505
20
16481
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Faculty Publications
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Pati, Dinesh Kumar; Mahapatra, Shreyash; Mahapatra, Mitrabinda; Chakroborty, Subhendu; Malviya, Jitendra; Das, Piyush R.; Dwivedi, Rajeev; Parida, B.N.; Urkude, Rajashri R.; Padhee, R.
Title
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Effect of samarium doping on structural properties and electronic structure of Bi0.5Na0.5TiO3ceramic with antimicrobial applications
Date
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01-01-2025
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Ceramics International;Volume;51;Issue;30;pp.62395-62414
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<a href="https://doi.org/10.1016/j.ceramint.2025.10.369" target="_blank" rel="noreferrer noopener">https://doi.org/10.1016/j.ceramint.2025.10.369</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105024759177?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105024759177?origin=resultslist</a>
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Pati D.K., School of Physics, Sambalpur University, Jyoti Vihar, Odisha, Burla, 768019, India, Newton Higher Secondary School of Science, Debaipali, Odisha, Sambalpur, 768200, India; Mahapatra S., Newton Higher Secondary School of Science, Debaipali, Odisha, Sambalpur, 768200, India; Mahapatra M., School of Physics, Sambalpur University, Jyoti Vihar, Odisha, Burla, 768019, India; Chakroborty S., Department of Chemistry, School of Basic Sciences, Chandigarh University Uttar Pradesh, Uttar Pradesh, Unnao, 209859, India; Malviya J., Department of Microbiology, Barkatullah University, MP, Bhopal, 462026, India; Das P.R., Department of Physics, CUPGS, BPUT, Chhend, Odisha, Rourkela, 769015, India; Dwivedi R., Department of Physics, Bennett University, U.P., Greater Noida, 201310, India; Parida B.N., Department of Physics, Central Institute of Technology Kokrajhar, Deemed to be University, BTC, Assam, 783370, India; Urkude R.R., Beamline Development and Application Section, BARC, Trombay, Mumbai, 400085, India; Padhee R., School of Physics, Sambalpur University, Jyoti Vihar, Odisha, Burla, 768019, India
Description
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The research work reports a systematic examination of structure, microstructure, electronic and local structure, optical and antimicrobial properties of Bi0.5Na0.5TiO3 (BNT) and Bi0.45Sm0.05Na0.5TiO3 (BSNT) ceramics manufactured by the solid-state route. The influence of a small amount of samarium (Sm) doping at the A-site of BNT matrix is examined using X-ray diffraction (XRD), Fourier transform infrared (FTIR) spectroscopy, Raman spectroscopy, field emission-scanning electron microscopy (FESEM), energy dispersive X-ray spectroscopy (EDS), atomic force microscopy (AFM), X-ray photoelectron spectroscopy (XPS), X-ray absorption near edge spectroscopy (XANES) and extended X-ray absorption fine structure spectroscopy (EXAFS), UVvis diffused reflectance spectroscopy (DRS), and antimicrobial analyses. XRD reveals single-phase rhombohedral structures with R3c space group. FTIR provides evidence for perovskite structures. Raman spectra illustrate three prominent vibrations, like Bi - O/Na - O, Ti - O vibrations of TiO6, and oxygen ion vibrations of the TiO6 octahedron of both samples. FESEM reveals better grain developments, and elemental confirmations are evident from EDS. The topographical and morphological studies, such as RMS and average roughness, skewness, and kurtosis, are conducted by AFM. XPS reveals dual oxidation states of Ti and Sm, and oxygen vacancies (OVs). A detailed analysis of the electronic structure is performed by examining the Bi L3-edge and Ti K-edge XANES spectra of BNT and BSNT, along with those of the model compounds. The Fourier transform of EXAFS analysis is conducted to probe the local structure, and extracted parameters, like coordination number (N), bond distance (R(), and Debye-Waller factor (?2), are analyzed. UVvis studies reveal a minute reduction in the band gap in BSNT. Impedance study shows the negative temperature coefficient of resistance (NTCR) responses. The antibacterial activities are examined by determining the minimum inhibitory concentration (MIC) and zone of inhibition (ZOI) caused by the materials, along with a control antibiotic, against a variety of bacterial strains. 2025 Elsevier Ltd and Techna Group S.r.l. All rights are reserved, including those for text and data mining, AI training, and similar technologies.
Subject
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Antimicrobial properties; Electronic and local structure; Microstructure; Structure
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Elsevier Ltd
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ISSN: 2728842; CODEN: CINND
Language
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English
Type
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Article
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Restricted Access; Hardcopy may be available in the library
Format
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online
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Articles
Article
Faculty Publications -Articles
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Title
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Comparative analysis and suggestion of architectures for reduction of road accidents
Subject
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Architectures; Road Accidents; Sensors; Technology; Vehicles
Description
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As Road Accidents are increasing all over the world, it is very important to save peoples lives. With the advancement in technology we can make use of various real time sensors and technology to save peoples lives. This paper focuses on comparing various architectures which consists of various real time sensors like Eye blink sensor, Alcohol sensor, Speed sensor, load sensor, tilt and turning sensor and various technologies like GPS, GSM. After comparison paper suggests which architecture should be used in the vehicle based on certain attributes. For E.g. If the car always travels outside the city then this paper suggests the architecture which has Eye blink sensor, Speed Sensor GPS and GSM. IAEME Publication.
