34
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16481
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Dublin Core
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Title
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Conference Papers
Conference Paper
Faculty Publications- Conference Papers
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Title
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A Non-Linear Approach to Predict the Salary of NBA Athletes using Machine Learning Technique
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Non-Linear Model; Random Forest; Regression machine learning; Salary prediction; Sports analytics; XGBoost
Description
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Every sportsman traded/drafted receives monetary compensation in accordance with their contract. In this study, we propose a nonlinear approach based on performance and other aspects to determine the salary of a basketball player. We estimate the salary based on four regressive models. Whilst predicting we also Figure out the important features impacting the salary. Comparatively speaking, random forest outperformed other algorithms. Furthermore, we consider that our findings might benefit discussions between basketball teams and players. This model can also help set a benchmark for salary expectations by the players in accordance. 2022 IEEE.
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Jain A.; Jain S.; Pancinovia N.M.; George J.P.
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2022 International Conference on Trends in Quantum Computing and Emerging Business Technologies, TQCEBT 2022
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Institute of Electrical and Electronics Engineers Inc.
Date
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2022-01-01
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<a href="https://doi.org/10.1109/TQCEBT54229.2022.10041664" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/TQCEBT54229.2022.10041664</a>
<br /><br /><a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85149179224&doi=10.1109%2FTQCEBT54229.2022.10041664&partnerID=40&md5=67bfccfae94fade205a75628f1961e7f" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85149179224&doi=10.1109%2fTQCEBT54229.2022.10041664&partnerID=40&md5=67bfccfae94fade205a75628f1961e7f</a>
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ISBN: 978-166545361-5
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Online
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English
Type
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Conference paper
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Jain A., CHRIST (Demeed to Be University), India; Jain S., CHRIST (Demeed to Be University), India; Pancinovia N.M., CHRIST (Demeed to Be University), India; George J.P., CHRIST (Demeed to Be University), India
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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/.
Title
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Articles
Article
Faculty Publications -Articles
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Title
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A note on ?(k)-colouring of the Cartesian product of some graphs
Subject
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bad edge; Improper colouring; near proper colouring; ?(k)-colouring
Description
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The chromatic number, x(G) of a graph G is the minimum number of colours used in a proper colouring of G. In an improper colouring, an edge uv is bad if the colours assigned to the end vertices of the edge is the same. Now, if the available colours are less than that of the chromatic number of graph G, then colouring the graph with the available colours lead to bad edges in G. The number of bad edges resulting from a ? (k)-colouring of G is denoted by bk(G). In this paper, we use the concept of (k)-colouring and determine the number of bad edges in Cartesian product of some graphs. 2022 by the authors.
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Ellumkalayil M.T.; Naduvath S.
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Communications in Combinatorics and Optimization, Vol-7, No. 1, pp. 113-120.
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Azarbaijan Shahid Madani University
Date
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2022-01-01
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<a href="https://doi.org/10.22049/cco.2021.27114.1211" target="_blank" rel="noreferrer noopener">https://doi.org/10.22049/cco.2021.27114.1211</a>
<br /><br /><a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85126861554&doi=10.22049%2Fcco.2021.27114.1211&partnerID=40&md5=6d190b48285f613b9fca7373095eea97" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85126861554&doi=10.22049%2fcco.2021.27114.1211&partnerID=40&md5=6d190b48285f613b9fca7373095eea97</a>
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ISSN: 25382128
Format
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Online
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English
Type
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Article
Coverage
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Ellumkalayil M.T., Department of Mathematics, CHRIST (Deemed to be University), Karnataka, Bangalore, 560029, India; Naduvath S., Department of Mathematics, CHRIST (Deemed to be University), Karnataka, Bangalore, 560029, India
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Title
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Articles
Article
Faculty Publications -Articles
Dublin Core
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Title
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A Note on J-colouring of Jahangir Graphs
Subject
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Cordial graph; J-colouring; Jahangir graph; Rainbow neighbourhood
Description
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In this paper, we discuss J-colouring of the family of Jahangir graphs.Note that the family of Jahangir graphs is a wide ranging family of graphs which by a generalised definition includes wheel graphs. We characterise the subset of Jahangir graphs which admit a J-colouring. 2019, The National Academy of Sciences, India.
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Naduvath S.; Kok J.
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Proceedings of the National Academy of Sciences India Section A - Physical Sciences, Vol-90, No. 5, pp. 819-821.
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Springer
Date
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2020-01-01
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<a href="https://doi.org/10.1007/s40010-019-00638-z" target="_blank" rel="noreferrer noopener">https://doi.org/10.1007/s40010-019-00638-z</a>
<br /><br /><a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85069849596&doi=10.1007%2Fs40010-019-00638-z&partnerID=40&md5=6c265f7b08ac434d5ef4344181490ece" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85069849596&doi=10.1007%2fs40010-019-00638-z&partnerID=40&md5=6c265f7b08ac434d5ef4344181490ece</a>
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ISSN: 3698203
Format
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Online
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English
Type
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Article
Coverage
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Naduvath S., Department of Mathematics, CHRIST (Deemed to be University), Bangalore, 560029, Karnataka, India; Kok J., Department of Mathematics, CHRIST (Deemed to be University), Bangalore, 560029, Karnataka, India
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Title
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Articles
Article
Faculty Publications -Articles
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Title
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A note on perfect lucky k-colourable graphs
Subject
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Lucky k-polynomial; Lucky ?-polynomial; Perfect Lucky k-colouring
Description
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This paper presents the notion of perfect Lucky k-colouring. Basic conditions for a perfect Lucky k-colourable graph are presented. Application thereof is then presented by obtaining the Lucky 4-polynomials for all connected graphs G on six vertices with ten edges. The chromatic number of these connected graphs is ?(G) = 3 or 4. For k = max{?(G): 3 or 4g = 4, it is possible to find Lucky 4-polynomials for all graphs on six vertices and ten edges. The methodology improves substantially on the fundamental methodology such that, vertex partitions begin with Lucky partition forms immediately. Finally, further problems for research related to this study are presented. 2020, International Scientific Research Publications. All rights reserved.
