Vaibhav Tummalapalli | Machine learning | Excellence in Innovation Award

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Mr. Vaibhav Tummalapalli l Machine learning
| Excellence in Innovation Award

Epsilon Data Management, LLC | United States

Mr. Vaibhav Tummalapalli’s research focuses on the advancement of applied machine learning methodologies, predictive modeling, and data-driven optimization across large-scale industrial domains, particularly automotive and telecommunications. His work emphasizes the integration of artificial intelligence in lifecycle analytics, customer engagement, and personalization strategies to enhance business intelligence and operational efficiency. His studies explore innovative modeling frameworks such as EV Conquest modeling, VIN-level mileage prediction, and vehicle recommendation systems, which apply behavioral, telematics, and demographic data to drive precision marketing and service optimization. Additionally, his contributions to outlier detection, cohort-based stratified sampling, and KNN imputation distance metrics extend theoretical and applied understanding in data preprocessing and imbalanced learning. His research also addresses model monitoring and drift management using SAS Viya and PySpark-based architectures, ensuring robust model performance in production environments. Through the development of scalable ML pipelines, channel propensity models, and retention-focused predictive systems, his work demonstrates the transformative potential of AI in driving measurable business outcomes, customer retention, and ethical personalization. His scholarly and technical pursuits collectively aim to advance the design of intelligent, explainable, and sustainable machine learning systems for real-world, high-impact applications

Featured Publications

Tummalapalli, V. (2025). Understanding distance metrics in KNN imputation: Theoretical insights and applications. Journal of Mathematical & Computer Applications, 4(4), 1–4. https://doi.org/10.47363/JMCA

Tummalapalli, V. (2025). Machine learning pipeline for automotive propensity models. International Journal of Core Engineering & Management, 8(3), [Issue-03].

Tummalapalli, V. (2025). Outlier detection & treatment for machine learning models. International Journal of Innovative Research and Creative Technology, 11(3).

Tummalapalli, V. (2025). Stratified sampling in cohort-based data for machine learning model development. International Scientific Journal of Engineering and Management, 4.

Reshma R | Mathematics | Best Scholar Award

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Ms. Reshma R l Mathematics | Best Scholar Award

Amrita Vishwa Vidyapeetham, Coimbatore | India

Ms. Reshma R is a dedicated researcher in applied and computational mathematics, with her work primarily focusing on nonlinear dynamics, fractional-order systems, and neural network synchronization. Her research explores the mathematical modeling, control, and stability analysis of complex systems with applications in secure communication and biometric image encryption. She has published in high-impact journals such as Communications in Nonlinear Science and Numerical Simulation and Mathematical Methods in the Applied Sciences, contributing to advancements in Mittag-Leffler synchronization, hybrid control strategies, and event-triggered systems. Her studies on fractional-order disturbed chaotic neural networks and Lyapunov-based stability conditions offer innovative approaches to enhancing security and robustness in image communication systems. Additionally, her work on hybrid event-triggered singular time-delay systems presents new methodologies for exponential stabilization, bridging theoretical mathematics with engineering applications. Ms. Reshma’s interdisciplinary approach integrates differential calculus, control theory, and computational modeling, reflecting her strong analytical foundation and research depth. She actively participates in national and international conferences, engaging with emerging areas in nonlinear complex systems and mathematical modeling. Her commitment to mathematical innovation and interdisciplinary collaboration underpins her growing impact within the mathematical sciences research community.

Featured Publications

Ramaswami, R., Arumugam, V., & Pathmanaban, S. (2026). Mittag-Leffler synchronization of fractional order disturbed chaotic neural networks with varying time-delay using hybrid controller and its application to biometric image encryption. Communications in Nonlinear Science and Numerical Simulation, 119, 109350. https://doi.org/10.1016/j.cnsns.2025.109350

Ramaswami, R., Arumugam, V., & Pathmanaban, S. (2025). Lyapunov conditions for the finite-time stability of fractional order disturbed nonlinear systems and neural networks: The secure image communication using encryption. Communications in Nonlinear Science and Numerical Simulation, 117, 108716. https://doi.org/10.1016/j.cnsns.2025.108716