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Patil A.; Das D.
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International Journal of Civil Engineering and Technology, Vol-9, No. 3, pp. 945-954.
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IAEME Publication
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2018-01-01
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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-s2.0-85045054306&partnerID=40&md5=8b62ab7436fe69fb35cbf5e88f7dcd10" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85045054306&partnerID=40&md5=8b62ab7436fe69fb35cbf5e88f7dcd10</a>
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ISSN: 9766308
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Online
Language
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English
Type
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Article
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Patil A., Department of Computer Science, Christ University, Bangalore, India; Das D., Department of Computer Science, Christ University, Bangalore, India
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Articles
Article
Faculty Publications -Articles
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Big data-Industry 4.0 readiness factors for sustainable supply chain management: Towards circularity
Subject
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Big data; Circular Economy; Digitalization; Industry 4.0; Sustainable Supply Chain
Description
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Big data-Industry 4.0 interaction is expected to revolutionize the existing supply chains in recent years. While increased operational efficiency and enhanced decision-making are the primary advantages studied widely, the sustainable aspects of digital supply chain in the circular economy era have received limited attention. The previous literature rarely explores the industry readiness for a digital supply chain. Thus, the present study objectives to explore Big data-Industry 4.0 readiness factors for sustainable supply chain management. A detailed literature analysis was performed to identify a total of seventeen readiness factors for sustainable supply chain management. A team of six experts were consulted to perform the pairwise comparison for the identified potential readiness factors. This study adopts a fuzzy best-worst method to prioritize the readiness factors according to their degree of influence. The results from the study reflect that readiness towards information system infrastructure, Internet stability for developing I4.0 infrastructure, and circular process and awareness are the most significant readiness factors. The potential recommendation of this study includes the increased attention from sustainable supply chain stakeholders on developing infrastructure, including knowledge building exercise and training process focused on circular economy process. The findings from the study will assist sustainable supply chain stakeholders to frame strategies and action plans during the digitalization of supply chains. 2023 Elsevier Ltd
Creator
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Patil A.; Dwivedi A.; Abdul Moktadir M.; Lakshay
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Computers and Industrial Engineering, Vol-178
Publisher
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Elsevier Ltd
Date
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2023-01-01
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<a href="https://doi.org/10.1016/j.cie.2023.109109" target="_blank" rel="noreferrer noopener">https://doi.org/10.1016/j.cie.2023.109109</a>
<br /><br /><a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85149638220&doi=10.1016%2Fj.cie.2023.109109&partnerID=40&md5=655fa597973507a2da5d5f29d541d9fe" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85149638220&doi=10.1016%2fj.cie.2023.109109&partnerID=40&md5=655fa597973507a2da5d5f29d541d9fe</a>
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ISSN: 3608352; CODEN: CINDD
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Online
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English
Type
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Article
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Patil A., Christ University, Bengaluru, India; Dwivedi A., Jindal Global Business School, O.P. Jindal Global University, Sonipat, India; Abdul Moktadir M., Institute of Leather Engineering and Technology, University of Dhaka, Dhaka, 1209, Bangladesh; Lakshay, Department of Mechanical Engineering, Indian Institute of Technology (BHU), Varanasi, India
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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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Effects of Macro Economic Indicators on Foreign Portfolio Investments
Subject
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Foreign Portfolio Investment; Gross Domestic Product; Monetary Policy; National Stock Exchange Volume; the VECM (Vector Error Correction) Model
Description
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In this study, both institutional and retail investors were observed making exits and entries based on macroeconomic data, utilizing measurable indicators such as GDP, inflation, bank rates, foreign exchange rates, trade volume on the national stock exchange, and portfolio investments. Employing a Vector Error Correction Model (VECM) in an econometric analysis, the study found a significant association between macroeconomic indicators and portfolio investments in India. Investors followed a discernible pattern of entering and exiting markets, with economic growth fostering greater investments. Notably, GDP, NSE Volume, and bank rates were identified as variables impacting foreign portfolio investments. In the long run, GDP positively affected foreign portfolio investments, while inflation and foreign exchange rates exhibited a detrimental influence, leading to decreased portfolio investments. Foreign Institutional Investors, prioritizing profits over business operations, focused on market sentiments, directing investments towards economies with potential performance and resulting in a higher volume of capital inflow. Overall, the study concludes that a robust economic condition attracts superior foreign portfolio investments. 2024 IEEE.
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Patil A.; Lakshmi R.B.; Niranjan L.R.; Vas J.
