Euclidean distance; Key frames; Spatio temporal; Threshold values; Video summarization
Video summarization is the most important video content service which gives us a short and condensed representation of the whole video content. It also ensures the browsing, mining, and storage of the original videos. The multi- view video summaries will produce only the most vital events with more detailed information than those of less salient ones. As such, it allows the interface user to get only the important information or the video from different perspectives of the multi-view videos without watching the whole video. In our research paper, we are focusing on a series of approaches to summarize the video content and to get a compact and succinct visual summary that encapsulates the key components of the video. Its main advantage is that the video summarization can turn numbers of hours long video into a short summary that an individual viewer can see in just few seconds. Springer India 2016.
Advances in Intelligent Systems and Computing, Vol-435, pp. 461-473.
Thingom C., Centre for Digital Innovation, Christ University, Bangalore, India; Yeon G., Centre for Digital Innovation, Christ University, Bangalore, India
Design and Development of Dual Fuzzy Technique to Optimize Job Scheduling and Execution Time in Cloud Environment
Cloud computing is a type of computing that relies on sharing a pool of computing resources, rather than deploying local or personal hardware and software. It enables convenient, on-demand network access to a shared pool of configurable computing re- sources (e.g., applications, storage, networks, services, and servers) that can be swiftly provisioned and released with minimal management control or through the interaction of the cloud service provider. The increasing demand for computing resources in the cloud has made elasticity an important issue in the cloud. The availability of extending the resources pool for the user provides an effective alternative to deploying applications with high scalability and processing requirements. Providing a satisfactory Quality of Service (QoS) is an important objective in cloud data centers. The QoS is measured in terms of response time, job completion time and reliability. If the user jobs cannot be executed in high load and the job is crashed, it will enormously increase the response time and also push up the job completion time. Also due to load, the jobs may be still in the waiting queue and could not find a resource to execute. In such a situation, the user notices a big response delay and it will affect the QoS. Towards ensuring QoS, this research proposes the following solution - Dual Fuzzy Load Balancing for jobs. Dual Fuzzy Load Balancing balances the load in the data center with an overall goal of reduction of response and execution time for tasks. The proposed solutions were simulated in the Cloudsim simulator and performance metrics in terms of job response time, job completion time, resource utilization, a number of SLA violations, and along with the cost comparison to the existing algorithms of Load Balancing. The proposed solutions are also implemented in a real cloud environment and the effectiveness of the solution is evaluated.
Design and development of dual fuzzy technique to optimize job scheduling and execution time in cloud environment /
Hybrid scheme image compression using DWT and SVD
DCT; DWT; Image compression; SVD; Wavelet filters
Image compression is process of reducing data size to represent an image by removing redundant data. Hybrid scheme image compression is combination of methods performed in order or as an amalgam to form a new technique. In this paper, we proposed a new approach to compress the image by collaborating Discrete Wavelet Transformation (DWT) and Singular Value Decomposition (SVD). Image is decomposed into wavelets using DWT and approximate wavelet is subsequently transformed into four bands. Different wavelet filters are implemented for transformation namely Haar, Daubechies, Biorthogonal and Coiflets. Apart from approximate image, SVD is applied on remaining wavelets (Horizontal, Vertical and Diagonal Details) at each decomposition level. On reconstruction, various singular values are selected depending on the level transformation. The performance of the proposed method is compared and evaluated with SVD, DCT-SVD and DWT-DCT-SVD. Evaluation is carried out based on Compression Ratio (CR), Mean Squared Error (MSE), Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity (SSIM) index. From the experimental results, it is observed that proposed method yields better MSE, PSNR and SSIM compared to state-of-the-art methods. 2017, Institute of Advanced Scientific Research, Inc. All rights reserved.
Journal of Advanced Research in Dynamical and Control Systems, Vol-2018, pp. 225-233.
Institute of Advanced Scientific Research, Inc.
Thinley T., Department of Computer Science, Christ University, Bengaluru, India; Praveen Kumar P.U., Department of Computer Science, Christ University, Bengaluru, India
Analysis of zoochemical from Meretrix casta (Mollusca: Bivalvia) extracts, collected from Rameswaram, Tamil Nadu, India and their pharmaceutical activities
Marine Anti-diabetic Agents; Marine Ethnomedicine; Marine natural products; Marine Pharmacognosy; Therapeutic exploration
The marine ecosystem's diverse animal species offer a unique opportunity to discover marine-derived natural products. While numerous invertebrates have been studied, research on Indian marine invertebrates, especially Meretrix casta, remains limited. This study explores the zoochemical composition of ethyl acetate and methanolic extracts from Meretrix casta off Rameswaram, Tamil Nadu, India, and evaluates their bioactive potential, focusing on antioxidant properties, glucose uptake in yeast cells, and alpha-amylase activity. The results reveal the presence of alkaloids, flavonoids, polyphenols, sterols, terpenoids, and cardiac glycosides in both extracts, highlighting their bioactive potential. Although their antioxidant capacity is slightly lower than ascorbic acid, the extracts demonstrated significant alpha-amylase inhibition, suggesting their potential in blood sugar regulation and diabetes management. These findings underscore the therapeutic potential of M. casta in developing anti-diabetic compounds, warranting further pharmacological exploration. Authors.
