Highly emissive dibenzofuranfluorophores with aggregation-induced emission for bioimaging in HeLa cell lines
Dibenzo[b,d]furans, structural analogs of furan, represent an emerging class of promising molecules whose solid-state emission properties remain largely unexplored. The aim of the present work is to design and synthesize new dibenzo[b,d]furan-based organic fluorophores for bioimaging applications. The synthesis involved a single-step Schiff base reaction of 4-(dibenzo[b,d]furan-4-yl)aniline with two different ortho-hydroxy aldehydes, furnishing DBF1 and DBF2 in high yields. Both fluorophores DBF1 and DBF2 exhibited high fluorescence in their solid and aggregated states. The photophysical properties in solution, solid, and aggregated states were investigated using absorbance and emission spectroscopy. The aggregation process was confirmed by the particle size analysis using dynamic light scattering (DLS). The cytotoxicity of the molecules was investigated against HeLa cell lines by the standard MTT assay. DBF1 and DBF2 demonstrated exceptional photoluminescence with quantum yields reaching up to 17.89% and 2.26% respectively, highlighting their potential as excellent materials for imaging applications. Both DBF1 and DBF2 exhibited significant toxicity towards HeLa cells with IC50 values of 42.08 ?g ml?1 and 39.74 ?g ml?1, respectively, showcasing notable anti-proliferative activity against HeLa cells. Both fluorophores exhibited excellent emission in HeLa cells with mean relative fluorescence intensities of 1.008 0.77 a.u. and 1.44 0.65 a.u. for DBF1 and DBF2, respectively. Thus, this work presents the lesser explored dibenzo[b,d]furan-based organic fluorophores for bioimaging applications with potential inhibitory activity against HeLa cells. 2025 The Royal Society of Chemistry.
Durgi, Nikita Kush; Pujar, Prasad Pralhad
RSC Advances;Volume;15;Issue;42;pp.35253-35264
All Open Access; Gold Open Access; Green Open Access
https://doi.org/10.1039/d5ra05626h https://www.scopus.com/pages/publications/105017120139?origin=resultslist
Durgi N.K., Department of Chemistry, Christ University, Hosur Road, Karnataka, Bangalore, 560029, India, Centre for Renewable Energy and Environmental Sustainability, Christ University Bangalore, 560029, India; Pujar P.P., Department of Chemistry, Christ University, Hosur Road, Karnataka, Bangalore, 560029, India, Centre for Renewable Energy and Environmental Sustainability, Christ University Bangalore, 560029, India
WOMEN'S SENSE OF BELONGING: MODERATING ECONOMIC, SOCIOCULTURAL, AND ENVIRONMENTAL IMPACTS ON COMMUNITY-BASED TOURISM DEVELOPMENT
and environmental impacts; community-based tourism; economic; PLS-SEM; sense of belonging; sociocultural; sustainable tourism development
Despite the tremendous development in the literature on sustainable tourism, the focus remains especially on government regulation and international cooperation. As will be seen, the present study is rare in adopting a bottom-up approach that focuses on the perceptions, attitudes, and beliefs of the communities. Therefore, this study aims to explore the positive impacts of community-based tourism, including economic, sociocultural, and environmental factors, on sustainable tourism development. Furthermore, it investigates the moderating role of women's sense of belonging in such relationships. Partial Least Squares Structural Equation Modelling (PLS-SEM) was adopted to analyze the survey results from 183 Omani women. The participants were selected via convenience sampling, therefore facilitating efficient data gathering over the course of the investigation. Convenience sampling was approved given the exploratory nature of the study and the need to gain understanding from a particular demographic group - Omani women - who significantly participate in community and tourism-related activities in Oman. Data gathering ran from January 21 to the end of February 2024. The authors and research assistants distributed the questionnaires to local markets, cultural institutions, university students and community gatherings, among other places, in order to get a diverse sample of Omani women. The study findings illustrate that economic and environmental variables significantly influence sustainable tourism development within the community-based tourism framework; however, sociocultural aspects show a lesser association. Interestingly, women's sense of belonging functions as a crucial moderating factor, amplifying the positive outcomes of economic and environmental factors on tourism sustainability. The results of the study considerably progress the theoretical knowledge of community-based tourism (CBT) by defining the complex connections between economic, social, and environmental components as well as the moderating function of a feeling of belonging (PS). This research delivers important practical uses to policymakers and tourist planners as well as community leaders in Oman who aim to develop community-based tourism. Environmental stewardship holds significant importance because its positive moderation effects strengthen the connection between ENV and STD, according to study findings. 2025 Editura Universitatii din Oradea. All rights reserved.
