324
20
16488
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Conference Papers
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Faculty Publications- Conference Papers
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A Novel CNN Approach for Condition Monitoring of Hydraulic Systems
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CNN; Deep Learning; Industry 4.0; Operations Management; Predictive Maintenance
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In the dynamic landscape of Industry 4.0, the ascendancy of predictive analytics methods is a pivotal paradigm shift. The persistent challenge of machine failures poses a substantial hurdle to the seamless functioning of factories, compelling the need for strategic solutions. Traditional reactive maintenance checks, though effective, fall short in the face of contemporary demands. Forward-thinking leaders recognize the significance of integrating data-driven techniques to not only minimize disruptions but also enhance overall operational productivity while mitigating redundant costs. The innovative model proposed herein harnesses the robust capabilities of Convolutional Neural Networks (CNN) for predictive analytics. Distinctively, it selectively incorporates the most influential variables linked to each of the four target conditions, optimizing the model's predictive precision. The methodology involves a meticulous process of variable extraction based on a predetermined threshold, seamlessly integrated with the CNN framework. This nuanced and refined approach epitomizes a forward-looking strategy, empowering the model to discern intricate failure patterns with a high degree of accuracy. 2024 IEEE.
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Khan R.; Bhagat V.; Ganie A.H.
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TQCEBT 2024 - 2nd IEEE International Conference on Trends in Quantum Computing and Emerging Business Technologies 2024
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Institute of Electrical and Electronics Engineers Inc.
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2024-01-01
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<a href="https://doi.org/10.1109/TQCEBT59414.2024.10545150" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/TQCEBT59414.2024.10545150</a>
<br /><br /><a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85204501324&doi=10.1109%2FTQCEBT59414.2024.10545150&partnerID=40&md5=203d93fad9f6cc2f8de0909a5cea615b" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85204501324&doi=10.1109%2fTQCEBT59414.2024.10545150&partnerID=40&md5=203d93fad9f6cc2f8de0909a5cea615b</a>
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ISBN: 979-835038427-7
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Online
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English
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Conference paper
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Khan R., Dept. of Data Science, Christ (Deemed to Be) University, Bangalore, India; Bhagat V., Dept. of Data Science, Christ (Deemed to Be) University, Bangalore, India; Ganie A.H., Dept. of Data Science, Christ (Deemed to Be) University, Bangalore, India
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Articles
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Faculty Publications -Articles
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Title
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Design requirements of a spectropolarimeter for solar extreme-ultraviolet observations and characterization of a K-mirror based on Brewster's angle
Description
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Measuring the linear polarization signal in extreme-ultraviolet (EUV) spectral lines, produced by the Hanle effect, offers a promising technique for studying magnetic fields in the solar corona. The required signal-to-noise ratio for detecting the Hanle polarization signals is on the order of 101 (off-limb) to 106 (disk center). Measuring such low signals in the photon starved observations demands highly efficient instruments. In this paper, we present the design of an instrument, SpectroPOLarimeter for Extreme-ultraviolet Observations (SPOLEO), which utilizes reflective components with suitable mirror coatings and thicknesses to minimize the throughput losses. We analyze the system performance within the spectral range from 740 to 800 The K-mirror-based polarimeter model provides a polarizing power of 20%40% in this wavelength range. Based on the system throughput and polarizing power, we discuss various possibilities for achieving the required signal-to-noise ratio, along with their limitations. Due to lack of facilities for fabrication and testing in the EUV, we have calibrated a prototype of the reflection-based polarimeter setup in the laboratory at the visible wavelength of 700 nm. 2024 Optica Publishing Group.
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Khan R.; Dharmadhikari R.; Mathur H.; Nagaraju K.; Jain S.R.; Phanindra D.V.S.; Sagayanathan K.
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Applied Optics, Vol-63, No. 34, pp. 8738-8747.
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Optica Publishing Group (formerly OSA)
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2024-01-01
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<a href="https://doi.org/10.1364/AO.537460" target="_blank" rel="noreferrer noopener">https://doi.org/10.1364/AO.537460</a>
<br /><br /><a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85210967116&doi=10.1364%2FAO.537460&partnerID=40&md5=e213331f98ec813db6d0f3769091f169" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85210967116&doi=10.1364%2fAO.537460&partnerID=40&md5=e213331f98ec813db6d0f3769091f169</a>
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ISSN: 1559128X; PubMed ID: 39602680; CODEN: APOPA
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Online
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English
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Article
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Khan R., Indian Institute of Astrophysics, Bengaluru, 560034, India, University of Calcutta, College Street, Kolkata, 700073, India, High Altitude Observatory, National Center for Atmospheric Research, Boulder, 80307, CO, United States; Dharmadhikari R., Indian Institute of Astrophysics, Bengaluru, 560034, India, University of Calcutta, College Street, Kolkata, 700073, India; Mathur H., Indian Institute of Astrophysics, Bengaluru, 560034, India, University of Calcutta, College Street, Kolkata, 700073, India; Nagaraju K., Indian Institute of Astrophysics, Bengaluru, 560034, India; Jain S.R., Christ University, Bengaluru, 560034, India; Phanindra D.V.S., Indian Institute of Astrophysics, Bengaluru, 560034, India; Sagayanathan K., Indian Institute of Astrophysics, Bengaluru, 560034, India
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Articles
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Title
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Shell script to clone AODV routing protocol in network Simulator-2
Subject
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Access Protocol; Ad-hoc Protocols; AODV Routing Protocol; Clone AODV; Network Simulator-2; Wireless Networks
Description
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Background and Objective: Most of the research that are carried in ad hoc routing protocol is through simulation. While working with a simulator, the codes are enclosed in a component that is accessible to all the developers. The difficulty arises as there is no enough documentation and users find it difficult to modify different C++ and TCL files. Even if one component is modified then the entire Network Simulator-2 (NS-2) suite must be reconfigured. Cloning the protocol manually takes a lot of time and prone to error. Our objective is to ease the work of developers and researchers by showing the procedure to clone the AODV protocol automatically using a script. Methodology: In this study, a shell script is developed that will clone the AODV protocol by modifying 18 C++ and TCL files of the protocol and NS-2 suite by automatically inserting the code in exact files at exact position. It also configures the NS-2 and installs the entire NS-2 suite along with setting the path in .bash files. Results: In this research work, a comparison of cloned protocol with AODV protocol is done based on throughput time and packet loss metrics and the results generated are exactly same for both the protocols. The results of the study reveal that the proposed script clones the AODV protocol successfully. Conclusion: This work proves that the proposed script can clone the AODV protocol faster with just one execution of shell script. This methodology will save the time and help the developers or research to focus more on their study on the protocol. 2018 Authors.