Ramaswami, R., Thoppilkalam, N. V., Arumugam, V., & Alzabut, J. (2024). Exponential stabilization of hybrid event-triggered singular time-delay systems. Mathematical Methods in the Applied Sciences, 47(14), 10190. https://doi.org/10.1002/mma.10190

Dharmalingam Kirubakaran | Pharmacology | Young Scientist Award

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Dr. Dharmalingam Kirubakaran l Pharmacology
| Young Scientist Award

Saveetha University | India

Dr. Dharmalingam Kirubakaran’s research focuses on the green synthesis of nanoparticles and their multifaceted biomedical applications, encompassing antioxidant, antibacterial, anti-inflammatory, antidiabetic, and anticancer activities. His work extensively explores plant-mediated nanoparticle synthesis using bioactive extracts from species such as Strobilanthes cordifolia, Acmella caulirhiza, Cajanus albicans, Lantana camara, and Mangifera indica, combining traditional phytochemistry with advanced nanotechnology approaches. Characterization techniques employed include UV-Visible spectrophotometry, Fourier Transform Infrared (FTIR) spectroscopy, X-ray Diffraction (XRD), Scanning and Transmission Electron Microscopy (SEM/TEM), Energy Dispersive Spectroscopy (EDAX), and Dynamic Light Scattering (DLS), ensuring comprehensive analysis of particle morphology, size, and stability. Complementing synthesis studies, his in vitro assessments encompass antibacterial activity against Gram-positive and Gram-negative bacteria, antioxidant assays such as DPPH, ABTS, H₂O₂, and FRAP, anti-inflammatory and antidiabetic evaluations, anticholinergic enzyme inhibition, and anticancer effects on multiple cell lines including MCF-7, A431, HeLa, and A549. Additional investigations include mosquito larvicidal bioassays, photocatalytic dye degradation, and ethnobotanical studies of medicinal plants. His publications reveal a strong emphasis on sustainable, plant-based nanomaterials for biomedical applications, with work on PVA–PEG and chitosan-based nanocomposites highlighting potential in food packaging, therapeutic interventions, and drug delivery. Collectively, his research integrates phytochemistry, nanotechnology, and pharmacological evaluation to advance environmentally friendly and biologically effective solutions for healthcare and environmental challenges.

Featured Publications

  • Kirubakaran, D., Wahid, J. B. A., Karmegam, N., Jeevika, R., Sellapillai, L., … (2025). A comprehensive review on the green synthesis of nanoparticles: Advancements in biomedical and environmental applications. Biomedical Materials & Devices, 1–26.

  • Manimegalai, P., Selvam, K., Loganathan, S., Kirubakaran, D., Shivakumar, M. S., … (2024). Green synthesis of zinc oxide (ZnO) nanoparticles using aqueous leaf extract of Hardwickia binata: Their characterizations and biological applications. Biomass Conversion and Biorefinery, 14(11), 12559–12574.

  • Kirubakaran, D., Selvam, K., Prakash, P., Shivakumar, M. S., Rajkumar, M. (2023). In-vitro antioxidant, antidiabetic, anticholinergic activity of iron/copper nanoparticles synthesized using Strobilanthes cordifolia leaf extract. OpenNano, 14, 100188.

  • Rajkumar, M., Presley, S. I. D., Govindaraj, P., Kirubakaran, D., Farahim, F., Ali, T., … (2025). Synthesis of chitosan/PVA/copper oxide nanocomposite using Anacardium occidentale extract and evaluating its antioxidant, antibacterial, anti-inflammatory and … Scientific Reports, 15(1), 3931.

  • Kirubakaran, D., Bupesh, G., Wahid, J. B. A., Murugeswaran, R., Ramalingam, J., … (2025). Green synthesis of zinc oxide nanoparticles using Acmella caulirhiza leaf extract: Characterization and assessment of antibacterial, antioxidant, anti … Biomedical Materials & Devices, 1–22.

