Kishan Kesari Gupta | Artificial Intelligence | Best Researcher Award

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Mr. Kishan Kesari Gupta | Artificial Intelligence | Best Researcher Award

Software Engineer at Capgemini Technology Services India Limited, India

Mr. Kishan Kesari Gupta is a proficient consultant and researcher with extensive expertise in developing scalable, efficient, and user-focused applications using modern technologies such as React, Vue, Java, Spring Boot, Ruby on Rails, and Python. With significant experience across industries like banking, finance, and insurance, he has successfully delivered complex projects involving microservices, cloud platforms, blockchain, and artificial intelligence. Beyond his professional contributions, he has actively engaged in research, publishing impactful papers with reputed publishers including IEEE, Springer, and Elsevier, while also serving as a reviewer and session chair at international conferences. His thought leadership is further reflected through technical blogs and articles that simplify complex concepts for the wider community. With a strong foundation in continuous learning, team mentorship, and innovation, Mr. Gupta bridges the gap between cutting-edge research and real-world applications, making him a well-rounded professional and a strong candidate for prestigious recognitions in research and technology.

Professional Profile 

Google Scholar

Education

Mr. Kishan Kesari Gupta holds a Master of Science in Computer Applications from Symbiosis International University, Pune, where he honed his technical and analytical abilities with a strong focus on software engineering, programming, and emerging technologies. He also earned his Bachelor of Business Administration in Computer Applications from Savitribai Phule Pune University, where he developed a strong interdisciplinary foundation blending business principles with computer science. His academic journey not only equipped him with comprehensive technical expertise but also instilled a problem-solving mindset, enabling him to bridge theory with practical applications. Through his education, he developed proficiency in programming languages, software design, databases, and system architecture, which later served as the cornerstone of his professional and research endeavors. His continuous pursuit of learning, complemented by engagement in academic research and knowledge sharing, reflects his commitment to both technical excellence and innovation in the field of computer science and information technology.

Experience

Mr. Kishan Kesari Gupta has built a distinguished professional career as a consultant with leading organizations, delivering high-impact projects across finance, insurance, and banking domains. At TechVerito Software Solutions, he worked extensively with modern frameworks such as Vue.js, React, Ruby on Rails, Java, and Spring Boot, developing scalable applications and deploying microservices through Docker and CI/CD pipelines. His expertise extended to building RESTful APIs, implementing secure architectures, and contributing to test-driven development practices. Later, at Capgemini, he contributed to strategic digital transformation initiatives, including customer onboarding and digital asset solutions for global banks, leveraging technologies such as blockchain, cloud computing, and artificial intelligence. His work consistently emphasized innovation, system efficiency, and user-centric design, while he actively contributed to agile processes through sprints, PR reviews, and deployment strategies. With experience in leadership, technical mentoring, and project management, he has demonstrated the ability to translate complex requirements into impactful technology-driven outcomes.

Research Focus

Mr. Kishan Kesari Gupta’s research focus lies at the intersection of artificial intelligence, machine learning, computer vision, blockchain, and scalable software systems. He has contributed significantly to academic research, with publications in reputed platforms such as IEEE, Springer, and Elsevier, covering topics like machine vision for heart health monitoring and human activity recognition using deep learning and CNN architectures. His work emphasizes applying advanced computational methods to solve real-world challenges, particularly in healthcare analytics, automation, and secure digital ecosystems. Additionally, he explores generative AI, large language models, and agentic AI, examining their potential for reshaping industries and human-technology interaction. His interest extends to leveraging blockchain for secure financial transactions and digital asset management, merging his industry experience with academic inquiry. By blending theoretical advancements with practical applications, he contributes to both the academic community and the technology industry, striving to build impactful solutions that are innovative, ethical, and future-ready.

Award and Honor

Mr. Kishan Kesari Gupta has been recognized for his dual contributions to both industry and academia, positioning him as a strong candidate for awards in research and technology innovation. His role as a session chair and research reviewer at international conferences reflects the academic community’s trust in his expertise and leadership. His impactful research publications with globally recognized publishers highlight his commitment to advancing knowledge and delivering practical solutions. Additionally, his leadership in major industry projects, particularly in digital transformation and financial technology, has earned him credibility as a technology innovator. His blogs and articles further enhance his reputation, as they simplify complex concepts and provide valuable insights to professionals and learners worldwide. These achievements, combined with his ongoing exploration of artificial intelligence, machine learning, and blockchain, underline his stature as a thought leader deserving of recognition through awards that honor excellence in research and applied technology.