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Khan R.U.; Vijayalakshmi A.
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International Journal of Engineering and Technology(UAE), Vol-7, No. 3, pp. 17-23.
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Science Publishing Corporation Inc
Date
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2018-01-01
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<a href="https://doi.org/10.14419/ijet.v7i3.4.14668" target="_blank" rel="noreferrer noopener">https://doi.org/10.14419/ijet.v7i3.4.14668</a>
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All Open Access; Bronze Open Access
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ISSN: 2227524X
Format
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Online
Language
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English
Type
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Article
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Khan R.U., Department of Computer Science, CHRIST (Deemed to be University), Bengaluru, India; Vijayalakshmi A., Department of Computer Science, CHRIST (Deemed to be University), Bengaluru, India
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Articles
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Title
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Comparing machine learning and ensemble learning in the field of football
Subject
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Decision trees; Ensemble learning; Prescision; Support vector machines; XGBoost
Description
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Football has been one of the most popular and loved sports since its birth on November 6th, 1869. The main reason for this is because it is highly unpredictable in nature. Predicting football matches results seems like the perfect problem for machine learning models. But there are various caveats such as picking the right features from an enormous number of available features. There have been many models which have been applied to various football-related datasets. This paper aims to compare Support Vector Machines a machine learning model and XGBoost an Ensemble learning model and how Ensemble Learning can greatly improve the accuracy of the predictions. Copyright 2019 Institute of Advanced Engineering and Science. All rights reserved.
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Khan S.; Kirubanand V.B.
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International Journal of Electrical and Computer Engineering, Vol-9, No. 5, pp. 4321-4325.
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Institute of Advanced Engineering and Science
Date
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2019-01-01
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<a href="https://doi.org/10.11591/ijece.v9i5.pp4321-4325" target="_blank" rel="noreferrer noopener">https://doi.org/10.11591/ijece.v9i5.pp4321-4325</a>
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All Open Access; Gold Open Access; Green Open Access
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ISSN: 20888708
Format
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Online
Language
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English
Type
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Article
Coverage
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Khan S., Department of Computer Science, CHRIST (Deemed to Be University), Hosur Main Road, Bengaluru, Karnataka, 560029, India; Kirubanand V.B., Department of Computer Science, CHRIST (Deemed to Be University), Hosur Main Road, Bengaluru, Karnataka, 560029, India
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Title
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Conference Papers
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Faculty Publications- Conference Papers
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Simulation of IoT-based Smart City of Darwin: Leading Cyber Attacks and Prevention Techniques
Subject
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Cyber Attacks; Cybersecurity; Internet of Things; Network Simulation; Smart City; Vulnerabilities
Description
An account of the resource
The Rise of the Internet of Things (IoT) technology made the world smarter as it has embedded deeply in several application areas such as manufacturing, homes, cities, and health etc. In the developed cities, millions of IoT devices are deployed to enhance the lifestyle of citizens. IoT devices increases the efficiency and productivity with time and cost efficiency in smart cities, on the other hand, also set an attractive often easy targets for cybercriminals by exposing a wide variety of vulnerabilities. Cybersecurity risks, if ignored can results as very high cost to the citizens and management as well. In this research, simulated IoT network of Darwin CBD has been used with different IoT simulation tools. The treacherous effects of vulnerable IoT environment are demonstrated in this research followed by implementation of security measures to avoid the illustrated threats. 2023 IEEE.
Creator
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Khan S.A.; Sundaram J.; Palendeng M.; Azam S.; Shanmugam B.
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2023 14th International Conference on Computing Communication and Networking Technologies, ICCCNT 2023
Publisher
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Institute of Electrical and Electronics Engineers Inc.