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Kok J.; Kureethara J.V.
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Journal of Mathematics and Computer Science, Vol-21, No. 3, pp. 192-197.
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International Scientific Research Publications
Date
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2020-01-01
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<a href="https://doi.org/10.22436/jmcs.021.03.03" target="_blank" rel="noreferrer noopener">https://doi.org/10.22436/jmcs.021.03.03</a>
<br /><br /><a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85085048819&doi=10.22436%2Fjmcs.021.03.03&partnerID=40&md5=9d074ab326abe12e4f6fc9ecbc63c6cc" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85085048819&doi=10.22436%2fjmcs.021.03.03&partnerID=40&md5=9d074ab326abe12e4f6fc9ecbc63c6cc</a>
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All Open Access; Gold Open Access
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ISSN: 2008949X
Format
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Online
Language
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English
Type
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Article
Coverage
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Kok J., City of Tshwane, South Africa; Kureethara J.V., CHRIST (Deemed to be a University), Bangalore, India
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Title
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Articles
Article
Faculty Publications -Articles
Dublin Core
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Title
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A Note on the Rainbow Neighbourhood Number of Certain Graph Classes
Subject
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Colour classes; Rainbow neighbourhood; Rainbow neighbourhood number
Description
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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 r ? (G). In this paper, rainbow neighbourhood number of certain graph classes are discussed. 2018, The National Academy of Sciences, India.
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Naduvath S.; Chandoor S.; Kalayathankal S.J.; Kok J.
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National Academy Science Letters, Vol-42, No. 2, pp. 135-138.
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Springer
Date
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2019-01-01
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<a href="https://doi.org/10.1007/s40009-018-0702-6" target="_blank" rel="noreferrer noopener">https://doi.org/10.1007/s40009-018-0702-6</a>
<br /><br /><a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85064542082&doi=10.1007%2Fs40009-018-0702-6&partnerID=40&md5=553041830605b8467382ebc5f95333e7" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85064542082&doi=10.1007%2fs40009-018-0702-6&partnerID=40&md5=553041830605b8467382ebc5f95333e7</a>
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ISSN: 0250541X
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Online
Language
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English
Type
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Article
Coverage
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Naduvath S., Department of Mathematics, CHRIST (Deemed to be University), Bengaluru, 560029, Karnataka, India; Chandoor S., Department of Mathematics, Research and Development Centre, Bharathiar University, Coimbatore, 641046, Tamil Nadu, India; Kalayathankal S.J., Department of Mathematics, Kuriakose Elias College, Mannanam, Kottayam, 686561, Kerala, India; Kok J., Centre for Studies in Discrete Mathematics, Vidya Academy of Science and Technology Thalakkottukara, Thrissur, 680501, Kerala, India
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Title
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Articles
Article
Faculty Publications -Articles
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Title
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A Note on the Significance of Quartic Autocatalysis Chemical Reaction on the Motion of Air Conveying Dust Particles
Subject
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Air; Dust Particles; Flow's Velocity; Fluid-Particle Interaction; Paraboloid of Revolution; Quartic Autocatalytic Chemical Reaction
Description
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Little is known on the significance of fluid-particle interaction for velocity and temperature as in the case of centrifuge for separating particles of different types, launching of rockets, and motion of space shuttle through the air when there exist chemical reactions between the flow and the wall. The aim of this study is not only to explore the significance of quartic autocatalytic chemical reaction on the flow of dusty fluid in which the transmission of energy in form of electromagnetic is nonlinear but also to unravel the effects of buoyancy on the velocity of the dust and temperature of the dust. The nonlinear partial differential equations that model the transport phenomenon was transformed, nondimensionalised, and parameterised using suitable variables. The corresponding boundary value problems were converted to an initial value problem using the method of superposition and solved numerically. The outcome of the study indicates that enhancement of buoyancy is a yardstick to increase the vertical velocity, horizontal velocity, and shear stress within the fluid domain; increase the velocity of the dust particles; increase the temperature distribution across the flow of dusty fluid; increase the concentration of dusty fluid; and decrease the concentration of the catalyst. It is worth noticing that utmost velocity of the dust occurs at a minimum value of fluid-particle interaction for velocity in the flow over a larger thickness of paraboloid of revolution. 2019 Walter de Gruyter GmbH, Berlin/Boston 2019.
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Animasaun I.L.; Koriko O.K.; Mahanthesh B.; Dogonchi A.S.
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Zeitschrift fur Naturforschung - Section A Journal of Physical Sciences, Vol-74, No. 10, pp. 879-904.
