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                <text>Soundarabai P.B., Department of Computer Science, Christ University, Bangalore, Hosur Main Road, India; Thriveni J., Department of Computer Science and Engineering, University Visvesvaraya College of Engineering, Bangalore University, Bangalore, India; Manjunatha H.C., Department of Computer Science and Engineering, University Visvesvaraya College of Engineering, Bangalore University, Bangalore, India; Venugopal K.R., Department of Computer Science and Engineering, University Visvesvaraya College of Engineering, Bangalore University, Bangalore, India; Patnaik L.M., Indian Institute of Science, Bangalore, India</text>
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                <text>Nexus Between Interest Rate Risk and Economic Value of Equity of Banks</text>
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                <text>This analytical study looks to provide recommendations to the banking sector on different policies and regulations by examining certain aspects of the Basel III accord, which was designed to manage specific operational, capital and market risks of banks. A review of extant literature reveals that only a few papers have been written on simulation-based approaches, using basis and re-pricing risks. We look to connect this as a source while attempting to define and measure the impact of interest rate risk (IRR) on the economic value of equity (EVE) of banks. We propose to use the driverdriven method, wherein interest rate shocks are derived through prime lending rate (PLR) for the period of 20162019 in the context of India. Monte Carlo Simulation and OLS regression was performed to predict the IRR; Granger causality was used to examine the cause and effect relationship; the impulse response function (IRF) was used for sensitivity analysis; and the vector error correction model (VECM) technique was used for co-integrating relationships. Notably, the EVE movement caused due to shocks in interest rates had to be traced as it envisages probable EVE losses. Importantly, our study is among the first few to show the relationship between IRR and EVE of banks, especially after the deregulation of Indian banking sector.  2021 International Management Institute, New Delhi.</text>
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                <text>Soundariya G., Christ University, Bangalore, Karnataka, India; David T.A., Christ University, Bangalore, Karnataka, India; Suresh G., Christ University, Bangalore, Karnataka, India</text>
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                <text>The convergence of smart product packaging and IoT marketing has transformed commerce. This study examines the fundamental ramifications of convergence and its potential to improve customer engagement. Our research shows the transformational potential of these technologies via quantitative and qualitative analyses.Smart packaging outperforms non-smart items, giving firms an advantage, according to quantitative data. Regression and correlation analysis confirm IoT data-customer interaction. Our study also emphasizes ethical data acquisition, which supports data privacy and consumer protection.Consumers may expect personalized experiences, transparency, and real-time feedback from this technology transformation. Smart product packaging and IoT marketing enable readers to make educated decisions and influence product development to meet changing consumer expectations.This research allows academics to study the ideas and models that affect consumer engagement. Data privacy and consumer protection may inform IoT marketing and smart device packaging policy.Our research guides organizations and customers towards better customer interactions, data-driven decision-making, and ethical data practices in this changing age. The future promises revolutionary customer contact.  2023 IEEE.</text>
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                <text>Soundarraj P.; Aarif M.; Gangadharan S.; Naqvi S.R.; Assihalaf N.K.; Salih Mahdi A.</text>
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                <text>Soundarraj P., Christ University, Pune, School of Business &amp;amp; Management, Maharashtra, Pune, 412112, India; Aarif M., CAG Study Center, UP, Noida, India; Gangadharan S., St. Joseph's College of Engineering, Department of Management Studies, Chennai, India; Naqvi S.R., Amity University, Department of Human Resource and Organizational Behaviour, Amity Business School, Up, Noida, India; Assihalaf N.K., National University of Science and Technology, DhiQar, Iraq; Salih Mahdi A., AL-Nisour University College, Department of Medical Laboratories Technology, Baghdad, Iraq</text>
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                <text>Internet banking has revolutionised the way customers interact with their banks, providing them with convenient access to a wide range of financial services from the comfort of their homes or mobile devices. Customer satisfaction the success of an endeavour is contingent upon a vital component internet banking Service provision, as it pertains directly impacts customer retention and loyalty. This research explores the application of artificial intelligence (AI) techniques, specifically random forest and convolutional neural networks (CNN), to prioritise the factors that affect customer satisfaction in internet banking. The study begins with data collection from a diverse sample of internet banking customers, including demographic information, transaction history, and customer feedback. These may include the ease of navigation, the response time of the platform, and the level of trust in the bank's security measures. Furthermore, convolutional neural networks (CNN) are utilised to analyse unstructured data such as customer feedback and reviews. By applying natural language processing techniques, CNN s extract sentiment and topic information from customer comments. This approach can ultimately lead to improved customer retention and loyalty, ensuring the long-term success and competitiveness of internet banking platforms. In conclusion, this study showcases the power of AI, specifically Random Forest and CNN, in prioritising factors affecting customer satisfaction in internet banking. It highlights the significance of using both quantitative and qualitative investigations in order to attain a comprehensive comprehension of customer sentiments and preferences in the digital banking landscape.   2024 IEEE.</text>
