In situ fabricated MOF-cellulose composite as an advanced ROS deactivator-convertor: Fluoroswitchable bi-phasic tweezers for free chlorine detoxification and size-exclusive catalytic insertion of aqueous H2O2
Combining the merits of structural diversity, and purposeful implantation of task-specific functionalities, metal-organic frameworks (MOFs) instigate targeted reactive oxygen species (ROS) scavenging and concurrent detoxification via self-calibrated emission modulation. Then again, grafting of catalytically active sites in MOFs can benefit developing a greener protocol to convert ROS generators to technologically important building blocks, wherein tailorable MOF-composite fabrication is highly sought for practical applications, yet unexplored. The chemo-robust and hydrogen-bonded framework encompassing free -NH2 moiety affixed pores serves as an ultra-fast and highly regenerable fluoro-probe for selective detection of toxic ROS producers hypochlorite ion (ClO-) and H2O2 with record-level nanomolar sensitivity. While the bio-relevant antioxidant l-ascorbic acid (AA) imparts notable quenching to the MOF, a significant 3.5 fold emission enhancement with bi-phasic colorimetric variation ensues when it selectively scavenges ClO- from uni-directional porous channels through an unprecedented molecular tweezer approach. Apart from a battery of experimental evidence, density functional theory (DFT) results validate "on-off-on"fluoroswitching from redistribution of MOF orbital energy levels, and show guest-mediated exclusive transition from "Tight state"to "Loose state". The coordination frustrated metal site engineered pore-wall benefits the dual-functionalized MOF in converting the potential ROS generator H2O2via selective alkene epoxidation under mild-conditions. Importantly, sterically encumbered substrates exhibit poor conversion and demonstrate first-ever pore-fitting-induced size selectivity for this benign oxidation. Judiciously planned control experiments in combination with DFT-optimized intermediates provide proof-of-concept to the ionic route of ROS conversion. Considering an effective way to broaden the advanced applications of this crystalline material, reconfigurable MOF@cotton fiber (CF) is fabricated via in situ growth, which scavenges free chlorine and concomitantly squeezes it upon exposure to AA with obvious colorimetric changes over multiple real-life platforms. Furthermore, multi-cyclic alkene epoxidation by MOF@CF paves the way to futuristic continuous flow reactors that truly serves this smart composite as a bimodal ROS deactivator-convertor and explicitly denotes it as an advanced promising analogue from contemporary state-of-the-art materials. The Royal Society of Chemistry.
Goswami R.; Bankar B.D.; Rajput S.; Seal N.; Pillai R.S.; Biradar A.V.; Neogi S.
Journal of Materials Chemistry A, Vol-10, No. 8, pp. 4316-4332.
https://doi.org/10.1039/d1ta10504c https://www.scopus.com/inward/record.uri?eid=2-s2.0-85125461429&doi=10.1039%2fd1ta10504c&partnerID=40&md5=56ee98dc875c34ff685139624a5fed6b
Goswami R., Academy of Scientific and Innovative Research (AcSIR), Ghaziabad, 201002, India, Inorganic Materials and Catalysis Division, CSIR-Central Salt and Marine Chemicals Research Institute, Gujarat, Bhavnagar, 364002, India; Bankar B.D., Academy of Scientific and Innovative Research (AcSIR), Ghaziabad, 201002, India, Inorganic Materials and Catalysis Division, CSIR-Central Salt and Marine Chemicals Research Institute, Gujarat, Bhavnagar, 364002, India; Rajput S., Inorganic Materials and Catalysis Division, CSIR-Central Salt and Marine Chemicals Research Institute, Gujarat, Bhavnagar, 364002, India; Seal N., Academy of Scientific and Innovative Research (AcSIR), Ghaziabad, 201002, India, Inorganic Materials and Catalysis Division, CSIR-Central Salt and Marine Chemicals Research Institute, Gujarat, Bhavnagar, 364002, India; Pillai R.S., Department of Chemistry, Christ University, Bangalore, 560029, India; Biradar A.V., Academy of Scientific and Innovative Research (AcSIR), Ghaziabad, 201002, India, Inorganic Materials and Catalysis Division, CSIR-Central Salt and Marine Chemicals Research Institute, Gujarat, Bhavnagar, 364002, India; Neogi S., Academy of Scientific and Innovative Research (AcSIR), Ghaziabad, 201002, India, Inorganic Materials and Catalysis Division, CSIR-Central Salt and Marine Chemicals Research Institute, Gujarat, Bhavnagar, 364002, India
System and method for transmission of data /
Patent Number: 201841003452, Applicant: Bhargavi Goswami. System and method for transmission of data are provided. The method includes computing a minimum congestion window size and a maximum congestion window size to initiate the transmission of data. The method also includes determining a congestion window size for an instantaneous transmission of data. The method further includes setting a rate for a determined congestion window size based on a computed minimum congestion window size and a computed maximum congestion window size, adjusting the congestion window size to the maximum congestion window size, when the congestion window size exceeds the maximum congestion window size, adjusting the congestion window size when the congestion window size is less than a threshold congestion window size, transmitting the data based on an adjusted congestion window size, updating the rate of the congestion window size based on a round trip time.
https://drive.google.com/file/d/1a_-JcVmSwoWMyCNSY9a8aG8qBBqtuGlq/view
System and method to secure data using substitution box /
Patent Number: 201841035014, Applicant: Bhargavi Goswami. System and method to secure data using substitution box are provided. The method include generating a round key of a pre-defined size of a byte, computing a sum for each value of the round key, applying a mod function on a computed sum with a value equal to the pre-defined size of the byte associated with the round key for generating an index value, computing a dynamic substitution box based on the compound sum and the index value, wherein computing the dynamic substitution box based on the computed sum and the index value comprises computing the dynamic substitution box is equal to an exclusive OR (XOR) operation of inverse of a static substitution box and a byte value of the round key.
https://drive.google.com/file/d/1ufucHN_wlEtBGQtE-J5aBRofxOq_UYvd/view
Real-Time Football Match Analysis with Region-Independent Player Tracking Using Deep Learning
ball detection; ByteTrack; Frames; Full frame detection; Homography; Multi object tracking; Yolov8
This project explores the application of AI in analyzing football games by tracking players across the entire video frame. Unlike traditional methods that focus on limited areas, the system here uses YOLO for detecting players everywhere in the frame and ByteTrack to follow them throughout the match. The goal is to get a clearer picture of each player's movement, particularly their speed and distance covered. Manual methods or GPS-based tools often fall short in providing quick, reliable data, especially in real-time scenarios. This study compensates for camera motion and adjusts for different viewpoints to get more accurate tracking results. As a way to test player identity consistency, the system randomly assigns popular player names to different tracking IDs. Experiments on public match videos show that the system can keep track of players even during zoom-ins, crowding, or partial visibility. Code snippets show how the model works in practice. Our results show that using full-frame AI tracking gives coaches more detailed tactical insights and helps them develop more effective strategies. 2025 IEEE.