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De Gruyter
Date
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2019-01-01
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<a href="https://doi.org/10.1515/zna-2019-0180" target="_blank" rel="noreferrer noopener">https://doi.org/10.1515/zna-2019-0180</a>
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ISSN: 9320784
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Online
Language
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English
Type
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Article
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Animasaun I.L., Fluid Dynamics Research Group, Department of Mathematical Sciences, Federal University of Technology, Akure, Nigeria; Koriko O.K., Fluid Dynamics Research Group, Department of Mathematical Sciences, Federal University of Technology, Akure, Nigeria; Mahanthesh B., Department of Mathematics, Christ University, Bengaluru, Karnataka, 560029, India; Dogonchi A.S., Department of Mechanical Engineering, Aliabad Katoul Branch, Islamic Azad University, Aliabad Katoul, Iran
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Title
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Articles
Article
Faculty Publications -Articles
Dublin Core
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Title
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A novel African buffalo based greedy routing technique for infrastructure and cluster based communication in vehicular ad-hoc network
Subject
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Data transmission ratio (DTR); End-to-end delay; Energy consumption; Throughput; Vehicular ad-hoc network (VANET)
Description
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In this modern era, the wire free replica is utilized Vehicular Ad hoc Networks (VANETs) to converse each other. Also, the VANET paradigm not required any specific fixed infrastructure. Furthermore, the vehicle in VANET framework is movable like as mobile nodes. Also, the wireless connectivity between the vehicular nodes is not stable in all cases, it often changes their structure. Research have recommended various responses to control these issues and furthermore to lessen blockage in VANET environment. Therefore, the infrastructure of a network changes frequently which results in communication overheads, energy consumption and lifetime of the nodes. Consequently, in this paper a novel African Buffalo based Greedy Routing (ABGR) technique is to improve the performance of infrastructure and cluster based communication of the node. Moreover, the routing overhead and infrastructure communication can be enhanced by this proposed protocol. Consequently, the energy consumption solution is enhanced based on the CH. Sequentially, the proposed routing protocol is compared with existing protocols in terms of end-to-end delay, throughput, Data transmission Ratio (DTR), and energy consumption and so on. Therefore, it shows that the energy utilization and lifetime of the nodes in the proposed network has been enhanced. 2021 Little Lion Scientific.
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Sree Divya N.; Bobba V.; Vatambeti R.
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Journal of Theoretical and Applied Information Technology, Vol-99, No. 5, pp. 1232-1243.
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Little Lion Scientific
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2021-01-01
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<a href="" target="_blank" rel="noreferrer noopener"></a>
<br /><br /><a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85105133104&partnerID=40&md5=f3808042abc11bec7408acb175c9529f" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85105133104&partnerID=40&md5=f3808042abc11bec7408acb175c9529f</a>
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ISSN: 19928645
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Online
Language
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English
Type
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Article
Coverage
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Sree Divya N., Dept. of CSE, KLEF Deemed to be University, A.P., India; Bobba V., Department of CSE, TKR College of Engineering and Technology, Hyderabad, Telangana, India; Vatambeti R., Department of CSE, CHRIST (Deemed to be University), Bangalore, India
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Articles
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Title
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A novel AI model for the extraction and prediction of Alzheimer disease from electronic health record
Subject
The topic of the resource
Big data; Dark data; Electronic health record; Machine learning; Natural language processing
Description
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Dark data is an emerging concept, with its existence, identification, and utilization being key areas of research. This study examines various aspects and impacts of dark data in the healthcare domain and designs a model to extract essential clinical parameters for Alzheimer's from electronic health records (EHR). The novelty of dark data lies in its significant impact across sectors. In healthcare, even the smallest data points are crucial for diagnosis, prediction, and treatment. Thus, identifying and extracting dark data from medical data corpora enhances decision-making. In this research, a natural language processing (NLP) model is employed to extract clinical information related to Alzheimer's disease, and a machine learning algorithm is used for prediction. Named entity recognition (NER) with SpaCy is utilized to extract clinical departments from doctors' descriptions stored in EHRs. This NER model is trained on custom data containing processed EHR text and associated entity annotations. The extracted clinical departments can then be used for future Alzheimer's diagnosis via support vector machine (SVM) algorithms. Results show improved accuracy with the use of extracted dark data, highlighting its importance in predicting Alzheimer's disease. This research also explores the presence of dark data in various domains and proposes a dark data extraction model for the clinical domain using NLP. 2025 Institute of Advanced Engineering and Science. All rights reserved.
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Maju S.V.; Prakasi G.; Pushpam O.S.
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Indonesian Journal of Electrical Engineering and Computer Science, Vol-37, No. 2, pp. 1023-1031.
