Mirabijalones S-W, rotenoids from rhizomes of white Mirabilis jalapa Linn. and their cell proliferative studies
Cytotoxicity; Mirabijalones S-W; Mirabilis jalapa Linn; MTT; Nyctaginaceae; Rotenoids
Five undescribed (2-6) rotenoid derivatives along with three known rotenoids (1, 7 and 8) were isolated from the rhizomes of white colored variety of Mirabilis jalapa Linn. The structures of these undescribed compounds were elucidated based on UV, IR, HR-MS (ESI), 1D and 2D NMR spectroscopic techniques. Selected compounds were evaluated for their cell viability and proliferation in two cancer cell lines namely, cervical (HeLa), breast (SKBR-3) and normal lung fibroblast (WI-38). Among them, the compounds Boeravinone C (1), Mirabijalone S (2), Mirabijalone T (3) and 4, 6, 11-trihydroxy-9-methoxy-10-methylchromeno [3, 4-b] chromen-12(6H)-one (8) showed moderate cytotoxicity against HeLa cells with IC50 values in the 8.40 ? 12.9 ?M range, and compound 8 exhibited cytotoxicity against SKBR-3 cells with IC50 value of 17.6 ?M. Molecular docking studies of isolated compounds were performed with three apoptosis proteins, 3H11, 2AR9 and 1X0X. These results revealed that the isolated compounds were found to interact with Caspase 8 and 9 along with the anti-apoptotic protein Survivin. Since these compounds exhibit cytotoxic effects against SKBR3 and HeLa cells, they are expected to show apoptosis and may be further utilized for wet lab apoptotic studies. 2021 Phytochemical Society of Europe
Sharathna P.; Alisha V.; Sasikumar P.; Ajesh V.; Ayisha F.; Shibi I.G.; Sivan V.V.; Kaustabh Kumar M.; Ravi S. L.; Radhakrishnan K.V.
Phytochemistry Letters, Vol-44, pp. 178-184.
https://doi.org/10.1016/j.phytol.2021.06.017 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85109004524&doi=10.1016%2fj.phytol.2021.06.017&partnerID=40&md5=8ef001731a67e0ae61cc850ffb87e4fa
Sharathna P., Organic Chemistry Section, CSIR-National Institute for Interdisciplinary Science and Technology (NIIST), Thiruvananthapuram-19, India, Academy of Scientific and Innovative Research (AcSIR), Ghaziabad-002, India; Alisha V., Organic Chemistry Section, CSIR-National Institute for Interdisciplinary Science and Technology (NIIST), Thiruvananthapuram-19, India, Academy of Scientific and Innovative Research (AcSIR), Ghaziabad-002, India; Sasikumar P., Organic Chemistry Section, CSIR-National Institute for Interdisciplinary Science and Technology (NIIST), Thiruvananthapuram-19, India; Ajesh V., Department of Chemistry, CHRIST (Deemed to Be University), Hosur Road, Bengaluru, 560029, India; Ayisha F., Organic Chemistry Section, CSIR-National Institute for Interdisciplinary Science and Technology (NIIST), Thiruvananthapuram-19, India; Shibi I.G., Department of Chemistry, Sree Narayana College, Chempazhanthy, Thiruvananthapuram, India; Sivan V.V., M. S. Swaminathan Research Foundation-Community Agrobiodiversity Centre (MSSRF-CAbC), Puthurvayal, Wayanad, Kerala, India; Kaustabh Kumar M., Organic Chemistry Section, CSIR-National Institute for Interdisciplinary Science and Technology (NIIST), Thiruvananthapuram-19, India, Academy of Scientific and Innovative Research (AcSIR), Ghaziabad-002, India; Ravi S. L., Organic Chemistry Section, CSIR-National Institute for Interdisciplinary Science and Technology (NIIST), Thiruvananthapuram-19, India, Academy of Scientific and Innovative Research (AcSIR), Ghaziabad-002, India; Radhakrishnan K.V., Organic Chemistry Section, CSIR-National Institute for Interdisciplinary Science and Technology (NIIST), Thiruvananthapuram-19, India, Academy of Scientific and Innovative Research (AcSIR), Ghaziabad-002, India
MIST-based Tuning of Cyber-Physical Systems Towards Holistic Healthcare Informatics
Body Area Network; Cyber-Physical Systems; Healthcare Informatics; MIST; Recommender Systems
The entire world seems shaken and disrupted since the strike of Covid-19 ever since its outbreak towards the end of 2019 and its continued perils. During this unprecedented event of the century, people's health emerged as the most vulnerable and affected area either directly or indirectly by the coronavirus and its new variants. Disrupting almost all spheres of life, patients' health and care systems required timely support from healthcare professionals to provide the needed medical advice on one hand and a prescriptive mechanism to avoid another impending casualty. Similarly, a proactive approach became desirable from the health ministry, pharmaceutical firms, medical insurance companies, and other stakeholders in fine-tuning their offerings to the patients as per the recommender systems. The devices to measure the vitals of a person, became more efficient and ergonomically sound with the advent of wearable gadgets. These devices monitored the physical activities of the user and transferred the vital signals wirelessly to any base computing device and cloud-based repositories. This mechanism, however, was reported to fail in addressing the issues with non-communicating or stand-alone devices that were used by the masses in developing countries including India. If the real-time data could be used from these devices, the healthcare diagnosis and analysis of a patient's medical condition could have taken a progressive dimension with the addition of missing data points. This research thus aims to fill the information gap and proposes a transforming approach towards existing non-communicating devices used to measure the vitals like blood pressure, oxygen level, blood sugar, etc. The proposed MIST-based Cyber-Physical System shall create extensive scalability towards the retrieval of the vital details from the devices which were otherwise captured offline previously and were unused at multiple critical points of healthcare processes. 2022 IEEE.