Durrah, Omar; Elbaz, Ahmed Mohamed; El-Maghraby, Lujain; Irfan, Mohammad
Geojournal of Tourism and Geosites;Volume;60;pp.1221-1230
https://doi.org/10.30892/gtg.602spl19-1495 https://www.scopus.com/pages/publications/105010245425?origin=resultslist
Durrah O., Dhofar University, College of Commerce and Business Administration, Department of Management, Salalah, Oman; Elbaz A.M., University of Technology and Applied Sciences-Salalah, College of Economics and Business Administration, Department of Economics and Business Administration, University of Sadat City, Faculty of Tourism and Hotels, Sadat City, Egypt; El-Maghraby L., Majan University College, Faculty of Business Management, Department of Management, Muscat, Oman; Irfan M., Christ University, School of Business and Management, Bengaluru, India
Smart therapist: The mental health detector
In this fast-moving world, mental health disorder has turned to be one of the important issues which need utmost care and concern. According to the National Mental Health Survey Report, at least 14% of the India's population needs attention for mental disorders, of which stress, depression, and anxiety are the most common. An automated system that helps in detecting these mental disorders and thereby helps us to lead a better life by moulding our lifestyle is desirable. This chapter proposes an Intelligent Therapist based on machine learning which diagnoses a person's mental state through a set of questionnaires that helps in determining his/her mental state. The chatbot analyses the response and classifies them into different categories as per the severity of the disorder. The model is trained and tested on varying datasets to provide better accuracy in the understanding of human behaviour. This web-based chatbot will be accessible to anyone having access to the internet and will act as a preliminary self-check-up point. 2025 selection and editorial matter, Neha Goel and Ravindra Kumar Yadav; individual chapters, the contributors.
Dutt A.; Nizar Banu P.K.; Bansal A.; Bansal K.
Internet of Things enabled Machine Learning for Biomedical Applications, pp. 167-187.
Dutt A., CHRIST (Deemed to be University), Karnataka, Bangalore, India; Nizar Banu P.K., CHRIST (Deemed to be University), Karnataka, Bangalore, India; Bansal A., CHRIST (Deemed to be University), Karnataka, Bangalore, India; Bansal K., CHRIST (Deemed to be University), Karnataka, Bangalore, India
Corrosion behavior of AlCuFeMn alloy in aqueous sodium chloride solution
EIS; Medium entropy alloy; Pitting corrosion; XPS
Medium Entropy Alloy AlCuFeMn possesses high room temperature strength and oxidation endurance. In present work, the aqueous corrosion resistance of the as-cast as well as low temperature oxidized AlCuFeMn alloy in 3.5 wt% NaCl solution, is explored. Equimolar proportions of high purity copper, manganese, iron, and aluminum were arc melted and cast in a copper mold. The alloy primarily consists of a face-centered cubic and a body-centered cubic phase. Potentiodynamic polarization tests on the alloy after low temperature surface oxidation reveal an aqueous corrosion resistance comparable to AISI 304 steel and CoCrFeMnNi high entropy alloy. The X-ray photoelectron spectroscopic studies confirmed that the free surface in the as-cast alloy is in partially oxidized state. The same completely oxidizes after low-temperature surface oxidation. Such low temperature surface oxidation improves pitting corrosion resistance in AlCuFeMn alloy due to increased metal/oxide layer resistance. The electrochemical impedance spectroscopy tests coupled with microscopy confirmed that the principal corrosion mechanisms in the alloy are of the uniform and pitting type. The energy dispersive spectroscopy experiments indicate that a copper oxide enriched layer is formed on the surface oxidized specimen during corrosion. 2021 Elsevier B.V.
Dutta A.; Sivaji T.; Ghosh M.; Fernandes R.; De P.S.; Nayak D.; Sharma R.
Materials Chemistry and Physics, Vol-276
https://doi.org/10.1016/j.matchemphys.2021.125397 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85118968199&doi=10.1016%2fj.matchemphys.2021.125397&partnerID=40&md5=53be1348f527ab31e455574911bffb26
Dutta A., School of Minerals, Metallurgical and Materials Engineering, Indian Institute of Technology Bhubaneswar, India; Sivaji T., School of Minerals, Metallurgical and Materials Engineering, Indian Institute of Technology Bhubaneswar, India; Ghosh M., Materials Engineering Division, CSIR-National Metallurgical Laboratory, Jamshedpur, India; Fernandes R., Department of Physics and Electronics, Christ University, Bangalore, India; De P.S., School of Minerals, Metallurgical and Materials Engineering, Indian Institute of Technology Bhubaneswar, India; Nayak D., School of Minerals, Metallurgical and Materials Engineering, Indian Institute of Technology Bhubaneswar, India, CSIR-Institute of Minerals & Materials Technology, Bhubaneswar, India; Sharma R., School of Minerals, Metallurgical and Materials Engineering, Indian Institute of Technology Bhubaneswar, India
Comparative Analysis of Various Ensemble Approaches for Web Page Classification
ensemble learning models; Machine learning; web page classification; web scraping
The amount of data available on web pages is enormous, and extracting the relevant information and classifying them is an important task. Web page classification finds applications in web content filtering, maintaining and expanding web directories, building efficient crawlers, etc. Machine Learning methods known for their well-established classification approaches have proved to be effective in web page classification. The present work uses ensemble methods like Bagging Meta Estimator, Random Forest, Adaptive boosting, Gradient Tree boosting, Extreme Gradient boosting and stacking to improve single classifiers results. One dataset is manually created to classify web pages into IoT projects and non-IoT projects. Another publicly available dataset is used to classify publications- and conference-related web pages. The advantage of the Ensemble methods over single classifiers has been validated, and various parameters to tune the Ensemble classifiers have been presented and analysed, with accuracy being the metric for performance. Features like learning rate, number of estimators, and maximum number of features have been tuned besides other parameters, and a comparison has been presented. 2023 Scrivener Publishing LLC.
