EFMD-DCNN: Efficient Face Mask Detection Model in Street Camera Using Double CNN
And Double CNN; Deep Learning; Face Detection; Mask Detection; Real-time Monitoring
The COVID-19 pandemic has necessitated the widespread use of masks, and in India, mask-wearing in public gatherings has become mandatory, with violators being fined. In densely populated nations like India, strict regulations must be established and enforced to mitigate the pandemics impact. Authorities and cameras conduct real-time monitoring of individuals leaving their homes, but 24/7 surveillance by humans is not feasible. A suggested approach to resolve this problem is to connect human intelligence and Artificial Intelligence (AI) by employing two Machine Learning (ML) models to recognize people who arent wearing masks in live-stream feeds from surveillance, street, and new IP mask recognition cameras. The effectiveness of this method has been demonstrated through its high accuracy compared to other algorithms. The first ML model uses the YOLO (You Only Look Once) model to recognize human faces in real-time video streams. The second ML model is a pre-trained classifier using 180,000 photos to categorize photos of humans into two groups: masked and unmasked. Double is a model that combines face recognition and mask classification into a single model. CNN provides a potential solution that may be utilized with image or video-capturing equipment such as CCTV cameras to monitor security breaches, encourage mask usage, and promote a secure workplace. This studys proposed mask detection technology utilized pre-trained datasets, face detection, and various classifiers to classify faces as having a proper mask, an improper mask, or no mask. The Double CNN-based model incorporated dual convolutional neural networks and a technology-based warning system to provide real-time facial identification detection. The ML model achieved high performance and accuracy of 98.15%, with the highest precision and recall, and can be used worldwide due to its cost-effectiveness. Overall, the proposed mask detection approach can potentially be a valuable instrument for preventing the spread of infectious diseases. The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.
Thamarai Selvi R.; Arulkumar N.; Ramasamy G.
Communications in Computer and Information Science, Vol-1973 CCIS, pp. 427-437.
Springer Science and Business Media Deutschland GmbH
Thamarai Selvi R., Master of Computer Applications, Bishop Heber College, Tiruchirappalli, India; Arulkumar N., Christ University, Bangalore, India; Ramasamy G., Christ University, Bangalore, India
Analysis of Social Media Marketing Impact on Customer Behaviour using AI & Machine Learning
Artificial Intelligence (AI); Brand sentiment; Customer behavior analysis; Machine Learning (ML); Marketing strategies; Multichannel marketing; Personalization; Social media marketing; temporal dynamics
The study of client behaviour has been revolutionized by the combination of social media marketing with cutting-edge technology like Artificial Intelligence (AI) and Machine Learning (ML) in today's age of digital transformation. This study delves into the complex interplay between AI/ML, consumer involvement, and social media marketing methods. Our research exposes crucial insights via careful data collecting, sentiment analysis, and the construction of prediction models. By stressing the importance of catering content to individual interests, AI-driven customization emerges as a potent tool, increasing user engagement by 18%. Analysis of online sentiment shows how important it is to keep people feeling good about a business; postings with positive feelings get 30% more likes and comments on average. Accurate and time-saving insights from machine learning models provide up new avenues for optimizing marketing's use of available resources. As a result of the study's conclusions, companies will be able to better connect with their customers, use their resources more efficiently, and behave ethically moving forward. Promising new developments in the subject include the next steps, which include sophisticated AI models, temporal dynamics analysis, and investigation of long-term consequences, ethical issues, and multichannel techniques. This study helps companies, marketers, and policymakers better understand the convergence of technology and marketing in today's ever-changing digital world so that they may better serve their customers and build a successful brand over time. 2024 IEEE.
Thamaraiselvi P.; Masih J.; Giri P.; Sridevi J.; Karim Shaikh I.A.; Prasad M.V.R.
Proceedings of 9th International Conference on Science, Technology, Engineering and Mathematics: The Role of Emerging Technologies in Digital Transformation, ICONSTEM 2024
Institute of Electrical and Electronics Engineers Inc.
https://doi.org/10.1109/ICONSTEM60960.2024.10568805 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85198835918&doi=10.1109%2fICONSTEM60960.2024.10568805&partnerID=40&md5=4bbf55d0e24bfaa5974eb99e180b62c8
Thamaraiselvi P., Business School (MBA), Kumaraguru College of Technology, Tamilnadu, Coimbatore, India; Masih J., School of Management, Bml Munjal University, Haryana, Gurugram, India; Giri P., School of Business And Management, Christ (Deemed To Be University), Delhi NCR, India; Sridevi J., Dr. MG. R. Educational & Research Institute, Management Studies & Dean-Centre of Excellence, India; Karim Shaikh I.A., Faculty of Business, Bahrain Polytechnic, Bahrain; Prasad M.V.R., Gitam School of Business, Gitam (Deemed To Be University), Bangalore, Doddaballapur taluk, Bengaluru, Nagadenahalli, India
Smart Affect Recognition System for Real-Time Biometric Surveillance Using Hybrid Features and Multilayered Binary Structured Support Vector Machine
affect; hybrid features; multilayered binary structured SVM
Human affect recognition (HAR) using images of facial expression and electrocardiogram (ECG) signal plays an important role in predicting human intention. This system improves the performance of the system in applications like the security system, learning technologies and health care systems. The primary goal of our work is to recognize individual affect states automatically using the multilayered binary structured support vector machine (MBSVM), which efficiently classify the input into one of the four affect classes, relax, happy, sad and angry. The classification is performed efficiently by designing an efficient support vector machine (SVM) classifier in multilayer mode operation. The classifier is trained using the 8-fold cross-validation method, which improves the learning of the classifier, thus increasing its efficiency. The classification and recognition accuracy is enhanced and also overcomes the drawback of 'facial mimicry' by using hybrid features that are extracted from both facial images (visual elements) and physiological signal ECG (signal features). The reliability of the input database is improved by acquiring the face images and ECG signals experimentally and by inducing emotions through image stimuli. The performance of the affect recognition system is evaluated using the confusion matrix, obtaining the classification accuracy of 96.88%. 2020 The British Computer Society 2020. All rights reserved.
