Mr. Ranran Wang | Artificial Intelligence | Research Excellence Award

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Mr. Ranran Wang | Artificial Intelligence | Research Excellence Award 

Mr. Ranran Wang | Artificial Intelligence | Shandong Agricultural University | China

Mr. Ranran Wang is an accomplished PhD scholar, associate professor, IEEE member, and seasoned academic in mechanical, electronic, and agricultural engineering, widely recognized for his contributions to intelligent detection systems, agricultural mechanization, precision management technologies, and integrated engineering innovations. With a strong educational foundation beginning with a Bachelor of Science in Electrical Engineering from Shandong University of Technology, followed by a master’s degree from the School of Electrical Engineering at Shandong University, and a PhD in Mechanical and Electronic Engineering from Shandong Agricultural University, Wang Ranran further expanded his expertise through postdoctoral research at the Plant Protection Postdoctoral Research Station and international academic collaboration as a visiting scholar at Iwate University in Japan. Professionally, Wang Ranran has maintained a long-standing academic role at the College of Mechanical and Electronic Engineering at Shandong Agricultural University, where he has contributed to teaching, research, academic evaluation, scientific leadership, and interdisciplinary innovation. He has served as a reviewer for multiple journals, a mentor for elite engineering talent programs, an expert reviewer for investment projects, and a key member of numerous provincial and municipal scientific committees. His professional service includes roles such as expert reviewer for forestry, agricultural engineering, water and fertilizer integration facilities, rural revitalization, electric power industry development, and technological innovation alliances, as well as leadership positions including technology commissioner, science and technology mayor, and vice chairman in provincial agricultural technology extension associations.

Citation Metrics (Scopus)

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Featured Publications

Ms. Martina Formichini | Artificial Intelligence | Best Researcher Award

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Ms. Martina Formichini | Artificial Intelligence | Best Researcher Award 

Ms. Martina Formichini | Artificial Intelligence | Sant’Anna School of Advanced Studies | Italy

Ms Martina Formichini is an Italian researcher whose interdisciplinary academic and professional background positions her strongly within the domains of physics, artificial intelligence, remote sensing, and large-scale data analytics. Ms Martina Formichini completed her Bachelor’s Degree in Physics at Sapienza University of Rome, followed by a Master’s Degree in Physics of Biosystems at the same institution, where she developed foundational expertise in top-down visual perception modelling using fMRI and in the application of physical-statistical methods to complex economic and technological networks. She further strengthened her skill set through a Master in Big Data Analytics & Social Mining at the University of Pisa, gaining advanced training in data science, neural networks, scalable architectures, and machine learning for satellite imagery. Professionally, Ms Martina Formichini worked in research collaboration at Sapienza University investigating motif significance in economic-technological networks, later serving as a Programmer at Eustema S.p.A., a Senior Analyst and Solution Developer at Avanade, and an intern at Almaviva S.p.A., where she contributed to deep learning projects in computer vision and environmental monitoring using aerial and satellitar imagery. Her current role as a Ph.D. researcher at Scuola Superiore Sant’Anna focuses on artificial intelligence systems for terrain, vegetation, and soil classification, using segmentation techniques and deep learning frameworks. Her research interests include AI-based remote sensing, environmental monitoring, image segmentation, complex networks, NLP, statistical modelling, and high-performance data processing. Ms Martina Formichini possesses strong skills in machine learning, computer vision, Python ecosystems, SQL, scalable analytics, cloud-based cognitive services, data engineering workflows, and end-to-end predictive modelling. Her collaborative research mindset, leadership in group projects, and experience across academic and industrial settings demonstrate strong potential for impactful multidisciplinary contributions.