Date
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2023-01-01
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<a href="https://doi.org/10.1109/ICCCNT56998.2023.10306646" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/ICCCNT56998.2023.10306646</a>
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ISBN: 979-835033509-5
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Online
Language
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English
Type
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Conference paper
Coverage
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Khan S.A., Charles Darwin University, Energy and Resources Institute, Faculty of Science and Technology, Darwin, Australia; Sundaram J., Christ (Deemed to Be University), School of Sciences, Delhi-NCR, India; Palendeng M., Charles Darwin University, Energy and Resources Institute, Faculty of Science and Technology, Darwin, Australia; Azam S., Charles Darwin University, Energy and Resources Institute, Faculty of Science and Technology, Darwin, Australia; Shanmugam B., Charles Darwin University, Energy and Resources Institute, Faculty of Science and Technology, Darwin, Australia
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Marine macrolides as an efficient source of FMS-like tyrosine kinase 3 inhibitors: A comprehensive approach of in silico virtual screening
Subject
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Docking; Drug; FMS-like tyrosine kinase 3; Marine macrolides; Virtual screening
Description
An account of the resource
Marine organisms are a definitive source of antibiotics and kinase inhibitors which provide cues for discovering novel drug leads. Marine macrolides are getting much attraction due to their enzyme inhibitory potential. The present study comprehensively dealt with the virtual screening and structure-based prediction of macrolide compounds against FMS-like tyrosine kinase 3 receptors (FLT3). The FLT3 was chosen as a biological target against the 990 marine macrolides. Before the virtual screening of macrolide compounds, validation of molecular docking was carried out by re-docking of co-crystallized Gilteritinib within the FLT3. Among the selected 990 candidates of marine macrolides, 311 were failed due to the generation of insufficient conformers. Amongst the successful compounds, 22 compounds were also failed to dock within the receptor, while the remaining 657 marine macrolide entities elicited successful docking. The HYBRID Chemguass4 Score ranged from -10.17 to -0.02. This vast difference in the HYBRID ChemGuass4 score is attributed to the difference in binding potential with the receptor's binding pocket. The top ten compounds were selected based on the HYBRID ChemGuass4 Score lower than -8.0 against FLT3. The pharmacokinetics and ADME properties revealed the drug likeliness of the macrolides. 2022 SAAB
Creator
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Khan S.U.; Pushparaj K.; Balasubramanian B.; Meyyazhagan A.; Alghamdi S.; Alghamdi A.; Kadaikunnan S.; Alharbi N.S.; Khaled J.M.; Thiruvengadam M.
Source
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South African Journal of Botany, Vol-148, pp. 93-103.
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Elsevier B.V.
Date
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2022-01-01
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<a href="https://doi.org/10.1016/j.sajb.2022.04.006" target="_blank" rel="noreferrer noopener">https://doi.org/10.1016/j.sajb.2022.04.006</a>
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All Open Access; Hybrid Gold Open Access
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A related resource
ISSN: 2546299; CODEN: SAJBD
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Online
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English
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Article
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Khan S.U., Department of Pharmacy, Abasyn University Peshawar, Khyber Pakhtunkhwa, 25000, Pakistan; Pushparaj K., Department of Zoology, School of Biosciences, Avinashilingam Institute for Home Science and Higher Education for Women, Tamil Nadu, Coimbatore, 641043, India; Balasubramanian B., Department of Food Science and Biotechnology, College of Life Science, Sejong University, Seoul, 05006, South Korea; Meyyazhagan A., Department of Life Science, CHRIST (Deemed to be University), Karnataka, Bengaluru, 560076, India; Alghamdi S., Pharmaceutical Chemistry Department, Faculty of Clinical Pharmacy, Albaha University, Al Baha, P.O. Box 1988, Saudi Arabia; Alghamdi A., Department of Clinical Pharmacy, Faculty of Clinical Pharmacy, Albaha University, Albaha, P.O. Box 1988 Al Baha, Saudi Arabia; Kadaikunnan S., Department of Botany and Microbiology, College of Science, King Saud University, Riyadh, 11451, Saudi Arabia; Alharbi N.S., Department of Botany and Microbiology, College of Science, King Saud University, Riyadh, 11451, Saudi Arabia; Khaled J.M., Department of Botany and Microbiology, College of Science, King Saud University, Riyadh, 11451, Saudi Arabia; Thiruvengadam M., Department of Crop Science, College of Sanghuh Life Science, Konkuk University, Seoul, South Korea
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Articles
Article
Faculty Publications -Articles
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Title
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Did Russia's Invasion of Ukraine Induce Herding Behavior in the Indian Stock Market?
Subject
The topic of the resource
event window; geopolitical conflicts; herding behavior; Hurst exponent; MFDFA; multifractal; stock market; war
Description
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This study empirically examines the herding behavior of the Indian stock market investors during the heightened geopolitical tensions between Russia and Ukraine in 2022. An intensified Russia-Ukraine geopolitical event window was constructed, and the high-frequency trading data (intraday) of the Nifty index was analyzed using Multifractal Detrended Fluctuation Analysis (MFDFA) to compute the 5th-order Hurst exponent (Hq (5)) that detects herding behavior. The study's empirical results revealed the presence of profound herding behavior during the intensified Russia-Ukraine geopolitical event window. The study contributes to the existing literature on herding behavior by examining the impact of a geopolitical event on the Indian stock market. Additionally, the study utilizes MFDFA to compute Hurst exponents, a relatively new approach to detecting herding behavior in financial markets. The findings of this study may assist investors and policymakers in understanding the impact of geopolitical events on financial markets and the potential for herding behavior among investors during times of heightened uncertainty. The study's results demonstrate the interconnectedness of global events and financial markets, highlighting the need for policymakers to consider the potential social and economic consequences of geopolitical events. 2023 The Author(s).
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Khan T.; Natchimuthu N.; Krishna T.A.
Source
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Theoretical and Practical Research in the Economic Fields, Vol-14, No. 2, pp. 484-498.
