Chitra P | Image Processing | Best Researcher Award

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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. šŸŒšŸ“ššŸ”¬

Publication Profile :Ā 

Scopus

Google Scholar

Educational Background šŸŽ“

  • 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 šŸ’¼

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 šŸ”¬

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 & AchievementsšŸŽ“

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 šŸ“š

  • Chitra P., Beryl Vedha Y. Johnson, Retnaraj Samuel S., et al. (2024), ā€œClassification of Microglial Cells using Deep Learning Techniquesā€, 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), ā€œBrain Tumor Detection- ISM Band SAR Reduction Analysis Using Microstrip Patch Antennaā€, Proceedings of the 2024 10th International Conference on Communication and Signal Processing, ICCSP 2024
  • Chitra P., Srilatha K., Sumathi M., et al. (2023), ā€œAutomated Detection of Polycystic Ovaries using Pretrained Deep Learning Modelsā€, AICERA/ICIS 2023
  • Chitra P., Srilatha K., Jayasudha F.V., et al. (2023), ā€œLung Cancer Detection Using Classification Algorithmā€, RAEEUCCI 2023
  • Amudha S., Shobana J., Satheesh Kumar M., Chitra P. (2022), ā€œModelling Air Pollution and Traffic Congestion Problem Through Mobile Applicationā€, IconDeepCom 2022
  • Chitra P., Sheela Rani B., Venkataraman B., et al. (2011), ā€œComparison of Image Enhancement Techniques for Radiographic Weld Imagesā€, Instrumentation Society Of India
  • Chitra P., Sheela Rani B., Venkataraman B., et al. (2011), ā€œEvaluation of Signal To Noise in Different Radiographic Methods and Standard Digitizerā€, Indian Journal of Computer Science and Engineering
  • Chitra P., Sheela Rani B., Manoharan N., et al. (2007), ā€œA Comparative Study on the Digitization Parameters of Radiographic Weld Image Digitizers for Weld Defect Detectionā€, ECHDEM 2007, Chennai
  • Chitra P., Arulmozhi N., Sheela Rani B., et al. (2008), ā€œEvaluation of Radiographic Image Quality through Standard Weld Image Digitizersā€, ESSTA 2008
  • Chitra P., Sheela Rani B. (2012), ā€œStudy and Analysis on the Effect of Source to Film Distance on the Radiographic Imageā€, ICCCT 2012
  • Chitra P., Arulmozhi N., et al. (2009), ā€œSNR Based Evaluation of Radiographic Weld Image Using Selenium 75, Ir-192, and X-raysā€, National Seminar and Exhibition on NDE
  • Chitra P., Sheela Rani B., et al. (2011), ā€œExtraction of Radiographic Weld Defects Using Pixel Based Segmentationā€, NCICM 2011

 

 

 

Muhammad Wajid | Computer Vision | Best Researcher Award

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Mr. Muhammad Wajid | Computer Vision | Best Researcher Award

Lecturer at Sir syed Case Institute of Technology Islamabad, Pakistan

šŸŒŸ Muhammad Wajid is a passionate computer scientist specializing in deep learning šŸ¤–, natural language processing šŸ“–, and computer vision šŸ‘ļøā€šŸ—Øļø. 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 šŸŽ“, he has earned recognition for his impactful research, including publications in prestigious journals šŸ“œ. Muhammadā€™s expertise spans Python, TensorFlow, PyTorch, and Scikit-Learn, making him a skilled innovator in AI and machine learning šŸš€.

Publication Profile :Ā 

Google Scholar

Education šŸŽ“

Muhammad Wajid earned his Master of Science in Computer Science from COMSATS University Islamabad (2020ā€“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 (2015ā€“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 ExperiencešŸ’¼

Muhammad is a Lecturer at the Sir Syed CASE Institute of Technology in Islamabad, Pakistan (Feb 2024ā€“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 2019ā€“Jul 2021), contributing to impactful research under expert supervision. His efforts have led to publications and advancements in deep learning and NLP.