Publication Top Notes

Title: Framework-Agnostic JavaScript Component Libraries: Benefits, Implementation Strategies, and Commercialization Models
Authors: KK Gupta, P Awasthi, M Shaik, PR Kaveri
Year: 2024
Citations: 6

Title: Understanding the need for machine learning as a solution for financial analysis of IT industries
Authors: KK Gupta, A Anil, A Anand, PR Kaveri
Year: 2020
Citations: 1

Title: Development of an Autonomous Weeding Robot for Roadside Weeds
Authors: Y Matsushita, KK Gupta, Y Fujii, DT Tran, JH Lee
Year: 2025

Title: iSpace Coding: A System for User-Defined Flexible Spatial Functions in Intelligent Spaces
Authors: S Yoshida, KK Gupta, Y Fujii, DT Tran, JH Lee
Year: 2025

Title: Integrating Human Motion Dynamics in CNN Architecture to Recognize Human Activity from Different Camera Angles
Authors: KK Gupta, JH Lee, PR Kaveri, P Awasthi
Year: 2025

Title: Implementing Machine Vision Process to Analyze Echocardiography for Heart Health Monitoring
Authors: KK Gupta, A Anil, PR Kaveri
Year: 2022

Conclusion

The publication record of Mr. Kishan Kesari Gupta reflects a strong and evolving research trajectory that bridges computer science, artificial intelligence, and applied technologies. His contributions span diverse areas such as machine learning for financial analysis, computer vision for healthcare, deep learning for human activity recognition, and innovative applications in robotics and intelligent spaces. The cited works, particularly in IEEE and Springer, indicate both academic relevance and growing recognition within the research community. His most cited paper on framework-agnostic JavaScript component libraries highlights his ability to merge practical software engineering with academic exploration. Recent works in robotics, human motion dynamics, and medical imaging demonstrate his focus on impactful, interdisciplinary research with real-world applications. Collectively, his publications showcase technical depth, innovation, and a commitment to advancing knowledge, positioning him as a promising researcher whose work continues to contribute meaningfully to both academia and industry.

Eduardo Coronel | Computer Science | Best Researcher Award

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Dr. Eduardo Coronel | Computer Science | Best Researcher Award

M.Sc. Eng. at Facultad Politécnica,  Paraguay

Eduardo Damián Coronel Torales, born on March 5, 1991, in Asunción, Paraguay, is a distinguished researcher and engineer specializing in electrical engineering, automation, and artificial intelligence applications. He has actively contributed to academia, industry, and international conferences, earning recognition for his innovative work in energy distribution and automation systems. His professional journey has taken him from academic research to practical implementations in one of the world’s largest hydroelectric plants, Itaipu Binacional. With a strong foundation in engineering and computational intelligence, Coronel Torales has made significant contributions to optimizing power distribution and developing automation solutions. His research extends beyond Paraguay, reaching international platforms and collaborations. He continues to push the boundaries of technology by integrating advanced optimization techniques, machine learning, and smart grid systems, positioning himself as a leader in his field.

Professional Profile

Education

Coronel Torales holds a Master’s degree in Electrical Engineering with an emphasis on Energy Systems Planning from the Facultad Politécnica of the Universidad Nacional del Este, obtained in 2021. His postgraduate research focused on optimizing power distribution using computational intelligence. He completed his undergraduate degree in Electronics Engineering with a specialization in Mechatronics at the Universidad Nacional de Asunción in 2017. During his academic career, he demonstrated exceptional analytical and problem-solving skills, engaging in multiple research projects related to automation, robotics, and energy systems. His academic journey reflects a strong commitment to technological advancements and interdisciplinary research. The combination of these degrees has provided him with a robust foundation in both theoretical and practical aspects of energy optimization, artificial intelligence, and industrial automation, equipping him with the expertise to tackle complex engineering challenges at both research and industrial levels.