Sharma D.; Singhal T.K.; Singh D.; Qadir A.
8th International Conference on Advanced Computing and Communication Systems, ICACCS 2022, pp. 1752-1757.
Institute of Electrical and Electronics Engineers Inc.
Sharma D., School of Business and Management, Christ University, U.P., Delhi-NCR, Ghaziabad, India; Singhal T.K., School of Business and Management, Christ University, U.P., Delhi-NCR, Ghaziabad, India; Singh D., School of Business and Management, Christ University, U.P., Delhi-NCR, Ghaziabad, India; Qadir A., School of Business and Management, Christ University, U.P., Delhi-NCR, Ghaziabad, India
Misuse of Internet Among School Children: Risk Factors and Preventative Measures
Misuse of internet; Neurodevelopment; Prevention; Psychosocial; Risk factors; School children
The Internet has been one of the most transformative and rapidly growing technologies. In recent years, it has improved the quality of life in areas such as communication, education, recreation. On the contrary, there are growing concerns about the use of the Internet that have created adverse consequences in the areas of social life, interpersonal relationships, family environment, and school activities. School-going children were vulnerable to such unhealthy outcomes due to readily available high-speed Internet and ease of access to different Internet platforms, which resulted in risky behaviours, decreased academic performance, poor nutrition, decreased sleep quality, and a high incidence of inter-social conflicts. While the majority of the research has focused on the adolescent population in terms of problematic Internet use, only a few studies have identified the vulnerabilities of school-going children in the same context. The research also confirmed that the risk factors for problematic Internet use start as early as middle childhood. Heightened risky use of the Internet was observed in children with neurodevelopmental concerns. This study explores risk factors associated with problematic Internet use among school-going children, identifying relevant warning signs followed with preventative measures. The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022.
Child Safety, Welfare and Well-being: Issues and Challenges, Second Edition, pp. 321-334.
Reddy K.J., Department of Psychology, Centre for Research, Christ University, Bangalore, India; Balasubramanian G., Aadya Tathva, India, QPR Institute, Certified Seizure First Aid Professional from Epilepsy Foundation, United States, National Council for Mental Wellbeing, United States, Indian Epilepsy Association (IEA), Bangalore, India, International Association of Applied Psychology (IAAP), Canada
Mitigating Abiotic Stress Using Nanoparticles and Involved Cellular Processes Studied by Multiple Omics
Abiotic stress; Agriculture; Climate change; Nanoparticles; Omics approach
Crop yield is greatly impacted by abiotic stress, necessitating creative methods to increase plant resilience. In order to reduce these stressors, this chapter focusses on the possibilities of integrating multi-omics technology with nanoparticle applications. Studies suggest that nanoparticles improve plant tolerance by improving antioxidant activity and minimizing oxidative damage. In addition, multi-omics techniques (genomics, transcriptomics, proteomics, and metabolomics) offer a comprehensive understanding of the molecular processes that stress initiates. The creation of nanoparticles designed to specifically target particular stress-response pathways is made possible by these findings. The chapter's case studies demonstrate how tailored nanoparticles made with multi-omics data might improve crop performance in difficult conditions. The chapter highlights a promising avenue for developing targeted, sustainable solutions to increase crop resilience under abiotic stress conditions by combining nanotechnology with omics techniques, providing long-term sustainability. CAB International 2025. All rights reserved.
