Chitra P | Image Processing | Best Researcher Award

Published on by Innovation Technologist

Dr. Chitra P | Image Processing | Best Researcher Award

Professor at Sathyabama Institute of Science and Technology, India

Dr. P. Chitra is an accomplished academic and researcher with a Ph.D. in Applied Electronics from Sathyabama University (2014) and a Master’s in Applied Electronics (M.E.) from Coimbatore Institute of Technology (2004). She has been serving as an Assistant Professor in Sathyabama University, Chennai, since 2004. Her research interests span across image processing, signal processing, communication systems, and electronic circuit analysis. She has secured multiple sponsored projects, including collaborations with the Indira Gandhi Centre for Atomic Research and the Department of Biotechnology. Dr. Chitra has contributed significantly to the field with numerous publications in both international journals and conferences, with notable works in deep learning for medical diagnostics, radiographic image processing, and PCOS detection using AI. Apart from her academic duties, she is a life member of the Indian Society for Technical Education (MISTE) and Institution of Engineers (IEI), and actively participates as a reviewer for various journals. Her dedication to advancing technology is reflected in her extensive training, certifications, and contributions to AI and machine learning. 🌐📚🔬

Publication Profile : 

Scopus

Google Scholar

Educational Background 🎓

  • Ph.D. in Electronics and Communication Engineering (2014), Sathyabama University, Chennai
  • M.E. in Applied Electronics (2004), Coimbatore Institute of Technology, Coimbatore (CGPA: 8.16)
  • B.E. in Electronics and Communication Engineering (2002), Nooral Islam College of Engineering, Kumaracoil, Nagercoil (73.55%)
  • H.S.C. (1998), Duthie Girls Higher Secondary School, Nagercoil (85.58%)
  • S.S.L.C. (1996), Duthie Girls Higher Secondary School, Nagercoil (87.00%)

Professional Experience 💼

Dr. P. Chitra has been an Assistant Professor at Sathyabama University since June 2004. With a career spanning over two decades, she has a rich academic experience in teaching and research, specializing in image processing, signal processing, and communication systems. She has been actively involved in various sponsored projects, including collaborations with Indira Gandhi Centre for Atomic Research and the Department of Biotechnology. She has led and contributed to multiple funded projects, including the development of digitization protocols for weld images and the application of AI for detecting PCOS (Polycystic Ovary Syndrome). Her expertise also extends to deep learning and AI-based algorithms in medical imaging and diagnostics.

Research Interests 🔬

Dr. Chitra’s research primarily focuses on image processing, signal processing, communication systems, and electronic circuit analysis. She is particularly passionate about applying AI and deep learning techniques for medical image analysis and diagnostic applications. Her ongoing research explores areas such as PCOS detection, brain tumor detection, and lung cancer classification, leveraging AI for better healthcare solutions.

Certifications & Achievements🎓

Dr. Chitra is a proud recipient of numerous NPTEL certifications, including courses in Deep Learning, Data Science for Engineers, and Introduction to AI. She is also a Life Member of Indian Society for Technical Education (ISTE) and the Institution of Engineers (IEI).