Journal of Integrated Science and Technology, Vol-12, No. 6
https://doi.org/10.62110/sciencein.jist.2024.v12.835 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85201557358&doi=10.62110%2fsciencein.jist.2024.v12.835&partnerID=40&md5=d62e90c966de0fdecd7b174448c197e3
Thippesh D., Department of Life Sciences, CHRIST (Deemed to be University), Bangalore Central Campus, Dharmaram Post, Hosur Road, Karnataka, Bengaluru, 560029, India; Velayudhannair K., Department of Life Sciences, CHRIST (Deemed to be University), Bangalore Central Campus, Dharmaram Post, Hosur Road, Karnataka, Bengaluru, 560029, India
Exploring the Pharmaceutical Potential of Meretrix casta (Gmelin, 1791) (Mollusca: Bivalvia)
Medicinal marine mollusks; Meretrix casta; Natural antioxidants; Therapeutic marine resources
Meretrix casta, a marine mollusk, has been recognized traditionally for its nutritional and medicinal properties. This study aims to investigate the pharmacological potential of M. casta extracts, specifically focusing on the antioxidant, hemolytic, anti-inflammatory and antimicrobial activities. The antioxidant activity, assessed via hydrogen peroxide scavenging and phosphomolybdate assays, revealed concentration-dependent inhibition, with the methanol extract showing 61.04% inhibition at 100 g/mL compared to 61.19% for ethyl acetate, while the standard ascorbic acid exhibited 87.80%. Anti-inflammatory activity was evaluated using heat-induced hemolysis, hypotonicity-induced hemolysis and protein denaturation assays. Both extracts demonstrated significant anti-inflammatory effects, with the ethyl acetate extract achieving 85.40% inhibition of hemolysis, closely matching acetylsalicylic acids 90.50% and methanol extract showing 87.60% at 100 g/mL. Antibacterial and antifungal assays demonstrated significant inhibitory effects against pathogenic bacteria and fungi, with the methanolic extract frequently exhibiting higher efficacy. These findings highlight the therapeutic potential of M. casta extracts as natural bioactive agents. Future investigations should aim to isolate and characterize the specific bioactive compounds underlying these pharmacological effects and to explore their mechanisms of action in detail. Such studies could pave the way for new therapies derived from marine biodiversity, addressing various health challenges. 2025 Asian Publication Corporation. All rights reserved.
Thippesh, Deepak; Velayudhannair, Krishnakumar
Asian Journal of Chemistry;Volume;37;Issue;6;pp.1469-1476
https://doi.org/10.14233/ajchem.2025.33827 https://www.scopus.com/pages/publications/105012758639?origin=resultslist
Thippesh D., Department of Life Sciences, Christ University, Dharmaram College Post, Hosur Road,, Bengaluru, 560029, India; Velayudhannair K., Department of Life Sciences, Christ University, Dharmaram College Post, Hosur Road,, Bengaluru, 560029, India
Roadmap of effects of biowaste-synthesized carbon nanomaterials on carbon nano-reinforced composites
Biowaste; Carbon; PNCs; Porosity; Reinforced composites
Sustainable growth can be achieved by recycling waste material into useful resources without affecting the natural ecosystem. Among all nanomaterials, carbon nanomaterials from biowaste are used for various applications. The pyrolysis process is one of the eco-friendly ways for synthesizing such carbon nanomaterials. Recently, polymer nanocomposites (PNCs) filled with bio-waste-based carbon nanomaterials attracted a lot of attention due to their enhanced mechanical properties. A variety of polymers, such as thermoplastics, thermosetting polymers, elastomers, and their blends, can be used in the formation of composite materials. This review summarizes the synthesis of carbon nanomaterials, polymer nanocomposites, and mechanical properties of PNCs. The review also focuses on various biowaste-based precursors, their nanoproperties, and turning them into proper composites. PNCs show improved mechanical properties by varying the loading per-centages of carbon nanomaterials, which are vital for many defence-and aerospace-related indus-tries. Different synthesis processes are used to achieve enhanced ultimate tensile strength and mod-ulus. The present review summarizes the last 5 years work in detail on these PNCs and their appli-cations. 2021 by the authors. Licensee MDPI, Basel, Switzerland.
