Sachin Karmuse | Image Processing | Best Researcher Award

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Dr. Sachin Karmuse | Image Processing | Best Researcher Award

Assistant Professor at DKTE’s Textile & Engineering Institute | India

Dr. Sachin Mohan Karmuse is an accomplished academic and researcher in the field of Electronics and Telecommunication Engineering with nearly two decades of teaching and research experience. He currently serves as an Assistant Professor at DKTE Society’s Textile and Engineering Institute, Ichalkaranji, where he has been actively engaged in teaching, research, and academic development. He earned his Ph.D. in Electronics Engineering from Visvesvaraya Technological University with a thesis on an improved system for heart rate monitoring using webcam-based analysis. His research interests include image processing, embedded systems, database engineering, artificial intelligence, and machine learning, supported by strong expertise in programming languages such as C, C++, Python, Java, and ASP.net, as well as software tools like Matlab, OrCAD, Proteus, and Xilinx. Dr. Karmuse has published 22 research papers in reputed journals and conferences, contributing significantly to the advancement of knowledge in his domain. His research impact is reflected in 15 citations by 15 documents, 7 indexed documents, and an h-index of 2. In addition to his research contributions, he is a life member of the Indian Society for Technical Education and has served as a Board of Studies member in his department. His academic profile reflects dedication to innovative teaching, applied research, and advancing technology in healthcare and intelligent systems.

Profile: Scopus

Featured Publications

(2025). A robust rPPG approach for continuous heart rate measurement based on face. Journal of the Institution of Engineers (India): Series B. Advance online publication.

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. 🌐📚🔬

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

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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]

 

 

 

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 Bachelor’s degree and Master’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 : 

Scopus

 

🎓 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 Bachelor’s degree in Computer Science and Engineering from Anna University (2012–2016), followed by a Master’s in the same field, earned with first-class distinction at Anna University (2016–2018). 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.

💼 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 Alzheimer’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 program’s reputation through academic rigor and practical industry integration. 📚💡

📚 Research Interests : 

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. Ganesan’s unique blend of academic insight, mentorship, and professional innovation showcases her commitment to advancing AI and machine learning for impactful, real-world applications. 🌟

📝 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), 33–44.
  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.