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                <text>Should Crypto Integrate Micro-Finance option?</text>
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                <text>awareness; cryptocurrency; economically challenged; investment behaviour; Micro finance; perception; policy makers; readiness; trustworthiness; younger population</text>
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                <text>Purpose - The purpose of the study is to identify the readiness or acceptance by the younger population specifically, the school and university students towards the investment in cryptocurrency if the micro-finance option is included in such new asset investments. Further to this the research also focusses on the mediating factor as trustworthiness to identify the impact or influence of the variable towards the acceptance of the new asset investment.Design/methodology/approach - The research conducted through relevant literature with sufficient variables measuring with five point Likert's scale. The research also tested with hypothesis on the relationship with variables. A total of 293 valid respondents data were collected and analysed through Structural Equation model.Findings - The analysis and results suggested that the perception, awareness and trustworthiness has positive impact towards the readiness towards crypto investments. Whereas, the investment behaviour has complex acceptability towards the readiness as it failed to accept the hypothesis.Research limitations/implications - the research is limited with the younger population however the research did not focusses on the economically challenged population as they may not be afford to invest in such platforms. The future studies can also be focussed on the same area with more towards the other factors that influence the economically challenged population and identify solution their economic growth. Furthermore, the study may be game changer for the policy makers in legalising the crypto investments in the country.Originality/value - According the wider background study and with substantial literature the research is of first in its kind as per the author's knowledge to integrate the micro finance concept in crypto investments to promote the investment habit among the younger population.   2024 IEEE.</text>
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                <text>Subramanian P.; Williams M.J.; Cheriyan A.; Monica L.; Ganesh L.; Rao P.N.; Adhikary D.</text>
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                <text>TQCEBT 2024 - 2nd IEEE International Conference on Trends in Quantum Computing and Emerging Business Technologies 2024</text>
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                <text>Institute of Electrical and Electronics Engineers Inc.</text>
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                <text>Subramanian P., Christ University, India; Williams M.J., Sri Krishna Arts &amp;amp; Science College, India; Cheriyan A., Christ University, India; Monica L., Christ University, India; Ganesh L., Christ University, India; Rao P.N., Christ University, India; Adhikary D., Christ University, India</text>
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                <text>Channel Selection Using Stochastic Diffusion Search Algorithm for Classification in Brain-Computer Interface</text>
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                <text>-Channel selection; Adaboost; Classification and Regression Tree (CART); Common Spatial Pattern (CSP); K-Nearest Neighbor (KNN); Naive Bayes (NB); Stochastic Diffusion Search (SDS)</text>
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                <text>Utilization of the Brain-Computer Interfaces (BCI) is done via Electroencephalogram (EEG) signals that provide several environmental interactions among individuals having restricted movements owing to neurodegenerative diseases or strokes. However, the BCI system was based on Motor Imagery (MI). It was not used for any form of real-life application owing to a decrease in the performance of various Common Spatial Pattern (CSP) algorithms, especially while the actual number of channels was high. A multi-channel structure of such EEG signals can increase cost and bring down speed. Due to this, a reduction in the system cost by the detection of active electrodes during the process can increase accessibility. This way, optimization techniques in choosing electrodes can be used to determine other effective channels by employing a method of random selection. For this work, a Stochastic Diffusion Search (SDS) algorithm based on herd optimization techniques was used with four different classifiers, which were the AdaBoost, the Classification and Regression Tree (CART), the Naive Bayes (NB) as well as the K-Nearest Neighbor (KNN). The channels that were chosen frequently were determined to improve the system performance with regard to accuracy and speed. The results proved that the approach proposed was successful in bringing down the channel number and run time without affecting the accuracy of classification.  2024 Seventh Sense Research Group.</text>
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                <text>Subramanian S.C.; Daniel D.</text>
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                <text>SSRG International Journal of Electronics and Communication Engineering, Vol-11, No. 9, pp. 56-63.</text>
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                <text>&lt;a href="https://doi.org/10.14445/23488549/IJECE-V11I9P106" target="_blank" rel="noreferrer noopener"&gt;https://doi.org/10.14445/23488549/IJECE-V11I9P106&lt;/a&gt;