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Proceedings of 9th International Conference on Science, Technology, Engineering and Mathematics: The Role of Emerging Technologies in Digital Transformation, ICONSTEM 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/ICONSTEM60960.2024.10568614" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/ICONSTEM60960.2024.10568614</a>
<br /><br /><a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85198841367&doi=10.1109%2FICONSTEM60960.2024.10568614&partnerID=40&md5=a4db7b165063fbdba1ba75ad2609c45e" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85198841367&doi=10.1109%2fICONSTEM60960.2024.10568614&partnerID=40&md5=a4db7b165063fbdba1ba75ad2609c45e</a>
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ISBN: 979-835036509-2
Format
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Online
Language
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English
Type
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Conference paper
Coverage
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Patil A., School of Business & Management, Christ University, Bangalore, India; Lakshmi R.B., School of Business & Management, Christ University, Bangalore, India; Niranjan L.R., School of Business & Management, Christ University, Bangalore, India; Vas J., School of Business & Management, Christ University, Bangalore, India
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Book Chapter
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Faculty Publications- Book Chapter
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Title
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An in-depth investigation into financial literacy levels in Indian households
Description
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In a complex financial world, lack of awareness complicates money management and savings. Emphasizing financial literacy is vital for informed decision-making. This study explores global financial illiteracy, advocating international initiatives. In India, it assesses disparities and government activities and reviews tax-saving and mobile banking. Gaps include limited studies on Indian households, necessitating gender-specific analyses and research on education's impact. The methodology outlines justification, operational definitions, and data collection techniques. With ANOVA and descriptive statistics on 285 respondents, the study reveals demographic analysis, indicating higher financial literacy with age and a gender gap. Education. positively correlates with financial literacy. Recommendations include interventions like financial seminars, collaboration with regulators, and destigmatizing money talks at home to enhance financial literacy and bridge gaps. 2024 by IGI Global. All rights reserved.
Creator
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Patil A.; Niranjan L.R.; Patavardhan R.; Jain V.
Source
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Contemporary Research and Practices for Promoting Financial Literacy and Sustainability, pp. 113-146.
Publisher
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IGI Global
Date
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2024-01-01
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<a href="https://doi.org/10.4018/979-8-3693-0863-9.ch005" target="_blank" rel="noreferrer noopener">https://doi.org/10.4018/979-8-3693-0863-9.ch005</a>
<br /><br /><a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85204521168&doi=10.4018%2F979-8-3693-0863-9.ch005&partnerID=40&md5=6e307a8ec7d60f5150d64ac6239ce4b9" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85204521168&doi=10.4018%2f979-8-3693-0863-9.ch005&partnerID=40&md5=6e307a8ec7d60f5150d64ac6239ce4b9</a>
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ISBN: 979-836930864-6; 979-836930863-9
Format
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Online
Language
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English
Type
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Book chapter
Coverage
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Patil A., Christ University, India; Niranjan L.R., Christ University, India; Patavardhan R., Christ University, India; Jain V., Christ University, India
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Book Chapter
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Dublin Core
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Title
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Role of ICT in economic empowerment of women by being an effective facilitator for women entrepreneurship
Description
An account of the resource
Information and communication technology (ICT) is steadily gaining dominance over other channels as a standalone medium for gaining and sharing knowledge, hybrid working environment, network strengthening and funding, business collaborations, new venture set-up, marketing and branding. ICT being most effectively featured with accessibility and omnipresence has been capable of empowering a lot of developing and talent-rich areas, and women empowerment is one among them. This chapter makes an attempt to bring out the efficient role played by ICT in enhancing the lifestyle of women. It proposes to provide a detailed description of how ICT has empowered women, and entrepreneurs, to set up and develop their business ventures giving them access to required resources and making them more competent through information and wider access to the market. This chapter presents its findings based on a systematic review of different case analyses by using secondary data. The findings will be also supported by the evidence and information gathered from credible reports, articles, and publications. 2023, IGI Global.
Creator
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Patil A.; Prathibha Raj M.S.; Thomas R.; Sarkar B.
Source
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ICT as a Driver of Women's Social and Economic Empowerment, pp. 77-101.
Publisher
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IGI Global
Date
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2023-01-01
Identifier
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<a href="https://doi.org/10.4018/978-1-6684-6118-1.ch006" target="_blank" rel="noreferrer noopener">https://doi.org/10.4018/978-1-6684-6118-1.ch006</a>
<br /><br /><a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85162923529&doi=10.4018%2F978-1-6684-6118-1.ch006&partnerID=40&md5=de50a32a2a9b45b3d7b8b9a5b9c8b76b" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85162923529&doi=10.4018%2f978-1-6684-6118-1.ch006&partnerID=40&md5=de50a32a2a9b45b3d7b8b9a5b9c8b76b</a>
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ISBN: 978-166846119-8; 1668461188; 978-166846118-1
Format
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Online
Language
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English
Type
The nature or genre of the resource
Book chapter
Coverage
The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant
Patil A., CHRIST University (Deemed), India; Prathibha Raj M.S., CHRIST University (Deemed), India; Thomas R., CHRIST University (Deemed), India; Sarkar B., CHRIST University (Deemed), India
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Articles
Article
Faculty Publications -Articles
Dublin Core
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Title
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An integrated framework for digitalization of humanitarian supply chains in post COVID-19 era
Subject
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Digitalization; Humanitarian supply chain; Neutrosophic DEMATEL; Post COVID-19; Principal component analysis
Description
An account of the resource
Digital Supply Chains (DSCs) are transforming industries across various domains. Digitalization can improve coordination, increase data collection and retention capacities, enhance funding mechanisms, and improve operational performance and resource utilization. However, DSC adoption is constrained by lack of funding, operational complexities, infrastructure issues, etc. Thus, the need emerges to explore the digitalization of the Humanitarian Supply Chain (HSC) and provide solutions that can ease the adoption of DSC. In this study, a framework is created to facilitate the digitalization process of HSC in post COVID-19 era. Nineteen related drivers are identified with the potential to digitalize the HSC. The drivers are identified from the previous literature and finalized with the assistance of HSC stakeholders. A Principal Component Analysis is carried out to discover the most pertinent drivers from the identified list of drivers. A Kappa analysis is adopted to perfect the priority map of the digitalization drivers. Further, the neutrosophic DEMATEL methodology is adopted to prioritize the potential drivers and find their dependency on each other. The results from the study indicate that the most influential drivers fall under the operational and technological categories. However, the social drivers have the potential to play a significant contribution in an effort to HSC digitalization. In addition, the study presents strategies for enhancing funds collection and data management using emerging technologies. These strategies can assist HSC decision-makers in formulating relevant policies and strategic interventions. 2023 Elsevier Ltd
Creator
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Patil A.; Shardeo V.; Dwivedi A.; Paul S.K.
