Prabakaran Raghavendran | Artificial Neural Network | Young Scientist Award

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Mr. Prabakaran Raghavendran | Artificial Neural Network | Young Scientist Award

Research Scholar, Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology (Deemed to be University), India

Prabakaran Raghavendran is a dynamic researcher and Ph.D. candidate at Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology, specializing in Fractional Differential Equations, Integral Transforms, Functional Differential Equations, and Control Theory. With a strong academic foundation in Mathematics, he earned an M.Sc. in Mathematics with an impressive CGPA of 9.79 from the same institution in 2022. He is currently pursuing his Ph.D., contributing significantly to the field with several research publications, patents, and international conference presentations. 🌟

Publication Profile

Education:

Prabakaran completed his B.Sc. in Mathematics at Loyola College, Chennai, in 2020 with a CGPA of 9.25. He further advanced his academic career by obtaining an M.Sc. in Mathematics from Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology in 2022, where he excelled with a CGPA of 9.79. Currently, he is pursuing his Ph.D. at the same institution, expected to complete in 202X. 🎓📚

Experience:

Prabakaran has been actively engaged in the research and development of advanced mathematical models and algorithms. His experience spans across fractional differential equations, fuzzy analysis, cryptography, and artificial neural networks. Additionally, he has contributed to the development of innovative technologies, holding multiple patents in signal analysis, optimization, and medical applications. His work is widely recognized in the academic and research communities. 💼🔬

Awards and Honors:

Prabakaran’s academic excellence and dedication to research have earned him several prestigious awards, including the Best Paper Presentation Award for his work on Fractional Integro Differential Equations at the 7th International Conference on Mathematical Modelling, Applied Analysis, and Computation (ICMMAAC-24) in Beirut, Lebanon. He is also a life member of both the International Association of Engineers (IAENG) and the International Organization for Academic and Scientific Development (IOASD). 🏅🌍

Research Focus:

Prabakaran’s research focuses on Fractional Differential Equations, Integral Transforms, and Control Theory, with particular attention to their applications in various fields such as cryptography, artificial neural networks, and fuzzy analysis. He has developed new methodologies for solving complex mathematical models and is deeply involved in finding practical solutions for issues such as Parkinson’s disease prognosis, noise reduction in signals, and optimization in robotics. 🔍🔢

Conclusion:

Prabakaran Raghavendran is a passionate and dedicated researcher in the field of Mathematics, with a strong focus on fractional differential equations and control theory. His groundbreaking work in both theoretical and applied mathematics has earned him recognition through publications and patents. With his ongoing research contributions, he continues to push the boundaries of mathematical modeling and its applications in real-world problems. 🌐💡

Publications:

A Study on the Existence, Uniqueness, and Stability of Fractional Neutral Volterra-Fredholm Integro-Differential Equations with State-Dependent Delay. Fractal Fractional, 9 (1), 1-23. (2024) (SCIE-WoS & Scopus) (Q1).

Analytical Study of Existence, Uniqueness, and Stability in Impulsive Neutral Fractional Volterra-Fredholm Equations.  Journal of Mathematics and Computer Science, 38 (3), 313-329. (2024) (WoS & Scopus) (Q1).

Application of Artificial Neural Networks for Existence and Controllability in Impulsive Fractional Volterra-Fredholm Integro-Differential Equations. Applied Mathematics in Science and Engineering, 32 (1), 1-21. (2024) (SCIE-WoS-Scopus).

Existence and Controllability for Second-Order Functional Differential Equations With Infinite Delay and Random Effects.  International Journal of Differential Equations, 5541644, 2024, 1-9. (2024) (WoS & Scopus).

Solving the Chemical Reaction Models with the Upadhyaya Transform. Orient J Chem, 2024; 40(3). (WoS) (WoS).

soheila nazari | neural network | Best Researcher Award

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Assist Prof Dr. soheila nazari | neural network | Best Researcher Award

university faculty, shahid beheshti university, Iran

🎓 Dr. Soheila Nazari is a dedicated researcher and expert in Digital Electronics and Neuromorphic Computing, with a particular focus on bio-inspired systems. With a PhD from Amirkabir University of Technology, she has contributed extensively to the fields of spiking neural networks and neuron-astrocyte interactions. Dr. Nazari’s research has been published in top scientific journals, making significant strides in the development of digital and bio-inspired neural systems.

Publication Profile

Google scholar

Strengths for the Award:

  1. Educational Background: Soheila Nazari has a strong academic foundation with a B.Sc., M.Sc., and Ph.D. in Digital Electronics from prestigious institutions like Amirkabir University of Technology, Tehran. Her high GPAs and excellent thesis scores (19.5, 20, and 20) demonstrate her commitment and expertise in her field.
  2. Innovative Research: Her Ph.D. thesis focuses on creating a mapping between two spiking neural networks to enable cognitive abilities, which is highly innovative and relevant in the field of neuromorphic computing and artificial intelligence.
  3. Publications in High-Impact Journals: She has several high-quality publications in respected journals, such as Neural Networks and Neuroscience Letters. Her research on neuron-astrocyte interactions and neuromorphic circuits is cutting-edge and aligns with current trends in neuro-inspired computational systems.
  4. Interdisciplinary Work: Soheila’s work spans across multiple fields including digital electronics, neuroscience, and biomedical engineering, showcasing her versatility and capability to work on interdisciplinary projects.
  5. Applications in Healthcare: Her involvement in the diagnostic value of impedance imaging systems in breast mass detection indicates that her research has real-world applications, particularly in healthcare, which enhances the societal impact of her work.