Goswami, Jai; Shoran, Preety; Shanbhog, Manjula
2025 IEEE International Conference on Computer, Electronics, Electrical Engineering and their Applications, IC2E3 2025;
Institute of Electrical and Electronics Engineers Inc.
Restricted Access; Hardcopy may be available in the library
https://doi.org/10.1109/IC2E365635.2025.11167431 https://www.scopus.com/pages/publications/105018462669?origin=resultslist
Goswami J., CHRIST University, Computer Science, Bengaluru, India; Shoran P., CHRIST University, Computer Science, Bengaluru, India; Shanbhog M., CHRIST University, Computer Science, Bengaluru, India
Air quality index improvement through machine learning and quantum computing: a framework for advancing air quality prediction using quantum-inspired metaheuristics on climate change to achieve positive health
Climate change, machine learning, Sustainable Development Goals, quantum-inspired metaheuristics, climate action, artificial intelligence
Climate change significantly exacerbates air quality deterioration, intensifying health risks and environmental instability. Air pollution poses significant challenges to public health and environmental sustainability. Accurate prediction of the Air Quality Index (AQI) is crucial for timely interventions and policy-making. As urbanization and industrial activities intensify, there is an urgent need for accurate and real-time air quality monitoring systems. Advanced machine learning (ML) techniques have shown promise in air quality forecasting and classification. Recently, quantum-inspired computational paradigms have emerged as innovative tools to overcome the limitations of traditional models, particularly in areas like feature selection, optimization, and spatial-temporal pattern recognition. This study presents a comprehensive analysis of various machine learning and deep learning models for AQI prediction, utilizing pollutant concentration data. It also explores quantum computing-inspired approaches. We explore the efficacy of different algorithms, datasets, and preprocessing techniques. This paper critically reviews high-impact research that explores the intersection of climate-induced changes and air quality prediction using ML. It identifies trends, gaps, and emerging methodologies. We conduct a comparative analysis of datasets, prediction models, and performance metrics. The paper focuses on three case studies. The first case study focuses on the Indian aspect using an Indian dataset and the global aspects with different global datasets, and the second case study uses quantum-inspired approaches. We further evaluate the performance of 10 state-of-the-art ML models, offering a roadmap for future research and deployment. Effective air quality forecasting is vital in urban planning decisions. This also plays an essential role need in environmental management and the protection of public health. This issue directly deals with Sustainable Development Goal (SDG) 3 and SDG 13. SDG 3 is related to positive health and SDG 13 is related to climate action. Conventional predictive models in ML face challenges due to multiple reasons. Effective feature selection is one such challenge as well as effective hyperparameter tuning. These challenges limit the effectiveness of artificial intelligence models. In the proposed framework, searching is enhanced using quantum jump- and quantum mechanics-related principles. This approach leads to the development of a quantum-inspired particle swarm optimization called QPSO. QPSO is able to provide more promising results by bridging the gaps of traditional optimization techniques. Model convergence is accelerated by using quantum-inspired feature selection techniques. 2026 Elsevier Inc. All rights reserved.
Multilevel Quantum Metaheuristics: Applications in Data Exploration;pp.341-384
https://doi.org/10.1016/B978-0-443-33136-7.00021-9 https://www.scopus.com/pages/publications/105032975207?origin=resultslist
Goswami M., AI, ML and Data Science Department, CHRIST (Deemed to be University), Karnataka, Bangalore, India
Decoding sustainability: A machine learning-based analysis of socioeconomic drivers in global sustainable developmental goals progress
Sustainability, a concept that gained prominence with the Brundtland Report in 1987, is defined as a development approach that addresses present needs without jeopardizing the ability of future generations to meet theirs. Over the years, sustainability has evolved beyond its initial environmental focus, now encompassing economic, social, and political dimensionsmaking it an essential pillar of modern development initiatives. To drive global sustainable development forward, the United Nations adopted the 2030 Agenda, featuring 17 Sustainable Development Goals (SDGs). These goals aim to resolve some of the most pressing challenges faced by humanity, including poverty eradication, climate action, gender equality, and economic growth. The SDG Index, which evaluates a countrys progress toward these goals, helps measure and compare performance across nations. The Intersection of Socioeconomic Factors and SDG Progress is significant for the growth of a country. A countrys Gross Domestic Product (GDP) has often been seen as a key economic indicator, reflecting its ability to invest in sustainable initiatives. However, sustainability is not solely dependent on financial resourcessocial factors play a critical role. To assess the connection between well-being and sustainability, researchers often analyze the Happiness Index alongside SDG scores. Countries demonstrating both high happiness levels and strong sustainability scores provide valuable insights into the relationship between social welfare and global progress. Furthermore, machine learning (ML) techniques have emerged as powerful tools in sustainability research. By analyzing vast datasets, AI-driven approaches can predict trends, optimize resources, and enhance policy implementationaccelerating progress toward a sustainable future. The Evolving Landscape of Sustainability and Its Global Impact is realized using statistical and ML approaches in this study. Rethinking Strategies for a Sustainable Tomorrow is very important in 2025 as we are approaching 2030 very fast. Understanding the underlying factors influencing SDG scores allows nations to refine their approaches to sustainability. By tailoring action plans based on socioeconomic conditions, governments can improve their policies, ensuring both environmental stewardship and enhanced quality of life for their citizens. As global challenges evolve, interdisciplinary approachesspanning technology, economics, and social scienceswill continue to shape sustainability efforts, fostering a future where development aligns seamlessly with environmental and societal well-being. 2026 selection and editorial matter, Siddhartha Bhattacharyya, Jan Plato, Soumyadip Dhar, Naba Kumar Mondal, Ivan Zelinka, Jyoti Sekhar Banerjee and Abhijit Das; individual chapters, the contributors.
