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                <text>In today's world, weather forecasting is essential for decision-making in a variety of fields, including agriculture, transportation, and disaster preparedness. It's not simple to make weather predictions. Today, both in business and academia, data analytics is growing in importance as a tool for decision-making. The adoption of data-driven concepts is for our graduates, enhancing their marketability. Data Analytics us a study belonging to science that analyses gathered raw data, which makes conclusions about the particular information. Data analytics has been used by many sectors recently, such as hospitality, where this industry can collect data, find out where the problem is, and manage to fix the problem. Nominal, ordinal, interval, and ratio data levels are the four types of data measurement. Applications of data analytics can be found in many industries, including shipping and logistics, manufacturing, security, education, healthcare, and web development. Any business that wants to succeed in the modern digital economy should make analytics a core focus. To make such data meaningful, a transformation engine was used with types from several sources. Ironically, this has made analytics harder for businesses. As businesses employ more platforms and applications, the amount of data available has grown tremendously. This article focuses on different applications of data analytics in the modern world. Weather forecasting is a highly intricate and multifaceted process that draws upon data from various sources. It relies on a combination of scientific studies and sophisticated weather models to decipher the vast amount of information available.  2023 IEEE.</text>
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                <text>International Conference on Self Sustainable Artificial Intelligence Systems, ICSSAS 2023 - Proceedings, pp. 765-770.</text>
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                <text>Jahnavi A., Christ (Deemed to Be University), Department of Cse, India; Tanni N., Christ (Deemed to Be University), Department of Cse, India; Jayapandian N., Christ (Deemed to Be University), Department of Cse, India</text>
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                <text>India has a thriving and varied agricultural sector, which has long served as the foundation of the economy. Agriculture contributes significantly to Indias economy and is essential to the nations food security because a sizable percentage of the countrys agricultural population works in farming and associated industries. Indian farmers have managed to successfully produce a variety of commodities, including cash crops like cotton and sugarcane as well as staples like rice and wheat, despite confronting numerous obstacles like small landholdings, poor infrastructure, and unpredictable weather. In this context, it is crucial to examine the status of Indian agriculture at the moment, its advantages and disadvantages, and the possibilities and difficulties confronting farmers and policymakers.  2024 Sachi Nandan Mohanty, Preethi Nanjundan and Tejaswini Kar.</text>
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                <text>Artificial Intelligence in Forecasting: Tools and Techniques, pp. 219-232.</text>
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                <text>Jahnavi A.G., CHRIST (Deemed to be University), Pune, India; Bashir S., Department of Data Science, CHRIST (Deemed to be University), Pune, India</text>
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                <text>Impact of Globalization and Multinational Corporations on Farmer Suicides in India: An Overview, Effects, Strategies and Policies</text>
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                <text>Globalization; MNCs; privatization of the seed sector; unsustainable agriculture</text>
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                <text>The tragic rise of farmer suicides in India brought to light some of the high social and ecological costs associated with globalization and unsustainable agriculture. The study analyzes the impact of globalization and MNCs on farmer suicides and suggests strategies and policies. The crucial findings show regressive agricultural policies, output declines, insufficient credit support, private parties intervention, land fragmentation, and the high cost of cultivation due to the privatization of the seed sector that led to worst debt traps among other factors as major contributors to this turmoil. This research underlines the ongoing efforts in understanding and tackling these issues.  2023 Taylor &amp;amp; Francis Group, LLC.</text>
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                <text>Jahnavi A.G.; Dastagiri M.B.</text>
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                <text>International Trade Journal, Vol-38, No. 1, pp. 99-112.</text>
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                <text>Jahnavi A.G., Department of Economics and Analytics, Christ (Deemed to be University), Bangalore, India; Dastagiri M.B., Department of Research Systems Division, ICAR-National Academy of Agriculture Research Management, Hyderabad, India</text>
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                <text>Artificial Intelligence and Machine Learning-Based Systems for Controlling Medical Robot Beds for Preventing Bedsores</text>
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                <text>actuators; AI; centralized control; integrated data; IoT; microcontroller; pressure ulcer; robotics</text>
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                <text>Artificial Intelligence is one of the most important technologies of the modern world which is continuously changing the dimensions of almost every sector. AI and IoT have together resulted in multiple outstanding technological innovations which have also impacted the healthcare sector massively. This study has critically focused on the role of AI and robotics in the treatment outcomes for patients. This study has done deep research regarding the role of automated beds in reducing pressure ulcers or bed sores among patients who are recovering from any chronic disease. This entire study has secondary qualitative data collection for analyzing the design and microcontroller systems in automated beds. This has provided a detailed data analysis with relevant equations and tables for reaching its proposed outcomes.   2022 IEEE.</text>
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                <text>Jaichandran R.; Rayavarapu S.; Salve A.R.; Rajagopal M.; Prasad G.N.R.; Muda I.</text>
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                <text>Jaichandran R., Aarupadai Veedu Institute of Technoloyg, Vinayaka Mission's Research Foundation, India; Rayavarapu S., Liverpool John Moores University, United Kingdom; Salve A.R., Indira College of Engineering and Management, India; Rajagopal M., School of Business and Management Christ Deemed to Be University, India; Prasad G.N.R., Chaitanya Bharathi Institute of Technology, India; Muda I., Universitas Sumatera Utara, Indonesia</text>