Data Engineering and Data Science: Concepts and Applications, pp. 137-172.
Dutta J., Centre for Digital Innovation, CHRIST (Deemed to be University), Mysore Road, Kumbalgodu, Bangalore, India; Kim Y.W., Centre for Digital Innovation, CHRIST (Deemed to be University), Mysore Road, Kumbalgodu, Bangalore, India; Dominic D., Centre for Digital Innovation, CHRIST (Deemed to be University), Mysore Road, Kumbalgodu, Bangalore, India
Comparison of Gradient Boosting and Extreme Boosting Ensemble Methods for Webpage Classification
Extreme Gradient Boosting; Gradient Tree Boosting; Web page classification; Web scraping
Web page classification is an important task in various areas like web content filtering, contextual advertising and maintaining or expanding web directories etc. Machine Learning methods have been found to perform well to classify web pages, and ensemble models have been used to improve the results obtained from single classifiers. The Gradient Boosting and Extreme Boosting ensemble models are used in this work for binary classification. The dataset containing URLs of web pages have been collected manually. The comparison between the two boosting algorithms validated the improvement in accuracy and speed obtained through Extreme boosting. Extreme boosting has been found to be around ten times faster than Gradient boosting and also shows improvement in accuracy. The effect of three preprocessing techniques; lemmatization, stop words removal and regular expressions shows that these preprocessing techniques improves the accuracy of the results but not significantly. 2020 IEEE.
Proceedings - 2020 5th International Conference on Research in Computational Intelligence and Communication Networks, ICRCICN 2020, pp. 77-82.
Institute of Electrical and Electronics Engineers Inc.
https://doi.org/10.1109/ICRCICN50933.2020.9296176 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85099206710&doi=10.1109%2fICRCICN50933.2020.9296176&partnerID=40&md5=b9e0ad5cb0c563be8a36598bd06647c9
Dutta J., CHRIST (Deemed to Be University), Centre for Digital Innovation, Bangalore, India; Kim Y.W., CHRIST (Deemed to Be University), Centre for Digital Innovation, Bangalore, India; Dominic D., CHRIST (Deemed to Be University), Centre for Digital Innovation, Bangalore, India
Spatial variations of landslide severity with respect to meteorological and soil related factors
GWR; Landslide; Meteorological factors; Soil variables; Spatial heterogeneity
Landslides, a prevalent natural disaster, wreak havoc on both human lives and vital infrastructure, making them a significant global concern. Their devastating impact is immeasurable, necessitating proactive measures to minimize their occurrence. The ability to accurately forecast the severity of a landslide, including its potential fatality rate and the scale of destruction it may cause, holds tremendous potential for prevention and mitigation to reduce the risk and the damage caused by a landslide to infrastructure and life. In this study, the spatial variability in severity of landslides (in terms of mortality rates) and its dependence on various meteorological, geographical and soil composition has been attempted to be established. To do this, Ordinary Least Squares (global) and various Geographically Weighted (local) models have been employed to observe the varying relation between mortality rates and its various causative factors. Existence of geographical heterogeneity in the relationships is also investigated. The spatial pattern of landslide mortality and its associations with various causative variables in the South Asian Region are investigated and analysed. Through this, insights into targeting of prevention and mitigation measures for landslides based on a given location can be obtained by studying the various forms of heterogeneous spatial associations observed. The outcomes highlight that the local models in the form of Gaussian GWR and Poisson GWR outperform their global counterparts by a huge margin with better R2 and Adj R2 values. In comparison with Poisson GWR and Gaussian GWR, it is seen that Poisson GWR outperforms Gaussian GWR in terms of Mean Absolute Error, Mean Squared Error and Corrected Akaike Information Criterion. Furthermore, several intriguing local relationships patterns are also noted. The Author(s), under exclusive licence to Springer Nature B.V. 2024.
Dutta K.; Poddar A.; Middya A.I.; Roy S.
Springer Science and Business Media B.V.
Dutta K., Department of Statistics and Data Science, Christ (Deemed to be) University, Bengaluru, India; Poddar A., Department of Statistics and Data Science, Christ (Deemed to be) University, Bengaluru, India; Middya A.I., Department of Computer Science and Engineering, Jadavpur University, Kolkata, India; Roy S., Department of Computer Science and Engineering, Jadavpur University, Kolkata, India
Decolonizing Open Science: Southern Interventions
Culture-Centered Approach; Data Colonialism; Data Resistance; Data Sovereignty; Data Sovereignty; Decolonization; Global South; Open Science
Hegemonic Open Science, emergent from the circuits of knowledge production in the Global North and serving the economic interests of platform capitalism, systematically erase the voices of the subaltern margins from the Global South and the Southern margins inhabiting the North. Framed within an overarching emancipatory narrative of creating access for and empowering the margins through data exchanged on the global free market, hegemonic Open Science processes co-opt and erase Southern epistemologies, working to create and reproduce new enclosures of extraction that serve data colonialism-capitalism. In this essay, drawing on our ongoing negotiations of community-led culture-centered advocacy and activist strategies that resist the racist, gendered, and classed structures of neocolonial knowledge production in the metropole in the North, we attend to Southern practices of Openness that radically disrupt the whiteness of hegemonic Open Science. These decolonizing practices foreground data sovereignty, community ownership, and public ownership of knowledge resources as the bases of resistance to the colonial-capitalist interests of hegemonic Open Science. The Author(s) 2021.