Goswami, Mausumi; Lakshmipriya, Shreenidhi Prasath
Data-Driven Environmental Intelligence;pp.304-328
https://doi.org/10.1201/9781003545743-12 https://www.scopus.com/pages/publications/105030811550?origin=resultslist
Goswami M., Department of Computer Science and Engineering, CHRIST (Deemed to be University), Bangalore, India; Lakshmipriya S.P., Department of Computer Science and Engineering, CHRIST (Deemed to be University), Bangalore, India
State-of-the-Art and Upcoming Trends in IoT-Enabled Smart Cities
Applications; Internet of things; Smart cities; Smart structures; Technology
Modern cities tremendous development of urbanization necessitates smart responses to pressing problems like mobility, medical care, power, and civil construction. The Internet of Things (IoT), which can use sustainable data and communication innovations, is evolving into the foundation for the upcoming trends of smart cities. To meet the demands of the expanding populace, several demands of the smart city must be taken into account. The IoT expansion has greatly generated a variety of study avenues for the smart city on the flip side of developing innovation. The suggested research proposal offers the analytic network procedure (ANP) for analyzing smart cities while maintaining in mind application instances of the smart city. In complicated circumstances when there are ambiguous options, the ANP technique performs effectively. The projected methods experimental findings demonstrate its viability for use case-based evaluation of IoT-enabled smart cities. 2024 selection and editorial matter, Prof. (Dr.) Dorota Jelonek, Prof. (Dr.) Narendra Kumar, Prof. (Dr.) Mamta Chahar, Prof. (Dr.) Rusudan Kinkladze and Prof. (Dr.) Lilla Knop; individual chapters, the contributors.
Goswami, Sushmita; Nassa, Vinay Kumar; Hussain, Shaik Saddam; Anandhi, R.J.; Gupta, Lovi Raj; Bikash, Purnendu
Recent Trends in Engineering and Science for Resource Optimization and Sustainable Development;pp.210-213
https://doi.org/10.1201/9781003596721-49 https://www.scopus.com/pages/publications/85218308872?origin=resultslist
Goswami S., Institute of Business Management, GLA University, Uttar Pradesh, Mathura, India; Nassa V.K., Department of Information Communication Technology(ICT), Tecnia Institute of Advanced Studies(Delhi), Affiliated with Guru Gobind Singh Indraprastha University, India; Hussain S.S., Department of Information Technology, Institute of Aeronautical Engineering, Telangana, Hyderabad, India; Anandhi R.J., Department of Information Science Engineering, New Horizon College of Engineering, Bangalore, India; Gupta L.R., Lovely Professional University, Phagwara, India; Bikash P., CHRIST University, Pune, India
Machine Learning Technique to Detect Radiations in the Brain
Brain morphology; Electromagnetic field radiations; Image processing; Machine learning; Segmentation
The brain of humans and other organisms is affected in various ways through the electromagnetic field (EMF) radiations generated by mobile phones and cell phone towers. Morphological variations in the brain are caused by the neurological changes due to the revelation of EMF. Cellular level analysis is used to measure and detect the effect of mobile radiations, but its utilization seems very expensive, and it is a tedious process, where its analysis requires the preparation of cell suspension. In this regard, this research article proposes optimal broadcasting learning to detect changes in brain morphology due to the revelation of EMF. Here, Drosophila melanogaster acts as a specimen under the revelation of EMF. Automatic segmentation is performed for the brain to attain the microscopic images from the prejudicial geometrical characteristics that are removed to detect the effect of revelation of EMF. The geometrical characteristics of the brain image of that is microscopic segmented are analyzed. Analysis results reveal the occurrence of several prejudicial characteristics that can be processed by machine learning techniques. The important prejudicial characteristics are given to four varieties of classifiers such as nae Bayes, artificial neural network, support vector machine, and unsystematic forest for the classification of open or nonopen microscopic image of D. melanogaster brain. The results are attained through various experimental evaluations, and the said classifiers perform well by achieving 96.44% using the prejudicial characteristics chosen by the feature selection method. The proposed system is an optimal approach that automatically identifies the effect of revelation of EMF with minimal time complexity, where the machine learning techniques produce an effective framework for image processing. This work is licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Gothai E.; Baseera A.; Prabu P.; Venkatachalam K.; Saravanan K.; SathishKumar S.
Computer Systems Science and Engineering, Vol-42, No. 1, pp. 149-163.
All Open Access; Hybrid Gold Open Access
https://doi.org/10.32604/CSSE.2022.020619 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85121609894&doi=10.32604%2fCSSE.2022.020619&partnerID=40&md5=bf5b14750fa5d3b433c59a2534f30d31
Gothai E., Department of Computer Science and Engineering, Kongu Engineering College, Erode, 638060, India; Baseera A., School of Computing Science and Engineering, VIT Bhopal University, Bhopal, 466114, India; Prabu P., Department of Computer Science, CHRIST (Deemed to be University), Bangalore, 560029, India; Venkatachalam K., Department of Computer Science and Engineering, CHRIST (Deemed to be University), Bangalore, India; Saravanan K., Department of Computer Science and Engineering, Erode Sengunthar Engineering College, Thudupathi, 638057, India; SathishKumar S., Department of EEE, M.Kumarasamy College of Engineering, Tamilnadu, Karur, 639113, India
The psychosocial impact of and access to mental health care for individuals with visual impairments
Access; attitudes; awareness; stigma; well-being
Blindness and Visual Impairment is one of the first recognized disabilities out of the 21 disabilities delineated by the Rights for Persons with Disabilities Act. Despite its recognition, a large research gap was identified regarding access to mental health care in India. Purpose: Further, the distinction between impairment and disability characterized by differentiating the medical impact and psychosocial impact was not widely explored in existing literature. Methods and materials: This qualitative phenomenological research explored the lived experiences of 11 adults with visual impairments in India, examining psychosocial challenges and mental healthcare access. Results: Four Key themes were derived with the use of the Braun and Clarke model of thematic analysisImpact of Visual Impairment, Coping Strategies, Perceived Support and Access to Mental Healthcare. Conclusion: The findings revealed unique experiences and influences on wellbeing and daily functioning as well as the role stigma plays in perpetuating contemporary disability rhetoric. The study emphasizes the need for tailored policies and interventions to address the psychosocial and mental healthcare challenges faced by individuals with visual impairments. 2025 Informa UK Limited, trading as Taylor & Francis Group.