Hellen Agumba | Education | Best Scholar Award

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Dr. Hellen Agumba l Education | Best Scholar Award

University of Johannesburg | South Africa

Dr. Hellen Agumba’s research focuses on the intersection of rurality, education access, and curriculum development within higher education contexts. Her scholarly work examines how students from rural backgrounds navigate challenges in entering and succeeding in higher education institutions, with particular attention to socio-economic disparities, institutional support mechanisms, and curriculum inclusivity. Through qualitative and interpretive research approaches, she explores how geographic and cultural contexts shape educational trajectories, student identities, and academic performance. Her contributions extend to curriculum studies, formative assessment practices, and the professional development of educators in economic and management sciences. Agumba’s ongoing research engages with the transformation of educational practices through contextualized pedagogy and equitable learning opportunities, aiming to inform both policy and teaching frameworks that respond to the realities of diverse student populations. She has contributed to national and international collaborative projects, such as the South African Rurality in Higher Education (SARIHE) initiative, which investigates rural transitions and access within post-apartheid education systems. Her current and emerging publications continue to advance discourse on decoloniality, inclusivity, and student agency in curriculum design and implementation, reinforcing her commitment to fostering socially just and responsive higher education in South Africa and beyond.

Featured Publications

Agumba, H., & Simpson, Z. (2024). Towards student engagement: Recognising alternative forms of capital among students from rural backgrounds. In Reimagining South African higher education: Towards a student-centred learning and teaching future (pp. xx–xx). DOI: https://doi.org/10.52779/9781991260468/06

Agumba, H., Simpson, Z., & Ndofirepi, A. (2023). Towards understanding the influence of rurality on students’ access to and participation in higher education. Critical Studies in Teaching and Learning, 11(1), Article 643. https://doi.org/10.14426/cristal.v11i1.643

Motsaathebe, S., Agumba, H., & Van Vuuren, C. (2022). Situating inquiry pedagogical practices in the classroom to foster a high-impact research-minded learning experience. African Journal of Inter/Multidisciplinary Studies, 4(1), Article 1030. https://doi.org/10.51415/ajims.v4i1.1030

Rakesh kumar Ramanathan | Chemistry | Best Review Paper Award

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Mr. Rakesh kumar Ramanathan l Chemistry
| Best Review Paper Award

Aakash institute of technology | India

Mr. Rakesh Kumar Ramanathan’s research primarily focuses on the synthesis and characterization of organic and inorganic nanoparticles, emphasizing their structural, thermal, optical, and antibacterial properties for advanced material applications. His recent open-access publication, “Structural, Thermal, Optical, Mechanical, and Antibacterial Properties of PLA/Nanoclay/TiO₂ Nanocomposite Films” in Letters in Applied Nanobioscience (2023), explores polymer nanocomposite films that enhance biocompatibility, strength, and antibacterial efficiency—contributing to potential applications in biomedical and packaging industries. His experimental and computational chemistry background enables him to integrate green synthesis techniques using natural extracts and hydrothermal processes for developing CuO, ZnO, and MnO₂ nanoparticles. His projects demonstrate interdisciplinary approaches, including natural nano-medicine for carcinoma treatment, solar cell efficiency enhancement using organic dyes, and chemosensor formation for detecting reactive nitrogen species such as peroxynitrite. With a strong foundation in spectroscopy and instrumentation (including NMR), he has presented his work at national conferences and workshops in computational and applied chemistry. Through his innovative nanoparticle synthesis and application-oriented projects, Mr. Ramanathan’s research contributes to sustainable nanotechnology, clean energy development, and biomedical advancements—reflecting a growing expertise in the field of chemical and material science

Profile:  Google Scholar

Featured Publication

Mukherjee, C., Varghese, D., Krishna, J. S., Boominathan, T., Rakeshkumar, R., & … (2023). Recent advances in biodegradable polymers–properties, applications and future prospects. European Polymer Journal, 192, 112068. https://doi.org/10.1016/j.eurpolymj.2023.112068

Yunwen Xu | Engineering | Best Researcher Award

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Dr. Yunwen Xu l Engineering
| Best Researcher Award

