthy S., Department of Mathematics, Bharathiar University, Coimbatore, 641 046, India; Ramya L., Department of Mathematics, Bharathiar University, Coimbatore, 641 046, India; Baleanu D., Department of Mathematics, Cankaya University, Balgat, Ankara, Turkey, Institute of Space Sciences, Magurele-Bucharest, Romania; Kureethara J.V., CHRIST (Deemed to be University), Bangalore, India; Annapoorani V., Department of Mathematics, Bharathiar University, Coimbatore, 641 046, India</text>
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                <text>The use of latent heat storage capacity from phase change material (PCM) to regulate asphalt pavement temperature is an innovative way to mitigate temperature-related pavement distresses, such as thermal cracking and rutting. In this review, a detailed discussion on the classification and incorporation methods of PCM in asphalt mixture is presented. Further, the physical and chemical performances of PCM modified asphalts were reviewed, followed by their thermal and mechanical properties. It was observed that temperature related performance of asphalt mixtures can be well controlled in the phase change temperature range. Polyethylene glycol (PEG) and n-Tetradecane have been generally used to regulate the high and low temperatures of asphalt pavement, respectively. Mixed results were obtained on rheological properties of binders with PCMs as well as on road performance properties of asphalt mixtures with PCMs. It is important to note that PCMs with high latent heat and thermal conductivity are preferred for effective thermal regulation.  2020 Elsevier Ltd</text>
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                <text>Chen Y., School of Highway, Chang'an University, Middle Section of NanErhuan Road, Xi'an, 710064, China; Wang H., School of Highway, Chang'an University, Middle Section of NanErhuan Road, Xi'an, 710064, China; You Z., Civil and Environmental Engineering, Michigan Technological University, Houghton, 49931, MI, United States; Hossiney N., Department of Civil Engineering, Christ University, Kumbalgodu, Bengaluru, 560074, Karnataka, India</text>
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          <element elementId="50">
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            <elementTextContainer>
              <elementText elementTextId="166233">
                <text>Application of Regression Analysis of Student Failure Rate</text>
              </elementText>
            </elementTextContainer>
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          <element elementId="49">
            <name>Subject</name>
            <description>The topic of the resource</description>
            <elementTextContainer>
              <elementText elementTextId="166234">
                <text>Machine learning; Regression; Student failure</text>
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            </elementTextContainer>
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                <text>The education sector has been rapidly growing and is currently facing several challenges. One such challenge is identifying students who are at risk of failing, as this can help educators provide targeted interventions to improve student performance. Machine learning models have been developed to predict the probability of student failure based on various student performance metrics to address this issue. In this paper, we present a regression-based model that predicts the probability of student failure using student performance metrics such as attendance, previous academic performance, and demographic information. The model was trained on a dataset of students and achieved high accuracy in predicting the probability of student failure. While the model performs well in predicting the probability of student failure, there is always room for improvement. Possible enhancements to the model include feature engineering, ensemble learning, hyperparameter tuning, deep learning, and interpretability. These enhancements can improve the models accuracy, stability, and transparency, leading to better predictions and targeted interventions for at-risk students.  The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.</text>
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            <elementTextContainer>
              <elementText elementTextId="166236">
                <text>Sybol S.S.; Srivasatava S.; Sharma S.</text>
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          <element elementId="48">
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            <elementTextContainer>
              <elementText elementTextId="166237">
                <text>Lecture Notes in Networks and Systems, Vol-994 LNNS, pp. 897-910.</text>
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            <name>Publisher</name>
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              <elementText elementTextId="166238">
                <text>Springer Science and Business Media Deutschland GmbH</text>
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            <description>A point or period of time associated with an event in the lifecycle of the resource</description>
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                <text>&lt;a href="https://doi.org/10.1007/978-981-97-3180-0_60" target="_blank" rel="noreferrer noopener"&gt;https://doi.org/10.1007/978-981-97-3180-0_60&lt;/a&gt;
&lt;br /&gt;&lt;br /&gt;&lt;a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85200688709&amp;amp;doi=10.1007%2F978-981-97-3180-0_60&amp;amp;partnerID=40&amp;amp;md5=d269e62e262b31349558f23eba348da6" target="_blank" rel="noreferrer noopener"&gt;https://www.scopus.com/inward/record.uri?eid=2-s2.0-85200688709&amp;amp;doi=10.1007%2f978-981-97-3180-0_60&amp;amp;partnerID=40&amp;amp;md5=d269e62e262b31349558f23eba348da6&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="166241">
                <text>Restricted Access</text>
              </elementText>
            </elementTextContainer>
