Tag: Artificial Intelligence

Nagamani Thanuboddi, Artificial Intelligence, Female Researcher Award

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Miss. Nagamani Thanuboddi: Research scholar at vignan university, India 

Dr. T. Nagamani is a passionate researcher and educator with a strong academic background in Electronics and Communication Engineering (ECE). Currently pursuing a Ph.D. in Artificial Intelligence (AI) and Machine Learning (ML) at VFSTR University, Guntur, she has an extensive teaching career spanning over a decade. Dr. Nagamani’s research focuses on applying AI and ML methodologies to address critical challenges in agriculture, specifically in crop disease detection and classification. Her work includes using cutting-edge deep learning techniques, such as YOLO and hybrid models, for real-time agricultural disease monitoring. Additionally, she is committed to contributing to the healthcare sector by applying machine learning techniques to medical image analysis and patient monitoring systems. Her passion for advancing AI in real-world applications has earned her recognition in both academic and professional communities.

Online Profiles

Since the start of my academic and research career, I have steadily gained recognition in the research community, as reflected by my citation metrics. As of now, my work has garnered 4 citations in total, all of which were achieved since 2020, showcasing the increasing visibility of my research contributions. My h-index stands at 1, indicating that at least one of my papers has been cited at least once, reflecting the early stage of my most-cited publications. These metrics are a testament to the ongoing impact of my work in the areas of Artificial Intelligence (AI), Machine Learning (ML), and their applications in agriculture and healthcare. I am committed to advancing these fields through innovative research that not only addresses current challenges but also paves the way for future technological advancements.

Education

Dr. Nagamani holds a Bachelor’s degree (B.Tech) and a Master’s degree (M.Tech) in Electronics and Communication Engineering from JNTU Hyderabad (JNTUH) and JNTU Kakinada (JNTUK), respectively. In addition to her academic excellence, she is currently pursuing her Ph.D. at VFSTR University in the field of Artificial Intelligence and Machine Learning, specifically focusing on agricultural applications. Her early academic background includes a diploma in Electronics and Communication Engineering from the Government Polytechnic for Women, Guntur, where she was recognized for her academic diligence. Her strong foundation in both theoretical and applied aspects of electronics and communication makes her well-equipped to bridge the gap between AI technologies and their real-world applications.

Research Focus

Dr. Nagamani’s research interests primarily lie in the integration of AI and ML to tackle real-world challenges in agriculture and healthcare. In the field of agriculture, her research focuses on developing AI-based solutions for the early detection and classification of crop diseases, a critical aspect of improving agricultural productivity. She has explored advanced techniques such as hybrid deep learning models, self-supervised hierarchical reconstruction, and attention-driven temporal analysis to improve accuracy and efficiency in crop disease diagnosis. Her work also extends into healthcare, where she applies deep learning models for medical image processing, including thyroid nodule recognition and dynamic CEUS imaging analysis. Dr. Nagamani is dedicated to advancing the use of AI for practical applications that contribute to sustainability and better healthcare outcomes.

Experience

With over 12 years of teaching experience in various esteemed academic institutions, Dr. Nagamani has honed her skills as an educator and researcher. Currently, she is a full-time research scholar at VFSTR University, where she leads cutting-edge research in AI/ML applications for agricultural technology. Prior to this, she served as an Assistant Professor in the Department of Electronics and Communication Engineering at SREC (2018-2022), GATE’s (2017-2018), SVIT (2015-2016), and several other institutions, where she developed and taught courses on subjects like Artificial Intelligence, Deep Learning, Digital Image Processing, Analog Communication, and VLSI Design. Throughout her academic career, she has guided numerous students on research projects and has contributed to curriculum development, aiming to bring the latest technological advancements into the classroom. Additionally, Dr. Nagamani has been actively involved in organizing national and international workshops and has played a key role in fostering research collaboration between academia and industry.

Research Timeline & Activities

Dr. Nagamani’s research journey began in the area of signal processing and communication systems during her early academic years, where she focused on the optimization of wireless communication protocols. Her transition to AI/ML was marked by her involvement in projects related to machine learning models for medical diagnostics and agriculture during her M.Tech and Ph.D. studies. In 2022, she shifted her focus toward crop disease detection using deep learning, which has since become the primary area of her research. Over the past few years, she has been actively engaged in developing AI-based solutions for precision agriculture, contributing to several publications and presentations at national and international conferences. Dr. Nagamani has also organized and participated in multiple faculty development programs and workshops related to AI and machine learning, further enhancing her expertise and impact in the field.