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                <text>The waterbodies of a city play a major role in its biodiversity and ecological well-being. The main aspect of this study was to select lakes close to urban areas that are affected due to garbage dumping or have wastewater treatment plants inlets in them and check for microplastics (MPs) presence in them. Seetharampalya and Puttenahalli lakes in Bangalore both showed the presence of microplastics in their water and bank sediment soil samples, which were segregated by the wet peroxide oxidation process. In scanning electron microscopy (SEM) analysis, the microplastics segregated from the water of Seetharampalya lake were found to be clumped and in clusters of uneven form and shape. Microplastics extracted from the soil of Seetharampalya lake were found to have sheet, like structures with occasional dumps or clusters. The microplastics sorted out from Puttenahalli lake water were uneven and had roughly rectangular structures. The soil microplastics recovered from Puttenahalli lake were found to be sheaths of globular masses. The energy dispersive spectroscopy (EDS) analysis majorly showed presence of carbon and oxygen. In Fourier transform infrared spectroscopy (FTIR) analysis, characteristic peaks at 719/cm and 1469/cm were observed. Similarly, in x-ray diffraction (XRD), the 26 values around 20 could be seen in all four samples. This is the first reported study of microplastics in these lakes of Bangalore.  2024 - Kalpana Corporation.</text>
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                <text>Indian Journal of Environmental Protection, Vol-44, No. 11, pp. 963-976.</text>
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                <text>ISSN: 2537141; CODEN: IJEPD</text>
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                <text>Jaikumar I.M., Christ (Deemed to be University, Department of Life Sciences, Karnataka, Bengaluru, 560 029, India; Tomson M., Christ (Deemed to be University, Department of Life Sciences, Karnataka, Bengaluru, 560 029, India</text>
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                <text>A comprehensive review of microplastic pollution in freshwater and marine environments</text>
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                <text>Water popularly termed the The Elixir of Life is now polluted beyond control in several regions. Microplastics, the tiny contaminants have found their way into all walks of life. They have also been found to be present in human blood, multiple organs, and even breast milk. There is an abundance of microplastics in the air we breathe, the food we eat, and the water we drink. Curbing them has to start with a ban of all forms of primary microplastics, and single use plastics with preference being given to biodegradable alternatives. India in particular banned single use plastics in 2022, which put an end to several commonly used plastic items being replaced with biodegradables. Paint is one of the largest contributors to microplastics, followed by textile industry, cosmetic, pharmaceutical industry, packaging industry are all top contributors to microplastics. The wastewater treatment plants aren't designed to filter microplastics from the source and this results in microplastics polluting all water resources. Though several novel techniques for microplastic segregation exist such as sieving, filtration, density separation, visual sorting, alkali digestion exist, they aren't fully employed as the initial process of microplastic segregation from waste is still in question.  2024 The Author(s)</text>
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                <text>Jaikumar I.M.; Tomson M.; Meyyazhagan A.; Balamuralikrishnan B.; Baskaran R.; Pappuswamy M.; Kamyab H.; Khalili E.; Farajnezhad M.</text>
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                <text>Green Analytical Chemistry, Vol-12</text>
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                <text>Jaikumar I.M., Department of Life Sciences, Christ University, Karnataka, Bengaluru, 560029, India; Tomson M., Department of Life Sciences, Christ University, Karnataka, Bengaluru, 560029, India; Meyyazhagan A., Department of Life Sciences, Christ University, Karnataka, Bengaluru, 560029, India; Balamuralikrishnan B., Department of Food Science and Biotechnology, College of Life Sciences, Sejong University, Seoul, 05006, South Korea; Baskaran R., Department of Bioinformatics and Medical Engineering, Asia University, Taichung, 413305, Taiwan; Pappuswamy M., Department of Life Sciences, Christ University, Karnataka, Bengaluru, 560029, India; Kamyab H., UTE University, Faculty of Architecture and Urbanism, Architecture Department, TCEMC Investigation Group, Calle Rumipamba S/N and Bourgeois, Quito, Ecuador, Department of Biomaterials, Saveetha Dental College and Hospital, Saveetha Institute of Medical and Technical Sciences, Chennai, 600077, India; Khalili E., Department of Engineering, University of Surrey, Guildford, GU2 7XH, United Kingdom; Farajnezhad M., Faculty of Business and Communication, Inti International University, N. Sembilan, Nilai, 71800, Malaysia</text>
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                <text>The world is ever evolving and new technologies are popping up everywhere. New inventions and discoveries have created a better world, but not a sustainable one. The whole earth is drowning in various pollutants and garbage. Plastic pollution has garnered sufficient attention and there are various teams and organizations working toward cleaning our beaches, parks, and environment. However, all these actions will not suffice as plastics have trickled down into microplastics, which are posing a greater threat to our water systems and aquatic fauna. Several ongoing researches focus on marine microplastics, while only 13% of studies are on freshwater. Research on microplastics is now on the rise, with new strategies and restrictions being put into place to curb its accumulation in our marine and freshwater environments. In a recent study, microplastics were found to be present in human blood with. Out of 22 people tested, 17 test subjects had microplastics present in their blood. This review focuses on the adverse effects of microplastics in marine and freshwater ecosystems, with special focus on aquatic fauna.  2023 Jaikumar, et al.</text>
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                <text>Jaikumar I.M.; Tomson M.; Pappuswamy M.; Krishnakumar V.; Shitut A.; Meyyazhagan A.; Balasubramnaian B.; Arumugam V.A.</text>
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                <text>Journal of Applied Biology and Biotechnology, Vol-11, No. 1, pp. 28-35.</text>
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                <text>All Open Access; Gold Open Access</text>
