Ankush Kumar | Medicinal Chemistry | Young Researcher Award

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Mr. Ankush Kumar | Medicinal Chemistry | Young Researcher Award

Chitkara University Punjab | India

Ankush Kumar is a dedicated pharmaceutical researcher whose work spans medicinal chemistry, molecular modeling, and targeted drug design with a strong focus on anticancer therapeutics. He holds a solid academic foundation with qualifications in pharmaceutical chemistry and extensive research training that includes computational drug design, structure-activity exploration, and medicinal insights into heterocyclic scaffolds. His professional experience includes serving as a Junior Research Fellow and working as an Assistant Professor across multiple academic institutions, where he contributed to teaching, mentoring, and research development. His research outputs reflect a strong contribution to oncology-oriented drug discovery, with publications covering molecular targets, biomarkers, EGFR inhibition, heterocyclic synthesis, and computational repurposing studies. He has authored numerous research papers in reputed journals, contributing to a total of 561 citations, supported by an h-index of 6 and i10-index of 4, demonstrating the growing influence of his scientific contributions. His work includes book chapters and multiple peer-reviewed articles that highlight new therapeutic strategies and structural insights into various biological targets. He has received academic distinctions such as top positions in academic competitions and has served as a reviewer for international journals, further reflecting his scholarly engagement. Overall, his research trajectory reflects a commitment to advancing pharmaceutical innovation and precision-driven drug discovery.

Profile: Google Scholar

Featured Publications

Ye, F., Dewanjee, S., Li, Y., Jha, N. K., Chen, Z. S., Kumar, A., Vishakha, Jha, S. K., et al. (2023). Advancements in clinical aspects of targeted therapy and immunotherapy in breast cancer. Molecular Cancer, 22(1), 105.

Kumar, A., Sehgal, A., Singh, S., Sharma, N., Yadav, S., Rashid, S., Ali, N., et al. (2023). Understanding the mechanistic pathways and clinical aspects associated with protein and gene based biomarkers in breast cancer. International Journal of Biological Macromolecules, Article 126595.

Zheng, M., Kumar, A., Sharma, V., Sehgal, A., Wal, P., Shinde, N. V., Kawaduji, B. S., et al. (2024). Revolutionizing pediatric neuroblastoma treatment: unraveling new molecular targets for precision interventions. Frontiers in Cell and Developmental Biology, 12, Article 1353860.

Kumar, A., Kumar, B., & Bhatia, R. (2023). Design, synthesis, molecular docking, and biological evaluation of isatin-based fused heterocycles as epidermal growth factor receptor inhibitors. Assay and Drug Development Technologies, 21(5), 222–233.

Kumar, A., Narang, R. K., & Bhatia, R. (2023). Recent advancements in NS5B inhibitors (2011–2021): Structural insights, SAR studies and clinical status. Journal of Molecular Structure, 1293, Article 136272.

Sivakamasundari | Computer Science | Best Researcher Award

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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).

Sunil Datt Sharma | Computer Science | Best Researcher Award

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Dr. Sunil Datt Sharma | Computer Science | Best Researcher Award

Central University of Jammu | India

Dr. Sunil Datt Sharma is a distinguished researcher in the fields of Digital Signal Processing, Adaptive Signal Processing, and Machine Learning Applications, recognized for his contributions across computational biology, biomedical signal analysis, and intelligent imaging systems. With 289 citations, an h-index of 9, and 9 indexed documents, his research is widely acknowledged for its technical depth and interdisciplinary impact. He has authored numerous journal articles, conference papers, and book chapters covering areas such as CpG island detection, promoter identification using deep learning, image de-noising, transfer learning for fault diagnosis, micro-Doppler signature analysis, anisotropic diffusion models, and advanced frequency-domain algorithms. His academic background encompasses strong training in electronics, computing, and signal processing, complemented by extensive experience in teaching, research, and scholarly reviewing for reputed international journals. His research interests span computational genomics, machine learning-based biomedical systems, pattern recognition, and intelligent signal analysis. He has been actively engaged in professional peer-review activities for more than twenty journals, reflecting his standing within the global research community. His work integrates innovative algorithms with real-world applications, contributing to both theoretical advancement and practical solutions. Dr. Sharma continues to advance cutting-edge research aimed at addressing complex challenges across science and engineering.

Profile: Google Scholar

Featured Publications

Sharma, S. D., Shakya, K., & Sharma, S. N. (2011). Evaluation of DNA mapping schemes for exon detection. In 2011 International Conference on Computer, Communication and Electrical Technology (ICCCET). (Cited by: 42).