Toppo, Varsha; Shaikh, Akash; Al-Khayri, Jameel M.; Nagella, Praveen
Stress-Resilient Crops: Coordinated omics-CRISPR-nanotechnology strategies;pp.141-156
Restricted Access; Hardcopy may be available in the library
https://doi.org/10.1079/9781800627307.0006 https://www.scopus.com/pages/publications/105023939403?origin=resultslist
Toppo V., Department of Life Sciences, School of Sciences, Christ University, Hosur Road, Bengaluru, 560029, India; Shaikh A., Department of Life Sciences, School of Sciences, Christ University, Hosur Road, Bengaluru, 560029, India; Al-Khayri J.M., Department of Agricultural Biotechnology, College of Agriculture and Food Sciences, King Faisal University, Al-Ahsa, 31982, Saudi Arabia; Nagella P., Department of Life Sciences, School of Sciences, Christ University, Hosur Road, Bengaluru, 560029, India
Mitigating Mental Health Burden of Youth During COVID-19 Through Resilience and Hope: Evidences from India and Germany
Anticipatory Crisis; COVID-19; Hope; Mental Health; Resilience; Stress
In the global crisis caused by the COVID-19 pandemic, young professionals and graduating students experience considerable psychological adversity due to the uncertainty surrounding their futures. Given the positive psychological outcomes and the potential to alleviate stress, we examine the role of resilience and hope in causing a substantial variance in the stress response to anticipation of crisis among Indians living in India and Germany. Resilience, hope, crisis apprehension, and the psychological response to the COVID-19 pandemic were measured among participants from India and Germany (n = 650) via an online survey using non-probability convenient sampling. Parallel mediation and conditional indirect effects showcase the differential roles of resilience and hope among socio-culturally similar but geographically divergent groups. Hope mediates the effect of pandemic-led crisis apprehension on perceived stress among those residing in India; resilience operates to mitigate stress among those from Germany. Findings highlight the contradistinctive role of resilience and hope in reducing stress and imply an urgent need for promotion of ameliorative practices. Resilience effectively mitigates the psychological burden of the COVID-19 crisis and can be promoted to reskill individuals; however, elevating hope in a crisis obligates prudence. The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023.
Handbook of Youth Development: Policies and Perspectives from India and Beyond, pp. 515-529.
Mukherjee T., Work and Organizational Psychologist, Muenster, Germany; Deb S., Ministry of Youth Affairs and Sports (MYAS), Government of India (GOI), Sriperumbudur Tamil Nadu, Pennalur, 602 105, India; Deb S., Department of Psychology, CHRIST (Deemed to Be University), Bengaluru, India
Mitigating post-harvest losses through IoT-based machine learning algorithms in smart farming
This research paper explores the transformative potential of Internet of Things (IoT) technology in mitigating the longstanding issue of post-harvest losses within the agriculture sector. These losses, which encompass both quantitative and qualitative deterioration of food commodities from harvest to consumption, have posed persistent challenges, resulting in economic losses and food wastage. By delving into the current landscape of post-harvest losses and the application of IoT technology, the paper offers valuable insights into how IoT can be harnessed to reduce these losses effectively. It not only highlights the benefits and existing IoT solutions but also addresses the inherent challenges, providing recommendations for their resolution. Moreover, the research introduces a machine learning-based model, specifically Random Forest ML, to identify and prevent losses in tandem with IoT devices, empowering farmers with timely alert messages for informed decision-making, thus fostering a more sustainable and efficient agricultural ecosystem. 2024 Author(s).
Kanna R.R.; Priya T.M.; Sivakumar V.; Nataraj C.; Thomas J.
AIP Conference Proceedings, Vol-3161, No. 1
https://doi.org/10.1063/5.0229500 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85204010477&doi=10.1063%2f5.0229500&partnerID=40&md5=868f046d8561d500a5b299f2ac2dfb7e
Kanna R.R., Department of Computer Science, CHRIST (Deemed to Be University), Bangalore, India; Priya T.M., Department of Computer Science, CHRIST (Deemed to Be University), Bangalore, India; Sivakumar V., School of Computing, Faculty of Computing Engineering and Technology, Asia Pacific University of Technology and Innovation, Kuala Lumpur, Malaysia; Nataraj C., School of Engineering, Faculty of Computing Engineering and Technology, Asia Pacific University of Technology and Innovation, Kuala Lumpur, Malaysia; Thomas J., Department of Sociology and Social Work, CHRIST (Deemed to Be University), Bangalore, India
Mitigating Subjectivity and Annotation Inconsistencies in Sentiment Analysis via an SVM-RoBERTa Ensemble
Annotation Inconsistency; Ensemble Learning; RoBERTa; Sentiment Analysis; Support Vector Machine
This research addresses a main limitation in the Natural Language Processing that is the impact of subjectivity and annotation inconsistencies on the accuracy of the sentiment classification. We did a systematic comparison of two fundamentally different architectures. A traditional feature based Support Vector Machine and a deep contextual fine tuned RoBERTa transformer using a challenging, noisy, real-world Twitter dataset. This corpus retains ambiguity and sarcasm on purpose and serve the crucible for testing model robustness. We developed a soft voting ensemble method that combines the probability scores from both models to obtain the best predictive capabilities. The results showed a clear technological hierarchy. The RoBERTa model with its deep semantic grasp outperformed the SVM by a substantial margin achieving 90% accuracy against 83.5% accuracy. But the hybrid ensemble model attained the highest overall accuracy of 91.35% and showed better reliability across all the sentiment classes. These findings shows that a hybrid approach fusing a transformer's nuanced understanding with the stabilization provided by ensemble learning is the most effective and robust method for mitigating data imperfections in modern sentiment analysis. 2025 IEEE.
Roy, Alphin; Roy, Apash; Shrivallabha, S.