Professional Experience

With extensive experience in academia and industry, Coronel Torales has worked as a research engineer at Itaipu Binacional, contributing to the modernization of automation systems. His expertise in failure analysis using PI tools and machine learning models has been instrumental in enhancing the reliability of large-scale energy infrastructure. He has also served as a postgraduate lecturer at the Universidad Nacional del Este, teaching heuristic optimization methods. Additionally, he has worked as an instructor at the Paraguay-Korea Advanced Technology Center (SNPP-KOICA), where he trained professionals in digital electronics and industrial automation. His work experience blends research, teaching, and industry applications, allowing him to bridge the gap between theory and practice. Through his diverse roles, he has been actively involved in developing intelligent systems, optimizing automation processes, and mentoring students and professionals in engineering disciplines.

Research Interests

Coronel Torales’ research interests lie at the intersection of power systems optimization, automation, and artificial intelligence. He has extensively explored the use of metaheuristic and multi-objective optimization techniques for enhancing the efficiency of electrical power distribution systems. His research also focuses on computer vision, machine learning, and control systems, particularly for applications in autonomous vehicles, industrial automation, and smart grids. Additionally, he is interested in the integration of AI-driven fault detection and predictive maintenance in large-scale energy infrastructures. His work contributes to improving the reliability and efficiency of energy management systems through data-driven solutions. By combining engineering principles with computational intelligence, he aims to develop sustainable and intelligent solutions for modern energy challenges. His forward-thinking research aligns with global trends in smart energy systems, IoT-enabled automation, and digital transformation in power distribution networks.

Awards and Honors

Coronel Torales has received international recognition for his research contributions, including multiple conference presentations at IEEE and other prestigious platforms. His work on remote-controlled switch optimization in power distribution systems has been published in IEEE Latin America Transactions and presented at international computing and engineering conferences such as CLEI, ICDIM, and INTERCON. He has been acknowledged for his contributions to automation failure analysis at Itaipu Binacional, influencing modernization decisions in one of the world’s largest hydroelectric plants. Additionally, his early research in autonomous vehicle navigation and fuzzy logic control earned him invitations to research symposiums in Argentina, Peru, South Korea, and the United States. His ability to translate research into practical applications has cemented his reputation as an emerging leader in electrical engineering and computational intelligence. His continued contributions are setting a benchmark for innovation in energy systems and industrial automation.

Conclusion

Eduardo Damián Coronel Torales has a strong research background with impactful contributions in energy systems optimization, automation, and AI applications. His publications, international recognition, and industry collaboration make him a strong candidate for the Best Researcher Award. However, to further strengthen his candidacy, he should aim for higher-impact journal publications, more independent research leadership, and broader contributions in emerging fields.

Publications Top Noted

  • Coronel, E., Barán, B., & Gardel, P. (2025). A Survey on Data Mining for Data-Driven Industrial Assets Maintenance Technologies. Journal article. DOI: 10.3390/technologies13020067.
  • Coronel Torales, E. D. (2024). Leveraging Machine Learning for Multi-Step Failure Forecasting in RTU Analog Modules and Estimating Key Performance Indicators to Support Management Decision-Making. CIGRE Paris Session 2024, Conference poster.
  • Coronel, E., Barán, B., & Gardel, P. (2022). Optimal Placement of Remote Controlled Switches in Electric Power Distribution Systems with a Meta-heuristic Approach. IEEE Latin America Transactions. DOI: 10.1109/TLA.2022.9675464.
  • Coronel Torales, E. D. (2021). Optimal Placement of Remote Controlled Switches in Electric Power Distribution Systems with a Multi-Objective Approach. 2021 XLVII Latin American Computing Conference (CLEI). DOI: 10.1109/clei53233.2021.9639970.
  • Coronel Torales, E. D. (2020). Optimización en la Ubicación de Seccionadores Tele-comandados en Sistemas de Distribución de Energía Eléctrica con enfoque meta-heurístico y soporte de decisión multi-criterio. Edited book. DOI: 10.13140/RG.2.2.32305.92002.
  • Coronel Torales, E. D. (2017). Estimación de disponibilidad de energía eléctrica de la Central Hidroeléctrica Itaipú y del crecimiento de la energía cedida al Paraguay hasta el 2023. Facultad Politécnica – Universidad Nacional del Este. DOI: 10.13140/RG.2.2.11838.79685.
  • Coronel Torales, E. D. (2015). Reliable navigation-path extraction system for an autonomous mobile vehicle. 2015 Tenth International Conference on Digital Information Management (ICDIM). DOI: 10.1109/icdim.2015.7381882.
  • Coronel Torales, E. D. (2015). PROTOTIPO DE VEHÍCULO AUTÓNOMO CON RNA Y VISIÓN POR COMPUTADORA. Simposio Argentino de Sistema Embebidos (SASE), Conference poster.
  • Coronel Torales, E. D. (2015). SISTEMA DE ALGORITMOS DE VISIÓN POR COMPUTADOR, APRENDIZAJE DE MÁQUINA, LOCALIZACIÓN Y NAVEGACIÓN DESARROLLADOS EN MATLAB, CON IMPLEMENTACIÓN EN VEHÍCULOS TERRESTRES PARA AUTO-CONDUCCIÓN. XXII Congreso Internacional de Ingeniería Eléctrica, Electrónica, Computación y Afines INTERCON 2015, Conference paper.
  • Coronel Torales, E. D. (2014). STABILITY COMMAND OF A TILT-ROTOR VEHICLE WITH A FUZZY LOGIC CONTROLLER. 3rd Conference of Computational Interdisciplinary Sciences – CCIS 2014, Conference poster. ISBN: 978-85-68888-00-1.
  • Coronel Torales, E. D. (2014). BALANCEADOR AERODINÁMICO CON LÓGICA DIFUSA. XXI Congreso Internacional de Ingeniería Electrónica, Eléctrica y Computación INTERCON 2014, Conference poster.