Professional Profiles: ORCID  

 Selected Publications

A Comparative Analysis of Deep Learning-Based Segmentation Techniques for Terrain Classification in Aerial Imagery

Deep Learning-Based Segmentation for Terrain Classification in Aerial Imagery

Dr. Seyedeh Tina Sefati | Reinforcement Learning | Best Researcher Award

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Dr. Seyedeh Tina Sefati | Reinforcement Learning | Best Researcher Award

Dr. Seyedeh Tina Sefati | Reinforcement Learning | University of Tabriz | Iran

Dr. Seyedeh Tina Sefati is a highly skilled and innovative Ph.D. candidate in Artificial Intelligence at the University of Tabriz, Iran, whose academic and professional trajectory reflects a strong commitment to advancing the fields of deep learning, generative adversarial networks, and game theory. Her doctoral research focuses on unsupervised multivariate time-series anomaly detection, contributing significantly to intelligent sensing and automated decision-making systems. Dr. Seyedeh Tina Sefati holds a Master’s degree in Artificial Intelligence from the University of Tabriz, where she explored spam filtering through game theory, an MBA from the Iran Technical and Vocational Training Organization, and a Bachelor’s degree in Computer Engineering from Seraj University with a thesis on solving optimization problems using ant colony algorithms. Professionally, Dr. Seyedeh Tina Sefati serves as the CEO and AI Architect at Saman Digital Eurasia, leading high-impact projects that integrate deep learning, natural language processing, and image analysis for clients across more than ten countries. Her prior experience as an AI Project Manager at Rayin Samaneh Arta and as a Programming Instructor at MFTabriz showcases her multifaceted expertise in both applied and academic contexts. Her research interests center around deep learning architectures, machine learning, NLP, image processing, and federated reinforcement learning for secure data transmission in wireless sensor networks. She has been involved in several international collaborations and industrial projects, including data-driven solutions for HepsiBurada and AndMe in Turkey, where she developed large-scale AI-based recommendation and forecasting systems. Dr. Seyedeh Tina Sefati’s technical skill set includes advanced proficiency in Python, TensorFlow, PyTorch, CNN, LSTM, GANs, and Transformers, demonstrating her ability to bridge theoretical concepts with real-world applications. Her research excellence is reflected in publications in Scopus and IEEE-indexed journals such as The Journal of Supercomputing and Mathematics. She is a recognized member of professional organizations such as IEEE and ACM and has received honors for her research contributions in deep learning and anomaly detection.

Professional Profiles: Google Scholar

Featured Publications 

  1. Sefati, S. T., Razavi, S. N., & Salehpour, P. (2025). Enhancing autoencoder models for multivariate time series anomaly detection: The role of noise and data amount. The Journal of Supercomputing, 81(4), 559. (2 citations)

  2. Sefati, S. T., Feizi-Derakhshi, M. R., & Razavi, S. N. (2016). Improvement of Persian spam filtering by game theory. International Journal of Advanced Computer Science and Applications, 7(6). (1 citation)

  3. Sefati, S. S., Sefati, S. T., Nazir, S., Farkhady, R. Z., & Obreja, S. G. (2025). Federated reinforcement learning with hybrid optimization for secure and reliable data transmission in wireless sensor networks (WSNs). Mathematics, 13(19), 1–37.

  4. Sefati, S. T., Razavi, S. N. (2024). Hybrid deep learning approach for intelligent anomaly detection in IoT sensor data. IEEE Internet of Things Journal. (3 citations)

  5. Sefati, S. T., Salehpour, P. (2023). GAN-based synthetic data generation for anomaly detection in multivariate time series. Expert Systems with Applications. (4 citations)

  6. Sefati, S. T., Feizi-Derakhshi, M. R. (2022). Game-theoretic optimization in distributed deep learning systems. Applied Intelligence. (2 citations)

  7. Sefati, S. T., Nazir, S. (2021). Deep learning-based adaptive framework for real-time sensor data analysis. IEEE Access. (3 citations)

Prof. Dr. Osman Erogul | Artificial | Best Researcher Award

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Prof. Dr. Osman Erogul | Artificial | Best Researcher Award 

Prof. Dr. Osman Erogul | TOBB University of Economics and Technology | Turkey

Prof. Dr. Osman Erogul is a distinguished academic and researcher in the field of biomedical engineering, medical device design, and artificial intelligence applications in healthcare. With a strong foundation in electrical and electronics engineering, his career has spanned academia, medical research, and international collaboration, earning him recognition as one of the leading figures in integrating engineering innovation with medical sciences. He has held various leadership positions, contributed to high-impact research, authored numerous scientific publications, and secured patents in the field of medical devices. Currently, he serves as the Dean of the Faculty of Engineering and Director of the Graduate School of Natural and Applied Sciences at TOBB University of Economics and Technology, where he continues to guide research and development in cutting-edge medical technologies.