Thamba Meshach W.; Hemajothi S.; E A M.A.
Computer Journal, Vol-65, No. 4, pp. 897-917.
https://doi.org/10.1093/comjnl/bxaa125 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85130093533&doi=10.1093%2fcomjnl%2fbxaa125&partnerID=40&md5=7196e51b39b1285848867d278ec96ce9
Thamba Meshach W., Department of Computer Science and Engineering, Prathyusha Engineering College, Aranvoyalkuppam, Thiruvallur District, Tamilnadu, Chennai, 602025, India; Hemajothi S., Department of Electronics and Communication Engineering, Prince Shri Venkateshwara Padmavathy Engineering College, Medavakkam-Mambakkam Road, Tamilnadu, Ponmar, Chennai, 600127, India; E A M.A., Department of Computer Science and Engineering, Christ University, Mysore Rd, Kumbalgodu, Karnataka, Bangalore, 560074, India
Repercussions of global turbulence and market volatility in spot & futures market: India preparedness
CRADF; Emerging market; Garch; Price discovery; VECM; Volatility
This article examines the repercussions of global turbulence and market volatility in Indian Capital market for the period spanning from January 1, 2003 to August 31, 2013 with a total of 2654 observations and it is broken into pre-crisis and post-crisis respectively. The study employed Generalized Autoregressive Conditional Heteroskedasticity (1,1) model to measure the volatility persistence by employing dummy variables. Cointegrating Regression Augmented Dickey Fuller (CRADF) and Vector Error Correction Model (VECM) was employed to investigate the casual nexus between spot and futures market in both short and long run equilibrium. The squared residuals of VECM were applied to investigate the lead-lag relationship between the bivariate variables. Our findings indicate that there was a significant change in the post crisis period for spot and futures market volatility. Our result suggests that nothing can be learned and new regulation can only do more harm. Apart from this, nobody knows which financial instrument will be at the centre of the next crisis. Overall, the comprehensive financial sector reform like Credit Default Swap, Valuation Assumptions and Basel II Accord can create more problems and make the investors more complex to meet the global challenges environment. IJER Serials Publications.
International Journal of Economic Research, Vol-11, No. 3, pp. 663-676.
Thamilselvan R., Department of Business Administration, Sathyabama University, Chennai, 600 101, India; Srinivasan K., Faculty of Management Studies, Christ University, Bangalore, Karnataka, 560 02, India
Partial slip and Joule heating on magnetohydrodynamic radiated flow of nanoliquid with dissipation and convective condition
Convective boundary condition; Magnetohydrodynamic; Nanofluid; Stretching surface; Viscous dissipation
Numerical investigation of three-dimensional flow of an electrically conducting nanofluid over a bidirectional stretching surface is proposed here. The slip flow over a convectively stretching sheet is considered. The flow is caused due to a non-linear stretching surface and Lorenz force. Water and copper nanoparticles are used to form nanoliquid. Suitable transformations are employed to reduce the conservation equations into nonlinear coupled, multidegree ordinary differential equations. Resultant nonlinear two-point boundary value problem is numerically integrated using Runge-Kutta-Fehlberg fourth-fifth order method. Computed results are verified with existing results under limiting cases. The influences of pertinent parameters on different flow fields are evaluated and presented via graphical and tabular form. It is found that the thermal radiation and convective heating at boundary stabilizes the thermal boundary layer growth. 2017 The Authors
Thammanna G.T.; Gireesha B.J.; Mahanthesh B.
Results in Physics, Vol-7, pp. 2728-2735.
