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.

Ritu Tanwar | Artificial intelligence | Best Researcher Award

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Ms. Ritu Tanwar | Artificial intelligence | Best Researcher Award

Research Scholar, NIT Uttarakhand, India

Ms. Ritu Tanwar is a dedicated Research Scholar at the National Institute of Technology, Uttarakhand, India, specializing in stress and emotion recognition through advanced machine learning techniques. Her innovative research harnesses deep learning and artificial intelligence to interpret physiological signals, contributing significantly to the field of affective computing. Ritu’s academic journey and teaching roles underline her commitment to advancing both theoretical and practical aspects of her research.

Profile

Scopus

Research for “Best Researcher Award” for Ms. Ritu Tanwar

Strengths for the Award

Ms. Ritu Tanwar, currently pursuing her PhD at the National Institute of Technology, Uttarakhand, has demonstrated exceptional strengths in her field of research. Her primary area of focus—stress and emotion recognition through physiological signals—highlights her deep engagement with cutting-edge technology and data analysis. Ritu’s work utilizes advanced techniques in deep learning and machine learning to address significant challenges in affective state recognition.

Innovative Research Contributions: Ritu’s research integrates multimodal physiological signals to enhance stress recognition, showcasing her ability to develop and implement novel frameworks. Her attention-based hybrid deep learning models for wearable stress recognition, published in prestigious journals like Engineering Applications of Artificial Intelligence and Computers and Electrical Engineering, underline her proficiency in blending theory with practical application.

High-Impact Publications: Her publications in high-impact journals and conferences, including Computers in Biology and Medicine and the International Conference on Artificial Intelligence, reflect the substantial impact of her work on the field. Her innovative models, such as the CNN-LSTM based stress recognition system, are well-received and contribute to advancing the state of the art in affective computing.

Diverse Expertise: Ritu’s skill set spans various domains, from deep learning and artificial intelligence to data analysis and signal processing. Her ability to apply these skills effectively in her research demonstrates a well-rounded expertise that is crucial for a leading researcher.

Areas for Improvement

While Ms. Tanwar’s achievements are commendable, there are areas where she could further enhance her profile:

Broader Research Collaboration: Expanding her collaborative network with researchers from diverse fields could provide new insights and foster interdisciplinary approaches. Engaging in more collaborative projects could also increase the visibility and applicability of her research outcomes.

Broadened Publication Scope: Although Ritu has published extensively, diversifying her publication portfolio to include more interdisciplinary journals or higher-impact venues could further amplify the reach and influence of her research.

Enhanced Outreach: Increasing her participation in academic and industry conferences, workshops, and seminars could boost her professional network and provide more platforms to showcase her research. Additionally, contributing to review articles or special issues in her field could enhance her visibility as a thought leader.

Education 🎓

Ms. Tanwar is currently pursuing a PhD in Electronics Engineering at the National Institute of Technology, Uttarakhand, India, focusing on developing a deep learning framework for affective state recognition using multimodal physiological signals (April 2021-present). She earned her M.Tech. in Electronics & Communication Engineering from the University Institute of Engineering & Technology, Kurukshetra, India, with a thesis on emotion recognition from audio signals (July 2018). Her foundational B.Tech. in Electronics & Communication Engineering was also completed at the same institute (July 2013).

Experience 💼

Ms. Tanwar has a robust academic background, having worked as a Teaching Assistant at the National Institute of Technology, Uttarakhand, where she taught courses on Microcontroller and Interfacing, Digital Signal Processing, and Speech & Image Processing. Her research experience includes contributions as an Assistant/Associate Supervisor for undergraduate students and active participation in administrative and outreach activities, including her roles as Session Coordinator and Reviewer for the IC2E3 IEEE Conference.

Research Interests 🔬

Ms. Tanwar’s research interests are centered around stress and emotion recognition, physiological signals, and advanced data analysis techniques. She specializes in applying deep learning, machine learning, and artificial intelligence to improve the accuracy and applicability of affective state recognition systems.

Awards 🏆

Senior Research Fellow Scholarship (2021-present): Awarded for her exceptional research capabilities and contributions to her field.

Publication Recognition: Her work has been accepted and recognized in leading journals and conferences, reflecting her significant contributions to the field of artificial intelligence and machine learning.

Publications Top Notes

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.

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).

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).

Tanwar, R., Singh, G., & Pal, P. K. (2024). Stress-Wed: Stress recognition autoencoder using Wearables Data. In Second International Conference on Artificial Intelligence: Towards Sustainable Intelligence. Springer (Accepted).

Conclusion

Ms. Ritu Tanwar’s research on stress and emotion recognition using physiological signals is both innovative and impactful, making her a strong candidate for the “Best Researcher Award.” Her contributions to deep learning and machine learning in affective computing are significant, and her academic and teaching experiences add to her profile as a dedicated and knowledgeable researcher. By addressing areas for improvement, such as expanding collaboration and publication scope, Ritu can further strengthen her position as a leading researcher in her field. Her ongoing research promises to make substantial contributions to both theoretical and applied aspects of artificial intelligence and emotion recognition.