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Institute of Advanced Engineering and Science
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2025-01-01
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<a href="https://doi.org/10.11591/ijeecs.v37.i2.pp1023-1031" target="_blank" rel="noreferrer noopener">https://doi.org/10.11591/ijeecs.v37.i2.pp1023-1031</a>
<br /><br /><a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85210744676&doi=10.11591%2Fijeecs.v37.i2.pp1023-1031&partnerID=40&md5=a8cd65f57f1f383add56616138d82584" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85210744676&doi=10.11591%2fijeecs.v37.i2.pp1023-1031&partnerID=40&md5=a8cd65f57f1f383add56616138d82584</a>
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ISSN: 25024752
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Online
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English
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Article
Coverage
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Maju S.V., Department of Computer Science and Engineering, CHRIST University, Bengaluru, India; Prakasi G., Department of Computer Science and Engineering, CHRIST University, Bengaluru, India; Pushpam O.S., Department of Computer Science and Engineering, CHRIST University, Bengaluru, India
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Faculty Publications
Article
Faculty Publications -Articles
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Maju, Sonam V.; Prakasi, Gnana; Pushpam, Oliver Sirya
Title
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A novel AI model for the extraction and prediction of Alzheimer disease from electronic health record
Date
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01-01-2025
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Indonesian Journal of Electrical Engineering and Computer Science;Volume;37;Issue;2;pp.1023-1031
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<a href="https://doi.org/10.11591/ijeecs.v37.i2.pp1023-1031" target="_blank" rel="noreferrer noopener">https://doi.org/10.11591/ijeecs.v37.i2.pp1023-1031</a> <br /><br /><a href="https://www.scopus.com/pages/publications/85210744676?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/85210744676?origin=resultslist</a>
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Maju S.V., Department of Computer Science and Engineering, CHRIST University, Bengaluru, India; Prakasi G., Department of Computer Science and Engineering, CHRIST University, Bengaluru, India; Pushpam O.S., Department of Computer Science and Engineering, CHRIST University, Bengaluru, India
Description
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Dark data is an emerging concept, with its existence, identification, and utilization being key areas of research. This study examines various aspects and impacts of dark data in the healthcare domain and designs a model to extract essential clinical parameters for Alzheimer's from electronic health records (EHR). The novelty of dark data lies in its significant impact across sectors. In healthcare, even the smallest data points are crucial for diagnosis, prediction, and treatment. Thus, identifying and extracting dark data from medical data corpora enhances decision-making. In this research, a natural language processing (NLP) model is employed to extract clinical information related to Alzheimer's disease, and a machine learning algorithm is used for prediction. Named entity recognition (NER) with SpaCy is utilized to extract clinical departments from doctors' descriptions stored in EHRs. This NER model is trained on custom data containing processed EHR text and associated entity annotations. The extracted clinical departments can then be used for future Alzheimer's diagnosis via support vector machine (SVM) algorithms. Results show improved accuracy with the use of extracted dark data, highlighting its importance in predicting Alzheimer's disease. This research also explores the presence of dark data in various domains and proposes a dark data extraction model for the clinical domain using NLP. 2025 Institute of Advanced Engineering and Science. All rights reserved.
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Big data; Dark data; Electronic health record; Machine learning; Natural language processing
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Institute of Advanced Engineering and Science
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ISSN: 25024752;
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English
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Article
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All Open Access; Gold Open Access
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online
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Articles
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A Novel AI-Based Stock Market Prediction Using Machine Learning Algorithm
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The time series forecasting system can be used for investments in a safe environment with minimized chances of loss. The Holt-Winters algorithm followed various procedures and observed the multiple factors applied to the neural network. The final module helps filter the system to predict the various factors and provides a rating for the system. This research work uses real-time dataset of fifteen stocks as input into the system and, based on the data, predicts or forecasts future stock prices of different companies belonging to different sectors. The dataset includes approximately fifteen companies from different sectors and forecasts their results based on which the user can decide whether to invest in the particular company or not; the forecasting will give an accurate result for the customer investments. 2022 Iyyappan. M et al.
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Iyyappan M.; Ahmad S.; Jha S.; Alam A.; Yaseen M.; Abdeljaber H.A.M.
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Scientific Programming, Vol-2022
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Hindawi Limited
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2022-01-01
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<a href="https://doi.org/10.1155/2022/4808088" target="_blank" rel="noreferrer noopener">https://doi.org/10.1155/2022/4808088</a>
<br /><br /><a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85128330200&doi=10.1155%2F2022%2F4808088&partnerID=40&md5=ad4e5ad0dab4324e706ee58de8712bf2" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85128330200&doi=10.1155%2f2022%2f4808088&partnerID=40&md5=ad4e5ad0dab4324e706ee58de8712bf2</a>
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All Open Access; Gold Open Access
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ISSN: 10589244; CODEN: SCIPE
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Online
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English
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Article
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Iyyappan M., School of Sciences, Christ (Deemed to Be University), Delhi-NCR, Ghaziabad, 201003, India; Ahmad S., Department of Computer Science, College of Computer Engineering and Sciences, Prince Sattam Bin Abdulaziz University, Alkharj, 11942, Saudi Arabia; Jha S., School of Sciences, Christ (Deemed to Be University), Delhi-NCR, Ghaziabad, 201003, India; Alam A., Department of Computer Science, Bakhtar University, Kabul, Afghanistan; Yaseen M., Faculty of Information Technology, University of Central Punjab, Lahore, Pakistan; Abdeljaber H.A.M., Department of Computer Science, Faculty of Information Technology, Applied Science Private University, Amman, Jordan
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Faculty Publications
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Pawaskar, Praveen; Yogish, H.K.; Pakruddin, B.; Deepa, Y.