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                <text>Soundarraj P.L.; Javaraj P.; Meetna S.; Elaiyaraja A.; Naqvi S.R.; Gamadia R.</text>
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                <text>Proceedings of 9th International Conference on Science, Technology, Engineering and Mathematics: The Role of Emerging Technologies in Digital Transformation, ICONSTEM 2024</text>
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                <text>&lt;a href="https://doi.org/10.1109/ICONSTEM60960.2024.10568595" target="_blank" rel="noreferrer noopener"&gt;https://doi.org/10.1109/ICONSTEM60960.2024.10568595&lt;/a&gt;
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                <text>Soundarraj P.L., School of Business And Management, Christ University, Maharashtra, Pune, 412112, India; Javaraj P., Sri Ramakrishna College of Arts And Science, Department of B. Com It, Coimbatore, India; Meetna S., Amrita School of Business, Coimbatore, India; Elaiyaraja A., PanimalarEngineering College, Department of Management Studies, Chennai, India; Naqvi S.R., Amity Business School, Amity University, Department of Human Resource And Organizational Behaviour, Up, Noida, India; Gamadia R., C K Shah Vijapurwala Institute of Management, Department of Management, Gujarat, Vadodara, India</text>
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                <text>Stress Management among Employees in Information Technology Sector Using Deep Learning</text>
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                <text>Deep Learning; Employees; Information Technology; Management; Stress</text>
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                <text>Information technology is one of the areas in India that is developing the quickest India's information technology (IT) administrations industry has become more merciless. The information technology area has been managing additional difficult issues like specialized development, administration enhancement, and worldwide overhauling starting from the beginning of this long period. Along these lines, it is unimaginable for everybody to adjust to the moving difficulties they experience in the field of information technology, which causes stress. Stress is something that individuals battle with for most of their lives. Albeit the information technology (IT) industry is notable for its hazardous turn of events and development, it is likewise portrayed by high worker burnout and stress levels. This theoretical proposes an original strategy for overseeing stress in the IT business that utilizes deep learning methods. This study utilizes deep learning calculations to expect, distinguish, and decrease stress makes all together location the earnest issue of stress among IT experts. The principal objective is to make a shrewd framework that can help organizations proactively recognize stress-related issues in their labor force and proposition specific cures.   2024 IEEE.</text>
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                <text>Soundarraj P.L.; Pelleti S.; John E.P.; Gadgil A.A.; Sehrawat A.; Yadav L.K.</text>
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                <text>A novel coronavirus (COVID-19), belonging to a family of severe acute respiratory syndrome coronavirus 2 (SARs-CoV-2), was identified in Wuhan city, Hubei, China, in November 2019. The disease had already infected more than 681.529665 million people as of March 13, 2023. Hence, early detection and diagnosis of COVID-19 are essential. For this purpose, radiologists use medical images such as X-ray and computed tomography (CT) images for the diagnosis of COVID-19. It is very difficult for researchers to help radiologists to do automatic diagnoses by using traditional image processing methods. Therefore, a novel artificial intelligence (AI)-based deep learning model to detect COVID-19 from chest X-ray images is proposed. The proposed work uses a wavelet and stacked deep learning architecture (ResNet50, VGG19, Xception, and DarkNet19) named WavStaCovNet-19 to detect COVID-19 from chest X-ray images automatically. The proposed work has been tested on two publicly available datasets and achieved an accuracy of 94.24% and 96.10% on 4 classes and 3 classes, respectively. From the experimental results, we believe that the proposed work can surely be useful in the healthcare domain to detect COVID-19 with less time and cost, and with higher accuracy.  2023 Elsevier Ltd</text>
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                <text>Soundrapandiyan R., School of Computer Science and Engineering, Vellore Institute of Technology, Vellore, 632014, India; Naidu H., ServiceNow, Telangana, Hyderabad, 500081, India; Karuppiah M., School of Computer Science and Engineering &amp;amp; Information Science, Presidency University, Karnataka, Bengaluru, 560064, India; Maheswari M., Department of Computer Science and Engineering, Sathyabama Institute of Science and Technology, Chennai, 600119, India; Poonia R.C., Department of Computer Science, CHRIST (Deemed to be University), Karnataka, Bengaluru, 560029, India</text>
