Mr. Dimitrios Gerontitis | Neural Networks | Best Researcher Award

Mr. Dimitrios Gerontitis | Neural Networks – PhD student at International Hellenic University, Greece

Dimitrios Gerontitis is an accomplished researcher renowned for his contributions to theoretical informatics, neural network modeling, and numerical linear algebra. His work, rooted in advanced mathematical frameworks, has significantly impacted the fields of recurrent neural networks (RNNs) and matrix theory. With a strong academic foundation and an analytical approach to problem-solving, Dimitrios has emerged as a leading figure in computational mathematics, applying his expertise to both theoretical challenges and practical applications.

Profile:

Orcid | Scopus | Google Scholar

Education:

Dimitrios earned his Bachelor’s degree in Mathematics from Aristotle University of Thessaloniki, followed by a Master’s in Theoretical Informatics and Systems & Control Theory. His academic journey was marked by academic excellence, reflecting his deep interest in mathematical models, computational algorithms, and system optimization. His educational background laid the groundwork for his groundbreaking research in neural networks and matrix theory, where he has developed innovative solutions to complex mathematical problems.

Experience:

Dimitrios has a rich academic and professional background, including roles as a Teaching Assistant for undergraduate mathematics courses at the International Hellenic University. He has also served as a reviewer for esteemed international journals, showcasing his analytical acumen and commitment to advancing scientific discourse. His research collaborations span global projects, including partnerships with institutions like the University of Bremen, where he contributed to studies on generalized multipole techniques and electron energy loss spectroscopy. His practical experience in teaching and academic service complements his research, fostering the growth of future scientists.

Research Interests:

Dimitrios’s research interests lie at the intersection of recurrent neural networks (RNNs), matrix theory, and numerical linear algebra. He is particularly focused on developing advanced models for matrix inversion, time-varying optimization, and computational simulations. His work explores the theoretical underpinnings of neural dynamics, including zeroing neural networks (ZNNs) and their applications in solving complex mathematical equations. His dedication to pushing the boundaries of computational mathematics has led to significant advancements in the field, particularly in optimizing algorithms for real-time applications.

Awards and Recognition:

Dimitrios has been recognized for his scholarly contributions through various academic honors and awards. His work has been published in high-impact journals and presented at international conferences, earning him recognition within the global scientific community. Notably, his paper on the “Improved Finite-Time Zeroing Neural Network for Time-Varying Division” (2020) was among the Top Cited Articles in its journal, highlighting the influence of his research. His innovative approach to solving numerical linear algebra problems has positioned him as a prominent figure in computational mathematics.

Publications 📚 :

“ZNN Models for Computing Matrix Inverse Based on Hyperpower Iterative Methods” (2017) — Filomat 📖
“Conditions for Existence, Representations, and Computation of Matrix Generalized Inverses” (2017) — Complexity 📈
“Gradient Neural Network with Nonlinear Activation for Computing Inner Inverses” (2018) — Neural Processing Letters ⚡
“A Varying-Parameter Finite-Time Zeroing Neural Network for Solving Linear Algebraic Systems” (2019) — Electronics Letters 📊
“Novel Discrete-Time Recurrent Neural Networks Handling Discrete-Form Time-Variant Multi-Augmented Sylvester Matrix Problems” (2021) — IEEE Transactions on Neural Networks and Learning Systems 🚀
“Improved Finite-Time Zeroing Neural Network for Time-Varying Division” (2020) — Studies in Applied Mathematics 🌐 (Top Cited)
“Simulation of Varying Parameter Recurrent Neural Network with Application to Matrix Inversion” (2022) — Mathematics and Computers in Simulation 🔍

Conclusion:

Dimitrios Gerontitis stands out as an exceptional researcher whose work has profoundly influenced the fields of computational mathematics, neural network dynamics, and system optimization. His extensive publication record, coupled with his dedication to academic excellence and innovative problem-solving, makes him a strong candidate for the Best Researcher Award. Through his groundbreaking research and academic leadership, Dimitrios continues to inspire the next generation of scientists and contribute to the global advancement of knowledge.

