K ASHWINI | Computer Science | Best Researcher Award

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K ASHWINI | Computer Science | Best Researcher Award

K ASHWINI, National Institute of Technology Rourkela, India

K. Ashwini is a dedicated Ph.D. candidate in Computer Science and Engineering at NIT Rourkela, specializing in deep learning applications for grading diabetic retinopathy. She holds an M.Tech. from VSSUT Burla and a B.Tech. from Synergy Institute of Engineering & Technology, Dhenkanal. Her research includes notable publications, such as her work on CNN-based diabetic retinopathy grading in Biomedical Signal Processing and Control. Skilled in Python, MATLAB, and LaTeX, she has actively participated in workshops on machine learning and signal processing. Ashwini is fluent in Hindi, Telugu, and English.

Publication profile

google scholar

Academic Background

Ms. K. Ashwini is a Research Scholar in Computer Science and Engineering (CSE) at NIT Rourkela, currently pursuing her Ph.D., with her research focused on diabetic retinopathy grading using deep learning techniques. Her advanced studies in deep learning, combined with an M.Tech. in CSE from VSSUT Burla, highlight her dedication to exploring complex topics within biomedical and computational research. She has maintained a strong academic record throughout her studies, underscoring her commitment and expertise in her field.

Research Focus and Publications

Ashwini’s primary research area is in biomedical signal processing, specifically targeting diabetic retinopathy grading using CNNs and soft attention mechanisms. She has contributed a journal article to Biomedical Signal Processing and Control and presented multiple conference papers at reputable IEEE and Springer conferences, indicating her active participation in disseminating her research findings. Notably, her publications demonstrate her capacity to employ and innovate with advanced computational methods for impactful health-related applications, a relevant focus for this award.

Technical Skills and Training

Her technical skill set, including Python, MATLAB, and LaTeX, complements her research competencies. Ashwini’s training in SQL and experience with clustering and fraud detection in mobile networks contribute to a robust and versatile research portfolio. Her academic research skills and fluency in programming languages further solidify her qualifications as a proficient researcher in her domain.

Workshops and Professional Development

Ms. Ashwini has participated in several workshops and short-term training programs across India, including those focused on biomedical signal processing, machine learning, and image processing applications. Her engagement in diverse professional development initiatives, such as faculty development programs and national seminars, showcases her continuous effort to enhance her knowledge base and technical skills.

Publication top notes

Grading diabetic retinopathy using multiresolution based CNN

Soft attention with convolutional neural network for grading diabetic retinopathy

Application of Generalized Possibilistic Fuzzy C-Means Clustering for User Profiling in Mobile Networks

Improving Diabetic Retinopathy grading using Feature Fusion for limited data samples

An intelligent ransomware attack detection and classification using dual vision transformer with Mantis Search Split Attention Network

Check for updates Modified Inception V3 Using Soft Attention for the Grading of Diabetic Retinopathy

Modified InceptionV3 Using Soft Attention for the Grading of Diabetic Retinopathy

Grading of Diabetic Retinopathy using iterative Attentional Feature Fusion (iAFF)

Conclusion

Ms. K. Ashwini exemplifies a suitable candidate for the Research for Best Researcher Award. Her specialized research in diabetic retinopathy grading, supported by a solid academic and technical background, positions her as a promising researcher. Her publications and active participation in workshops further validate her dedication and contributions to biomedical signal processing and computer vision applications, aligning well with the award’s criteria for excellence in research and innovation.

Noor .A. Rashed | Computer Science Award | Women Researcher Award

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Dr . Noor .A. Rashed | Computer Science Award | Women Researcher Award

Dr. Noor Rashid, Iraq

Dr. Noor Rashid is a Ph.D. candidate at the University of Technology, Baghdad, specializing in Computer Science. She earned her master’s degree from the University of al-Anbar in 2018. Her research covers areas such as Artificial Intelligence, secure data systems, machine learning, data mining, image processing, and project management automation. Her current focus is on optimization algorithms, particularly multi-objective optimization (2022-2023). Dr. Rashid has contributed significantly to the field, including her recent publication on evolutionary and swarm-based algorithms. She continues to advance AI and optimization research in her academic journey.

 

Publication profile

Google Scholar

Orcid

Employment

Dr. Noor Rashid is currently employed at the University of Technology, Baghdad, Iraq, in the Department of Computer Science. As a dedicated researcher and educator, she contributes to the university’s mission by advancing studies in Artificial Intelligence, secure data systems, and optimization algorithms. Her role involves teaching and mentoring students while conducting innovative research in multi-objective optimization and machine learning. Dr. Rashid’s work continues to impact both the academic community and the broader technological landscape through her involvement in cutting-edge computer science projects.

 

Education and Qualifications 🎓📜

Dr. Noor Rashid is currently pursuing her Ph.D. in Computer Science at the University of Technology, Baghdad, Iraq, from November 2021 to November 2024. Her doctoral research focuses on advanced areas such as optimization algorithms and Artificial Intelligence, contributing to cutting-edge technological advancements. Prior to this, Dr. Rashid earned her master’s degree from the College of Computer Science and Information Technology at the University of al-Anbar in 2018. Her academic background equips her with a strong foundation in secure data, machine learning, and project management systems, preparing her for continued success in the field.

 

Research Focus 🎯🔬

Dr. Noor Rashid’s research primarily focuses on Artificial Intelligence (AI), particularly in machine learning, optimization algorithms, and data mining. Her studies delve into complex areas such as multi-objective optimization and evolutionary algorithms, aiming to solve real-world computational problems. Additionally, Dr. Rashid has worked extensively on medical image processing, applying AI techniques like ANN and SVM to detect and classify diseases like diabetic retinopathy. Her research bridges the gap between AI and healthcare, making significant contributions to secure data, networks, and advanced algorithmic developments. 🚀🧠

 

Publication Top Notes

  • Diagnosis retinopathy disease using GLCM and ANN – N. Rashed, S. Ali, A. Dawood – J. Theor. Appl. Inf. Technol 96, 6028-6040, 2018 (Cited by: 4) 📖
  • Unraveling the Versatility and Impact of Multi-Objective Optimization: Algorithms, Applications, and Trends for Solving Complex Real-World Problems – N.A. Rashed, Y.H. Ali, T.A. Rashid, A. Salih – arXiv preprint, 2024 (Cited by: 2) 🌐
  • Advancements in Optimization: Critical Analysis of Evolutionary, Swarm, and Behavior-Based Algorithms –  Rashed, Y.H. Ali, T.A. Rashid – Algorithms 17(9), 416, 2024 📑
  • ANN and SVM to recognize Texture features for spontaneous Detection and Rating of Diabetic Retinopathy –  Rashed (Upcoming) 🔍