 

 

María Inmaculada Mohino-Herranz | Artificial Intelligence| Best Research Article Award

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Dr. María Inmaculada Mohino-Herranz | Artificial Intelligence| Best Research Article Award

Investigador, INTA, Spain

Inmaculada Mohíno Herranz is a distinguished researcher in the fields of signal processing, pattern recognition, and emotion detection. She currently works at the National Institute of Aerospace Technology (INTA), bringing her extensive expertise in physiological signal analysis to the forefront of innovative research. 🌟 Her career reflects a commitment to advancing technology and science, contributing to both academia and industry.

Publication profile

Scopus

Education

Inmaculada holds an impressive academic background, beginning with her M.Eng. in Telecommunication Engineering (2010), followed by a second degree in Electronics Engineering (2012), and a Master’s degree in Information and Communication Technologies (2015). 📚 She culminated her academic journey with a Ph.D. in Information and Communication Technologies (2017, with honors) from the University of Alcalá, Madrid, Spain. 🎓

Experience

She has built a solid career in academia and research, having worked at the Signal Theory and Communications Department of the University of Alcalá, where she was part of the Applied Signal Processing research group until 2021. 📡 Currently, she continues her research at INTA, contributing to projects related to aerospace technology. She has also been actively involved in supervising final degree and master’s projects, shaping future innovators. 👩‍🏫

Research Focus

Inmaculada’s research revolves around physiological signal processing, pattern recognition, emotion recognition, and stress detection. 💡 Her work is especially significant in understanding how physiological data can be used to monitor emotional states, which has applications ranging from healthcare to technology-enhanced well-being. 💻

Awards and Honors

Inmaculada has received recognition for her outstanding contributions to the field of Information and Communication Technologies, including supervising several successful degree projects and participating in numerous public and private-funded research initiatives. 🏆 Her efforts in academic and industrial projects further solidify her reputation as a leading researcher.

Publication Top Notes

Inmaculada Mohíno Herranz has authored various impactful papers. She has published nine journal papers, six of which are indexed in the Journal Citation Report. 📄 She has also written a book chapter and around 20 conference papers, showcasing her active engagement in research dissemination.

Metrological analysis on measuring techniques used to determine solubility of solids in supercritical carbon dioxide – Published in Measurement: Journal of the International Measurement Confederation (2025), this article has no citations yet.

Initializing the weights of a multilayer perceptron for activity and emotion recognition – Published in Expert Systems with Applications (2024), this article has no citations yet.

Introducing the ReaLISED Dataset for Sound Event Classification – Published in Electronics (2022), cited by two articles.

Linear detector and neural networks in cascade for voice activity detection in hearing aids – Published in Applied Acoustics (2021), cited by one article.

A wrapper feature selection algorithm: An emotional assessment using physiological recordings from wearable sensors – Published in Sensors (2020), this open-access article focuses on emotion assessment using physiological data from wearable sensors.