Mrs . Preeti Sharma | Deep Learning | Women Researcher Award

Assistant Professor , DIT University, Dehradun, Uttrakhand , India

Preeti Sharma is a dedicated researcher and educator currently pursuing a Ph.D. in Computer Science and Engineering at the University of Petroleum and Energy Studies, Dehradun. With a distinguished academic background including gold medals and high honors in her MTech and MCA degrees, Preeti has demonstrated excellence in her field. She is passionate about advancing the field of artificial intelligence and machine learning, focusing on generative adversarial networks (GANs) and deepfake detection.

Education

Preeti Sharma is pursuing a Ph.D. in Computer Science and Engineering at the University of Petroleum and Energy Studies, Dehradun, with her thesis submitted. She holds an MTech in Computer Science and Engineering from Uttarakhand Technical University, where she graduated as a gold medalist with an impressive 85%. Preeti completed her M.C.A. from M.D.U. (Campus), Rohtak, with a strong academic record of 82%.

Experience

Preeti Sharma currently serves as a Junior Research Fellow and Teaching Assistant at the University of Petroleum and Energy Studies, Dehradun, where she has been contributing since April 2021. Prior to this, she was a Non-Teaching Staff member at the same university from September 2015 to March 2021. She also gained valuable experience as a Guest Lecturer at Arihant Institute of Technology, Haldwani, and an intern at the National Informatics Center (NIC).

Research Interests

Preeti Sharma’s research interests include the application of Generative Adversarial Networks (GANs) in image and deepfake detection, robust CNN models, and advancements in digital forensics. Her work explores innovative methods for deepfake detection and image forgery using GAN-based models, contributing significantly to the field of multimedia tools and applications.

Awards

Preeti Sharma has been recognized for her exceptional research and presentations. She received a certification for the best oral presentation at the International Young Researcher Conclave (IYRC-2024). Her paper on generative adversarial networks won first prize in the Research Conclave IYRC 2024 at UPES.

Publications

Sharma, P., Kumar, M., Sharma, H.K. et al. Generative adversarial networks (GANs): Introduction, Taxonomy, Variants, Limitations, and Applications. Multimedia Tools and Applications (2024). Link

Sharma, P., Kumar, M., & Sharma, H.K. Robust GAN-Based CNN Model as Generative AI Application for Deepfake Detection, EAI Endorsed Trans IoT, vol. 10 (2024).

Sharma, P., Kumar, M., & Sharma, H.K. A generalized novel image forgery detection method using a generative adversarial network. Multimedia Tools and Applications (2023). Link

Sharma, P., Kumar, M., & Sharma, H.K. A GAN-based model of deepfake detection in social media. Procedia Computer Science, 218, 2153-2162 (2023).

Sharma, P., Kumar, M., & Sharma, H.K. Comprehensive analyses of image forgery detection methods from traditional to deep learning approaches: an evaluation. Multimedia Tools and Applications, 82(12), 18117-18150 (2023).

Sharma, P., Kumar, M., & Sharma, H.K. A Guide to Digital Forensic: Theoretical to Software-Based Investigations. Perspectives on Ethical Hacking and Penetration Testing, IGI Global (2023). Link

Sharma, P., Kumar, M., & Sharma, H.K. CNN-based Facial Expression Recognition System Using Deep Learning Approach. Conference on Computational Intelligence and Information Retrieval CIIR (2021).

Sharma, P. Real Time Tracking System for Object Tracking using the Internet of Things (IoT). Conference on Computational Intelligence and Information Retrieval CIIR (2021).

Sharma, P. Leach and Improved Leach: A Review. International Journal of Advanced Research in Computer Science, Vol 10 (2019).

Conclusion

Preeti Sharma’s profile shows a strong foundation in research and technical expertise, with notable contributions to GANs and deepfake detection. Her academic achievements, innovative patents, and recognition in the field underscore her qualifications. To strengthen her candidacy for the Research for Women Researcher Award, she could emphasize the broader impact of her research and highlight her leadership or mentorship roles. Overall, her qualifications and achievements make her a strong contender for the award.