NEHA KATIYAR | Machine Learning | Best Research Article Award

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MS. NEHA KATIYAR | Machine Learning | Best Research Article Award

RESEARCH SCHOLAR, Bennett university, India

 

Neha Katiyar is an Assistant Professor at Noida Institute of Technology, Greater Noida, India. With a robust background in Information Technology and Computer Science, she has contributed significantly to academia through teaching, research, and project management.

Profile

Scopus

Education

🎓 Doctorate
Bennett University, Greater Noida (July 2023 – Present)
Department: Computer Science & Engineering

🎓 Master of Technology
Madan Mohan Malviya University of Technology, Gorakhpur (Aug 2018 – July 2020)
Department: Information Technology and Computer Application
Percentage: 69% (First Division)

🎓 Bachelor of Technology
Sir Chootu Ram Institute of Engineering & Technology, Meerut (July 2015 – June 2018)
University: Chaudhary Charan Singh University, Meerut, U.P
Course: Information Technology
Percentage: 74% (First Division)

🎓 Diploma in Engineering
Government Girls Polytechnic, Lucknow (July 2010 – December 2013)
University: Board of Technical Education, Lucknow UP
Course: Information Technology
Percentage: 70% (First Division)

🎓 High School
Soni Pariya Inter College, Farrukhabad (Apr 2009 – Mar 2010)
Board: Board of High School and Intermediate Education, U.P.
Percentage: 58% (Second Division)

Experience

💼 Assistant Professor
Noida Institute of Technology, Greater Noida (11 April 2022 – 17 May 2023)
Responsibilities included evaluation of copies, research work, academic work, and preparation of question banks and presentations.

💼 Academic Associate
Indian Institute of Management, Rohtak (22 July 2021 – 7 April 2022)
Assisted faculties, conducted empirical research, managed conferences, and evaluated copies.

💼 Research Assistant
Ajay Kumar Garg Engineering College, Ghaziabad (12 Oct 2020 – 16 Jul 2021)
Worked on a project titled “Compressed parallel wavelet tree based on semantic search” funded by the Council of Science And Technology, Uttar Pradesh (UPCST).

Research Interest

🔬 Neha’s research interests include Cyber Security, Internet of Things (IoT), Machine Learning, and Artificial Intelligence. She has actively participated in various projects and research works, contributing to advancements in these fields.

Publications Top Notes

📚 Neha has authored several research papers and articles in reputed journals and conferences. Below are some of her notable publications:

Diabetes detection using IoT techniques and platform: A Survey – Published in the 1st International Conference on Recent Trends in Computer Science and Information Technology (ICRCSIT-20) at St. Martin’s Engineering College Secunderabad Telangana, on 17-18 June 2020.

A review: Target Based Sentiment Analysis using Machine Learning – Published in the 4th International Conference on Microelectronics and Telecommunications at SRM Institute of Science and Technology NCR Campus, on 26-27 Sept. 2020 (Springer Conference).

Index Optimization using Wavelet Tree and Compression – Published in the 2nd International Conference on Data Analytics and Management Conference (ICDAM 2021) at Panipat Institute of Engineering and Technology, on 26 June 2021.

A Survey on Wavelet Tree ensembles with Machine Learning and its classification – Published in 2021 at Sreyas Institute of Engineering and Technology, Hyderabad, on 9-10 July 2021.

A perspective towards 6G Networks – Published in the 5th ICMETE21 at SRM Institute of Science and Technology NCR Campus, on 24-25 Sept. 2021 (Springer Conference).

Trending IoT platform Middleware layer – Published in Taylor and Francis Group Journal on 3 May 2023.

Everton Tetila | Artificial intelligence | Scientific Breakthrough Award

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Assoc Prof Dr. Everton Tetila | Artificial intelligence | Scientific Breakthrough Award

professor/researcher, Universidade Federal da Grande Dourados – UFGD/FACET, Brazil

Assoc. Prof. Dr. Everton Tetila of Universidade Federal da Grande Dourados (UFGD), FACET, Brazil, stands at the forefront of artificial intelligence (AI) research, earning acclaim with the prestigious Scientific Breakthrough Award 🏆. His groundbreaking contributions to the field have propelled advancements in AI, reshaping industries and pioneering innovative solutions. With a keen focus on pushing the boundaries of technological innovation, Dr. Tetila’s work represents a fusion of academic rigor and real-world impact. As a respected professor and researcher, he continues to inspire future generations, fostering a culture of excellence and discovery in AI research.