IC-DECON 2025 - 2025 International Conference on Data, Energy and Communication Network, Proceedings;
https://doi.org/10.1109/DECoN67170.2025.11447642 https://www.scopus.com/pages/publications/105037371064?origin=resultslist
Roy A., Christ (Deemed to be University), Dept. of Statistics and Data Science, Bangalore, India; Roy A., Christ (Deemed to be University), Dept. of Statistics and Data Science, Bangalore, India; Shrivallabha S., Christ (Deemed to be University), Dept. of Statistics and Data Science, Bangalore, India
Mitigation of harmonics for five level multilevel inverter with fuzzy logic controller
cascade H-bridge multilevel inverters; fuzzy logic controller; selective pulse width modulation technique; total harmonic distortion
Introduction. The advantages of a high-power quality waveform and a high voltage capability of multilevel inverters have made them increasingly popular in recent years. These inverters reduce harmonic distortion and improve the voltage output. Realistically speaking, as the number of voltage levels increases, so does the quality of the multilevel output-voltage waveform. When it comes to industrial power converters, these inverters are by far the most critical. Novelty. Multilevel cascade inverters can be used to convert multiple direct current sources into one direct current. These inverters have been getting a lot of attention recently for high-power applications. A cascade H-bridge multilevel inverter controller is proposed in this paper. A change in the pulse width of selective pulse width modulation modulates the output of the multilevel cascade inverter. Purpose. The total harmonic distortion can be reduced by using filters on controllers like PI and fuzzy logic controllers. Methods. The proposed topology is implemented with MATLAB/Simulink, using gating pulses and pulse width modulation methodology and fuzzy logic controllers. Moreover, the proposed model also has been validated and compared to the hardware system. Results. Total harmonic distortion, number of power switches, output voltage and number of DC sources are analyzed with conventional topologies. Practical value. The proposed topology has been very supportive for implementing photovoltaic based multilevel inverter, which is connected to large demand in grid and industry. M.S. Sujatha, S. Sreelakshmi, E. Parimalasundar, K. Suresh.
Sujatha M.S.; Sreelakshmi S.; Parimalasundar E.; Suresh K.
Electrical Engineering and Electromechanics, Vol-2023, No. 4, pp. 52-56.
National Technical University "Kharkiv Polytechnic Institute"
All Open Access; Gold Open Access; Green Open Access
Sujatha M.S., Department of Electrical & Electronics Engineering, Sree Vidyanikethan Engineering College, AP, Tirupati, 517102, India; Sreelakshmi S., Department of Electrical and Electronics Engineering, Jawaharlal Nehru Technological University Anantapur, AP, Anantapuramu, 517102, India; Parimalasundar E., Department of Electrical & Electronics Engineering, Sree Vidyanikethan Engineering College, AP, Tirupati, 517102, India; Suresh K., Department of Electrical and Electronics Engineering, Christ (Deemed to be University), Bangalore, India
Mixed convection 3D radiating flow and mass transfer of eyring-powell nanofluid with convective boundary condition
Brownian motion; Convective conditions; Eyring-Powell fluid; Thermal radiation; Thermophoresis; Three dimensional flows
Three-dimensional mixed convection flow, heat and mass transfer of Eyring-powell fluid over a convectively heated stretched sheet is inspected in this paper. The encouragement of Brownian motion, thermophoresis, convective condition and thermal radiations are accounted. Appropriate transformations are used to reduce the principal PDE's into set of coupled highly nonlinear ODE's which are then solved numerically using RKF fourth-fifth order method. The consequence of several parameters on flow, heat and mass transfer characteristics are deliberated with the help of graphs and tables. It is observed that the temperature and concentration profiles diminish for higher values mixed convection parameter. Further, the temperature and its related boundary layer thickness is increases with increasing the Biot number and thermal radiation effects. 2018 Trans Tech Publications, Switzerland.
Kumar P.B.S.; Mahanthesh B.; Gireesha B.J.; Manjunatha S.
Defect and Diffusion Forum, Vol-388, pp. 158-170.
https://doi.org/10.4028/www.scientific.net/DDF.388.158 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85063086573&doi=10.4028%2fwww.scientific.net%2fDDF.388.158&partnerID=40&md5=2bbce3adf1a03adfc1a86a3506e62305
Kumar P.B.S., Department of Studies and Research in Mathematics, Kuvempu University, Shankaraghatta, Shimoga, Karnataka, 577 451, India; Mahanthesh B., Department of Mathematics, Christ University, Bangalore, 560029, India; Gireesha B.J., Department of Studies and Research in Mathematics, Kuvempu University, Shankaraghatta, Shimoga, Karnataka, 577 451, India; Manjunatha S., Department of Engineering Mathematics, Faculty of Engineering, Christ University, Mysore Road, Bengaluru, 560074, India
Mixed convection in the stagnation-point flow over a vertical stretching sheet in the presence of thermal radiation
Boundary layer flow; Dusty fluid; Free stream velocity; Mixed convective parameter; Numerical solution; Stretching porous surface; Thermal radiation
An unsteady two-dimensional stagnation-point mixed convection flow of a viscous, incompressible dusty fluid towards a vertical stretching sheet has been examined. The stretching velocity and the free stream velocity are assumed to vary linearly with the distance from the stagnation point. The problem is analyzed using similarity solutions. The similarity ordinary differential equations were then solved numerical by using the RKF-45 method. The effects of various physical parameters on the velocity profile and skin-friction coefficient are also discussed in this paper. Some important findings reported in this work reveal that the effect of radiation has a significant impact on controlling the rate of heat transfer in the boundary layer region.
