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                <text>Rotor Dynamics of TurbineGenerator Shaft with Dampers During Subsynchronous Resonance Generated by Series Capacitors</text>
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                <text>Dampers; Finite element method; Generator; Shaft; Subsynchronous resonance; Turbine</text>
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                <text>Purpose In this paper, an electromechanical approach to study the turbinegenerator shaft stability with and without dampers is made. The shaft is subjected to electrical disturbances created by series capacitors. The high power capacitors help the electric power system to improve the reactive power in high voltage transmission lines. Methods Finite element method is used to study the stability of the shaft under subsynchronous resonance when compared to the traditional methods like eigenvalue analysis, frequency scanning method and digital time simulation techniques. At the same time, it leads to subsynchronous resonance. Results Electromechanical stress in the rotating shaft arises when the resonance is created in the system. Maximum stress and strain of the shaft are calculated with other necessary parameters to prove the system instability. In order to maintain stability, dampers are installed at an optimum location. Conclusion Best location of installing damper is found using ANSYS 16.0 by modal analysis, harmonic and phase response analysis. The damper installed at the point reduces the stress caused by subsynchronous resonance and maintains the stability of the system.  2021, Springer Nature Singapore Pte Ltd.</text>
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                <text>Manikandan P.; Khan F.A.</text>
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                <text>Lecture Notes in Mechanical Engineering, pp. 191-202.</text>
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                <text>Springer Science and Business Media Deutschland GmbH</text>
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                <text>&lt;a href="https://doi.org/10.1007/978-981-15-5701-9_16" target="_blank" rel="noreferrer noopener"&gt;https://doi.org/10.1007/978-981-15-5701-9_16&lt;/a&gt;
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                <text>ISSN: 21954356; ISBN: 978-981155700-2</text>
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                <text>Manikandan P., Department of EEE, CHRIST (Deemed to be University), Bangalore, Karnataka, India; Khan F.A., Department of EEE, Ghousia College of Engineering (Affiliated to Visvesvaraya Technological University, Belagavi, Karnataka), Ramanagaram, Karnataka, India</text>
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                <text>Experimental Investigations on Turbine-Generator Shaft Under Subsynchronous Resonance</text>
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                <text>Damper; Finite element method; Modal analysis; Rotor dynamics; Subsynchronous resonance; Turbo-generator shaft</text>
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                <text>Energy exchange takes place between turbine and generator in the power system during subsynchronous resonance (SSR) which leads to torsional interaction between the shafts. Resonance in the power system is caused by the series capacitors connected to the transmission line. This paper aims to present an electromechanical approach to analyse and interpret subsynchronous resonance using the Finite element method. Subsynchronous resonance is introduced in two test rigs consisting of turbine, generator, shaft, and coupler with capacitors. Experiments and simulations (torque analysis and frequency response analysis) are conducted in test rigs and ANSYS workbench 16.0. Moreover, a spring damper is modelled to improve the stability of the shaft. From the results, it is clear that mechanical stress is increased when capacitors are connected to the test rig. A spring damper is installed at the point where the deformation is high. The damper reduced the stress and the vibration.  2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.</text>
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                <text>Manikandan P.; Sushanth G.; Haneesh K.M.</text>
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                <text>Lecture Notes in Electrical Engineering, Vol-977, pp. 693-703.</text>
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                <text>Springer Science and Business Media Deutschland GmbH</text>
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                <text>ISSN: 18761100; ISBN: 978-981197752-7</text>
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                <text>Manikandan P., Department of EEE, CHRIST (Deemed to be University), Bengaluru, India; Sushanth G., Department of ECE, CHRIST (Deemed to be University), Bengaluru, India; Haneesh K.M., Department of EEE, CHRIST (Deemed to be University), Bengaluru, India</text>