          </element>
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                <text>ISSN: 23673370; ISBN: 978-981973179-4</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="166244">
                <text>English</text>
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                <text>Sybol S.S., CHRIST (Deemed to be University), Delhi NCR, India; Srivasatava S., CHRIST (Deemed to be University), Delhi NCR, India; Sharma S., Uttaranchal University, Dehradun, India</text>
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            <elementTextContainer>
              <elementText elementTextId="116940">
                <text>Application of response surface methodology to optimize lead(Ii) ion adsorption by activated carbon fabricated from de oiled soya</text>
              </elementText>
            </elementTextContainer>
          </element>
          <element elementId="49">
            <name>Subject</name>
            <description>The topic of the resource</description>
            <elementTextContainer>
              <elementText elementTextId="116941">
                <text>Adsorption; Adsorption capacity; Central composite design; De-oiled soya; Response surface methodology</text>
              </elementText>
            </elementTextContainer>
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              <elementText elementTextId="116942">
                <text>Lead(II) ion a heavy metal is known for its toxicity. An initiative has been taken in this study, to adsorb toxic lead(II) ion using activated carbon made of de oiled soya, by an aqueous solution through batch adsorption methodology. Adsorption process variables such as adsorbent dose, contact time, solution pH, and lead(II) ion concentration were optimized by central composite design (CCD). To find the interaction between process variables, response surface plots were utilized using response surface methodology. Design-Expert software version 7 was resorted to in this experiment. It was observed that the components from the analysis of variance of the CCD revealed that the selective process independent variables had significant control over adsorption capacity. Desirability function was used to appraise the factors and response in adsorption experiments to find an optimum point where the preferred adsorption could be obtained. Adsorption process with the application of activated carbon developed from de-oiled soya meritoriously removed lead(II) ion with an optimum adsorption capacity of 26.279 mg/g for an initial concentration of lead(II) at 60 mg/L.  2021 Desalination Publications. All rights reserved.</text>
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              <elementText elementTextId="116943">
                <text>Sujatha S.; Sivarethinamohan R.</text>
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              <elementText elementTextId="116944">
                <text>Desalination and Water Treatment, Vol-226, pp. 242-250.</text>
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                <text>Desalination Publications</text>
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                <text>2021-01-01</text>
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                <text>&lt;a href="https://doi.org/10.5004/dwt.2021.27240" target="_blank" rel="noreferrer noopener"&gt;https://doi.org/10.5004/dwt.2021.27240&lt;/a&gt;
&lt;br /&gt;&lt;br /&gt;&lt;a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85110310388&amp;amp;doi=10.5004%2Fdwt.2021.27240&amp;amp;partnerID=40&amp;amp;md5=94fd398aa2787e9e5c25d3a5ef5e9959" target="_blank" rel="noreferrer noopener"&gt;https://www.scopus.com/inward/record.uri?eid=2-s2.0-85110310388&amp;amp;doi=10.5004%2fdwt.2021.27240&amp;amp;partnerID=40&amp;amp;md5=94fd398aa2787e9e5c25d3a5ef5e9959&lt;/a&gt;</text>
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            <description>Information about rights held in and over the resource</description>
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              <elementText elementTextId="116948">
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                <text>ISSN: 19443994</text>
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                <text>Online</text>
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            <description>A language of the resource</description>
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              <elementText elementTextId="116951">
                <text>English</text>
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              <elementText elementTextId="116953">
                <text>Sujatha S., K. Ramakrishnan College of Technology, Tiruchirapalli, Tamilnadu, India; Sivarethinamohan R., Christ (Deemed to be University), Bangalore, Karnataka, India</text>
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              <elementTextContainer>
                <elementText elementTextId="3139">
                  <text>Faculty Publications</text>
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      <name>Book Chapter</name>
      <description>Faculty Publications- Book Chapter</description>
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              <elementText elementTextId="286746">
                <text>Jha, Babita; DavDevanesan, Mark; Jha, Deepak; Giri, Pratibha</text>
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          <element elementId="50">
            <name>Title</name>
            <description>A name given to the resource</description>
            <elementTextContainer>
              <elementText elementTextId="286747">
                <text>Application of Sensory and Neuromarketing to Sustain in Emerging Economies</text>
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            </elementTextContainer>
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                <text>01-01-2025</text>
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            <elementTextContainer>
              <elementText elementTextId="286749">
