79
20
2150
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Conference Papers
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Optimizing Portfolio for Highly Funded Industries Within Budget Constraints for the Period of 20232024
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Budget 2023; Portfolio Optimization; Time Series Analysis
Description
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This research paper aims to analyze and optimize portfolios for the top funded industries based on the budget23. The study uses a data-driven approach to identify the best investment opportunities within these industries. The methodology involves collecting financial data, conducting market analysis, and using optimization techniques to create an optimal portfolio. The results of the study show that the top funded industries have a high potential for growth, and the optimized portfolios can maximize returns while minimizing risk. The findings can provide valuable insights for investors and fund managers who are seeking to make informed investment decisions in these industries. The study also highlights the importance of considering the budget constraints while optimizing portfolios. The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.
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Nanjundan P.; George J.P.; Birari A.; Geetha P.S.; Manwali M.
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Communications in Computer and Information Science, Vol-2047 CCIS, pp. 168-179.
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Springer Science and Business Media Deutschland GmbH
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2024-01-01
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<a href="https://doi.org/10.1007/978-3-031-55486-5_13" target="_blank" rel="noreferrer noopener">https://doi.org/10.1007/978-3-031-55486-5_13</a>
<br /><br /><a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85188706853&doi=10.1007%2F978-3-031-55486-5_13&partnerID=40&md5=ba684025d9e0131182bdec35340fbb0a" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85188706853&doi=10.1007%2f978-3-031-55486-5_13&partnerID=40&md5=ba684025d9e0131182bdec35340fbb0a</a>
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ISSN: 18650929; ISBN: 978-303155485-8
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Online
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English
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Conference paper
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Nanjundan P., CHRIST(Deemed to be University), Bengaluru, India; George J.P., CHRIST(Deemed to be University), Bengaluru, India; Birari A., CHRIST(Deemed to be University), Bengaluru, India; Geetha P.S., Koneru Lakshmaiah Education Foundation, Andhra Pradesh, Vijayawada, India; Manwali M., Graphic Era Hill University, Dehradun, India
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Conference Papers
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An Effective BiLSTM-CRF Based Approach to Predict Student Achievement: An Experimental Evaluation
Subject
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Bidirectional Long Short-Term Memory (Bi-LSTM); Gated Convolution (GC); Self - Attention (SA)
Description
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Currently, massive volumes of data are accumulated in databases when people configure new requirements and services. Data mining techniques and intelligent systems are emerging for managing large amounts of data and extracting actionable insights for policy development. As digital technology has grown, it has naturally become intertwined with e-learning practices. In order to facilitate communication between instructors and a diverse student body located all over the world, distance learning programs rely on Learning Management Systems (LMSs). Colleges can better accommodate their students' individual needs by using and analyzing interaction data that reveals variances in their learning progress. Predicting pupils' success or failure is a breeze with the help of learning analytics tools. Better learning outcomes might be achieved through early prediction leading to swift focused action. Preprocessing, feature selection, and model training are the three components of the proposed method. Data cleansing, data transformation, and data reduction are the preprocessing steps used here. It used a CFS to enable feature selection. This study has used a BiLSTM-CRF hybrid approach to train the model. When compared to tried-and-true techniques like CNN and CRF, the proposed method performs effectively. 2024 IEEE.
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Manigandan E.; Anispremkoilraj P.; Suresh Kumar B.; Satre S.M.; Chauhan A.; Jeyaganthan C.
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2nd International Conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2024, pp. 779-784.
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Institute of Electrical and Electronics Engineers Inc.
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2024-01-01
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<a href="https://doi.org/10.1109/IDCIoT59759.2024.10467973" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/IDCIoT59759.2024.10467973</a>
<br /><br /><a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85190148893&doi=10.1109%2FIDCIoT59759.2024.10467973&partnerID=40&md5=aea52580983eefb6edc5e0fd2b555c4c" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85190148893&doi=10.1109%2fIDCIoT59759.2024.10467973&partnerID=40&md5=aea52580983eefb6edc5e0fd2b555c4c</a>
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ISBN: 979-835032753-3
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Online
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English
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Conference paper
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Manigandan E., Saveetha College of Liberal Arts and Sciences, SIMATS, Department of Computer Applications, Tamilnadu, Chennai, India; Anispremkoilraj P., KSR Institute for Engineering and Technology, Tiruchengode, India; Suresh Kumar B., Chaitanya Bharathi Institute of Technology, Dept of EEE, Telangana, Hyderabad, India; Satre S.M., Bharati Vidyapeeth College of Engineering, Department of Information Technology, Navi Mumbai, India; Chauhan A., School of Sciences, CHRIST (Deemed to Be University), Department of Life Sciences, Bengaluru, Karnataka, India; Jeyaganthan C., AMET Business School, AMET University, Chennai, India
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Conference Papers
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Dublin Core
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Title
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Integrating AI Tools into HRM to Promote Green HRM Practices
Subject
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AI; Green HRM; HRM; Sustainability
Description
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The image of Human Resource Management (HRM) is undergoing a drastic transformation. The conventional methods are evolving due to the emergence of technology, especially with the integration of Artificial Intelligence (AI) and data analytics into the HR processes. With the rapidly changing concept of the overall growth of an organization, AI is becoming a vital stimulant for sustainable growth. AI-powered tools promote data-driven decision-making for talent acquisition, performance management, workforce training and development, optimization of energy consumption and waste reduction. Green HRM aligns these efforts by integrating sustainability considerations into talent management strategies, nurturing employees eco-engagement, and promoting environmentally responsible practices within the workforce. This research paper aims to explore the synergies between AI tools and Green HRM practices, investigating how the integration of AI technologies into HR processes can contribute to the promotion of environmental sustainability. By examining real-world case studies, this study aims to investigate the potential of AI-powered solutions in shaping the future of HRM through the lens of sustainability. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.