Sharma, S., Sharma, S. N., & Saxena, R. (2020). Identification of short exons disunited by a short intron in eukaryotic DNA regions. IEEE/ACM Transactions on Computational Biology and Bioinformatics, 17(5). (Cited by: 33).

Sharma, S. D., Saxena, R., & Sharma, S. N. (2015). Identification of microsatellites in DNA using adaptive S-transform. IEEE Journal of Biomedical and Health Informatics, 19(3), 1097–1105. (Cited by: 23).

Garg, P., & Sharma, S. (2020). Identification of CpG islands in DNA sequences using short-time Fourier transform. Interdisciplinary Sciences: Computational Life Sciences, 12(3), 355–367. (Cited by: 19).

Sharma, S. D., Saxena, R., Sharma, S. N., & Singh, A. K. (2015). Short tandem repeats detection in DNA sequences using modified S-transform. International Journal of Advances in Engineering and Technology, 8(2). (Cited by: 16).

Kabil Dev Mahato | Spectroscopy | Editorial Board Member

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Mr. Kabil Dev Mahato | Spectroscopy | Editorial Board Member

National Institute of Technology Jamshedpur | India

Kapil Dev Mahato is a researcher in physics whose work spans laser spectroscopy, computational chemistry, and data-driven modeling of organic dyes. His scientific contributions focus on predicting photophysical properties such as absorption and emission wavelengths, quantum yields, Stokes shift values, and Förster distance parameters using advanced machine-learning approaches. He has authored multiple peer-reviewed journal articles, conference papers, review works, and a book chapter, reflecting strong academic engagement and interdisciplinary impact. His Google Scholar profile reports 153 citations, an h-index of 7, and an i10-index of 6, demonstrating growing recognition in spectroscopy and machine-learning research communities. His academic training includes strong foundations in physics at both undergraduate and postgraduate levels, further strengthened by competitive national research fellowships. He has collaborated with experts in spectroscopy and computer science, contributing to work on fluorescent dyes, sol-gel systems, optical materials, and predictive ML models for scientific and biomedical data. His research interests include organic dye photophysics, fluorescence mechanisms, nanomaterials, ensemble learning methods, and scientific data modeling. He has participated in multiple national and international conferences, presenting work on spectroscopy and machine-learning applications. His overall profile highlights consistent research productivity, interdisciplinary collaboration, and commitment to advancing spectroscopy and computational modeling.

Profile: Google Scholar | Research gate | Linked In

Featured Publications

Mahato, K. D., & Kumar, U. (2024). Optimized machine learning techniques enable prediction of organic dyes photophysical properties: Absorption wavelengths, emission wavelengths, and quantum yields. Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy, 308, 123768.

Mahato, K. D., Das, S. S. K., Azad, C., & Kumar, U. (2024). Machine learning based hybrid ensemble models for prediction of organic dyes photophysical properties: Absorption wavelengths, emission wavelengths, and quantum yields. APL Machine Learning, 2(1).

Bhowmick, A., Mahato, K. D., Azad, C., & Kumar, U. (2022). Heart disease prediction using different machine learning algorithms. In 2022 IEEE World Conference on Applied Intelligence and Computing (AIC) (pp. 60–65).

Mahato, K. D., Das, S. S. G. K., Azad, C., & Kumar, U. (2024). Stokes shift prediction of fluorescent organic dyes using machine learning-based hybrid cascade models. Dyes and Pigments, 222, 111918.

Mahato, K. D., & Kumar, U. (2023). A review of organic dye-based nanoparticles: Preparation, properties, and engineering/technical applications. Mini-Reviews in Organic Chemistry, 20(7), 655–674.

Subhodeep Moitra | Artificial Intelligence | Young Researcher Award

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Mr. Subhodeep Moitra | Artificial Intelligence | Young Researcher Award

Techno College Hooghly | India

Subhodeep Moitra is a computer science researcher focused on advancing artificial intelligence through the fusion of human-like visual perception and cognition. His academic foundation spans computer applications at both undergraduate and postgraduate levels, where he built strong expertise in machine learning, deep learning, computer vision, neural networks, adversarial robustness, and cognitive modeling. His research explores self-supervised reconstruction, adversarial recovery, AGI-oriented theoretical computing, medical prediction systems, and environmental forecasting, with publications in journals, conferences, preprint platforms, and book chapters. He has contributed to projects ranging from temperature forecasting and brain-stroke detection to adversarially robust autoencoders and AGI theory. His professional experience includes serving as a visiting faculty member, teaching programming, mentoring research projects, and engaging in active collaborative work. His technical skills extend across Python, deep learning frameworks, MERN stack development, and cloud-based AI tools, supported by multiple certifications from NASA, NVIDIA, CERN, IBM, Oracle, and Coursera. He has presented papers at international conferences and earned best paper presentation awards for his contributions in machine learning–driven forecasting and adversarial perception. His long-term research interest lies in building unified AI systems capable of perceiving, reasoning, and adapting with human-inspired intelligence, aiming to push the boundaries of next-generation cognitive AI.