Manjunatha S.; Gireesha B.J.; Bagewadi C.S.
International Journal of Applied Mechanics and Engineering, Vol-20, No. 4, pp. 871-888.
https://doi.org/10.1515/ijame-2015-0056 https://www.scopus.com/inward/record.uri?eid=2-s2.0-84973151716&doi=10.1515%2fijame-2015-0056&partnerID=40&md5=9213000470825d1363f62a92374ca166
Manjunatha S., Department of Engineering Mathematics, Christ University Faculty of Engineering, Kanminike, Kumbalgodu, Mysore Road, Bengaluru, Karnataka-State, 560074, India; Gireesha B.J., Department of Studies and Research in Mathematics, Kuvempu University, Shankaraghatta, Shimoga, Karnataka, 577 451, India; Bagewadi C.S., Department of Studies and Research in Mathematics, Kuvempu University, Shankaraghatta, Shimoga, Karnataka, 577 451, India
Mixed CoO/Co3O4 phase nanoparticles encapsulated in a carbon shell derived from Co-MOF as a bifunctional electrocatalyst for scalable hydrogen production
Bifunctional electrocatalyst; Carbon layer; Hydrogen production; Metal organic framework; Water electrolysis
Designing high-performance and long-lasting electrocatalysts is essential to enable large-scale green hydrogen generation through water electrolysis. Herein, we present a bifunctional electrocatalyst, C@CoxOy-B/P-700, obtained by pyrolyzing a Co-MOF under N2 atmosphere, followed by phospho-boronization. Extensive characterization revealed the formation of nanoparticles composed of combined Co3O4 and CoO phases encapsulated by a thin carbon shell. This unique architecture provided electron transport and active site accessibility, while the co-incorporation of boron and phosphorus induced abundant oxygen vacancies, enhancing intrinsic kinetics. In alkaline media, low overpotentials of 220 and 79 mV are required at 10 mA/cm2 for OER and HER, along with excellent durability, maintaining performance over 10,000 cycles and 100 h of continuous operation. When implemented in a symmetric two-electrode configuration, it achieves a current density of 10 mA/cm2 at just ?1.57 V, rivalling noble-metal-based systems. Furthermore, scale-up in a zero-gap alkaline electrolyzer confirms industrial applicability, requiring ?1.72 V to achieve a 500 mA/cm2 at 60 C with a negligible degradation rate. These results emphasize the promise of C@CoxOy-B/P-700 as a sustainable and scalable solution for next-generation hydrogen production. 2025 Elsevier Ltd.
Bhabal, Rinkoo; Bhide, Aniruddha; Gupta, Suraj; Fernandes, Rohan; Gupta, Mukul; Patel, Nainesh
Carbon;Volume;247;Issue;;Article No.;121031;
https://doi.org/10.1016/j.carbon.2025.121031 https://www.scopus.com/pages/publications/105029025365?origin=resultslist
Bhabal R., Department of Physics and Electronics, Christ University, Bengaluru, 560029, India; Bhide A., Department of Physics and Electronics, Christ University, Bengaluru, 560029, India; Gupta S., Advanced Materials Department, Joef Stefan Institute, Jamova 39, Ljubljana, 1000, Slovenia; Fernandes R., Department of Physics and Electronics, Christ University, Bengaluru, 560029, India; Gupta M., UGC, DAE Consortium for Scientific Research, Khandwa Road, Madhya Pradesh, Indore, 452001, India; Patel N., Department of Physics and Electronics, Christ University, Bengaluru, 560029, India
Mixed radiated magneto Casson fluid flow with Arrhenius activation energy and Newtonian heating effects: Flow and sensitivity analysis
Arrhenius activation energy; Homotopy analysis method; Newtonian heating; Sensitivity analysis; Stefan blowing
The characteristics of Stefan blowing effects in a magneto-hydrodynamic flow of a Casson fluid past a stretching sheet are investigated. The effects of radiation, heat source/sink, Newtonian heating, Arrhenius activation energy and binary chemical reaction are considered for heat and mass transfer analysis. The homotopy analysis method (HAM) was utilised to solve the transformed non-dimensionalized equations analytically. The impact of various physical parameters affecting the flow are investigated. Further, the relationship of various parameters on the skin friction and rate of heat and mass transfer was explored using correlation and probable error. A sensitivity analysis was carried out based on the Response Surface Methodology to analyse the effect of Stefan blowing parameter, magnetic parameter and stretching/shrinking parameter on the reduced Nusselt number and reduced Sherwood number. A constant positive sensitivity for the reduced Nusselt number towards the Stefan blowing parameter for all levels of magnetic parameter and stretching/shrinking parameter was found. Further, the reduced Sherwood number indicated a negative sensitivity towards the Stefan blowing parameter. 2020 Faculty of Engineering, Alexandria University
Abdelmalek Z.; Mahanthesh B.; Md Basir M.F.; Imtiaz M.; Mackolil J.; Khan N.S.; Nabwey H.A.; Tlili I.