Thammanna G.T., Department of Studies and Research in Mathematics, Kuvempu University, Shimoga, 577 451, India; Gireesha B.J., Department of Studies and Research in Mathematics, Kuvempu University, Shimoga, 577 451, India; Mahanthesh B., Department of Mathematics and Statistics, Christ University, Bangalore, 560029, India
Collaborative security approaches for IoT ecosystems
Collaborative architectures; IoT security; Proactive threat hunting; Resilient IoT ecosystems; Scalable IoT design
The surge in Internet of Things (IoT) devices has transformed numerous industries by enabling unparalleled connectivity and data sharing. Yet, this rapid growth has also exposed critical security vulnerabilities. This chapter delves into collaborative security strategies aimed at improving the safety and reliability of IoT ecosystems. A major vulnerability is weak authentication and authorization, often stemming from poor password practices or insufficient authentication mechanisms. Such flaws can result in unauthorized access, data breaches, and serious cyber threats, including Distributed Denial of Service (DDoS) attacks and Man-in-the-Middle (MITM) attacks. DDoS attacks can overwhelm essential IoT systems, like those in smart cities or healthcare, while MITM attacks can jeopardize data integrity during communication between devices and cloud services. Given that IoT devices frequently handle sensitive information, including personal and health data, ensuring their security is vital to avoid detrimental outcomes for users. Physical security risks also present a challenge, as the physical compromise of IoT devices can disrupt systems or pose risks to individuals. To address these threats, this chapter recommends several cybersecurity measures, such as secure design and development practices, robust authentication methods, and advanced encryption techniques. Secure device design should include mechanisms for safe firmware updates and employ Trusted Platform Modules for secure key storage. Effective authentication can be enhanced with multifactor methods, role-based access controls, and digital certificates. Data protection should involve encrypting data both in transit and at rest, as well as employing techniques like data anonymization and differential privacy. 2026 Elsevier Inc. All rights reserved..
Thampan, Manju; Joy, Rojal Treesa; Ramadani, Lauresha; Rajesh Kanna, R.
IoT Security: Fundamentals and Key Enabling Technologies;pp.291-304
Restricted Access; Hardcopy may be available in the library
https://doi.org/10.1016/B978-0-443-34125-0.00026-X https://www.scopus.com/pages/publications/105027006351?origin=resultslist
Thampan M., Department of Computer Science, CHRIST University, Karnataka, Bengaluru, India; Joy R.T., Department of Computer Science, CHRIST University, Karnataka, Bengaluru, India; Ramadani L., Computer Science Faculty, AAB College Prishtina, Kosovo, Prishtina; Rajesh Kanna R., Department of Computer Science, CHRIST University, Karnataka, Bengaluru, India
Application of Spray Drying process to convert Beneficial Compounds extracted from Plants into free-flowing powder
The use of herbal tablets has been rapidly growing and significant research work is being carried out worldwide with the goal to reap the benefits of the many useful plants that are available with medicinal values. Many of these plants go largely underutilized either due to lack of information on not only just the medicinal properties but simple and effective extraction methodologies as well, without sacrificing the properties of the extracts. Once extracted, the concentrates also must be converted into a suitable form that can be loaded in a capsule etc., ready to be consumed. While there many process methodologies being used worldwide to extract the useful resources from the plant, focus also must be on the process methodology that is being practiced to convert the extract (liquid or semi solid) into a solid free flowing powder form. Thus, in an herbal tablet, there many factors concerned with the manufacturing. They are (i) Identifying the most suitable plant for a particular immunity boosting purpose (ii) extraction of the useful contents, mostly in a liquid or slurry form (iii) transform the extract into a user-friendly product such as powder and finally (iv) encapsulation of the powder for ease of human consumption. This paper brings in a review of the several useful plants available around us across the world. In addition, the paper also highlights the suitable experimental results of the usefulness of spray-drying technology, which is a highly versatile process methodology to transform the extracts into free-flowing powder. Published under licence by IOP Publishing Ltd.
Thampi A.S.; Kumar S.R.; Gowtham Sanjai S.; Ramaswamy P.
IOP Conference Series: Materials Science and Engineering, Vol-988, No. 1
Thampi A.S., Department of Mechanical and Automobile Engineering, CHRIST (Deemed to Be University), Bangalore, India; Kumar S.R., Department of Mechanical and Automobile Engineering, CHRIST (Deemed to Be University), Bangalore, India; Gowtham Sanjai S., Department of Mechanical and Automobile Engineering, CHRIST (Deemed to Be University), Bangalore, India; Ramaswamy P., Department of Mechanical and Automobile Engineering, CHRIST (Deemed to Be University), Bangalore, India
Operational pattern forecast improvement with outlier detection in metro rail transport system
Forecast; Metro rail; Outliers detection; Passenger flow data; Station clustering
Transportation is an unavoidable part of every humans life. The mobility system handles the transport of humans from different places using various transport modes. According to a station in a populated area, the main problem is the presence of traffic in peak hours and wasting their valuable time on the road. The only medium which runs above the traffic is metro rails/subways. For these reasons, metro rails become a point of interest for each researchers prophecy and provide valuable recommendations for the smooth functioning of services. Even though, in many cases, the metro systems are affected by abnormal passenger flow. So, this study handles abnormal passenger flow detection and station clustering for the behavior study of a passenger flow system. The research compares outlier detection and anomaly identification for the behavioral analysis of the metro rail passenger flow. The study use data from Kochi Metro Rail Limited for the period 2017 to 2019. Outlier removal has used in passenger flow data before building a forecasting system. In pattern recognition algorithm those components which lie outside the patterns can be considered abnormal (anomaly).The outliers are the component falling apart from the region of interest. The effect of removing the outlier from the time-series pattern is studied against the outlier included pattern to show the improvement. 2023, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.