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John J.E.; Pramila S.
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Lecture Notes in Networks and Systems, Vol-878, pp. 249-259.
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Springer Science and Business Media Deutschland GmbH
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2024-01-01
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<a href="https://doi.org/10.1007/978-981-99-9489-2_22" target="_blank" rel="noreferrer noopener">https://doi.org/10.1007/978-981-99-9489-2_22</a>
<br /><br /><a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85189563916&doi=10.1007%2F978-981-99-9489-2_22&partnerID=40&md5=4395bfc01da2b0b6604fb46b36f2cb5b" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85189563916&doi=10.1007%2f978-981-99-9489-2_22&partnerID=40&md5=4395bfc01da2b0b6604fb46b36f2cb5b</a>
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ISSN: 23673370; ISBN: 978-981999488-5
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Online
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English
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Conference paper
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John J.E., Christ (CHRIST (Deemed to be University), Delhi NCR, India; Pramila S., Christ (CHRIST (Deemed to be University), Delhi NCR, India
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Conference Papers
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Advancing Credit Card Fraud Detection Through Explainable Machine Learning Methods
Subject
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Credit card fraud; Explainable model; Machine Learning
Description
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The world of finance has experienced a significant shift in the way money flows, due to the advancements in technologies such as online banking, card payments, and QR-based payment systems. These innovative banking payment facilities are offered by ensuring the safety of the transaction and ensuring that only the authorized customer can access and utilize these banking services. Credit card fraud is innovative way to cheat the user of the card. Government all over the word encouraging to the people for the uses of digital money. This research work focuses on analyzing the machine learning database by using a labelled dataset to classify legitimate and fraudulent business transactions with explainable AI. This study is based on decision tree, logistic regression, support vector machine and random forest machine learning techniques. 2024 IEEE.
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Adhegaonkar V.R.; Thakur A.R.; Varghese N.
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2nd International Conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2024, pp. 792-796.
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Institute of Electrical and Electronics Engineers Inc.
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2024-01-01
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<a href="https://doi.org/10.1109/IDCIoT59759.2024.10467999" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/IDCIoT59759.2024.10467999</a>
<br /><br /><a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85190145545&doi=10.1109%2FIDCIoT59759.2024.10467999&partnerID=40&md5=f6f1220ab34db963bc6ab60aed17c866" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85190145545&doi=10.1109%2fIDCIoT59759.2024.10467999&partnerID=40&md5=f6f1220ab34db963bc6ab60aed17c866</a>
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ISBN: 979-835032753-3
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Online
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English
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Conference paper
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Adhegaonkar V.R., Christ University, Dept of Business Management, Pune, India; Thakur A.R., Sri Balaji University, Dept of Business Management, Pune, India; Varghese N., Christ University, Dept of Business Management, Pune, India
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Conference Papers
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A Deep Learning Method for Autism Spectrum Disorder
Subject
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Autism; Deep learning; VGG-16
Description
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The present study uses deep learning methods to detect autism spectrum disorder (ASD) in patients from global multi-site database Autism Brain Imaging Data Exchange (ABIDE) based on brain activity patterns. ASD is a neurological condition marked by repetitive behaviours and social difficulties. A deep learning-based approach using transfer learning for automatic detection of ASD is proposed in this study, which uses characteristics retrieved from the intracranial brain volume and corpus callosum from the ABIDE data set. T1-weighted MRI scans provide information on the intracranial brain volume and corpus callosum. ASD is detected using VGG-16 based on transfer learning. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.
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George B.; Chandra Blessie E.; Resmi K.R.
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Lecture Notes in Electrical Engineering, Vol-1106, pp. 1-9.
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Springer Science and Business Media Deutschland GmbH
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2024-01-01
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<a href="https://doi.org/10.1007/978-981-99-7954-7_1" target="_blank" rel="noreferrer noopener">https://doi.org/10.1007/978-981-99-7954-7_1</a>
<br /><br /><a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85188267619&doi=10.1007%2F978-981-99-7954-7_1&partnerID=40&md5=96c728bdafdfe5f2e1ecc8394a1be059" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85188267619&doi=10.1007%2f978-981-99-7954-7_1&partnerID=40&md5=96c728bdafdfe5f2e1ecc8394a1be059</a>
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ISSN: 18761100; ISBN: 978-981997953-0
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Online
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English
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Conference paper
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George B., Santhigiri College, Vazhithala, India; Chandra Blessie E., Department of Artificial Intelligence and Machine Learning, Coimbatore Institute of Technology, Coimbatore, India; Resmi K.R., CHRIST(Deemed to be) University, Karnataka, Bengaluru, India
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Conference Papers
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Digital Watermarking Techniques for Secure Image Distribution
Subject
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Digital Image; Digital Water Marking; Image Distributions; Pixel values
Description
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In the contemporary era of digital advancements, it is of utmost importance to prioritize the establishment of robust security measures and traceability protocols for photos. This necessity arises from the inherent risk associated with the effortless diffusion of unlicensed information. Digital watermarking, which implants hidden data into digital photographs to verify their validity, is frequently used. This level emphasizes the need of safe photo distribution, digital platform problems, and unauthorized reproductions. The purpose of this research is to explain digital watermarking fundamentals. It emphasizes verification, IP protection, and digital watermarking monitoring. This research compares spatial and frequency domain watermarking approaches. Direct pixel manipulation in spatial domain techniques is vulnerable to attacks. Integrating watermarks with transform domains like Discrete Cosine Transform improves robustness in frequency domain techniques. The study also studies adaptive watermarking, which adjusts the watermark to the image's content to balance visibility and durability. The purpose of this research is to explore watermark identification methods. These methods use blind and non-blind watermarking. We discuss the security risks that might compromise watermarked photographs and the ways to reduce their likelihood. 2024 IEEE.