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ASERS Publishing House
Date
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2023-01-01
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<a href="https://doi.org/10.14505/tpref.v14.2(28).24" target="_blank" rel="noreferrer noopener">https://doi.org/10.14505/tpref.v14.2(28).24</a>
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All Open Access; Hybrid Gold Open Access
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A related resource
ISSN: 20687710
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Online
Language
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English
Type
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Article
Coverage
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Khan T., School of Commerce, Finance and Accountancy CHRIST (Deemed to be University), Bengaluru, India; Natchimuthu N., School of Commerce, Finance and Accountancy CHRIST (Deemed to be University), Bengaluru, India; Krishna T.A., Department of Professional Studies CHRIST (Deemed to be University), Bengaluru, India
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Do all shocks produce embedded herding and bubble? An empirical observation of the Indian stock market
Subject
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econophysics; Hurst exponent; market shocks; multifractal; persistence
Description
An account of the resource
Herding has a history of igniting large, irrational market ups and downs, usually based on a lack of fundamental support. Intuitively, most herds start with an external shock. This empirical study seeks to detect shock-induced herding and the creation of nascent bubbles in the Indian stock market. Initially, the multifractal form of the detrended fluctuation analysis was applied. Then the Reformulated Hurst exponent for the Bombay stock exchange (BSE) was determined using Kantelhardt's calibration. The investigation found evidence of high-level herding and a bubble in 2012, with a high value of Hurst Exponent (0.7349). The other years of the research period (2011, 2013, 2016, 2018, 2020-2021) observed mild to significant herding with comparatively lower Hurst values. The results confirm that herding behavior occurs during a crisis and harsh situations emitting shocks. The study concludes that shock-based herding is prevalent in all six shocks: the economic meltdown, commodities and currency devaluation, geo-political problems, the Central Bank's decision on liquidity management, and the Pandemic. Additionally, the years following the Financial Crisis and the years of the Pandemic are when herding and bubble are prominent. Tabassum Khan, Suresh G., 2022.
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Khan T.; Suresh G.
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Investment Management and Financial Innovations, Vol-19, No. 3, pp. 346-359.
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LLC CPC Business Perspectives
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2022-01-01
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<a href="https://doi.org/10.21511/imfi.19(3).2022.29" target="_blank" rel="noreferrer noopener">https://doi.org/10.21511/imfi.19(3).2022.29</a>
<br /><br /><a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85139437220&doi=10.21511%2Fimfi.19%283%29.2022.29&partnerID=40&md5=d49ccbbc4d92f95ea82b8f653f777e6d" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85139437220&doi=10.21511%2fimfi.19%283%29.2022.29&partnerID=40&md5=d49ccbbc4d92f95ea82b8f653f777e6d</a>
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All Open Access; Gold Open Access
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ISSN: 18104967
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Online
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English
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Article
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Khan T., School of Commerce, Finance and Accountancy, CHRIST (Deemed to be University), India; Suresh G., School of Commerce, Finance and Accountancy, CHRIST (Deemed to be University), India
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Articles
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Faculty Publications -Articles
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Title
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ENHANCING FAKE NEWS DETECTION ON SOCIAL MEDIA THROUGH ADVANCED MACHINE LEARNING AND USER PROFILE ANALYSIS
Subject
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Linguistic Inquiry and Word Count features; Machine learning techniques; Principal Component Analysis (PCA); Rhetorical Structure Theory (RST) features; Unified feature set (ALL); User profile features (UPF)
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Social media news consumption is growing in popularity. Users find social media appealing because it's inexpensive, easy to use, and information spreads quickly. Social media does, however, also contribute to the spread of false information. The detection of fake news has gained more attention due to the negative effects it has on society. However, since fake news is created to seem like real news, the detection performance when relying solely on news contents is typically unsatisfactory. Therefore, a thorough understanding of the connection between fake news and social media user profiles is required. In order to detect fake news, this research paper investigates the use of machine learning techniques, covering important topics like feature integration, user profiles, and dataset analysis. To generate extensive feature sets, the study integrates User Profile Features (UPF), Linguistic Inquiry and Word Count (LIWC) features, and Rhetorical Structure Theory (RST) features. Principal Component Analysis (PCA) is used to reduce dimensionality and lessen the difficulties presented by high-dimensional datasets. The study entails a comprehensive assessment of multiple machine learning models using datasets from "Politifact" and "Gossipofact," which cover a range of data processing methods. The evaluation of the XGBoost classification model is further enhanced by the analysis of Receiver Operating Characteristic (ROC) curves. The results demonstrate the effectiveness of particular combinations of features and models, with XGBoost outperforming other models on the suggested unified feature set (ALL). 2023 Little Lion Scientific.
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Khan Z.A.; Rekha V.
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Journal of Theoretical and Applied Information Technology, Vol-101, No. 1, pp. 274-286.
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Little Lion Scientific
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2024-01-01
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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-s2.0-85182870014&partnerID=40&md5=2eec1cda6575d0567c0679390e008ab7" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85182870014&partnerID=40&md5=2eec1cda6575d0567c0679390e008ab7</a>
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Restricted Access
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ISSN: 19928645
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Online
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English
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Article
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Khan Z.A., Department of Computer Science and Engineering, School of Engineering and Technology, Christ University, Bangalore, India; Rekha V., Department of Computer Science and Engineering, School of Engineering and Technology, Christ University, Bangalore, India
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Articles
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Faculty Publications -Articles
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Fake News Detection Using TF-IDF Weighted with Word2Vec: An Ensemble Approach
Subject
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Convolutional Neural Networks; Machine learning; TF-IDF Weighted Vector; Word2Vec
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Social media platforms' utilization for news consumption is steadily growing due to their accessibility, affordability, appeal, and ability to propagate misinformation. False information, whether intentionally or unintentionally created, is being disseminated across the internet. Certain individuals spread inaccurate information on social media to gain attention, financial benefits, or political advantage. This has a detrimental impact on a substantial portion of society that is heavily influenced by technology. It is imperative for us to develop better discernment in distinguishing between fake and genuine news. In this research paper, we present an ensemble approach for detecting fake news by using TF-IDF Weighted Vector with Word2Vec. The extracted features capture specific textual characteristics, which are converted into numerical representations for training the models and balanced dataset with the Random over Sampling technique. The implementation of our proposed framework utilized the ensemble approach with majority voting which combines 2 machine learning models like Random Forest and Decision Tree. The proposed strategy was adopted empirically evaluated against contemporary techniques and basic classifiers, including Gaussian Nae Bayes, Logistic Regression, Multilayer Perceptron, and XGBoost Classifier. The effectiveness of our approach is validated through the evaluation of the accuracy, F1-Score, Precision, Recall, and Auc curve, yielding an impressive accuracy score of 94.24% on the FakeNewsNet dataset. 2023, Ismail Saritas. All rights reserved.