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                <text>All Open Access; Gold Open Access</text>
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                <text>Subramanian S.C., Department of Computer Science and Engineering, School of Engineering and Technology, CHRIST(Deemed to be University), Karnataka, Bengaluru, India; Daniel D., Department of Computer Science and Engineering, School of Engineering and Technology, CHRIST(Deemed to be University), Karnataka, Bengaluru, India</text>
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                <text>A Framework for Enhancing Classification in BrainComputer Interface</text>
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                <text>Adaboost; Braincomputer interface (BCI); Channel selection using recursive feature elimination; Common spatial pattern (CSP)</text>
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                <text>Over the past twenty years, the various merits of braincomputer interface (BCI) have garnered much recognition in the industry and scientific institutes. An increase in the quality of life is the key benefit of BCI utilization. The majority of the published works are associated with the examination and assessment of classification algorithms due to the ever-increasing interest in electroencephalography-based (EEG) BCIs. Yet, another objective is to offer guidelines that aid the reader in picking the best-suited classification algorithm for a given BCI experiment. For a given BCI system, selecting the best-suited classifier essentially requires an understanding of the features to be utilized, their properties, and their practical uses. As a feature extraction method, the common spatial pattern (CSP) will project multichannel EEG signals into a subspace to highlight the variations between the classes and minimize the similarities. This work has evaluated the efficacy of various classification algorithms like Naive Bayes, k-nearest neighbor classifier, classification and regression tree (CART), and AdaBoost for the BCI framework. Furthermore, the work has offered the proposal for channel selection with recursive feature elimination.  2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.</text>
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                <text>Subramanian S.C.; Daniel D.</text>
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                <text>Lecture Notes on Data Engineering and Communications Technologies, Vol-111, pp. 651-665.</text>
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                <text>Springer Science and Business Media Deutschland GmbH</text>
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                <text>ISSN: 23674512</text>
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                <text>Subramanian S.C., Department of Computer Science and Engineering, Christ (Deemed To Be University), Bengaluru, India, Department of Computer Science and Engineering, Sri Venkateswara College of Engineering, Tamil Nadu, Sriperumbudur, India; Daniel D., Department of Computer Science and Engineering, Christ (Deemed To Be University), Bengaluru, India</text>
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                <text>Algorithms; Braincomputer interface (BCI); Classification; Classifiers; Electroencephalography (EEG); Feature extraction; Feature selection; Optimization techniques</text>
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                <text>In braincomputer interface (BCI) systems, the electroencephalography (EEG) signal is extensively utilized, as the recording of EEG brain signals is having relatively low cost, the potentiality for user mobility, high time resolution, and non-invasive nature. The EEG features are extracted by the BCI to execute commands. In the feature set obtained, the computational complexity increases, and poor classifier generalization can be caused by the utilization of a lot of overlapping features. The irrelevant features accumulation could be avoided with the feature selection procedures application. The feature selection algorithms are utilized to select diverse features for each classifier. Classifiers are the algorithms that are run to attain the classification. The researchers have examined diverse classifier implementation techniques to identify the feature vectors class. A review of EEG-BCI techniques available in the literature for feature selection, classifiers, and optimization algorithms is presented in this work. The research challenges, gaps, and limitations are identified in this paper.  The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.</text>
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                <text>Subramanian S.C.; Daniel D.</text>
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                <text>Lecture Notes in Networks and Systems, Vol-865, pp. 79-93.</text>
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                <text>Subramanian S.C., Department of Computer Science and Engineering, School of Engineering and Technology, CHRIST (Deemed to be University), Karnataka, Bengaluru, India; Daniel D., Department of Computer Science and Engineering, School of Engineering and Technology, CHRIST (Deemed to be University), Karnataka, Bengaluru, India</text>
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                <text>A Deep Learning Method for Classification in Brain-Computer Interface</text>