https://doi.org/10.1080/09638288.2025.2605624 https://www.scopus.com/pages/publications/105026691648?origin=resultslist
Goturu S., Psychology (Clinical), Christ University, Bengaluru, India; Chander R J., Psychology (Clinical), Christ University, Bengaluru, India
A Novel Real-Time Posture Monitoring System Using Signal Processing and Computer Vision Techniques
Butterworth Filter; Computer Vision; Posture Monitoring; Signal Processing
This paper presents a novel real-time posture monitoring system using signal processing and computer vision techniques to provide accurate feedback on body posture. By measuring key angles between the head-shoulder and shoulder-hip regions, the system identifies deviations from ideal posture. A Butterworth low-pass filter is employed to smooth the posture data, significantly reducing noise and misclassification of sudden movements as poor posture. The proposed systems novelty lies in the integration of signal processing to enhance data interpretation, ensuring that momentary shifts are filtered out, resulting in more reliable classification and feedback. The system was tested in real-world scenarios, demonstrating its ability to offer immediate, high-accuracy posture feedback. Unlike conventional systems that rely solely on raw data, our approach uses smoothed, noise-free data to provide a clearer understanding of posture, making it suitable for deployment in workplaces, home offices, and rehabilitation centers. Future work will focus on multi-joint analysis, duration-based feedback mechanisms for sustained posture deviations, and the impact of camera angle on measurement accuracy. Overall, the system provides a cost-effective and efficient solution for continuous posture monitoring, aiming to improve health and ergonomics across various settings. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2026.
Gour, Shivashish; Kavitha, Rose; Arvind, Tripti; Saravanan, K.; Jeewankar, Ankita Nathuji
Lecture Notes in Electrical Engineering;Volume;1529 LNEE;pp.64-69
Springer Science and Business Media Deutschland GmbH
https://doi.org/10.1007/978-981-95-5835-3_11 https://www.scopus.com/pages/publications/105039435941?origin=resultslist
Gour S., Department of Computer Science and Engineering - BCT, Jain (Deemed-to-be) University, 45th km, NH - 209, Jakkasandra Post, Kanakapura Taluk, Ramanagara District, Karnataka, Bengaluru, 562112, India; Kavitha R., Department of Management Studies, New Horizon College of Engineering, Outer Ring Road, Marathahalli, Kadabeesanahalli, Karnataka, Bengaluru, 560103, India; Arvind T., School of Business and Management (MBA), Christ University, Hosur Main Road, Karnataka, Bengaluru, 560029, India; Saravanan K., Department of Information Science and Engineering, New Horizon College of Engineering, Outer Ring Road, KadabeesanahalliKarnataka, Marathahalli, Bengaluru, 560103, India; Jeewankar A.N., Department of Management Studies, New Horizon College of Engineering, Outer Ring Road, Marathahalli, Kadabeesanahalli, Karnataka, Bengaluru, 560103, India
Spectroscopic Studies of Galactic Field Be Stars
Be stars provide excellent opportunity to study circumstellar disks. But the disc formation mechanism of classical Be (CBe) stars- the Be phenomenon- is still poorly understood. This can be understood by studying CBe stars in various locations like clusters and fields. Spectra of Be stars show interesting emission lines of different elements like hydrogen, helium, iron, oxygen calcium, etc. These emission lines are valuable indicators in providing information about the circumstellar disks of Be stars. In the past several decades various aspects of Be stars have been studied. But literature review clearly indicates the need of further studies to frame a consolidated picture about Be phenomenon in CBe stars. It is found that especially, the region ?????? 7500 - 8800 ??? is a less studied, and thus poorly understood area in Be star research. But this area shows some interesting features like emission lines calcium, iron, oxygen and Paschen series. So, here we have studied a sample of 118 field CBe stars taken from the catalogue of Jaschek & Egret (1982) and whose medium resolution spectra were obtained in ?????? 3800 ?? 9000 ??? region during December, 2007 to January, 2009 with the 2.1-m Himalayan Chandra Telescope (HCT), located at Hanle, Ladakh, India and operated by the Indian Institute of Astrophysics (IIA), Bangalore. In this thesis, we present three works which investigate the disc properties of our 118 program Be stars by studying their spectral line features, focussing primarily on the less explored ?????? 7500 - 8800 ??? region. Firstly, we have analyzed the less studied Fe II 7712 ??? emission line for our stars to understand the possible Fe II line excitation mechanism in CBe stars. Our work predicts that Ly???fluorescence may be the possible Fe II line excitation mechanism in CBe stars. Secondly, we have studied the Ca II triplet emission lines for our stars and have developed a new technique for deblending Ca II components from their counterpart Paschen lines, thus providing a more efficient way to analyze Ca II lines. Analyzing Ca II lines through this technique, we suggest that the gas producing these lines is optically thick. This leads us to predict that Ca II lines may be an indicator of binarity in Be stars. Lastly, we have estimated the Balmer decrement values, D34 and D54 for 81 of our sample stars to shed light on opacity effects in Be star disks. Our work confirms the disc transient nature of Be stars through epoch-wise D34 and D54 variation study and also suggests that Be star disks are optically thick.