                <text>Adapting Global Communication and Marketing Strategies to Generative AI;pp.23-54</text>
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                <text>Neuromarketing involves the application of neuroscience in developing suitable marketing campaigns or messages (Morin, 2011). Neuromarketing involves understanding the schema of associations in the consumer's mind, through which a brand can subconsciously stimulate brand recall and drive purchase behavior (Briesemeister &amp;amp; Selmer, 2022). Use of neuromarketing tools can help build associations based on existing knowledge/ semantic memory or through past experiences/episodic memory (Tulving, 1972). Furthermore, sensory marketing can also create subconscious cues to episodic memory (Krishna, 2012) as sensory cues such as touch, taste, and smell are already encoded in a consumer's long- term memory. In the customer journey driven by traditional marketing messages, decision making is driven by reasoning of known facts, but sensory marketing messages can drive implicit decision making based on impulses (Friese et al., 2006).  2026, IGI Global Scientific Publishing. All rights reserved.</text>
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                <text>The application of machine learning to production is becoming a chief objective for businesses all around the world. Smart product-service systems enable digital business model innovation by merging digitized product and service components. The life cycle that comes with the realization of customer value is a critical component of these industrial solutions and manufacturing industry is undergoing significant changes as a result of digitalization and automation. As a result, smart services, or digital services that generate value from product data, are gaining popularity. Customers may now contribute in greater numbers in product design during the design process. Giving more people access, on the other hand, increases the security vulnerabilities associated with cloud manufacturing. Smart Manufacturing is one of the technology-driven approach to manufacturing that uses network-connected machines to monitor the process. Smart manufacturing has the ability to be used in a variety of ways, including putting sensors in manufacturing machines and collecting data on their operating state and performance. Thus, the main purpose here is to find ways to improve and automate production performance. This conceptual paper attempts to give a view of how a smart intelligence system may be used in business and how individuals and organizations can produce value.   2023 Author(s).</text>
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                <text>Parashar B., Department of Business School, Vit Bhopal University, Madhya Pradesh, Bhopal, India; Chaurasia A., Department of Commerce, Christ University, Ghaziabad, India; Sharma R., Department of Management Studies, Swami Rama Himalayan University, Uttarakhand, Dehradun, India</text>
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                <text>The use of herbal tablets has been rapidly growing and significant research work is being carried out worldwide with the goal to reap the benefits of the many useful plants that are available with medicinal values. Many of these plants go largely underutilized either due to lack of information on not only just the medicinal properties but simple and effective extraction methodologies as well, without sacrificing the properties of the extracts. Once extracted, the concentrates also must be converted into a suitable form that can be loaded in a capsule etc., ready to be consumed. While there many process methodologies being used worldwide to extract the useful resources from the plant, focus also must be on the process methodology that is being practiced to convert the extract (liquid or semi solid) into a solid free flowing powder form. Thus, in an herbal tablet, there many factors concerned with the manufacturing. They are (i) Identifying the most suitable plant for a particular immunity boosting purpose (ii) extraction of the useful contents, mostly in a liquid or slurry form (iii) transform the extract into a user-friendly product such as powder and finally (iv) encapsulation of the powder for ease of human consumption. This paper brings in a review of the several useful plants available around us across the world. In addition, the paper also highlights the suitable experimental results of the usefulness of spray-drying technology, which is a highly versatile process methodology to transform the extracts into free-flowing powder.  Published under licence by IOP Publishing Ltd.</text>
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                <text>Thampi A.S.; Kumar S.R.; Gowtham Sanjai S.; Ramaswamy P.</text>
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                <text>IOP Conference Series: Materials Science and Engineering, Vol-988, No. 1</text>
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                <text>Thampi A.S., Department of Mechanical and Automobile Engineering, CHRIST (Deemed to Be University), Bangalore, India; Kumar S.R., Department of Mechanical and Automobile Engineering, CHRIST (Deemed to Be University), Bangalore, India; Gowtham Sanjai S., Department of Mechanical and Automobile Engineering, CHRIST (Deemed to Be University), Bangalore, India; Ramaswamy P., Department of Mechanical and Automobile Engineering, CHRIST (Deemed to Be University), Bangalore, India</text>
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                <text>Application of SWOT And Breakeven Analysis in Strategic Decision-Making: A Quantitative Approach</text>