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                <text>Adaboost; Brain-Computer Interfaces (BCI); Deep Learning; Electroencephalogram (EEG) signals; Inception Network; Residual Network (ResNet); Visual Geometry Group Network (VGGNet)</text>
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                <text>Neural activity is the controlling signal used in enabling BCI to have direct communication with a computer. An array of EEG signals aid in the selection of the neural signal. The feature extractors and classifiers have a specific pattern of EEG control for a given BCI protocol, which is tailor-made and limited to that specific signal. Although a single protocol is applied in the deep neural networks used in EEG-based brain-computer interfaces, which are being used in the feature extraction and classification of speech recognition and computer vision, it is unclear how these architectures find themselves generalized in other area and prototypes. The deep learning approach used in transferring knowledge acquired from the source tasks to the target tasks is called transfer learning. Conventional machine learning algorithms have been surpassed by deep neural networks while solving problems concerning the real world. However, the best deep neural networks were identified by considering the knowledge of the problem domain. A significant amount of time and computational resources have to be spent to validate this approach. This work presents a deep learning neural network architecture based on Visual Geometry Group Network (VGGNet), Residual Network (ResNet), and inception network methods. Experimental results show that the proposed method achieves better performance than other methods.  2023 IEEE.</text>
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                <text>Subramanian S.C.; Daniel D.</text>
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                <text>Institute of Electrical and Electronics Engineers Inc.</text>
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                <text>&lt;a href="https://doi.org/10.1109/ICEEICT56924.2023.10157910" target="_blank" rel="noreferrer noopener"&gt;https://doi.org/10.1109/ICEEICT56924.2023.10157910&lt;/a&gt;
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                <text>Subramanian S.C., CHRIST(Deemed to Be University), Departement of Computer Science and Engineering, Karnataka, Bangalore, India; Daniel D., CHRIST(Deemed to Be University), Departement of Computer Science and Engineering, Karnataka, Bangalore, India</text>
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          <element elementId="50">
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              <elementText elementTextId="154906">
                <text>Smart medical sensor</text>
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            <description>The topic of the resource</description>
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              <elementText elementTextId="154907">
                <text>Accelerometers; Blood pressure; Bluetooth; Health-care sector; Medical sensor; Physical parameters</text>
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                <text>Medical sensors facilitate health-care applications to save a patient's life by continuously monitoring the patient's health. The combined feature of medical sensors and the fastest growing techniques that are Internet of things improves the accuracy of treatment. Internet of things techniques serve the smart and very effective medical service. Early diagnosis of the symptoms helps the health-care provider to get success in the treatment to save a patient's life. Many medical sensors are available in the health-care application that can monitor continuously patient health. Medical sensors can be wearable and nonwearable. There are some common parameters such as body temperature and a heart rate that are used to monitor human activity. These parameters are measured by using wearable and body-embedded sensors. The data collected from these parameters are analyzed by the medical devices for early detection of disease. The advanced internet of things techniques help to connect the sensors, patients, hospitals, and other medical devices. In this chapter, we highlight the use of different types of sensors with advanced technology (internet of things).  2023 Elsevier Inc. All rights reserved.</text>
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              <elementText elementTextId="154909">
                <text>Subramanian V.; Dahiya S.; Thiruppathiraju G.; Balusamy B.</text>
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                <text>Security and Privacy Issues in Internet of Medical Things, pp. 1-32.</text>
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              <elementText elementTextId="154911">
                <text>Elsevier</text>
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&lt;br /&gt;&lt;br /&gt;&lt;a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85152812505&amp;amp;doi=10.1016%2FB978-0-323-89872-0.00005-8&amp;amp;partnerID=40&amp;amp;md5=24d06e22493b2974695be518befe267b" target="_blank" rel="noreferrer noopener"&gt;https://www.scopus.com/inward/record.uri?eid=2-s2.0-85152812505&amp;amp;doi=10.1016%2fB978-0-323-89872-0.00005-8&amp;amp;partnerID=40&amp;amp;md5=24d06e22493b2974695be518befe267b&lt;/a&gt;</text>
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                <text>ISBN: 978-032389872-0; 978-032388594-2</text>