https://doi.org/10.3390/catal11121485 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85120643131&doi=10.3390%2fcatal11121485&partnerID=40&md5=caab7ac36e3789f9a07478b33fc7edab
Thippeswamy B.H., Department of Electronics & Communication Engineering, B.M.S. College of Engineering, Bengaluru, Bangalore, 560019, India; Maligi A.S., Department of Electronics & Communication Engineering, B.M.S. College of Engineering, Bengaluru, Bangalore, 560019, India; Hegde G., Department of Chemistry, CHRIST (Deemed to be University), Hosur Road, Bengaluru, Karnataka, 560029, India, Centre for Advanced Research and Development (CARD), CHRIST (Deemed to be University), Hosur Road, Bengaluru, Karnataka, 560029, India
Studies on the characterisation of thiophene substituted 1,3,4-oxadiazole derivative for the highly selective and sensitive detection of picric acid
1,3,4-oxadiazole; Binding constant; Fluorescence quenching; Nitrated explosives or nitroaromatic compounds; Picric acid
A novel thiophene substituted 1,3,4-oxadiazole based chemosensor namely 2-(4-(5-(5-hexylthiophen-2-yl) thiophen-2-yl)phenyl) -5-(5-(5-(5-hexylthiophen-2-yl) thiophen-2-yl)thiophen-2-yl)-1,3,4-oxadiazole [TKO] has been characterised for the efficient detection of picric acid (PA) based on fluorescence quenching mechanism. In this regard, the electronic absorption spectra, fluorescence spectra, and fluorescence lifetime of TKO are recorded in the presence of different nitroaromatic compounds (NACs) in ethanol at room temperature. The absorption studies exhibited a blue shift in the absorption maxima with the increase in the concentration of PA. In the fluorescence titration studies, TKO shows a remarkable fluorescence quenching with picric acid as compared to other nitroaromatic compounds. Using the Benesi-Hildebrand plot, the binding constant value of PA with TKO is determined and is of the order of 6.467 104 M?1. Job's plot analysis confirms the 1:1 binding stoichiometry ratio between TKO and PA and is supported by the 1H NMR studies. The detection limit is determined and is of the order of 10.08 M. The competitive studies revealed that TKO is highly selective for recognizing PA without the interference of other NACs. The theoretical studies were also carried out to understand the binding mechanisms of PA with TKO. The fluorescence quenching of TKO by PA may be attributed to photo induced electron transfer (PET). Overall, the experimental findings suggest that, the novel probe TKO may be used as a highly selective and sensitive chemosensor for the detection of explosives like picric acid. 2022 Elsevier B.V.
Thippeswamy M.S.; Naik L.; Maridevarmath C.V.; Savanur H.M.; Malimath G.H.
Journal of Molecular Structure, Vol-1264
https://doi.org/10.1016/j.molstruc.2022.133274 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85130194990&doi=10.1016%2fj.molstruc.2022.133274&partnerID=40&md5=f5c0123e50075c72a50f77b4ffe0e2b8
Thippeswamy M.S., UG and PG Department of Physics, Karnatak Science College, Karnataka, Dharwad, 580001, India; Naik L., Department of Physics and Electronics, CHRIST (Deemed to be University), Bangalore Central Campus, Karnataka, Bengaluru, 560029, India; Maridevarmath C.V., Department of Physics, Government First Grade College, Karnataka, Hubballi, 580032, India; Savanur H.M., Department of Chemistry, P.C. Jabin, Science College, Karnataka, Hubballi, India; Malimath G.H., UG and PG Department of Physics, Karnatak Science College, Karnataka, Dharwad, 580001, India
The Role of IoT in Revolutionizing Payment Systems and Digital Transactions in Finance
Digital transactions; Finance; Financial inclusion; IoT; Payment systems; Security; Technological innovation
The revolutionary impact of the Internet of Things (IoT) on payment systems and digital transactions within the financial industry is investigated so as to better understand its implications. During this period of unparalleled digitalization in the financial environment, the Internet of Things has emerged as a crucial participant in the process of altering traditional payment paradigms. For the purpose of improving efficiency, security, and the overall user experience, this article analyzes the incorporation of Internet of Things (IoT) devices into financial transactions. These devices include smart cards, wearables, and linked appliances. The paper elucidates how Internet of Things-driven innovations are expediting payment processes, reducing transaction costs, and mitigating fraud risks. This is accomplished through a comprehensive investigation of case Researches, technology breakthroughs, and regulatory frameworks. In addition to this, the article investigates the implications of the Internet of Things (IoT) in terms of promoting financial inclusion by providing digital payment services to groups that were previously underserved. This research gives useful insights for policymakers, financial institutions, and technologists who are looking to navigate and harness the potential of the Internet of Things in transforming payment systems. These insights are gained through an examination of the obstacles and opportunities related with the adoption of IoT in the financial sector. 2024 IEEE.
Thirumagal P.G.; Das T.; Das S.; Khatri E.; Kiran S.; Rani S.
5th International Conference on Recent Trends in Computer Science and Technology, ICRTCST 2024 - Proceedings, pp. 171-176.
Institute of Electrical and Electronics Engineers Inc.