Dutta M.; Ramasubramanian S.; Barrett M.; Elers C.; Sarwatay D.; Raghunath P.; Kaur S.; Dutta D.; Jayan P.; Rahman M.; Tallam E.; Roy S.; Falnikar A.; Johnson G.M.; Mandal I.; Dutta U.; Basnyat I.; Soriano C.; Pavarala V.; Sreekumar T.T.; Ganesh S.; Pandi A.R.; Zapata D.
Journal of Communication, Vol-71, No. 5, pp. 803-826.
https://doi.org/10.1093/joc/jqab027 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85125146052&doi=10.1093%2fjoc%2fjqab027&partnerID=40&md5=2faeefb3275e809c49d865ed8549dad1
Dutta M., Center for Culture-Centered Approach to Research and Evaluation (CARE), School of Communication, Journalism, and Marketing, Massey University, Palmerston North, 4410, Aotearoa, 4442, New Zealand; Ramasubramanian S., Department of Communication, Texas A&M University, College Station, 77843, TX, United States; Barrett M., School of Accountancy, Massey University, Aotearoa, New Zealand; Elers C., Center for Culture-Centered Approach to Research and Evaluation (CARE), School of Communication, Journalism, and Marketing, Massey University, Palmerston North, 4410, Aotearoa, 4442, New Zealand; Sarwatay D., Sarojini Naidu School of Arts and Communication, University of Hyderabad, Hyderabad, 500046, India; Raghunath P., Sarojini Naidu School of Arts and Communication, University of Hyderabad, Hyderabad, 500046, India; Kaur S., Center for Culture-Centered Approach to Research and Evaluation (CARE), School of Communication, Journalism, and Marketing, Massey University, Singapore; Dutta D., School of Communication, Journalism, and Marketing, Massey University, Palmerston North, 4410, Aotearoa, 4442, New Zealand; Jayan P., Center for Culture-Centered Approach to Research and Evaluation (CARE), School of Communication, Journalism, and Marketing, Massey University, Palmerston North, 4410, Aotearoa, 4442, New Zealand; Rahman M., Center for Culture-Centered Approach to Research and Evaluation (CARE), School of Communication, Journalism, and Marketing, Massey University, Palmerston North, 4410, Aotearoa, 4442, New Zealand; Tallam E., Department of Media Studies, University of the Witwatersrand, Johannesburg, South Africa; Roy S., Department of Languages, Cultures and Communication (LCC), Stephen F. Austin State University (SFASU), 75962, TX, United States; Falnikar A., Media Studies, Christ University, Bangalore, India; Johnson G.M.; Mandal I., Center for Culture-Centered Approach to Research and Evaluation (CARE), School of Communication, Journalism, and Marketing, Massey University, Palmerston North, 4410, Aotearoa, 4442, New Zealand, Center for Culture-Centered Approach to Research and Evaluation (CARE), School of Communication, Journalism, and Marketing, Massey University, Kharagpur, India; Dutta U., Hugh Downs School of Communication, Arizona State University, Arizona, United States; Basnyat I., Global Affairs Program, Department of Communication, George Mason University, Fairfax, 22030, VA, United States; Soriano C., Department of Communication, De Lasalle University, Philippines; Pavarala V., Sarojini Naidu School of Arts and Communication, University of Hyderabad, Hyderabad, 500046, India; Sreekumar T.T., Department of Communication, The English and Foreign Languages University, Hyderabad, India; Ganesh S., Communication Studies, Moody College of Communication, The University of Texas at Austin, Austin, TX, United States; Pandi A.R., Center for Culture-Centered Approach to Research and Evaluation (CARE), School of Communication, Journalism, and Marketing, Massey University, Johor Bahru, Malaysia; Zapata D., Center for Culture-Centered Approach to Research and Evaluation (CARE), School of Communication, Journalism, and Marketing, Massey University, Manila, Philippines
Facial pain expression recognition in real-time videos
Recognition of pain in patients who are incapable of expressing themselves allows for several possibilities of improved diagnosis and treatment. Despite the advancements that have already been made in this field, research is still lacking with respect to the detection of pain in live videos, especially under unfavourable conditions. To address this gap in existing research, the current study proposed a hybrid model that allowed for efficient pain recognition. The hybrid, which consisted of a combination of the Constrained Local Model (CLM), Active Appearance Model (AAM), and Patch-Based Model, was applied in conjunction with image algebra. This contributed to a system that enabled the successful detection of pain from a live stream, even with poor lighting and a low-resolution recording device. The final process and output allowed for memory for storage that was reduced up to 40%-55% and an improved processing time of 20%-25%. The experimental system met with success and was able to detect pain for the 22 analysed videos with an accuracy of 55.75%-100.00%. To increase the fidelity of the proposed technique, the hybrid model was tested on UNBC-McMaster Shoulder Pain Database as well. 2018 Pranti Dutta and Nachamai M.
