Padmini Sankaramurthy | Energy | Research Excellence Award

Published-on by Indian Scientist

Dr. Padmini Sankaramurthy | Energy | Research Excellence Award

SRM Institute of Science and Technology | India

Dr. S. Padmini is an accomplished academic and researcher with a strong foundation in Electrical and Electronics Engineering, advanced training in Power Systems Engineering, and doctoral work centered on intelligent algorithms for hydrothermal scheduling, further strengthened by an ongoing postdoctoral fellowship in multidisciplinary AI applications. With extensive teaching and research experience in a premier institution, she has contributed significantly to curriculum development, research supervision, academic leadership, and large-scale institutional activities. Her scholarly output includes publications indexed in major databases, patents, major project proposals, international collaborations, and diverse technical contributions across intelligent systems, power systems optimization, energy economics, and AI-driven engineering solutions. She has delivered numerous courses, created substantial digital learning resources, and guided students at multiple academic levels. Her research has earned citations totalling 341, along with an h-index of 10 and an i10-index of 10, demonstrating meaningful global impact. She is recognized for excellence through multiple awards, including distinguished scientific honours, innovation-driven recognitions, and accolades for academic contributions. Her ongoing collaborations across global universities reflect her commitment to advancing multidisciplinary research. Dr. Padmini’s work continues to integrate engineering intelligence, sustainable energy solutions, and advanced computational methods to support emerging technological needs and societal progress.

Profiles: Google Scholar | Orcid | Scopus

Featured Publication

Jeevadason, A. W., Padmini, S., Bharatiraja, C., & Kabeel, A. E. (2022). A review on diverse combinations and Energy-Exergy-Economics (3E) of hybrid solar still desalination. Desalination, 527, 115587.

Rebecca, B., Kumar, K. P. M., Padmini, S., Srivastava, B. K., Halder, S., & Boopathi, S. (2024). Convergence of Data Science-AI-Green Chemistry-Affordable Medicine: Transforming Drug Discovery. In Handbook of Research on AI and ML for Intelligent Machines and Systems (pp. 348–373).

Venkatesh, B., Sankaramurthy, P., Chokkalingam, B., & Mihet-Popa, L. (2022). Managing the demand in a micro grid based on load shifting with controllable devices using hybrid WFS2ACSO technique. Energies, 15(3), 790.

Lakshmi, K., Amaran, S., Subbulakshmi, G., Padmini, S., Joshi, G. P., & Cho, W. (2025). Explainable artificial intelligence with UNet based segmentation and Bayesian machine learning for classification of brain tumors using MRI images. Scientific Reports, 15(1), 690.

Sankaramurthy, P., Chokkalingam, B., Padmanaban, S., Leonowicz, Z., … Padmini, S. (2019). Rescheduling of generators with pumped hydro storage units to relieve congestion incorporating flower pollination optimization. Energies, 12(8), 1477.

Uma V | Materials Science | Women Researcher Award

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Dr. Uma V | Materials Science | Women Researcher Award

Government Thirumagal Mill’s College | India

Dr. V. Uma is a dedicated physics educator and researcher specializing in advanced functional materials, particularly multiferroic nanomaterials and thin-film systems. She serves as a Guest Lecturer in the Department of Physics at Government Thirumagal Mills College, where she has handled extensive teaching responsibilities across UG and PG programs, guided numerous student projects, and delivered lectures in areas such as material science, microprocessors, electronics, nuclear physics, and numerical methods. Her academic background includes a Ph.D. in Physics focused on the synthesis and characterization of bismuth iron oxide nanomaterials, supported by an M.Phil. in Physical Sciences, M.Sc. in Physics, and a B.Ed. in Physical Science, along with qualification in the State Eligibility Test. Her research expertise covers nanomaterial synthesis using sol-gel, hydrothermal, microwave combustion, and thin-film deposition techniques, along with characterization skills in XRD, FTIR, UV, VSM, SEM, dielectric studies, and electrochemical analysis. She has published impactful research contributions with a citation count of 8, two indexed documents, and an h-index of 2, demonstrating emerging recognition within the scientific community. She has actively participated and presented in several national and international conferences in materials science. Her work reflects strong commitment toward innovative research, quality teaching, and the advancement of materials science applications.

Profile: Scopus

Featured Publications

Uma, V., & Seenuvasakumaran, P. (2023). Synthesis, structural, microstructural, optical, magnetic and dielectric properties of Nd-doped multiferroic Bismuth iron oxide. Results in Materials. https://doi.org/10.1016/j.rinma.2022.100359