Optical Spectroscopy of Classical be stars in the galaxy /
Examining the impact of maternal experiences of domestic violence on the mental health of their adolescent children in India
Background Domestic violence (DV) is experienced by one in three women in India and is linked to poor mental health outcomes. We hypothesize that maternal experiences of DV can have negative impacts on the mental health of their children. Previous studies have demonstrated this link in Western countries, however culturally specific manifestations of DV and mental health disorders and socio-cultural differences in parent-child relationships and home environments necessitate deeper understanding of the impacts of maternal experiences of DV on children in the Indian context. Methods This study presents a secondary analysis of data collected from a seven-center study in urban and rural India examining mental health disorders among adolescents aged 1217 years and psychological, physical, and sexual abuse affecting their mothers. The Indian Family Violence and Control Scale (IFVCS) was used to examine experiences of DV among mothers and the Mini International Neuropsychiatric InterviewKid (MINI-Kid) was used to examine mental health outcomes among adolescents. Multivariate analyses examined the associations between maternal DV and adolescent mental disorders. Results Data from 2,784 adolescent-mother pairs were analyzed. In bivariate analyses, maternal experiences of physical, psychological, and sexual abuse were significantly associated with adolescent common mental disorders including anxiety and depression (p < 0.05). After adjusting for adolescent gender, site, and education status in the multivariate analysis, physical, sexual, and any DV were significantly associated with adolescent anxiety disorders and common mental disorders. Physical abuse was significantly associated with adolescent depressive disorders. Conclusions These results suggest that exposure to maternal DV significantly impacts adolescent mental health in India and underscore the need to develop trauma-informed school programs and enhance DV prevention for women in India. 2025 Gourisankar et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Gourisankar, Amritha; Ravi, Preethi; Kalokhe, Ameeta; Hall, Rachel Waford; Vaidya, Nilakshi; Sharma, Eesha; Holla, Bharath; Basu, Debasish; Bharath, Rose Dawn; Chakrabarti, Amit; Desrivieres, Sylvane; Hickman, Matthew; Kartik, Kamakshi; Ghattu, Krishnaveni; Kalyanaraman, Kumaran; Krishna, Murali; Kuriyan, Rebecca; Murthy, Pratima; Orfanos, Dimitri Papadopoulos; Purushottam, Meera; Kurpad, Sunita Simon; Singh, Rajkumar Lenin; Singh, Roshan Lourembam; Subodh, Bhagyalakshmi Nanjayya; Toledano, Mireille B.; Benegal, Vivek; Schumann, Gunter; Kalyanram, Kartik; Varghese, Mathew; Kandavel, Thennarasu; Urvakhsh, Metha; Girimaji, Satish; Jacob, Preeti; Jayarajan, Deepak; Kumar, Keshav; Narayanan, Gitanjali; Khullar, Madhu; Khandelwal, Niranjan; Ghosh, Abhishek; Amit; Joshi, Nainesh; Chanu, Ningthoujam Debala; Fujica, M.C.; Ph, Victoria; Phurailatpam, Celina; Bhattacharya, Debangana; Haque, Bidisha; Nagraj, Alisha; Ghosh, Arpita; Basu, Anirban; Pandit, Mriganka; Das, Subhadip; Yadav, Anupa; Das, Surajit; Roy, Sanjit; Maurya, Pawan; Rani, Geetha T.; Sujatha, B.; Rangaswamy, Madhavi; Fall, Caroline; Kiran, Kn; Ramya, Mc; Urs, Chaitra; Santhosh, N.; Somashekhara, R.; Divyashree, K.; Rao, Arathi; Poornima, R.; Tripathy, Saswathika; Parashar, Neha; Nayana, K.B.; Seshadri, Ashwini; Kumar, Sathish; Baligar, Suneela; Arumugam, Thamodharan; Safai, Apoorva; Cyril, Anthony; Rashmitha; Roy, Ashika; Dhanalakshmi, D.; Shivamma, D.; Bhavana, B.R.
All Open Access; Gold Open Access; Green Open Access
https://doi.org/10.1371/journal.pone.0304936 https://www.scopus.com/pages/publications/105006726457?origin=resultslist
Gourisankar A., Emory University School of Medicine, Atlanta, United States, Emory Rollins School of Public Health, Atlanta, United States; Ravi P., Emory University School of Medicine, Atlanta, United States, Emory Rollins School of Public Health, Atlanta, United States; Kalokhe A., Emory University School of Medicine, Atlanta, United States, Emory Rollins School of Public Health, Atlanta, United States; Hall R.W., Emory Rollins School of Public Health, Atlanta, United States; Vaidya N., Centre for Addiction Medicine, National Institute of Mental Health and Neurosciences, Bangalore, India, Centre for Population Neuroscience and Precision Medicine (PONS), Department of Psychiatry and Neuroscience, CharitUniversitsmedizin Berlin, Berlin, Germany; Sharma E., Department of Child and Adolescent Psychiatry, National Institute of Mental Health and Neurosciences, Bangalore, India; Holla B., Department of Psychiatry, National Institute of Mental Health and Neurosciences, Bangalore, India; Basu D., Department of Psychiatry, Regional Institute of Medical Sciences, Manipur, Imphal, India; Bharath R.D., Department of Neuroimaging and Interventional Radiology, National Institute of Mental Health and Neurosciences, Bangalore, India; Chakrabarti A., Centre for Ageing and Mental Health, Indian Council of Medical Research (ICMR), Kolkata, India; Desrivieres S., Centre for Population Neuroscience and Precision Medicine, MRC Social, Genetic, Developmental Psychiatry Centre, Institute of Psychology, Psychiatry & Neuroscience, Kings College London, London, United Kingdom; Hickman M., Bristol Medical School, University of Bristol, Bristol, United Kingdom; Kartik K., Rishi Valley Rural Health Center, Madanapalle, India; Ghattu K., Epidemiology Research Unit, CSI Holdsworth Memorial Hospital, Mysore, India; Kalyanaraman K., Epidemiology Research Unit, CSI Holdsworth Memorial Hospital, Mysore, India; Krishna M., Institute of Public Health, Bangalore, India; Kuriyan R., NeuroSpin, CEA, UniversitParis-Saclay, Paris, France; Murthy P., Department of Psychiatry, National Institute of Mental Health and Neurosciences, Bangalore, India; Orfanos D.P., Molecular Genetics Laboratory, National Institute of Mental Health and Neurosciences, Bangalore, India; Purushottam M., Division of Nutrition, St. Johns Research Institute, Bangalore, India; Kurpad S.S., Department of Psychiatry, Postgraduate