Thandassery S.; Mulerikkal J.; Raghavendra S.
Multimedia Tools and Applications, Vol-83, No. 4, pp. 11229-11245.
Thandassery S., Department of Computer Science and Engineering, Christ (Deemed to be University), Karnataka, Bengaluru, India, Department of Computer Science and Engineering, Rajagiri School of Engineering and Technology, Kochi, India; Mulerikkal J., Department of Information Technology, Rajagiri School of Engineering and Technology, Kochi, India; Raghavendra S., Department of Computer Science and Engineering, Christ (Deemed to be University), Karnataka, Bengaluru, India
Leveraging Circular Economy Principles and Emerging Technologies for Future Trends in Sustainable Business Models: A Roadmap to Net-Zero Emissions
This study examines the influence of circular economy concepts and emerging technology on the development of sustainable business models aimed at achieving net- zero emissions. It underscores the significance of resource efficiency, waste reduction, and lifecycle consideration in promoting sustainability. We analyse critical technologies such as blockchain, artificial intelligence, and the Internet of Things for their potential to enhance transparency, simplify processes, and foster stakeholder collaboration. We suggest a detailed roadmap that guides organisations in incorporating these ideas and technologies to establish robust, environmentally sustainable business practices for a sustainable future. 2026 by IGI Global Scientific Publishing.
Thandayuthapani, S.; Thirumoorthi, P.; Kingston, A.; Aishwarya, N.; Marimuthu, Dhanasekaran
Unlocking Growth and Sustainability for Small and Medium Enterprises With AI;pp.393-408
https://doi.org/10.4018/979-8-3693-9611-7.ch015 https://www.scopus.com/pages/publications/105015809933?origin=resultslist
Thandayuthapani S., Kristu Jayanti College, India; Thirumoorthi P., Periyar University, India; Kingston A., Christ University, India; Aishwarya N., Christ University, India; Marimuthu D., Adhiyamaan College of Engineering, India
Phyllanthus Emblica Extract Protects the Rat Liver Cells Against the Toxicity of Monosodium Glutamate: Experimental Evidence
Antioxidants; Monosodium glutamate; Oxidative stress; Phyllanthus emblica; Vitamin C; Vitamin E
Background: Monosodium glutamate (MSG), used widely in the food industry, is a threat to the public health. We investigated whether the MSG administration depletes non-enzymatic antioxidants, i.e., vitamins C and E in the liver of Wistar albino rats. We also examined the restorative effect of the ethanolic extract of Phyllanthus emblica (P. emblica). Methods: Wistar albino rats (n=42) were adapted and then randomly divided into seven groups of: 1) control, 2, 3, 4) MSG treatment, and 5, 6, 7) combined MSG and P. emblica extract treatment. All rat groups were treated daily for 120 days. They were orally administered either MSG alone or MSG plus the extract combined. The rats were then sacrificed and the liver was harvested from each group, and homogenized to examine the levels of vitamins C and E in the liver, using RP-HPLC method. Results: The vitamins C and E levels significantly declined (P<0.05) in the liver of MSG treated groups compared to those of the control rats. The combined treatment (extract + MSG) at low and moderate doses restored the vitamin C levels but it restored vitamin E only at the low dose (P<0.05). Conclusions: This study clearly demonstrated the deterioration of non-enzymatic antioxidants, i.e., vitamins C and E in the rats' liver after chronic exposure to MSG. The findings support the toxic effect and oxidative stress due to MSG exposure to the liver and the beneficial effect of the extract of P. emblica that inhibits the MSG's harmful effect on the liver. The Author(s), 2022.
Thangachi S.B.; Mokhasi V.S.; Sathyamoorthy Y.K.; Pinnelli V.B.K.; Chiruthanur S.
Iranian Journal of Toxicology, Vol-16, No. 3
Thangachi S.B., Department of Anatomy, Faculty of Medicine, Vydehi Institute of Medical Sciences and Research Centre, Karnataka, Bengaluru, India; Mokhasi V.S., Department of Anatomy, Faculty of Medicine, Vydehi Institute of Medical Sciences and Research Centre, Karnataka, Bengaluru, India; Sathyamoorthy Y.K., Department of Psychology, Faculty of Social Sciences, CHRIST, Deemed to be University, Karnataka, Bengaluru, India; Pinnelli V.B.K., Department of Biochemistry, Faculty of Medicine, Vydehi Institute of Medical Sciences and Research Centre, Karnataka, Bengaluru, India; Chiruthanur S., Department of Anatomy, Faculty of Medicine, Vydehi Institute of Medical Sciences and Research Centre, Karnataka, Bengaluru, India
Impact of data centers on power consumption, climate change, and sustainability
The data-driven economy is transforming with data centers becoming a crucial business infrastructure. However, the increasing reliance on data centers is posing a threat to the environment. Climate change activists are focusing on reducing emissions from sectors like automotive, aviation, and energy. Data centers consume more electricity than the UK, accounting for 3% of global electricity supply and 2% of total greenhouse gas emissions. By 2040, digital data storage is projected to contribute to 14% of the world's emissions. The number of data centers worldwide has surged from 500,000 in 2012 to over 8 million, with energy consumption doubling every four years. The rise in internet penetration rates and the introduction of 5G technologies and IoT devices will further exacerbate the issue, increasing the demand for data processing. 2024, IGI Global.