Sachin Dhawale | Medicinal Chemistry | Best Researcher Award

Published-on by Indian Scientist

Assoc. Prof. Dr. Sachin Dhawale | Medicinal Chemistry | Best Researcher Award

Shreeyash Institute of Pharmaceutical Education and Research | India

Dr. Sachin Ashok Dhawale is a dedicated researcher in medicinal chemistry and pharmaceutical sciences, recognized for his contributions to drug design, molecular modeling, and synthetic chemistry. With a strong academic foundation including a PhD in Pharmacy, M.Pharm in Pharmaceutical Chemistry, and a B.Pharm degree, he has built extensive experience across academic and research environments, serving in teaching positions and contributing to funded research projects as a JRF and SRF under national scientific bodies. His scientific impact is reflected in 136 citations, an h-index of 6, and an i10-index of 4, supported by more than 25 Scopus-indexed publications, several as first or corresponding author, and cumulative impact exceeding 50. His research spans anticancer agents, antimicrobial molecules, molecular docking, natural product-based therapeutics, ADMET studies, and computational chemistry approaches. He has guided multiple undergraduate and postgraduate research projects, presented papers at national and international conferences, and published a specialized book on computer-aided drug design. His work has earned several recognitions including first-prize awards at competitive scientific events and fellowship support from national and state agencies. With expertise in advanced cheminformatics tools and synthetic techniques, he continues to contribute to innovative therapeutic discovery aimed at improving human health.

Profile: Google Scholar | Orcid

Featured Publications

Gandla, K., Islam, F., Zehravi, M., Karunakaran, A., Sharma, I., Haque, M. A., … Dhawale, S. A. (2023). Natural polymers as potential P-glycoprotein inhibitors: Pre-ADMET profile and computational analysis as a proof of concept to fight multidrug resistance in cancer. Heliyon, 9(9).

Singu, P. S., Kanugala, S., Dhawale, S. A., Kumar, C. G., & Kumbhare, R. M. (2020). Synthesis and pharmacological evaluation of some amide functionalized 1H-benzo[d]imidazole-2-thiol derivatives as antimicrobial agents. ChemistrySelect, 5(1), 117–123.

Dube, P. N., Sakle, N. S., Dhawale, S. A., More, S. A., & Mokale, S. N. (2019). Synthesis, biological investigation and docking study of novel chromen derivatives as anti-cancer agents. Anti-Cancer Agents in Medicinal Chemistry, 13.

Gawai, A. A., Kharat, A. R., Chorge, S. S., & Dhawale, S. A. (2023). Green synthesis of silver nanoparticles mediated Azadirachta indica extract and study of their characterization, molecular docking, and antibacterial activity. Journal of Molecular Recognition, 36(10), e3051.

Suryawanshi, R. M., Shimpi, R. B., Muralidharan, V., Nemade, L. S., Gurugubelli, S., … Dhawale, S. A. (2025). ADME, toxicity, molecular docking, molecular dynamics, glucokinase activation, DPP-IV, α-amylase, and α-glucosidase inhibition assays of mangiferin and friedelin for antidiabetic potential. Chemistry & Biodiversity, 22(5), e202402738.

Sivakamasundari | Computer Science | Best Researcher Award

Published-on by Indian Scientist

Ms. Sivakamasundari | Computer Science | Best Researcher Award

SRM Institute of Science and Technology | India

Ms. P. Sivakamasundari is a dedicated academic and researcher in Computer Science and Engineering, recognized for her contributions to deep learning-based medical image analysis. With qualifications spanning Diploma, Bachelor’s, and Master’s degrees in Computer Science and Engineering, she is currently pursuing her Ph.D. at SRM Institute of Science and Technology. She has extensive teaching experience as an Assistant Professor for more than a decade, during which she has guided students in core computing subjects including algorithms, computation theory, compiler design, and image classification. Her research focuses on advanced deep learning frameworks for healthcare applications, particularly diabetic retinopathy and diabetic foot ulcer detection, resulting in book chapters, conference publications, and journal manuscripts under review. She has published and filed patents related to medical imaging and automated disease detection systems, demonstrating her innovation-driven approach. Her scholarly presence includes 1 citation, 1 h-index, and 0 i10-index, indicating emerging research visibility. She has completed multiple professional certifications and participated in workshops, FDPs, and internships in machine learning, biometrics, accelerated computing, and high-performance healthcare analytics. Her work reflects strong commitment toward applying AI for societal benefit, and she continues to advance her expertise through active research and academic contributions.

Profile: Google Scholar

Featured Publications

Sivakamasundari, P., Anandhi, S., Kumaran, A. A., Vijayakumar, K., Birnica, Y. J., & others. (2024). Early detection of glaucoma utilizing retinal nerve fiber layer (RNFL) investigation. International Conference on Innovative Computing, Intelligent Communication and Smart Electrical Systems.

Sivakamasundari, P., & Niranjana, G. (2025). An automatic detection and classification of diabetic foot ulcers using Chebyshev chaotic ladybug beetle optimized extended Swin Transformer–InceptionV3 model. Biomedical Signal Processing and Control, 110, 108268.

Gomathi, G., Sumathy, V., Sivakamasundari, P., & Deepa, R. (2024). A various approaches of machine learning algorithms for kidney disease prediction. International Conference on Innovative Computing, Intelligent Communication and Smart Electrical Systems.

Sivakamasundari, P., & Niranjana, G. (2024). Diabetic foot ulcer classification using deep learning approach. International Conference on Computer, Communication and Signal Processing (ICCCSP).

Sivakamasundari, P., & Niranjana, G. (2023). A critique on deep learning methodologies employed for the identification of diabetic retinopathy using fundus images. Intelligent Computing and Control for Engineering and Business Systems (ICCEBS).