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                <text>Subramanian V., Department of Data Science, CHRIST (Deemed to be University), Maharashtra, Lavasa, India; Dahiya S., School of Computing Science and Engineering, Galgotias University, Uttar Pradesh, Greater Noida, India; Thiruppathiraju G., School of Computing Science, KPR College of Arts Science and Research, Tamil Nadu, Coimbatore, India; Balusamy B., Shiv Nadar University, Tamil Nadu, Chennai, India</text>
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          <element elementId="50">
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            <description>A name given to the resource</description>
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              <elementText elementTextId="72148">
                <text>Designing a One-Pot Ternary Fe-Mn-Zn Oxide Positive Electrode with Enhanced Energy-Storage Properties for Hybrid Supercapacitors</text>
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              <elementText elementTextId="72149">
                <text>In recent years, ternary metal-oxide nanocomposite-based active electrodes have been investigated more effectively for supercapacitor applications due to the existence of a greater number of electroactive sites and the synergistic effect of three different transition-metal ions. Herein, Fe-Mn-Zn oxide ternary nanocomposites are synthesized using a simple and cost-effective one-pot hydrothermal approach. The characterizations of XRD, FTIR, FESEM, EDX, HRTEM, and XPS are analyzed for the synthesized Fe-Mn-Zn oxide nanocomposites to study their phases, functional groups, morphologies, purity, and binding energies. The electrochemical characteristics for the developed electrodes are studied in a three-electrode technique using CV, GCD, EIS, and a cyclic stability test. As expected, the ternary nanocomposite electrode of Fe-Mn-Zn oxide reveals a maximum specific capacitance (Cspc1) of 1673.4 F/g in comparison to other developed electrodes of ZnFe2O4 (271.7 F/g) and ZnMn2O4 (412.7 F/g) at the appropriate scan rate of 10 mV/s. In addition, the Fe-Mn-Zn oxide ternary nanocomposite active electrode exhibits 2616.25 F/g of total capacitance (qT**), 686.94 F/g of outer capacitance (qO**), and 1929.30 F/g of inner capacitance (qI**) which are determined by Trasatti analysis. Moreover, the fabricated hybrid supercapacitor device provides a good specific capacitance of 320.8 F/g, a high energy density of 75.3 Wh/kg at the power density of 649.9 W/kg at 1 A/g of current density range, and 88.75% of superior capacitive retention over 10,000 cycles at 10 A/g. Therefore, a ternary metal-oxide nanocomposite electrode is proposed to be a promising material for energy-storage devices.  2024 American Chemical Society.</text>
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              <elementText elementTextId="72150">
                <text>Subramaniyan S.; Veerasingam M.; Feroz Gandhi K.; Arumugam V.; Muthusamy K.K.; Sakkarapani S.</text>
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              <elementText elementTextId="72151">
                <text>Energy and Fuels, Vol-39, No. 1, pp. 906-920.</text>
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                <text>American Chemical Society</text>
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                <text>2025-01-01</text>
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                <text>&lt;a href="https://doi.org/10.1021/acs.energyfuels.4c04759" target="_blank" rel="noreferrer noopener"&gt;https://doi.org/10.1021/acs.energyfuels.4c04759&lt;/a&gt;
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                <text>Subramaniyan S., Department of Physics, Alagappa University, Tamil Nadu, Karaikudi, 630003, India; Veerasingam M., Department of Chemistry, Mount Zion College of Engineering and Technology, Thirumayam, Pudukkottai, 622 507, India; Feroz Gandhi K., Department of Physics, Alagappa University, Tamil Nadu, Karaikudi, 630003, India; Arumugam V., Department of Physics, Panimalar Engineering College, Tamilnadu, Chennai, 600 123, India; Muthusamy K.K., Department of Physics and Electronics, CHRIST (Deemed to be University), Bengaluru, 560 029, India; Sakkarapani S., Department of Physics, Alagappa University, Tamil Nadu, Karaikudi, 630003, India</text>
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                <text>A new stepwise method for selection of input and output variables in data envelopment analysis</text>
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                <text>Commercial banks; Data envelopment analysis; Dimensionality; Stepwise method; Welchs statistic</text>
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                <text>Data envelopment analysis (DEA) is one of the widely accepted optimization technique uses to measure the relative efficiency of organizational units where multiple inputs and outputs are present. The significance of DEA results depends on the variables selected for DEA modelling. One of the main challenges in data envelopment analysis modelling is of identify the significant input and output variables for DEA modelling. In this study, we propose an enhanced stepwise method to identify the significant and insignificant input and output variable by reducing the iterations process in stepwise method. The statistical significance of the input and output variables evaluated using the statistical methods: Least significance difference (LSD), and Welchs statistics. The proposed method applied to the Indian banking sector and the results have shown that the proposed model significantly identified the significant and insignificant input and output variables with least loss of information.  2021 the author(s).</text>