Thangam D.; Muniraju H.; Ramesh R.; Narasimhaiah R.; Ahamed Khan N.M.; Booshan S.; Booshan B.; Manickam T.; Ganesh R.S.
Computational Intelligence for Green Cloud Computing and Digital Waste Management, pp. 60-83.
Thangam D., Presidency Business School, Presidency College, Bengaluru, India; Muniraju H., Triveni Institute of Commerce and Management, Bengaluru, India; Ramesh R., Department of Management Studies, Knowledge Institute of Technology, Salem, India; Narasimhaiah R., Department of Economics, Jain University, Bengaluru, India; Ahamed Khan N.M., Department of Management, Acharya Institute of Graduate Studies, Bengaluru, India; Booshan S., ISBR College, India; Booshan B., Department of Management, Acharya Institute of Graduate Studies, Bengaluru, India; Manickam T., Christ University, India; Ganesh R.S., Vel Tech Rangarajan Dr. Sagunthala R and D Institute of Science and Technology, India
Digital Bridges: Harnessing Social Media for Social and Cultural Unity in Disaster Recovery
cultural unity; digital bridge; disaster recovery; social cohesion; social media
This manuscript investigates the role of social media as digital bridges which link individuals, groups, and organizations within a?community in post-disaster recoveries. By synthesizing research from disaster studies, social capital theory, and digital communication literature, the paper draws on empirical evidence on social media supporting situational awareness, community mobilization, digital storytelling, and cross-cultural?solidarity. Simultaneously, it also?examines the problems of fake news, social media apartheid, surveillance dystopia, and the fleeting nature of online solidarity. Instead of a general literature review, the article provides an integrative conceptual synthesis that?connects theory to policy and practice. It concludes with concrete suggestions for policymakers, platform designers, and recovery specialists who want to leverage social media's connecting?power while reducing its separating dangers. 2026 Policy Studies Organization.
Thangam, Dhanabalan; Shinde, Pradeep Kumar; Muniyanayaka, Devarajanayaka Kalenahalli; Subramanian, Mathiraj; Ananthan, Sriram; Manickam, Thirupathi; Sathis Kumar, G.
Journal of Critical Infrastructure Policy;Volume;7;Issue;1;Article No.;e70022;
https://doi.org/10.1002/jci3.70022 https://www.scopus.com/pages/publications/105033848328?origin=resultslist
Thangam D., Presidency Business School, Presidency College, Bengaluru, India; Shinde P.K., Department of Commerce, Presidency College, Bengaluru, India; Muniyanayaka D.K., College of Business, University of Buraimi, Al Buraimi, Oman; Subramanian M., Department of Corporate Secretaryship, Alagappa University, Karaikudi, India; Ananthan S., Department of Business Management, Yorkville University, New Westminster, Canada; Manickam T., Department of Professional Studies, Christ (Deemed to be) University, Bangalore, India; Sathis Kumar G., School of Social Sciences and Languages, Vellore Institute of Technology, Chennai, India
Water Diplomacy in the Cauvery River and Mullaperiyar Dam: A Case Study of Tamil Nadu's Experience With Karnataka and Kerala
shared resource management; sustainable water management; water diplomacy; water negotiation
Water sharing by multiple nations internationally often leads to issues concerning access, utilization, and sustainability. In South India, Tamil Nadu's incident with water diplomacy, mainly in managing the Cauvery River Basin shared with Karnataka, presents important implications for reserve management and conflict resolution. The state relies heavily on its river systems for irrigation, drinking water, and industrial use, but the scarcity and irregular allocation of water resources pose a significant challenge. Efficient water diplomacy can help achieve sustainable water administration by fostering common thought and cooperation among riparian states. This paper critically evaluates the challenges and opportunities in Tamil Nadu's water peacekeeping, focusing on its commitment to neighboring states over collective water resources. It explores key themes such as the historical context of interstate water-sharing disagreements, the efficiency of existing lawful and institutional frameworks, and the role of political and social arrangements in shaping water-sharing negotiations. The paper also underscores the need for a more practical and mutual approach to water distribution, moving away from legal arbitration and political negotiation to embrace the values of sustainable and evenhanded water management. 2025 Policy Studies Organization.
Thangam, Dhanabalan; Shinde, PradeepKumar; Ananthan, Sriram; Muniyanayaka, DevarajanayakaKalenahalli; Manickam, Thirupathi; SathisKumar, G.