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                <text>Impact on cardioprotective effect of Psidium guajava leaves extract in streptozotocin-induced Wistar mice with molecular in silico analysis</text>
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                <text>cardiac markers; lysosomal enzymes; mitochondrial enzymes; Psidium guajava</text>
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                <text>Cardiovascular disease (CVD) and its complications have been regarded as the leading cause of morbidity and mortality. The drugs available in the market are effective to treat CVD, but with many adverse reactions. Nowadays, herbal products are the attention of researchers because of their less adverse effects. In this study, the cardioprotective effects of ethanolic leaves extract of Psidium guajava Linn. (Guava) (P. guajava) were evaluated in streptozotocin (STZ)-treated animal models. Mice acquired for the study were divided into five groups, each consisting of six mice. The toxin-induced mice were treated with the ethanolic leaves extract of P. guajava (300 mg/ kg body weight [b.w.]). The results were compared to the standard drug (glibenclamide)-treated mice (3 mg/kg b.w.). The following parameters were considered for further investigations: creatine kinase-muscle brain (CK-MB), creatine kinase (CK), troponin, lysosomal, and mitochondrial enzymes. Then the docking study was accomplished. The levels of cardiac marker enzymes and lysosomal enzymes increased significantly in the toxin-induced mice, while the level of mitochondrial enzyme decreased significantly. During treatment with the ethanolic leaves extract of P. guajava, the levels of all parameters were notably reversed to normal range (P &amp;lt; 0.05). Further, in docking analysis, the interaction of compounds, such as alpha-terpineol, cyclopentanecarboxamide, guaiol (a sesquiterpenoid alcohol), 1H-cyclopropanaphthalene, tetracyclotridecan-9-ol, dormin/abscisic acid, and epiglobulol, with the respective protein molecules, evidenced the cardioprotective effect of P. guajava leaves. Hence, it was concluded that the ethanolic leaves extract of P. guajava leaves have a cardioprotective effect.  2023 Codon Publications.</text>
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                <text>Manikandan R.; Balamuralikrishnan B.; Boro A.; Karthika P.; Arun M.; Velayuthaprabhu S.; Malaisamy A.; Rengarajan R.L.; Anand A.V.</text>
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                <text>Quality Assurance and Safety of Crops and Foods, Vol-15, No. 2, pp. 209-221.</text>
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                <text>Codon Publications</text>
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                <text>&lt;a href="https://doi.org/10.15586/QAS.V15I2.1261" target="_blank" rel="noreferrer noopener"&gt;https://doi.org/10.15586/QAS.V15I2.1261&lt;/a&gt;
&lt;br /&gt;&lt;br /&gt;&lt;a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85160527512&amp;amp;doi=10.15586%2FQAS.V15I2.1261&amp;amp;partnerID=40&amp;amp;md5=7170ca118e0d7b813f4fbdb59b997659" target="_blank" rel="noreferrer noopener"&gt;https://www.scopus.com/inward/record.uri?eid=2-s2.0-85160527512&amp;amp;doi=10.15586%2fQAS.V15I2.1261&amp;amp;partnerID=40&amp;amp;md5=7170ca118e0d7b813f4fbdb59b997659&lt;/a&gt;</text>
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              <elementText elementTextId="101471">
                <text>All Open Access; Hybrid Gold Open Access</text>
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                <text>ISSN: 17578361</text>
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                <text>Manikandan R., Department of Biochemistry, Shrimati Indira Gandhi College, Tamil Nadu, Tiruchirappalli, India; Balamuralikrishnan B., Department of Food Science and Biotechnology, College of Life Science, Sejong University, Seoul, South Korea; Boro A., Department of Human Genetics and Molecular Biology, Bharathiar University, Tamil Nadu, Coimbatore, India; Karthika P., Department of Zoology, School of Biosciences, Avinashilingam Institute for Home Science and Higher Education for Women, Tamil Nadu, Coimbatore, India; Arun M., Department of Life Sciences, CHRIST Deemed to be University, Bengaluru, India; Velayuthaprabhu S., Department of Biotechnology, Bharathiar University, Tamil Nadu, Coimbatore, India; Malaisamy A., Transcription Regulation Group, International Centre for Genetic Engineering and Biotechnology (ICGEB), New Delhi, India; Rengarajan R.L., Rashtriya Uchchatar Shiksha Abhiyan (RUSA) Scheme, Madurai Kamaraj University, Tamil Nadu, Madurai, India; Anand A.V., Department of Human Genetics and Molecular Biology, Bharathiar University, Tamil Nadu, Coimbatore, India</text>
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                <text>Leveraging Deep Learning in Hate Speech Analysis on Social Platform</text>
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            <name>Description</name>
            <description>An account of the resource</description>