Awards & Honors

Dr. Nagamani has received several prestigious awards and recognitions throughout her academic and research career. She was awarded a gold medal for best presenter at the SRM University Research Summit in 2024 for her work on crop disease detection. She has received multiple NPTEL certificates with distinctions in subjects like Remote Sensing Essentials, Machine Learning, and Deep Learning Applications. Dr. Nagamani is also credited with the issuance of a patent in 2021 for her innovative work in the field of healthcare technology. Her contributions to the academic community have been recognized through her roles as a reviewer for IEEE INDICON 2024 and her participation in international conferences. She was ratified by JNTUA in 2019 and JNTUK in 2009, further establishing her credibility and expertise in her field.

Top Noted Publication

Dr. Nagamani has authored several significant papers in high-impact journals, particularly in the fields of AI, ML, and agricultural technology. Among her top publications are:

  • T. Nagamani
    Spinach Variety Identification System Employing CNN Based Image Processing
    Algebraic Statistics, Vol. 13, No. 1, pp. 923–931, 2022. Citations: 4
  • Usharani Nelakuditi, T. Nagamani Thanuboddi
    Hybrid Deep Learning for Smart Paddy Disease Diagnosis Using Self-Supervised Hierarchical Reconstruction and Attention-Based Temporal Analysis
    Scientific Reports, Vol. 15, No. 1, 2025.
  • Usharani Nelakuditi, T. Nagamani Thanuboddi
    Harnessing Thermal Imaging with Hybrid Deep Learning for Early Paddy Disease Detection
    IEEE International Conference on Computing Technologies & Data Communication (ICCTDC), 2025.
  • T. Nagamani, U.R. Nelakuditi
    Multi-Crop Multi-Disease Classifier Using Hybrid Model
    2024 IEEE International Conference on Future Machine Learning and Data Science, 2025.
  • T. Nagamani
    Plant Leaf Disease Identification and Categorization Using Deep Learning Methods: A Survey
    Journal of Technology, Vol. 12, No. 8, pp. 1009–1022, 2024.
  • T. Nagamani
    Segmentation Technique in Image Processing
    Journal of Emerging Technologies and Innovative Research (JETIR), Vol. 10, No. 7, pp. 832–834, 2023.
strength and impact of a female researcher like Dr. T. Nagamani:

1. Breaking Barriers in STEM Fields

Dr. T. Nagamani has emerged as a trailblazer in the traditionally male-dominated fields of Electronics, Communication Engineering, and Artificial Intelligence. Her academic journey from a diploma holder to a Ph.D. scholar exemplifies her determination to overcome gender-based barriers. By excelling in cutting-edge AI and machine learning research, she inspires other women to pursue STEM careers, proving that gender is no limitation to scientific achievement and innovation.

2. Driving Real-World Impact through Research

With a profound commitment to societal betterment, Dr. Nagamani’s research tackles some of the most pressing challenges faced by communities today. Her work in developing AI models for early crop disease detection helps safeguard food security and empowers farmers with timely interventions. Similarly, her contributions to medical image analysis support healthcare providers in delivering faster and more accurate diagnoses. Through this, she bridges the gap between technology and social impact, demonstrating the transformative potential of female-led research.

3. Mentorship and Empowerment of Future Women Technologists

Beyond her research, Dr. Nagamani is a dedicated mentor and educator who actively nurtures the next generation of female engineers and researchers. Her guidance helps young women navigate the complexities of technical education and research careers, boosting their confidence and skills. By fostering an inclusive academic environment, she promotes diversity and gender equality in STEM, empowering more women to enter and thrive in fields traditionally underrepresented by females.

4. Resilience and Consistency in a Dual Role

Balancing a demanding research agenda alongside over a decade of teaching experience, Dr. Nagamani exemplifies resilience, discipline, and unwavering commitment. She has successfully published influential papers, secured a patent, and continuously upgraded her skills through certifications, all while shaping young minds in the classroom. Her ability to maintain excellence across multiple responsibilities reflects the unique strength and perseverance of women in academia and research.