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Baskar R.; Dwibedi R.K.; Kumar T.R.; Vijayalakshmi N.; Rajesh Kanna R.; Reddy G.N.
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Proceedings of the 2nd International Conference on Intelligent and Innovative Technologies in Computing, Electrical and Electronics, ICIITCEE 2024
Publisher
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Institute of Electrical and Electronics Engineers Inc.
Date
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2024-01-01
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<a href="https://doi.org/10.1109/IITCEE59897.2024.10467416" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/IITCEE59897.2024.10467416</a>
<br /><br /><a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85189794243&doi=10.1109%2FIITCEE59897.2024.10467416&partnerID=40&md5=699abb6a44210a1f49c3cc1fe5ecc119" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85189794243&doi=10.1109%2fIITCEE59897.2024.10467416&partnerID=40&md5=699abb6a44210a1f49c3cc1fe5ecc119</a>
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ISBN: 979-835030641-5
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Online
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English
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Conference paper
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Baskar R., Saveetha Institute of Medical and Technical Sciences, Saveetha School of Engineering, Chennai, India; Dwibedi R.K., Vmrf, Aarupadai Veedu Institute of Technology, Chennai, India; Kumar T.R., Saveetha Institute of Medical and Technical Sciences, Saveetha School of Engineering, Department of Cse, Chennai, India; Vijayalakshmi N., Sns College of Technology, Department of Cse, Coimbatore, India; Rajesh Kanna R., CHRIST(Deemed to Be University), Department of Cse, Bangalore, India; Reddy G.N., Vel Tech Rangarajan Dr.Sagunthala R&d Institute of Science and Technology, Department of Chemistry, Chennai, India
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Conference Papers
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Faculty Publications- Conference Papers
Dublin Core
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/.
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A Secure Data Encryption Mechanism in Cloud Using Elliptic Curve Cryptography
Subject
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Cloud computing; cryptography; data security; ECC; encryption
Description
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Cloud computing is undergoing continuous evolution and is widely regarded as the next generation architecture for computing. Cloud computing technology allows users to store their data and applications on a remote server infrastructure known as the cloud. Cloud service providers, such Amazon, Rackspace, VMware, iCloud, Dropbox, Google's Application, and Microsoft Azure, provide customers the opportunity to create and deploy their own applications inside a cloud-based environment. These providers also grant users the ability to access and use these applications from any location worldwide. The subject of security poses significant challenges in contemporary times. The primary objective of cloud security is to establish a sense of confidence between cloud service providers and data owners inside the cloud environment. The cloud service provider is responsible for ensuring user data's security and integrity. Therefore, the use of several encryption techniques may effectively ensure cloud security. Data encryption is a commonly used procedure utilised to ensure the security of data. This study analyses the Elliptic Curve Cryptography method, focusing on its implementation in the context of encryption and digital signature processes. The objective is to enhance the security of cloud applications. Elliptic curve cryptography is a very effective and robust encryption system due to its ability to provide reduced key sizes, decreased CPU time requirements, and lower memory utilisation. 2024 IEEE.
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Sudha I.; Donald C.; Navya S.; Nithya G.; Balamurugan M.; Saravanan S.
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Proceedings of the 2nd International Conference on Intelligent and Innovative Technologies in Computing, Electrical and Electronics, ICIITCEE 2024
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Institute of Electrical and Electronics Engineers Inc.
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2024-01-01
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<a href="https://doi.org/10.1109/IITCEE59897.2024.10467407" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/IITCEE59897.2024.10467407</a>
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ISBN: 979-835030641-5
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Online
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English
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Conference paper
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Sudha I., Saveetha Institute of Medical and Technical Sciences - Simats, Department of Computer Science and Engineering, Saveetha School of Engineering, Chennai, India; Donald C., Christ (Deemed to Be University), Department of Computer Science, Bengaluru, India; Navya S., Raghu Engineering College, Department of Computer Science Engineering, Visakhapatnam, India; Nithya G., Department of Cse, V. S. B College of Engineering, Technical Campus, Coimbatore, India; Balamurugan M., Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology, Department of Mathematics, Tamil Nadu, Avadi, India; Saravanan S., Saveetha Institute of Medical and Technical Sciences - Simats, Department of Computer Science and Engineering, Saveetha School of Engineering, Chennai, India
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Conference Papers
Conference Paper
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War Strategy Optimization for Optimal Integration of Public Fast Charging Stations in Radial Feeders
Subject
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Electrical distribution networks; Loss; Public fast charging stations; Voltage profile; Voltage stability index; Voltage stability margin; War strategy optimization
Description
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In light of rising pollution and global warming, there is need for raising the acceptance rate of electric vehicles (EVs) across the globe for sustainable and clean transportation. However, low-voltage electrical distribution networks (EDN) are necessary to provide the electrical power needed to charge the EV batteries. Due to their radial construction and high r/x ration branches; these networks typically suffer from significant energy losses, inadequate voltage profiles, and low stability margins. Therefore, the performance of EDNs shouldnt be further compromised by the incorporation of EV charging stations. In light of this, this work presents a unique heuristic war strategy optimization (WSO) for integrating fast charging stations (FCS) as efficiently as possible utilizing the voltage stability index (VSI). The effect of equivalent EV load penetration in EDS is initially evaluated in terms of loss, voltage profile, and voltage stability for a certain number of EVs. Simulations are executed for IEEE 15-bus system for three different scenarios. The technological advantages seen in the simulations illustrate the efficiency of the suggested technique for real-time adaptation. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.