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                <text>In preparation of the changing environment of 5th wave (5G) and prospective networks of cells, this study explores new methods to meet challenges that result from the erratic character of the communication medium. Traditionally viewed as a chance factor, the relationship between broadcast radio waves with surrounding factors lowers signal quality in modern times of wireless communications. This paper performs a full literature review on customizable autonomous surfaces (RISs) alongside their uses, stressing the chance for network managers to control radio wave features and minimize environmental spread problems. RISs allow effective control over waveform parameters, including the amplitude, phase, number, and polarization, that without needing complex encoder, decoder, or radio wave processing methods. Leveraging technical developments, metasurfaces, reflectarrays, phase shifts, and liquid crystals appear as potential options for RIS application, placing them as pioneers in the realization of 5G as well as subsequent networks. The study dives into current actions in the RIS-operated mobile phone network area and covers core research issues that deserve exploration to feed unlocking the full promise of RISs at wireless communication networks.  2024 IEEE.</text>
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                <text>2024 4th International Conference on Advance Computing and Innovative Technologies in Engineering, ICACITE 2024, pp. 229-234.</text>
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                <text>Cultural Expression of Anxiety Symptoms in Kannada Language: A Qualitative Study</text>
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                <text>Anxiety; Culture; Culture-specific tool; Qualitative; Tool development</text>
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                <text>Background: In anxiety disorders, culture is important in symptom presentation and help-seeking. Most tools for anxiety disorders are not validated in India and thus might not capture culture-specific aspects of anxiety. This study aims to identify and generate culturally specific terms to describe symptoms of anxiety as part of the development of the Kannada version of the Panic and Anxiety National Indian Questionnaire (PANIQ). The PANIQ is a tool under development to identify anxiety and panic in Indian settings. Methods: This study used qualitative methods like focus group discussions (FGDs) and in-depth interviews (IDIs) to identify and generate items related to anxiety and panic in Kannada from stakeholders like individuals with anxiety disorders, their caregivers, healthcare workers, and mental health professionals who treat individuals with anxiety and panic disorders. Five FGDs (n = 28), one triad (n = 3), and 34 IDIs (n = 34) were conducted. Results: The mean age of the participants was 38.9 (standard deviation: 12.28) years; 57.1% were from rural areas. We generated 615 Kannada items. These were classified into 21 domains and facets. Items in domains like Somatic symptoms, Fear, and Impairment in day-to-day life were higher than those noted in existing tools for anxiety that focus more on cognitive symptoms of anxiety. Conclusions: This study generated culturally specific items of anxiety through a qualitative process of tool development incorporating subjective experiences of persons with anxiety disorders and other stakeholders. This is among the first steps toward the development of PANIQ.  2022 The Author(s).</text>
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                <text>Sowmya H.R.; Ghani S.; Sreedaran P.; Sahu M.K.; Mysore A.; Sharan P.</text>
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              <elementText elementTextId="94161">
                <text>Indian Journal of Psychological Medicine, Vol-45, No. 5, pp. 496-502.</text>
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              <elementText elementTextId="94162">
                <text>SAGE Publications Ltd</text>
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                <text>2023-01-01</text>
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                <text>&lt;a href="https://doi.org/10.1177/02537176221140742" target="_blank" rel="noreferrer noopener"&gt;https://doi.org/10.1177/02537176221140742&lt;/a&gt;
&lt;br /&gt;&lt;br /&gt;&lt;a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85145209847&amp;amp;doi=10.1177%2F02537176221140742&amp;amp;partnerID=40&amp;amp;md5=55b017545f2646c5e790ce090c1f47fa" target="_blank" rel="noreferrer noopener"&gt;https://www.scopus.com/inward/record.uri?eid=2-s2.0-85145209847&amp;amp;doi=10.1177%2f02537176221140742&amp;amp;partnerID=40&amp;amp;md5=55b017545f2646c5e790ce090c1f47fa&lt;/a&gt;</text>
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            <description>Information about rights held in and over the resource</description>
            <elementTextContainer>
              <elementText elementTextId="94165">
                <text>All Open Access; Gold Open Access; Green Open Access</text>
              </elementText>
            </elementTextContainer>
          </element>
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              <elementText elementTextId="94166">
                <text>ISSN: 2537176</text>
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              <elementText elementTextId="94167">
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              <elementText elementTextId="94168">
                <text>English</text>
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            <description>The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant</description>
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              <elementText elementTextId="94170">
                <text>Sowmya H.R., St. Johns Research Institute, Karnataka, Bangalore, India; Ghani S., Christ University, Karnataka, Bangalore, India; Sreedaran P., Dept. of Psychiatry, St. Johns Medical Hospital, Karnataka, Bangalore, India; Sahu M.K., Dept. of Psychiatry, Pt. J.N.M. Medical College, Karnataka, Raipur, India; Mysore A., Dept. of Psychiatry, St. Johns Medical Hospital, Karnataka, Bangalore, India; Sharan P., Dept. of Psychiatry, All India Institute of Medical Sciences, New Delhi, India</text>
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                  <text>Conference Papers</text>
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          <element elementId="50">
            <name>Title</name>
            <description>A name given to the resource</description>
            <elementTextContainer>
              <elementText elementTextId="187070">