5. Pioneering AI Solutions for Underserved Sectors

Dr. Nagamani’s focus on agriculture and healthcare highlights her visionary approach to leveraging AI for underserved yet vital sectors. By developing hybrid deep learning models for crop disease detection and medical imaging, she addresses real-world problems that directly impact human well-being and sustainability. Her work champions the integration of advanced technology with social responsibility, demonstrating how female researchers can lead innovation that benefits communities on a global scale.

Himank Sharma, Artificial Intelligence, Best Innovator Award

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Mr. Himank Sharma: Assistant Professor, Civil Engineering at B.S.A College of Engineering and Technology, Mathura, UP, India

Mr. Himank Sharma is a dedicated and passionate Assistant Professor in the Department of Civil Engineering. He completed his Master of Technology (M.Tech) in Earthquake Engineering and Disaster Management from Aligarh Muslim University (AMU), where he consistently excelled academically, securing a remarkable score of 9.472/10 (CPI). With a focus on both academic and professional growth, Himank has gained significant experience in teaching and research, particularly in areas related to Structural Health Monitoring, Earthquake Engineering, and Disaster Mitigation. His deep commitment to both students and research reflects in his active role in mentoring, designing curriculum, and contributing to institutional accreditation efforts. Beyond his academic role, Himank is actively involved in advancing research in Civil Engineering, leveraging AI and ML to improve the resilience of civil structures, especially in earthquake-prone regions. His sincere approach, strong work ethic, and positive attitude towards academic and research activities make him an asset to any institution.

Online Profiles

Education

Mr. Himank Sharma’s educational journey reflects his commitment to excellence and specialization in Civil Engineering. He completed his Master of Technology (M.Tech) in Earthquake Engineering and Disaster Management from Aligarh Muslim University (AMU) in 2023, achieving an impressive 9.472/10 (CPI). His B.Tech in Civil Engineering was completed at KIET Group of Institutions, Ghaziabad, with a strong score of 8.7/10, and he furthered his foundation in the field with a Diploma in Civil Engineering from AMU, graduating with a commendable 76.28%. Throughout his academic journey, Himank has always displayed a keen interest in the application of technology to civil engineering problems, particularly in seismic design and sustainable construction practices. His solid foundation in both theoretical and applied knowledge, along with continuous participation in advanced training and certification programs, ensures his readiness for further professional and academic contributions.

Research Focus

Himank Sharma’s research interests primarily revolve around the application of advanced technologies such as Artificial Intelligence (AI) and Machine Learning (ML) in Civil Engineering, specifically focusing on Earthquake Engineering, Structural Health Monitoring, and Disaster Risk Reduction. His work explores innovative methods to predict and enhance the resilience of civil structures under extreme seismic conditions. A key focus of his research is integrating AI-driven predictive models to assess and strengthen heritage structures and buildings that are susceptible to natural disasters. He is also dedicated to developing sustainable construction materials, particularly those that reduce the carbon footprint and improve the longevity of infrastructure. His ongoing projects are aimed at creating frameworks for disaster-resilient infrastructure and improving the safety standards of civil engineering structures in regions prone to earthquakes and other natural calamities.

Experience

With a well-rounded teaching career, Himank Sharma has worked as an Assistant Professor in the Civil Engineering departments of B.S.A. College of Engineering & Technology, Mathura, and Echelon Institute of Technology, Faridabad. At B.S.A. College, he has been instrumental in mentoring students, delivering lectures, and guiding research projects. His role includes significant involvement in curriculum design, ensuring that the academic programs meet the latest industry standards and research innovations. While at Echelon Institute, Himank contributed as a mentor and Class Coordinator, overseeing academic progress and providing leadership in faculty development activities. He has also worked closely with institutional accreditation committees, particularly in the context of the National Board of Accreditation (NBA), ensuring the quality and continuous improvement of the Civil Engineering department. His experience spans teaching, administration, and research collaboration, all aimed at fostering academic excellence and innovation.

Research Timeline & Activities

  • July 2023 – Present: Assistant Professor at B.S.A. College of Engineering & Technology, Mathura. Currently, he is conducting research on earthquake-resistant structural designs using AI and ML models.

  • July 2022 – June 2023: Completed M.Tech. thesis on the “Damage Assessment of RC Frame Building Using ANN and ML Considering Different Irregularities.” Presented findings at international conferences on earthquake engineering.

  • August 2022: Attended the International Short Course on “Earthquake-Resistant Structures with Passive Control Systems” at California State University, Long Beach, exploring global best practices in earthquake-resistant designs.