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Khan Z.A.; Rekha V.
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International Journal of Intelligent Systems and Applications in Engineering, Vol-11, No. 3, pp. 1065-1076.
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Ismail Saritas
Date
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2023-01-01
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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-s2.0-85174857890&partnerID=40&md5=fffe94079b40d894912d13c37d245571" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85174857890&partnerID=40&md5=fffe94079b40d894912d13c37d245571</a>
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Restricted Access
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ISSN: 21476799
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Online
Language
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English
Type
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Article
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Khan Z.A., CSE Dept, School of Engineering and Technology, CHRIST (Deemed to be University), Bangalore, India; Rekha V., CSE Dept, School of Engineering and Technology, CHRIST (Deemed to be University), Bangalore, India
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Faculty Publications
Conference Paper
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Khan, Fazal; Priya, Sharon Roji C.; Burhanuddin, Ibrahim; Kurian, Joshua P.K.
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Automated Waste Segregation using Raspberry Pi and Deep Learning
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01-01-2025
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16th International Conference on Advances in Computing, Control, and Telecommunication Technologies, ACT 2025;Volume;2;pp.8620-8625
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<a href="https://www.scopus.com/pages/publications/105034833401?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105034833401?origin=resultslist</a>
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Khan F., Department of Computer Science and Engineering, Christ University, Bangalore, India; Priya S.R.C., Department of Computer Science and Engineering, Christ University, Bangalore, India; Burhanuddin I., Department of Computer Science and Engineering, Christ University, Bangalore, India; Kurian J.P.K., Department of Computer Science and Engineering, Christ University, Bangalore, India
Description
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With rapid urbanization and increasing waste generation, efficient waste segregation has become a critical challenge for sustainable waste management. Traditional waste disposal methods rely heavily on manual sorting, which is inefficient, labor-intensive, and prone to errors, leading to improper recycling and environmental hazards. To address these problems, a clever waste segregation method is presented in this research. It automatically sorts waste into four categoriesglass, metal, plastic, and paper/cardboardusing computer vision and machine learning. A 720p webcam is used to collect images in real time, and the system is powered by a Raspberry Pi 4B with 4GB of RAM. A Convolutional Neural Network (CNN) model that was developed using the TrashNet dataset forms its basis. The model can correctly identify the waste in the photos due to an optimized training method that incorporates data augmentation, regularization strategies, and early stopping to prevent overfitting. An SG90 servo motor controls the lid, ensuring the garbage is placed in the appropriate compartment, while an MG996R servo motor swings the bin into place after the waste has been classified. The bin and lid go back to their initial places once the garbage has been dumped, preparing the system for usage again. Here, we are able to combine automated mobility, automated categorization, and real-time waste detection with embedded technologies, machine learning, and automation to separate waste with the least amount of human intervention. Furthermore, the system's scalability and adaptability make it appropriate for smart city initiatives, urban trash management, and wider industrial application. Consequently, this technology helps to tackle intelligent waste management problems, which facilitates the emergence of a sustainable and eco-friendly future. The system achieved a testing accuracy of 88.1%, showcasing its effectiveness and reliability. Grenze Scientific Society, 2025.
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automation; computer vision; Convolutional Neural Network (CNN); environmental sustainability; machine learning; Real-time waste detection; smart waste management; TrashNet dataset; Waste segregation
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Grenze Scientific Society
Language
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English
Type
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Conference paper
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Restricted Access; Hardcopy may be available in the library
Format
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online
-
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Faculty Publications
Conference Paper
Faculty Publications- Conference Papers
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Khan, Hasib Mohammad; Singh, Monisha; Lakshmi, R.
Title
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Beyond Transcripts: A Learner-Centred Review for Closing the Graduate Skills Gap
Date
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01-01-2025
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Proceedings of 6th International Conference on IoT Based Control Networks and Intelligent Systems, ICICNIS 2025;pp.1509-1515
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<a href="https://doi.org/10.1109/ICICNIS66685.2025.11315761" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/ICICNIS66685.2025.11315761</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105032736729?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105032736729?origin=resultslist</a>
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Khan H.M., Christ University, Dept. of Statistics and Data Science, Bangalore, India; Singh M., Christ University, Dept. of Statistics and Data Science, Bangalore, India; Lakshmi R., Christ University, Dept. of Statistics and Data Science, Bangalore, India
Description
An account of the resource
The majority of the university graduates leave their courses with high grades, but they usually do not have the necessary skills needed in the working environments including teamwork, problem-solving and digital skills. This disconnect between higher education training and the needs of the industry is what is referred to as the graduate skills gap. The article consists of a literature review from 2020-2025 to explore how learner-centred pedagogy can be used to reduce this gap. The results have shown that project-based learning, real-life assessment, internship and micro-credential equip students better than conventional exams. Employers prefer technical and soft skills to academic performance, but most universities are facing problems with stiff curricula and lack of faculty training. This review proposes the incorporation of practice projects, industry partnership, and online skill records to fill the gap. These are some of the strategies that can be used to equip the students with the competencies needed in the current dynamically changing labour market. 2025 IEEE.
Subject
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Authentic assessment; Digital skills; Employability; Experiential learning; Graduate skills gap; Higher education reform; Learner-centred pedagogy
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Institute of Electrical and Electronics Engineers Inc.