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Kurian J.; Janamala V.
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Lecture Notes in Networks and Systems, Vol-865, pp. 251-260.
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Springer Science and Business Media Deutschland GmbH
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2024-01-01
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<a href="https://doi.org/10.1007/978-981-99-9043-6_21" target="_blank" rel="noreferrer noopener">https://doi.org/10.1007/978-981-99-9043-6_21</a>
<br /><br /><a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85190368388&doi=10.1007%2F978-981-99-9043-6_21&partnerID=40&md5=7edb716ae70a87f4bfccd83d06bf07c5" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85190368388&doi=10.1007%2f978-981-99-9043-6_21&partnerID=40&md5=7edb716ae70a87f4bfccd83d06bf07c5</a>
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ISSN: 23673370; ISBN: 978-981999042-9
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English
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Conference paper
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Kurian J., Department of Electrical and Electronics Engineering, School of Engineering and Technology, CHRIST (Deemed to Be University), Karnataka, Bangalore, 560074, India; Janamala V., Department of Electrical and Electronics Engineering, School of Engineering and Technology, CHRIST (Deemed to Be University), Karnataka, Bangalore, 560074, India
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Conference Papers
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Faculty Publications- Conference Papers
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New Paradigm of Marketing-Financial Integration Modelling for Business Performance: An IMC Model
Subject
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Business performance; Competitive advantage; Finance market integration; Integrated marketing communication (IMC)
Description
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When it comes to the provision of financial services, the integrated marketing communication (IMC) process is crucial in the creation and maintenance of client-provider bonds. This research presents a literature assessment on the theoretical basis for using marketing communication tools in the provision of financial services. This research is an attempt to bolster the little theoretical literature on the effectiveness of marketing communication techniques in the provision of financial services. Financial service providers use marketing communication as a channel for two-way exchanges with their clientele, with the ultimate goal of maximising the benefits their customers bring to the company. When it comes to providing financial services, an organisations success hinges on its ability to effectively manage its relationships with both current and potential consumers. As a result, it is important for practical reasons to be guided by well-defined marketing communications goals to identify the extent of usage and within the constraints of available resources. In this regard, businesses are free to establish specific communications objectives in accordance with their unique situations to direct the implementation of their IMC plan. This study aims to find out an impact of financial integration with IMC on business performance. This study is descriptive in nature. Primary data is collected with the help of questionnaire. The study finds that the financial integration in the IMC model has a statistically significant impact on business success. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.
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Paralkar T.A.; Goyal A.A.; Haque M.; Ramteke N.; Upreti K.; Shukla S.
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Lecture Notes in Electrical Engineering, Vol-1115, pp. 155-172.
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Springer Science and Business Media Deutschland GmbH
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2024-01-01
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<a href="https://doi.org/10.1007/978-981-99-8661-3_12" target="_blank" rel="noreferrer noopener">https://doi.org/10.1007/978-981-99-8661-3_12</a>
<br /><br /><a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85187689666&doi=10.1007%2F978-981-99-8661-3_12&partnerID=40&md5=d89d960b9ae98932a31faeacb6c8605c" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85187689666&doi=10.1007%2f978-981-99-8661-3_12&partnerID=40&md5=d89d960b9ae98932a31faeacb6c8605c</a>
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ISSN: 18761100; ISBN: 978-981998660-6
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Online
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English
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Conference paper
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Paralkar T.A., Shri. Ramdeobaba College of Engineering and Management, Nagpur, India; Goyal A.A., G H Raisoni University Business Management, Saikheda, Madhya Pradesh, Chhindwara, India; Haque M., Dr. D.Y. Patil Vidyapeeths Centre For Online Learning, Dr. D.Y. Patil Vidyapeeth (Deemed to Be University), Maharashtra, Pune, India; Ramteke N., Indira Institute of Management, Pune, India; Upreti K., CHRIST (Deemed to Be University), Delhi NCR Campus, Ghaziabad, India; Shukla S., Department of Computer Science and Engineering, Christ (Deemed to Be University), Kengeri Campus, Bangalore, India
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Conference Papers
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Title
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UAV Security Analysis Framework
Subject
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Attacks; Database; Neural network; Threats; UAV; Vulnerabilities
Description
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This study presents a framework that allows for various types of checks to detect weaknesses in UAV subsystems. The UAV testing process is automated and allows the operator only to select the types of checks or types of structural and functional characteristics that the operator wants to test. To ensure the possibility of automated verification, implemented databases are used, which include a catalog of structural characteristics, threats, vulnerabilities, and attacks. These catalogs are many-to-many related, and thanks to these links, it is possible to identify threats or vulnerabilities specific to a particular structural characteristic. In essence, such an architecture is a knowledge base based on an ontological model. Thanks to this architecture of the system, it is enough for the operator to determine what types of structural characteristics need to be checked and the system will give him information about the vulnerabilities of the UAV. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.