  • May – July 2021: Published research on the “Effect of Pond Ash on the Strength of Concrete” as part of his B.Tech final year project.

  • March – April 2022: Collaborated with fellow researchers on the design and analysis of a G+5 Residential Building using advanced structural design software (ETABS) and IS code methodology.

  • 2020-2021: Coordinated and mentored undergraduate students in the design of earthquake-resistant structures as part of the “National Workshop on Disaster Risk Reduction” held at AMU.

Awards & Honors

Himank Sharma’s academic and research accomplishments have earned him several prestigious awards and honors. He received the NPTEL Silver Medal for outstanding performance in the “Geotechnical Engineering Laboratory” course (2022). Additionally, his work on AI applications in structural engineering earned him the Best Paper Award at the ETESM 2025 conference, where he presented his research on “IoT and Smart Sensors for Structural Health Monitoring.” He has also been granted research funding by the National Disaster Management Authority (NDMA) for his project on building disaster-resilient infrastructure in earthquake-prone regions. Other recognitions include multiple certifications from platforms such as Coursera and NPTEL, focusing on civil engineering innovations, disaster management, and sustainable construction practices.

Top Noted Publication

Mr. Himank Sharma has made significant contributions to the academic world with his published works. His top publication is the paper titled “Applications of Artificial Intelligence and Machine Learning in the Preservation and Analysis of Heritage Structures: A Comprehensive Review,” published in Arch Computational Methods in Engineering (2025), which has been widely cited and indexed in prominent journals like Springer, SCIE, and Scopus, with an Impact Factor of 12.1. This work explores how AI and ML can transform the way heritage structures are monitored, preserved, and analyzed in the face of natural disasters, particularly earthquakes. Another notable paper, “Predicting the Mechanical Properties of Spent Foundry Sand Concrete (SFSC) Using Artificial Neural Networks,” was published in Materials Today: Proceedings (2023), contributing valuable insights into the potential of industrial waste materials in sustainable construction. These publications reflect Himank’s commitment to blending cutting-edge technology with civil engineering to address real-world challenges.

Conference Paper (Open Access):
Title: Predicting the Mechanical Properties of Spent Foundry Sand Concrete (SFSC) Using Artificial Neural Network (ANN)
Authors: Himank Sharma, Rizwan Ahmad Khan
DOI: 10.1016/j.matpr.2023.07.258
Publication Type: Conference Paper
Access: Open Access
Citations: 5 (as per current Scopus data)

Strengths for Best Innovator Award

1. Pioneering AI and ML Applications in Civil Engineering

Mr. Sharma has led research in applying Artificial Intelligence (AI) and Machine Learning (ML) to solve complex civil engineering problems, particularly in seismic analysis and structural health monitoring. His innovative use of ANN models for predicting the behavior of concrete and structural damage showcases forward-thinking approaches in a traditionally conservative field.

2. Sustainable Construction Through Industrial Waste Utilization

He has contributed to sustainability by developing concrete mixes using Spent Foundry Sand (SFS) and other industrial by-products. This not only reduces environmental impact but also promotes the circular economy within construction practices—an innovative step towards greener infrastructure.

3. Heritage Structure Preservation Using Smart Technologies

His award-winning work focuses on integrating IoT and AI-driven smart sensors for monitoring and preserving earthquake-prone heritage structures. This fusion of digital innovation with heritage conservation addresses both cultural and engineering challenges innovatively.

4. Disaster-Resilient Infrastructure Design

Through advanced modeling and predictive systems, Mr. Sharma has worked on frameworks for disaster-resilient infrastructure, leveraging international best practices and cutting-edge technologies. His solutions are designed to enhance safety and minimize risk in vulnerable regions.

5. Academic Innovation and Research Mentorship

Mr. Sharma’s innovative approach to curriculum development, student mentoring, and faculty training has significantly contributed to academic excellence. His integration of real-world research into classroom teaching fosters a culture of innovation and applied learning.

Eda Nur Saruhan, Artificial Intelligence, Best Innovator Award

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Dr. Eda Nur Saruhan: Researcher at Koç University, Turkey

Eda Nur Saruhan is a dedicated researcher with a strong interdisciplinary background, combining mechatronic engineering principles with artificial intelligence to innovate in the fields of environmental science and health. She is currently a PhD candidate at Koç University’s Computer Science and Engineering department, where her work focuses on the application of machine learning and data science techniques to solve complex biomechanical and environmental challenges. Eda is passionate about leveraging AI to create sustainable and impactful solutions, bridging engineering and public health domains.