Thirumagal P.G., VISTAS, Department of MBA, Tamil Nadu, Chennai, India; Das T., Christ University, Department of School of Business Management, Delhi NCR Campus, UP, Ghaziabad, India; Das S., Christ University, Department of School of Commerce, Finance & Accountancy, Delhi NCR Campus, UP, Ghaziabad, India; Khatri E., Nitte Meenakshi Institute of Technology, Department of Management Studies, Karnataka, Bengaluru, India; Kiran S., CMR Institute of Technology, Department of MBA, Karnataka, Bengaluru, India; Rani S., Kalasalingam Academy of Research and Education, Department of Commerce, Tamil Nadu, Sriviliputtur, India
IoT-Driven Credit Scoring Models: Improving Loan Decision Making in Banking
Banking analytics; financial inclusion; IoT data in lending; IoT-driven credit scoring; Loan approval optimization; Loan decision-making; Risk assessment
By the game-changing possibilities of credit scoring models driven by the Internet of Things, this hopes to shed light on how the banking sector may enhance its loan decision-making procedures. Financial organisations are putting more and more faith in Internet of Things technologies to improve their risk assessment and lending processes. These IoT-driven models provide a more accurate and thorough assessment of creditworthiness by including real-time and detailed data on borrowers' activities, spending habits, and asset utilisation. This research examines the practicality and accuracy of Internet of Things (IoT) credit scoring by comparing it to conventional methods, looking closely at case researches, and analysing empirical data. The findings shed light on potential ways to enhance the loan approval and risk prediction procedures while also addressing concerns and considerations related to data privacy, security, and regulatory compliance. It is possible that decision-making frameworks could be altered by IoT-driven credit scoring algorithms, which could lead to a more inclusive and informed lending atmosphere. The contributes to the growing area of banking credit evaluation by showing that these models have promise. 2024 IEEE.
Thirumagal P.G.; Das T.; Das S.; Vinit Sikka C.S.; Amit Kumar C.S.; Kusuma T.
5th International Conference on Recent Trends in Computer Science and Technology, ICRTCST 2024 - Proceedings, pp. 193-197.
Thirumagal P.G., VISTAS, Department of MBA, Tamil Nadu, Chennai, India; Das T., Christ University, Department of School of Business Management, Delhi NCR Campus, UP, Ghaziabad, India; Das S., Christ University, Department of School of Commerce, Finance & Accountancy, UP, Ghaziabad, India; Vinit Sikka C.S., School of Leadership and Management, Manav Rachna International Institute of Research and Studies, Department of UG Management Studies, Haryana, Faridabad, India; Amit Kumar C.S., School of Leadership and Management, Manav Rachna International Institute of Research and Studies, Department of UG Management Studies, Haryana, Faridabad, India; Kusuma T., Sree Vidyanikethan Engineering College, Department of Commerce, Andhra Pradesh, Tirupati, India
AI-Enhanced IoT Data Analytics for Risk Management in Banking Operations
AI; Banking Operations; Data Analytics; Financial Risk; IoT; Real-time Analysis; Risk Management
Using IoT data analytics in conjunction with artificial intelligence (AI) has the potential to improve banking operations' risk management. Sophisticated analytical methods are necessary for the detection and management of possible risks due to the increasing complexity and amount of data generated by the banking industry. This research proposes a novel method for analysing real-time data from IoT devices by employing artificial intelligence algorithms. The risks associated with financial transactions and operations can be better and more accurately assessed using this method. Through the integration of AI's pattern recognition, anomaly detection, and predictive modelling capabilities with the massive amounts of data generated by Internet of Things devices, this project aims to substantially enhance the efficacy and efficiency of risk management approaches in the banking sector. Research like this could lead to innovative solutions that make financial institutions more resistant to rising risks by enhancing decision-making, reducing operational weaknesses, and so on. 2024 IEEE.
Thirumagal P.G.; Vaddepalli S.; Das T.; Das S.; Madem S.; Immaculate P.S.
5th International Conference on Recent Trends in Computer Science and Technology, ICRTCST 2024 - Proceedings, pp. 177-181.
https://doi.org/10.1109/ICRTCST61793.2024.10578533 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85199358472&doi=10.1109%2fICRTCST61793.2024.10578533&partnerID=40&md5=cb3c7936415ed5b0a248ef13d338b17b
Thirumagal P.G., VISTAS, Department of MBA, Tamil Nadu, Chennai, India; Vaddepalli S., University of Technology and Applied Sciences, Department of Economics and Business Administration, India; Das T., Christ University, Department of School of Business Management, UP, Ghaziabad, India; Das S., Christ University, Department of School of Commerce, Finance & Accountancy, Delhi NCR Campus, UP, Ghaziabad, India; Madem S., Vignan's Institute of Information Technology, Department of Management Studies, India; Immaculate P.S., Sri SaiRam Institute of Technology, Department of MBA, West Tambaram, TamilNadu, Chennai, India
Enhancing Banana Cultivation: Disease Identification through CNN and SVM Analysis for Optimal Plant Health
Convolutional Neural Network; Deep Learning; Image processing; Machine Learning
Detection and effective remedies play a crucial role in revolutionizing banana crop health. The banana industry faces numerous challenges, including the prevalence of diseases and pests that can lead to significant yield losses. This paper explores the potential impact of detection techniques and remedies on improving banana crop management. Disease detection models based on machine learning, image processing and deep learning offer high accuracy in identifying diseases like Fusarium Wilt, Yellow Sigatoka, and Black Sigatoka. Implementing detection and targeted treatments can enhance crop productivity, reduce pesticide usage, and ensure sustainable banana production. 2024 IEEE.
Thirumeninathan V.; Vijayalakshmi S.; Palathara S.T.