                <text>Framework for Controlling Interference and Power Consumption on Femto-Cells In-Wireless System</text>
              </elementText>
            </elementTextContainer>
          </element>
          <element elementId="49">
            <name>Subject</name>
            <description>The topic of the resource</description>
            <elementTextContainer>
              <elementText elementTextId="187071">
                <text>Cellular network; Coverage; Energy efficiency; Femto-cells; Power consumption</text>
              </elementText>
            </elementTextContainer>
          </element>
          <element elementId="41">
            <name>Description</name>
            <description>An account of the resource</description>
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              <elementText elementTextId="187072">
                <text>Utilization of femto-cells is one of the cost effective solution to increase the internal network connectivity and coverage. However, there are various impediment in achieving so which has caused a consistent research work evolving out with solution. Review of existing literature shows that maximum focus was given for energy problems in cellular network and not much on problems that roots out from interference. Therefore, the proposed system has presented a very simple and novel approach where the problems associated with interference and energy in using large groups of femto-cells are addressed. Adopting analytical research methodology, the proposed model offers on-demand utilization of the selective femto-cells on the basis of the traffic demands. The study outcome shows that proposed system offers better performance in contrast to existing approach.  Springer Nature Switzerland AG 2019.</text>
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          <element elementId="39">
            <name>Creator</name>
            <description>An entity primarily responsible for making the resource</description>
            <elementTextContainer>
              <elementText elementTextId="187073">
                <text>Sowmya Naik P.T.; Narasimha Murthy K.N.</text>
              </elementText>
            </elementTextContainer>
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          <element elementId="48">
            <name>Source</name>
            <description>A related resource from which the described resource is derived</description>
            <elementTextContainer>
              <elementText elementTextId="187074">
                <text>Advances in Intelligent Systems and Computing, Vol-1046, pp. 199-208.</text>
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            </elementTextContainer>
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            <name>Publisher</name>
            <description>An entity responsible for making the resource available</description>
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              <elementText elementTextId="187075">
                <text>Springer</text>
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            <name>Date</name>
            <description>A point or period of time associated with an event in the lifecycle of the resource</description>
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              <elementText elementTextId="187076">
                <text>2019-01-01</text>
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                <text>&lt;a href="https://doi.org/10.1007/978-3-030-30329-7_19" target="_blank" rel="noreferrer noopener"&gt;https://doi.org/10.1007/978-3-030-30329-7_19&lt;/a&gt;
&lt;br /&gt;&lt;br /&gt;&lt;a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85075606968&amp;amp;doi=10.1007%2F978-3-030-30329-7_19&amp;amp;partnerID=40&amp;amp;md5=9f80bba8cada0e90e5ae29d542bcfb02" target="_blank" rel="noreferrer noopener"&gt;https://www.scopus.com/inward/record.uri?eid=2-s2.0-85075606968&amp;amp;doi=10.1007%2f978-3-030-30329-7_19&amp;amp;partnerID=40&amp;amp;md5=9f80bba8cada0e90e5ae29d542bcfb02&lt;/a&gt;</text>
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            </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="187078">
                <text>Restricted Access</text>
              </elementText>
            </elementTextContainer>
          </element>
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            <description>A related resource</description>
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              <elementText elementTextId="187079">
                <text>ISSN: 21945357; ISBN: 978-303030328-0</text>
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          <element elementId="42">
            <name>Format</name>
            <description>The file format, physical medium, or dimensions of the resource</description>
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              <elementText elementTextId="187080">
                <text>Online</text>
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            <description>A language of the resource</description>
            <elementTextContainer>
              <elementText elementTextId="187081">
                <text>English</text>
              </elementText>
            </elementTextContainer>
          </element>
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            <name>Type</name>
            <description>The nature or genre of the resource</description>
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                <text>Conference paper</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="187083">
                <text>Sowmya Naik P.T., Department of Computer Science and Engineering, City Engineering College, Bengaluru, India; Narasimha Murthy K.N., Faculty of Engineering, Christ (Deemed to be University), Bengaluru, India</text>
              </elementText>
            </elementTextContainer>
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          <elementContainer>
            <element elementId="50">
              <name>Title</name>
              <description>A name given to the resource</description>
              <elementTextContainer>