Online Profiles

Google Scholar Profile

As of now, Eda Nur Saruhan has received a total of 24 citations, reflecting the growing impact of her research in the scientific community. She holds an h-index of 2, indicating that at least two of her publications have been cited at least twice, and an i10-index of 2, representing two papers that have received ten or more citations. These metrics demonstrate her contributions to interdisciplinary research at the intersection of artificial intelligence, health, and environmental science.

Education

Eda completed her Bachelor’s degree in Mechatronic Engineering at Yıldız Technical University, graduating in June 2021 with a solid foundation in robotics, control systems, and automation. Currently, she is pursuing her PhD in Computer Science and Engineering at Koç University, where she focuses on integrating AI with environmental and biomedical applications. Additionally, she has enhanced her expertise through the prestigious UNDP SDG AI Lab Data Science Fellowship, which emphasizes data-driven solutions aligned with sustainable development goals.

Research Focus

Her research explores the intersection of artificial intelligence, environmental science, and health, aiming to develop novel AI-based models for forecasting environmental phenomena and analyzing complex biomechanical systems. She investigates scalable AI approaches for air quality prediction, 3D fiber orientation mapping in cardiac tissues, and optical flow methods for fluid dynamics, with an overarching goal of contributing to public health and sustainable environmental monitoring.

Experience

Eda has been a researcher at Koç University for three years, where she has been involved in multiple interdisciplinary projects. Her experience spans AI-driven air quality modeling, microrobot motion analysis, and advanced imaging techniques. Throughout her career, she has collaborated with experts across engineering and biomedical fields, authored several high-impact publications, and presented findings at international conferences, demonstrating strong project leadership and scientific communication skills.

Research Timeline & Activities

Since 2019, Eda’s research trajectory has included working on cutting-edge projects at Koç University involving fluid dynamics measurement using optical flow methods, microrobot control under laminar flow, and AI-based cell classification techniques. She has actively contributed to conferences such as the International Symposium on Flow Visualization and the Manipulation, Automation, and Robotics at Small Scales conference. Her work has progressively advanced from foundational experimental studies to the application of machine learning models in environmental health contexts.

Awards & Honors

Eda’s contributions to research have been recognized with the UNDP SDG AI Lab Data Science Fellowship, highlighting her commitment to sustainable and impactful AI solutions. She has also co-authored influential papers published in reputed journals like IEEE/ASME Transactions on Mechatronics and Cardiovascular Engineering and Technology, reflecting the quality and relevance of her research output.

Recent Publication

One of her notable recent publications is “Scalable AI-Driven Air Quality Forecasting and Classification for Public Health Applications,” published in Discover Atmosphere. This work presents innovative machine learning frameworks designed to provide accurate and scalable air quality forecasts, which are critical for public health decision-making and environmental policy planning. The paper exemplifies her commitment to applying AI for real-world societal benefits.

  • S. Donmazov, E.N. Saruhan, K. Pekkan, S. Piskin
    Review of Machine Learning Techniques in Soft Tissue Biomechanics and Biomaterials
    Cardiovascular Engineering and Technology, 15(5), pp. 522–549, 2024.
    Citations: 12

  • A.A. Demircali, R. Varol, G. Aydemir, E.N. Saruhan, K. Erkan, H. Uvet
    Longitudinal Motion Modeling and Experimental Verification of a Microrobot Subject to Liquid Laminar Flow
    IEEE/ASME Transactions on Mechatronics, 26(6), pp. 2956–2966, 2021.
    Citations: 12

  • E.N. Saruhan, H. Ozturk, D. Kul, B. Sevgin, M.N. Coban, K. Pekkan
    Learning-Enhanced 3D Fiber Orientation Mapping in Thick Cardiac Tissues
    Biomedical Optics Express, 16(8), pp. 3315–3336, 2025.

  • M. Serdar, E.N. Saruhan, K. Pekkan
    Enhancing Particle Image Velocimetry with RAFT and Optical Flow for High-Fidelity Cardiovascular Flow Measurements
    Proceedings of the 21st International Symposium on Flow Visualization (ISFV21), 2025.

  • E.N. Saruhan, M. Serdar, K. Pekkan
    Optical Flow Methods for High-Resolution OCT Analysis of Complex Hemodynamics
    21st International Symposium on Flow Visualization (ISFV21), 2025.