Title
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A Novel and Efficient Deep Learning Models for Assessing AIs Impact on Disease Diagnosis in Agriculture
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01-01-2025
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Indian Journal of Agricultural Research;Volume;59;Issue;9;pp.1395-1403
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<a href="https://doi.org/10.18805/IJARe.A-6388" target="_blank" rel="noreferrer noopener">https://doi.org/10.18805/IJARe.A-6388</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105016859404?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105016859404?origin=resultslist</a>
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Pawaskar P., Department of Computer Science and Engineering, MS Ramaiah Institute of Technology, Bengaluru, Visvesvaraya Technological University, Karnataka, Belagavi, 590 018, India, School of Computer Science and Engineering and Information Science and Engineering, Presidency University, Karnataka, Bengaluru, 560 064, India; Yogish H.K., Department of Computer Science and Engineering, MS Ramaiah Institute of Technology, Bengaluru, Visvesvaraya Technological University, Karnataka, Belagavi, 590 018, India; Pakruddin B., School of Computer Science and Engineering and Information Science and Engineering, Presidency University, Karnataka, Bengaluru, 560 064, India; Deepa Y., Department of Computer Science and Engineering, Christ University, Karnataka, Bengaluru, 560 029, India
Description
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Background: Agriculture sustains human life by providing food, raw materials and employment opportunities. However, climate change and resource limitations pose significant challenges to crop production. AI-driven smart farming has emerged as a solution to enhance agricultural efficiency, with Explainable AI (XAI) improving transparency in decision-making. Innovations such as smart sensors and automated systems have benefited key agricultural sectors, including crops, forestry, livestock and aquaculture. Turmeric, valued for its medicinal and economic significance, requires careful monitoring to combat diseases like leaf spot and leaf blotch, which can impact yield and quality. Methods: This study introduces turmeric net, a Convolutional Neural Network (CNN)-based model leveraging transfer learning to detect and classify turmeric leaf diseases. The dataset used consists of 791 original images and 3,702 augmented images obtained from mendeley data, categorized into four classes: healthy leaf, dry leaf, leaf blotch and rhizome rot. The model development was carried out using TensorFlow, with ResNet50V2 as a baseline for comparison. The models were trained on processed image data, incorporating augmentation techniques to improve robustness and generalizability. Result: The accuracy of both models was evaluated. ResNet50V2 achieved an accuracy exceeding 99%, demonstrating high effectiveness in disease classification. Meanwhile, TurmericNet attained a competitive accuracy of 98%, making it a reliable alternative for turmeric disease identification. These results indicate that deep learning-based models can significantly aid in early disease detection, providing farmers with a valuable tool to enhance crop management and productivity. 2025 , Agricultural Research Communication Centre. All rigths reserved.
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Agriculture; Classification; Disease detection; Turmeric
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Agricultural Research Communication Centre
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ISSN: 3678245;
Language
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English
Type
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Article
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All Open Access; Gold Open Access
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online
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Articles
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Title
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A novel and secured bitcoin method for identification of counterfeit goods in logistics supply management within online shopping
Subject
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Bitcoin; Blockchain technology; Cryptographic hashes; Decentralized ledger; Smart contracts
Description
An account of the resource
Counterfeit merchandise poses significant challenges for both consumers and retailers. When counterfeit goods infiltrate the market, they damage the trustworthiness and reputation of legitimate companies, leading to negative publicity. Furthermore, these imitations can be harmful, especially in critical sectors like food and pharmaceuticals. To address this issue, it is essential to identify and prevent counterfeit products from reaching consumers. Our proposed solution leverages blockchain technology to authenticate products. Blockchains decentralized database securely stores all transaction data, ensuring transparency and traceability. Additionally, we introduce a tool that records ownership and product details. By utilizing a Quick Response (QR) code, consumers can easily verify the authenticity of a product, thus accessing its manufacturing and ownership information. This approach not only safeguards consumer safety but also protects the reputation and financial performance of legitimate business. Bharati Vidyapeeth's Institute of Computer Applications and Management 2024.
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Singh A.; Ramachandran K.K.; Krishna S.H.; Nayak C.; Anusha K.; Acharjee P.B.; Srivastava S.
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International Journal of Information Technology (Singapore), Vol-16, No. 8, pp. 5371-5377.
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Springer Science and Business Media B.V.
Date
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2024-01-01
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<a href="https://doi.org/10.1007/s41870-024-02021-7" target="_blank" rel="noreferrer noopener">https://doi.org/10.1007/s41870-024-02021-7</a>
<br /><br /><a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85200549370&doi=10.1007%2Fs41870-024-02021-7&partnerID=40&md5=1b6c7dafc2ccb7d08f52bafb49a3eee7" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85200549370&doi=10.1007%2fs41870-024-02021-7&partnerID=40&md5=1b6c7dafc2ccb7d08f52bafb49a3eee7</a>
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Restricted Access
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ISSN: 25112104
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Online
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English
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Article
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Singh A., Institute of Business Management, GLA University, Mathura, India; Ramachandran K.K., Dr. G R D College of Science, Tamil Nadu, Coimbatore, India; Krishna S.H., Department of Business Management, Vignana Bharathi Institute of Technology, Telangana, Hyderabad, India; Nayak C., Pimpri Chinchwad College of Engineering, Pune, India; Anusha K., KL Education Foundation, KL Business School, Andhra Pradesh, Guntur, India; Acharjee P.B., Computer Science, CHRIST University, Pune, India; Srivastava S., CSE Department, M.M. Engineering College, Maharishi Markandeshwar (Deemed to be University), Haryana, Mullana, India
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Conference Papers
Conference Paper
Faculty Publications- Conference Papers
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Title
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A Novel Approach for Detection and Recognition of Traffic Signs for Automatic Driver Assistance System Under Cluttered Background
Subject
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Driver assistance systems; SVM (Support Vector Machine) classifiers; YCbCr
Description
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Traffic sign detection and recognition is a core phase of Driver Assistance and Monitoring System. This paper focuses on the development of an intelligent driver assistance system there by achieving road safty. In this paper a novel system is proposed to detect and classify traffic signs such as warning and compulsory signs even for occluded and angular tilt images using Support Vector Machines. Exhaustive experiments are performed in order to demonstrate the efficiency of proposed method. 2019, Springer Nature Singapore Pte Ltd.
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Manjunatha H.T.; Danti A.; ArunKumar K.L.
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Communications in Computer and Information Science, Vol-1035, pp. 407-419.