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                <text>Jaikumar I.M., Department of Life Sciences, CHRIST University, Karnataka, Bengaluru, India; Tomson M., Department of Life Sciences, CHRIST University, Karnataka, Bengaluru, India; Pappuswamy M., Department of Life Sciences, CHRIST University, Karnataka, Bengaluru, India; Krishnakumar V., Department of Life Sciences, CHRIST University, Karnataka, Bengaluru, India; Shitut A., Department of Life Sciences, CHRIST University, Karnataka, Bengaluru, India; Meyyazhagan A., Department of Life Sciences, CHRIST University, Karnataka, Bengaluru, India; Balasubramnaian B., Department of Food Science and Bio-Technology, Sejong University, Seoul, South Korea; Arumugam V.A., Department of Human Genetics and Molecular Biology, Bharathiar University, Tamil Nadu, Coimbatore, India</text>
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                <text>Jaikumar, Irene Monica; Tomson, Majesh</text>
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                <text>Exploring Microplastic Pollution in Bellandur and Madiwala Lakes of Bangalore: A Preliminary Study</text>
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                <text>Water Resources;Volume;52;Issue;2;pp.355-371</text>
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                <text>&lt;a href="https://doi.org/10.1134/S0097807824604977" target="_blank" rel="noreferrer noopener"&gt;https://doi.org/10.1134/S0097807824604977&lt;/a&gt; &lt;br /&gt;&lt;br /&gt;&lt;a href="https://www.scopus.com/pages/publications/105003798763?origin=resultslist" target="_blank" rel="noreferrer noopener"&gt;https://www.scopus.com/pages/publications/105003798763?origin=resultslist&lt;/a&gt;</text>
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                <text>Jaikumar I.M., Department of Life Sciences, CHRIST (Deemed to be University), Dharmaram College, Bengaluru, 560029, India; Tomson M., Department of Life Sciences, CHRIST (Deemed to be University), Dharmaram College, Bengaluru, 560029, India</text>
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                <text>Abstract: Microplastics are plastic debris/fragments measuring less than 5 mm, either produced as such or obtained due to the weathering and fragmentation of bigger plastics. They are an emerging pollutant that have already caused great havoc on aquatic organisms and are a major environmental concern. Microplastics are also found in the human body, but the extent of their harmful effects is not yet fully understood. The sources of microplastic entry into humans include food, water, and air. This study assesses the microplastic occurrence in water and bank sediment of Bellandur (L1) and Madiwala (L2) lakes in Bangalore. The lakes are surrounded by urban areas and are affected by sewage inlet, waste dumping, and commercial boating. The microplastics in the bank sediment and water samples were segregated using the Wet Peroxide Oxidation process. There were no visible microplastics obtainable from the water samples of both lakes. However, microplastics were segregated from the bank sediment of both lakes. The scanning electron micrographs of the microplastics segregated from the bank sediment of L1 were found to be like sheaths and in uneven form and shape. The microplastics retrieved from L2 bank sediment were uneven and sheaths of globular masses when observed using SEM. The EDAX analysis majorly showed the presence of carbon and oxygen in both samples. From the FTIR analysis, the microplastics derived from L1 bank sediment had characteristic peaks of Acrylonitrile Butadiene Styrene (ABS), and Polystyrene (PS). Whereas, the microplastics from L2 had unique peaks corresponding to both Polypropylene (PP), PS and ABS as identified by the absorption peaks pertaining to both CH3 bending (1366 cm1) and aromatic CH (2922 cm1) stretching vibrations. Similarly, in XRD, the 2? values around 20 could be seen in both samples. These peaks correspond to some form of crystallinity, but the microplastics may have degraded due to several factors in the environment leading to changes in their structural form. The SEM, EDAX, FTIR and XRD data confirm the presence of microplastics in both the samples. This is the first reported study of microplastics in these lakes of Bangalore and it implies the need to focus on mitigation strategies for retrieval of microplastics that continues to accumulate in these lakes.  Pleiades Publishing, Ltd. 2025.</text>
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                <text>analytical techniques; diversity; ecology; freshwater; microplastics; pollution</text>
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                <text>ISSN: 978078; CODEN: WARED</text>
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                <text>Restricted Access; Hardcopy may be available in the library</text>
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          <element elementId="50">
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              <elementText elementTextId="152457">
                <text>An approach for mobile application design using Figma</text>
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              <elementText elementTextId="152458">
                <text>With the increased mobile usage throughout the world, there is an enormous demand for mobile applications that provide not only good functionality but also good experience to the user, which is why it is important to have a good user interface for a mobile application. In this chapter, various components of an app are designed and illustrated in Figma software to make the work easier for a developer to create them. It also demonstrates the usage of Fig-ma. Meanwhile it shows the design thinking behind adding the correct colour schemes, right font, using proper white space, proper placement of buttons, and other design principles.  2023, IGI Global. All rights reserved.</text>
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              <elementText elementTextId="152459">
                <text>Jain A.</text>
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            <elementTextContainer>
              <elementText elementTextId="152460">
                <text>Designing and Developing Innovative Mobile Applications, pp. 165-197.</text>
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              <elementText elementTextId="152461">
                <text>IGI Global</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/10.4018/978-1-6684-8582-8.ch010" target="_blank" rel="noreferrer noopener"&gt;https://doi.org/10.4018/978-1-6684-8582-8.ch010&lt;/a&gt;
&lt;br /&gt;&lt;br /&gt;&lt;a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85163336047&amp;amp;doi=10.4018%2F978-1-6684-8582-8.ch010&amp;amp;partnerID=40&amp;amp;md5=c3486e443ef1ee3867930cb924ac2943" target="_blank" rel="noreferrer noopener"&gt;https://www.scopus.com/inward/record.uri?eid=2-s2.0-85163336047&amp;amp;doi=10.4018%2f978-1-6684-8582-8.ch010&amp;amp;partnerID=40&amp;amp;md5=c3486e443ef1ee3867930cb924ac2943&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="152464">
                <text>Restricted Access</text>