Shanghai Jiao Tong University | China

Dr. Yunwen Xu’s research focuses on advancing intelligent transportation systems, autonomous driving control, and predictive control for complex and embedded systems. Her innovative work integrates graph-based spatial-temporal modeling, data-driven control algorithms, and real-time optimization to enhance vehicle trajectory prediction, traffic signal management, and collaborative control in large-scale dynamic environments. Through over 50 high-impact publications, including 15 in top-tier journals and several ESI highly cited papers, Dr. Xu has significantly contributed to the theoretical and practical foundations of predictive control and intelligent mobility. Her research achievements include developing FPGA-based predictive controllers, robust model predictive frameworks, and reinforcement learning-based control systems for V2X-enabled autonomous vehicles. By leading national and provincial research projects and collaborating internationally with institutions like Purdue University and industrial partners such as Shanghai Electric Wind Power Group, she bridges the gap between academic innovation and industrial application. Her patents and successful technology transfers in microgrid energy management and advanced temperature control demonstrate the translational strength of her research. Recognized with prestigious honors, including the Best Paper Award at the Chinese Process Control Conference and championship at the Autonomous Driving Algorithm Challenge, Dr. Xu continues to pioneer next-generation control and automation technologies that drive the evolution of intelligent, efficient, and sustainable transportation ecosystems.

Profile:  Google Scholar 

Featured Publications

Gidado M. J | Agricultural and Biological Sciences | Young Scientist Award

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Dr. Gidado M. J. l Agricultura l and Biological Sciences
| Young Scientist Award

University Malaysia Perlis | Nigeria

Dr. Gidado M. J. is a distinguished researcher in postharvest technology, food innovation, and nanotechnology, with a focus on developing sustainable solutions for fruit quality preservation and food safety. His work integrates green chemistry, biopolymer-based coatings, and hydrophobic deep eutectic solvent nanoemulsions to extend shelf life, control postharvest pathogens, and improve the physiological and biochemical quality of horticultural produce. By combining experimental, computational, and AI-driven approaches, he has advanced understanding of fruit physiology, microbial inhibition, and smart packaging systems. His research contributions include the design of nanobiocomposite films, edible coatings functionalized with bioactive compounds, and intelligent sensing platforms for real-time monitoring of postharvest quality. With over 20 publications in high-impact journals, one patent, and multiple awards for scientific excellence, Dr. Gidado has demonstrated significant impact on sustainable agriculture, circular food systems, and postharvest innovation. His interdisciplinary work bridges academia and industry, fostering eco-friendly technologies, digital biotechnology integration, and knowledge transfer for enhanced food security and preservation practices worldwide.

Profile:  Google Scholar  | Scopus | Orcid | Researchgate | LinkedIn 

Featured Publications

Gidado, M. J., Gunny, A. A. N., Gopinath, S. C. B., Ali, A., Wongs-Aree, C., … (2024). Challenges of postharvest water loss in fruits: Mechanisms, influencing factors, and effective control strategies–A comprehensive review. Journal of Agriculture and Food Research, 101249. https://doi.org/10.1016/j.jafr.2024.101249

Gidado, M. J., Gunny, A. A. N., Gopinath, S. C. B., Wongs-Aree, C., Yusoff, N. H. A., … (2024). Effect of hydrophobic deep eutectic oil-in-water nano coating on the quality preservation of postharvest ‘Harumanis’ mango. Colloids and Surfaces A: Physicochemical and Engineering Aspects, 691, 133818. https://doi.org/10.1016/j.colsurfa.2024.133818

Gidado, M. J., Gunny, A. A. N., Gopinath, S. C. B., Wongs-Aree, C., Makhtar, M. M. Z., … (2023). Formulation of selective hydrophobic deep eutectic oil-in-water nanoemulsion as green fungicides for mitigating anthracnose fungus Colletotrichum gloeosporioides. Process Biochemistry, 135, 40–49. https://doi.org/10.1016/j.procbio.2023.01.010

Gidado, M. J., Gunny, A. A. N., Sankari, R. S. A., Gopinath, S. C. B., … (2023). Delaying the ripening of banana fruit and increased storage shelf-life using hydrophobic deep eutectic oil (Menthol–Thymol)-In-Water nanoemulsion coating. International Conference on Biomass Utilization and Sustainable Energy, 13–22.