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Basan E.; Abramov E.; Gladkov N.; Lapina M.; Lapin V.; Mary Anita E.A.; Kumar S.
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Lecture Notes in Networks and Systems, Vol-865, pp. 379-391.
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Springer Science and Business Media Deutschland GmbH
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2024-01-01
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<a href="https://doi.org/10.1007/978-981-99-9043-6_31" target="_blank" rel="noreferrer noopener">https://doi.org/10.1007/978-981-99-9043-6_31</a>
<br /><br /><a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85190386256&doi=10.1007%2F978-981-99-9043-6_31&partnerID=40&md5=b1199c268b2177cc0a4ccd12af0aea4c" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85190386256&doi=10.1007%2f978-981-99-9043-6_31&partnerID=40&md5=b1199c268b2177cc0a4ccd12af0aea4c</a>
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ISSN: 23673370; ISBN: 978-981999042-9
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Online
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English
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Conference paper
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Basan E., Southern Federal University, Taganrog, 347922, Russian Federation; Abramov E., Southern Federal University, Taganrog, 347922, Russian Federation; Gladkov N., Southern Federal University, Taganrog, 347922, Russian Federation; Lapina M., North Caucasian Federal University, Stavropol, 355017, Russian Federation; Lapin V., Stavropol Regional Clinical Advisory and Diagnostic Center, Stavropol, 355000, Russian Federation; Mary Anita E.A., CHRIST (Deemed to be University), Karnataka, Bangalore, 560074, India; Kumar S., CHRIST (Deemed to be University), Karnataka, Bangalore, 560074, India
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Conference Papers
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Augmented Reality based Navigation for Indoor Environment using Unity Platform
Subject
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Computer vision; GPS; Image recognition; Indoor navigation; Unity platform
Description
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This paper proposes an augmented reality (AR) navigation system developed for indoor environment. The proposed navigation system is developed using Unity platform which is usually used for developing gaming applications. The proposed navigation system without the aid of Global Positioning System (GPS) tracks users position and orientation accurately by making use of computer vision and image processing techniques. The user can navigate to the desired location using its user friendly and intuitive interface. The proposed system can be extended further to provide indoor navigational guidance within lager buildings such as malls, airports, universities and medical facilities. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.
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Aswanth M.; Amal; Alok C.; Nagaraju S.; Elappila M.
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Lecture Notes in Networks and Systems, Vol-865, pp. 199-207.
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Springer Science and Business Media Deutschland GmbH
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2024-01-01
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<a href="https://doi.org/10.1007/978-981-99-9043-6_17" target="_blank" rel="noreferrer noopener">https://doi.org/10.1007/978-981-99-9043-6_17</a>
<br /><br /><a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85190393669&doi=10.1007%2F978-981-99-9043-6_17&partnerID=40&md5=8641ca7243d7c1ec2f820d65d24fd872" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85190393669&doi=10.1007%2f978-981-99-9043-6_17&partnerID=40&md5=8641ca7243d7c1ec2f820d65d24fd872</a>
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ISSN: 23673370; ISBN: 978-981999042-9
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Online
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English
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Conference paper
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Aswanth M., Department of Computer Science and Engineering, CHRIST (Deemed to be University), Karnataka, Bangalore, 560074, India; Amal, Department of Computer Science and Engineering, CHRIST (Deemed to be University), Karnataka, Bangalore, 560074, India; Alok C., Department of Computer Science and Engineering, CHRIST (Deemed to be University), Karnataka, Bangalore, 560074, India; Nagaraju S., Department of Computer Science and Engineering, CHRIST (Deemed to be University), Karnataka, Bangalore, 560074, India; Elappila M., Department of Computer Science and Engineering, CHRIST (Deemed to be University), Karnataka, Bangalore, 560074, India
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Conference Papers
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Title
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Deep CNN Based Interpolation Filter for High Efficiency Video Coding
Subject
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Deep CNN; HEVC; MV; SRCNN
Description
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Video coding is a current focus in research area as the world focus more on multimedia transfer. High Efficiency Video Coding (HECV) is prominent among existing one. The interpolation in HEVC with fixed half-pel interpolation filter uses fixed interpolation filter derived from traditional signal processing methods. Some research came up with CNN based interpolation filter too, here we are proposing a deep learning-based interpolation filter to perform interpolation in inter prediction in HEVC. The network extracts the low-resolution image and extract the patch and feature in that to predict a high-resolution image. The network is trained to predict the HR image for the given patch, it can be repeated to generate the full frame in the HEVC. The system uses cleave approach to reduce the computational complexity. The trained network is validated and tested for different inputs. The results show an improvement of 2.38% in BD-bitrate saving for low delay configuration. 2024 IEEE.
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Joy H.K.; Kounte M.R.
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2nd International Conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2024, pp. 519-524.
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Institute of Electrical and Electronics Engineers Inc.