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                <text>The scope and usage of the Internet have surpassed the expected growth and have proven beyond the basic purpose of being used for networking and telecommunications. It serves as the backbone of the web, and one of the predominant domains that uses the Internet is social media. The concept was conceived in the early 1990s and went on to grow as a powerful medium of people networking along with the Internet. Social networking sites (SNS) acquired a predominant element of the Internet owing to their use and services they offer through the Internet. A few of the most used social networking sites include Twitter and Facebook, which are used synonymous to expressions of text. These SNS allow the users to post photos, videos, and other multimedia content along with text and voice messages that are shared among other users. As with any technology or application, these also have the risk of users posting offensive material and textual content. Hate is being spread through messages, which are in the form of text and also through other materials posted. There is no control to check for the message for the hate content as and when it is posted, and by the time it is deleted by admins, it could have already reached millions of users. This chapter proposes a technique for detecting hate texts in reviews from registered users in the Twitter dataset. The proposed work makes use of improved principle component analysis (IPCA) and modified convolution neural network (MCNN) for detecting hate texts. The advantage of natural language processing is used for building an automated system for the analysis of syntax and semantics of the words. The proposed methodology consists of phases like pre-processing, feature extraction, and process to classify the text. The white spaces in the text are removed through normalization in the pre-processing phase, and also remove special characters such as question marks, punctuations, and exclamatory symbols to remove stop words. The features that are pre-processed are then subjected to feature extraction using IPCA. A set of correlated features are made used for identifying more important features in the data set under consideration. Next, the classification is done for identifying the hate text or for any language abuse. MCNN is applied for the classification of the text into HATE and NON-HATE from the text with better accuracy. The experiments prove that the proposed method has a high level of accuracy even for a large dataset. The results show that the proposed method has better performance in terms of precision, recall, and F-measure when compared with other state-of-the-art methods.  2024 Taylor &amp;amp; Francis Group, LLC.</text>
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              <elementText elementTextId="149533">
                <text>Manikandan R.; Hariharasitaraman S.; Ramkumar S.; Gobinath R.</text>
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                <text>Deep Learning for Smart Healthcare: Trends, Challenges and Applications, pp. 38-53.</text>
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                <text>CRC Press</text>
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                <text>2024-01-01</text>
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                <text>&lt;a href="https://doi.org/10.1201/9781003469605-3" target="_blank" rel="noreferrer noopener"&gt;https://doi.org/10.1201/9781003469605-3&lt;/a&gt;
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            <description>Information about rights held in and over the resource</description>
            <elementTextContainer>
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                <text>Restricted Access</text>
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                <text>ISBN: 978-104002137-8; 978-103245581-5</text>
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                <text>Manikandan R., CHRIST (Deemed to be University), India, School of Business and Management, India; Hariharasitaraman S., Division of Cyber Security and Digital Forensics, Faculty of Computing Science and Engineering, VIT Bhopal University, India; Ramkumar S., Christ University, Bangalore, India; Gobinath R., Christ University, Bangalore, India</text>
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          <element elementId="50">
            <name>Title</name>
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                <text>AI Based Variable Step Size Block Least Mean Square Filter for Noise Cancellation System</text>
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          </element>
          <element elementId="49">
            <name>Subject</name>
            <description>The topic of the resource</description>
            <elementTextContainer>
              <elementText elementTextId="172439">
                <text>Adaptive filter; Blocked Least Mean Square (BLMS); FIR filter; Normalized LMS</text>
              </elementText>
            </elementTextContainer>
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            <description>An account of the resource</description>