Alexandria Engineering Journal, Vol-59, No. 5, pp. 3991-4011.
https://doi.org/10.1016/j.aej.2020.07.006 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85088968806&doi=10.1016%2fj.aej.2020.07.006&partnerID=40&md5=8c6ba6c68d08e7857f6a1a8425f79c9e
Abdelmalek Z., Institute of Research and Development, Duy Tan University, Da Nang, 550000, Viet Nam, Faculty of Medicine, Duy Tan University, Da Nang, 550000, Viet Nam; Mahanthesh B., Department of Mathematics, CHRIST (Deemed to be University), Bangalore, 560029, Karnataka, India; Md Basir M.F., Department of Mathematical Sciences, Faculty of Science, Universiti Teknologi Malaysia, UTM Johor Bahru, 81310, Malaysia; Imtiaz M., Department of Social and Behavioral Sciences, National University of Medical Sciences, Rawalpindi, Pakistan; Mackolil J., Department of Mathematics, CHRIST (Deemed to be University), Bangalore, 560029, Karnataka, India; Khan N.S., Department of Mathematics, Abdul Wali Khan University, Mardan, 23200, Khyber Pakhtunkhwa, Pakistan; Nabwey H.A., Department of Mathematics, College of Science and Humanities in Al-Kharj, Prince Sattam bin Abdulaziz University, Al-Kharj, 11942, Saudi Arabia, Department of Basic Engineering Science, Faculty of Engineering, Menoufia University, Shebin El-Kom, 32511, Egypt; Tlili I., Department of Mechanical and Industrial Engineering, College of Engineering, Majmaah University, Al-Majmaah, 11952, Saudi Arabia
ML Algorithms and Their Approach on COVID-19 Data Analysis
classification; covid analysis; covid data visualization; Machine learning; regression; supervised learning; un-supervised learning
This chapter begins with characterizing Supervised Learning and Unsupervised learning and investigates Machine Learning algorithms in every one of the sub domains of Regression, Classification, Clustering, and so forth. It also talks about the engineering of calculations like Linear Regression, Logistic Regression, K-Means, K Nearest Neighbors, Hierarchical, DB Scan, Decision Tree, Random Forest Regression, and Random Forest classifier. Utilization of every algorithm to investigate the dataset will be displayed by carrying out it on renowned dataset model, and output of each piece of code is displayed with their preview. This section likewise takes care of the issue of predicting the future number of COVID-19 cases and the precision behind each model or algorithm is shown and investigated utilizing different measurements dependent on situation or issue articulation, for example, either issue is on forecast or order. This chapter does not focus on the solution of COVID-19 data analysis or expectation, rather it will be followed and will task different models dependent on need with conclusive target being clear comprehension of the Machine Learning algorithms and its execution in Python. 2023 Scrivener Publishing LLC.
Data Engineering and Data Science: Concepts and Applications, pp. 335-349.
Ashok K., Department of CS, Kyungpook National University, Daegu, South Korea; Reddy P.A., Department of CSE, Christ University, Karnataka, Bangalore, India; Kumar K.P., Department of CSE, Christ University, Karnataka, Bangalore, India
ML based sign language recognition system
Automated SLR; Classification models; Feature extraction; Preprocessing; Segmentation
This paper reviews different steps in an automated sign language recognition (SLR) system. Developing a system that can read and interpret a sign must be trained using a large dataset and the best algorithm. As a basic SLR system, an isolated recognition model is developed. The model is based on vision-based isolated hand gesture detection and recognition. Assessment of ML-based SLR model was conducted with the help of 4 candidates under a controlled environment. The model made use of a convex hull for feature extraction and KNN for classification. The model yielded 65% accuracy. 2021 IEEE.
2021 International Conference on Innovative Trends in Information Technology, ICITIIT 2021
https://doi.org/10.1109/ICITIIT51526.2021.9399594 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85104831889&doi=10.1109%2fICITIIT51526.2021.9399594&partnerID=40&md5=46dea1c1a1f0e4fb41987b4917a0c61b
Amrutha K., CHRIST(Deemed to Be University), Dept. of Computer Science, Bangalore, India; Prabu P., CHRIST(Deemed to Be University), Dept. of Computer Science, Bangalore, India
ML in drug delivery-current scenario and future trends
Machine learning (ML) has enabled transformative applications and emerged as a domain-agnostic decision-making tool as a virtue of its rapid democratization. The authors believe that a systematic assortment of important publications on this issue is indispensable in this context. In terms of data ingestion, data curation, data preprocessing, data handling, and model cross-validation, this review gathers together several studies that have demonstrated a minimum ML framework approach. In general, ML models are described as black-box models, with limited information supplied about their transparency. The authors propose techniques based on the US Food and Drug Administration (FDA)'s current good ML practice (GMLP) in order to improve the ML framework and minimize the aforementioned gap, especially for data. Considering this, the conversation around a model's logic and interpretability are additionally provided. Explicitly, the authors explore the challenges and constraints that ML execution confronts throughout the development of pharmaceuticals. In this context, a structural approach in statistics is presented to allow the scientist to assess the quality of data and incorporate important ideas and techniques that would be implemented in modern ML. The data analytics tetrahedron proposed here can be applied to data of any size. To further contextualize, selected case studies capturing good practices are highlighted to provide pharmaceutical scientists, pharmaceutical ML enthusiasts, readers, reviewers, and regulatory authorities an exposure to fundamental and cuttingedge techniques of ML and data science with respect to chemistry, manufacture, and control (CMC) of drug products. In addition, the authors believe that leveraging ML within CMC procedures can assist in improving decision-making, increasing quality, and enhancing the speed of pharmaceutical product development. IOP Publishing Ltd 2023. All rights reserved.