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                <text>Subramanyam T.; Donthi R.; Satish Kumar V.; Amalanathan S.; Zalki M.</text>
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                <text>Journal of Mathematical and Computational Science, Vol-11, No. 1, pp. 703-715.</text>
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                <text>All Open Access; Gold Open Access</text>
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                <text>ISSN: 19275307</text>
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                <text>Subramanyam T., Department of Mathematics &amp;amp; Statistics, M.S. Ramaiah University of Applied Sciences, Bangalore, 560054, India; Donthi R., Department of Mathematics, St. MARTINS Engineering College, Telangana, 500100, India; Satish Kumar V., Department of Computer Science, East Point College of Higher Education, Bangalore, 560049, India; Amalanathan S., Department of Business and Management, Christ (Deemed to be University), Bangalore, 560029, India; Zalki M., Department of Mathematics, REVA University, Bangalore, 560064, India</text>
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                <text>Efficiency of Indian Banks with Non-Performing Assets as Undesirable Outputs</text>
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              <elementText elementTextId="170132">
                <text>Data Envelopment Analysis; efficiency; Indian Banks; Non-performing assets; Undesirable output</text>
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                <text>The performance evaluation of any banks is of utmost importance for bank management, investors, and policymakers. Due to globalization, all the banks are working in a competitive environment. Several risk factors affect the operational efficiency of banking system. This study aims to evaluate the efficiency of Indian banks with NPAs as uncontrolled variables. Due to the nature of NPAs, these are assumed as undesirable outputs in the DEA modelling. The results reveal that public sector banks experienced more input losses due to NPAs compared to private banks. The private banks experienced more loss in inputs due to the scale of operation. The Wilcoxon Signed-Rank test shown that the impact of NPAs and scale of operation are statistically significant at 0.05 level.  2023 American Institute of Physics Inc.. All rights reserved.</text>
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                <text>Subramanyam T.; Zalki M.; Satish Kumar V.; Amalanathan S.</text>
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                <text>AIP Conference Proceedings, Vol-2649</text>
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                <text>American Institute of Physics Inc.</text>
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                <text>ISSN: 0094243X; ISBN: 978-073544553-6</text>
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                <text>Subramanyam T., Department of Mathematics and Statistics, M.S. Ramaiah University of Applied Sciences, Bangalore, India; Zalki M., Department of Mathematics, REVA University, Bangalore, India; Satish Kumar V., Department of Computer Science, East Point College of Higher Education, Bangalore, India; Amalanathan S., Department of Business and Management, Christ University, Bangalore, India</text>
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                <text>The impact of Covid-19 on global upstream and downstream supply chain management activities</text>
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          <element elementId="49">
            <name>Subject</name>
            <description>The topic of the resource</description>
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              <elementText elementTextId="176644">
                <text>Corona Virus; COVID-19; Downstream Supply chain; Pandemic; Upstream Supply chain</text>
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                <text>COVID-19 pandemic has affected thousands of people worldwide; with significant economic changes in the past and to the changes to be made for future. Many organisations especially; The Intergovernmental economic organisation (OECD - The Organisation for Economic Co-operation and Development) warned the companies and industries on the global economic cut, the corona virus will be boarding. The global economy and international markets pitched down with the spread of corona virus spreading from China which is the world's second largest economy to other countries including Asia; Europe; Australia; Europe; America and the Middle East. Many economies came up with many policies to prevent the further spread of this virus; including restrictions on travel and quarantines; which has disrupted international supply chains affecting a lot of business operations and dwindle revenues. About 75 percent of business including Wholesale; Manufacturing; Retail and Services in China and about 51,000 companies have this impact at a global level according to data from Dun and Bradstreet. The success or failure of every Business depends on how well they manage their supply chain management activities. The impact of corona virus on supply chain activities is twofold. One is; Upstream Supply chain management where companies should monitor the backward integrated activities in procuring the inventory; which has accommodated a loss in the production because of closure of factories and a slowdown in the economy. Second is; Downstream Supply chain management where the intermediaries and middlemen face a lot of problems because of scarcity in inventory and many quarantine measures taken by many economies. Many disruptions in both Upstream and Downstream Supply chains lead to severe scarcity of inventory which was experienced globally by all the economies. This situation has made many economies to think of the inter connectivity and inter dependency among global nations in terms of supply chain. This article is aimed to highlight the effects and changes COVID-19 pandemic has brought in the supply chain industry from both Upstream and Downstream perspective.  2022 Author(s).</text>