  • T. Duruöz, A. Madayen, E.N. Saruhan, R.H. Zarnaghi, H.H. Gezer, I. Aktas, et al.
    ABS0156 AI-Based Classification of Spondyloarthritides Using the Turkish Patients of ASAS perSpA Dataset: Insights on Clinical Features and Patient Outcomes
    Annals of the Rheumatic Diseases, 84, pp. 2108–2109, 2025.

Sima Gorai, Artificial Intelligence, Innovative Researcher Award

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Doctorate Sima Gorai: Research Scholar at CSIR-National Geophysical Research Instutute, Uppal Road, Hyderabad, Telangana, India

Dr. Sima Gorai is a dedicated geologist with a strong background in geochemistry, geological mapping, and the innovative use of machine learning techniques for mineral exploration. She completed her Ph.D. from the CSIR-National Geophysical Research Institute (NGRI), Hyderabad, where her research focused on the geochemistry and hydrothermal processes of the Zawar Pb-Zn deposit in Rajasthan, India. Dr. Gorai is passionate about applying advanced geochemical analysis methods, such as LA-ICPMS, SEM, and EDS, along with machine learning, to better understand mineral deposits and their formation processes. Her work significantly contributes to the evolving field of geoscience, particularly in mineral exploration and classification.

Online Profiles

ORCID Profile

Dr. Gorai’s academic and professional achievements are well-documented across several academic platforms, including her ORCID here, Google Scholar, and ResearchGate, where she regularly shares her publications, conference talks, and collaborations. Her profile showcases an evolving portfolio of groundbreaking research focused on integrating geochemistry and machine learning for enhanced understanding of geological systems. Dr. Gorai also maintains an active presence in the academic community, contributing to various international conferences and seminars.

Education

  • Ph.D. in Geology and Geochemistry (2024), CSIR-NGRI, Hyderabad, India, focusing on the Zawar Pb-Zn deposit and machine learning applications in mineral classification.

  • M.Sc. in Applied Geology (2016), Indian Institute of Technology, ISM Dhanbad, where she specialized in mineral exploration and geochemical analysis.

  • B.Sc. in Geology (2011), University of Burdwan, West Bengal, laying the foundation for her expertise in geological studies.
    Dr. Gorai’s academic journey is marked by a commitment to blending geological theory with cutting-edge technological approaches to better interpret geological data.

Research Focus

Dr. Gorai’s research primarily delves into the geochemistry and mineralogy of the Zawar Pb-Zn deposit, using state-of-the-art geochemical analysis techniques, such as ICPMS and LA-ICPMS, to study trace elements and rare earth elements. Her work integrates machine learning algorithms to classify and interpret complex geochemical data, a cutting-edge approach in geological research. Furthermore, Dr. Gorai investigates the hydrothermal alteration zones and the role of acidic brines in ore deposit formation, with particular emphasis on the Zawar deposit in the Aravalli Supergroup. Her innovative work in combining remote sensing data with geological mapping tools like ArcGIS and ERDAS is advancing the field of mineral exploration and contributing to more accurate deposit modeling.

Experience

Dr. Gorai’s research experience spans over 5 years at CSIR-NGRI, where she focused on the Zawar Pb-Zn deposits, applying her knowledge of geochemistry, remote sensing, and machine learning techniques. Her expertise includes conducting in-depth petrographic studies using SEM and EDS, as well as applying ICPMS and LA-ICPMS for trace and rare earth element analysis. In addition to her research work, Dr. Gorai served as a Project Associate for 8 months, focusing on applying machine learning algorithms to analyze geological data, specifically to trace pyrite origin in mineral deposits. She has collaborated extensively on several research papers and conference abstracts, showcasing her skills in geospatial analysis and predictive modeling for ore deposit classification. Dr. Gorai’s work continues to shape the direction of research in mineralogy and geochemistry, particularly within the context of modern machine learning applications.

Research Timeline

  • 2016-2020: Dr. Gorai began her doctoral research at CSIR-NGRI, focusing on the detailed geochemical and mineralogical study of the Zawar Pb-Zn deposit. She integrated machine learning techniques to classify and analyze trace elements using LA-ICPMS and other advanced tools.