Gidado, M. J., Gunny, A. A. N., Gopinath, S. C. B., Devi, M., Jayavalli, R., Ilyas, R. A. (2025). Challenges in selecting edible coating materials for fruit postharvest preservation and recent advances in edible coating techniques: A review. Journal of Food Science and Technology, 1–11. https://doi.org/10.1007/s13197-025-07015-4

Vanlalhmangaihsanga | Veterinary | Young Scientist Award

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Assist. Prof. Dr. Vanlalhmangaihsanga l Veterinary
| Young Scientist Award

Skisha ‘O’ Anusandhan | India

Assist. Prof. Dr. Vanlalhmangaihsanga research focuses on livestock production management, with particular emphasis on swine and poultry performance, welfare, and sustainable animal husbandry practices. His studies explore the physiological, behavioral, and production impacts of various management interventions such as castration methods, weaning systems, and stocking density optimization under Indian farming conditions. He has conducted comparative evaluations of surgical and chemical castration agents (AgNO₃ and KMnO₄) on growth and carcass quality in pigs, providing valuable insights for humane and cost-effective production systems. His research extends to assessing the effects of environmental and management factors—such as flooring type, weaning age, and feed conversion efficiency—on animal welfare and productivity. In poultry science, his collaborative studies analyze the performance and economic viability of different broiler and layer strains (COBB 430 Y and BV 300) under intensive rearing systems, focusing on feed conversion ratio, mortality rate, and financial sustainability. His contributions also include the molecular detection of haemoprotozoan pathogens in ticks, highlighting his interdisciplinary approach that connects animal health with production efficiency. Dr. Vanlalhmangaihsanga’s research outcomes, published in journals such as Veterinaria, Journal of Advances in Biology & Biotechnology, and International Journal of Chemical Studies, contribute significantly to optimizing livestock productivity, improving animal welfare, and promoting sustainable farming practices. His ongoing investigations in livestock behavior, stress mitigation, and performance enhancement aim to support evidence-based strategies for efficient, ethical, and resilient animal production systems in tropical environments.

Profile:  Google Scholar 

Featured Publications

Roy, N. K., Praharaj, N., Mohanty, S., & Majumder, S. (2025). Performance and financial analysis in broiler (COBB 430 Y strain): A case study on the feed conversion ratio and mortality rate. Veterinaria, 74(1), 113–123.

Sandhu, K., Singh, A. K., Singh, Y., Chahal, U., & Malik, D. S. (2024). Impact of stocking density on welfare, behavior, and growth performance of Large White Yorkshire pigs under Indian condition. Journal of Advances in Biology & Biotechnology, 27(12), 21–30.

Vanlalhmangaihsanga, L. H. (2021). Comparison of carcass characteristics between KMnO₄ and AgNO₃ as chemical castration in finisher pigs. Journal of Veterinary Medicine & Surgery, 5(3), 37.

Vanlalhmangaihsanga, L. H., Kalita, G., Goswami, R., & Samanta, A. K. (2019). Effect of surgical castration and chemical castrations (AgNO₃ and KMnO₄) on the production performance of growing finishing pigs. International Journal of Chemical Studies (IJCS), 7(4), 2819–2821.

Majumder, S., Vanlalhmangaihsanga, S. S., & Kumar, N. (n.d.). A comprehensive study on production performance in COBB 430 Y strain of broiler and BV 300 strain layer reared in intensive poultry farm of IVSAH, SOA.