Journal of Healthcare Engineering, Vol-2018
Dutta P., Department of Computer Science, Christ University, Bengaluru, 560029, India; Nachamai M., Siemens India, Bengaluru, 560100, India
Detection of faces from video files with different file formats
face detection; object recognition; pattern recognition; stored video database
Face detection is the primary approach of all fundamental problems of human computer interaction system (HCIS). This paper evaluates the performance of detection system on single face from stored videos that are stored in different file formats. Stored videos contain raw homemade datasets as well as ready-made datasets. This proposed work concludes detection percentage of face detection system in different video formats. The implementation is done in two phases. The raw homemade dataset is tested on.3gp,.avi,.mov,.mp4 and a ready-made dataset is tested on.wmv,.m4v,.asf,.mpg file formats. The coding part for face detection has been done in MATLAB R2013a. The detection of faces from video file was 72.79 % for homemade dataset and 82.78% for ready-made dataset. 2016 IEEE.
International Conference on Microelectronics, Computing and Communication, MicroCom 2016
https://doi.org/10.1109/MicroCom.2016.7522448 https://www.scopus.com/inward/record.uri?eid=2-s2.0-84983554951&doi=10.1109%2fMicroCom.2016.7522448&partnerID=40&md5=d92f769c7db61bf1d5e4fe433eb57f5c
Dutta P., Department of Computer Science, Christ University, Bengaluru, India; Nachamai M., Department of Computer Science, Christ University, Bengaluru, India
Extraction of features from video files using different image algebraic point operations
Feature extraction; Image algebra; Point operation; Video frame
In the human-computer interaction (HCI) field, facial feature analysis and extraction are the most decisive stages which can lead to a robust and efficient classification system like facial expression recognition, emotion classification. In this paper, an approach to the problem of automatic facial feature extraction from different videos are presented using several image algebraic operations. These operations deal with pixel intensity values individually through some mathematical theory involved in image analysis and transformations. In this paper, 11 operations (point subtraction, point addition, point multiplication, point division, edge detecting, average neighborhood filtering, image stretching, log operation, exponential operation, inverse filtering, and image thresholding) are implemented and tested on the images (video frames) extracted from three different self-recorded videos named as video1, video2, video3. The videos are in .avi, .mp4 and .wmv format respectively. The work is tested on two types of data: grayscale and RGB (Red, Green, Blue). To assess the efficiency of each operation, three factors are considered: processing time, frames per second (FPS) and sharpness of edges of feature points based on image gradients. The implementation has been done in MATLAB R2017a. 2019 Association for Computing Machinery.
ACM International Conference Proceeding Series, Vol-Part F147765, pp. 65-72.
Dutta P., Department of Computer Science, Christ University, Bengaluru, 560029, India; Nachamai N., Siemens India, Bengaluru, India
Data Analytics for Social Microblogging Platforms
Data Analysis for Social Microblogging Platforms explores the nature of microblog datasets, also covering the larger field which focuses on information, data and knowledge in the context of natural language processing. The book investigates a range of significant computational techniques which enable data and computer scientists to recognize patterns in these vast datasets, including machine learning, data mining algorithms, rough set and fuzzy set theory, evolutionary computations, combinatorial pattern matching, clustering, summarization and classification. Chapters focus on basic online micro blogging data analysis research methodologies, community detection, summarization application development, performance evaluation and their applications in big data. 2023 Elsevier Inc. All rights reserved.
Dutta S.; Das A.K.; Ghosh S.; Samanta D.
Data Analytics for Social Microblogging Platforms, pp. 1-303.
Dutta S., Department of Computer Application and Science, Institute of Engineering & Management (IEM), Kolkata, India; Das A.K., Department of Computer Science and Technology, IIEST Shibpur, Howrah, India; Ghosh S., Department of Computer Science and Engineering, Indian Institute of Technology, Kharagpur, India; Samanta D., Department of Computer Science, CHRIST (Deemed to be University), Bangalore, India
Photocatalytic driven self-cleaning IPN membranes infused with a host-guest pair consisting of metal-organic framework encapsulated anionic nano-clusters for water remediation
Antimicrobial; Interpenetrating polymer networks; Metal-organic framework; Photocatalytic membrane; Polyoxometalate; Self-cleaning; Water treatment
Traditional water treatment membranes frequently encounter challenges in attaining an ideal equilibrium between permeability and selectivity. The performance of membranes is further hampered by hydrophobicity, scalability, and fouling problems, as well as excessive energy consumption. Hence, the current research is dedicated to the development of highly effective antifouling membranes, aiming for a significant balance between water permeance and separation efficiency, and featuring exceptional photocatalytic self-cleaning properties to ensure the sustainable reuse of membranes. In this study, a unique nanocomposite-based membrane is designed containing metal-organic frameworks (MOFs) MIL-101 (Fe) encapsulated copper-containing polyoxometalate (Cu-POM) incorporated into an interpenetrating polymer networks (IPNs) membrane. POMs are highly electronegative, oxo-enriched nanosized metal-oxygen cluster species and when composited with MOF yields POMOF which can help in the removal of pollutants from water through electrostatic site-specific binding. The IPN membrane designed by polymerizing aniline in the presence of polyvinylidene fluoride (PVDF) offers tunable pores of the membrane. The infusion of POMOF imparts a strong negative charge to the membrane surface, improving membrane hydrophilicity. This enhances pollutant removal through the Donnan exclusion principle and adds anti-fouling properties. Furthermore, the reduced pore size achieved by the IPN architecture in the POMOF@IPNs membrane effectively sieves out both cationic and anionic dyes, as well as pharmaceutical pollutants. Additionally, POMOF enhances the photocatalytic degradation of CR and MB dyes, coupled with essential self-cleaning attributes vital for separation processes. The IPNs structure, apart from housing POMOF, fortifies the membrane's mechanical strength with its distinctive network-like configuration. Furthermore, these advanced membranes showcase robust antibacterial and antiviral characteristics, while remaining non-cytotoxic to mammalian cells. Our findings indicate that the state-of-the-art POMOF@IPNs membrane is scalable and holds substantial promise for industrial wastewater treatment. 2024 Elsevier B.V.