Muthudoss P.; Ali H.; Ramasamy G.; Allada R.; Fink E.; Kalaiselvan V.; Jayakrishnan B.; Shahane S.; Ramalingam M.; Kanakaraj L.; Kaliappan I.; Paudel A.
Advances in Drug Delivery Systems for Healthcare: From concept to clinic, pp. 9-.
Muthudoss P., A2Z4.0 Research and Analytics Private Limited, Chennai, 600062, India; Ali H., Christ University, Bangalore, 560029, India; Ramasamy G., Christ University, Bangalore, 560029, India; Allada R., Novugen Pharma (Malaysia) Sdn Bhd, Hicomglenmarie Industrial Park, 3, Jalan Jururancang U1/21, Selangor, 40150, Malaysia; Fink E., Research Center Pharmaceutical Engineering GmbH (RCPE), Inffeldgasse 13, Graz, 8010, Austria; Kalaiselvan V., Indian Pharmacopoeia, Commission, Ministry of Health and Family Welfare, Government of India, Sector 23, Rajnagar, 201002, India; Jayakrishnan B., Business School, Vellore Institute of Technology (VIT), Chennai Campus, Kekottaiyur, 600127, India; Shahane S., The Machine Learning Company, Maharashtra, India; Ramalingam M., Chettinad School of Pharmaceutical Sciences, Chettinad Academy of Research and Education, Chettinad Health City, Chennai, 603103, India; Kanakaraj L., Chettinad School of Pharmaceutical Sciences, Chettinad Academy of Research and Education, Chettinad Health City, Chennai, 603103, India; Kaliappan I., SRM College of Pharmacy, SRM Institute of Science and Technology, Kattankulathur, India; Paudel A., Graz University of Technology, Institute of Process and Particle Engineering, Inffeldgasse 13/3, Graz, 8010, Austria
ML Use in Fraud Detection in the Financial Sector
Detecting fraud is complex, requiring analysis of vast data over time, making it resource- intensive for human investigators. This chapter explores key forms of financial fraud, including those involving corporations, banks, and insurance sectors. It provides a thorough evaluation of various machine learning techniques, such as supervised and unsupervised learning, deep learning, and ensemble methods, used to detect and prevent these financial crimes. The chapter examines the effectiveness of algorithms like LR, DT, SVM, and NN in identifying complex fraud patterns. Additionally, it discusses advanced approaches like anomaly detection, clustering, and real- time fraud detection systems. It highlights ongoing challenges and proposes potential future developments, including explainable AI, federated learning, and improved data processing techniques. This comprehensive exploration aims to deepen the understanding of machine learning's role in fraud detection and guide future research toward more effective and reliable fraud prevention strategies. 2025 by IGI Global Scientific Publishing. All rights reserved.
Yadav, Sameer; Singh, Archana; Peddapalli, Nagaraj; Chauhan, Amit; Ahmed, Syed Arfath; Bhoopathy, V.
Utilizing AI and Machine Learning in Financial Analysis;pp.427-447
https://doi.org/10.4018/979-8-3693-8507-4.ch0023 https://www.scopus.com/pages/publications/105007268498?origin=resultslist
Yadav S., University of Allahabad, India; Singh A., University of Allahabad, India; Peddapalli N., GITAM School of Business, GITAM University, India; Chauhan A., Christ University, India; Ahmed S.A., Maulana Azad National Urdu University, India; Bhoopathy V., Sree Rama Engineering College, India
ML-Based Fall Risk Prediction to Substitute Personal Assistance for Hospitalized Elderly: Integrating Geriatric Assessment and E-Health Records
Geriatric assessment serves as a holistic evaluation tool, encompassing various aspects of the elderly individual's health, including physical function, cognition, and psychosocial factors. Integration of CGA data with EHRs allows for a comprehensive analysis of the individual's health status and medical history, providing valuable insights into their risk factors for falls. The ML-based predictive model developed in this study utilizes these integrated data sources to identify patterns and trends associated with fall occurrences among hospitalized elderly patients. By analysing various variables, including mobility indicators, medication usage, and previous fall history, the model can generate accurate predictions of fall risks for individual patients. This ML-driven approach has the potential to significantly improve patient safety and quality of care by enabling healthcare providers to pre-emptively identify and address fall risks among hospitalized elderly individuals, thereby reducing the reliance on constant personal assistance while ensuring optimal patient outcomes. 2025 by IGI Global Scientific Publishing.