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Springer Verlag
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2019-01-01
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<a href="https://doi.org/10.1007/978-981-13-9181-1_36" target="_blank" rel="noreferrer noopener">https://doi.org/10.1007/978-981-13-9181-1_36</a>
<br /><br /><a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85070200947&doi=10.1007%2F978-981-13-9181-1_36&partnerID=40&md5=c6210343358312d9050ac12dc5fea8b4" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85070200947&doi=10.1007%2f978-981-13-9181-1_36&partnerID=40&md5=c6210343358312d9050ac12dc5fea8b4</a>
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ISSN: 18650929; ISBN: 978-981139180-4
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Online
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English
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Conference paper
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Manjunatha H.T., Department of MCA, Jawaharlal Nehru National College of Engineering, Shimoga, Karnataka, India; Danti A., Computer Science and Engineering, CHRIST (Deemed to be University), Bengaluru, Karnataka, India; ArunKumar K.L., Department of MCA, Jawaharlal Nehru National College of Engineering, Shimoga, Karnataka, India
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Articles
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A Novel Approach for Fractional (1 + 1 ) -Dimensional BiswasMilovic Equation
Subject
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Caputo fractional derivative; Fractional BiswasMilovic equation; Laplace transform; q-Homotopy analysis method
Description
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In this paper, we find the solution for (1 + 1 ) -dimensional fractional Biswas-Milovic (FBM) equation using the q-homotopy analysis transform method (q-HATM). The Biswas-Milovic equation is a generalization of the nonlinear Schringer (NLS) equation. The future scheme is the elegant mixture of q-homotopy analysis scheme with Laplace transform technique and fractional derivative considered in Caputo sense. To validate and illustrate the competence of the method, we examine the projected model in terms of arbitrary order. Moreover, the nature of the attained results have been presented in 3D plots and contour plots for different value of the order. The gained consequences show that, the hired algorithm is highly accurate, easy to implement, and very operative to investigate the nature of complex nonlinear models ascended in science and engineering. 2021, The Author(s), under exclusive licence to Springer Nature India Private Limited.
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Prakasha D.G.; Veeresha P.; Baskonus H.M.
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International Journal of Applied and Computational Mathematics, Vol-7, No. 5
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Springer
Date
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2021-01-01
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<a href="https://doi.org/10.1007/s40819-021-01124-0" target="_blank" rel="noreferrer noopener">https://doi.org/10.1007/s40819-021-01124-0</a>
<br /><br /><a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85113394854&doi=10.1007%2Fs40819-021-01124-0&partnerID=40&md5=252a88d63dbc43701420cd88f8f0b7fc" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85113394854&doi=10.1007%2fs40819-021-01124-0&partnerID=40&md5=252a88d63dbc43701420cd88f8f0b7fc</a>
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ISSN: 23495103
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Online
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English
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Article
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Prakasha D.G., Department of Mathematics, Davangere University, Shivagangothri, Davangere, 577007, India; Veeresha P., Center for Mathematical Needs, Department of Mathematics, CHRIST (Deemed To Be University), Bengaluru, 560029, India; Baskonus H.M., Department of Mathematics and Science Education, Faculty of Education, Harran University, Sanliurfa, Turkey
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Conference Papers
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Faculty Publications- Conference Papers
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Title
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A Novel Approach for Implementing Conventional LBIST by High Execution Microprocessors
Subject
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conventional LBIS T; high execution microprocessor; power consumption and binning
Description
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The major VLS I circuits like sequential circuits, linear chips and op amps are very important elements to provide many logic functions. Today's competitive devices like cell phone, tabs and note pads are most prominent and those are used to get function the 5G related operations. In this work lower built-in self-test (LBIS T) mechanism is used to designing a microprocessor. The proposed methodology is giving performance measure like power efficiency 97.5%, improvement of delay is 2.5% and 32% development of area had been attained. This methodology attains more out performance and compete with present technology. The proposed equipment and execution for our approach requiring a constrained range overhead (lower than 3% power) over conventional LBIS T. 2022 IEEE
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Shravani C.; Bollam H.L.; Krishna G.R.; Vatambeti R.; Saikumar K.
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Proceedings - 4th International Conference on Smart Systems and Inventive Technology, ICSSIT 2022, pp. 804-809.
Publisher
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Institute of Electrical and Electronics Engineers Inc.
Date
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2022-01-01
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<a href="https://doi.org/10.1109/ICSSIT53264.2022.9716411" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/ICSSIT53264.2022.9716411</a>
<br /><br /><a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85127367540&doi=10.1109%2FICSSIT53264.2022.9716411&partnerID=40&md5=d9aae90c454ff76a9e8a570f3b872d2c" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85127367540&doi=10.1109%2fICSSIT53264.2022.9716411&partnerID=40&md5=d9aae90c454ff76a9e8a570f3b872d2c</a>
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ISBN: 978-166540118-0
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Online
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English
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Conference paper
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Shravani C., Department of ECE, Vignan's Institute of Information Technology (A), Duvvada, Andhra Pradesh, India; Bollam H.L., Dept. of ECE, Nalla Malla Reddy Engineering College, Divyanagar, Narapally, India; Krishna G.R., CSE Department, Koneru Lakshmaiah Education Foundation, Green Fields, Vaddeswaram, Andhra Pradesh, 522502, India; Vatambeti R., Dept. of CSE, CHRIST (Deemed to be University), Bangalore, India; Saikumar K., Dept. of CSE, Malla Reddy University, MRUH, Hyderabad, India
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Articles
Article
Faculty Publications -Articles
Dublin Core
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A novel approach for integrating cryptography and blockchain into IoT system
Subject
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Block chain; Cryptography; End-to-end security; Integrating approach; IoT; Privacy; Security
Description
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The quick advancement of Internet of Things (IoT) emphasizes the significance of cryptography and blockchain ensuring the security of sensitive data and connected devices. Blockchain technology and encryption play key roles in ensuring the security of the expansive IoT network. Blockchain offers decentralized trust, immutability, and transparency to IoT networks and transactions, while encryption serves to protect IoT data from unauthorized access. It is a novel approach for integrating cryptography and blockchain into IoT System, cryptography and blockchain stand out as robust technologies that enhance the security of IoT systems. The implementation of an integrated architecture, along with a strategic integration approach, further strengthens the security measures. This methodology proves valuable for managing and validating digital transactions on decentralized, immutable networks. This work also explores the potential significance of integrating cryptography and blockchain into IoT System, this functions and applications in enhancing IoT security. This methodology introduces encryption techniques tailored for resource-constrained IoT devices, which are essential for ensuring end-to-end security. 2024, Taru Publications. All rights reserved.