Source
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International Journal of Disaster Risk Reduction, Vol-87
Publisher
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Elsevier Ltd
Date
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2023-01-01
Identifier
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<a href="https://doi.org/10.1016/j.ijdrr.2023.103574" target="_blank" rel="noreferrer noopener">https://doi.org/10.1016/j.ijdrr.2023.103574</a>
<br /><br /><a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85147880035&doi=10.1016%2Fj.ijdrr.2023.103574&partnerID=40&md5=db7ce3a8269f7b407e7489253b4f2bdf" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85147880035&doi=10.1016%2fj.ijdrr.2023.103574&partnerID=40&md5=db7ce3a8269f7b407e7489253b4f2bdf</a>
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All Open Access; Bronze Open Access
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ISSN: 22124209
Format
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Online
Language
A language of the resource
English
Type
The nature or genre of the resource
Article
Coverage
The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant
Patil A., Christ University, Bengaluru, India; Shardeo V., Lal Bahadur Shastri Institute of Management, New Delhi, India; Dwivedi A., Jindal Global Business School, O.P. Jindal Global University, Sonipat, India; Paul S.K., UTS Business School, University of Technology Sydney, Sydney, Australia
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Articles
Article
Faculty Publications -Articles
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Title
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Digital twins' readiness anditsimpacts on supply chaintransparency andsustainable performance
Subject
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Digital twins technology; Supply chain; Sustainable performance; Transparency
Description
An account of the resource
Purpose: Production systems occupy geographically dispersed organizations with limited visibility and transparency. Such limitations create operational inefficiencies across the Supply Chain (SC). Recently, researchers have started exploring applications of Digital Twins Technology (DTT) to improve SC operations. In this context, there is a need to provide comprehensive theoretical knowledge and frameworks to help stakeholders understand the adoption of DTT. This study aims to fulfill the research gap by empirically investigating DTT readiness to enable transparency in SC. Design/methodology/approach: A comprehensive literature survey was conducted to develop a theoretical model related to Supply Chain Transparency (SCT) and DTT readiness. Then, a questionnaire was developed based on the proposed theoretical model, and data was collected from Indian manufacturers. The data was analyzed using Confirmatory Factor Analysis (CFA) and Structural EquationModelling (SEM) to confirm the proposed relationships. Findings: The findings from the study confirmed a positive relationship between DTT implementation and SCT. This study reported that data readiness, perceived values and benefits of DTT, and organizational readiness and leadership support influence DTT readiness and further lead to SCT. Originality/value: This study contributes to the literature and knowledge by uniquely mapping and validating various interactions between DTT readiness and sustainable SC performance. 2024, Emerald Publishing Limited.
Creator
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Patil A.; Srivastava S.; Paul S.K.; Dwivedi A.
Source
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Industrial Management and Data Systems, Vol-124, No. 8, pp. 2532-2566.
Publisher
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Emerald Publishing
Date
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2024-01-01
Identifier
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<a href="https://doi.org/10.1108/IMDS-10-2023-0767" target="_blank" rel="noreferrer noopener">https://doi.org/10.1108/IMDS-10-2023-0767</a>
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ISSN: 2635577; CODEN: IMDSD
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Patil A., International Management Institute, New Delhi, India; Srivastava S., Christ University, Bangalore, India; Paul S.K., UTS Business School, University of Technology Sydney, Sydney, Australia; Dwivedi A., Jindal Global Business School, Jindal Global University, OP, Sonipat, India
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A Study of Segmentation Techniques to Detect Leukaemia in Microscopic Blood Smear Images
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Active contour; Acute lymphoblastic Leukemia; Artificial neural network; Fuzzy c-mean; Image segmentation; k-means; Region growing; Thresholding; watershed
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In medical image processing, the segmentation of the image is considered to be a vital stage and is effectively used to extract the region of interest. Automated diagnosis of leukaemia is highly associated with the accurate segmentation of the cell nucleus. The purpose of this paper is to review and analyze literature related to some of the major segmentation techniques used in the field of Acute lymphoblastic leukaemia (ALL) detection. This paper presents an overview of segmentation methods along with the experimental results of six implemented methods and highlights some of the advantages and disadvantages of implemented segmentation techniques. 2020 IEEE.
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Patil A.P.
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Proceedings of the 2020 IEEE International Conference on Communication, Computing and Industry 4.0, C2I4 2020
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Institute of Electrical and Electronics Engineers Inc.