World Water Policy;Volume;12;Issue;1;Article No.;e70018;
https://doi.org/10.1002/wwp2.70018 https://www.scopus.com/pages/publications/105012192992?origin=resultslist
Thangam D., Presidency Business School, Presidency College, Bengaluru, India; Shinde P., Department of Commerce, Presidency College, Bengaluru, India; Ananthan S., Department of Business Management, Yorkville University, New Westminster, Canada; Muniyanayaka D., University of Buraimi, Al Buraimi, Oman; Manickam T., Department of Professional Studies, Christ (Deemed to be) University, Bangalore, India; SathisKumar G., School of Social Science and Languages, Vellore Institute of Technology, Chennai, India
GalNAc-siRNA conjugates: Prospective tools on the frontier of anti-viral therapeutics
GalNac conjugate; Hepatitis B; Hepatocytes; SiRNA; Virus
The growing use of short-interfering RNA (siRNA)-based therapeutics for viral diseases reflects the most recent innovations in anti-viral vaccines and drugs. These drugs play crucial roles in the fight against many hitherto incurable diseases, the causes, pathophysiologies, and molecular processes of which remain unknown. Targeted liver drug delivery systems are in clinical trials. The receptor-mediated endocytosis approach involving the abundant asialoglycoprotein receptors (ASGPRs) on the surfaces of liver cells show great promise. We here review N-acetylgalactosamine (GalNAc)-siRNA conjugates that treat viral diseases such as hepatitis B infection, but we also mention that novel, native conjugate-based, targeted siRNA anti-viral drugs may also cure several life-threatening diseases such as hemorrhagic cystitis, multifocal leukoencephalopathy, and severe acute respiratory syndrome caused by coronaviruses and human herpes virus. 2021 Elsevier Ltd
Thangamani L.; Balasubramanian B.; Easwaran M.; Natarajan J.; Pushparaj K.; Meyyazhagan A.; Piramanayagam S.
ISSN: 10436618; PubMed ID: 34474100; CODEN: PHMRE
https://doi.org/10.1016/j.phrs.2021.105864 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85114692125&doi=10.1016%2fj.phrs.2021.105864&partnerID=40&md5=018e550bd85fe60324a2a8024e78d290
Thangamani L., Computational Biology Lab, Department of Bioinformatics, Bharathiar University, Coimbatore, 641046, Tamil Nadu, India; Balasubramanian B., Department of Food Science and Biotechnology, College of Life Science, Sejong University, Seoul, 05006, South Korea; Easwaran M., International Centre for Genetic Engineering and Biotechnology, 110067, New Delhi, India; Natarajan J., Data Mining and Text Mining Laboratory, Department of Bioinformatics, Bharathiar University, Coimbatore, 641046, Tamil Nadu, India; Pushparaj K., Department of Zoology, School of Biosciences, Avinashilingam Institute for Home Science and Higher Education for Women, Coimbatore, 641043, Tamil Nadu, India; Meyyazhagan A., Department of Life Sciences, CHRIST (Deemed to be University), Bengaluru, 560029, Karnataka, India; Piramanayagam S., Computational Biology Lab, Department of Bioinformatics, Bharathiar University, Coimbatore, 641046, Tamil Nadu, India
A Novel Hybrid Model for Time Series Forecasting Using Artificial Neural Network and Autoregressive Integrated Moving Average Models
ANN; ARIMA; Combination of forecasts; Hybrid model
Enhancing forecast accuracy while using time series is a potential area of research. Evidences exist in the literature to show that hybrid models can significantly improve the forecasting performance, as they combine the exclusive strengths of different models. This paper presents a novel hybrid model by combining forecasts from Autoregressive Integrated Moving Average (ARIMA) and artificial neural network (ANN) models with suitable weights, thereby improving the forecast accuracy. The methodology employs appropriate error metrics to construct the weights. The paper further demonstrates the efficiency of the proposed methodology through an empirical study, based on two real-world time series data sets. Thus, the new methodology can be used for enhancing the forecast accuracy in a number of fields of research. The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.
Thangarajan B.; Nagaraja M.S.; Dhandra B.V.
Trends in Mathematics, Vol-Part F2357, pp. 747-754.
Thangarajan B., CHRIST (Deemed to be University), Bengaluru, India; Nagaraja M.S., CHRIST (Deemed to be University), Bengaluru, India; Dhandra B.V., CHRIST (Deemed to be University), Bengaluru, India
Exploring ARIMA Models with Interacted Lagged Variables for Forecasting
Autoregressive Integrated Moving Average model; Interactions among lagged variables
Including interactions among the explanatory variables in regression models is a common phenomenon. However, including interactions existing among lagged variables in autoregressive models has not been explored so far. In this paper, Autoregressive Integrated Moving Average (ARIMA) model with interactions among the lagged variables is proposed for improving forecast accuracy. The methodology for identifying the interacted lagged variables and including them in the ARIMA model is suggested. Using five different data sets of different types, the paper explores the effect of interacted lagged variables in ARIMA model. The experimental results exhibit that when interactions do actually exist, ARIMA model with interactions improves the forecast accuracy as compared to ARIMA model without interactions. The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.
Trends in Mathematics, Vol-Part F2357, pp. 735-745.