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ISBN: 979-833151520-1;
Language
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English
Type
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Conference paper
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Restricted Access; Hardcopy may be available in the library
Format
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online
-
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Title
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Faculty Publications
Book Chapter
Faculty Publications- Book Chapter
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Khan, Muhammad Adnan; Di Virgilio, Francesca; Baber, Ruturaj
Title
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Talent motivation and knowledge transfer to support cultural entrepreneurship: Evidence from italian higher education contest
Date
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01-01-2025
Source
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Handbook of Talent Management and Learning Organizations: A Post-Pandemic Perspective;pp.295-311
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<a href="https://doi.org/10.1201/9781003429890-21" target="_blank" rel="noreferrer noopener">https://doi.org/10.1201/9781003429890-21</a> <br /><br /><a href="https://www.scopus.com/pages/publications/85215091777?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/85215091777?origin=resultslist</a>
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Khan M.A., Department of Business Administration, Barani Institute of Sciences, Pakistan; Di Virgilio F., Department of Economics, University of Molise, Italy; Baber R., School of Business and Management, Christ University, Bengaluru, Karnataka, India
Description
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[No abstract available]
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CRC Press
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ISBN: 978-104030026-8; 978-103254681-0;
Language
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English
Type
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Book chapter
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Restricted Access; Hardcopy may be available in the library
Format
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online
-
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Title
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Faculty Publications
Book Chapter
Faculty Publications- Book Chapter
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Khan, Nishat; Garg, Seema; Arora, Isha; Hegde, Gurumurthy; Sagadeven, Suresh; Ledwani, Lalita; Chandra, Amrish
Title
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Immobilization of TiO2 on Various Substrates
Date
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01-01-2025
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: Synthesis, Photocatalytic Properties, and Applications;pp.87-114
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<a href="https://doi.org/10.1002/9783527848348.ch05" target="_blank" rel="noreferrer noopener">https://doi.org/10.1002/9783527848348.ch05</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105020122343?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105020122343?origin=resultslist</a>
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Khan N., Department of Chemistry, Amity Institute of Applied Sciences, Amity University, Uttar Pradesh, Noida, India; Garg S., Department of Chemistry, Amity Institute of Applied Sciences, Amity University, Uttar Pradesh, Noida, India; Arora I., Department of Chemistry, Amity Institute of Applied Sciences, Amity University, Uttar Pradesh, Noida, India; Hegde G., Department of Chemistry, Centre for Advanced Research and Development (CARD), CHRIST (Deemed to be University), Karnataka, Bengaluru, India; Sagadeven S., Nanotechnology & Catalysis Research Centre, University of Malaya, Kuala Lumpur, Malaysia; Ledwani L., Manipal University Jaipur, Dehmi Kalan, Jaipur-Ajmer Expressway, Rajasthan, Jaipur, India; Chandra A., Lexgin IP LLP, T21/1602, Paras Tierea, Sector 137, Uttar Pradesh, Noida, India
Description
An account of the resource
Recovery of photocatalytic materials after the degradation of organic pollutants remains a challenge. To address this issue, immobilizing the material on a suitable substrate presents a viable solution. Immobilization of the commonly used titanium dioxide (TiO2) photocatalyst onto various substrates is typically achieved through adsorption, hydrogen bonding, or chemical bonding. Coating TiO2 onto different substrates is a common approach to enhance its durability, reusability, and catalytic efficiency across multiple applications, such as photocatalysis, sensors, and heterogeneous catalysis. The choice of substrate depends on the specific application, desired properties, and its ability to improve the photocatalytic performance. Substrates such as glass/quartz, polymeric materials, metal oxides, carbon-based materials, textiles, and cellulose each offer unique characteristics that enhance the potential of the photocatalytic material. 2026 WILEY-VCH GmbH.
Subject
The topic of the resource
atomic layer deposition; chemical vapor deposition; dip coating; heat treatment; immobilization; microplasma; photodegradation; PVA matrix; sintering; substrate coating
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wiley
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ISBN: 978-352784834-8; 978-352735417-7;
Language
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English
Type
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Book chapter
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Restricted Access; Hardcopy may be available in the library
Format
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online
-
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Faculty Publications
Book Chapter
Faculty Publications- Book Chapter
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Khan, Sofia; Singh, Kartikeya
Title
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Navigating Digital Transformation: The Role of Technology in HRM Efficiency and Effectiveness
Date
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01-01-2025
Source
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The Future of Human Resource Management in a Multigenerational Workforce;pp.245-260
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<a href="https://doi.org/10.1108/978-1-83708-808-920251013" target="_blank" rel="noreferrer noopener">https://doi.org/10.1108/978-1-83708-808-920251013</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105023249585?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105023249585?origin=resultslist</a>
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Khan S., School of Management Sciences, India; Singh K., School of Business and Management, Christ Deemed to be University, India
Description
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Technology has grown increasingly common in contemporary workplaces, helping to improve the efficiency and efficacy of Human Resource Management (HRM) processes. The purpose of this research is to investigate the ever-changing role of technology in HRM, especially its impact on organizational efficiency and effectiveness. The goal of this study is to look at the usage of artificial intelligence (AI) and automation in human resources (HR) activities in order to identify the possible advantages and obstacles of incorporating technology. Furthermore, the goal is to identify the best techniques for using technology to improve HR operations and decision-making processes. The goal of this research is to highlight the importance of HR analytics in allowing data-driven decision-making and improving organizational performance by a thorough examination of current literature and empirical data. Furthermore, this study investigates the practical implications and empirical examples of technology integration in the field of HRM, focusing on its influence on both organizational success and employee engagement. To leverage the benefits of technology-driven HRM practices, the passage highlights the need of aligning HR strategy with organizational goals and cultivating a data-driven culture. This study adds to the current literature by giving valuable insights into the changing environment of HRM in the digital age. It also advises professionals and academics on how to utilize technology most effectively in order to achieve organizational success. 2025 by Dr Sofia Khan and Dr Kartikeya Singh. All rights reserved.