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2024-01-01
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<a href="https://doi.org/10.1109/IDCIoT59759.2024.10467280" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/IDCIoT59759.2024.10467280</a>
<br /><br /><a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85190119176&doi=10.1109%2FIDCIoT59759.2024.10467280&partnerID=40&md5=9be0cee7f27834e4cadffae41d5f503f" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85190119176&doi=10.1109%2fIDCIoT59759.2024.10467280&partnerID=40&md5=9be0cee7f27834e4cadffae41d5f503f</a>
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ISBN: 979-835032753-3
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Online
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English
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Conference paper
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Joy H.K., CHRIST University, Department of Computer Science, Bangaluru, India; Kounte M.R., School of ECE, REVA University, Bangaluru, India
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Conference Papers
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Prediction of Hazardous Asteroids Using Machine Learning
Subject
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Asteroid Impact; data analysis - minor planets; Machine Learning; Planetary defense; Potentially Hazardous Asteroids; Prediction; Random Forest Classification
Description
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As the need for early detection and mitigation of potential threats from near-Earth objects continues to grow, this study presents a comprehensive approach to predicting hazardous asteroids through the application of machine learning techniques. With the increasing interest in safeguarding our planet from potential impact events, the accurate classification and prediction of hazardous asteroids is of paramount importance. This research leverages a diverse dataset comprising a wide array of asteroid characteristics, including orbital parameters, physical properties, and historical impact data, to train and validate machine learning models. The study employs a combination of feature engineering, data preprocessing, and state-of-the-art machine learning algorithms to assess the risk posed by asteroids in near-Earth space. 2024 IEEE.
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Surekha N.P.; Nanjundan P.; Bashir S.
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ESIC 2024 - 4th International Conference on Emerging Systems and Intelligent Computing, Proceedings, pp. 169-174.
Publisher
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Institute of Electrical and Electronics Engineers Inc.
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2024-01-01
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<a href="https://doi.org/10.1109/ESIC60604.2024.10481589" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/ESIC60604.2024.10481589</a>
<br /><br /><a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85190992971&doi=10.1109%2FESIC60604.2024.10481589&partnerID=40&md5=3a4c445df8078adc6d2ead14b9dd5358" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85190992971&doi=10.1109%2fESIC60604.2024.10481589&partnerID=40&md5=3a4c445df8078adc6d2ead14b9dd5358</a>
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ISBN: 979-835034985-6
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Online
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English
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Conference paper
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Surekha N.P., Christ University Pune, Department of Data Science, Lavasa, India; Nanjundan P., Christ University Pune, Department of Data Science, Lavasa, India; Bashir S., Christ University Pune, Department of Data Science, Lavasa, India
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Conference Papers
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Smart Air Pollution Monitoring System Using Arduino Based on Wireless Sensor Networks
Subject
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Air pollution; IoT; LCD; Population growth
Description
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Impurity levels in air have risen throughout time as a result of several reasons, such as population expansion, increased automobile use, industry, and urbanization. All of these elements harm the health of individuals who are exposed to them, which has a detrimental effect on human well-being. We will create an air pollution monitoring system based on an IoT that uses a Internet server to track the air quality online in order to keep track of everything. An alert will sound when the level of harmful gases such CO2, smoking, alcohol, benzene, and NH3 is high enough or when the air quality drops below a specified threshold. The air quality will be displayed on the LCD in PPM. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.
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Thaiyalnayaki S.; Sambandam R.K.; K.Vidhyalakshmi M.; Shanthi S.; Jenefa J.; Vetriveeran D.
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Lecture Notes in Networks and Systems, Vol-840, pp. 497-504.
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Springer Science and Business Media Deutschland GmbH
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2024-01-01
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<a href="https://doi.org/10.1007/978-981-99-8451-0_42" target="_blank" rel="noreferrer noopener">https://doi.org/10.1007/978-981-99-8451-0_42</a>
<br /><br /><a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85187697753&doi=10.1007%2F978-981-99-8451-0_42&partnerID=40&md5=5998b84933d57db41e6f3065bda29da3" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85187697753&doi=10.1007%2f978-981-99-8451-0_42&partnerID=40&md5=5998b84933d57db41e6f3065bda29da3</a>
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ISSN: 23673370; ISBN: 978-981998450-3
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Online
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English
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Conference paper
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Thaiyalnayaki S., Department of CSE, Bharath Institute of Higher Education and Research (Deemed to Be University), Tamilnadu, Chennai, India; Sambandam R.K., Department of CSE, SOET, CHRIST (Deemed to Be University), Kengeri Campus, Karnataka, Bengaluru, India; K.Vidhyalakshmi M., Department of Computing Technologies, Faculty of Engineering and Technology, SRM Institute of Science and Technology, Kattankulathur, Tamil Nadu, Chengalpattu District, 603203, India; Shanthi S., Department of CSE, Malla Reddy College of Engineering and Technology, Telangana, Hyderabad, India; Jenefa J., Department of CSE, SOET, CHRIST (Deemed to Be University), Kengeri Campus, Karnataka, Bengaluru, India; Vetriveeran D., Department of CSE, SOET, CHRIST (Deemed to Be University), Kengeri Campus, Karnataka, Bengaluru, India
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Conference Papers
Conference Paper
Faculty Publications- Conference Papers
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Effects of Peer Monitoring on Student Stress Level of College Students Based on Multi-Layer Perceptron Approach
Subject
The topic of the resource
Individuals with Disabilities Education Act (IDEA); Linear Discriminant Analysis (LDA); MultiLayer Perceptron (MLP)
Description
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The classroom is just one of many places where the proposed approach encounter stress. Previous studies have shown that college students experience high rates of stress. It is not known if the Student Stress Inventory-Stress Manifestations (SSI-SM) is useful in identifying stressors and evaluating stress manifestations among college students. To this end, it was created a college-specific version of the Student Stress Inventory-Stress Manifestations (SSI-SM) and administered it to students to determine its validity and reliability. These procedures comprise the proposed technique and include preprocessing, feature selection, and model training. It uses Normalization as a preprocessing approach. The term' normalization' refers to the procedure of rescaling or modifying data so that all categories have the same variance. The proposed approach employed linear discriminant analysis as a means of selecting features. The models are then trained using MLP after information gain has been used to choose relevant features. The proposed approach achieves better results than the two leading alternatives, CNN and RNN. 2024 IEEE.