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                <text>This chapter investigates the extent to which the conjunction of SWOT analysis and quantitative analysis (such as breakeven analysis) enhances the quality of strategic decision-making. In addition to the usual limitations of a qualitative SWOT analysis, the paper embraces financial feasibility tools and promotes a multi-criteria decisionmaking approach. After a brief initial chapter noting the rationale for combining qualitative and quantitative analyses in the strategic planning process, the chapter continues with the theoretical foundation relating to strategic decision-making models (including SWOT, breakeven analysis, Analytic Hierarchy Process (AHP), Hybrid SWOT-AHP, and Quantitative Strategic Planning Matrix (QSPM). A casestudy approach with examples from both the private and public sectors illustrates the practical application of these tools through numerical data tables, breakeven calculations, and decision matrices.  2026 by IGI Global Scientific Publishing.</text>
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                <text>Application of the Taguchi method and RSM for process parameter optimization in AWSJ machining of CFRP composite-based orthopedic implants</text>
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                <text>abrasive water suspension jet machining; carbon fiber-reinforced polymer composite; orthopedic implant; response surface methodology; Taguchi method</text>
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                <text>Abrasive water suspension jet (AWSJ) machining on carbon fiber-reinforced polymer (CFRP) composite-based orthopedic implants yielded insightful results based on experimental data and subsequent statistical validations. Underwater AWSJ cutting consistently outperformed free air cutting, with numerical findings demonstrating its superiority. For instance, at #100 abrasive size and 5 mm standoff distance (SOD), the material removal rate (MRR) peaked at 2.44 g/min with a kerf width of 0.89 mm and a surface roughness (SR) of 9.25 ?m. Notably, the increase in abrasive size correlated with higher MRR values, such as achieving 2.15 g/min at #120 grit and 3 mm SOD. Furthermore, optimization techniques like the Taguchi method and response surface methodology (RSM) were applied to refine machining parameters. These methodologies enhanced MRR, exemplified by achieving 2.10 g/min with #120 abrasive size and 5 mm SOD in underwater cutting conditions. The research explored the impact of key process parameters, namely, the speed, feed, and SOD on the MRR, kerf width, and SR in both free air cutting and underwater cutting conditions, which is one of the novel research endeavors in the domain of abrasive jet machining of composites.  2024 the author(s)</text>
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                <text>Kodandappa R.; Nagaraja S.; Chowdappa M.M.; Krishnappa M.; Poornima G.S.; Ammarullah M.I.</text>
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                <text>Open Engineering, Vol-14, No. 1</text>
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                <text>Kodandappa R., Department of Mechanical and Automobile Engineering, School of Engineering and Technology, CHRIST (Deemed to be University), Karnataka, Bangalore, 560074, India; Nagaraja S., Department of Mechanical Engineering, MVJ College of Engineering, Karnataka, Bangalore, 560067, India; Chowdappa M.M., Department of Mechanical Engineering, Bangalore Institute of Technology, Karnataka, Bangalore, 560004, India; Krishnappa M., Department of Mechanical Engineering, S.E.A. College of Engineering and Technology, Karnataka, Bangalore, 560049, India; Poornima G.S., Department of Chemistry, New Horizon College of Engineering, Karnataka, Bangalore, 560103, India; Ammarullah M.I., Department of Mechanical Engineering, Faculty of Engineering, Universitas Diponegoro, Central Java, Semarang, 50275, Indonesia, Undip Biomechanics Engineering &amp;amp; Research Centre (UBM-ERC), Universitas Diponegoro, Central Java, Semarang, 50275, Indonesia</text>
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                <text>This study tests the conceptual model theorizing democratic leadership, servant leadership, learning organization, and performance of manufacturing industries using Structural Equation Modeling (SEM). The impact of democratic and servant leadership on learning organizations and the performance of manufacturing industries in Ethiopia is analyzed, and the role of learning organizations as a mediating variable is examined. Confirmatory Factor Analysis was performed, which includes a well-established Chi-square test, the Chi-square ratio to degrees of freedom, the goodness-of-fit index, the TuckerLewis index, the comparative fit index, the adjusted goodness-of-fit, and the root mean square error of approximation. Further, the performance of manufacturing industries has been assessed using XAI which helps in having a higher clarity on understanding the complexities in production. Based on linear regression, two methods SHAP and LIME have been used for precise predictions and forecast for future production plans in the manufacturing industry. This research contributes to the existing body of knowledge by dissecting the nuanced relationships between the two leadership styles and learning organization and further, their implications for an organizations performance. The findings of the study would provide insights for policymakers and practitioners to improve the performance of manufacturing industries.  The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.</text>
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                <text>Lecture Notes in Networks and Systems, Vol-1116 LNNS, pp. 123-138.</text>
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                <text>Badada B., Kalasalingam Academy of Research and Education (Deemed to be University), Tamil Nadu, Krishnankoil, Srivilliputhur, 626126, India; Delina G., Kalasalingam Academy of Research and Education (Deemed to be University), Tamil Nadu, Krishnankoil, Srivilliputhur, 626126, India; Krishnaraj R., Dambi Dolo University, Dembi Dollo, 000260, Ethiopia; Thiruthuvanathan M.M., Christ (Deemed to be University), Karnataka, Bengaluru, 560029, India</text>
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