Jing Fan | Environmental Science | Best Researcher Award

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Assoc. Prof. Dr. Jing Fan l Environmental Science
| Best Researcher Award

Zhaotong University | China

Assoc. Prof. Dr. Jing Fan the research focuses on advancing understanding of geological and hydrological disaster mechanisms with particular emphasis on rock fracture behavior, structural evolution, and fluid migration in complex subsurface environments. Central themes include the analysis of structural plane connectivity in landslide-prone rock masses and the development of models to predict multi-factor disaster-pregnancy mechanisms in tectonically active regions such as the Jinsha River Basin. By integrating field investigations, laboratory experiments, and numerical simulations, the studies aim to reveal how geological structures influence water conduction, stability, and energy transfer within fractured rock systems. Recent work explores the coupling effects between mechanical stress, fluid flow, and seismic responses to improve risk assessment and early warning for geological disasters. A key contribution includes the development of a double-layered seismo-electric method for characterizing groundwater seepage fields in high-level waste disposal sites, offering a novel approach to detect and evaluate subsurface hydrogeological conditions. The research collectively contributes to disaster prevention, sustainable resource management, and environmental safety by providing quantitative frameworks and innovative methodologies to understand, model, and mitigate risks associated with complex geological processes.

Profile:  Orcid  

Featured Publications

Fan, J., Meiliya, Y., Wu, S., Du, G., & Chen, L. (2025, September). A double-layered seismo-electric method for characterizing groundwater seepage fields in high-level waste disposal. Water. Multidisciplinary Digital Publishing Institute. https://doi.org/10.3390/w17192848

Fan, J., Wang, R., & Hua, J. (2025, September 22). Ground sound recognition method for evolution of post-sliding fractures at the slope edge based on GIS and machine learning models. Proceedings of the 2025 Annual Conference of the Chinese Geographical Society.

Meiliya, Y., Fan, J., & Zhang, H. (2025, July 9). Legal regulation of algorithm discrimination and construction of digital fairness platform. Law.

Fan, J., Yusuupjiang, M., & Lv, X. (2025, June 14). Characteristics of permeable structure of Beishan granite and three-dimensional detection research by high-density electromagnetic method. Soil Science.

Mujeeb Abiola Abdulrazaq | engineering | Young Scientist Award

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Mr. Mujeeb Abiola Abdulrazaq l engineering
| Young Scientist Award

University of North Carolina at Charlotte | United States

Mr. Mujeeb Abiola’s research focuses on advancing transportation safety and efficiency through data-driven methodologies and emerging technologies. His work extensively employs large-scale traffic and crash data, including millions of federal highway administration records, to investigate the spatiotemporal dynamics of pedestrian crashes and the evolution of crash hotspots. Utilizing advanced statistical and machine learning models, he has developed predictive frameworks that outperform traditional Highway Safety Manual standards, providing robust insights into risk factors and injury severity in both human-driven and autonomous vehicle contexts. His research on connected and autonomous vehicles (CAVs) has led to the development of traffic control algorithms that significantly enhance safety, operational efficiency, and environmental sustainability in freeway work zones. Furthermore, his studies integrate GPU-accelerated data processing, simulation-based optimization, and multi-level heterogeneity modeling to evaluate vulnerable road user behavior and assess dynamic collision risks. Through simulation platforms such as VISSIM and SUMO, combined with Python-based data analysis and GIS applications, his work systematically addresses complex traffic scenarios, including merging, diverging, and weaving segments, while also accounting for seasonal variations and temporal constraints in crash determinants. His contributions include empirical analyses of autonomous vehicle incidents, methodological advancements in microsimulation accuracy, and development of actionable strategies for real-world traffic management, ultimately aiming to improve roadway safety, inform policy, and guide evidence-based planning in modern transportation systems.

Profile:  Google Scholar 

Featured Publications

  • Abdulrazaq, M. A., & Fan, W. D. (2024). Temporal dynamics of pedestrian injury severity: A seasonally constrained random parameters approach. International Journal of Transportation Science and Technology, 9.

  • Abdulrazaq, M. A., & Fan, W. (2025). A priority based multi-level heterogeneity modelling framework for vulnerable road users. Transportmetrica A: Transport Science, 1–34. https://doi.org/10.1080/23249935.2025.2516817

  • Abdulrazaq, M. A., & Fan, W. (2025). Seasonal instability in crash determinants: A partially temporally constrained modeling analysis. SSRN 5341417. https://doi.org/10.2139/ssrn.5341417