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                <text>ISBN: 978-166848584-2; 978-166848582-8</text>
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                <text>Jain A., CHRIST University (Deemed), India</text>
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  <item itemId="13142" public="1" featured="0">
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                <elementText elementTextId="64">
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      <description>Faculty Publications -Articles</description>
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        <elementContainer>
          <element elementId="50">
            <name>Title</name>
            <description>A name given to the resource</description>
            <elementTextContainer>
              <elementText elementTextId="80921">
                <text>Impact of the operational parameters of a dual fuel engine operating on a blend of Water Hyacinth biodiesel and Mesua ferrea biodiesel with hydrogenA clean development mechanism</text>
              </elementText>
            </elementTextContainer>
          </element>
          <element elementId="49">
            <name>Subject</name>
            <description>The topic of the resource</description>
            <elementTextContainer>
              <elementText elementTextId="80922">
                <text>Dual fuel; Hydrogen; Mesua ferrea biodiesel; Water Hyacinth biodiesel</text>
              </elementText>
            </elementTextContainer>
          </element>
          <element elementId="41">
            <name>Description</name>
            <description>An account of the resource</description>
            <elementTextContainer>
              <elementText elementTextId="80923">
                <text>The study was conducted to uncover the emission, combustion, and performance features of the blend of Water Hyacinth biodiesel and Mesua Ferrea seed oil biodiesel with Hydrogen addition on a diesel engine in dual fuel. Pilot fuel is a blend of 50% Water Hyacinth biodiesel and 50% Mesua Ferrea seed oil biodiesel. A single-cylinder compression ignition engine was modified to operate on dual fuel mode with hydrogen. Variations of engine operating parameters such as injection timing, and engine load were performed. The study was conducted with three pilot fuel injection timings (23, 26, and 29bTDC) and variable engine loadings (20%100% with an increment of 20%) at an injection pressure of 200 bar and compression ratio of 18. The results indicated that the maximum brake thermal efficiency of 28.11% and a replacement of liquid fuel by 85% was obtained for the WHMF blend powered dual fuel diesel engine at pilot fuel injection timings of 26bTDC at 100% load. HC, CO, and smoke emissions are reduced with hydrogen due to faster combustion. On the other hand, there was a slight increase in NOx emissions noticed with hydrogen enrichment.  2024 Hydrogen Energy Publications LLC</text>
              </elementText>
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          </element>
          <element elementId="39">
            <name>Creator</name>
            <description>An entity primarily responsible for making the resource</description>
            <elementTextContainer>
              <elementText elementTextId="80924">
                <text>Jain A.; Bora B.J.; Kumar R.; Sharma P.; Barik D.; Balasubramanian D.; Ramegowda R.; Josephin JS F.; Varuvel E.G.; Nguyen Le D.T.; Truong T.H.; Cao D.N.; Le T.T.</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="80925">
                <text>International Journal of Hydrogen Energy, Vol-66, pp. 689-702.</text>
              </elementText>
            </elementTextContainer>
          </element>
          <element elementId="45">
            <name>Publisher</name>
            <description>An entity responsible for making the resource available</description>
            <elementTextContainer>
              <elementText elementTextId="80926">
                <text>Elsevier Ltd</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="80927">
                <text>2024-01-01</text>
              </elementText>
            </elementTextContainer>
          </element>
          <element elementId="43">
            <name>Identifier</name>
            <description>An unambiguous reference to the resource within a given context</description>
            <elementTextContainer>
              <elementText elementTextId="80928">
                <text>&lt;a href="https://doi.org/10.1016/j.ijhydene.2024.03.111" target="_blank" rel="noreferrer noopener"&gt;https://doi.org/10.1016/j.ijhydene.2024.03.111&lt;/a&gt;
&lt;br /&gt;&lt;br /&gt;&lt;a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85190523485&amp;amp;doi=10.1016%2Fj.ijhydene.2024.03.111&amp;amp;partnerID=40&amp;amp;md5=63a38350b76ec3b3f211f40dc3403faf" target="_blank" rel="noreferrer noopener"&gt;https://www.scopus.com/inward/record.uri?eid=2-s2.0-85190523485&amp;amp;doi=10.1016%2fj.ijhydene.2024.03.111&amp;amp;partnerID=40&amp;amp;md5=63a38350b76ec3b3f211f40dc3403faf&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="80929">
                <text>Restricted Access</text>
              </elementText>
            </elementTextContainer>
          </element>
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            <description>A related resource</description>
            <elementTextContainer>
              <elementText elementTextId="80930">
                <text>ISSN: 3603199; CODEN: IJHED</text>
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            <name>Format</name>
            <description>The file format, physical medium, or dimensions of the resource</description>
            <elementTextContainer>
              <elementText elementTextId="80931">
                <text>Online</text>
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          <element elementId="44">
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            <description>A language of the resource</description>
            <elementTextContainer>
              <elementText elementTextId="80932">
                <text>English</text>
              </elementText>
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          </element>
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            <name>Type</name>
            <description>The nature or genre of the resource</description>
            <elementTextContainer>
              <elementText elementTextId="80933">
                <text>Article</text>
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            </elementTextContainer>
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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="80934">