                <elementText elementTextId="51377">
                  <text>Conference Papers</text>
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              </elementTextContainer>
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      <description>Faculty Publications- Conference Papers</description>
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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="175643">
                <text>Systematic Literature Review on Industry Revolution 4.0 to Predict Maintenance and Life Time of Machines in Manufacturing Industry</text>
              </elementText>
            </elementTextContainer>
          </element>
          <element elementId="49">
            <name>Subject</name>
            <description>The topic of the resource</description>
            <elementTextContainer>
              <elementText elementTextId="175644">
                <text>Artificial Intelligence; CBM; Industry 4.0; PHM; Predictive maintenance</text>
              </elementText>
            </elementTextContainer>
          </element>
          <element elementId="41">
            <name>Description</name>
            <description>An account of the resource</description>
            <elementTextContainer>
              <elementText elementTextId="175645">
                <text>Industry 4.0 is digitized revolution for manufacturers or companies where in new technologies are imbibed into their production system for their day-to-day operations or activities. So that their overall economic needs and efficiency can be improved. In manufacturing industry maintenance of the equipment is the key concern. When the equipment requires maintenance, it has to be done at the earliest, failing which companies will have to face consequences in terms of loss of customers, time and money. Solution is provided to this problem in terms of a technique called predictive maintenance. The content of the article focuses on different predictive maintenance strategies, which help manufacturers to forecast if equipment/component will fail so that its maintenance and repair can be scheduled exactly before the component fails. The results will be useful for manufacturers to understand the importance of industry 4.0 for predictive maintenance.  2023 IEEE.</text>
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          <element elementId="39">
            <name>Creator</name>
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            <elementTextContainer>
              <elementText elementTextId="175646">
                <text>Sowmya P.; Ravichandran S.K.; Rakshitha</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="175647">
                <text>Proceedings of the 3rd International Conference on Artificial Intelligence and Smart Energy, ICAIS 2023, pp. 194-199.</text>
              </elementText>
            </elementTextContainer>
          </element>
          <element elementId="45">
            <name>Publisher</name>
            <description>An entity responsible for making the resource available</description>
            <elementTextContainer>
              <elementText elementTextId="175648">
                <text>Institute of Electrical and Electronics Engineers Inc.</text>
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            </elementTextContainer>
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            <name>Date</name>
            <description>A point or period of time associated with an event in the lifecycle of the resource</description>
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              <elementText elementTextId="175649">
                <text>2023-01-01</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/10.1109/ICAIS56108.2023.10073753" target="_blank" rel="noreferrer noopener"&gt;https://doi.org/10.1109/ICAIS56108.2023.10073753&lt;/a&gt;
&lt;br /&gt;&lt;br /&gt;&lt;a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85152452157&amp;amp;doi=10.1109%2FICAIS56108.2023.10073753&amp;amp;partnerID=40&amp;amp;md5=95e003f0406753caa9a21f7d7086d49e" target="_blank" rel="noreferrer noopener"&gt;https://www.scopus.com/inward/record.uri?eid=2-s2.0-85152452157&amp;amp;doi=10.1109%2fICAIS56108.2023.10073753&amp;amp;partnerID=40&amp;amp;md5=95e003f0406753caa9a21f7d7086d49e&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="175651">
                <text>Restricted Access</text>
              </elementText>
            </elementTextContainer>
          </element>
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                <text>ISBN: 978-166546216-7</text>
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            <description>The file format, physical medium, or dimensions of the resource</description>
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              <elementText elementTextId="175653">
                <text>Online</text>
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            <name>Language</name>
            <description>A language of the resource</description>
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              <elementText elementTextId="175654">
                <text>English</text>
              </elementText>
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          </element>
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            <description>The nature or genre of the resource</description>
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              <elementText elementTextId="175655">
                <text>Conference paper</text>
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            <description>The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant</description>
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                <text>Sowmya P., Nmam Institute of Technology-Affiliated to NITTE(Deemed to Be University), Department of Robotics and Artificial Intelligence, Karnataka, Nitte, India; Ravichandran S.K., Christ University, School of Engineering and Technology, Department of Computer Science, Karnataka, Bangalore, India; Rakshitha, Nmam Institute of Technology-Affiliated to NITTE(Deemed to Be University), Department of Artificial Intelligence and Machine Learning, Karnataka, Nitte, India</text>