Professional Profile

Orcid

Google Scholar

Suitability Summary 

Prof. Dr. Osman Erogul is highly suitable for the Best Researcher Award due to his outstanding academic, research, and leadership contributions in biomedical engineering and medical technology. With a solid educational background and advanced training across the USA, Germany, Holland, and Japan, he has established himself as an international authority in medical device innovation and healthcare technologies.

Education

Prof. Dr. Osman Erogul began his academic journey with a Bachelor of Science degree in Electrical and Electronics Engineering from the Military Academy, building a strong technical base. He pursued his Master of Science degree at Middle East Technical University, where he specialized further in electrical and electronics engineering with a focus on medical applications. His academic training culminated with a Ph.D. in Electronics Engineering from Ankara University, where he deepened his expertise in biomedical instrumentation and signal processing. Alongside these formal qualifications, he undertook specialized professional training in the United States, Germany, Holland, and Japan, focusing on advanced imaging technologies such as Computed Tomography, Digital Angiography, and Magnetic Resonance Imaging.

Experience

His professional experience is marked by leadership and innovation. He served as the Head of the Biomedical Engineering Centre and the Medical Design and Manufacturing Centre at Gulhane Military Medical Academy (GATA), where he directed groundbreaking projects in medical device development and healthcare technology. Additionally, he gained international exposure as a research scientist at the Communications Research Centre in Ottawa, Canada, enhancing his expertise in medical imaging and communication technologies. His experience in these roles not only bridged medical sciences and engineering but also positioned him as a key contributor to Turkey’s advancement in biomedical technologies. As a representative of Turkey, he was also designated to the Medical Applications of Knowledge Transfer Forum at CERN in Switzerland, underscoring his global standing in scientific collaboration. Currently, as Dean and Director at TOBB University of Economics and Technology, he leads engineering education and graduate research programs, preparing the next generation of researchers and innovators.

Research Interests

Prof. Dr. Osman Erogul research encompasses a wide array of domains in biomedical engineering and applied sciences. His key focus areas include physiological signals and image processing, sleep signals analysis, artificial intelligence for medical diagnostics, and the design and manufacturing of custom-made implants. He has a strong interest in additive manufacturing techniques for medical applications, ensuring that patient-specific solutions are developed with high precision. His research also spans medical technology management, quality assurance systems, and radiation physics, contributing to both the academic community and the healthcare industry. His interdisciplinary approach demonstrates a unique integration of engineering principles with real-world medical applications.

Awards

Throughout his career, Prof. Dr. Osman Erogul has been recognized for his leadership in biomedical engineering research and academic excellence. His contributions to medical device innovation, training of young researchers, and international collaboration have positioned him as a valuable asset to both the scientific and healthcare communities. His designation as the national representative in international knowledge transfer forums such as CERN highlights the trust placed in his expertise and his role in advancing Turkey’s global scientific presence. He has also been honored through various institutional and professional recognitions for his contributions to medical device design and applied research.

Publication Top Notes

  • Effects of electromagnetic radiation from a cellular phone on human sperm motility: an in vitro study
    Year: 2006
    Citation: 410

  • Epileptic EEG detection using the linear prediction error energy
    Year: 2010
    Citation: 228

  • An efficient method for snore/nonsnore classification of sleep sounds
    Year: 2007
    Citation: 167

  • Efficient sleep spindle detection algorithm with decision tree
    Year: 2009
    Citation: 119

  • Selective brain cooling seems to be a mechanism leading to human craniofacial diversity observed in different geographical regions
    Year: 2004
    Citation: 97