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2020-01-01
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<a href="https://doi.org/10.1109/C2I451079.2020.9368928" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/C2I451079.2020.9368928</a>
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ISBN: 978-172818312-1
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English
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Patil A.P., Department of Computer Science, CHRIST (Deemed to be University), Bengaluru, India, Department of Computer Application, CMR Institute of Technology, Bengaluru, India
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Title
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Acute Leukemia Subtype Recognition in Blood Smear Images with Machine Learning
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Acute Leukemia; cell analysis; Image processing; Leukemia Classification; Segmentation
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Acute leukemia is a swiftly progressing blood cancer affecting white blood cells which poses a significant threat to the immune system and often leads to fatal outcomes if not detected and treated promptly. The current manual diagnostic method, being time-consuming and prone to errors, necessitates an urgent shift toward a comprehensive automated system. This paper presents an innovative approach to automatically identify acute leukemia cells and their subtypes by analyzing microscopic blood smear images. The proposed methodology involves the segmentation of clustered lymphocytes, isolation of nuclei, and extraction of diverse features from each nucleus. A random forest classifier is then trained to categorize nuclei into healthy or cancerous, with further precision in classifying cancerous nuclei into specific subtypes. The method achieves an impressive 97% accuracy across all evaluations, holding profound implications for pathologists and medical practitioners in their decision-making processes. 2024, J.J. Strossmayer University of Osijek, Faculty of Electrical Engineering, Computer Science and Information Technology. All rights reserved.
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Patil A.P.; Hiremath M.
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International Journal of Electrical and Computer Engineering Systems, Vol-15, No. 7, pp. 563-570.
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J.J. Strossmayer University of Osijek , Faculty of Electrical Engineering, Computer Science and Information Technology
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2024-01-01
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<a href="https://doi.org/10.32985/ijeces.15.7.2" target="_blank" rel="noreferrer noopener">https://doi.org/10.32985/ijeces.15.7.2</a>
<br /><br /><a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85199139463&doi=10.32985%2Fijeces.15.7.2&partnerID=40&md5=9bd641d0c97f8c41adb4912167c1d886" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85199139463&doi=10.32985%2fijeces.15.7.2&partnerID=40&md5=9bd641d0c97f8c41adb4912167c1d886</a>
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ISSN: 18476996
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English
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Article
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Patil A.P., Department of Computer Science, CHRIST (Deemed to be University), Bengaluru, India; Hiremath M., Department of Computer Science, CHRIST (Deemed to be University), Bengaluru, India
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Conference Papers
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Segment Anything Model (SAM) to Segment lymphocyte from Blood Smear Images
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Deep learning; Lymphocyte; Segment Anything Model; Segmentation; white blood cells
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Automated lymphocyte segmentation from smear images plays an important role in disease diagnosis and monitoring, aiding in the assessment of immune system function and pathology detection. This study proposes an approach for lymphocyte segmentation utilizing Segment Anything Model (SAM) which is a deep learning model. Our method leverages a pre trained SAM architecture and fine-tunes it on a custom dataset comprising smear images containing lymphocytes. The pretrained model's ability of versatile segmentation combined with fine-tuning on the specific dataset enhances its performance in accurately identifying lymphocyte boundaries. We evaluate the proposed approach on a diverse set of smear images, demonstrating its effectiveness in segmenting lymphocytes with impressive IOU score and Dice Score. SAM deep learning model, fine-tuned on custom datasets, holds promise for robust and efficient lymphocyte segmentation from blood smear images. 2024 IEEE.
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Patil A.P.; Hiremath M.
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2024 IEEE 3rd World Conference on Applied Intelligence and Computing, AIC 2024, pp. 1080-1084.
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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/AIC61668.2024.10730867" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/AIC61668.2024.10730867</a>
<br /><br /><a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85210268951&doi=10.1109%2FAIC61668.2024.10730867&partnerID=40&md5=36cc6adea1bbf1eee90d6f1b7b2f1179" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85210268951&doi=10.1109%2fAIC61668.2024.10730867&partnerID=40&md5=36cc6adea1bbf1eee90d6f1b7b2f1179</a>
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ISBN: 979-835038459-8
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Online
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English
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Conference paper
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Patil A.P., Christ (Deemed to Be University), Department of Computer Science, India, Cmr Institute of Technology, Department of Computer Application, Bengaluru, India; Hiremath M., Christ (Deemed to Be University), Department of Computer Science, Bengaluru, India
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Title
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Machine Learning Model to Detect Chronic Leukemia in Microscopic Blood Smear Images
Subject
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Chronic lymphocytic Leukemia; Chronic Myeloid Leukemia; image processing; Machine Learning; Random Forest; Watershed Segmentation
Description
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Chronic leukemia is a slow-progressing form of disease, If not diagnosed on time can progress and increase the risk of life-threatening complications. It is essential to develop a fully automated system to recognize and categorize type of leukemia for proper evaluation and treatment. This paper aims to provide a machine learning model to identify and classify chronic lymphocytic leukemia, chronic myeloid Leukemia and healthy cells. Digital microscopic blood smear images were automatically cropped into single nucleus and segmented using watershed algorithm. Grey level co-occurrence matrix (GLCM) and geometrical features were extracted from the segmented nucleus images and random forest algorithm is used to classify chronic leukemia and healthy cells. This prognosis aids pathologists and physicians in identifying leukemic patients early and selecting the most effective course of action. 2023 IEEE.
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Patil A.P.; Hiremath M.
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Proceedings of IEEE InC4 2023 - 2023 IEEE International Conference on Contemporary Computing and Communications
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Institute of Electrical and Electronics Engineers Inc.