Dutta S.; Patel B.G.M.; Singh Y.; Hegde G.; Bose S.
https://doi.org/10.1016/j.memsci.2024.122422 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85182907943&doi=10.1016%2fj.memsci.2024.122422&partnerID=40&md5=34bda78dcbbcac82e1515a248f6f053c
Dutta S., Department of Materials Engineering, Indian Institute of Science, Karnataka, Bengaluru, 560012, India; Patel B.G.M., Centre for Advanced Research and Development (CARD), CHRIST (Deemed to be University), Karnataka, Bengaluru, 560029, India, Department of Chemistry, CHRIST (Deemed to be University), Karnataka, Bengaluru, 560029, India; Singh Y., Department of Chemistry, CHRIST (Deemed to be University), Karnataka, Bengaluru, 560029, India; Hegde G., Centre for Advanced Research and Development (CARD), CHRIST (Deemed to be University), Karnataka, Bengaluru, 560029, India, Department of Chemistry, CHRIST (Deemed to be University), Karnataka, Bengaluru, 560029, India; Bose S., Department of Materials Engineering, Indian Institute of Science, Karnataka, Bengaluru, 560012, India
Enhancing Educational Adaptability: A Review and Analysis of AI-Driven Adaptive Learning Platforms
Adaptive Learning Platforms (ALPs); Education; Intelligence; Personalized Learning Paths; Student Engagement
This study explores the transformative potential of AI-powered adaptive learning platforms (ALPs) in education, specifically focusing on personalized learning paths and their impact on student engagement and outcomes. Through a comprehensive analysis of four prominent ALPs - Carnegie Learning, DreamBox Learning, Smart Sparrow, and Knewton - this study investigates their approaches to content tailoring and feedback delivery. The comparative analysis highlights each platform's strengths and limitations, providing educators with valuable insights for informed selection and implementation. This study also considers the broader landscape of ALPs, acknowledging concerns such as bias, data privacy, and the role of educators in the tech-driven educational environment. The findings contribute to our understanding of how ALPs can empower educators, personalize learning, and address achievement gaps, offering a nuanced perspective on the complex tapestry of AI in education. 2024 IEEE.
Dutta S.; Ranjan S.; Mishra S.; Sharma V.; Hewage P.; Iwendi C.
4th International Conference on Innovative Practices in Technology and Management 2024, ICIPTM 2024
Dutta S., Kalinga Institute of Industrial Technology, India; Ranjan S., Kalinga Institute of Industrial Technology, India; Mishra S., Kalinga Institute of Industrial Technology, India; Sharma V., Christ (Deemed to Be University), Department of Computational Sciences, Delhi NCR, India; Hewage P., University of Bolton, School of Creative Technologies, United Kingdom; Iwendi C., University of Bolton, School of Creative Technologies, United Kingdom
Benchmark test functions; Border Collie optimization; Friedman test; Kruskal-Wallis test; metaheuristic; optimization; swarm intelligence
In recent times, several metaheuristic algorithms have been proposed for solving real world optimization problems. In this paper, a new metaheuristic algorithm, called the Border Collie Optimization is introduced. The algorithm is developed by mimicking the sheep herding styles of Border Collie dogs. The Border Collie's unique herding style from the front as well as from the sides is adopted successfully in this paper. In this algorithm, the entire population is divided into two parts viz., dogs and sheep. This is done to equally focus on both exploration and exploitation of the search space. The Border Collie utilizes a predatory move called eyeing. This technique of the dogs is utilized to prevent the algorithm from getting stuck into local optima. A sensitivity analysis of the proposed algorithm has been carried out using the Sobol's sensitivity indices with the Sobol g-function for tuning of parameters. The proposed algorithm is applied on thirty-five benchmark functions. The proposed algorithm provides very competitive results, when compared with seven state-of-the-art algorithms like Ant Colony optimization, Differential algorithm, Genetic algorithm, Grey-wolf optimizer, Harris Hawk optimization, Particle Swarm optimization and Whale optimization algorithm. The performance of the proposed algorithm is analytically and visually tested by different methods to judge its supremacy. Finally, the statistical significance of the proposed algorithm is established by comparing it with other algorithms by employing Kruskal-Wallis test and Friedman test. 2013 IEEE.