TQCEBT 2024 - 2nd IEEE International Conference on Trends in Quantum Computing and Emerging Business Technologies 2024
https://doi.org/10.1109/TQCEBT59414.2024.10545264 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85204452673&doi=10.1109%2fTQCEBT59414.2024.10545264&partnerID=40&md5=e2a9ee31bb435d440aea7235e5cdf919
Thirumeninathan V., CHRIST(Deemed to Be University), Department of Data Science, Pune, India; Vijayalakshmi S., CHRIST(Deemed to Be University), Department of Data Science, Pune, India; Palathara S.T., CHRIST(Deemed to Be University), Department of Data Science, Pune, India
Phytofabricated bimetallic synthesis of silver-copper nanoparticles using Aerva lanata extract to evaluate their potential cytotoxic and antimicrobial activities
In this study, we demonstrate the green synthesis of bimetallic silver-copper nanoparticles (AgCu NPs) using Aerva lanata plant extract. These NPs possess diverse biological properties, including in vitro antioxidant, antibiofilm, and cytotoxic activities. The synthesis involves the reduction of silver nitrate and copper oxide salts mediated by the plant extract, resulting in the formation of crystalline AgCu NPs with a face-centered cubic structure. Characterization techniques confirm the presence of functional groups from the plant extract, acting as stabilizing and reducing agents. The synthesized NPs exhibit uniform-sized spherical morphology ranging from 7 to 12nm. They demonstrate significant antibacterial activity against Staphylococcus aureus and Pseudomonas aeruginosa, inhibiting extracellular polysaccharide secretion in a dose-dependent manner. The AgCu NPs also exhibit potent cytotoxic activity against cancerous HeLa cell lines, with an inhibitory concentration (IC50) of 17.63gmL?1. Additionally, they demonstrate strong antioxidant potential, including reducing capability and H2O2 radical scavenging activity, particularly at high concentrations (240gmL?1). Overall, these results emphasize the potential of A. lanata plant metabolite-driven NPs as effective agents against infectious diseases and cancer. 2024, The Author(s).
Thirumoorthy G.; Balasubramanian B.; George J.A.; Nizam A.; Nagella P.; Srinatha N.; Pappuswamy M.; Alanazi A.M.; Meyyazhagan A.; Rengasamy K.R.R.; Veerappa Lakshmaiah V.
All Open Access; Gold Open Access; Green Open Access
Thirumoorthy G., Department of Life Sciences, CHRIST (Deemed to be University), Hosur Rd, Karnataka, Bengaluru, 560029, India; Balasubramanian B., Department of Food Science and Biotechnology, College of Life Sciences, Sejong University, Seoul, South Korea; George J.A., Department of Life Sciences, CHRIST (Deemed to be University), Hosur Rd, Karnataka, Bengaluru, 560029, India; Nizam A., Department of Chemistry, CHRIST (Deemed to be University), Hosur Rd, Karnataka, Bengaluru, 560029, India; Nagella P., Department of Life Sciences, CHRIST (Deemed to be University), Hosur Rd, Karnataka, Bengaluru, 560029, India; Srinatha N., Department of Physics, RV Institute of Technology and Management, Bengaluru, 560 076, India; Pappuswamy M., Department of Life Sciences, CHRIST (Deemed to be University), Hosur Rd, Karnataka, Bengaluru, 560029, India; Alanazi A.M., Pharmaceutical Chemistry Department, College of Pharmacy, King Saud University, Riyadh, 11451, Saudi Arabia; Meyyazhagan A., Department of Life Sciences, CHRIST (Deemed to be University), Hosur Rd, Karnataka, Bengaluru, 560029, India; Rengasamy K.R.R., Laboratory of Natural Products and Medicinal Chemistry (LNPMC), Center for Global Health Research, Saveetha Medical College and Hospital, Saveetha Institute of Medical and Technical Sciences (SIMATS), Thandalam, Chennai, 602105, India, Centre of Excellence for Pharmaceutical Sciences, North-West University, Potchefstroom, 2520, South Africa; Veerappa Lakshmaiah V., Department of Life Sciences, CHRIST (Deemed to be University), Hosur Rd, Karnataka, Bengaluru, 560029, India
Identification of potential ZIKV NS2B-NS3 protease inhibitors from Andrographis paniculata: An insilico approach
Andrographis paniculata; Andrographolide; Molecular docking; Zika virus; ZIKV NS2B-NS3 protease
Andrographis paniculata is a widely used medicinal plant for treating a variety of human infections. The plant's bioactives have been shown to have a variety of biological activities in various studies, including potential antiviral, anticancer, and anti-inflammatory effects in a variety of experimental models. The present investigation identifies a potent antiviral compound from the phytochemicals of Andrographis paniculata against Zika virus using computational docking simulation. The ZIKV NS2B-NS3 protease, which is involved in viral replication, has been considered as a promising target for Zika virus drug development. The bioactives from Andrographis paniculata, along with standard drugs as control were screened for their binding energy using AutoDock 4.2 against the viral protein. Based on the higher binding affinity the phytocompounds Bisandrographolide A (-11.7), Andrographolide (-10.2) and Andrographiside (-9.7) have convenient interactions at the binding site of target protein (ZIKV NS2B-NS3 protease) in comparison with the control drug. In addition, using insilico tools, the selected high-scoring molecules were analysed for pharmacological properties such as ADME (Absorption, Distribution, Metabolism, and Excretion profile) and toxicity. Andrographolide was reported to have strong pharmacodynamics properties and target accuracy based on the Lipinski rule and lower binding energy. The selected bioactives showed lower AMES toxicity and has potent antiviral activity against zika virus targets. Further, MD simulation studies validated Bisandrographolide A & Andrographolide as a potential hit compound by exhibiting good binding with the target protein. The compounds exhibited good hydrogen bonds with ZIKV NS2B-NS3 protease. As a result, bioactives from the medicinal plant Andrographis paniculata can be studied in vitro and in vivo to develop an antiviral phytopharmaceutical for the successful treatment of zika virus. Communicated by Ramaswamy H. Sarma. 2021 Informa UK Limited, trading as Taylor & Francis Group.