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2023-01-01
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<a href="https://doi.org/10.1109/InC457730.2023.10263162" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/InC457730.2023.10263162</a>
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ISBN: 979-835033577-4
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Online
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English
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Patil A.P., CHRIST (Deemed to Be University), Department of Computer Science, Bengaluru, India; Hiremath M., CMR Institute of Technology, Department of Computer Application, Bengaluru, India
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A Study of Preprocessing Techniques on Digital Microscopic Blood Smear Images to Detect Leukemia
Subject
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Bilateral; Gaussian; Leukemia; Median; Noise reduction; Wiener
Description
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Digital microscopic blood smear images can get distorted due to the noise as a result of excessive staining during slide preparation or external factors during the acquisition of images. Noise in the image can affect the output of further steps in image processing and can have an impact on the accuracy of results. Hence, it is always better to denoise the image before feeding it to the automatic diagnostic system. There are many noise reduction filters available; the selection of the best filter is also very important. This paper presents a comparative study of some common spatial filters like wiener filter, bilateral filter, Gaussian filter, median filter and mean filter which are efficient in noise reduction, along with their summary and experimental results. Performing comparative analysis of result based on PSNR, SNR and MSE values, it can be determined that median filter is most suitable method for denoising digital blood smear images. 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
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Patil A.P.; Hiremath M.; Kavipriya K.
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Lecture Notes in Networks and Systems, Vol-462, pp. 275-282.
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Springer Science and Business Media Deutschland GmbH
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2022-01-01
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<a href="https://doi.org/10.1007/978-981-19-2211-4_24" target="_blank" rel="noreferrer noopener">https://doi.org/10.1007/978-981-19-2211-4_24</a>
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ISSN: 23673370; ISBN: 978-981192210-7
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English
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Conference paper
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Patil A.P., Department of Computer Science, CHRIST (Deemed to be University), Bengaluru, India, Department of Computer Application, CMR Institute of Technology, Bengaluru, India; Hiremath M., Department of Computer Science, CHRIST (Deemed to be University), Bengaluru, India; Kavipriya K., Department of Computer Science, CHRIST (Deemed to be University), Bengaluru, India
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A Unified Approach to Predict and Understand Acute Myeloid Leukemia Diagnosis
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Acute myeloid leukemia; Classification; Explainable AI; LIME; SHAP feature importance
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Acute myeloid leukemia (AML) is a rapidly progressing disease that affects myeloid cells in blood and bone marrow. These abnormal cancerous cells called blast cells are non-functional cells that increase rapidly in bone marrow and are released into blood stream which crowd out the healthy functional cells leading to weak immune system. This life-threatening disease needs to be diagnosed at early stage and hence requires fully automated system for early detection of leukemia to aid pathologists and doctors. Most of the automated machine learning and AI models are not transparent and require techniques to explain model prediction. This paper presents methods to classify blood microscopic images into healthy or acute myeloid leukemia. Among all the methods implemented, Gradient Boosting outperforms with an accuracy of 96.67%. This paper also focuses on explainable AI to interpret model prediction and feature importance which further helps in understanding decision-making process of classification model and optimize it. 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
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Patil A.P.; Hiremath M.; Pawar V.
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Lecture Notes in Electrical Engineering, Vol-1098, pp. 233-242.
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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/10.1007/978-981-99-7383-5_18" target="_blank" rel="noreferrer noopener">https://doi.org/10.1007/978-981-99-7383-5_18</a>
<br /><br /><a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85184280202&doi=10.1007%2F978-981-99-7383-5_18&partnerID=40&md5=dbd26b286e33f0012295dd712498e7f3" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85184280202&doi=10.1007%2f978-981-99-7383-5_18&partnerID=40&md5=dbd26b286e33f0012295dd712498e7f3</a>
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ISSN: 18761100; ISBN: 978-981997382-8
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English
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Conference paper
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Patil A.P., Department of Computer Science, CHRIST (Deemed to be University), Bengaluru, India; Hiremath M., Department of Computer Science, CHRIST (Deemed to be University), Bengaluru, India; Pawar V., Department of Computer Applications, CMR Institute of Technology, Bengaluru, India
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Quantitative assessment of blockchain applications for Industry 4.0 in manufacturing sector
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Blockchain; Industry 4.0; Manufacturing; Technology
Description
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Blockchain is one of the emerging digital technologies that will play a role in the breakthroughs of the fourth industrial revolution. The use of blockchain technology has the potential to greatly benefit businesses of all sizes by increasing their data's integrity, privacy, and openness. The term Industry 4.0? refers to the amalgamation of recent advances in manufacturing technology that have helped businesses cut production times significantly. The industrial and supply chain industries can benefit from these technological advancements in a number of ways. Increased efficiency in production and a more stable supply chain are just two of the many benefits that blockchain promises to bring to the manufacturing industry. The study focuses on Blockchain's huge potential in the context of Industry 4.0. Understanding the role of Blockchain technology in Industry 4.0 is examined, along with its various drivers, enablers, and associated capabilities. The several sub-domains of Industry 4.0 that can benefit from the implementation of Blockchain technology are also covered. The present research is primary and exploratory in nature. The sample size of the study is 256. The responses obtained from workers working in manufacturing sector in Delhi/NCR. The responses from workers obtained through structured questionnaire. The several sub-domains of Industry 4.0 found that can benefit from the implementation of Blockchain technology. At last, the existing study found the most important uses of Blockchain technology in the fourth industrial revolution. 2023
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Patil B.S.; .K. Sharma M.; Soubhari T.; Ashok J.; Pandey V.; Joshi G.