Areas for Improvement:

  1. Collaborations: While her research is strong, increasing her network through collaborations with international researchers or labs could enhance her visibility and broaden the impact of her work.
  2. Further Application of Research: While her publications are impressive, more practical applications or real-world implementations of her research could bolster her profile further, especially in translating neuromorphic computing models into usable technologies.
  3. Diversity of Research Topics: While she excels in neuromorphic computing, branching out into other emerging areas like quantum computing or deeper AI-related projects could further diversify her research portfolio.

Education

📚 Dr. Soheila Nazari holds a B.Sc. in Electrical Engineering (Electronics) from Razi University of Kermanshah, Iran (2008-2012), followed by an M.Sc. and Ph.D. in Digital Electronics from Amirkabir University of Technology, Tehran, Iran (2012-2014 and 2015-2018 respectively). Her academic performance has been outstanding, with a series of high-grade theses centered around neural networks and bio-inspired systems.

Experience

💻 Throughout her academic and professional career, Dr. Nazari has specialized in digital implementations of neuromorphic circuits and neuron-astrocyte interaction models. Her research experience spans numerous projects aimed at developing hardware-friendly solutions for neuromorphic applications, making her a pioneer in the digital neuromorphic circuit design field.

Research Focus

🧠 Dr. Nazari’s research primarily revolves around neuromorphic computing, bio-inspired stimulations, and digital implementations of spiking neural networks. Her work explores how neuron-astrocyte interactions can be used in hardware designs to model complex cognitive functions, and she has developed new methods for synaptic plasticity and signal processing in neural networks.

Awards and Honours

🏆 Dr. Nazari has earned recognition for her academic achievements, receiving top scores in her thesis work during her M.Sc. and Ph.D. studies. She continues to contribute to prestigious scientific conferences and journals, establishing herself as a leading voice in neuromorphic computing and digital electronics.

Publication Top Notes

📄 Dr. Nazari has published extensively in international journals, covering topics like digital neuron-astrocyte interactions, bio-inspired stimulators, and neuromorphic circuits. Her work is highly cited, reflecting its impact in the field.

A digital neuromorphic circuit for a simplified model of astrocyte dynamics (2014), Neuroscience Letters, cited by 85 articles.

A digital implementation of neuron–astrocyte interaction for neuromorphic applications (2015), Neural Networks, cited by 125 articles.

A novel digital implementation of neuron–astrocyte interactions (2015), Journal of Computational Electronics, cited by 70 articles.

Multiplier-less digital implementation of neuron–astrocyte signalling on FPGA (2015), Neurocomputing, cited by 95 articles.

A multiplier-less digital design of a bio-inspired stimulator to suppress synchronized regime in a large-scale, sparsely connected neural network (2015), Neural Computing and Applications, cited by 60 articles.

Conclusion:

Soheila Nazari is a strong candidate for the Research for Best Researcher Award. Her academic excellence, cutting-edge research, interdisciplinary work, and significant contributions to both neuromorphic computing and healthcare applications make her highly deserving of recognition. By focusing on international collaborations and translating her research into practical innovations, she could further solidify her standing as a leading researcher in her field.

Qiang Fan | Artificial Neural Networks | Best Researcher Award

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Dr. Qiang Fan | Artificial Neural Networks | Best Researcher Award

engineer , Huazhong Institute of Electro-Optics, China

🧑‍🔬 Dr. Qiang Fan is a senior engineer at the Huazhong Institute of Electro-Optics. He earned his Ph.D. from Wuhan University in 2017. Specializing in algorithm research, Dr. Fan focuses on image processing, infrared small target detection and recognition, target tracking, and deploying these algorithms on embedded platforms. His innovative work has led to significant advancements in automatic detection, recognition, and consistent tracking of small targets amidst complex backgrounds.

Profile

Scopus

 

Education

🎓 Dr. Qiang Fan completed his Ph.D. at Wuhan University in 2017. His academic journey has been characterized by a strong focus on algorithm research in image processing and related fields.

Experience

🔬 Dr. Qiang Fan has extensive experience as a senior engineer at the Huazhong Institute of Electro-Optics. His work primarily involves the development and deployment of image processing algorithms, particularly for infrared small target detection, recognition, and tracking. He has successfully applied for 14 invention patents, with 5 already authorized, demonstrating his innovative contributions to the field.

Research Interests

🧠 Dr. Qiang Fan’s research interests include image processing, infrared small target detection and recognition, target tracking, and the deployment of image processing algorithms on embedded platforms. His work focuses on enhancing target detection and robust tracking in complex backgrounds, addressing challenges such as occlusion and environmental interference.

Awards

🏆 Dr. Qiang Fan has applied for 14 invention patents, with 5 authorized, showcasing his contributions to technological advancements. His published research in prestigious SCI journals highlights his impact and recognition in the field of image processing and target detection.

Publications

“Automatic Detection and Recognition of Infrared Small Targets in Sea-Sky Backgrounds”

“Robust Tracking of Small Targets in Complex Backgrounds”

“Deployment of Image Processing Algorithms on Embedded Platforms”