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                <text>Most of the Active Noise Cancellation (ANC) systems working properly in low-frequency noises only. To make it suitable for isolating high-frequency noise, it needs an additional circuit which consumes more energy. This problem is mitigated in this study by designing a Variable Step size Block Least Mean Square (VSBLMS) filter which is suitable for an effective noise cancellation system. VSBLMS filter is designed with RCA to make a design area efficient and it is designed with a novel adder to achieve high speed as well as less energy consumption. The proposed filter is designed and simulated using Xilinx ISE 13.2. The simulation results shows that the proposed VSBLMS filter design mitigates the unwanted noises in various frequency bands. The proposed VSBLMS reduces the energy consumption by 9.32%, 27.63%, 13.53%, 11.80%, 10.71 %, 13.14% and 9.26% when compared with state of the art methods.  2023 IEEE.</text>
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            <name>Creator</name>
            <description>An entity primarily responsible for making the resource</description>
            <elementTextContainer>
              <elementText elementTextId="172441">
                <text>Manikandan R.; Kumar K.S.; Ramkumar S.</text>
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            <description>A related resource from which the described resource is derived</description>
            <elementTextContainer>
              <elementText elementTextId="172442">
                <text>International Conference on Self Sustainable Artificial Intelligence Systems, ICSSAS 2023 - Proceedings, pp. 122-125.</text>
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            <name>Publisher</name>
            <description>An entity responsible for making the resource available</description>
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              <elementText elementTextId="172443">
                <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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              <elementText elementTextId="172444">
                <text>2023-01-01</text>
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                <text>&lt;a href="https://doi.org/10.1109/ICSSAS57918.2023.10331847" target="_blank" rel="noreferrer noopener"&gt;https://doi.org/10.1109/ICSSAS57918.2023.10331847&lt;/a&gt;
&lt;br /&gt;&lt;br /&gt;&lt;a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85181131705&amp;amp;doi=10.1109%2FICSSAS57918.2023.10331847&amp;amp;partnerID=40&amp;amp;md5=3e9b2f41a38c5da84186289abab77b28" target="_blank" rel="noreferrer noopener"&gt;https://www.scopus.com/inward/record.uri?eid=2-s2.0-85181131705&amp;amp;doi=10.1109%2fICSSAS57918.2023.10331847&amp;amp;partnerID=40&amp;amp;md5=3e9b2f41a38c5da84186289abab77b28&lt;/a&gt;</text>
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            <description>Information about rights held in and over the resource</description>
            <elementTextContainer>
              <elementText elementTextId="172446">
                <text>Restricted Access</text>
              </elementText>
            </elementTextContainer>
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              <elementText elementTextId="172447">
                <text>ISBN: 979-835030085-7</text>
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            <description>The file format, physical medium, or dimensions of the resource</description>
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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>Manikandan R., School of Business and Management Christ (Deemed to Be University), Department of Lean Operations and Systems, Karnataka, Bengaluru, India; Kumar K.S., Alliance University, Computer Science and Engineering, Alliance College of Engineering and Design, Karnataka, Bengaluru, India; Ramkumar S., School of Sciences, Christ (Deemed to Be University), Department of Computer Science, Karnataka, Bengaluru, India</text>
              </elementText>
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          <description>The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/.</description>
          <elementContainer>
            <element elementId="50">
              <name>Title</name>
              <description>A name given to the resource</description>
              <elementTextContainer>
                <elementText elementTextId="51377">
                  <text>Conference Papers</text>
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          <element elementId="50">
            <name>Title</name>
            <description>A name given to the resource</description>
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              <elementText elementTextId="172074">
                <text>Area and Energy Efficient Method Using AI for Noise Cancellation in Ear Phones</text>
              </elementText>
            </elementTextContainer>
          </element>
          <element elementId="49">
            <name>Subject</name>