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          <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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      <name>Article</name>
      <description>Faculty Publications -Articles</description>
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        <description>The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/.</description>
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          <element elementId="50">
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            <description>A name given to the resource</description>
            <elementTextContainer>
              <elementText elementTextId="94808">
                <text>A comprehensive review of AI based intrusion detection system</text>
              </elementText>
            </elementTextContainer>
          </element>
          <element elementId="49">
            <name>Subject</name>
            <description>The topic of the resource</description>
            <elementTextContainer>
              <elementText elementTextId="94809">
                <text>Artificial intelligence; Deep learning; Ensemble learning; Intrusion detection system; Machine learning</text>
              </elementText>
            </elementTextContainer>
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          <element elementId="41">
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            <description>An account of the resource</description>
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              <elementText elementTextId="94810">
                <text>In today's digital world, the tremendous amount of data poses a significant challenge to cyber security. The complexity of cyber-attacks makes it difficult to develop efficient tools to detect them. Signature-based intrusion detection has been the common method used for detecting attacks and providing security. However, with the emergence of Artificial Intelligence (AI), particularly Machine Learning, Deep Learning and ensemble learning, promising results have been shown in detecting attacks more efficiently. This review discusses how AI-based mechanisms are being used to detect attacks effectively based on relevant research. To provide a broader view, the study presents taxonomy of the existing literature on Machine Learning (ML), Deep learning (DL), and ensemble learning. The analysis includes 72 research papers and considers factors such as the algorithm and performance metrics used for detection. The study reveals that AI-based intrusion detection methods improve accuracy, but researchers have primarily focused on improving performance for detecting attacks rather than individual attack classification. The main objective of the study is to provide an overview of different AI-based mechanisms in intrusion detection and offer deeper insights for future researchers to better understand the challenges of multi-classification of attacks.  2023</text>
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              <elementText elementTextId="94811">
                <text>Sowmya T.; Mary Anita E.A.</text>
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            <elementTextContainer>
              <elementText elementTextId="94812">
                <text>Measurement: Sensors, Vol-28</text>
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            <elementTextContainer>
              <elementText elementTextId="94813">
                <text>Elsevier Ltd</text>
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              <elementText elementTextId="94814">
                <text>2023-01-01</text>
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                <text>Sowmya T., Department of CSE, Christ University Bangalore, CMR Institute of Technology, Bengaluru, India; Mary Anita E.A., Department of CSE, Christ University, Bangalore, India</text>
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                <text>Intelligent machine learning approach for cidscloud intrusion detection system</text>
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                <text>Cloud intrusion detection system; Feature selection; KLS framework; Machine learning; UNSW-NB15 dataset</text>
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                <text>In this new era of information technology world, security in cloud computing has gained more importance because of the flexible nature of the cloud. In order to maintain security in cloud computing, the importance of developing an eminent intrusion detection system also increased. Researchers have already proposed intrusion detection schemes, but most of the traditional IDS are ineffective in detecting attacks. This can be attained by developing a new ML based algorithm for intrusion detection system for cloud. In the proposed methodology, a CIDS is incorporated that uses only selected features for the identification of the attack. The complex dataset will always make the observations difficult. Feature reduction plays a vital role in CIDS through time consumption. The current literature proposes a novel faster intelligent agent for data selection and feature reduction. The data selection agent selects only the data that promotes the attack. The selected data is passed through a feature reduction technique which reduces the features by deploying SVM and LR algorithms. The reduced features which in turn are subjected to the CIDS system. Thus, the overall time will be reduced to train the model. The performance of the system was evaluated with respect to accuracy and detection rate. Then, some existing IDS is analyzed based on these performance metrics, which in turn helps to predict the expected output. For analysis, UNSW-NB15 dataset is used which contains normal and abnormal data. The present work mainly ensures confidentiality and prevents unauthorized access.  The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021.</text>