Profile: Google Scholar

Featured Publications

Moitra, S., & Banerjee, D. (n.d.). Robustness as Latent Symmetry: A Theoretical Framework for Semantic Recovery in Deep Learning. OSF.

Moitra, S., & Banerjee, D. (n.d.). Are We Even on the Right Track? A Theoretical Framework for AGI Beyond Classical Computation. Authorea Preprints.

Moitra, S., & Banerjee, D. (n.d.). Skip the Chaos: A Self-Supervised Learning-Powered Autoencoder for Adversarial Recovery. OSF.

Pintu, P., Subhodeep, M., & Deblina, B. (n.d.). The mystery of Neural Network: Linked with quantum mechanics and universe. ResearchGate.

Pal, P., Moitra, S., & Banerjee, D. (n.d.). The mystery of Neural Network: Linked with quantum mechanics and universe.

Jayachithra R | Bio-Medical | Best Researcher Award

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Dr. Jayachithra R | Bio-Medical | Best Researcher Award

Urumu Dhanalakshmi College | India

Dr. R. Jayachitra is a dedicated researcher and academic specializing in material characterization with extensive contributions to nanomaterial synthesis and application-oriented studies. With advanced qualifications in physics and related disciplines, she has accumulated strong experience in teaching at both postgraduate and undergraduate levels and long-standing research involvement. Her work focuses on graphene-based transition metal oxide nanocomposites for wastewater treatment and the development of nanosized functional materials such as yttrium-doped cerium dioxide for photocatalytic applications. She has also contributed to computational, electronic spectral analysis, and molecular docking investigations of complex organic compounds, highlighting their electronic activity, molecular interactions, and medicinal potential. Her research portfolio includes publications in SCI and Scopus-indexed journals, authorship of several ISBN-listed books, and a patent under process, demonstrating a consistent commitment to scientific innovation. Her Google Scholar profile reflects 366 citations, an h-index of 11, and an i10-index of 11, indicating the visibility and relevance of her scholarly work. She has collaborated with leading national institutes for ultrasonic and metallurgical characterization, adding interdisciplinary strength to her research outcomes. With expertise spanning material science, nanotechnology, and computational studies, she continues to contribute impactful findings aligned with the Best Researcher Award and Women Research Award categories.

Profile: Google Scholar

Featured Publications

Vijayalakshmi, K., Muthupandi, V., & Jayachitra, R. (2011). Influence of heat treatment on the microstructure, ultrasonic attenuation and hardness of SAF 2205 duplex stainless steel. Materials Science and Engineering: A, 529, 447–451.

Kanagavalli, A., Jayachitra, R., Thilagavathi, G., Padmavathy, M., Elangovan, N., & others. (2023). Synthesis, structural, spectral, computational, docking and biological activities of Schiff base (E)-4-bromo-2-hydroxybenzylidene) amino)-N-(pyrimidin-2-yl) benzenesulfonamide. Journal of the Indian Chemical Society, 100(1), 100823.

Thilagavathi, G., Kanagavalli, A., Jayachitra, R., Padmavathy, M., Elangovan, N., & others. (2022). Synthesis, structural, computational, electronic spectra, wave function properties and molecular docking studies of (Z)-4-(((5-methylfuran-2-yl)methylene)amino)-N-(thiazol-2-yl) …. Journal of the Indian Chemical Society, 99(12), 100786.

Jayachitra, R., Thilagavathi, G., Kanagavalli, A., Elangovan, N., Sirajunnisa, A., & others. (2023). Synthesis, computational, electronic spectra, and molecular docking studies of 4-((diphenylmethylene)amino)-N-(pyrimidin-2-yl) benzenesulfonamide. Journal of the Indian Chemical Society, 100(1), 100836.