            <description>The topic of the resource</description>
            <elementTextContainer>
              <elementText elementTextId="172075">
                <text>Active Noise Cancellation; Active Noise Reduction; Digital Signal Processing; Normalized Least Mean Square; Recursive Least Square and Block LMS</text>
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            <elementTextContainer>
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                <text>Adaptive filters are suitable for most of the Digital Signal Processing (DSP) applications such as channel equalization, noise cancellation, echo cancellation, channel estimation and system identification. Nowadays due to the advancement in semiconductor technology, the need for Active Noise Cancellation (ANC) headphones in compact devices is increased. The major idea behind this proposed work is to design an area and energy efficient novel adaptive filter suitable for in-ear headphones by combining Normalized Least Mean Square (NLMS) and Block LMS (BLMS). The proposed filter is designed and simulated using Xilinx ISE 13.2. The simulation results shows that the proposed design mitigates the unwanted noises in various frequency bands.  2023 IEEE.</text>
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            <name>Creator</name>
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            <elementTextContainer>
              <elementText elementTextId="172077">
                <text>Manikandan R.; Ramkumar S.; Prabhakaran M.</text>
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            </elementTextContainer>
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            <elementTextContainer>
              <elementText elementTextId="172078">
                <text>International Conference on Self Sustainable Artificial Intelligence Systems, ICSSAS 2023 - Proceedings, pp. 117-121.</text>
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            <description>An entity responsible for making the resource available</description>
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              <elementText elementTextId="172079">
                <text>Institute of Electrical and Electronics Engineers Inc.</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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                <text>2023-01-01</text>
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                <text>&lt;a href="https://doi.org/10.1109/ICSSAS57918.2023.10331813" target="_blank" rel="noreferrer noopener"&gt;https://doi.org/10.1109/ICSSAS57918.2023.10331813&lt;/a&gt;
&lt;br /&gt;&lt;br /&gt;&lt;a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85181067301&amp;amp;doi=10.1109%2FICSSAS57918.2023.10331813&amp;amp;partnerID=40&amp;amp;md5=463fa972febd5d576d8b59dbf93e7068" target="_blank" rel="noreferrer noopener"&gt;https://www.scopus.com/inward/record.uri?eid=2-s2.0-85181067301&amp;amp;doi=10.1109%2fICSSAS57918.2023.10331813&amp;amp;partnerID=40&amp;amp;md5=463fa972febd5d576d8b59dbf93e7068&lt;/a&gt;</text>
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            <description>Information about rights held in and over the resource</description>
            <elementTextContainer>
              <elementText elementTextId="172082">
                <text>Restricted Access</text>
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            </elementTextContainer>
          </element>
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                <text>ISBN: 979-835030085-7</text>
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              <elementText elementTextId="172085">
                <text>English</text>
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                <text>Manikandan R., School of Business and Management Christ (Deemed to Be University), Department of Lean Operations and Systems, Karnataka, Bengaluru, India; Ramkumar S., School of Sciences, Christ (Deemed to Be University), Department of Computer Science, Karnataka, Bengaluru, India; Prabhakaran M., Alliance College of Engineering and Design, Alliance University, Department of Computer Science and Engineering, Karnataka, Bengaluru, India</text>
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            <element elementId="50">
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          <element elementId="50">
            <name>Title</name>
            <description>A name given to the resource</description>
            <elementTextContainer>
              <elementText elementTextId="196374">
                <text>Current scenario of Gastric cancer (GC): A systematic review</text>
              </elementText>
            </elementTextContainer>
          </element>
          <element elementId="49">
            <name>Subject</name>
            <description>The topic of the resource</description>
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              <elementText elementTextId="196375">
                <text>Digestive system disorder; Food habits; Gastric Cancer.; Genetic factor</text>
              </elementText>