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Mallikarjuna B.; Berar G.; Noonia A.; Rani L.; Pushpalatha C.; Sebastian S.
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Journal of Discrete Mathematical Sciences and Cryptography, Vol-27, No. 7, pp. 2087-2099.
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Taru Publications
Date
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2024-01-01
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<a href="https://doi.org/10.47974/JDMSC-2082" target="_blank" rel="noreferrer noopener">https://doi.org/10.47974/JDMSC-2082</a>
<br /><br /><a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85211109084&doi=10.47974%2FJDMSC-2082&partnerID=40&md5=4a4d70116f905b3f3b1ef8513261cbec" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85211109084&doi=10.47974%2fJDMSC-2082&partnerID=40&md5=4a4d70116f905b3f3b1ef8513261cbec</a>
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ISSN: 9720529
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Online
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English
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Article
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Mallikarjuna B., Department of Information Technology, Institute of Aeronautical Engineering, Telangana, Dundigal, Hyderabad, 500043, India; Berar G., Department of Computer Science and Engineering, Chitkara University Institute of Engineering and Technology, Chitkara University, Punjab, 140401, India; Noonia A., Department of Computer Science and Engineering, Manipal University Jaipur, Rajasthan, Jaipur, 303007, India; Rani L., Department of Computer Science and Engineering, Chitkara University Institute of Engineering and Technology, Chitkara University, Punjab, 140401, India; Pushpalatha C., Department of Master of Business Administration, Institute of Aeronautical Engineering, Telangana, Dundigal, Hyderabad, 500043, India; Sebastian S., School of Business and Management, CHRIST University, Delhi NCR Campus Uttar Pradesh, 201003, India
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Articles
Article
Faculty Publications -Articles
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 Novel Approach for Linguistic Steganography Evaluation Based on Artificial Neural Networks
Subject
The topic of the resource
Artificial intelligence; linguistic steganography; LSTM; Markov chain model; natural language processing; NLP; recurrent neural networks; RNN; statistical language model; steganography
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Increasing prevalence and simplicity of using Artificial Intelligence (AI) techniques, Steganography is shifting from conventional model building to AI model building. AI enables computers to learn from their mistakes, adapt to emerging inputs, and carry out human-like activities. Traditional Linguistic Steganographic approaches lack automation, analysis of Cover text and hidden text volume and accuracy. A formal methodology is used in only a few Steganographic approaches. In the vast majority of situations, traditional approaches fail to survive third-party vulnerability. This study looks at evaluation of an AI-based statistical language model for text Steganography. Since the advent of Natural Language Processing (NLP) into the research field, linguistic Steganography has superseded other types of Steganography. This paper proposes the positive aspects of NLP-based Markov chain model for an auto-generative cover text. The embedding rate, volume, and other attributes of Recurrent Neural Networks (RNN) Steganographic schemes are contrasted in this article between RNN-Stega and RNN-generated Lyrics, two RNN methods. Here the RNN model follows Long Short Term Memory (LSTM) neural network. The paper also includes a case study on Artificial Intelligence and Information Security, which discusses history, applications, AI challenges, and how AI can help with security threats and vulnerabilities. The final portion is dedicated to the study's shortcomings, which may be the subject of future research. 2013 IEEE.
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Gurunath R.; Alahmadi A.H.; Samanta D.; Khan M.Z.; Alahmadi A.
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IEEE Access, Vol-9, pp. 120869-120879.
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Institute of Electrical and Electronics Engineers Inc.
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2021-01-01
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<a href="https://doi.org/10.1109/ACCESS.2021.3108183" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/ACCESS.2021.3108183</a>
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All Open Access; Gold Open Access
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ISSN: 21693536
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Online
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English
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Article
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Gurunath R., Department of Computer Science, Christ (Deemed to Be University), Karnataka, Bengaluru, India; Alahmadi A.H., Department of Computer Science and Information, Taibah University, Medina, Saudi Arabia; Samanta D., Department of Computer Science, Christ (Deemed to Be University), Karnataka, Bengaluru, India; Khan M.Z., Department of Computer Science and Information, Taibah University, Medina, Saudi Arabia; Alahmadi A., Department of Computer Science, Christ (Deemed to Be University), Karnataka, Bengaluru, India
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Conference Papers
Conference Paper
Faculty Publications- Conference Papers
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A Novel Approach for Machine Reading Comprehension using BERT-based Large Language Models
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ALBERT; DistilBERT; Longformer; Machine Reading Comprehension; Span Extraction; Transformers
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Teaching machines to learn the information from the natural language documents remains an arduous task because it involves natural language understanding of contexts, excerpting the meaningful insights, and deliver the answer to the questions. Machine Reading Comprehension (MRC) tasks can identify and understand the content from the natural language documents by asking the model to answer questions. The model can extract the answer from the given context or other external repositories. This article proposes a novel Context Minimization method for MRC Span Extraction tasks to improve the accuracy of the models. The Context Minimization method constitutes two subprocedures, Context Reduction and Sentence Aggregation. The proposed model reduced the context with the most relevant sequences for answering by estimating the sentence embedding between the question and the sequences involved in the context. The Context Reduction facilitates the model to retrieve the answer efficiently from the minimal context. The Sentence Aggregation improves the quality of answers by aggregating the most relevant shreds of evidence from the context. Both methods have been developed from the two notable observations from the empirical analysis of existing models. First, the models with minimal context with efficient masking can improve the accuracy and the second is the issue with the scatted sequences on the context that may lead to partial or incomplete answering. The Context Minimization method with Fine-Tuned BERT model compared with the ALBERT, DistilBERT, and Longformer models and the experiments with these models have shown significant improvement in results. 2024 IEEE.