Subject
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AI; Data-driven decision making; Digital transformation; HR analytics; HRM; Technology
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Emerald Publishing
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ISBN: 978-183708808-9; 978-183708809-6;
Language
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English
Type
The nature or genre of the resource
Book chapter
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Restricted Access; Hardcopy may be available in the library
Format
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online
-
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Title
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Faculty Publications
Conference Paper
Faculty Publications- Conference Papers
Dublin Core
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Khan, Zahir Abbas; Rekha, V.
Title
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Optimizing Fake News Classification Using Data Fusion and NLP-Based Machine Learning Techniques
Date
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01-01-2025
Source
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Lecture Notes in Networks and Systems;Volume;1355 LNNS;pp.315-326
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<a href="https://doi.org/10.1007/978-981-96-4883-2_26" target="_blank" rel="noreferrer noopener">https://doi.org/10.1007/978-981-96-4883-2_26</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105010596080?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105010596080?origin=resultslist</a>
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Khan Z.A., Department of Computer Science and Engineering, School of Engineering and Technology, Christ (Deemed to Be University), Bangalore, India; Rekha V., Department of Computer Science and Engineering, School of Engineering and Technology, Christ (Deemed to Be University), Bangalore, India
Description
An account of the resource
In this research, the performance of different machine learning algorithms for identifying fake news using a dataset of news articles labeled as fake or real. The dataset was preprocessed to remove stop words, punctuation, digits, and special characters, and text normalization was applied. Two feature extraction methods, BOW (Bag-of-Words) and TF-IDF, were utilized to convert text data into numerical features. The dataset was split into training and testing phases to train and evaluate models, including Support Vector Classifier, Logistic Regression, Decision Trees, Gradient Boosting Classifier, Random Forest, and Multinomial Naive Bayes. Ensemble models combining various classifiers were also tested. Performance metrics, including precision, recall, and F1-score, were assessed, and confusion matrices were analyzed. Results showed that TF-IDF generally outperformed BOW. The Random Forest model achieved the highest precision (93%) but had a lower recall (83%). The SVC model showed a balanced performance with a precision of 90%, recall of 87%, and an F1-score of 86%. Ensemble models like GB?+?RF exhibited high precision (99%) but lower recall. These findings highlight the strengths of different algorithms in fake news detection and inform the development of practical classification tools. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.
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Count vectorizer; Fake news detection; Machine learning algorithms; Natural language processing (NLP); Random forest classifier; Support vector classifier
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Springer Science and Business Media Deutschland GmbH
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ISSN: 23673370; ISBN: 978-981964882-5;
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English
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Conference paper
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online
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Articles
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Education suffering within structural inequalities: A Critical Discourse Analysis of a policy framework
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Critical Discourse Analysis; Political Economy; Public Policy Research; Samagra Shiksha Abhiyan (SAMSA); Structural inequalities
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Education acts as an important catalyst for socioeconomic and democratic evolution in society and is a critical tool for building an equitable system. In our paper, we have historicized one of the most important educational policies, viz. Samagra Shiksha Abhiyan (SAMSA) in India that carries large expectations to minimize the educational divide. We have studied the policy through the lens of Political Economy and have further critiqued it through the framework of Critical Discourse Analysis. We find in our paper that the budget allocated to SAMSA was revised in 2022, from its preceding years with a 28 per cent slash. We critically reflect on the principles mentioned in the policy and find that although there has been an attempt to mitigate the hazards of banking education the Public-Private Partnership initiative reinforces struggles for equitable education, and further, the privatization sets the government free from any accountability. Moreover, a constitutional right like the Right to Education (RTE) is not sufficient enough to meet the goals of universalisation of education. Besides, we analyse the principles such as Education for All, Equity, Equal Opportunity, Access, Gender Concern, Centrality of teacher, Moral Compulsion, and Convergent and integrated system of education management, and argue that although some of the facets of societal structural inequalities are addressed, however, there exists hardly a proper roadmap that could be monitoring the process of creating an inclusive educational paradigm. 2023, Institute for Education Policy Studies. All rights reserved.
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Khandal U.; Das S.; Gaur R.
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Journal for Critical Education Policy Studies, Vol-20, No. 3, pp. 243-285.
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Institute for Education Policy Studies
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2023-01-01
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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-s2.0-85146779522&partnerID=40&md5=9c844d62c79e31c19a6d3a9931acc54e" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85146779522&partnerID=40&md5=9c844d62c79e31c19a6d3a9931acc54e</a>
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ISSN: 20510969
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Online
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English
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Article
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Khandal U., Manipal University Jaipur, Rajasthan Off Jaipur-Ajmer Expressway, Jaipur, 303007, India; Das S., Deemed to be University, Department of Media Studies Christ, Dharmaram College Post, Hosur Road, Bengaluru, 560029, India; Gaur R., Manipal University Jaipur, Rajasthan Off Jaipur-Ajmer Expressway, Jaipur, 303007, India
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Faculty Publications
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Khandelwaal, Chirag; Dhaundiyal, Pankaj; Kataria, Abhinav
Title
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The role of artificial intelligence autonomy in higher education in India
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01-01-2025
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Rethinking the Pedagogy of Sustainable Development in the AI Era;pp.307-327
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<a href="https://doi.org/10.4018/979-8-3693-9062-7.ch014" target="_blank" rel="noreferrer noopener">https://doi.org/10.4018/979-8-3693-9062-7.ch014</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105003148022?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105003148022?origin=resultslist</a>
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Khandelwaal C., Christ University, India; Dhaundiyal P., Christ University, India; Kataria A., Christ University, India
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To leverage the benefits of artificial intelligence applications for experiential learning, many higher education institutes have started using artificial intelligence by adopting many artificial intelligencedriven technologies. Some of them are chatbots, generative AI, concepts of virtual tutors, and providing students with various automated assessment tools for their own assessment, which might change the traditional teaching methodology. This chapter examines the role of artificial intelligence autonomy in higher education in India, deep-diving into AI's impact on students' learning outcomes by leveraging AI-driven technologies in education. This research will specifically have five major variables that will be examined and measured. These variables are usage intention, thought autonomy, action autonomy, sensing autonomy, and culture. This study will examine the five main dimensions of AI autonomy: usage intention, thought autonomy, action autonomy, sensing autonomy, and culture. 2025, IGI Global Scientific Publishing. All rights reserved.