Thirumoorthy G.; Tarachand S.P.; Nagella P.; Veerappa Lakshmaiah V.
Journal of Biomolecular Structure and Dynamics, Vol-40, No. 21, pp. 11203-11215.
ISSN: 7391102; PubMed ID: 34319220; CODEN: JBSDD
Thirumoorthy G., Department of Life Sciences, CHRIST (Deemed to be University), Karnataka, Bengaluru, India; Tarachand S.P., Department of Life Sciences, CHRIST (Deemed to be University), Karnataka, Bengaluru, India; Nagella P., Department of Life Sciences, CHRIST (Deemed to be University), Karnataka, Bengaluru, India; Veerappa Lakshmaiah V., Department of Life Sciences, CHRIST (Deemed to be University), Karnataka, Bengaluru, India
Intelligent Retrieval and Secure Content Generation in Consumer Healthcare Electronics Using Quantum Blockchain and Edge-Fog-Cloud Intelligence
consumer applications; Federated learning; privacy preserving; smart homes; spatiotemporal data analytics
To address the growing need for intelligent retrieval and personalized content generation in consumer healthcare electronic devices, this work proposes a secure, scalable, and AI-enhanced framework integrating wearable IoMT devices with edgefogcloud infrastructures. The system leverages quantum blockchain with Quantum Key Distribution (QKD) for tamper-proof storage of sensor data and applies a hybrid Practical Byzantine Fault Tolerance (pBFT) and Proof of Work (PoW) consensus for low-latency validation. At the edge layer, consumer medical devices, such as smart watches, smart patches, and mobile health assistants perform preliminary anomaly detection using lightweight BiLSTM-CNN models integrated with Quantum Neural Networks (QNN). When emergencies or anomalies are detected, the fog layer handles intelligent data retrieval and prioritization based on task urgency, network quality, and energy constraints. The cloud layer supports long-term storage and AI-driven content generation, such as personalized health summaries, alerts, and predictive reports. The architecture enables fast retrieval of user-specific biomedical data across consumer platforms and generates real-time decision support notifications through smartphones, wearables, and connected home healthcare centers. The simulation results demonstrate improved responsiveness, security, and retrieval efficiency compared to traditional IoMT architectures. This framework positions consumer healthcare electronic devices as intelligent, context-aware, and secure systems capable of real-time predictive assistance, data retrieval, and adaptive content generation for smart living environments. 2026 IEEE. All rights reserved.
Thirunavukkarasan, M.; Remamany, Krishna Priya; Vaishnnave, M.P.; Mary, S.A. Sahaaya Arul; Devarajan, Ganesh Gopal; Mahapatra, Rajendra Prasad
IEEE Transactions on Consumer Electronics;Volume;72;Issue;1;pp.2235-2242
Restricted Access; Hardcopy may be available in the library
https://doi.org/10.1109/TCE.2025.3650338 https://www.scopus.com/pages/publications/105026687691?origin=resultslist
Thirunavukkarasan M., Department of Computational Intelligence, School of Computer Science and Engineering, Vellore Institute of Technology, Tamil Nadu, Vellore, 632014, India; Remamany K.P., Department of Research and Consultancy, College of Engineering and Technology, University of Technology and Applied Sciences, Khasab, 811, Oman; Vaishnnave M.P., Department of Computer Science and Engineering, Sathyabama Institute of Science and Technology, Deemed to be University), Chennai, 600119, India; Mary S.A.S.A., Department of AIML and Data Science, Faculty of Engineering and Technology, CHRIST University, Kengeri Campus, Karnataka, Bengaluru, 560074, India; Devarajan G.G., Department of Computer Science and Engineering, Amity School of Engineering and Technology, Amity University, Uttar Pradesh, Noida, 201313, India; Mahapatra R.P., Department of Computer Science and Engineering, Faculty of Engineering and Technology, SRM Institute of Science and Technology, Delhi NCR Campus, Uttar Pradesh, 201204, India
Effect of Mentha arvensis enriched diet to promote the growth and immune response of Clarias batrachus against Aeromonas hydrophila challenge
Growth performance; Innate immune-related genes; Serum biochemical parameters; Walking cat fish; Wild mint
The study was conducted to investigate the effects of fish fed diet Mentha arvensis extract on growth performance, non-specific immunity and expression of some immune-related genes and resistance to Aeromonas hydrophila in Clarias batrachus. Five diets were formulated with 0, 1, 2, 3, and 4% of M. arvensis leaf extract. The results indicated that, compared to the control groups, 2-4% dietary inclusion increased growth and feed consumption. In the dietary inclusion of 3-4% M. arvensis extract groups were increased relative on weight gain, specific growth rate, RBC, WBC, total hemocyte counts, total protein, globulin than control. Fed diet supplements with 3% mint-extract increased the total protein, WBC and globulin and phagocytic indexes and lysozyme activity increased at the 2, 3 and 4% of mint groups relative to the control. The PCR analysis showed that TNF, IL-1, MyD88, and TLRs were increased in the 2-4% fed diet M. arvensis extract groups than the control. These results suggest that 3% of M. arvensis extract significantly influences the immunomodulatory activity and immune-specific genes of C. batrachus. 2024 The Authors
Thirunavukkarasu P.; Balasubramanian B.; Shanmugam S.; Lee J.-W.; Anjali Devi D.; Meyyazhagan A.; Liu W.-C.; Pappuswamy M.; Kim I.H.