Dutta T.; Bhattacharyya S.; Dey S.; Platos J.
https://doi.org/10.1109/ACCESS.2020.2999540 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85087330032&doi=10.1109%2fACCESS.2020.2999540&partnerID=40&md5=032469ae52626dbd629b5aadec388109
Dutta T., Department of Computer Science and Engineering, University Institute of Technology, Burdwan, 713104, India; Bhattacharyya S., Department of Computer Science and Engineering, CHRIST, Deemed to Be University, Bengaluru, 560029, India; Dey S., Department of Computer Science, Sukanta Mahavidyalaya, Jalpaiguri, 735210, India; Platos J., Department of Computer Science, FEECS, VSB-Technical University of Ostrava, Ostrava, 70800, Czech Republic
Quantum fractional order Darwinian particle swarm optimization for hyperspectral multi-level image thresholding
Fractional order Darwinian particle swarm optimization; Hyperspectral image thresholding; Multilevel quantum systems; Quantum disaster management
A Hyperspectral Image (HSI) is a data cube consisting of hundreds of spatial images. Each captured spatial band is an image at a particular wavelength. Thresholding of these images is itself a tedious task. Two procedures, viz., Qubit Fractional Order Particle Swarm Optimization and Qutrit Fractional Order Particle Swarm Optimization are proposed in this paper for HSI thresholding. The Improved Subspace Decomposition Algorithm, Principal Component Analysis, and a Band Selection Convolutional Neural Network are used in the preprocessing stage for band reduction or informative band selection. For optimal segmentation of the HSI, modified Otsu's criterion, Masi entropy and Tsallis entropy are used. A new method for quantum disaster operation is implemented to prevent the algorithm from getting stuck into local optima. The implementations are carried out on three well known datasets viz., the Indian Pines, the Pavia University and the Xuzhou HYSPEX. The proposed methods are compared with state-of-the-art methods viz., Particle Swarm Optimization (PSO), Ant Colony Optimization, Darwinian Particle Swarm Optimization, Fractional Order Particle Swarm Optimization, Exponential Decay Weight PSO and Heterogeneous Comprehensive Learning PSO concerning the optimal thresholds, best fitness value, computational time, mean and standard deviation of fitness values. Furthermore, the performance of each method is validated with Peak signal-to-noise ratio and SensenDice Similarity Index. The KruskalWallis test, a statistical significance test, is conducted to establish the superiority in favor of the proposed methods. The proposed algorithms are also implemented on some benchmark functions and real life images to establish their universality. 2021 Elsevier B.V.
Dutta T.; Dey S.; Bhattacharyya S.; Mukhopadhyay S.
https://doi.org/10.1016/j.asoc.2021.107976 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85118722812&doi=10.1016%2fj.asoc.2021.107976&partnerID=40&md5=12741412cd2bcfa99175ef92b497fcc2
Dutta T., Department of Computer Science & Engineering, Presidency University, Bangalore, Karnataka, India; Dey S., Department of Computer Science, Sukanta Mahavidyalaya, Dhupguri, Jalpaiguri, West Bengal, India; Bhattacharyya S., Department of Computer Science & Engineering, Christ University, Bangalore, Karnataka, India; Mukhopadhyay S., Department of Computer Science & Engineering, Assam University, Silchar, Assam, India
Hyperspectral multi-level image thresholding using qutrit genetic algorithm
Hyperspectral image thresholding; Multilevel quantum systems; Quantum genetic algorithm; Quantum mutation operator; Qutrit
Hyperspectral images contain rich spectral information about the captured area. Exploiting the vast and redundant information, makes segmentation a difficult task. In this paper, a Qutrit Genetic Algorithm is proposed which exploits qutrit based chromosomes for optimization. Ternary quantum logic based selection and crossover operators are introduced in this paper. A new qutrit based mutation operator is also introduced to bring diversity in the off-springs. In the preprocessing stage two methods, called Interactive Information method and Band Selection Convolutional Neural Network are used for band selection. The modified Otsu Criterion and Masi entropy are employed as the fitness functions to obtain optimum thresholds. A quantum based disaster operation is applied to prevent the quantum population from getting stuck in local optima. The proposed algorithm is applied on the Salinas Dataset, the Pavia Centre Dataset and the Indian Pines dataset for experimental purpose. It is compared with classical Genetic Algorithm, Particle Swarm Optimization, Ant Colony Optimization, Gray Wolf Optimizer, Harris Hawk Optimization, Qubit Genetic Algorithm and Qubit Particle Swarm Optimization to establish its effectiveness. The peak signal-to-noise ratio and Sensen-Dice Similarity Index are applied to the thresholded images to determine the segmentation accuracy. The segmented images obtained from the proposed method are also compared with those obtained by two supervised methods, viz., U-Net and Hybrid Spectral Convolutional Neural Network. In addition to this, a statistical superiority test, called the one-way ANOVA test, is also conducted to judge the efficacy of the proposed algorithm. Finally, the proposed algorithm is also tested on various real life images to establish its diversity and efficiency. 2021 Elsevier Ltd
Dutta T.; Dey S.; Bhattacharyya S.; Mukhopadhyay S.; Chakrabarti P.