Institute of Medical Education and Research, Chandigarh, India; Singh R.L., Department of Psychiatry, Regional Institute of Medical Sciences, Manipur, Imphal, India; Singh R.L., Department of Psychiatry, Regional Institute of Medical Sciences, Manipur, Imphal, India; Subodh B.N., Medical Research Council Centre for Environment and Health, School of Public Health, Imperial College London, London, United Kingdom; Toledano M.B., Institute for Science and Technology of Brain-inspired Intelligence (ISTBI), Fudan University, Shanghai, China; Benegal V., Centre for Addiction Medicine, National Institute of Mental Health and Neurosciences, Bangalore, India; Schumann G., Centre for Population Neuroscience and Precision Medicine (PONS), Department of Psychiatry and Neuroscience, CharitUniversitsmedizin Berlin, Berlin, Germany, Department of Psychiatry, Department of Medical Ethics, St. Johns Medical College and Hospital, Bangalore, India; Kalyanram K., Rishi Valley Rural Health Center, Madanapalle, India; Varghese M., Department of Psychiatry, National Institute of Mental Health and Neurosciences, Bangalore, India; Kandavel T., Department of Biostatistics, National Institute of Mental Health and Neurosciences, Bangalore, India; Urvakhsh M., Department of Psychiatry, National Institute of Mental Health and Neurosciences, Bangalore, India; Girimaji S., Department of Child & Adolescent Psychiatry, National Institute of Mental Health and Neurosciences, Bangalore, India; Jacob P., Department of Child & Adolescent Psychiatry, National Institute of Mental Health and Neurosciences, Bangalore, India; Jayarajan D., Department of Psychiatry, National Institute of Mental Health and Neurosciences, Bangalore, India; Kumar K., Department of Mental Health and Clinical Psychology, National Institute of Mental Health and Neurosciences, Bangalore, India; Narayanan G., Department of Mental Health and Clinical Psychology, National Institute of Mental Health and Neurosciences, Bangalore, India; Khullar M., Department of Experimental Medicine, Post Graduate Institute of Medical Education and Research, Chandigarh, India; Khandelwal N., Department of Radiodiagnosis and Imaging, Post Graduate Institute of Medical Education and Research, Chandigarh, India; Ghosh A., Department of Psychiatry, Post Graduate Institute of Medical Education and Research, Chandigarh, India; Amit, Department of Radiodiagnosis and Imaging, Post Graduate Institute of Medical Education and Research, Chandigarh, India; Joshi N., Department of Experimental Medicine, Post Graduate Institute of Medical Education and Research, Chandigarh, India; Chanu N.D., Regional Institute of Medical Sciences, Manipur, Imphal, India; Fujica M.C., Regional Institute of Medical Sciences, Manipur, Imphal, India; Ph V., Regional Institute of Medical Sciences, Manipur, Imphal, India; Phurailatpam C., Regional Institute of Medical Sciences, Manipur, Imphal, India; Bhattacharya D., Indian Council for Medical Research, Centre for Ageing and Mental Health, Kolkata, India; Haque B., Indian Council for Medical Research, Centre for Ageing and Mental Health, Kolkata, India; Nagraj A., Indian Council for Medical Research, Centre for Ageing and Mental Health, Kolkata, India; Ghosh A., Indian Council for Medical Research, Centre for Ageing and Mental Health, Kolkata, India; Basu A., Indian Council for Medical Research, Centre for Ageing and Mental Health, Kolkata, India; Pandit M., Indian Council for Medical Research, Centre for Ageing and Mental Health, Kolkata, India; Das S., Indian Council for Medical Research, Centre for Ageing and Mental Health, Kolkata, India; Yadav A., Indian Council for Medical Research, Centre for Ageing and Mental Health, Kolkata, India; Das S., Indian Council for Medical Research, Centre for Ageing and Mental Health, Kolkata, India; Roy S., Indian Council for Medical Research, Centre for Ageing and Mental Health, Kolkata, India; Maurya P., Indian Council for Medical Research, Centre for Ageing and Mental Health, Kolkata, India; Rani G.T., Rishi Valley Rural Health Centre, Andhra Pradesh, India; Sujatha B., Rishi Valley Rural Health Centre, Andhra Pradesh, India; Rangaswamy M., Department of Psychology, Christ University, Bangalore, India; Fall C., MRC Lifecourse Epidemiology Unit, University of Southampton, United Kingdom; Kiran K., Epidemiology Research Unit, CSI Holdsworth Memorial Hospital, Mysore, India; Ramya M., Epidemiology Research Unit, CSI Holdsworth Memorial Hospital, Mysore, India; Urs C., Epidemiology Research Unit, CSI Holdsworth Memorial Hospital, Mysore, India; Santhosh N., Epidemiology Research Unit, CSI Holdsworth Memorial Hospital, Mysore, India; Somashekhara R., Epidemiology Research Unit, CSI Holdsworth Memorial Hospital, Mysore, India; Divyashree K., Epidemiology Research Unit, CSI Holdsworth Memorial Hospital, Mysore, India; Rao A., Division of Nutrition, St. Johns Research Institute, Bangalore, India; Poornima R., Division of Nutrition, St. Johns Research Institute, Bangalore, India; Tripathy S., Centre for Addiction Medicine, National Institute of Mental Health and Neurosciences, Bangalore, India; Parashar N., Centre for Addiction Medicine, National Institute of Mental Health and Neurosciences, Bangalore, India; Nayana K.B., Centre for Addiction Medicine, National Institute of Mental Health and Neurosciences, Bangalore, India; Seshadri A., Centre for Addiction Medicine, National Institute of Mental Health and Neurosciences, Bangalore, India; Kumar S., Centre for Addiction Medicine, National Institute of Mental Health and Neurosciences, Bangalore, India; Baligar S., Molecular Genetics Laboratory, National Institute of Mental Health and Neurosciences, Bangalore, India; Arumugam T., Department of Neuroimaging and Interventional Radiology, National Institute of Mental Health and Neurosciences, Bangalore, India; Safai A., Department of Neuroimaging and Interventional Radiology, National Institute of Mental Health and Neurosciences, Bangalore, India; Cyril A., Centre for Addiction Medicine, National Institute of Mental Health and Neurosciences, Bangalore, India; Rashmitha, Molecular Genetics Laboratory, National Institute of Mental Health and Neurosciences, Bangalore, India; Roy A., Department of Neuroimaging and Interventional Radiology, National Institute of Mental Health and Neurosciences, Bangalore, India; Dhanalakshmi D., Centre for Addiction Medicine, National Institute of Mental Health and Neurosciences, Bangalore, India; Shivamma D., Centre for Addiction Medicine, National Institute of Mental Health and Neurosciences, Bangalore, India; Bhavana B.R., Centre for Addiction Medicine, National Institute of Mental Health and Neurosciences, Bangalore, India