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                <text>Lecture Notes on Data Engineering and Communications Technologies, Vol-66, pp. 873-885.</text>
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                <text>Sowmya T., School of Engineering and Technology CHRIST (Deemed to be University), Bangalore, India; Muneeswari G., Department of Computer Science and Engineering, School of Engineering and Technology, CHRIST (Deemed to be University), Bangalore, India</text>
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                <text>An Intelligent Hybrid GA-PI Feature Selection Technique for Network Intrusion Detection Systems</text>
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                <text>Feature selection; Hybrid GA-PI algorithm; Network Intrusion Detection Systems (NIDS); Support vector machines (SVM)</text>
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                <text>The development of Network Intrusion Detection Systems (NIDS) has become increasingly important due to the growing threat of cyber-attacks. However, with the vast amount of data generated in networks, handling big data in NIDS has become a major challenge. To address this challenge, this research paper proposes an intelligent hybrid GA-PI algorithm for feature selection and classification tasks in NIDS using support vector machines (SVM). The proposed approach is evaluated using two sub-datasets, Analysis and Normal, and Reconnaissance and Normal, which are generated from the publicly available UNSWNB-15 dataset. In this work, instead of considering all possible attacks, the focus is on two attacks, emphasizing the importance of the feature selection agent in determining the optimal features based on the attack type. The experimental results show that the proposed hybrid feature selection approach outperforms existing methodologies in terms of accuracy and execution time. Moreover, the selection of features can be subjective and dependent on the domain knowledge of the researcher. Additionally, the proposed approach requires computational resources for feature selection and classification tasks, which can be a limitation for resource-constrained systems. To be brief, this research paper presents a promising approach for feature selection and classification tasks in NIDS using an intelligent hybrid GA-PI algorithm. While there are some challenges and limitations, the proposed approach has the potential to contribute to the development of effective and efficient NIDS.  2023, Ismail Saritas. All rights reserved.</text>
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                <text>Sowmya; Mary Anita T.</text>
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                <text>International Journal of Intelligent Systems and Applications in Engineering, Vol-11, No. 7s, pp. 718-731.</text>
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                <text>Ismail Saritas</text>
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                <text>ISSN: 21476799</text>
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                <text>Sowmya, Christ University &amp;amp; Assistant professor, CMRIT, Karnatka, Bangalore, India; Mary Anita T., Christ University Bangalore, Karnatka, Bangalore, India</text>
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                <text>Innovative Method for Detecting Liver Cancer using Auto Encoder and Single Feed Forward Neural Network</text>
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                <text>Auto Encoder (AE); Convolutional Neural Network (CNN); Extreme Learning Machine; Support Vector Machine (SVM)</text>
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                <text>Liver cancer ranks sixth among all cancers in frequency of incidence. A CT scan is the gold standard for diagnosis. These days, CT scan images of the liver and its tumor can be segmented using deep learning and Neural Network techniques. In this proposed approach to identifying cancer cells, it's focus on four important areas: To enhance a photo by taking out imperfections and unwanted details. An ostu method is used for this purpose. Specifically, this proposed approach to use the watershed segmentation technique for image segmentation, followed by feature extraction, in an effort to isolate the offending cancer cell. After finishing the model training with AE-ELM. To do this, Extreme Learning Machine incorporates an auto encoder. To achieve effective and supervised recognition, the network's strengths of Extreme Learning Machine (ELM) are thoroughly leveraged, including its few training parameters, quick learning speed, and robust generalization ability. The auto encoder-extreme learning machine (AE-ELM) network has been shown to have a respectable recognition impact when the sigmoid activation function is used and the number of hidden layer neurons is set to 1200. According to the results of this investigation, a method based on AE-ELM can be utilized to detect the liver tumor. As compared to the CNN and ELM models, this technique achieves superior accuracy (around 99.23%).  2023 IEEE.</text>
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                <text>Sowparnika B., Nandha Engineering College, Department of Biomedical Engineering, Tamilnadu, Erode, India; Yamini K., Sriher University, Cyber Security in Computer Science and Engineering, Sri Ramachandra Faculty of Engineering, Tamilnadu, Chennai, India; Walid M.A.A., Khulna University of Engineering &amp;amp; Technology (KUET), Department of Computer Science and Engineering, Bangladesh; Prasad J., Shri Shankaracharya College of Pharmaceutical Sciences, Chhattisgarh, Junwani, Bhilai, India; Aparna N., Dhaanish Ahmed Institute of Technology, Department of Bio Medical Engineering, Tamilnadu, Coimbatore, India; Chauhan A., Christ (Deemed to Be University), Department of Life Sciences, Karnataka, Bengaluru, India</text>
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                <text>Assessing the Skill Sets from the NEP Policy 2020: Scale Development and Validation</text>