Publications 📚

  • Chitra P., Beryl Vedha Y. Johnson, Retnaraj Samuel S., et al. (2024), “Classification of Microglial Cells using Deep Learning Techniques”, Proceedings – 2nd International Conference on Advancement in Computation and Computer Technologies, InCACCT 2024
  • Sheeba I.R., Jegan G., Jayasudha F.V., Chitra P., et al. (2024), “Brain Tumor Detection- ISM Band SAR Reduction Analysis Using Microstrip Patch Antenna”, Proceedings of the 2024 10th International Conference on Communication and Signal Processing, ICCSP 2024
  • Chitra P., Srilatha K., Sumathi M., et al. (2023), “Automated Detection of Polycystic Ovaries using Pretrained Deep Learning Models”, AICERA/ICIS 2023
  • Chitra P., Srilatha K., Jayasudha F.V., et al. (2023), “Lung Cancer Detection Using Classification Algorithm”, RAEEUCCI 2023
  • Amudha S., Shobana J., Satheesh Kumar M., Chitra P. (2022), “Modelling Air Pollution and Traffic Congestion Problem Through Mobile Application”, IconDeepCom 2022
  • Chitra P., Sheela Rani B., Venkataraman B., et al. (2011), “Comparison of Image Enhancement Techniques for Radiographic Weld Images”, Instrumentation Society Of India
  • Chitra P., Sheela Rani B., Venkataraman B., et al. (2011), “Evaluation of Signal To Noise in Different Radiographic Methods and Standard Digitizer”, Indian Journal of Computer Science and Engineering
  • Chitra P., Sheela Rani B., Manoharan N., et al. (2007), “A Comparative Study on the Digitization Parameters of Radiographic Weld Image Digitizers for Weld Defect Detection”, ECHDEM 2007, Chennai
  • Chitra P., Arulmozhi N., Sheela Rani B., et al. (2008), “Evaluation of Radiographic Image Quality through Standard Weld Image Digitizers”, ESSTA 2008
  • Chitra P., Sheela Rani B. (2012), “Study and Analysis on the Effect of Source to Film Distance on the Radiographic Image”, ICCCT 2012
  • Chitra P., Arulmozhi N., et al. (2009), “SNR Based Evaluation of Radiographic Weld Image Using Selenium 75, Ir-192, and X-rays”, National Seminar and Exhibition on NDE
  • Chitra P., Sheela Rani B., et al. (2011), “Extraction of Radiographic Weld Defects Using Pixel Based Segmentation”, NCICM 2011

 

 

 

Muhammad Wajid | Computer Vision | Best Researcher Award

Published on by Innovation Technologist

Mr. Muhammad Wajid | Computer Vision | Best Researcher Award

Lecturer at Sir syed Case Institute of Technology Islamabad, Pakistan

🌟 Muhammad Wajid is a passionate computer scientist specializing in deep learning 🤖, natural language processing 📖, and computer vision 👁️‍🗨️. As a Lecturer at Sir Syed CASE Institute of Technology, he teaches and mentors in cutting-edge fields. With a strong academic foundation from COMSATS University and Gomal University 🎓, he has earned recognition for his impactful research, including publications in prestigious journals 📜. Muhammad’s expertise spans Python, TensorFlow, PyTorch, and Scikit-Learn, making him a skilled innovator in AI and machine learning 🚀.

Publication Profile : 

Google Scholar

Education 🎓

Muhammad Wajid earned his Master of Science in Computer Science from COMSATS University Islamabad (2020–2023) with a CGPA of 3.43/4.00. His thesis focused on using deep learning and preprocessing techniques to detect communities in graph-structured data. Prior to this, he completed his Bachelor of Computer Science at Gomal University (2015–2020), graduating with a stellar CGPA of 3.79/4.00. His undergraduate thesis involved the classification and identification of iris flowers using machine learning. Muhammad also holds a certification in machine learning from Stanford and freelancing from the Virtual University of Pakistan, further showcasing his commitment to technical growth.

Professional Experience💼

Muhammad is a Lecturer at the Sir Syed CASE Institute of Technology in Islamabad, Pakistan (Feb 2024–Present). He teaches advanced courses in Deep Learning, Natural Language Processing (NLP), Programming Fundamentals, and Web Technology. He also supervises final-year projects in cutting-edge domains like computer vision, deep learning, and NLP. Previously, he served as a Research Assistant at COMSATS University Islamabad (Feb 2019–Jul 2021), contributing to impactful research under expert supervision. His efforts have led to publications and advancements in deep learning and NLP.

Research Interests 🔬

Muhammad’s research interests lie at the intersection of deep learning, natural language processing, computer vision, and medical imaging. He is particularly passionate about applying AI techniques to tackle real-world challenges, including tumor segmentation, financial forecasting, and community detection in graph-structured data.

Publications Top Notes 📚

  • Wajid, Muhammad, Iqbal, Ahmed, Malik, Isra, Syed Jawad Hussain, and Yasir Jan.
    “A semi-supervised approach for breast tumor segmentation using sparse transformer attention UNet.”
    Pattern Recognition Letters, 2024.
    [Impact Factor: 3.9]
    DOI: 10.1016/j.patrec.2024.11.008
  • Wajid, Muhammad, and Ahmad Kamran Malik.
    “A Stacked GRU-based Recurrent Deep Learning Approach for Bitcoin Price Prediction to Maximize Profit.”
    Complex & Intelligent Systems, 2024.
    [Impact Factor: 5.0]
    [Under Review]