Krishna Pada Das | Biomathematics | Best Researcher Award

Published-on by Indian Scientist

Assoc. Prof. Dr. Krishna Pada Das | Biomathematics | Best Researcher Award

Mahadevananda Mahavidyalaya | India

Dr. Krishna Pada Das is an Associate Professor of Mathematics whose research primarily focuses on mathematical biology, especially population dynamics and eco-epidemiological systems. His work explores how infectious diseases interact with ecological food webs, including predator-prey and plankton systems, and how factors such as alternative food, nutrient levels, environmental toxins, diffusion, seasonal effects, and harvesting influence system stability or generate complex behaviors like chaos and oscillation. He employs mathematical modeling through differential equations (ODE, DDE, stochastic systems), fractional calculus, spatio-temporal dynamics, and computational simulation using MATLAB, MAPLE, and MATHEMATICA to analyze stability, bifurcation, and long-term behavior of biological systems. His research has contributed to understanding control strategies for harmful algal blooms, disease propagation in ecosystems, phytoplankton-zooplankton interactions, and tri-trophic food chain dynamics. In recent work, he has also examined epidemic models including COVID-19 and HIV transmission, applying sensitivity analysis, parameter estimation, and optimal control strategies. With an extensive list of research publications in international journals and collaborative studies, his contributions support improved ecological system management, disease control measures, and deeper theoretical insights into nonlinear biological dynamics.

Profile: Google Scholar

Featured Publications

Das, K., & Mukherjee, A. K. (2007). Differential utilization of pyrene as the sole source of carbon by Bacillus subtilis and Pseudomonas aeruginosa strains: Role of biosurfactants in enhancing availability. Journal of Applied Microbiology, 102(1), 195–203.

Dutta, S. K., Das, K., Ghosh, B., & Blackman, C. F. (1992). Dose dependence of acetylcholinesterase activity in neuroblastoma cells exposed to modulated radio-frequency electromagnetic radiation. Bioelectromagnetics, 13(4), 317–322.

Soni, B. K., Das, K., & Ghose, T. K. (1982). Bioconversion of agro-wastes into acetone butanol. Biotechnology Letters, 4(1), 19–22.

Kooi, B. W., van Voorn, G. A. K., & Das, K. P. (2011). Stabilization and complex dynamics in a predator–prey model with predator suffering from an infectious disease. Ecological Complexity, 8(1), 113–122.

Das, C. R., Mondal, N. K., Aditya, P., Datta, J. K., Banerjee, A., & Das, K. (2012). Allelopathic potentialities of leachates of leaf litter of some selected tree species on gram seeds under laboratory conditions. Asian Journal of Experimental Biological Sciences, 3(1), 59–65.

Major Singh Goraya | Computer Science | Best Researcher Award

Published-on by Indian Scientist

Prof. Major Singh Goraya | Computer Science | Best Researcher Award

Sant Longowal Institute of Engineering and Technology | India

Dr. Major Singh Goraya is a Professor in the Department of Computer Science and Engineering at Sant Longowal Institute of Engineering and Technology (SLIET), Longowal. He holds a Ph.D. from Punjabi University, complemented by strong academic foundations in computer engineering. His primary research areas include cloud computing, resource management, green computing, fault tolerance, and load balancing. With 27 Scopus-indexed publications, 460 citations across 399 documents, and an h-index of 10, Dr. Goraya has made consistent scholarly contributions to high-performance and sustainable computing. His studies have explored dynamic resource allocation, energy-efficient scheduling frameworks, and deep learning-based optimization techniques. He has supervised several Ph.D. and M.Tech. research scholars and continues to guide emerging researchers in cloud resource efficiency and intelligent computation. His international exposure through conferences in the UK, Canada, and Malaysia reflects his active engagement in global research forums. In addition, he has successfully organized numerous academic workshops, conferences, and training programs. Dr. Goraya’s innovative contributions strengthen the integration of artificial intelligence and cloud technology, promoting scalable and eco-efficient computational solutions that advance modern computer engineering research.

Profiles: Scopus | Orcid | Google Scholar

Featured Publications

Goyal, N., Singh, T., & Goraya, M. S. (2025). Deep convolutional neural networks vs. vision transformers for video-based human activity recognition. In Proceedings of Lecture Notes in Computer Science (pp. xx–xx). Springer.

Singh, J., & Goraya, M. S. (2023). An autonomous multi-agent framework using quality of service to prevent service level agreement violations in cloud environment. International Journal of Advanced Computer Science and Applications, 14(3).

Goyal, N., Goraya, M. S., & Singh, T. (2023). An axiomatic analysis for object detection and recognition using deep learning. In Smart Innovation, Systems and Technologies (pp. xx–xx). Springer.

Thakur, A., & Goraya, M. S. (2022). A workload and machine categorization-based resource allocation framework for load balancing and balanced resource utilization in the cloud. International Journal of Grid and High Performance Computing, 14(2).

Hasan, M., Goraya, M. S., & Garg, T. (2022). E-FFTF: An extended framework for flexible fault tolerance in cloud. In Lecture Notes in Networks and Systems (pp. xx–xx). Springer.