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                <text>commerce and management skill set; communal-based skills; employability skills; higher education; individualistic skills; national education policy (NEP); social and emotional learning</text>
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                <text>Purpose: The central aspect of this paper is the National Education Policy (NEP) 2020, an education-based policy in the Indian context. The article captured the opportunities from the perspective of the skill set it offers to its important stakeholders, students; the current research has aimed to propose and validate an instrument to measure the skill set identified from the NEP document and academic expertise, which could be used to measure students skill-based performance. Design/Methodology/Approach: The first section used EFA to establish four skill sets: Employability Skills, Communal-Based Skills, Social and Emotional Skills, and Individualistic Skills. The CFA model was further deployed to confirm the factors; the study was administered to a sample of 820 students from various commerce and management colleges from South and North Bangalore. Findings: The research results could be applied to evaluate the effectiveness of skill sets on students performance to create holistic education. Originality: The first step is to develop and validate scales for measuring skill sets under NEP 2020. Second, in accordance with the pupils order of priority, to close the gap between the talents stated and those observed. Research Limitations/Implications: The study was conducted only with students of commerce and management disciplines in Bangalore. Practical Implications: NEP has placed a greater emphasis on competency development and skill enhancement, which aids in the development of higher-order cognitive, social-emotional, and 21st-century abilities necessary for employment in the future. NEP gave greater autonomy to students to choose their learning pathway and develop skill sets, which are likely to make them job creators, thereby giving rise to the entrepreneurial culture. Social Implications: To cultivate an aspirational student body, it is critical to instill the necessary skill sets in pupils. Policymakers and decision-makers could then organize their courses using these skill sets.  2024, Associated Management Consultants Pvt. Ltd.. All rights reserved.</text>
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                <text>Spoorthy Reddy M.; Pavithra V.; Thomas M.R.</text>
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                <text>Prabandhan: Indian Journal of Management, Vol-17, No. 9, pp. 44-59.</text>
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                <text>Associated Management Consultants Pvt. Ltd.</text>
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                <text>&lt;a href="https://doi.org/10.17010/pijom/2024/v17i9/173941" target="_blank" rel="noreferrer noopener"&gt;https://doi.org/10.17010/pijom/2024/v17i9/173941&lt;/a&gt;
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                <text>ISSN: 9752854</text>
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                <text>Spoorthy Reddy M., School of Commerce, Finance and Accountancy, Central Campus, CHRIST (Deemed to be University), Karnataka, Bangalore, 560 029, India; Pavithra V., School of Commerce, Finance and Accountancy, Central Campus, CHRIST (Deemed to be University), Karnataka, Bangalore, 560 029, India; Thomas M.R., School of Commerce, Finance and Accountancy, Central Campus, CHRIST (Deemed to be University), Karnataka, Bangalore, 560 029, India</text>
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                <text>Recent advances in electrochemical and optical sensing of the organophosphate chlorpyrifos: a review</text>
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                <text>carbon nanomaterials; Chlorpyrifos; electrochemical sensors; molecularly-imprinted sensors; optical sensors</text>
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                <text>Chlorpyrifos (CP) is one of the most popular organophosphorus pesticides that is commonly used in agricultural and nonagricultural environments to combat pests. However, several concerns regarding contamination due to the unmitigated use of chlorpyrifos have come up over recent years. This has popularized research on various techniques for chlorpyrifos detection. Since conventional methods do not enable smooth detection, the recent trends of chlorpyrifos detection have shifted toward electrochemical and optical sensing techniques that offer higher sensitivity and selectivity. The objective of this review is to provide a brief overview of some of the important and innovative contributions in the field of electrochemical and optical sensing of chlorpyrifos with a primary focus on the comparative advantages and shortcomings of these techniques. This review paper will help to offer better perspectives for research in organophosphorus pesticide detection in the future.  2022 Informa UK Limited, trading as Taylor &amp;amp; Francis Group.</text>
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                <text>Sradha S A.; George L.; P K.; Varghese A.</text>
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                <text>Critical Reviews in Toxicology, Vol-52, No. 6, pp. 431-448.</text>
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                <text>Taylor and Francis Ltd.</text>
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                <text>&lt;a href="https://doi.org/10.1080/10408444.2022.2122770" target="_blank" rel="noreferrer noopener"&gt;https://doi.org/10.1080/10408444.2022.2122770&lt;/a&gt;
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                <text>Sradha S A., Department of Chemistry, CHRIST (Deemed to be University), Bangalore, India; George L., Department of Chemistry, CHRIST (Deemed to be University), Bangalore, India; P K., Department of Chemistry, CHRIST (Deemed to be University), Bangalore, India; Varghese A., Department of Chemistry, CHRIST (Deemed to be University), Bangalore, India</text>
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