                <text>Jain A., Energy Institute Bengaluru, Centre of Rajiv Gandhi Institute of Petroleum Technology, Karnataka, Bengaluru, 562157, India; Bora B.J., Energy Institute Bengaluru, Centre of Rajiv Gandhi Institute of Petroleum Technology, Karnataka, Bengaluru, 562157, India; Kumar R., Rajiv Gandhi Institute of Petroleum Technology, Jais, Amethi, 229305, U.P, India; Sharma P., Delhi Skill and Entrepreneurship University, Delhi, 110089, India; Barik D., Department of Mechanical Engineering, Karpagam Academy of Higher Education, Coimbatore, 641021, India; Balasubramanian D., Department of Mechanical Engineering, Mepco Schlenk Engineering College, Sivakasi, India; Ramegowda R., Department of Mechanical and Automobile Engineering, CHRIST University, Bangalore, India; Josephin JS F., Department of Computer Engineering, Faculty of Engineering and Natural Sciences, Istinye University, Istanbul, Turkey, Department of Autotronics, Institute of Automobile Engineering, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, Tamil Nadu, Chennai, 602105, India; Varuvel E.G., Department of Mechanical Engineering, Faculty of Engineering and Natural Sciences, Istinye University, Istanbul, Turkey, Department of Automobile Engineering, Faculty of Engineering and Technology, SRM Institute of Science and Technology, Tamil Nadu, Kattankulathur, 603203, India; Nguyen Le D.T., Faculty of Automotive Engineering, Dong-A University, Danang, Viet Nam; Truong T.H., PATET Research Group, Ho Chi Minh City University of Transport, Viet Nam; Cao D.N., PATET Research Group, Ho Chi Minh City University of Transport, Viet Nam; Le T.T., Institute of Engineering, HUTECH University, Ho Chi Minh City, Viet Nam</text>
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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="51377">
                  <text>Conference Papers</text>
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              </elementTextContainer>
            </element>
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                <text>The step-by-step process of creating a bookshelf design is specified, including the ProBoolean compound primitive, applying edit poly modifier, using detach option, making use of lattice modifier, using bend modifier, using twist modifier. The manner in which materials are added to the model, together with environment lighting and renderer configuration, is defined. Procedures and methods for rendering are also defined. What we aim to achieve through our research is to create a Bookshelf design that uses materials to enhance the models. The shapes used in the model were Box, Teapot, sphere, chamfercyl, Oiltank, ProBoolean compound. The modifiers used were edit poly, bend, twist, lattice. Afterwards we used the Arnold light and material editor to enhance and glorify the model.  2023 IEEE.</text>
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                <text>Institute of Electrical and Electronics Engineers Inc.</text>
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                <text>Jain A., Christ University, Dept. of Computer Science, Karnataka, Bangalore, India; Dangi P., Christ University, Dept. of Computer Science, Karnataka, Bangalore, India; Samanta D., Rit Kosovo (A.U.K), Dept. of Computing and Information Technologies, Kosovo, Europe, Prishtina, 10000, Serbia, Rochester Institute of Technology, Rit Global Campus, Kosovo, Europe, Prishtina, 10000, Serbia; Dutta S., Institute of Engineering &amp;amp; Management, Dept. of Computer Science &amp;amp; Applications, Kolkata, India; Bhattacharya A., Institute of Engineering &amp;amp; Management, Dept. of Computer Science &amp;amp; Applications, Kolkata, India; Joseph N.P., Christ University, Dept. of Computer Science, Karnataka, Bangalore, India</text>
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                <text>A Non-Linear Approach to Predict the Salary of NBA Athletes using Machine Learning Technique</text>
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                <text>Non-Linear Model; Random Forest; Regression machine learning; Salary prediction; Sports analytics; XGBoost</text>
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                <text>Every sportsman traded/drafted receives monetary compensation in accordance with their contract. In this study, we propose a nonlinear approach based on performance and other aspects to determine the salary of a basketball player. We estimate the salary based on four regressive models. Whilst predicting we also Figure out the important features impacting the salary. Comparatively speaking, random forest outperformed other algorithms. Furthermore, we consider that our findings might benefit discussions between basketball teams and players. This model can also help set a benchmark for salary expectations by the players in accordance.   2022 IEEE.</text>
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                <text>Jain A.; Jain S.; Pancinovia N.M.; George J.P.</text>
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                <text>Institute of Electrical and Electronics Engineers Inc.</text>
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              <elementText elementTextId="177630">
                <text>Restricted Access</text>
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                <text>ISBN: 978-166545361-5</text>
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                <text>Jain A., CHRIST (Demeed to Be University), India; Jain S., CHRIST (Demeed to Be University), India; Pancinovia N.M., CHRIST (Demeed to Be University), India; George J.P., CHRIST (Demeed to Be University), India</text>
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                <text>RCBAM-CNN: Rebuild Convolution Block Attention Module-based Convolutional Neural Network for Lung Nodule Classification</text>
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          <element elementId="49">
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            <elementTextContainer>
              <elementText elementTextId="84226">
                <text>Computed tomography; data augmentation; horizontal flips; rebuild convolution block attention module-based convolutional neural network; U-Net</text>
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                <text>Lung cancer remains the leading cause of cancer-related deaths worldwide. Pulmonary nodules, indicative of tumor growth, present significant diagnostic challenges due to their varying sizes and shapes. Computed Tomography (CT) is commonly used for lung cancer screening due to its high sensitivity and efficacy in detecting these nodules. However, differentiating between benign and malignant nodules can be difficult due to their overlapping characteristics. To address this challenge, we propose a Rebuild Convolution Block Attention Module-based Convolutional Neural Network (RCBAM-CNN) designed to accurately classify lung nodules from CT scans. The RCBAM-CNN integrates a Rebuild Convolution Block Attention Module (RCBAM), which includes reshaped layers and redefined spatial attention mechanisms to enhance the networks focus on relevant features while minimizing noise. The performance of the proposed method is evaluated using the LIDC-IDRI dataset. Data augmentation techniques, including rotation, rescaling, and both vertical and horizontal flips, are applied to improve the models robustness and generalization. Subsequently, U-Net is employed for precise image segmentation, ensuring accurate delineation of nodule regions. The proposed RCBAM-CNN demonstrates exceptional performance, achieving an accuracy of 99.72%, surpassing existing methods such as adaptive morphology with a Gabor Filter (GF) and Capsule Network-based CNN. This approach represents a significant advancement in lung nodule classification, offering improved diagnostic accuracy and reliability.  2024 River Publishers.</text>