Semantic Analysis and Topic Modelling of Web-Scrapped COVID-19 Tweet Corpora through Data Mining Methodologies
BiLSTM; COVID-19 sentiment analysis; Latent Dirichlet Allocation (LDA); natural language processing; topic modeling
The evolution of the coronavirus (COVID-19) disease took a toll on the social, healthcare, economic, and psychological prosperity of human beings. In the past couple of months, many organizations, individuals, and governments have adopted Twitter to convey their sentiments on COVID-19, the lockdown, the pandemic, and hashtags. This paper aims to analyze the psychological reactions and discourse of Twitter users related to COVID-19. In this experiment, Latent Dirichlet Allocation (LDA) has been used for topic modeling. In addition, a Bidirectional Long Short-Term Memory (BiLSTM) model and various classification techniques such as random forest, support vector machine, logistic regression, naive Bayes, decision tree, logistic regression with stochastic gradient descent optimizer, and majority voting classifier have been adapted for analyzing the polarity of sentiment. The effectiveness of the aforesaid approaches along with LDA modeling has been tested, validated, and compared with several benchmark datasets and on a newly generated dataset for analysis. To achieve better results, a dual dataset approach has been incorporated to determine the frequency of positive and negative tweets and word clouds, which helps to identify the most effective model for analyzing the corpora. The experimental result shows that the BiLSTM approach outperforms the other approaches with an accuracy of 96.7%. 2022 by the authors. Licensee MDPI, Basel, Switzerland.
Gourisaria M.K.; Chandra S.; Das H.; Patra S.S.; Sahni M.; Leon-Castro E.; Singh V.; Kumar S.
https://doi.org/10.3390/healthcare10050881 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85130318553&doi=10.3390%2fhealthcare10050881&partnerID=40&md5=f406b997b389f51e257aecb8692b61b4
Gourisaria M.K., School of Computer Engineering, KIIT Deemed to be University, Odisha, Bhubaneswar, 751024, India; Chandra S., School of Computer Engineering, KIIT Deemed to be University, Odisha, Bhubaneswar, 751024, India; Das H., School of Computer Engineering, KIIT Deemed to be University, Odisha, Bhubaneswar, 751024, India; Patra S.S., School of Computer Applications, KIIT Deemed to be University, Odisha, Bhubaneswar, 751024, India; Sahni M., Department of Mathematics, Pandit Deendayal Energy University, Gujarat, Gandhinagar, 382426, India; Leon-Castro E., Faculty of Economics and Administrative Sciences, Universidad Catica de la Santima Concepci, Concepci, 4030000, Chile; Singh V., Department of Computer Science and Engineering, School of Computing and Information Technology, Manipal University Jaipur, Rajasthan, Jaipur, 303007, India, ERCIM Postdoctoral Fellow, Department of ICT and Science, Norwegian University of Science and Technology, Ankeret, B-315, esund, Torgarden, Trondheim, 7491, Norway; Kumar S., Department of CSE, School of Engineering and Technology, CHRIST Deemed to be University, Karnataka, Bengaluru, 560029, India
Computational Modelling of Complex Systems for Democratizing Higher Education: A Tutorial on SAR Simulation
Colaboratory; Jupyter; Python; Reproducible research; SAR
Engineering systems like Synthetic Aperture Radar (SAR) are complex systems and require multi-domain knowledge to understand. Teaching and learning SAR processing is intensive in terms of time and resources. It also requires software tools and computational power for preprocessing and image analysis. Extensive literature exists on computational models of SAR in MATLAB and other commercial platforms. Availability of computational models in open-source reproducible platforms like Python kernel in Jupyter notebooks running on Google Colaboratory democratizes such difficult topics and facilitates student learning. The model, discussed here, generates SAR data for a point scatterer using SAR geometry, antenna pattern, and range equation and processes the data in range and azimuth with an aim to generate SAR image. The model demonstrates the generation of synthetic aperture and the echo signal qualities as also how the pulse-to-pulse fluctuating range of a target requires resampling to align the energy with a regular grid. The model allows for changing parameters to alter for resolution, squint, geometry, radar elements such as antenna dimensions, and other factors. A successful learning outcome would be to understand where parameters need to be changed, to affect the model in a specific way. Factors affecting Range Doppler processing are demonstrated. Use of the discussed model nullifies use of commercial software and democratizes SAR topic in higher education. 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
Lecture Notes in Networks and Systems, Vol-613 LNNS, pp. 723-734.
Govind P.J., CHRIST (Deemed to be University), Kengeri, Bangalore, India; Kumar N., CHRIST (Deemed to be University), Kengeri, Bangalore, India
Deep Learning Based Performance Prediction of Sustainable Microwave Absorbers
absorber; CNN; deep learning; microwave; sustainable
This paper proposed a convolutional neural network (CNN) based deep learning (DL) approach to predict performance of sustainable microwave absorbers. This study explores the transformative potential of DL in predicting and optimizing microwave absorber performance, offering a datadriven alternative to traditional approaches. The absorber is a composite of tea and carbon powder considered as waste mixed in various composition percentages. The measured S21 data is used for training the proposed DL model. The prediction of absorber's S21 performance shows an accuracy of above 98 %. 2025 IEEE.