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                <text>AIP Conference Proceedings, Vol-2421</text>
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                <text>Sucharitha M.M., Christ (Deemed to Be University), Department of Professional Studies, Bengaluru, India; Sowjanya S., Rajeev Gandhi Memorial College of Engineering and Technology, Department of Management Studies, Nandyal, India; Sumalatha K.N., Maris Stella College, Vijayawada, India; Ayesha S., Christ (Deemed to Be University), Department of Professional Studies, Bengaluru, India; Basha M.S.A., Gandhi Institute of Technology and Management (Deemed to Be University), Gitam School of Business, Bengaluru, India</text>
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                <text>Kidney stones have been more common in recent years, leading many to believe that the condition is common. The condition's strong relationship with other terrible diseases makes it a major threat to public health. The development of instruments and procedures that facilitate the diagnosis and treatment of this ailment has the potential to enhance the effectiveness and efficiency of health care. Preprocessing, feature extraction, level set segmentation, and model training are the four steps that make up this approach. Part of the preprocessing includes eliminating the skeletal skeleton and soft-organs. Level set segmentation is commonly used for object tracking, motion segmentation, and image segmentation. An extremely effective feature extraction method called Gray level co-occurrence matrix (GLCM) is suggested for extracting the necessary characteristics from the segmented image. That ELM-I-AdaBoost-RT was used all during training. This cutting-edge technique achieves an average accuracy of 95.83%, surpassing both ELM and AdaBoost.   2024 IEEE.</text>
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                <text>Sudarsan M.S.V.D., Deemed To Be University, College Siddhartha Academy of Higher Education(SAHE), Department of Mathematics V.R. Siddhartha Engineering, Vijayawada, India; Sapra P., Rayat Bahra University, Department of Cse, Punjab, Mohali, India; Suganthi D., Saveetha University, Saveetha College of Arts and Sciences, Department of Computational Intelligence, Chennai, India; Jeevitha S., Kalasalingam Academy of Research and Education, Department of Computer Science and Information Technology, Krishnankoil, India; Ankit R., Lovely Professional University, School of Physical Education, Punjab, Phagwara, India; Chauhan A., Christ University, Department of Life Sciences, Bengaluru, India</text>
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                <text>Sudarshanam G., Department of Political Science, University of Hyderabad, Telangana, India, Department of Political Science, University of Hyderabad, Telangana, India; Pradeep N.C., School of Law, Christ University, Karnataka, Bengaluru, India</text>
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                <text>Automatic Target Recognition; Deep learning; Feature extraction; Image Classification; ISAR Image Detection; Ship Classification</text>
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                <text>Inverse Synthetic Aperture Radar (ISAR) is used to image sea surface targets during day/night and all-weather capabilities for applications such as coastal surveillance, ship self-defense, suppression of drug trafficking etc. Hence automating classification of ships by means of machine learning methods has become more significant. Typical classification approaches consist of pre-processing, feature extraction and processing by classifiers. Image processing techniques are applied for pre-processing ISAR images. Transformation invariant features are then extracted to which classifiers such as SVM, Neural Networks (NNs) are applied. The performance of these algorithms depend on the manually chosen features and is trained to perform well for different target profiles. The target image (profile of target) varies depending on the target type, aspect angle and motion introduced due to different sea states. In addition, Deep learning methods are also being explored for classification of ships. The challenge is to classify ships for different sea conditions and image acquisition parameters with limited database and processing resource.  2023 IEEE.</text>
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                <text>6th International Conference on Inventive Computation Technologies, ICICT 2023 - Proceedings, pp. 953-956.</text>
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                <text>Institute of Electrical and Electronics Engineers Inc.</text>
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                <text>&lt;a href="https://doi.org/10.1109/ICICT57646.2023.10134428" target="_blank" rel="noreferrer noopener"&gt;https://doi.org/10.1109/ICICT57646.2023.10134428&lt;/a&gt;
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                <text>ISBN: 979-835039849-6</text>