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              <elementText elementTextId="84228">
                <text>Jain D.; Hundekari S.; Upreti K.; Jain N.; Rose M.; Singh N.; Singhai R.; Kumar M.</text>
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                <text>Journal of Mobile Multimedia, Vol-20, No. 5, pp. 1039-1066.</text>
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              <elementText elementTextId="84230">
                <text>River Publishers</text>
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                <text>2024-01-01</text>
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                <text>&lt;a href="https://doi.org/10.13052/jmm1550-4646.2053" target="_blank" rel="noreferrer noopener"&gt;https://doi.org/10.13052/jmm1550-4646.2053&lt;/a&gt;
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              <elementText elementTextId="84233">
                <text>All Open Access; Gold Open Access</text>
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                <text>ISSN: 15504646</text>
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                <text>Online</text>
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                <text>English</text>
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                <text>Jain D., Department of CSE-AIML, ABES Engineering College, U.P, Ghaziabad, India; Hundekari S., School of Engineering and Technology, Pimpri Chinchwad University, Pune, India; Upreti K., Christ University, Delhi NCR Campus, Ghaziabad, India; Jain N., Vivekananda School of Engineering and Technology, VIPS-TC, Delhi, Pitampura, India; Rose M., G. D. Goenka University, Haryana, Gurgaon, India; Singh N., G. D. Goenka University, Haryana, Gurgaon, India; Singhai R., IIPS, Devi Ahilya University, Indore, India; Kumar M., Gurukula Kangri University, Uttarakhand, Haridwar, India</text>
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      <description>Faculty Publications- Conference Papers</description>
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                <text>Alkali-Activated Materials - A Review for Sustainable Construction</text>
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              <elementText elementTextId="176386">
                <text>Alkali Activated Materials; Blast Furnace Slag; Fly Ash; Metakaolin; Sustainability</text>
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                <text>New, sustainable low-Carbon Dioxide (CO2) construction materials must be developed for the global building sector to decrease its environmental impact. During the last several decades, Alkali-activated Materials (AAMs) is a Portland cement-free form, have been intensively researched as a potential alternative for ordinary Portland cement concrete (OPCC), with the objective of lowering CO2 emissions while repurposing a large volume of industrial waste by-products. The suitability of using AAMs made up of industrial waste by-products such as blast furnace slag (BFS), calcined clay (metakaolin), and fly ash (FA) was investigated in this study utilizing a performance-based approach that was unaffected by binder chemistry, history, or environmental effect, Binder paste microstructural assessment and influence on engineering effectiveness, including fresh and hardened characteristics of these materials, In the Viewpoints area, we analyze specific premature phase and long-phase performance of AAMs, as well as Upcoming scientific breakthroughs are also discussed in the Viewpoints section.  2022 American Institute of Physics Inc.. All rights reserved.</text>
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                <text>Jain D.K.; Alex J.; Aravind H.B.</text>
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              <elementText elementTextId="176389">
                <text>AIP Conference Proceedings, Vol-2426</text>
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                <text>ISSN: 25738240</text>
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                <text>Jain G., Digital Transformation, Keele Business School, Keele University, United Kingdom; Kumar N., Department of Business, Christ University, Bangalore, India; Rigby C., Entrprise Lead, Keele Business School, Keele University, United Kingdom</text>
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                <text>Effective time context based collaborative filtering recommender system inspired by Gowers coefficient</text>
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                <text>Collaborative filtering; Gowers coefficient; Recommendation system; Similarity measure; Time decay function</text>
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                <text>The fast growth of Internet technology in recent times has led to a surge in the number of users and amount of information generated. This substantially contributes to the popularity of recommendation systems (RS), which provides personalized recommendations to users based on their interests. A RS assists the user in the decision-making process by suggesting a suitable product from various alternatives. The collaborative filtering (CF) technique of RS is the most prevalent because of its high accuracy in predicting users' interests. The efficacy of this technique mainly depends on the similarity calculation, determined by a similarity measure. However, the traditional and previously developed similarity measures in CF techniques are not able to adequately reveal the change in users' interests; therefore, an efficient measure considering time into context is proposed in this paper. The proposed method and the existing approaches are compared on the MovieLens-100k dataset, showing that the proposed method is more efficient than the comparable methods. Besides this, most of the CF approaches only focus on the historical preference of the users, but in real life, the people's preferences also change over time. Therefore, a time-based recommendation system using the proposed method is also developed in this paper. We implemented various time decay functions, i.e., exponential, convex, linear, power, etc., at various levels of the recommendation process, i.e., similarity computation, rating matrix, and prediction level. Experimental results over three real datasets (MovieLens-100k, Epinions, and Amazon Magazine Subscription) suggest that the power decay function outperforms other existing techniques when applied at the rating matrix level.  2022, The Author(s) under exclusive licence to The Society for Reliability Engineering, Quality and Operations Management (SREQOM), India and The Division of Operation and Maintenance, Lulea University of Technology, Sweden.</text>