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Materials Today: Proceedings
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Elsevier Ltd
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2023-01-01
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<a href="https://doi.org/10.1016/j.matpr.2023.04.366" target="_blank" rel="noreferrer noopener">https://doi.org/10.1016/j.matpr.2023.04.366</a>
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ISSN: 22147853
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English
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Article
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Patil B.S., Dayananda Sagar Business School, Karnataka, Bengaluru, India; .K. Sharma M., Department of Mathematics, Chaudhary Charan Singh University, U.P, Meerut, India; Soubhari T., N.S.S. College, Manjeri, University of Calicut, Kerala, India; Ashok J., School of Business and Management, CHRIST (Deemed to be University), Karnataka, Bangalore, India; Pandey V., Department of Management, Amity University, Bihar, Patna, India; Joshi G., Lal Bahadur Shastri Institute of Management, New Delhi, India
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Book Chapter
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A Comparison of Similarity Measures in an Online Book Recommendation System
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Books; Collaborative filtering; Machine learning; Recommendation system; Similarity; User
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To assist users in identifying the right book, recommendation systems are crucial to e-commerce websites. Methodologies that recommend data can lead to the collection of irrelevant data, thus losing the ability to attract users and complete their work in a swift and consistent manner. Using the proposed method, information can be used to offer useful information to the user to help enable him or her to make informed decisions. Training, feedback, management, reporting, and configuration are all included. Our research evaluated user-based collaborative filtering (UBCF) and estimated the performance of similarity measures (distance) in recommending books, music, and goods. Several years have passed since recommendation systems were first developed. Many people struggle with figuring out what book to read next. When students do not have a solid understanding of a topic, it can be difficult determining which textbook or reference they should read. 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
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Patil D.; Preethi N.
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Lecture Notes on Data Engineering and Communications Technologies, Vol-131, pp. 341-349.
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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/10.1007/978-981-19-1844-5_26" target="_blank" rel="noreferrer noopener">https://doi.org/10.1007/978-981-19-1844-5_26</a>
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ISSN: 23674512
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English
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Book chapter
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Patil D., Christ University, Bengaluru, India; Preethi N., Christ University, Bengaluru, India
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Dublin Core
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Title
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Conference Papers
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Faculty Publications- Conference Papers
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Title
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Investigation on AI-Based Techniques in Applications for Detecting Fatal Traffic Accidents
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IoT; Road Accidents; Smart vehicle systems and Accident detection systems; Traffic
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The difficulties with road accident rates today rank among the top concerns for health and social policy in nations across the continents. In this essay, we've spoken about the fatalities and injuries brought on by traffic accidents in several Indian states. We have also shed light on the various factors that contribute to traffic accidents. Many researchers have reported various methods for identifying automobile crashes or accidents that are discussed in this work. Additionally, we covered collision avoidance systems and their various kinds. An examination of the analysis techniques used to comprehend the numerous causes causing accidents is also included in the study. Traditional models are frequently used to identify problems such driver weariness, drowsiness, driving while intoxicated, and distractions. 2023 IEEE.
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Patil H.P.; Upreti K.; Poonia R.C.
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2023 3rd International Conference on Emerging Smart Technologies and Applications, eSmarTA 2023
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Institute of Electrical and Electronics Engineers Inc.
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2023-01-01
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<a href="https://doi.org/10.1109/eSmarTA59349.2023.10293467" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/eSmarTA59349.2023.10293467</a>
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ISBN: 979-835030533-3
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Online
Language
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English
Type
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Conference paper
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Patil H.P., Symbiosis Skills and Professional University, Dept. of Mechatronics, Pune, India; Upreti K., CHRIST(Deemed to Be University), Dept. of Computer Science, Ghaziabad, India; Poonia R.C., CHRIST(Deemed to Be University), Dept. of Computer Science, Ghaziabad, India
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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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Concept Drift Detection for Social Media: A Survey
Subject
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real-time data; streaming data; temporal data; Twitter
Description
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The research over information retrieval from social media data has progressed for streaming data since the last decade. Recently, academic researchers have witnessed users' changing topics, trends, and intent on social media. This change of information with time takes into account the temporal attribute for real-time data, and thus, advances in this domain are exponentially growing. Although concept drift is still not explored due to a shortage of available datasets, concept drift for social media is minimally explored. This manuscript makes attempts to identify the types of concept drift for social media data, discuss the historical perspective of concept drift on social media, and enlist the possible research directions. 2021 IEEE.
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Patil M.A.; Kumar S.; Kumar S.; Garg M.
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Proceedings - 2021 3rd International Conference on Advances in Computing, Communication Control and Networking, ICAC3N 2021, pp. 12-16.