Kanagavalli, A., Thilagavathi, G., Jayachitra, R., Elangovan, N., Sowrirajan, S., & others. (2023). Synthesis, electronic structure, UV–vis, wave function, and molecular docking studies of Schiff base (Z)-N-(thiazol-2-yl)-4-((thiophene-2-ylmethylene)amino) benzenesulfonamide. Polycyclic Aromatic Compounds, 43(10), 8710–8728.

Aishwarya Jaiswal | Numerical Analysis | Best Researcher Award

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Ms. Aishwarya Jaiswal | Numerical Analysis | Best Researcher Award

IIT BHU| India

Aishwarya Jaiswal is a dedicated researcher in numerical analysis of partial differential equations, contributing to the advancement of efficient and uniformly convergent computational methods. With strong academic preparation in mathematics and computing from premier Indian institutes, she has developed expertise in numerical schemes for singularly perturbed systems, convection–diffusion models, parabolic reaction–diffusion equations, and multiscale interface problems. Her scholarly output includes multiple peer-reviewed publications in international journals, supported by citation metrics that reflect early research impact, including 1 citation, 1 h-index, and 0 i10-index, along with 1 indexed document. She has worked on diverse research themes such as boundary and interior layer phenomena, component-wise splitting algorithms, higher-order numerical schemes, and efficient discretization techniques. Her academic journey includes hands-on research experience through conference presentations, workshops, and collaborative visits at reputed institutions, contributing to global knowledge exchange in applied mathematics. Her interests span numerical PDEs, error analysis, computational methods, and scientific computing. She has been recognized with prestigious competitive awards, including highly regarded research fellowships that support her doctoral investigations. Through her continued focus on accuracy, robustness, and computational efficiency, she aims to contribute impactful advancements to the field of numerical mathematics and applied scientific computation.

Profile: Google Scholar

Featured Publications

Jaiswal, A., Kumar, S., & Ramos, H. Boundary and interior layer phenomena in coupled multiscale parabolic convection–diffusion interface problems: Efficient numerical resolution and analysis. International Journal of Numerical Methods for Heat & Fluid Flow., Cited by: 1

Jaiswal, A., Kumar, S., & Clavero, C. Efficient component-wise splitting approach to solve coupled singularly perturbed parabolic reaction–diffusion systems with interior layers. Numerical Algorithms.

Jaiswal, A., Kumar, S., & Ramos, H. Efficient uniformly convergent numerical methods for singularly perturbed parabolic reaction–diffusion systems with discontinuous source term. Journal of Applied Mathematics and Computing.

Jaiswal, A., Kumar, S., & Kumar, S. A priori and a posteriori error analysis for a system of singularly perturbed Volterra integro-differential equations. Computational and Applied Mathematics, 42(6), 278.

Banushri S | Deep Learning | Best Researcher Award

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Mrs. Banushri S | Deep Learning | Best Researcher Award

Impact College of Engineering and Applied Sciences| India

Dr. Banushri S is an accomplished academician and researcher in Computer Science and Engineering, recognized for her contributions to machine learning, deep learning, image processing, and human activity recognition. She has published impactful research across reputed journals and conferences, contributing to the scientific community with multiple documents, citations, and an evolving h-index that reflects her growing influence in the field. Her academic journey includes strong foundational degrees in engineering and digital electronics, shaping her expertise in logic design, embedded systems, computer architecture, networking, operating systems, programming, and advanced AI-driven technologies. With extensive teaching experience as an Assistant Professor in leading engineering institutions, she has guided numerous undergraduate and postgraduate learners, supervised research projects, and supported curriculum development and departmental academic activities. Her research works span areas such as fall detection, wearable sensor data analysis, transfer learning, and long-range context modeling for human activity recognition, including contributions indexed in Scopus and other reputed platforms. She has actively participated in faculty development programs, workshops, and conferences focusing on artificial intelligence, computer vision, and full-stack technologies, while also being a member of professional bodies like ISTE and CSI. Her work reflects a commitment to advancing intelligent systems research and contributing to academic excellence.

Profile: Scopus

Featured Publications

Banushri, S. (2026). Attention-guided residual shrinkage with gated recurrent unit for human activity recognition. Information Processing and Management.