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                <text>Sudarson Rama Perumal T., Dept. of CSE, Rohini College of Engineering and Technology, TamilNadu, India; Gaurav G.S., Dept. of. ISE, CMR Institute of Technology, Bengaluru, India; Helen Josephine V.L., Business Analytics, School of Business and Management, Christ University, Bangalore, India; Joshua Samuel Raj R., Faculty of EDICT, Bahrain Polytechnic, Isa Town, Bahrain</text>
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                <text>Some new results on equitable coloring parameters of graphs</text>
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              <elementText elementTextId="131318">
                <text>Coloring mean; Coloring variance; Graph coloring; ?&lt;sub&gt;e&lt;/sub&gt;-chromatic mean; ?&lt;sub&gt;e&lt;/sub&gt;-chromatic variance</text>
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                <text>An equitable coloring of a graph G is a proper vertex coloring C of G such that the cardinalities of any two color classes in G with respect to C differ by at most one. Coloring the vertices of a graph G subject to given conditions can be considered as a random experiment. In this context, a discrete random variable X can be defined as the color of a vertex chosen at random, with respect to the given type of coloring of G and a probability mass function for this random variable can be defined accordingly. In this paper, we discuss two statistical parameters of the powers of certain graph classes with respect to their equitable colorings.  2019, Univerzita Komenskeho. All rights reserved.</text>
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              <elementText elementTextId="131320">
                <text>Sudev N.</text>
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                <text>Acta Mathematica Universitatis Comenianae, Vol-89, No. 1, pp. 109-122.</text>
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                <text>Univerzita Komenskeho</text>
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                <text>&lt;a href="" target="_blank" rel="noreferrer noopener"&gt;&lt;/a&gt;
&lt;br /&gt;&lt;br /&gt;&lt;a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85077273641&amp;amp;partnerID=40&amp;amp;md5=d28a25f8af5171c2019b51aad8026b72" target="_blank" rel="noreferrer noopener"&gt;https://www.scopus.com/inward/record.uri?eid=2-s2.0-85077273641&amp;amp;partnerID=40&amp;amp;md5=d28a25f8af5171c2019b51aad8026b72&lt;/a&gt;</text>
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                <text>ISSN: 8629544</text>
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                <text>Sudev N., Department of Mathematics, CHRIST (Deemed to be University), Bangalore, 560029, Karnataka, India</text>
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                <text>The sparing number of certain graph powers</text>
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          </element>
          <element elementId="49">
            <name>Subject</name>
            <description>The topic of the resource</description>
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              <elementText elementTextId="128889">
                <text>graph powers; integer additive set-indexers; mono-indexed elements of a graph; sparing number of a graph; weak integer additive set-indexers</text>
              </elementText>
            </elementTextContainer>
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            <description>An account of the resource</description>
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                <text>Let N0 be the set of all non-negative integers and P(N0) be its power set. Then, an integer additive set-indexer (IASI) of a given graph G is an injective function f : V(G) ! P(N0) such that the induced function f+ : E(G) ! P(N0) deffned by f+(uv) = f(u) + f(v) is also injective. An IASI f is said to be a weak IASI if jf+(uv)j = max(jf(u)j; jf(v)j) for all u; v 2 V(G). A graph which admits a weak IASI may be called a weak IASI graph. The set-indexing number of an element of a graph G, a vertex or an edge, is the cardinality of its set-labels. The sparing number of a graph G is the minimum number of edges with singleton set-labels, required for a graph G to admit a weak IASI. In this paper, we study the admissibility of weak IASI by certain graph powers and their corresponding sparing numbers.  2019 Sciendo. All rights reserved.</text>
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              <elementText elementTextId="128891">
                <text>Sudev N.K.; Chithra K.P.; Germina K.A.</text>
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              <elementText elementTextId="128892">
                <text>Acta Universitatis Sapientiae, Mathematica, Vol-11, No. 1, pp. 186-202.</text>
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                <text>&lt;a href="https://doi.org/10.2478/ausm-2019-0015" target="_blank" rel="noreferrer noopener"&gt;https://doi.org/10.2478/ausm-2019-0015&lt;/a&gt;
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              <elementText elementTextId="128896">
                <text>All Open Access; Gold Open Access; Green Open Access</text>
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                <text>ISSN: 18446094</text>
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                <text>The sparing number of the powers of certain Mycielski graphs</text>
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                <text>Integer additive set-labeled graphs; Mycielski graphs; Sparing number of a graph; Weak integer additive set-labeled graphs</text>