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IGI Global
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ISBN: 979-836939064-1; 979-836939062-7;
Language
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English
Type
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Book chapter
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Restricted Access; Hardcopy may be available in the library
Format
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online
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Conference Papers
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Faculty Publications- Conference Papers
Dublin Core
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Machine Learning Methods leveraging ADFA-LD Dataset for Anomaly Detection in Linux Host Systems
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ADFA-LD; Anomaly Detection; Host Intrusion Detection System; Linux; Machine Learning
Description
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Advancement in network technology and revolution in the global internet transformed the overall Information Technology (IT) infrastructure and its usage. In the era of the Internet of Things (IoT) and the Internet of Everything (IoE), most everyday gadgets and electronic devices are IT-enabled and can be connected over the internet. With the advancements in IT technologies, operating systems also evolved to leverage these advancements. Today's operating systems are more user-friendly and feature-rich to support current IT requirements and provide sophisticated functionalities. On the one hand, these features enabled operating systems accomplish all current requirements, but on the other hand, these modern operating systems increased their attack surface considerably. Intrusion detection systems play a significant role in providing security against the broad spectrum of attacks on host systems. Intrusion detection systems based on anomaly detection have become a prominent research area among diverse areas of cyber security. The traditional approaches for anomaly detection are inadequate to discover the operating system level anomalies. The advancement and research in Machine Learning (ML) based anomaly detection open new opportunities to tackle this challenge. The dataset plays a significant role in ML-based system efficacy. The Australian Defence Force Academy Linux Dataset (ADFA-LD) comprises thousands of normal and attack processes system call traces for the Linux platform. It is the benchmark dataset used for dynamic approach-based anomaly detection. This paper provided a comprehensive and structured study of various research works based on the ADFA-LD for host-based anomaly detection and presented a comparative analysis. 2022 IEEE.
Creator
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Khandelwal P.; Likhar P.; Yadav R.S.
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2022 2nd International Conference on Intelligent Technologies, CONIT 2022
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Institute of Electrical and Electronics Engineers Inc.
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2022-01-01
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<a href="https://doi.org/10.1109/CONIT55038.2022.9848305" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/CONIT55038.2022.9848305</a>
<br /><br /><a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85137264153&doi=10.1109%2FCONIT55038.2022.9848305&partnerID=40&md5=a4795dce4b9f313b3542507b66cc4a82" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85137264153&doi=10.1109%2fCONIT55038.2022.9848305&partnerID=40&md5=a4795dce4b9f313b3542507b66cc4a82</a>
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ISBN: 978-166548407-7
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Online
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English
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Conference paper
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Khandelwal P., Christ(Deemed to Be University), Department of Computer Science, Bengaluru, India; Likhar P., Centre for Artificial Intelligence and Robotics (CAIR), Defence Research and Development Organisation (DRDO), Bengaluru, India; Yadav R.S., Centre for Artificial Intelligence and Robotics (CAIR), Defence Research and Development Organisation (DRDO), Bengaluru, India
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The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/.
Title
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Faculty Publications
Book Chapter
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Khandelwal, Parth; Yadav, Lata; Sharma, Vandana
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Enhancing Cloud Security and Privacy With Blockchain Technology
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01-01-2025
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Safe Data-Driven Control for Cyber-Physical Systems;pp.53-102
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<a href="https://doi.org/10.4018/979-8-3373-1832-5.ch003" target="_blank" rel="noreferrer noopener">https://doi.org/10.4018/979-8-3373-1832-5.ch003</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105022764537?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105022764537?origin=resultslist</a>
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Khandelwal P., Christ University, India; Yadav L., Christ University, India; Sharma V., Christ University, India
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This chapter explores blockchain's potential to address cloud computing security challenges. Despite cloud computing's scalability and cost efficiency, it faces risks like data breaches and regulatory non-compliance, as seen in the 2019 Capital One AWS breach. Blockchain's decentralized ledger, cryptographic hashing, smart contracts, and consensus mechanisms (e.g., PoW, PoS) enhance security through decentralized access control, secure storage, and intrusion detection. Privacy techniques like homomorphic encryption and zero-knowledge proofs protect data. Case studies, including IBM Food Trust and MedRec, show practical applications. However, scalability, interoperability, regulatory conflicts (e.g., GDPR), and high costs pose barriers. Solutions like sharding and layer-2 protocols aim to overcome these. Future research focuses on scalability, privacy, hybrid cloud integration, and AI-driven security. Blockchain strengthens cloud security but requires innovation to achieve widespread adoption. 2026, IGI Global Scientific Publishing. All rights reserved.
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IGI Global
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ISBN: 979-833731834-9; 979-833731832-5;
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English
Type
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Book chapter
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Restricted Access; Hardcopy may be available in the library
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online