  • Investigation of sequential properties of snoring episodes for obstructive sleep apnoea identification
    Year: 2008
    Citation: 71

  • Automatic recognition of vigilance state by using a wavelet-based artificial neural network
    Year: 2005
    Citation: 60

  • Obstructive sleep apnea prediction from electrocardiogram scalograms and spectrograms using convolutional neural networks
    Year: 2021
    Citation: 38

Conclusion

Prof. Dr. Osman Erogul professional journey reflects a blend of technical mastery, innovative research, and academic leadership. His contributions to the fields of biomedical engineering, medical imaging, and artificial intelligence highlight his dedication to advancing healthcare through engineering. By combining extensive educational training, practical experience, and global collaboration, he has shaped impactful research that benefits both academia and industry. His publications, patents, and leadership roles reinforce his reputation as a pioneer in medical technology innovation. Prof. Dr. Osman Erogul continues to inspire the academic community while driving forward new discoveries and applications that integrate engineering with medicine.

Dr. Sampath Dakshina Murthy Achanta | Machine Learning | Best Innovation Award

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Dr. Sampath Dakshina Murthy Achanta | Machine Learning | Best Innovation Award 

Dr. Sampath Dakshina Murthy Achanta, Vignan’s Institute of Information Technology, India

Dr. Achanta Sampath Dakshina Murthy is a distinguished academic and innovator currently serving as a Senior Associate Professor in the Department of Electronics and Communication Engineering at Vignan’s Institute of Information Technology (Autonomous), Visakhapatnam. He is also the Head of the Vignan Centre for Innovations and Start-ups. With a Ph.D. in Image Processing from KL University, Andhra Pradesh, Dr. Murthy brings a strong foundation in digital electronics, communication systems, and biomedical signal analysis. He has over nine years of teaching experience and one year of industry exposure. His contributions to academia include more than 70 research publications, 20 research awards, six granted patents, ten design patents, and multiple funded projects. His research interests encompass human motion analysis, biomedical image and video processing, and IoT-enabled assistive technologies. Notably, his doctoral work focused on intelligent gait analysis using AI-driven IoT sensors for fall prediction in physically challenged individuals. He actively mentors students, coordinates institutional quality assurance (IQAC), and fosters innovation through successful incubation projects like ScitiSense and Illuminisense. Recognized for his academic leadership and innovation ecosystem contributions, Dr. Murthy is also an editor of the “Futuristic Trends in IoT” book series and a reviewer on multiple editorial boards.

Professional Profile:

ORCID

SCOPUS

GOOGLE SCHOLAR

🏆 Summary of Suitability: Best Innovation Award

Dr. Achanta Sampath Dakshina Murthy stands out as a trailblazer in academic innovation, applied research, and translational technology development, making him an ideal candidate for the Best Innovation Award. With a strong foundation in Image Processing, Biomedical Engineering, and IoT-enabled healthcare, his contributions are both technologically advanced and socially impactful.

🎓 Education

  • Ph.D. in Image Processing – 2023, KL University, Andhra Pradesh

  • M.Tech in Digital Electronics and Communication Systems – 2015, JNTU Kakinada

  • B.Tech in Electronics and Communication Engineering – 2013, JNTU Kakinada

  • Intermediate (M.P.C) – 2009, Board of Intermediate, A.P

  • X (CBSE) – 2007, Central Board of Secondary Education, Delhi

💼 Work Experience

  • 👨‍🏫 Senior Associate Professor (From April 1, 2025 – Present)
    Vignan’s Institute of Information Technology (A), Visakhapatnam

  • 👨‍🏫 Associate Professor (Dec 27, 2023 – March 31, 2025)
    Same institute

  • 👨‍🏫 Assistant Professor (June 15, 2016 – Dec 26, 2023)

  • 🛠️ Technician, Nirmala Industries, Auto Nagar (June 1, 2015 – June 13, 2016)

Current Roles:

  • 🚀 Head, Vignan’s Centre for Innovations & Start-ups (Aug 19, 2024 – Present)

  • 🧪 Associate Dean, R&D (Feb 19, 2024 – Aug 19, 2024)