            </elementTextContainer>
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                <text>Gastric cancer (GC) is amongst the most difficult cancers to treat and, hence, has to be studied from the roots to be properly analysed. The disease starts as precancerous lesions which have to be observed. This includes dysplasia, atrophic gastritis and intestinal metaplasia. This is effective for detecting the prognosis of GC so that it can be resolved in early stages. This is dependent on a variety of risk factors which include blood type, gender, age, geography, gene polymorphisms, acid secretion, pernicious anemia, oxidative damage, previous gastric surgery, etc. Probably the most important, however, is infection by H. Pylori. With respect to virulence factors and host factors, both are significant players in the development of several cases of malignancies. Other genetic factors and lifestyle choices including alcohol use, tobacco usage and obesity, will also be analysed. There are also several genetic conditions that cause high risk of development into a malignancy including Menetrier s disease, hereditary diffuse GC, Lynch syndrome, Peutz-Jeghers syndrome, Li Fraumani syndrome etc. Several diagnostic procedures such as lab tests, physical examinations and biopsy are used. The current review analysed the factors which are involved in the prognosis and causative agents in the development of GC. This is to better understand how GC can be diagnosed and confirmed. This may include molecular testing, including genes, which can be of high risk followed by modern treatment methods discussing surgery, chemotherapy, immunotherapy and radiotherapy. Chemotherapy includes discussion of several drugs and how they target the malignancy in question. By understanding the disease in detail, through causes, prognosis, detection and treatment, we can go one step closer to find the cure for GC.  2022 World Research Association. All rights reserved.</text>
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              <elementText elementTextId="196377">
                <text>Manikantan P.; Anushka S.; Arun M.; Balamuralikrishnan B.; Anand A.V.</text>
              </elementText>
            </elementTextContainer>
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            <description>A related resource from which the described resource is derived</description>
            <elementTextContainer>
              <elementText elementTextId="196378">
                <text>Research Journal of Biotechnology, Vol-17, No. 9, pp. 134-144.</text>
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                <text>Manikantan P., Department of Life Sciences, CHRIST (Deemed to Be) University, Bangalore, 560029, India; Anushka S., Department of Life Sciences, CHRIST (Deemed to Be) University, Bangalore, 560029, India; Arun M., Department of Life Sciences, CHRIST (Deemed to Be) University, Bangalore, 560029, India; Balamuralikrishnan B., Department of Food Science and Biotechnology, College of Life Science, Sejong University, Seoul, South Korea; Anand A.V., Department of Human Molecular Genetics, Bharathiar University, Coimbatore, 641046, India</text>
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                <text>Chromosomal aberration assay-micronucleus assay-oxidative stress-type 2 diabetes mellitus</text>
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                <text>Introduction: Type 2 diabetes mellitus is a metabolic disorder characterized by insulin resistance and disrupted insulin secretion. It is often linked to injuries, malfunction and failure of several organs in the long term. The elevated chromosomal disruptions and genetic complications in diabetic patients are due to the increased production of reactive oxygen species. Materials and Methods: The current study used chromosomal aberration assay and micronucleus assay to analyze the extent of abnormalities in the subjects. Results: The results showed increase in frequency of chromosomal aberrations in diabetic patients when compared to the control group (2.761.65 and 0.470.75 respectively). They also showed higher levels of micronuclei formation than the control participants (13.288.63 and 4.128.89 respectively). The correlation analysis indicated positive relationship between total aberrations and duration of diabetes. Conclusion: These results indicate that diabetes is associated with genomic instability and studies at a genetic level can be employed for early detection.  2020, West Asia Organization for Cancer Prevention.</text>
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                <text>All Open Access; Gold Open Access</text>
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                <text>Manikantan P., Department of Life Sciences, CHRIST (Deemed to be University), Bengaluru, Karnataka, India; Rajesh N., Department of Life Sciences, CHRIST (Deemed to be University), Bengaluru, Karnataka, India; Philip A.M., Department of Life Sciences, CHRIST (Deemed to be University), Bengaluru, Karnataka, India</text>