Profile

Orcid

Academic graduation

In 2019, I obtained my PhD in Local Development from Dom Bosco Catholic University, Brazil, focusing on the innovative use of unmanned aerial vehicles and computer vision techniques for detecting and classifying soybean diseases and pests 🌱🔍. Prior to that, I completed my Master’s degree in Production Engineering at Universidade Paulista in 2007, where my research centered on software estimation processes. My academic journey began with a Bachelor’s degree in Computer Science from the State University of Mato Grosso do Sul in 2004, where I delved into bioinformatics and biological sequence analysis under the guidance of André Chastel Lima 🧬

Professional performance

In the realm of environmental sustainability and academic prowess, I’ve traversed diverse roles and responsibilities with unwavering dedication. From steering projects as a Coordinator at SEMADESC to delving into doctoral pursuits at UCDB, and nurturing minds as a Professor at UFGD, my journey embodies a mosaic of commitment and expertise. Whether it’s crafting innovative solutions in Vision Computing or delving into the depths of Database intricacies, my passion resonates across varied domains. Additionally, collaborations with esteemed institutions like UFMS and IEEE-GRSS underscore my commitment to scholarly contributions. Each engagement, be it as a Collaborator, Professor, or Reviewer, fuels my resolve to champion sustainable development and technological advancement. 🌱🎓

Publications Top Notes

  1. YOLO performance analysis for real-time detection of soybean pests
    • Authors: Tetila, Everton Castelão; Godoy da Silveira, Fábio Amaral; Da Costa, Anderson Bessa; Amorim, Willian Paraguassu; Astolfi, Gilberto; Pistori, Hemerson; Barbedo, Jayme Garcia Arnal
    • Journal: Smart Agricultural Technology
    • Year: 2024
  2. Classificação e monitoramento de áreas urbanizadas usando técnicas de visão computacional
    • Authors: Tetila, E. C.; Moraes, P. M.; Constantino, M.; Costa, R. B.; Ayres, F. M.; Reynaldo, G. O.; Colman, N. A.; Machado, F. C. A. P.; Soares, K. G.; Greco, M. M. D. M.; Pistori, H.
    • Journal: Desenvolvimento e Meio Ambiente (UFPR)
    • Year: 2023
  3. Pseudo-label Semi-supervised Learning for Soybean Monitoring
    • Authors: Menezes, Gabriel Kirsten; Astolfi, Gilberto; Martins, José Augusto Correa; Castelão Tetila, Everton; da Silva Oliveira Junior, Adair; Gonçalves, Diogo Nunes; Marcato Junior, José; Silva, Jonathan Andrade; Li, Jonathan; Gonçalves, Wesley Nunes; Pistori, Hemerson
    • Journal: Smart Agricultural Technology
    • Year: 2023
  4. System for quantitative diagnosis of COVID-19 associated Pneumonia based on Superpixels with deep learning and chest CT
    • Authors: Tetila, E. C.; Bressem, K. K.; Astolfi, G.; Sant’Ana, D. A.; Pache, M. C. B.; Wirti Junior, G.; Barbedo, J. G. A.; Pistori, H.
    • Journal: Observatorio de la Economía Latinoamericana
    • Year: 2023
  5. Desenvolvimento de uma plataforma web para sensoriamento remoto com VANT
    • Authors: Terenciani, Marcelo Figueiredo; Tetila, Everton Castelão; da Silva, Igor Donatti Gonçalves; Tetila, Juliana Queiroz da Silva; Barbedo, Jayme Garcia Arnal
    • Journal: Observatorio de la Economía Latinoamericana
    • Year: 2023
  6. Um sistema de visão computacional para reconhecimento de doenças da soja usando VANTs: resultados preliminares
    • Authors: Tetila, E. C.; Machado, B. B.; Silva, G. G.; Pistori, H.; Belete, N. A. S.; Tetila, J. Q. S.; Barbedo, J. G. A.
    • Journal: Revista Caribeña de Ciencias Sociales
    • Year: 2023
  7. An approach for applying natural language processing to image classification problems
    • Authors: Astolfi, Gilberto; Sant’Ana, Diego André; Porto, João Vitor de Andrade; Rezende, Fábio Prestes Cesar; Tetila, Everton Castelão; Matsubara, Edson Takashi; Pistori, Hemerson
    • Journal: Neurocomputing
    • Year: 2022
  8. Combining Syntactic Methods With LSTM to Classify Soybean Aerial Images
    • Authors: Astolfi, Gilberto; Pache, Marcio Carneiro Brito; Menezes, Geazy Vilharva; Oliveira Junior, Adair da Silva; Menezes, Gabriel Kirsten; Weber, Vanessa Aparecida de Moares; Castelao Tetila, Everton; Belete, Nicolas Alessandro de Souza; Matsubara, Edson Takashi; Pistori, Hemerson
    • Journal: IEEE Geoscience and Remote Sensing Letters
    • Year: 2021