Runu Banerjee Roy | Artificial Intelligence | Best Researcher Award

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Dr. Runu Banerjee Roy | Artificial Intelligence | Best Researcher Award

Jadavpur University| India

Dr. Runu Banerjee Roy is a Professor in Instrumentation and Electronics Engineering at Jadavpur University, widely recognized for her contributions to electronic olfaction, taste sensing, molecular imprinting, sensor development, and artificial intelligence–based instrumentation. She has an extensive research profile with 1213 citations, an h-index of 17, and an i10-index of 25, reflecting her strong scholarly impact and research productivity. Her publication record includes more than 50 peer-reviewed journal papers, around 40 conference papers, multiple book chapters, and several patents that are granted, published, or filed. She has successfully guided doctoral and postgraduate research scholars and completed multiple sponsored research projects funded by major scientific agencies, focusing on portable sensing devices, electronic nose and tongue systems, and electrochemical detection technologies for applications in food quality and safety. In academics, she has served in leadership roles such as Head of Department, NBA accreditation coordinator, and curriculum committee member, contributing to program development and quality enhancement. Her work has earned recognition through competitive research awards, scientific prizes, and support for international research presentations. With strong expertise spanning instrumentation, intelligent sensing systems, and applied electronics, she continues to advance innovative research, academic excellence, and technology-driven solutions in modern sensor engineering.

Profile: Google Scholar | Scopus

Featured Publications

Roy, R. B., Tudu, B., Shaw, L., Jana, A., Bhattacharyya, N., & Bandyopadhyay, R. (2012). Instrumental testing of tea by combining the responses of electronic nose and tongue. Journal of Food Engineering, 110(3), 356–363.

Banerjee, M. B., Roy, R. B., Tudu, B., Bandyopadhyay, R., & Bhattacharyya, N. (2019). Black tea classification employing feature fusion of E-Nose and E-Tongue responses. Journal of Food Engineering, 244, 55–63.

Banerjee, R., Tudu, B., Bandyopadhyay, R., & Bhattacharyya, N. (2016). A review on combined odor and taste sensor systems. Journal of Food Engineering, 190, 10–21.

Roy, R. B., Chattopadhyay, P., Tudu, B., Bhattacharyya, N., & Bandyopadhyay, R. (2014). Artificial flavor perception of black tea using fusion of electronic nose and tongue response: A Bayesian statistical approach. Journal of Food Engineering, 142, 87–93.

Nag, S., Pradhan, S., Naskar, H., Roy, R. B., Tudu, B., Pramanik, P., … et al. (2021). A simple nano cerium oxide modified graphite electrode for electrochemical detection of formaldehyde in mushroom. IEEE Sensors Journal, 21(10), 12019–12026.

Gaurang Sharma | Mathematics | Young Researcher Award

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Dr. Gaurang Sharma | Mathematics | Young Researcher Award

College of Agricultural Engineering & Technology | India

Dr. Sharma Gaurangkumar Bhanuprasad is a dedicated academician and researcher currently serving as an Assistant Professor at the College of Agricultural Engineering & Technology, Godhra. He holds a doctoral degree in Mathematics with a strong foundation in advanced mathematical concepts and their real-world applications. His primary research interests include mathematical modeling and epidemiology, with a focus on developing analytical frameworks for understanding complex biological and environmental systems. He has contributed significantly to the research community through multiple publications, including five journal papers indexed in reputable scientific databases, two research papers presented at international conferences, and two patents under process. His Google Scholar profile reflects his growing scholarly influence, supported by a citation count of three, documented research outputs, and measurable academic contributions. He is also associated with professional bodies through two memberships, reinforcing his involvement in collaborative scientific initiatives. His research identifiers include an ORCID profile and a Scopus Author ID, ensuring transparent documentation of his academic work. He has been honored with the SHODH scholarship awarded by the Education Department of Gujarat for research excellence. Dr. Sharma continues to advance innovative ideas in mathematical sciences, contributing meaningfully to the academic, scientific, and applied research communities.

Profile: Google Scholar

Featured Publications

Sharma, A., Sharma, G., & Singh, F. (2023). Computational models to study the infectious disease COVID-19: A review. International Journal of Mathematical Modelling and Numerical Optimisation.

Sharma, G., Sharma, A., & Parmar, N. (2024). A delay differential equation model on COVID-19 with vaccination strategy. RAIRO – Operations Research, 58(5), 4093–4117.

Sharma, G., & Sharma, A. (2024). The effect of double dose of vaccination on COVID-19 infection in India: A mathematical study. World Scientific News, 195, 7–18.

Sharma, G., & Sharma, A. (2025). Computational model on viral load dynamics in response to COVID-19 infection in a cell. The European Physical Journal Plus, 140(11), 1–15.

Sharma, G., Sharma, A., & Parmar, N. (2025). Mathematical model on COVID-19 transmission with absence and presence of second dose of vaccination in Turkey. TWMS Journal of Applied and Engineering Mathematics.