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Biography
Dr. Uma Mahesh R N is an accomplished academic and researcher based in Mysore, India. He currently serves as an Associate Professor in the Department of Computer Science & Engineering (AI and ML) at ATME College of Engineering, Mysore (pin‑570028). His tenure at ATME spans over 8½ years, evolving from Lecturer and Guest Lecturer roles into his present faculty position.
He earned his B.E. in Electronics & Communication Engineering from VTU in 2009 and went on to complete his M.Tech in VLSI Design and Embedded Systems from VTU in 2012. He furthered his education with research work at VIT Chennai and attained UGC‑NET qualification in December 2019. Currently, he is pursuing a Ph.D. at VIT University, as confirmed on his LinkedIn and ResearchGate profiles .
Dr. Mahesh is a member of the Optical Society of America (OSA) and brings expertise in digital holography, artificial intelligence, and machine learning . His significant scholarly contributions include work on deep CNN-based classification of 3D holographic objects—published in IgMin Research in July 2024 and widely cited in Google Scholar (h-index 4, ≈50 citations) .
With over a dozen publications and active engagement as an IgMin Research editor and conference speaker, Dr. Mahesh bridges cutting-edge AI and optical imaging research with practical education in AI/ML . His career reflects a dedication to advancing both research and pedagogy in rapidly evolving technological fields.
Research Interest
Dr. Uma Mahesh R N is deeply engaged in interdisciplinary research bridging Artificial Intelligence, Machine Learning, and Optical Computing. His primary research interests lie in deep learning models, particularly Convolutional Neural Networks (CNNs), and their applications in 3D object recognition and digital holography. He explores how AI can enhance precision in image classification, pattern recognition, and intelligent vision systems. His current work focuses on developing robust AI algorithms for non-contact object measurement, leveraging holographic datasets for real-time industrial and biomedical imaging. Dr. Mahesh is also keen on applying machine learning to embedded systems, IoT devices, and edge computing platforms to optimize performance and accuracy. Additionally, he has a growing interest in explainable AI, ensuring transparency and interpretability in algorithmic decisions. His research contributes to both theoretical advancements and real-world solutions, aiming to integrate smart automation into education, healthcare, and next-gen industrial systems.
Open Access Policy refers to a set of principles and guidelines aimed at providing unrestricted access to scholarly research and literature. It promotes the free availability and unrestricted use of research outputs, enabling researchers, students, and the general public to access, read, download, and distribute scholarly articles without financial or legal barriers. In this response, I will provide you with an overview of the history and latest resolutions related to Open Access Policy.
In this paper, the binary classification of skin images has been performed using deep learning technique. i.e the skin disease recognition has been performed using deep learning technique. Here, the binary classification of skin images namely melanocytic nevi and normal skin images has been classified using resnet50 deep learning network. Normal skin images have been considered in TRUE class. Melanocytic nevi skin images have been considered in FALSE class. Traditional method such as biopsy involves lot of computational procedures and consumes ...a lot of time which is tedious process. Therefore, deep learning-based skin disease recognition has been proposed here. The dataset for melanocytic nevi and normal skin images have been prepared for 9792 images. This dataset is passed through all five deep residual network models to obtain the results. The results such as error/accuracy curves, error matrix, false-positive-rate (FPR) vs. true-positive-rate (TPR) curve are shown for the confirmation of the work. The results obtained from the ResNet50 model were compared with other deep residual network models i.e ResNet18, ResNet34, ResNet101, and ResNet152 models.
Open Access Policy refers to a set of principles and guidelines aimed at providing unrestricted access to scholarly research and literature. It promotes the free availability and unrestricted use of research outputs, enabling researchers, students, and the general public to access, read, download, and distribute scholarly articles without financial or legal barriers. In this response, I will provide you with an overview of the history and latest resolutions related to Open Access Policy.
In this paper, we present a deep CNN-based approach for multi-class classification of three-dimensional (3-D) objects using phase-only digital holographic information. The 3-D objects considered for the multi-class (four-class) classification task are ‘triangle-square’, ‘circle-square’, ‘square-triangle’, and ‘triangle-circle’. The 3-D object ‘triangle-square’ is considered for Class-1 and the remaining 3-D objects ‘circle-square’, ‘square-circle’, and ‘tr...iangle-circle’ are considered for Class-2, Class-3, and Class-4. The digital holograms of 3-D objects were created using the two-step Phase-Shifting Digital Holography (PSDH) technique and were computationally post-processed to obtain phase-only digital holographic data. Subsequently, a deep CNN was trained on a phase-only image dataset consisting of 2880 images to produce the results. The loss and accuracy curves are presented to validate the performance of the model. Additionally, the results are validated using metrics such as the confusion matrix, classification report, Receiver Operating Characteristic (ROC) curve, and precision-recall curve.
