merve pınar | Machine Learning | Best Researcher Award

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Dr. merve pınar | Machine Learning | Best Researcher Award

Research Ass, Marmara University, Turkey

Merve Pinar is a Research Assistant in the Faculty of Technology, Computer Engineering Department at Marmara University, Turkey. She has been pursuing her doctorate since 2023 at Marmara University in the field of Computer Engineering. Her academic journey includes a postgraduate degree from the Institute for Graduate Studies in Pure and Applied Sciences (2019-2022) and an undergraduate degree from Çanakkale Onsekiz Mart University, where she studied Engineering (2009-2013). Merve’s work primarily focuses on artificial intelligence, machine learning, and their applications in various fields, especially healthcare. She is dedicated to exploring innovative solutions using deep learning and pattern recognition techniques. Her contributions to the academic community include publications in respected journals and conferences. She also actively collaborates with other researchers to advance the field.

Profile 

Education

  • Doctorate (2023-Present): Marmara University, Faculty of Technology, Computer Engineering, Turkey.
  • Postgraduate (2019-2022): Marmara University, Institute for Graduate Studies in Pure and Applied Sciences, Turkey. Dissertation: “Derinöğrenme yöntemleri kullanılarak beyin tümörü tiplerinin ve sınırlarının tahminlenmesi” (Prediction of brain tumor types and boundaries using deep learning methods).
  • Undergraduate (2009-2013): Çanakkale Onsekiz Mart University, Faculty of Engineering, Turkey.

Merve’s academic background provides a solid foundation in computer engineering, artificial intelligence, and data science. She continues to pursue advanced studies, focusing on leveraging machine learning and deep learning methods to address complex problems in health and technology.

Research Focus

Merve Pinar’s research focuses on the intersection of artificial intelligence, machine learning, and medical applications. Her primary interests are database management, data structures, pattern recognition, and deep learning. She specializes in using AI techniques for medical imaging, particularly in the classification and segmentation of brain tumor types using MRI and surgical microscope images. Her work aims to enhance diagnostic tools, improving the accuracy and efficiency of healthcare systems. Additionally, she is involved in hyperparameter optimization for big data applications, which helps improve recommendation systems. Merve’s interdisciplinary research is positioned at the cutting edge of AI, combining computer engineering with real-world applications, particularly in healthcare technology, where deep learning plays a crucial role in revolutionizing diagnostics and treatment strategies.

Publications

  • Deep Learning-Assisted Segmentation and Classification of Brain Tumor Types on Magnetic Resonance and Surgical Microscope Images 🧠💻 (2024)
  • Hyperparameter Optimization for Recommendation Systems with Big Data 📊🔍 (2017)

Ritu Tanwar | Artificial intelligence | Best Researcher Award

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Ms. Ritu Tanwar | Artificial intelligence | Best Researcher Award

Research Scholar | NIT Uttarakhand | India

Best Researcher Award

Strengths for the Award

Innovative Research Focus: Ms. Tanwar’s research is at the cutting edge of deep learning, artificial intelligence, and stress recognition. Her focus on multimodal physiological signals for affective state recognition and wearable technology is highly relevant and forward-looking.

Strong Academic Foundation: She is pursuing a PhD at the National Institute of Technology, Uttarakhand, with a well-defined thesis on deep learning frameworks for affective state recognition. Her previous education, including an M.Tech. in Emotion Recognition and a B.Tech. in Electronics & Communication Engineering, complements her current research focus.

Quality Publications: Ms. Tanwar has a strong publication record with peer-reviewed journal articles in high-impact journals like Engineering Applications of Artificial Intelligence and Computers in Biology and Medicine. Her conference papers and book chapters also demonstrate her active engagement with the academic community.

Recognition and Support: She has received a Senior Research Fellow Scholarship, highlighting her recognized potential in her field. Her involvement in teaching and supervision further indicates her commitment to academic excellence and leadership.

Technical Skills: Proficiency in Python, MATLAB, and deep learning frameworks enhances her ability to conduct high-quality research. Her experience with various software tools and programming languages supports her research in data analysis and machine learning.

Areas for Improvement

Broader Impact and Application: While Ms. Tanwar’s work is innovative, expanding the application of her research to practical, real-world scenarios could enhance its impact. Exploring collaborations with industry partners could provide valuable insights into the practical applications of her research findings.

Interdisciplinary Integration: Integrating her research with other disciplines, such as psychology or healthcare, could provide a more comprehensive understanding of stress recognition and its applications. This interdisciplinary approach might strengthen her research outcomes and broaden her impact.