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Shaikh I.A.K.; Gupta K.; Parasar A.; Thulasimani T.; Little Femilin Jana D.; Chauhan A.
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2nd International Conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2024, pp. 1370-1375.
Publisher
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Institute of Electrical and Electronics Engineers Inc.
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2024-01-01
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<a href="https://doi.org/10.1109/IDCIoT59759.2024.10467811" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/IDCIoT59759.2024.10467811</a>
<br /><br /><a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85190093258&doi=10.1109%2FIDCIoT59759.2024.10467811&partnerID=40&md5=d4b7ab7bb4a33cfde7647bd756591500" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85190093258&doi=10.1109%2fIDCIoT59759.2024.10467811&partnerID=40&md5=d4b7ab7bb4a33cfde7647bd756591500</a>
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ISBN: 979-835032753-3
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Online
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English
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Conference paper
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Shaikh I.A.K., Bahrain Polytechnic, Faculty of Business, Bahrain; Gupta K., School of Commerce and Management, IIMT University, Meerut, India; Parasar A., Galgotias University, India; Thulasimani T., Bannari Amman Institute of Technology, Department of Mathematics, Sathyamangalam, India; Little Femilin Jana D., Prince Shri Venkateshwara Padmavathy Engineering College, Department of Science & Humanities, Tamil Nadu, Chennai, India; Chauhan A., School of Sciences, CHRIST (Deemed to Be University), Department of Life Sciences, Bengaluru, Karnataka, India
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Conference Papers
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Sentiment Analysis of Online Hotel Reviews Employing Bidirectional GRU with Attention Mechanism
Subject
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attention mechanism; BI-GRU; customer satisfaction; Online reviews; Sentiment analysis
Description
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Online hotel reviews are a more reliable resource for potential hotel guests. Sentiment analysis is a branch of text mining, Natural Processing Language that seeks to identify personality traits, emotions, and other factors. Deep Learning algorithms such as LSTM and GRU have successfully generated context information in sequence learning. However, deep learning cannot focus on the words that contribute the most and cannot capture important content information. This research aims to overcome the inability of LSTM and GRU to capture information. The results are satisfactory, with 93.12% accuracy, 95% ROCAUC, and 95.28% precision recall. This research paper helps managers identify areas to improve their products and services, target marketing campaigns, and identify customer churn. 2024 IEEE.
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Mathew M.M.; Nanjundan P.; Bashir S.
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ESIC 2024 - 4th International Conference on Emerging Systems and Intelligent Computing, Proceedings, pp. 237-242.
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Institute of Electrical and Electronics Engineers Inc.
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2024-01-01
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<a href="https://doi.org/10.1109/ESIC60604.2024.10481617" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/ESIC60604.2024.10481617</a>
<br /><br /><a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85190984123&doi=10.1109%2FESIC60604.2024.10481617&partnerID=40&md5=44cbb2bbc324a3c0e3230b64c4724904" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85190984123&doi=10.1109%2fESIC60604.2024.10481617&partnerID=40&md5=44cbb2bbc324a3c0e3230b64c4724904</a>
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ISBN: 979-835034985-6
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Online
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English
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Conference paper
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Mathew M.M., Christ University, Department of Data Science, Lavasa, India; Nanjundan P., Christ University, Department of Data Science, Lavasa, India; Bashir S., Christ University, Department of Data Science, Lavasa, India
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Conference Papers
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Title
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Predicting of Credit Risk Using Machine Learning Algorithms
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Credit risk; Feature selection; Machine learning; Testing; Training
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Credit risk management is one of the key processes for banks and is crucial to ensuring the banks stability and success. However, due to the need for more rigid forecasting models with strong mapping abilities, credit risk prediction has become challenging for the banking industry. Therefore, this paper attempts to predict commercial banks credit risk (CR) by using various machine learning algorithms. Machine learning algorithms, namely linear regression, KNN, SVR, DT, RF, XGB, and MLP, are compared with and without feature selection and feature extraction techniques to examine their prediction capabilities. Various determinants of credit risk (features) have been extracted to predict credit risk, and these features have been used to train machine learning models. Findings revealed that the decision tree algorithm had the highest performance, with the lowest mean absolute error (MSE) value of 0.1637 and the lowest root mean squared error (RMSE) value of 0.2158. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.
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Antony T.M.; Kumar B.S.
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Lecture Notes in Networks and Systems, Vol-843, pp. 99-114.