Expert Systems with Applications, Vol-181
https://doi.org/10.1016/j.eswa.2021.115107 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85106241525&doi=10.1016%2fj.eswa.2021.115107&partnerID=40&md5=202dba8e8b7a8d1a28c9970749c13a67
Dutta T., Department of Computer Science & Engineering, Assam University, Silchar, Assam, India; Dey S., Department of Computer Science, Sukanta Mahavidyalaya, Dhupguri, Jalpaiguri, West Bengal, India; Bhattacharyya S., Department of Computer Science & Engineering, Christ University, Bangalore, Karnataka, India; Mukhopadhyay S., Department of Computer Science & Engineering, Assam University, Silchar, Assam, India; Chakrabarti P., Techno India NJR Institute of Technology, Udaipur, Rajasthan, India
Photocatalytic nanomaterials: Applications for remediation of toxic polycyclic aromatic hydrocarbons and green management
Bioremediation; Metal oxides; Nanomaterials; PAHs; Semiconductor photocatalysis
Nanomaterials (NMs) have piqued the attention of scientists and researchers across many biomedical sciences due to their superior physical, chemical, and magnetic properties. The efficacy and efficiency of NMs depend on adapting to specific site conditions and soil composition. NMs have lately received much attention in the context of polycyclic aromatic hydrocarbons (PAHs) polluted soil remediation and water mitigation because of their unique properties resulting from their nanoscale sizes. The remediation of hazardous PAHs in water and soil is a hot research subject. Because the exposure of PAHs in water and soil results in pollution, which raises major human health concerns. The current review reports novel advancements in NMs that subsidize enhancement for degradation of PAHs. Challenges to the fabrication of high activity-based photocatalytic materials are also discussed. Furthermore, this review delivers exclusive and wide-ranging perspectives on the fabrication of nanomaterial-based photocatalytic systems. The knowledge of both soil remediation and water mitigation is also updated. 2022
Dutta V.; Devasia J.; Chauhan A.; M J.; L V.V.; Jha A.; Nizam A.; Lin K.-Y.A.; Ghotekar S.
Chemical Engineering Journal Advances, Vol-11
https://doi.org/10.1016/j.ceja.2022.100353 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85133196987&doi=10.1016%2fj.ceja.2022.100353&partnerID=40&md5=64445090f8e19f3deb53adab85a2c158
Dutta V., Advanced School of Chemical Sciences, Shoolini University, Himachal Pradesh, Solan, 173212, India; Devasia J., Department of Chemistry, CHRIST (Deemed to be University), Karnataka, Bangalore, 560029, India; Chauhan A., Chettinad Hospital and Research Institute, Chettinad Academy of Research and Education, Tamil Nadu, Kelambakkam, Kanchipuram, 603103, India; M J., Department of Chemistry, CHRIST (Deemed to be University), Karnataka, Bangalore, 560029, India; L V.V., Department of Life Sciences, CHRIST (Deemed to be University), Karnataka, Bangalore, 560029, India; Jha A., University Institute of Biotechnology, Chandigarh University, Punjab, Gharuan, 140413, India; Nizam A., Department of Chemistry, CHRIST (Deemed to be University), Karnataka, Bangalore, 560029, India; Lin K.-Y.A., Department of Environmental Engineering & Innovation and Development Center of Sustainable Agriculture, National Chung Hsing University, 250 Kuo-Kuang Road, Taichung, Taiwan; Ghotekar S., Department of Chemistry, Smt. Devkiba Mohansinhji Chauhan College of Commerce and Science, University of Mumbai, Dadra and Nagar Haveli (UT), Silvassa, 396 230, India
Investigating the factors influencing health equity
This chapter determines various determinants of global health and wellbeing in relation to the Third Sustainable Development Goal of ensuring "Good Health and Well-being." It investigates the contribution of cultural, environmental, systemic, and socio-economic factors to health outcomes and the expected international cooperation towards closing the gaps in the health of nations. In the chapter, a qualitative methodology has been followed based on secondary data in the form of two peer-reviewed literature pieces. From there, the synthesis has been done to find an overview of global healthcare governance structures, their effectiveness, and major challenges.The study shows that while mechanisms of global health care governance are rated as being moderately effective, wide gaps in funding, transparency, and equitable access to services persist. Respondents also showed the need for increased international collaboration, especially on social determinants of health and strengthening health systems in low-and middle-income countries. 2026 by IGI Global Scientific Publishing. All rights reserved.
Public Sector Burnout and Wellness: Research and Experiences;pp.213-240
https://doi.org/10.4018/979-8-3373-5661-7.ch008 https://www.scopus.com/pages/publications/105013952969?origin=resultslist
Dutta A., Christ University, India; Ranjan S., Christ University, India
A psychoanalytical study of surrealist elements in films /
Psychoanalysis has over the years been a centre of attraction and intense research and study by critiques, psychologists, sociologists, etc. due to its unique outlook at the world, Freud’s psychoanalytical theories have found their way onto films, which use the creativity of visuals, sounds, effects etc to create the world Freud claims to be hidden behind the human consciousness. No film can escape psychoanalysis as it frames the underlying reasoning behind human behavior and thus this forms a most intriguing realm of study.
Master of Arts in Media and Communication Studies