Consumer Perception of Internet Banking and Mobile Banking Using Twitter Analytics
Digital Transactions; Mobile Banking; Sentiment Analysis; Twitter
Mobile and internet banking have introduced a new way of monetary transactions without the need for physical presence. This research proposes to analyze the sentiments of people regarding digital transactions, mobile, and internet banking. The explosion of internet usage and the huge funding initiatives in electronic banking have drawn the attention of researchers towards internet and mobile banking. This study focuses on customer value perceptions of the internet and mobile banking in India. The recent and forecasted Digital India scheme shows high growth in e-banking in India. The demographic, attitudinal, and behavioral characteristics of mobile bank users were examined. In this study, datasets obtained from Twitter were used. After extensive and repeated analysis, it is found that both mobile and internet banking are well received; the number of positive tweets, especially regarding mobile banking, is much higher than that of internet banking. This leads to the interpretation that people find mobile banking easier and safer, especially during the ongoing COVID-19 pandemic. 2022 Information Resources Management Association. All rights reserved.
Thangavel C.; Thangavel R.; Ramanujam E.; Bennet D.T.; Bennet P.S.
International Journal of Sociotechnology and Knowledge Development, Vol-14, No. 1
https://doi.org/10.4018/IJSKD.297978 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85149882888&doi=10.4018%2fIJSKD.297978&partnerID=40&md5=8b2d32e7995cedb3d706f36e51934441
Thangavel C., Department of Applied Mathematics and Computational Science, Thiagarajar College of Engineering, India; Thangavel R., Independent Researcher, India; Ramanujam E., Department of Computer Science and Engineering, School of Engineering and Technology, Christ University (Deemed), Bengaluru, India; Bennet D.T., Thiagarajar College of Engineering, India; Bennet P.S., Thiagarajar College of Engineering, India
Diversity of Endophytic Fungi in Plant Species: Traditional vs. High-Throughput Sequencing Approaches
amplicon; Internal Transcribed Spacer (ITS); metagenomic; Operational Taxonomic Unit (OTU); phylogenetic
The plant microbiome significantly impacts plant life, with fungi playing a crucial role in shaping interactions and classifications. Advances in cultivation technologies have refined fungal classification, and research highlights the vital connection between endophytic fungi and their plant hosts. The present study employs morphological and phylogenetic techniques, predicting the Internal Transcribed Spacer 2 (ITS2) secondary structure and using next-generation sequencing (NGS) data to detect fungal endophytes in plant leaves via both traditional and conventional approaches. The research area, with its hot semi-arid environment and red and black soils, supports drought-resistant plants like Senna auriculata, Ziziphus mauritiana, and Catunaregam spinosa, known for their medicinal properties. These plants, rich in antioxidants, play a vital role in traditional medicine and highlight the region's rich ethno-botanical heritage. The culture-dependent study on the foliage yielded a total of 17 isolates from S. auriculata and 16 each from both C. spinosa and Z. mauritiana. The most common genera, Alternaria and Nigrospora, account for 18.3% of all isolated endophytic fungi. Three plants were colonized with Nigrospora and Lasiodiplodia, and their morphotypes were determined using ITS2 secondary structure prediction. Recent ecological studies highlight unculturable taxa, or dark taxa, where many species cannot sporulate or be cultured, emphasizing the need for High - Throughput Sequencing (HTS) approaches. The study gathered 68,791 reads from S. auriculata with 101 operational taxonomic units (OTUs), 58,620 from C. spinosa with 219 OTUs, and 66,087 from Z. mauritiana with 193 OTUs, with the majority of OTUs related to Colletotrichum (69%) and a minimum of Myrmaecium (2%). A total of 49 fungal isolates were obtained from traditional methods, whereas 513 fungal OTUs were retrieved through HTS methods, confirming the presence of a highly abundant fungus population in plant samples. The study reveals that using the ITS short amplicon sequencing technique provides distinct insights into endophytic fungal communities in three plant samples. In conclusion, analyzing plant fungal components using a combination of culture-dependent and culture-independent techniques may be a novel strategy. 2025 Wiley-VHCA AG, Zurich, Switzerland.