2025 IEEE Microwaves, Antennas, and Propagation Conference, MAPCON 2025;
https://doi.org/10.1109/MAPCON65020.2025.11426455 https://www.scopus.com/pages/publications/105036394036?origin=resultslist
Govind P.J., CHRIST University, RF and Microwave Research Laboratory, ECE Department, Bangalore, India; Kumar N., CHRIST University, RF and Microwave Research Laboratory, ECE Department, Bangalore, India
Novel preemptive intelligent artificial intelligence-model for detecting inconsistency during software testing
Artificial intelligence; Automation; Error; Inconsistency; Software testing
The contribution of artificial intelligence (AI)-based modelling is highly significant in automating the software testing process; thereby enhancing the cost, resources, and productivity while performing testing. Review of existing AI-models towards software testing showcases yet an open-scope for further improvement as yet the conventional AI-model suffers from various challenges especially in perspective of test case generation. Therefore, the proposed scheme presents a novel preemptive intelligent computational framework that harnesses a unique ensembled AI-model for generating and executing highly precise and optimized test-cases resulting in an outcome of adversary or inconsistencies associated with test cases. The ensembled AI-model uses both unsupervised and supervised learning approaches on publicly available outlier dataset. The benchmarked outcome exhibits supervised learning-based AI-model to offer 21% of reduced error and 1.6% of reduced processing time in contrast to unsupervised scheme while performing software testing. 2025, Institute of Advanced Engineering and Science. All rights reserved.
Govinda, Sangeetha; Prasanthi, B.G.; Vincent, Agnes Nalini
IAES International Journal of Artificial Intelligence;Volume;14;Issue;3;pp.1781-1789
Institute of Advanced Engineering and Science
https://doi.org/10.11591/ijai.v14.i3.pp1781-1789 https://www.scopus.com/pages/publications/105008110548?origin=resultslist
Govinda S., Department of Computer Science, Christ University, Bangalore Central Campus, Bangaluru, India; Prasanthi B.G., Department of Computer Science and Applications, St. Josephs University, Bangaluru, India; Vincent A.N., Faculty of Information Technology, AMITY Institute of Higher Education, Quatre Bornes, Mauritius
Novel artificial intelligence-based ensemble learning for optimized software quality
Accuracy; Artificial intelligence; Defect density; Software engineering; Software quality
Artificial intelligence (AI) contributes towards improving software engineering quality; however, existing AI models are witnessed to deploy learning-based approaches without addressing various complexities associated with datasets. A literature review showcases an unequilbrium between addressing the accuracy and computational burden. Therefore, the proposed manuscript presents a novel AI-based ensemble learning model that is capable of performing an effective prediction of software quality. The presented scheme adopts correlation-based and multicollinearity-based attributes to select essential feature selection. At the same time, the scheme also introduces a hybrid learning approach integrated with a bio-inspired algorithm for constructing the ensemble learning scheme. The quantified outcome of the proposed study showcases 65% minimized defect density, 94% minimized mean time to failure, 62% minimized processing time of the algorithm, and 43% enhanced predictive accuracy. 2025, Institute of Advanced Engineering and Science. All rights reserved.
Govinda, Sangeetha; Vincent, Agnes Nalini; Babu, Merwa Ramesh
IAES International Journal of Artificial Intelligence;Volume;14;Issue;3;pp.1820-1828
https://doi.org/10.11591/ijai.v14.i3.pp1820-1828 https://www.scopus.com/pages/publications/105008100220?origin=resultslist
Govinda S., Department of Computer Science, Christ University, Bangalore, India; Vincent A.N., Faculty of Information Technology, AMITY Institute of Higher Education, Quatre Bornes, Mauritius; Babu M.R., Department of Computer Science, Bharathiar University, Coimbatore, India
RayleighBard and BardMarangoni magnetoconvection in variable viscosity finitely conducting liquids
magnetoconvection; RayleighBard and BardMarangoni; thermorheological effect
The thermorheological effect on magneto-Bard-convection is studied numerically in fluids with finite electrical conductivity. A nonlinear thermorheological equation is considered in the problem. The results are compared with the classical approach of constant viscosity, which depicts the fact that the effect of increasing the strength of the magnetic field is to delay the onset of convection. The magnetic field is shown to have a rheostatic influence on convective instabilities. The results obtained by the study have possible applications in the field of astrophysics, sunspots, and in space applications under microgravity. 2021 Wiley Periodicals LLC
Govindaraju M.V.; Siddheshwar P.G.; Ramachandramurthy V.
Heat Transfer, Vol-50, No. 6, pp. 5674-5696.
https://doi.org/10.1002/htj.22143 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85104092353&doi=10.1002%2fhtj.22143&partnerID=40&md5=2876843e4b184cda70c0fec0421851fe
Govindaraju M.V., Department of Mathematics, M. S. Ramaiah Institute of Technology, Bengaluru, Karnataka, India; Siddheshwar P.G., Department of Mathematics, CHRIST (Deemed to be University), Bengaluru, Karnataka, India; Ramachandramurthy V., Department of Mathematics, RRIT, Bangalore, Karnataka, India
Sulfamic acid catalyzed grinding: A facile one-pot approach for the synthesis of polysubstituted pyrazoles under green conditions
Four-component; One-pot; Pyrazole; Solvent-free; Sulfamic acid
A competent, rapid and simple grinding procedure for the synthesis of pharmacologically relevant polysubstituted pyrazoles catalyzed by sulfamic acid is reported via multicomponent reaction of substituted arylaldehydes, 4-nitrophenylacetonitrile, hydrazine hydrate, ethyl acetoacetate under solvent-free reaction conditions. In our reported protocol, four different reactants featuring diverse functional groups are assembled in one pot, enabling the synthesis of more diverse molecular structures in a facile manner. 2022
Govindaraju S.; Daniel N.K.; Tabassum S.
Materials Today: Proceedings, Vol-62, pp. 5336-5340.
https://doi.org/10.1016/j.matpr.2022.03.416 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85128337920&doi=10.1016%2fj.matpr.2022.03.416&partnerID=40&md5=48c1e7c470c7568adfb3ea3972c473ce
Govindaraju S., Department of Sciences and Humanities, School of Engineering and Technology, CHRIST (Deemed to be University), Kumbalagodu, Mysore Road, Bengaluru, 560074, India; Daniel N.K., Department of Sciences and Humanities, School of Engineering and Technology, CHRIST (Deemed to be University), Kumbalagodu, Mysore Road, Bengaluru, 560074, India; Tabassum S., Department of Chemistry, Surana College, South End Road, Basavanagudi, Bengaluru, 560004, India