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                <text>Algebra and Discrete Mathematics, Vol-28, No. 2, pp. 291-307.</text>
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                <text>Lugansk Taras Shevchenko National University</text>
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                <text>ISSN: 17263255</text>
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                <text>Sudev N.K., Department of Mathematics CHRIST (Deemed to be University), Bangalore, 560029, Karnataka, India; Chithra K.P., Department of Mathematics CHRIST (Deemed to be University), Bangalore, 560029, Karnataka, India; Germina K.A., Department of Mathematics Central, University of Kerala Kasaragod, Kerala, India</text>
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                <text>Arithmetic integer additive set-valued graphs: A creative review</text>
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          <element elementId="49">
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            <description>The topic of the resource</description>
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              <elementText elementTextId="198219">
                <text>Integer additive set-indexers; Set-indexers; Strong integer additive set-indexers; Weak integer additive set-indexers</text>
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                <text>For a non-empty ground set X, finite or infinite, the set-valuation or set-labeling of a given graph G is an injective function f: V (G) ? P(X), where P(X) is the power set of the set X. A set-indexer of a graph G is an injective set-valued function f: V (G) ? P(X) such that the function f?: E(G) ? P(X) ? { defined by f? (uv) = f (u)? f (v) for every uv?E(G) is also injective, where ? is a binary operation on sets. Let N0 be the set of all non-negative integers and P(N0) is its power set. An integer additive set-labeling (IASL) of a graph G is an injective function f: V (G) ? P(N0) such that the induced function f+: E(G) ? P(N0) is defined by f+ (uv) = f (u) + f (v), where f (u) + f (v) is the sumset of the sets f (u) and f (v). An IASL f of a graph G is said to be an integer additive set-indexer (IASI) of G if the induced function f+ is also injective. In this paper, we critically and creatively review the concepts and properties of a particular type integer additive set-valuation, called arithmetic integer additive set-valuation of graphs.  2020 the author(s).</text>
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                <text>Journal of Mathematical and Computational Science, Vol-10, No. 4, pp. 1020-1049.</text>
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              <elementText elementTextId="198226">
                <text>All Open Access; Gold Open Access</text>
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                <text>ISSN: 19275307</text>
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                <text>Sudev N.K., Department of Mathematics, CHRIST (Deemed to be University), Bangalore, Karnataka, India; Chithra K.P., Department of Mathematics, CHRIST (Deemed to be University), Bangalore, Karnataka, India; Germina K.A., Department of Mathematics, Central University of Kerala, Kasaragod, Kerala, India</text>
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                <text>On the rainbow neighbourhood number of Mycielski type graphs</text>
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          <element elementId="49">
            <name>Subject</name>
            <description>The topic of the resource</description>
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              <elementText elementTextId="133948">
                <text>Color classes; Rainbow neighbourhood; Rainbow neighbourhood number</text>
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              <elementText elementTextId="133949">
                <text>A rainbow neighbourhood of a graph G is the closed neighbourhood N[v] of a vertex v ? V (G) which contains at least one colored vertex of each color in the chromatic coloring C of G. Let G be a graph with a chromatic coloring C defined on it. The number of vertices in G yielding rainbow neighbourhoods is called the rainbow neighbourhood number of the graph G, denoted by rX(G). In this paper, we discuss the rainbow neighbourhood number of the Mycielski type graphs of graphs.  2018 Academic Publications.</text>
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              <elementText elementTextId="133950">
                <text>Sudev N.K.; Susanth C.; Kalayathankal S.J.</text>
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                <text>International Journal of Applied Mathematics, Vol-31, No. 6, pp. 797-803.</text>
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                <text>Academic Publications Ltd.</text>
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              <elementText elementTextId="133955">
                <text>All Open Access; Gold Open Access; Green Open Access</text>
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                <text>ISSN: 13111728</text>
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                <text>Sudev N.K., Department of Mathematics, CHRIST (Deemed to be University), Bengaluru, 560029, India; Susanth C., Department of Mathematics, Vidya Academy of Science and Technology Thalakkottukara, Thrissur, 680501, India; Kalayathankal S.J., Department of Mathematics, Kuriakose Elias College, Mannanam, Kottayam, 686561, India</text>
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