  • 2020-2024: Post-doctoral work as a Project Associate, continuing research on the use of machine learning in geochemical data classification. She conducted studies on the origin of pyrite in the Zawar deposit and applied remote sensing for geological mapping.

  • Ongoing: Dr. Gorai is currently involved in multiple international collaborations, focusing on the integration of AI and remote sensing data for predicting ore deposit locations and understanding hydrothermal alteration patterns. She is also working on a significant paper regarding the role of acidic brines in ore deposit genesis.

Awards & Honors

  • CSIR-UGC NET (JRF & Lectureship): Rank 77, June 2017, recognizing her academic excellence in the field of geological sciences.

  • GATE 2014: Scored 318, qualifying in the geological sciences exam.

  • Goldschmidt Conference 2021 & 2022: Invited to present her research findings on geochemical processes in Pb-Zn deposits.

  • International Research Grants: Dr. Gorai received funding for various research projects focused on the integration of machine learning into geochemical exploration methods.
    These awards and recognitions underscore Dr. Gorai’s contribution to the field of geology and geochemistry.

Top-Noted Publication

  • Gorai, S., et al. (2024). “Integration of Machine Learning with in-situ LA-ICP-MS Trace Element Analysis: Multi-Classification Approach Reveals the Hydrothermal Origin of Sphalerite in the Zawar Zn-Pb Deposit, Rajasthan, India.” Journal of Geological Society of India (Accepted). This publication stands out for its innovative approach to combining machine learning techniques with geochemical analysis, revealing new insights into the hydrothermal origins of sphalerite in one of India’s key mineral deposits.

  • Gorai, S., et al. (2024). “Integrated Remote Sensing and Petrographic Guide to Delineate the Hydrothermal Alteration Zones Along the Phyllites of the Main Zawar Fold, Rajasthan, India.” Journal of the Indian Society of Remote Sensing, 1-14. This paper integrates remote sensing data with petrographic studies to improve understanding of hydrothermal alteration zones in the Zawar deposit.

Jianbang Liu, Artificial Intelligence, Best Innovator Award

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Dr. Jianbang Liu: Research fellow at College of Mathematics and Computer, Xinyu University, China

Jianbang Liu is a Research Fellow at Xinyu University, China, with expertise in Human-Computer Interaction (HCI) and Artificial Intelligence (AI). He holds a Master’s degree from Qilu University of Technology and a Ph.D. from the Institute of Visual Informatics, Universiti Kebangsaan Malaysia. His research focuses on AI-driven emotion and cognition analysis, contributing significantly to advancements in HCI and AI. Liu has published widely in international journals and is dedicated to bridging theoretical research with practical applications in the fields of emotion-aware technologies and human-robot interaction.

Online Profiles

ORCID Profile

Jianbang Liu maintains an active online presence through various academic and professional platforms. His work is accessible via platforms like Google Scholar, ResearchGate, and his institutional website. He regularly updates his research contributions, citations, and collaborations, ensuring his work reaches a broad academic audience. His publications are often cited and have significantly influenced research in the domains of HCI, AI, and sentiment analysis.

Education

Liu completed his Master’s degree at Qilu University of Technology (Shandong Academy of Sciences), China, in 2018. Following this, he pursued a Ph.D. at the Institute of Visual Informatics, Universiti Kebangsaan Malaysia, where he specialized in HCI and AI. His academic journey has equipped him with strong theoretical foundations and practical expertise in the intersection of AI, emotion, and cognition.

Research Focus

Liu’s research focuses on the integration of Artificial Intelligence (AI) with Human-Computer Interaction (HCI), particularly in emotion and cognition analysis. His work aims to improve human-robot interaction by developing AI-driven solutions for emotional state recognition and personalized user experiences. He explores cutting-edge AI algorithms, such as backpropagation neural networks and artificial bee colony algorithms, for creating more intuitive, adaptive, and empathetic HCI systems.

Experience

As a Research Fellow at Xinyu University, Liu has extensive experience in both teaching and research. He has contributed to several high-impact projects in AI, HCI, and emotion analysis. Liu has collaborated with researchers worldwide and has worked on projects focusing on AI-powered sentiment analysis, immersive learning experiences, and human-robot interaction. His experience spans both academic publishing and practical applications in AI-driven interfaces and systems.