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                <text>Jain G.; Mahara T.; C.Sharma S.</text>
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                <text>International Journal of System Assurance Engineering and Management, Vol-14, No. 1, pp. 429-447.</text>
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                <text>&lt;a href="https://doi.org/10.1007/s13198-022-01813-z" target="_blank" rel="noreferrer noopener"&gt;https://doi.org/10.1007/s13198-022-01813-z&lt;/a&gt;
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                <text>ISSN: 9756809</text>
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                <text>Jain G., Indian Institute of Technology Roorkee, Uttarakhand, Roorkee, 247667, India; Mahara T., Christ University, Bengaluru, India; C.Sharma S., Indian Institute of Technology Roorkee, Uttarakhand, Roorkee, 247667, India</text>
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                <text>Performance Evaluation of Time-based Recommendation System in Collaborative Filtering Technique</text>
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                <text>Collaborative Filtering; Cosine Correlation Coefficient; Gower's Coefficient; Recommendation System; Similarity Measure; Time decay function</text>
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                <text>The Collaborative Filtering (CF) technique is the most common neighbourhood-based recommendation strategy, that provides personalized recommendation to a user for the items using a similarity measure. Hence, the selection of the appropriate similarity measure becomes crucial in the CF based recommendation system. The traditional similarity measures merely focus only on the historical ratings provided by the users to compute the similarity, completely ignoring the fact that preferences change over a period of time. Considering this, the paper aims to develop an effective Recommendation System that uses temporal information to capture the changes in the preferences over a period of time. For this, the existing exponential and power time decay functions are integrated with Cosine, Pearson Correlation, and Gower's similarity measures to compute similarity. The similarity is computed at the similarity computation and prediction levels of recommendation processes. Experimental findings in terms of MAE and RMSE on the MovieLens-100k demonstrate that performance of Gower's coefficient is better when applied with the exponential function at the similarity computation level of the recommendation process.  2022 Elsevier B.V.. All rights reserved.</text>
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                <text>Procedia Computer Science, Vol-218, pp. 1834-1844.</text>
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                <text>Elsevier B.V.</text>
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                <text>Jain G., Indian Institute of Technology Roorkee, Uttarakhand, Roorkee, India; Mahara T., Christ University, Bengaluru, India; Sharma S.C., Indian Institute of Technology Roorkee, Uttarakhand, Roorkee, India</text>
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                <text>TD?DNN: A Time Decay?Based Deep Neural Network for Recommendation System</text>
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                <text>Collaborative Filtering; Deep Neural Network; Matrix Factorization; noisy ratings; recommendation system; time decay functions</text>
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                <text>In recent years, commercial platforms have embraced recommendation algorithms to provide customers with personalized recommendations. Collaborative Filtering is the most widely used technique of recommendation systems, whose accuracy is primarily reliant on the computed similarity by a similarity measure. Data sparsity is one problem that affects the performance of the similarity measures. In addition, most recommendation algorithms do not remove noisy data from datasets while recommending the items, reducing the accuracy of the recommendation. Further-more, existing recommendation algorithms only consider historical ratings when recommending the items to users, but users tastes may change over time. To address these issues, this research presents a Deep Neural Network based on Time Decay (TD?DNN). In the data preprocessing phase of the model, noisy ratings are detected from the dataset and corrected using the Matrix Factorization approach. A power decay function is applied to the preprocessed input to provide more weight-age to the recent ratings. This non?noisy weighted matrix is fed into the Deep Learning model, con-sisting of an input layer, a Multi?Layer Perceptron, and an output layer to generate predicted rat-ings. The models performance is tested on three benchmark datasets, and experimental results con-firm that TD?DNN outperforms other existing approaches.  2022 by the authors. Li-censee MDPI, Basel, Switzerland.</text>
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                <text>Applied Sciences (Switzerland), Vol-12, No. 13</text>
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&lt;br /&gt;&lt;br /&gt;&lt;a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85132983140&amp;amp;doi=10.3390%2Fapp12136398&amp;amp;partnerID=40&amp;amp;md5=db330081d3f470cf8ade841ecf8e6576" target="_blank" rel="noreferrer noopener"&gt;https://www.scopus.com/inward/record.uri?eid=2-s2.0-85132983140&amp;amp;doi=10.3390%2fapp12136398&amp;amp;partnerID=40&amp;amp;md5=db330081d3f470cf8ade841ecf8e6576&lt;/a&gt;</text>
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                <text>All Open Access; Gold Open Access</text>
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                <text>ISSN: 20763417</text>
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                <text>Jain G., Electronics and Computer Discipline, Indian Institute of Technology, Roorkee, 247667, India; Mahara T., Institute of Management, Christ University, Bengaluru, 560029, India; Sharma S.C., Electronics and Computer Discipline, Indian Institute of Technology, Roorkee, 247667, India; Agarwal S., Amity School of Engineering &amp;amp; Technology, Amity University Uttar Pradesh, Noida, 201313, India; Kim H., School of Computer Science, Kyungil University, Kyungbuk, Gyeongsan, 38428, South Korea</text>
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                <text>A Cognitive Similarity-Based Measure to Enhance the Performance of Collaborative Filtering-Based Recommendation System</text>
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              <elementText elementTextId="104128">
                <text>Cognitive similarity (CgS); collaborative filtering (CF); cosine measure; Goweras coefficient; Jaccard coefficient; recommendation system (RS); sigmoid function; similarity measure</text>