  • 🏛️ IQAC Coordinator (Oct 26, 2021 – May 31, 2025)

🏆 Achievements

  • 📜 Ph.D. Awarded in presence of Hon’ble Sri Ramnath Kovind & Governor of A.P (Nov 30, 2024)

  • 📩 Letter of Appreciation as ARIIA Coordinator by IIC for innovation & entrepreneurship efforts (2022–23)

  • 📘 Editor of “Futuristic Trends in IoT” – IIP Series, 2023

  • 🙌 Appreciation for Research & Innovation by Chairman Dr. Lavu Rathaiah Garu (May 2023)

  • 🧠 SYRF Membership, Scholars Academic & Scientific Society (2021)

  • 📜 Ratified as Assistant Professor by JNTU Kakinada (2016)

  • 🏅 Outstanding Academic Performance in B.Tech, Vizag Institute of Technology (2011)

🧬 Research, Awards & Innovations

  • 📈 Scopus h-index: 15

  • 📝 Research Papers Published: 70

  • 🏅 Research Awards: 20

  • 🧠 Patents: 4 Published | 6 Granted

  • 🧩 Design Patents: 10

  • ©️ Copyrights: 4

  • 📚 Books Published: 7

  • 📋 Editorial Board/Reviewer: 15

  • 🏵️ Certificates of Appreciation: 100+

💡 Project Grants

  1. 🥾 Smart Shoe project under STPI Chunauti 2.0 – ₹90,000 Grant (2022–2023)

  2. 🧬 ScitiSense startup incubated at MedTech CoE, STPI Lucknow (2024)

  3. 🌐 Illuminisense incubated at AIC STPI, Bengaluru – OCP 6.0 (2024)

Publication Top Notes:

Numerical Investigation Using Machine Learning Process Combination of Bio PCM and Solar Salt for Thermal Energy Storage Applications

A wireless IOT system towards gait detection technique using FSR sensor and wearable IOT devices

Machine Learning Algorithms for Anomaly Detection in IoT Networks

Prediction and Prevention of Malicious URL Using ML and LR Techniques for Network Security

An IoT-based agriculture maintenance using pervasive computing with machine learning technique

An automated detection of heart arrhythmias using machine learning technique: SVM

Executing CNN-LSTM Algorithm for Recognizable Proof of Cervical Spondylosis Infection on Spinal Cord MRI Image

Implementation of online and offline product selection system using FCNN deep learning: Product analysis

Mr. Heon-Sung Park | Neural Networks Awards | Best Researcher Award

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Mr. Heon-Sung Park | Neural Networks Awards | Best Researcher Award 

Mr. Heon-Sung Park, School of Computer Science and Engineering, Chung-Ang University, South Korea

Heon-Sung Park is a Ph.D. student in the School of Computer Science and Engineering at Chung-Ang University, South Korea, under the guidance of Professor Dae-Won Kim. His research interests focus on artificial intelligence, continual learning, and on-device AI. He previously completed his Master’s degree in the same department and earned his Bachelor’s degree in Information Technology from Silla University. Heon-Sung has contributed to international conferences, including the IEEE International Conference on Consumer Electronics, where he presented his work on a Continual Gesture Recognition System. He has been involved in various projects, such as developing deep learning algorithms for structural adhesive inspection and creating frameworks for on-device AI. He has received several accolades, including the Chung-Ang University Graduate Research Scholarship and the Best Paper Award at the Winter Academic Conference of the Korean Society of Computer and Information. Proficient in Python, LaTeX, and machine learning tools like PyTorch and TensorFlow, Heon-Sung is committed to advancing research in AI and its applications in real-world scenarios.

Professional Profile:

GOOGLE SCHOLAR

Summary of Suitability for the Best Researcher Award: Heon-Sung Park

Heon-Sung Park is a highly qualified candidate for the Best Researcher Award, showcasing an exceptional academic background, significant research contributions, and a commitment to advancing the field of artificial intelligence.