Assistive Technology Solutions for Aging Adults and Individuals with Disabilities;pp.281-308
https://doi.org/10.4018/9798369363089.ch013 https://www.scopus.com/pages/publications/105007277384?origin=resultslist
Rajan J.S., Christ University, India; Vinay M., Christ University, India
ML-Based Prediction Model for Cardiovascular Disease
Cardiovascular disease (CVD); Logistic regression; Nae Bayes; Random forest
In this paper, the prediction of cardiovascular disease model based on the machine learning algorithm is implemented. In medical system applications, data mining and machine learning play an important role. Machine learning algorithms will predict heart disease or cardiovascular disease. Initially, online datasets are applied to preprocessing stage. Preprocessing stage will divide the data from baseline data. In the same way, CVD events are collected from data follow-ups. After that, data will be screened using the regression model. The regression model consists of logistic regression, support vector machine, nae Bayes, random forest, and K-nearest neighbors. Based on the techniques, the disease will be classified. Before classification, a testing procedure will be performed. At last from results, it can observe that accuracy, misclassification, and reliability will be increased in a very effective way. 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
Lecture Notes in Networks and Systems, Vol-490, pp. 91-98.
Springer Science and Business Media Deutschland GmbH
Nagavelli U., Dayananda Sagar Research Foundation, University of Mysore (UoM), Karnataka, Mysore, India; Samanta D., Dayananda Sagar Research Foundation, University of Mysore (UoM), Karnataka, Mysore, India, Department of Computer Science, CHRIST Deemed to be University, Karnataka, Bangalore, India; Thomas B., Department of Computer Science, CHRIST Deemed to be University, Karnataka, Bangalore, India
MLLR based speaker adaptation for indian accents
Automatic speech recognition (ASR); HMM (hidden markov model); Linear transformations; Maximum likelihood linear regression (MLLR); Speaker adaptation
Speech Recognition has become an inherent and important feature of today's mobile based apps. Speech input is a very popular option for people with limitations of using the keyboard / mouse in a computer system. Nowadays, more voice messages are used than written text as they also convey the emotions of the speakers. As solutions are developed with native speakers of a language, many of the English input systems have higher accuracy for native speakers than for people with English as their second language (L2), especially for Asian population. The complexity increases since the accent and intonation of Indian speakers are varied from region to region and state to state. This paper analyses an effective speaker adaptation mechanism implemented with Indian speaker profiles and with a very small amount of adaptation data. This research is to facilitate a speaker adaptive system for the speech disabled users with limited disabilities like stuttering and/or unintelligible speech due to illness like cerebral palsy. Experimental results show improvements in the recognition accuracy for speakers speaking small sentences. 2017 University of Bahrain. All rights reserved.
International Journal of Computing and Digital Systems, Vol-6, No. 5, pp. 293-301.
https://doi.org/10.12785/IJCDS/060508 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85104911803&doi=10.12785%2fIJCDS%2f060508&partnerID=40&md5=c9e9cbf48c5864d71fd361c3855030b5
Balaji V., Department of Computer Science, Christ University, Bangalore, India; Sadashivappa G., Department of Telecommunication Engineering, R V College of Engineering, Bangalore, India
MMOF: A Multi-Metric Objective Function for Congestion Detection Under Varying Transmission Ranges in RPL-Based WSN
Congestion detection in RPL; Multi-metric objective function; RPL congestion; RPL congestion using Cooja; Transmission range
The Routing Protocol for Low Power Lossy Networks (RPL) is prone to congestion under high traffic. The single-path routing strategy and single-parent selection make RPL energy and resource-efficient only when the traffic is low and uniform. Two Objective Functions (OFs) are defined for RPL, which use single routing metrics-Expected Transmission Count (ETX) and hop count, to select the best parent and path toward the root. However, considering a single metric for OFs is unsuitable for detecting congestion in Lossy Networks (LLNs) applications as each metric has limitations. The current study proposes a novel Multi-Metric Objective Function (MMOF) that combines these two metrics and removes the weakness of the existing OFs. The proposed MMOF works under the nodes' varying transmission ranges (Tx ranges) to reduce the congestion. By changing Tx ranges, we show that the congestion in a fixed topology RPL network reduces, and MMOF can detect this congestion state more accurately than the existing OFs. The research introduces a successful transmission probability metric that makes MMOF more efficient in detecting congestion than ETX and Hop-Count. We prove that considering these two parameters individually is misleading and cannot contribute 100% to detect congestion state. Increasing transmission range can decrease congestion, and MMOF can detect this state transition with 100% accuracy. Simulation results in Cooja show that MMOF outperforms these two metrics and that the robust metric shows a linear relationship with the Tx range. Finally, two quality of service (QoS) parameters are derived to prove the method's efficiency and novelty. The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.
Srivastava V., Department of Computer Science, Banasthali Vidyapith, Raj, India; Paul A., Department of Computer Science, Banasthali Vidyapith, Raj, India; Sinha S., Department of Computer Science, CHRIST Deemed to Be University, Bengaluru, India