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                <text>Advances in technology and high Internet penetration are leading to a large number of businesses going online. As a result, there is a substantial increase in the number of customers making online purchases and the number of items available online. However, with so many options available to choose from, users have to face the information overload problem. Several techniques have been developed to handle this, but the performance of the recommendation system (RS) has been recorded unprecedentedly. The collaborative filtering (CF) of RS is the most prevalent technique, which suggests personalized items to users based on their past preferences. The efficacy of this technique mainly depends on the similarity calculation, which the traditional or cognitive approach can ascertain. In the traditional approach, a similarity measure utilizes the user's ratings on an item to compute the similarity. Most similarity measures in this approach suffer from either data sparsity and/or cold-start problems. To address both of them, a new similarity measure based on the Jaccard and Gower coefficients, the efficient Gowers-Jaccard-Sigmoid Measure (EGJSM), is proposed in this article. It also includes a nonlinear sigmoid function to penalize the bad ratings. The performance of EGJSM is evaluated by conducting experiments on benchmark datasets, and the results depict that the proposed technique outperforms several existing methods. Along with this, a cognitive similarity (CgS) measure has been proposed, which considers cognitive features such as genre and year of release along with rating information, to calculate similarity. The CgS method also outperforms the proposed EGJSM method and produces almost 4% and 1% lower mean absolute error (MAE) and root-mean-squared error (RMSE) values than that.   2014 IEEE.</text>
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                <text>Jain G.; Mahara T.; Sharma S.C.; Sangaiah A.K.</text>
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              <elementText elementTextId="104131">
                <text>IEEE Transactions on Computational Social Systems, Vol-9, No. 6, pp. 1785-1793.</text>
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            <elementTextContainer>
              <elementText elementTextId="104132">
                <text>Institute of Electrical and Electronics Engineers Inc.</text>
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            <description>A point or period of time associated with an event in the lifecycle of the resource</description>
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                <text>&lt;a href="https://doi.org/10.1109/TCSS.2022.3187430" target="_blank" rel="noreferrer noopener"&gt;https://doi.org/10.1109/TCSS.2022.3187430&lt;/a&gt;
&lt;br /&gt;&lt;br /&gt;&lt;a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85134217229&amp;amp;doi=10.1109%2FTCSS.2022.3187430&amp;amp;partnerID=40&amp;amp;md5=5f34db2778fa985e5d20a8285a9b1576" target="_blank" rel="noreferrer noopener"&gt;https://www.scopus.com/inward/record.uri?eid=2-s2.0-85134217229&amp;amp;doi=10.1109%2fTCSS.2022.3187430&amp;amp;partnerID=40&amp;amp;md5=5f34db2778fa985e5d20a8285a9b1576&lt;/a&gt;</text>
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            <elementTextContainer>
              <elementText elementTextId="104135">
                <text>Restricted Access</text>
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              <elementText elementTextId="104136">
                <text>ISSN: 2329924X</text>
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              <elementText elementTextId="104137">
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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>Jain G., IIT Roorkee, Electronic and Computer Discipline, Department of Polymer and Process Engineering, Roorkee, 247667, India; Mahara T., IIT Roorkee, Department of Polymer and Process Engineering, Roorkee, 247667, India, Christ University, School of Business and Management, Bengaluru, 560029, India; Sharma S.C., IIT Roorkee, Electronic and Computer Discipline, Department of Paper Technology, Roorkee, 247667, India; Sangaiah A.K., National Yunlin University of Science and Technology, Department of Industrial Engineering and Management, Douliu, 64002, Taiwan</text>
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          <element elementId="50">
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                <text>Clustering-Based Recommendation System for Preliminary Disease Detection</text>
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            <description>The topic of the resource</description>
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                <text>Collaborative Filtering; Coronavirus; COVID-19; Health Recommender Systems; Similarity Measure</text>
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                <text>The catastrophic outbreak COVID-19 has brought threat to the society and also placed severe stress on the healthcare systems worldwide. Different segments of society are contributing to their best effort to curb the spread of COVID-19. As a part of this contribution, in this research, a clustering-based recommender system is proposed for early detection of COVID-19 based on the symptoms of an individual. For this, the suspected patients symptoms are compared with the patient who has already contracted COVID-19 by computing similarity between symptoms. Based on this, the suspected person is classified into either of the three risk categories: high, medium, and low. This is not a confirmed test but only a mechanism to alert the suspected patient. The accuracy of the algorithm is more than 85%.  2022 IGI Global. All rights reserved.</text>
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                <text>Jain G.; Mahara T.; Sharma S.C.; Verma O.P.; Sharma T.</text>
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                <text>International Journal of E-Health and Medical Communications, Vol-13, No. 4</text>
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                <text>&lt;a href="https://doi.org/10.4018/IJEHMC.313191" target="_blank" rel="noreferrer noopener"&gt;https://doi.org/10.4018/IJEHMC.313191&lt;/a&gt;
&lt;br /&gt;&lt;br /&gt;&lt;a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85202576722&amp;amp;doi=10.4018%2FIJEHMC.313191&amp;amp;partnerID=40&amp;amp;md5=a9653cf3a03b082fad618db4417b30ba" target="_blank" rel="noreferrer noopener"&gt;https://www.scopus.com/inward/record.uri?eid=2-s2.0-85202576722&amp;amp;doi=10.4018%2fIJEHMC.313191&amp;amp;partnerID=40&amp;amp;md5=a9653cf3a03b082fad618db4417b30ba&lt;/a&gt;</text>
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                <text>All Open Access; Gold Open Access</text>
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                <text>ISSN: 1947315X</text>
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                <text>Jain G., Indian Institute of Technology, Roorkee, India; Mahara T., Christ University, India; Sharma S.C., Indian Institute of Technology, Roorkee, India; Verma O.P., Dr. B. R. Ambedkar National Institute of Technology, Jalandhar, India; Sharma T., Shobhit University, Gangoh, India</text>
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