Research Interests šŸ”¬

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 šŸ“š

  • 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]

 

 

 

Kothai Ganesan | Computer Vision | Best Researcher Award

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Assist. Prof. Dr. Kothai Ganesan | Computer Vision | Best Researcher Award

Assistant Professor at KPR Institute of Engineering and Technology, India

Dr. Kothai Ganesan is an Assistant Professor in Computer Science and Engineering (Artificial Intelligence and Machine Learning) at KPR Institute of Engineering and Technology. She holds a Bachelorā€™s degree and Masterā€™s degree in Engineering from Anna University and a Ph.D. from SRM Institute of Science and Technology, with research focusing on Intelligent Transport Systems, traffic prediction, and secure data transmission. Her published work includes studies in vehicular networks, traffic congestion, and security protocols, alongside ongoing research in medical imaging, AI-enhanced diagnostic tools, and image processing. A recognized mentor and department coordinator, Dr. Kothai integrates modern tools like Docker into her curriculum, advancing industry-aligned education. Her contributions have been acknowledged in journals, conferences, and professional memberships, including IEEE Computational Intelligence Society.

Publication Profile :Ā 

Scopus

 

šŸŽ“ Educational Background :

Dr. Kothai Ganesan is an accomplished Assistant Professor in the Computer Science and Engineering (Artificial Intelligence and Machine Learning) department at KPR Institute of Engineering and Technology. Her academic journey began with a Bachelorā€™s degree in Computer Science and Engineering from Anna University (2012ā€“2016), followed by a Masterā€™s in the same field, earned with first-class distinction at Anna University (2016ā€“2018). Pursuing her passion for advanced research, Dr. Ganesan completed her Ph.D. in November 2023 at SRM Institute of Science and Technology, where her research centered on Intelligent Transport Systems, with a particular focus on traffic prediction, congestion avoidance, and secure data transmission, all aimed at improving urban mobility.

šŸ’¼ Professional Experience :

Professionally, Dr. Ganesan has contributed significantly to the field of AI-driven transport systems, publishing extensively on vehicular ad hoc networks (VANETs) and innovative machine learning techniques. Her research outputs include collision prediction and secure communication protocols, enhancing safety in smart cities. She has also expanded her focus into medical AI, exploring Alzheimerā€™s diagnosis, pediatric epilepsy recognition, and cancer detection using optimized learning models. Alongside her research, Dr. Ganesan is an active mentor, guiding student-led projects in machine learning and artificial intelligence. As a dedicated faculty member, she serves as the IQAC Coordinator, Exam Cell Coordinator, Autonomous Coordinator, and R&D Coordinator, furthering the programā€™s reputation through academic rigor and practical industry integration. šŸ“ššŸ’”

šŸ“š Research Interests :Ā 

Her current research interests are broad, covering deep learning, machine learning, computer vision, and natural language processing. Dr. Ganesan’s work has gained her recognition in prestigious journals and conferences, and she actively participates in the IEEE Computational Intelligence Society as a Faculty Advisor. As a reviewer for leading journals, she contributes her expertise to the scholarly community. Dr. Ganesanā€™s unique blend of academic insight, mentorship, and professional innovation showcases her commitment to advancing AI and machine learning for impactful, real-world applications. šŸŒŸ

šŸ“ Publication Top Notes :

  1. Ganesan, K., & [Co-authors, if applicable]. (2021). A New Hybrid Deep Learning Algorithm for Prediction of Wide Traffic Congestion in Smart Cities. Wireless Communications and Mobile Computing, 2021, Article ID 5583874, 13 pages. https://doi.org/10.1155/2021/5583874.
  2. Ganesan, K., & [Co-authors, if applicable]. (2020). Performance Analysis of Stationary and Deterministic AODV Model. International Journal of Interactive Mobile Technologies (IJIM), 14(17), 33ā€“44.
  3. Ganesan, K., & [Co-authors, if applicable]. (2022). IoT-Based Automatic SOP Adoption in Pandemic Scenario. International Journal of High Technology Letters, June 2022.
  4. Ganesan, K., & [Co-authors, if applicable]. (2024). A Hybrid CNN-GRU based Intrusion Detection System for Secure Communication in Vehicular Adhoc Network. Information Security Journal, Taylor & Francis, June 2024.