Journal of King Saud University - Science, Vol-36, No. 5
https://doi.org/10.1016/j.jksus.2024.103170 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85188995636&doi=10.1016%2fj.jksus.2024.103170&partnerID=40&md5=74d0820ffbf917480a4edf128c33762d
Thirunavukkarasu P., Department of Biotechnology, Nehru Arts and Science College, Tamil Nadu, Coimbatore, 641105, India; Balasubramanian B., Department of Food Science and Biotechnology, College of Life Science, Sejong University, Seoul, 05006, South Korea; Shanmugam S., Department of Animal Resource and Science, Dankook University, Chungnam, Cheonan-si, South Korea; Lee J.-W., Department of Integrative Biological Sciences and Industry, College of Life Science, Sejong University, Seoul, 05006, South Korea; Anjali Devi D., Veterinary Assistant Surgeon-Veterinary Dispensary, Kudumiyanmalai, Tamil Nadu, Pudukkottai, India; Meyyazhagan A., Department of Life Science, CHRIST University, Karnataka, Bangalore, 560029, India; Liu W.-C., Department of Animal Science, College of Coastal Agricultural Sciences, Guangdong Ocean University, Zhanjiang, 524088, China; Pappuswamy M., Department of Life Science, CHRIST University, Karnataka, Bangalore, 560029, India; Kim I.H., Department of Animal Resource and Science, Dankook University, Chungnam, Cheonan-si, South Korea
Multitask EfficientNet affective computing for student engagement detection
Affective computing; Behavioral analysis; Deep learning; Emotional analysis; Engagement analysis; Face detection; Multi task-EfficientNEtB0; Multi-modal student analysis
In the realm of education, feedback emerges as a pivotal component, serving to foster engagement and interaction while also facilitating the refinement of teaching methods to capture and maintain student attention. Traditional classroom assessment methods often struggle to accurately gauge the degree of comprehension among students during lectures, relying on manual comment collection that inherently carries the risk of inaccuracies. In response to this challenge, a novel system has been proposed, harnessing the power of Facial Emotion Recognition (FER) technology to capture student feedback. Within this framework, students are given a unique avenue to convey their emotions and reactions, employing facial expressions and gestures as the means to communicate. This innovative approach enables the analysis of students emotional responses and thereby provides invaluable insights into their comprehension levels, as well as the overall quality and engagement experienced during lectures. The approach takes shape through the utilization of Computer Vision techniques, with a particular focus on an unobtrusive methodology for assessing students overall engagement. Overcoming limitations of traditional assessment, our approach integrates compound scaling, employing the proposed Multitask EfficientNetB0 model recognized for its proved accuracy in emotion recognition (95.7%) and behavior analysis (96.3%) across diverse datasets (DAiSEE, iSED, iSAFFE). The behavioral classification system categorizes students into Engaged and Disengaged classes within a multi-class framework, providing nuanced insights into comprehension and Student engagement. Assessment metrics, including ROC Curves, Precision, Recall, and F1-Score, ensure a thorough evaluation. Our systems adaptability is demonstrated across varied educational environments, showcasing real-world efficacy in classrooms, laboratories, and seminar halls. The inclusion of MTCNN enhances face detection capabilities, facilitating robust analysis in dynamic scenarios. Expanding its applicability, the model has been put to the test in a range of educational settings, including classrooms, laboratory environments, and seminar halls, offering dual-capability analysis of both emotions and behavior. This comprehensive approach yields nuanced insights into student engagement and interaction, and its performance has been validated through real-world deployment within classrooms and seminars The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.
Thiruthuvanathan M.M., Computer Science and Engineering, CHRIST University, Kanmanike, Karnataka, Bengaluru, 560029, India; Krishnan B., Computer Science and Engineering, CHRIST University, Kanmanike, Karnataka, Bengaluru, 560029, India
Multimodal emotional analysis through hierarchical video summarization and face tracking
Deep neural networks; Emotional intelligence; Entropy; Face detection; Hierarchical summarization; Video summarization; Viola Jones
The era of video data has fascinated users into creating, processing, and manipulating videos for various applications. Voluminous video data requires higher computation power and processing time. In this work, a model is developed that can precisely acquire keyframes through hierarchical summarization and use the keyframes to detect faces and assess the emotional intent of the user. The key-frames are used to detect faces using recursive Viola-Jones algorithm and an emotional analysis for the faces extracted is conducted using an underlying architecture developed based on Deep Neural Networks (DNN). This work has significantly contributed in improving the accuracy of face detection and emotional analysis in non-redundant frames. The number of frames selected after summarization was less than 30% using the local minima extraction. The recursive routine introduced for face detection reduced false positives in all the video frames to lesser than 2%. The accuracy of emotional prediction on the faces acquired through the summarized frames, on Indian faces achieved a 90%. The computational requirement scaled down to 40% due to the hierarchical summarization that removed redundant frames and recursive face detection removed false localization of faces. The proposed model intends to emphasize the importance of keyframe detection and use them for facial emotional recognition. 2021, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.