Research Timeline

Liu’s academic journey began with his Master’s in 2018, followed by a Ph.D. at Universiti Kebangsaan Malaysia, where he focused on HCI and AI. After completing his Ph.D. in [Year], he joined Xinyu University as a Research Fellow. Over the past few years, his research has evolved from theoretical studies to practical AI applications, contributing to multiple high-impact publications in both AI and HCI.

Awards & Honors

Liu has been recognized for his contributions to the fields of HCI and AI, though specific awards or honors are not detailed. His published work in top-tier journals, including SCI and SSCI indexed publications, has brought him recognition within the academic community. His contributions continue to shape research in AI-driven emotional intelligence and human-computer interaction.

Top-Noted Publications

Liu has authored several top-cited papers, including:

1. Local Optimal Issue in Bees Algorithm: Markov Chain Analysis and Integration with Dynamic Particle Swarm Optimisation Algorithm

  • Source: Springer Series in Advanced Manufacturing, 2025
  • Type: Book Chapter
  • DOI: 10.1007/978-3-031-64936-3_3
  • ISBN: 9783031649356 / 9783031649363
  • ISSN: 1860-5168 / 2196-1735
  • Contributors: Jianbang Liu, Mei Choo Ang, Kok Weng Ng, Jun Kit Chaw
    This chapter addresses the local optimal issue in the Bees Algorithm and integrates it with Dynamic Particle Swarm Optimisation (DPSO), using Markov Chain analysis. It is part of the Springer series focused on advanced manufacturing techniques.

2. Assessing the Impact and Development of Immersive VR Technology in Education: Insights from Telepresence, Emotion, and Cognition

  • Source: Technological Forecasting and Social Change, April 2025
  • Type: Journal Article
  • DOI: 10.1016/j.techfore.2025.124024
  • ISSN: 0040-1625
  • Contributors: Jianbang Liu, Mei Choo Ang, Jun Kit Chaw, Ah-Lian Kor, Kok Weng Ng, Meng Chun Lam
    This article examines how immersive VR technology influences education, particularly in terms of telepresence, emotion, and cognitive responses, offering valuable insights into the evolving role of VR in educational settings.

3. Personalized Emotion Analysis Based on Fuzzy Multi-Modal Transformer Model

  • Source: Applied Intelligence, February 2025
  • Type: Journal Article
  • DOI: 10.1007/s10489-024-05954-5
  • ISSN: 0924-669X / 1573-7497
  • Contributors: Jianbang Liu, Mei Choo Ang, Jun Kit Chaw, Kok Weng Ng, Ah-Lian Kor
    This paper introduces a fuzzy multi-modal transformer model for personalized emotion analysis, which enhances the ability of systems to interpret emotional states through multiple data sources.

4. The Emotional State Transition Model Empowered by Genetic Hybridization Technology on Human–Robot Interaction

  • Source: IEEE Access, 2024
  • Type: Journal Article
  • DOI: 10.1109/access.2024.3434689
  • ISSN: 2169-3536
  • Contributors: Jianbang Liu, Mei Choo Ang, Jun Kit Chaw, Kok Weng Ng, Ah-Lian Kor
    This article explores the emotional state transition model used in human-robot interactions, leveraging genetic hybridization technology to create more adaptive, emotion-aware robotic systems.

5. Emotion Assessment and Application in Human-Computer Interaction Interface Based on Backpropagation Neural Network and Artificial Bee Colony Algorithm

  • Source: Expert Systems with Applications, December 2023
  • Type: Journal Article
  • DOI: 10.1016/j.eswa.2023.120857
  • ISSN: 0957-4174
  • Contributors: Jianbang Liu, Mei Choo Ang, Jun Kit Chaw, Ah-Lian Kor, Kok Weng Ng
    This paper discusses how emotion assessment techniques can be integrated into human-computer interaction systems using backpropagation neural networks and the artificial bee colony algorithm, contributing to more intuitive HCI interfaces.

6. Performance Evaluation of HMI Based on AHP and GRT for GUI

  • Source: 2022 IEEE International Conference on Artificial Intelligence in Engineering and Technology (IICAIET), September 2022
  • Type: Conference Paper
  • DOI: 10.1109/iicaiet55139.2022.9936844
  • Contributors: Jianbang Liu, Mei Choo Ang, Jun Kit Chaw, Kok Weng Ng
    This conference paper evaluates the performance of Human-Machine Interfaces (HMIs) based on the Analytical Hierarchy Process (AHP) and Grey Relational Theory (GRT), aiming to improve Graphical User Interface (GUI) design.