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Varghese N.; Shereef S.; Joy H.K.; Ramasamy G.; Sridevi R.; Cynthia T.; Rajeshkanna R.
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Proceedings of CONECCT 2024 - 10th IEEE International Conference on Electronics, Computing and Communication Technologies
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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/CONECCT62155.2024.10677199" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/CONECCT62155.2024.10677199</a>
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Restricted Access
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ISBN: 979-835038592-2
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Online
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English
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Conference paper
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Varghese N., Christ University, Department of Computer Science, Karnataka, Bangalore, 560029, India; Shereef S., Jain (Deemed to Be University), Department of Cs and It, Karnataka, Bangalore, 560059, India; Joy H.K., Christ University, Department of Computer Science, Karnataka, Bangalore, 560029, India; Ramasamy G., Christ University, Department of Computer Science, Karnataka, Bangalore, 560029, India; Sridevi R., Christ University, Department of Computer Science, Karnataka, Bangalore, 560029, India; Cynthia T., Christ University, Department of Computer Science, Karnataka, Bangalore, 560029, India; Rajeshkanna R., Christ University, Department of Computer Science, Karnataka, Bangalore, 560029, 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/.
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A Novel Approach for Segmenting Coronary Artery from Angiogram Videos
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Coronary angiogram; Coronary artery; Keyframe; Segmentation; Video
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This paper addresses the research focuses on coronary artery disease; it is one of the major heart diseases affecting the people all around the world in the recent era. This heart disease is primarily diagnosed using a medical test called angiogram test. During the angiogram procedure the cardiologist often physically selects the frame from the angiogram video to diagnose the coronary artery disease. Due to the waning and waxing changeover in the angiogram video, its hard for the cardiologist to identify the artery structure from the frame. So, finding the keyframe which has a complete artery structure is difficult for the cardiologist. To help the cardiologist a method is proposed, to detect the keyframe which has segmented artery from the angiogram video. 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
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Kavipriya K.; Hiremath M.
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Lecture Notes in Networks and Systems, Vol-528, pp. 191-200.
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Springer Science and Business Media Deutschland GmbH
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2023-01-01
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<a href="https://doi.org/10.1007/978-981-19-5845-8_14" target="_blank" rel="noreferrer noopener">https://doi.org/10.1007/978-981-19-5845-8_14</a>
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Restricted Access
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ISSN: 23673370; ISBN: 978-981195844-1
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Online
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English
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Conference paper
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Kavipriya K., CHRIST (Deemed to Be University), Bangalore, India; Hiremath M., CHRIST (Deemed to Be University), Bangalore, India
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PHD
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61000438
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A Novel Approach for Sensitive Crop Disease Prediction Based on Computer Vision Techniques
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Computer Science and Engineering
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Agriculture is a vital sector that plays an essential role in ensuring global food security, supporting economic development, and promoting environmental sustainability. Sustainable agriculture is an essential approach that aims to address the diffculties posed by conventional farming practices and ensure the long-term viability of our food production systems. Worldwide, crop leaf diseases seriously threaten food security and agricultural production. Early and accurate detection of crop leaf diseases is essential for effective crop productivity management and food prevention. Computer vision approaches offer promising solutions for automating the identifcation and prediction of crop leaf diseases. Analyzing digital images of plant leaves enables the identifcation of disease characteristics, such as discoloration, lesions, and patterns, which are often imperceptible to the naked eye. Machine Learning (ML) algorithms, such as Convolutional Neural Networks (CNN), have been widely employed in this domain to learn from large datasets of annotated images and accurately classify leaf diseases. The process of crop leaf disease classifcation using computer vision involves several stages. Initially, highresolution images of plant leaves are acquired using cameras or mobile devices. Preprocessing techniques, including image enhancement and noise reduction, are applied to improve image quality. Subsequently, feature extraction approaches extract pertinent data from the images, including texture, shape, and color. Deep Learning (DL) models are then trained and fne-tuned using these extracted features. newlineAlthough computer vision techniques have shown effective results in the classifcation of plant diseases, however, several challenges remain. Tomatoes and Potatoes newlineare widely cultivated and consumed vegetables worldwide and are a primary economic newlinesource for many countries. These sensitive plants are prone to various diseases during newlinegrowth, leading to signifcant losses in productivity and fnancial impact on farmers.
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Patil, Manoj
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Author's Submission
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Christ(Deemed to be University)
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2025-01-01
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Manohar, M
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Open Access
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PDF
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English
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PhD
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<a href="http://hdl.handle.net/10603/621302" target="_blank" rel="noreferrer noopener">http://hdl.handle.net/10603/621302</a>