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                <text>Antecedents of consumer attitude and purchase intention towards counterfeit products</text>
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                <text>counterfeit products; genuine store trustworthiness; hedonic behaviour; purchase intention; risk; value consciousness</text>
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                <text>This paper aims to investigate the effect of information reliability, risk, and value consciousness on hedonic behaviour, attitude towards counterfeit products, and genuine store trustworthiness. Using a structured survey instrument, this paper gathered data from 449 respondents from three cities major cities (Kochi, Bangalore, and Chennai) in southern part of India. The hierarchical regression render support that: 1) information reliability is positively related to hedonic behaviour and attitude towards counterfeit products; 2) risk is negatively related to hedonic behaviour and attitude towards counterfeit products; 3) value consciousness is positively related to hedonic behaviour and genuine store trustworthiness; 4) hedonic behaviour and attitude towards products are related to purchase intention. In addition, the results also support the moderation hypotheses of materialism, value consciousness and social status. The study suggests that marketers need to understand the importance of non-deceptive counterfeiting is useful and consumers have tendency to prefer to use these products once they are satisfied with their utility. The conceptual model developed and tested in this research enables the marketing managers to understand the antecedents of consumers purchase intention of counterfeit products. Copyright  2024 Inderscience Enterprises Ltd.</text>
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                <text>International Journal of Business and Globalisation, Vol-37, No. 3, pp. 340-369.</text>
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                <text>Manikoth S., Department of Commerce, CHRIST (Deemed to be University), Bengaluru, 560029, India; Olson B.J., Dhillon School of Business, University of Lethbridge, 4401 University Drive, Lethbridge, T1K 3M4, AB, Canada; Lakavath M., School of Management, Pondicherry University, Puducherry, India; Parayitam S., Department of Management and Marketing, Charlton College of Business, University of Massachusetts Dartmouth, 285 Old Westport Road, North Dartmouth, 02747, MA, United States</text>
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                <text>There are many theories on why managers do not (as a behavior) or should not (as a value) supplement profit orientation with people-centrism and planet sensitivity. In practice, managers do not supplement profit orientation with considerations for people and the planet unless they have the tools and know how to make that possible. This book seeks to address that by focusing on the normative dimension of organizational development. There are two competing norms for developing an organization: first, as a profit-oriented business enterprise; and second, as a people-centric, planet-sensitive, profit-oriented business or social enterprise. The performance of a business is a concern for all stakeholders. With the growing realization of the importance of indirect stakeholders like the society and the planet, it is increasingly important to raise awareness about the social and environmental responsibilities of businesses and organizations. This book is a must-read for academics, researchers, practitioners and policymakers who are concerned about the triple bottom-line (Planet-People-Profit) performance of businesses, which is critical for their long-term sustainability. It covers topics pertaining to the relationship between business and society, including social entrepreneurship and corporate social responsibility, among others, and draws from real-life case studies on social initiatives.  2024 by World Scientific Publishing Co. Pte. Ltd.</text>
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                <text>Entrepreneurship education has become a priority for policy-makers especially in developing countries. Such interventions in the education system are expected to create a culture of entrepreneurship in the society and thereby bring economic benefits through the enterprising behaviour of individuals resulting in better performance of existing organizations as well as creation of new ventures. While the process appears to be simple and straightforward, the experiences have often belied the expectations. The fact that it is rather difficult to assess the long-term impact of entrepreneurship education adds to the confusion and ambiguities. Educators therefore have been tinkering with various aspects of entrepreneurship education and training in the hope of arriving at the best design. Obviously, this has led to many innovations in the curriculum, pedagogy, target groups and institutions involved in entrepreneurship education. The present paper attempts to document these innovations and best practices under a 'WHAT-HOW-WHO-WHERE' framework to capture the four domains of activities involved. Based on a comprehensive review of the literature, we have developed a fairly comprehensive picture of what is happening in the field and proposed a theoretical model highlighting the dual role of entrepreneurship education, namely developing enterprising individuals in the society and providing knowledge and skills required for enterprise creation.  Springer Nature Singapore Pte Ltd. 2017. All rights are reserved.</text>