Education 🎓

  • Ph.D. in Computer Science and Engineering
    Chung-Ang University (2022 – Present)
    Academic Adviser: Prof. Dae-Won Kim
  • Master’s in Computer Science and Engineering
    Chung-Ang University (2020 – 2022)
    Academic Adviser: Prof. Dae-Won Kim
  • Bachelor of Science in Information Technology
    Silla University (2014 – 2020)

Work Experience 💼

  • Ph.D. Student
    School of Computer Science and Engineering, Chung-Ang University (2022 – Present)

Achievements 🏆

  • Best Paper Award at the Winter Academic Conference, Korean Society of Computer and Information (2019)
  • Chung-Ang University Graduate Research Scholarship (2022 – 2024)

Awards and Honors 🌟

  • Chung-Ang University Graduate Research Scholarship (2022 – 2024)
  • Best Paper Award (2019) for the research paper presented at the Winter Academic Conference of the Korean Society of Computer and Information

Publication Top Notes:

 

Prof. Yuguo Yu | Artificial Neural Awards | Best Researcher Award

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Prof. Yuguo Yu | Artificial Neural Awards | Best Researcher Award  

Prof. Yuguo Yu, Fudan University, China

Yuguo Yu, Ph.D., is a distinguished professor in Brain-inspired Artificial Intelligence and Computational Neuroscience at Fudan University, where he has been a faculty member since 2011. He currently serves as a professor at both the Research Institute of Intelligent Complex Systems and the National Key Laboratory of Medical Neurobiology. Yu obtained his Bachelor’s degree in Physics from Lanzhou University in 1995 and completed his Ph.D. in Condensed Matter Physics at Nanjing University in 2001. He pursued postdoctoral training in Computational/Behavior Neuroscience at Carnegie Mellon University from 2001 to 2004 and was an Associate Research Scientist at Yale University from 2005 to 2011, where he continues to contribute as a visiting Research Scientist since 2021. Yu has been recognized for his academic excellence through prestigious awards, including the Shanghai Eastern Scholar Professorship in 2013 and the Shanghai Excellent Academic Leader award in 2021. He is an active member of the Chinese Society of Computational Neuroscience and serves as an associate editor for several prominent journals, including IEEE Transactions on Cognitive and Developmental Systems and Frontiers in Computational Neuroscience. His research interests encompass brain-inspired neural networks, cellular mechanisms of energy-efficient cortical dynamics, synaptic learning mechanisms, and large-scale cortical network modeling, with over 100 publications in leading journals such as Nature and Neuron. Yu has also led or participated in numerous national foundation projects, advancing the field of computational neuroscience.

Professional Profile:

GOOGLE SCHOLAR

Research for Best Researcher Award

Candidate Overview: Dr. Yuguo Yu is a prominent researcher and professor in Brain-inspired artificial intelligence and computational neuroscience at Fudan University. With extensive academic and research experience, he is a strong candidate for the Best Researcher Award due to his significant contributions to the field, impactful publications, and leadership roles.

Education

  • B.Sc. in Physics
    Lanzhou University, 1995
  • Ph.D. in Condensed Matter Physics
    Nanjing University, 2001
  • Postdoctoral Researcher in Computational Neuroscience
    Carnegie Mellon University, 2001–2004
  • Research Scientist in Neurobiology
    Yale University, 2005–2011

Work Experience

  • Professor
    Research Institute of Intelligent Complex Systems, Fudan University, 2020–Present
  • Professor
    National Key Laboratory of Medical Neurobiology, Fudan University, 2013–Present
  • Visiting Research Scientist
    Yale University School of Medicine, 2021–Present
  • Associate Research Scientist
    Department of Neuroscience, Yale University, 2005–2011

Research Interests:

  • Brain-inspired Intelligence and Computational Neuroscience
  • Neural Computation Model
  • Neural Coding Theory
  • Network Topology Analysis
  • Sensory Fusion Mechanism
  • Brain Connectome Atlas
  • Self-organizing Learning Algorithm
  • Multi-sensory Fusion Model
  • Low-power Mechanism of the Human Brain 🔍

Publication Top Notes

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