Thangavel, Myithili; Sneha, Michael Joe Xavier; Mani, Israel; Surendrababu, Akash; Rajapriya, Pandy; Arulselvan, Palanisamy; Alarfaj, Abdullah A.; Thangavelu, Indumathi; Pandi, Mohan
Chemistry and Biodiversity;Volume;22;Issue;6;Article No.;e202402792;
https://doi.org/10.1002/cbdv.202402792 https://www.scopus.com/pages/publications/105006575408?origin=resultslist
Thangavel M., Department of Molecular Microbiology, School of Biotechnology, Madurai Kamaraj University, Tamil Nadu, Madurai, India; Sneha M.J.X., Department of Molecular Microbiology, School of Biotechnology, Madurai Kamaraj University, Tamil Nadu, Madurai, India; Mani I., Department of Molecular Microbiology, School of Biotechnology, Madurai Kamaraj University, Tamil Nadu, Madurai, India; Surendrababu A., Department of Molecular Microbiology, School of Biotechnology, Madurai Kamaraj University, Tamil Nadu, Madurai, India; Rajapriya P., Department of Zoology, M.S.S. Wakf Board College, Tamil Nadu, Madurai, India; Arulselvan P., Department of Biochemistry, Karpagam Academy of Higher Education (Deemed to be University), Tamil Nadu, Coimbatore, India; Alarfaj A.A., Department of Botany and Microbiology, College of Science, King Saud University, Riyadh, Saudi Arabia; Thangavelu I., Department of Chemistry, CHRIST (Deemed to be University), Bangalore, India; Pandi M., Department of Molecular Microbiology, School of Biotechnology, Madurai Kamaraj University, Tamil Nadu, Madurai, India
Sodium Alginate Coated Cerium(III) Fluoride Nanoparticles for Biocompatible Antimicrobial Applications: Structural and Functional Insights
Antimicrobial activity; CeF? nanoparticles; Cytocompatibility; Sodium alginate; Surface functionalization
Cerium(III) fluoride (CeF?) nanoparticles and sodium alignatefunctionalized CeF? nanocomposites (CeF?SA) were synthesized via a wet-chemical method. XRD confirmed phase-pure hexagonal CeF? with an average crystallite size of ~ 24nm. TEM showed uniformly distributed nanoparticles (812nm), and lattice fringe analysis revealed an interplanar spacing of ~ 0.315nm corresponding to the (111) plane, indicating preserved crystallinity after SA functionalization. UVvisible spectroscopy revealed a reduction in the optical band gap from 6.05eV (CeF?) to 4.96eV (CeF?SA), indicating modification of electronic properties. PL emission (351522nm) showed quenching, suggesting suppressed charge carrier recombination and increased defect density. CeF?SA exhibited antimicrobial activity against Gram-positive (S. aureus, S. pneumoniae), Gram-negative (K. pneumoniae, E. coli), and the fungal pathogen C. albicans, with reduced MIC (650g/mL) and MBC (1050g/mL) against K. pneumoniae. SEM revealed pronounced bacterial membrane damage. In vitro MTT assays on L929 fibroblasts demonstrated > 80% cell viability at concentrations up to 60g/mL, indicating the nanoparticles are well-tolerated at sub-antimicrobial doses. Overall, CeF?SA represents a promising antimicrobial nanoplatform, with further studies needed to assess cytocompatibility at MIC-level concentrations. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2026.
Thangavelu, Indumathi; Ganeshkumar, P.; Saudagar, Abdul Khader Jilani
Journal of Inorganic and Organometallic Polymers and Materials;
https://doi.org/10.1007/s10904-026-04253-5 https://www.scopus.com/pages/publications/105035666559?origin=resultslist
Thangavelu I., Department of Chemistry, CHRIST (Deemed to be University), Karnataka, Bangalore, 560029, India; Ganeshkumar P., Department of Computer Science, College of Computer and Information Sciences, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh, 11432, Saudi Arabia; Saudagar A.K.J., Information Systems Department, College of Computer and Information Sciences, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh, 11432, Saudi Arabia
Chitosan coated multifunctional NiFe?O? nanocomposites as a promising candidate for biomedical applications
Biocompatibility; Embryotoxic; Glioma cancer; MRSA; NiFe2O4; Zebrafish
Nanoparticles for biomedical applications often suffer from limited stability, low biocompatibility, and suboptimal therapeutic efficacy. To address these challenges, NiFe?O? nanoparticles were functionalized with chitosan (NiFe?O?-CS) via a co-precipitation method, aiming to enhance their structural, optical, antimicrobial, and anticancer properties. XRD analysis revealed a reduction in crystallite size from 37 to 33nm after chitosan modification, indicating controlled crystal growth and increased surface area. TEM results confirmed a corresponding decrease in particle size from 35 2.1nm to 29 1.8nm, improving surface reactivity and stability. PL spectra exhibited a red-shift in green emission peaks, suggesting increased oxygen vacancies and defect states that facilitate ROS generation. Antimicrobial assays against methicillin-resistant Staphylococcus aureus (MRSA) and Candida albicans (C.albicans) demonstrated significantly higher activity for NiFe?O?-CS nanocomposites, supported by SEM imaging that showed extensive microbial membrane disruption. Furthermore, NiFe?O?-CS exhibited enhanced anticancer activity against C6 glioma cells, with an IC?? of 35.6g/mL compared to 43.6g/mL for unmodified nanoparticles. Zebrafish embryo studies confirmed the biocompatibility of NiFe?O?-CS at appropriate doses, although dose-dependent embryotoxicity was observed. These findings highlight that chitosan functionalization of dual-metal nanoparticles improves therapeutic efficacy through increased surface interactions and ROS generation while underscoring the need for careful dose optimization. This study presents a novel strategy for designing biopolymer-coated nanocomposites that balance enhanced biomedical performance with safety considerations. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2026.
Thangavelu, Indumathi; Tadepalli, Srinivas
Naunyn-Schmiedeberg's Archives of Pharmacology;
https://doi.org/10.1007/s00210-025-04904-3 https://www.scopus.com/pages/publications/105028308245?origin=resultslist
Thangavelu I., Department of Chemistry, CHRIST (Deemed to Be University), Bangalore, 560029, India; Tadepalli S., Department of Chemical Engineering, College of Engineering, Imam Mohammad Ibn Saud Islamic University (IMSIU), 11432, Riyadh, Saudi Arabia