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                <text>Entrepreneurial ecosystem is the interacting socio-economic environment that facilitates entrepreneurs to start and develop their enterprises. A vibrant and supportive entrepreneurial ecosystem is necessary for the start-up and growth of an enterprise. The entrepreneurial action would largely depend on the perception of entrepreneurs about the ecosystem. In this context, a study was designed to understand the perceptions of actors (entrepreneurs) and observers (non-entrepreneurs) on various components of the entrepreneurial ecosystem. Data for this study were collected from 296 entrepreneurs and 315 non-entrepreneurs from India, who responded to a 77-item questionnaire by giving their ratings of various aspects of the ecosystem on a 5-point scale. Findings of the study showed that perceptions of the entrepreneurial ecosystem were significantly different for most of the subgroups. Most notable among these differences was those between entrepreneurs and non-entrepreneurs, where the mean scores on all dimensions were found to be significantly higher for non-entrepreneurs than for entrepreneurs except for entrepreneurial capability which was found to be higher for entrepreneurs. Hence, the hypothesis of actorobserver bias in the perceptions of entrepreneurs and non-entrepreneurs is supported.  2019 SAGE Publications.</text>
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                <text>Network Intrusion Detection System (NIDS) is the key technology for information security, and it plays significant role for classifying various attacks in the networks accurately. An NIDS gains an understanding of normal and anomalous behavior by examining the network traffic and can identify unknown and new attacks. Analyzing and Identifying unfamiliar attacks are one of the big challenges in Network IDS research. A huge response has been given to deep learning over the past several years and novelty in deep learning techniques are also improved regularly. Deep learning based Network Intrusion Detection approach is highly essential for improved performance. Nowadays, Machine learning algorithms made a revolution in the area of human computer interaction and achieved significant advancement in imitating human brain exactly. Convolutional Neural Network (CNN) is a powerful learning algorithm in deep learning model for improving the machine learning ability in order to achieve high attack classification accuracy and low false alarm rate. In this article, an overview of deep learning methodologies for commonly used NIDS such as Auto Encoder (AE), Deep Belief Network (DBN), Deep Neural Network (DNN), Restricted Boltzmann Machine (RBN). Moreover, the article introduces the most recent work on network anomaly detection using deep learning techniques for better understanding to choose appropriate method while implementing NIDS through widespread literature analysis. The experimental results designate that the accuracy, false alarm rate, and timeliness of the proposed CNN-NIDS model are superior than the traditional algorithms.  2020, Springer Science+Business Media, LLC, part of Springer Nature.</text>
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                <text>Manimaran A., Department of Computer Applications, Madanapalle Institute of Technology &amp;amp; Science, Madanapalle, India; Chandramohan D., Department of Computer Science &amp;amp; Engineering, Madanapalle Institute of Technology &amp;amp; Science, Madanapalle, India; Shrinivas S.G., Department of Computer Applications, Madanapalle Institute of Technology &amp;amp; Science, Madanapalle, India; Arulkumar N., Department of Computer Science, Christ (Deemed to be University), Bangalore, India</text>
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                <text>Manimekala B., Christ University, Department of Computer Science, Yeshwanthpur, Bengaluru, India; Aravind T., Vel TecRangarajanDr.Sagunthala R&amp;amp;d Institute of Science and Technology, Department of Cse, School of Computing, India; Sounderrajan T., Erode Sengunthar Engineering College, Department of Ai and Data Science, Tamil Nadu, Thuduppathi, India; Rajakumareswaran V., Erode Sengunthar Engineering College, Department of Computer Science and Design, Tamil Nadu, Thuduppathi, India; Kondreddi K., Chaitanya Bharathi Institute of Technology, Electrical and Electronics Engineering, Ranga Reddy, Telangana, Gandipet, 500075, India; Mohanambal K., Bannariamman Institute of Technology, Department of Computer Science and Engineering, Sathyamangalam, India</text>
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