Multimedia Tools and Applications, Vol-81, No. 25, pp. 35535-35554.
Thiruthuvanathan M.M., Department of Computer Science and Engineering, School of Engineering and Technology, CHRIST (Deemed to be University), Bangalore, India; Krishnan B., Department of Computer Science and Engineering, School of Engineering and Technology, CHRIST (Deemed to be University), Bangalore, India
EMONET: A Cross Database Progressive Deep Network for Facial Expression Recognition
Cross database; Deep neural network; Emonet; Facial expression recognition; Progressive resizing
Recognizing facial features to detect emotions has always been an interesting topic for research in the field of Computer vision and cognitive emotional analysis. In this research a model to detect and classify emotions is explored, using Deep Convolutional Neural Networks (DCNN). This model intends to classify the primary emotions (Anger, Disgust, Fear, Happy, Sad, Surprise and Neutral) using progressive learning model for a Facial Expression Recognition (FER) System. The proposed model (EmoNet) is developed based on a linear growing-shrinking filter method that shows prominent extraction of robust features for learning and interprets emotional classification for an improved accuracy. EmoNet incorporates Progressive- Resizing (PR) of images to accommodate improved learning traits from emotional datasets by adding more image data for training and Validation which helped in improving the model's accuracy by 5%. Cross validations were carried out on the model, this enabled the model to be ready for testing on new data. EmoNet results signifies improved performance with respect to accuracy, precision and recall due to the incorporation of progressive learning Framework, Tuning Hyper parameters of the network, Image Augmentation and moderating generalization and Bias on the images. These parameters are compared with the existing models of Emotional analysis with the various datasets that are prominently available for research. The Methods, Image Data and the Fine-tuned model combinedly contributed in achieving 83.6%, 78.4%, 98.1% and 99.5% on FER2013, IMFDB, CK+ and JAFFE respectively. EmoNet has worked on four different datasets and achieved an overall accuracy of 90%. 2020. All Rights Reserved.
International Journal of Intelligent Engineering and Systems, Vol-13, No. 6, pp. 31-41.
https://doi.org/10.22266/ijies2020.1231.04 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85100673488&doi=10.22266%2fijies2020.1231.04&partnerID=40&md5=5b8a1a7783439d9c0a4d637b180208bf
Engagement Detection through Facial Emotional Recognition Using a Shallow Residual Convolutional Neural Networks
Convolutional neural network; Emotion detection; Facial expression recognition.; Residual networks; Student engagement detection
Online teaching and learning has recently turned out to be the order of the day, where majority of the learners undergo courses and trainings over the new environment. Learning through these platforms have created a requirement to understand if the learner is interested or not. Detecting engagement of the learners have sought increased attention to create learner centric models that can enhance the teaching and learning experience. The learner will over a period of time in the platform, tend to expose various emotions like engaged, bored, frustrated, confused, angry and other cues that can be classified as engaged or disengaged. This paper proposes in creating a Convolutional Neural Network (CNN) and enabling it with residual connections that can enhance the learning rate of the network and improve the classification on three Indian datasets that predominantly work on classroom engagement models. The proposed network performs well due to introduction of Residual learning that carries additional learning from the previous batch of layers into the next batch, Optimized Hyper Parametric (OHP) setting, increased dimensions of images for higher data abstraction and reduction of vanishing gradient problems resulting in managing overfitting issues. The Residual network introduced, consists of a shallow depth of 50 layers which has significantly produced an accuracy of 91.3% on ISED & iSAFE data while it achieves a 93.4% accuracy on the Daisee dataset. The average accuracy achieved by the classification network is 0.825 according to Cohens Kappa measure. 2020, Intelligent Engineering & System. All rights reserved.
Thiruthuvanathan M.M.; Krishnan B.; Rangaswamy M.
International Journal of Intelligent Engineering and Systems, Vol-14, No. 2, pp. 236-247.
https://doi.org/10.22266/ijies2021.0430.21 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85102826027&doi=10.22266%2fijies2021.0430.21&partnerID=40&md5=e7de08338c059e769a72b0cbf4dcc854
Thiruthuvanathan M.M., Department of Computer Science and Engineering, School of Engineering and Technology, CHRIST (Deemed to be University), Bangalore, India; Krishnan B., Department of Computer Science and Engineering, School of Engineering and Technology, CHRIST (Deemed to be University), Bangalore, India; Rangaswamy M., Department of Psychology, School of Social Sciences, Bangalore, India