Public Engagement and Outreach: Increasing her presence in public forums and engaging with broader audiences could amplify the reach of her research. Participating in outreach activities and science communication initiatives might help in translating her research for non-specialist audiences.

Conclusion

Ms. Ritu Tanwar demonstrates significant promise as a researcher, with a strong foundation in innovative areas of deep learning and stress recognition. Her research contributions are noteworthy, and she has established a solid track record with quality publications and academic achievements.

For the “Best Researcher Award,” Ms. Tanwar’s strengths in cutting-edge research, quality publications, and technical expertise make her a strong candidate. Addressing the suggested areas for improvement could further enhance her research impact and recognition in the field.

Short Bio

👩‍🔬 Ms. Ritu Tanwar is a dedicated Research Scholar in Electronics Engineering at the National Institute of Technology, Uttarakhand, India. With a focus on stress and emotion recognition through innovative technologies, she is pursuing a PhD under the supervision of Dr. Pankaj Kumar Pal and Dr. Ghanapriya Singh. Her extensive background in deep learning and artificial intelligence positions her as a notable contributor to the field of physiological signal analysis.

Profile

Orcid

Education

🎓 PhD (pursuing) – April 2021-present
Department of Electronics Engineering, National Institute of Technology, Uttarakhand, India
Thesis: A deep learning framework for affective state recognition using multimodal physiological signals
Thesis Supervisors: Dr. Pankaj Kumar Pal and Dr. Ghanapriya Singh

🎓 M. Tech. – July 2018
Department of Electronics & Communication Engineering, University Institute of Engineering & Technology, Kurukshetra, India
Thesis: Emotion Recognition from Audio Signals
Thesis Supervisor: Dr. Deepti Chaudhary

🎓 B. Tech. – July 2013
Department of Electronics & Communication Engineering, University Institute of Engineering & Technology, Kurukshetra, India

Experience

📚 Teaching Assistant
Department of Electronics Engineering, National Institute of Technology, Uttarakhand, India

  • Microcontroller and Interfacing (Jan–May 2024)
  • Digital Signal Processing (July–Dec 2021, July–Dec 2023)
  • Speech Signal Processing (July–Dec 2022)
  • Image Processing (Jan–July 2022)

📝 Supervision Experience
National Institute of Technology, Uttarakhand, India

  • Undergraduate Supervision: Kunal Kavi and Shivam Purwal (Completion year: 2024)

Research Interests

🔬 Stress and Emotion Recognition: Focused on understanding and analyzing stress and emotional states through physiological signals.
🧠 Data Analysis and Deep Learning: Leveraging advanced data analysis techniques and deep learning models to enhance emotion and stress recognition.
🤖 Artificial Intelligence and Machine Learning: Applying AI and ML technologies to improve the accuracy and effectiveness of stress recognition systems.

Awards

🏆 Senior Research Fellow Scholarship – 2021-present
Awarded for exceptional research potential and academic performance in the field of Electronics Engineering.

Publications

  1. Tanwar, R., Phukan, O. C., Singh, G., Pal, P. K., & Tiwari, S. (2024). Attention based hybrid deep learning model for wearable based stress recognition. Engineering Applications of Artificial Intelligence, 127, 107391.
  2. Tanwar, R., Singh, G., & Pal, P. K. (2024). A Hybrid Transposed Attention Based Deep Learning Model for Wearable and Explainable Stress Recognition. Computers and Electrical Engineering (Accepted).
  3. Tanwar, R., Singh, G., & Pal, P. K. (2024). Explainable Artificial Intelligence System For Stress Recognition Using Multimodal Physiological Signals. Computers in Biology and Medicine (under review).
  4. Tanwar, R., Singh, G., & Pal, P. K. (2024). Stress-Wed: Stress recognition autoencoder using Wearables Data. Second International Conference on Artificial Intelligence: Towards Sustainable Intelligence, Springer (Accepted).
  5. Tanwar, R., Singh, G., & Pal, P. K. (2024, July). Wearables Based Personalised Stress Recognition Using Signal Processing and Hybrid Deep learning Model. 2024 2nd International Conference on Computer, Electronics, Electrical Engineering and their Applications (IC2E3), IEEE.
  6. Tanwar, R., Singh, G., & Pal, P. K. (2023, July). FuSeR: Fusion of wearables data for StrEss Recognition using explainable artificial intelligence models. 2023 14th International Conference on Computing Communication and Networking Technologies (ICCCNT), IEEE.
  7. Tanwar, R., Phukan, O. C., Singh, G., & Tiwari, S. (2022). CNN-LSTM Based Stress Recognition Using Wearables. CEUR Workshop Proceedings, Springer.