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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/ICAC3N53548.2021.9725548" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/ICAC3N53548.2021.9725548</a>
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ISBN: 978-166543811-7
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Online
Language
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English
Type
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Conference paper
Coverage
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Patil M.A., Amity University Rajasthan, Amity School of Engineering Technology, Jaipur, India; Kumar S., Amity University Rajasthan, Amity School of Engineering Technology, Jaipur, India; Kumar S., CHRIST (Deemed to Be University), Karnataka, Bangalore, 560029, India; Garg M., Amity University Rajasthan, Amity School of Engineering Technology, Jaipur, India
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Articles
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Enhanced radial basis function neural network for tomato plant disease leaf image segmentation
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Enhanced radial basis function; Feature extraction; Modified sunflower optimization; Segmentation; Tomato leaf disease
Description
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Primary crop losses in agriculture are due to leaf diseases, which farmers cannot identify early. If the diseases are not detected early and correctly, then the farmer will have to undergo huge losses. Therefore, in the field of agriculture, the detection of leaf diseases in tomato crops plays a vital role. Recent advances in computer vision and deep learning techniques have made disease prediction easy in agriculture. Tomato crop front side leaf images are considered for research due to their high exposure to diseases. The image segmentation process assumes a significant role in identifying disease affected areas on tomato leaf images. Therefore, this paper develops an efficient tomato crop leaf disease segmentation model using an enhanced radial basis function neural network (ERBFNN). The proposed ERBFNN is enhanced using the modified sunflower optimization (MSFO) algorithm. Initially, the noise present in the images is removed by a Gaussian filter followed by CLAHE (contrast-limited adaptive histogram equalization) based on contrast enhancement and un-sharp masking. Then, color features are extracted from each leaf image and given to the segmentation stage to segment the disease portion of the input image. The performance of the proposed ERBFNN approach is estimated using different metrics such as accuracy, Jaccard coefficient (JC), Dice's coefficient (DC), precision, recall, F-Measure, sensitivity, specificity, and mean intersection over union (MIoU) and are compared with existing state-of-the-art methods of radial basis function (RBF), fuzzy c-means (FCM), and region growing (RG). The experimental results show that the proposed ERBFNN segmentation model outperformed with an accuracy of 98.92% compared to existing state-of-the-art methods like RBFNN, FCM, and RG, as well as previous research work. 2022 Elsevier B.V.
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Patil M.A.; M M.
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Ecological Informatics, Vol-70
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Elsevier B.V.
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2022-01-01
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<a href="https://doi.org/10.1016/j.ecoinf.2022.101752" target="_blank" rel="noreferrer noopener">https://doi.org/10.1016/j.ecoinf.2022.101752</a>
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ISSN: 15749541
Format
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Online
Language
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English
Type
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Article
Coverage
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Patil M.A., Research Scholar, Department of Computer Science and Engineering, School of Engineering and Technology, CHRIST (Deemed to be University), Karnataka, Bangalore, 560074, India, Assistant Professor, Department of Information Technology, Vasavi College of Engineering, Ibrahimbagh, Telangana, Hyderabad, 500031, India; M M., Associate Professor, Department of Computer Science and Engineering, School of Engineering and Technology, CHRIST (Deemed to be University), Karnataka, Bangalore, 560074, 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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Plant Leaf Disease Classification Using Optimal Tuned Hybrid LSTM-CNN Model
Subject
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CNN; Deep Learning; Hybrid classifier; LSTM; SISS-OB
Description
An account of the resource
Tomatoes are widely cultivated and consumed worldwide and are susceptible to various leaf diseases during their growth. Therefore, early detection and prediction of leaf diseases in tomato crops are crucial. Farmers can take proactive measures to prevent the spread and minimize the impact on crop yield and quality by identifying leaf diseases in their early stages. Several Machine Learning (ML) and Deep Learning (DL) frameworks have been developed recently to identify leaf diseases. This research presents an efficient deep-learning approach based on a hybrid classifier by optimizing the CNN and LSTM models, which helps to enhance classification accuracy. Initially, Median Filtering (MF) is used for leaf image pre-processing. Then, an improved watershed approach is used for segmenting the leaf images. Subsequently, enhanced Local Gabor Pattern (LGP) and statistical and color features are extracted. An optimized CNN and LSTM are used for classification, and the weights are tuned using the SISS-OB (Self Improved Shark Smell With Opposition Behavior) algorithm. Finally, we have analyzed the performance using various measures. Since we have done segmentation, feature extraction, and optimization improvisations, our proposed methodology results are higher than other available methods and existing works. The results obtained at Learning Percentage (LP) is 90% which is far superior to those obtained at other LPs. The FNR (False Negative Rate) is much lower (0.05) at the 90th LP. The proposed model achieved better classification performance in terms of Accuracy of 97.13%, Sensitivity of 95.09%, Specificity of 95.24%, Precision of 94.31%, F measure of 96.71% and MCC 87.34%. 2023, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.
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Patil M.A.; Manohar M.
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SN Computer Science, Vol-4, No. 6
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Springer
Date
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2023-01-01
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<a href="https://doi.org/10.1007/s42979-023-02245-7" target="_blank" rel="noreferrer noopener">https://doi.org/10.1007/s42979-023-02245-7</a>
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ISSN: 2662995X
Format
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Online
Language
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English
Type
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Article
Coverage
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Patil M.A., Department of Computer Science and Engineering, Christ (Deemed to be University) School of Engineering and Technology, Kengeri, Karnataka, Bengaluru, 560074, India, Department of Computer Science and Engineering, G. Narayanamma Institute of Technology and Science, Shaikpet, Telangana, Hyderabad, 500104, India; Manohar M., Department of Computer Science and Engineering, Christ (Deemed to be University) School of Engineering and Technology, Kengeri, Karnataka, Bengaluru, 560074, India