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Springer Science and Business Media Deutschland GmbH
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2024-01-01
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<a href="https://doi.org/10.1007/978-981-99-8476-3_9" target="_blank" rel="noreferrer noopener">https://doi.org/10.1007/978-981-99-8476-3_9</a>
<br /><br /><a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85187671116&doi=10.1007%2F978-981-99-8476-3_9&partnerID=40&md5=a1d8be2d03d7a9942928c713d50331d2" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85187671116&doi=10.1007%2f978-981-99-8476-3_9&partnerID=40&md5=a1d8be2d03d7a9942928c713d50331d2</a>
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ISSN: 23673370; ISBN: 978-981998475-6
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Online
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English
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Conference paper
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Antony T.M., School of Commerce Finance and Accountancy, CHRIST (Deemed to be University), Bengaluru, India; Kumar B.S., School of Commerce Finance and Accountancy, CHRIST (Deemed to be University), Bengaluru, India
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Conference Papers
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Unsupervised Feature Selection Approach for Smartwatches
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Conjoint analysis; Laplacian score; Multi-cluster; Smartwatch; Spectral; Unsupervised Feature Selection
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Traditional feature selection methods can be time-consuming and labor-intensive, especially with large datasets. This studys unsupervised feature selection approach can automate the process and help identify important features preferred by a particular segment of users. The unsupervised feature selection method is applied for smartwatches. Smartwatches continue to gain popularity. It is important to understand which features are most important to users to design and develop smartwatches that are more engaging, user-friendly, and meet the needs and preferences of their target audience. The rapid pace of technological innovation in the smartwatch industry means that new features and functionalities are constantly being developed. Multi-cluster feature selection, Laplacian score, and unsupervised spectral feature are used. Conjoint analysis is done on the most common features in all three selection methods. The unsupervised feature selection technique is used for identifying the relevant and important features of new smartwatch users.The practical implication of the research is in the application of the technique in the new product design of smartwatches. The result of the study also informs smartwatch manufacturers and developers on the features they need to prioritize and invest in. This can ultimately result in better and more user-friendly smartwatches and a good overall experience for the user. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.
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Kapse M.; Sharma V.; Elangovan N.; Gupta S.
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Lecture Notes in Networks and Systems, Vol-869 LNNS, pp. 467-481.
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Springer Science and Business Media Deutschland GmbH
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2024-01-01
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<a href="https://doi.org/10.1007/978-981-99-9040-5_35" target="_blank" rel="noreferrer noopener">https://doi.org/10.1007/978-981-99-9040-5_35</a>
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ISSN: 23673370; ISBN: 978-981999039-9
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Online
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English
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Conference paper
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Kapse M., Symbiosis Centre for Management and Human Resource Development, Symbiosis International (Deemed University), Pune, India; Sharma V., Symbiosis Centre for Management and Human Resource Development, Symbiosis International (Deemed University), Pune, India; Elangovan N., School of Business Management, Christ University, Karnataka, Bangalore, India; Gupta S., Prestige Institute of Management and Research, Madhya Pradesh, Indore, India
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Conference Papers
Conference Paper
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Disaster resilience of flood in Kerala, India
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Decentralised disaster Management; Disaster Management; Displaced communities; Flood; Resilience capacity
Description
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Kerala, the southern state in the Indian peninsula, has been affected by floods for the last three consecutive years. Changing weather patterns leading to heavy monsoon and development without considering the ecological vulnerabilities of the region has been pointed out as the reasons for flooding. Displaced communities, the destruction of agricultural and industrial enterprises, and health concerns have made disaster management a challenge for communities and governments alike. Even though there were lots of difficulties, the way Keralites came out of all these miseries and their adaptation was really inexplicable and always provided scope for research in that area. This paper focuses on examining the flooding pattern and impact of floods in Kerala, India and assessing the resilience capacity of the affected community. Self-developed questionnaires were used to gather data from the flood-affected population in the most flood-affected districts in Kerala. To gauge the respondents' opinions, the questionnaire used a five-point variable Likert scale. When all was said and done, 260 valid questionnaires were successfully retrieved. The study found that communities show resilience to flood with partnership and decentralised management of disasters. The study could help recognise the strategies for building resilient communities through policy intervention and civil society participation. Published under licence by IOP Publishing Ltd.
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Mohan C.; Joy R.
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IOP Conference Series: Earth and Environmental Science, Vol-1306, No. 1
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Institute of Physics
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2024-01-01
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<a href="https://doi.org/10.1088/1755-1315/1306/1/012045" target="_blank" rel="noreferrer noopener">https://doi.org/10.1088/1755-1315/1306/1/012045</a>
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All Open Access; Gold Open Access
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ISSN: 17551307
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Online
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English
Type
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Conference paper
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Mohan C., School of Business and Management, Christ University, Bengaluru, India, School of Management, Presidency University, Bengaluru, India; Joy R., School of Business and Management, Christ University, Bengaluru, India
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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/.
Title
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Conference Papers
Conference Paper
Faculty Publications- Conference Papers
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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/.
Title
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A Survey on Feature Selection, Classification, and Optimization Techniques for EEG-Based BrainComputer Interface
Subject
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Algorithms; Braincomputer interface (BCI); Classification; Classifiers; Electroencephalography (EEG); Feature extraction; Feature selection; Optimization techniques
Description
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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.
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Subramanian S.C.; Daniel D.
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Lecture Notes in Networks and Systems, Vol-865, pp. 79-93.
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Springer Science and Business Media Deutschland GmbH
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2024-01-01
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<a href="https://doi.org/10.1007/978-981-99-9043-6_7" target="_blank" rel="noreferrer noopener">https://doi.org/10.1007/978-981-99-9043-6_7</a>
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ISSN: 23673370; ISBN: 978-981999042-9
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Online
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English
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Conference paper
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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