Pranav Parnerkar

Pranav Parnerkar

MS CS @ USC

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A list of all the posts and pages found on the site. For you robots out there is an XML version available for digesting as well.

Pages

Posts

Future Blog Post

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Blog Post number 4

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Blog Post number 3

less than 1 minute read

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Blog Post number 2

less than 1 minute read

Published:

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Blog Post number 1

less than 1 minute read

Published:

This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool.

portfolio

publications

Skin Lesion Classification using Deep learning and Image Processing

Published in 2020 3rd International Conference on Intelligent Sustainable Systems (ICISS), 2020

Skin Cancer is the most common (accounting for 40% of cancer cases globally) and potentially life-threatening type of cancers. It was diagnosed in about 5.6 million individuals last year. Automated classification of skin lesions through images has been a challenge throughout the years because of fine variability in their appearance. Deep Learning techniques exhibit potential in tackling fine-margined image-based analysis and manage to provide accurate results. The three modelling stages include data collection and augmentation, model architecture and finally prediction into 7 different types of skin cancer namely actinic keratoses, basal cell carcinoma, benign keratosis-like lesions, dermatofibroma, melanoma, melanocytic nevi and vascular lesions. A Convolutional Neural Network was fabricated (using TensorFlow) obtaining an accuracy of 81.24%. Further Transfer learning Approach was implemented in PyTorch, which yielded accuracies of 96.40%, 98.20%, 98.70% and 99.04% respectively for Wide Resnet101, Resnet50, Densenet121 and VGG19 with batch normalization, which are all trained end-to-end from images directly, to proliferate the scalability of these models and curtail initial diagnostic costs. The aim of this research paper is to render non-invasive skin cancer screening a common norm, making it simpler.

Recommended Citation: A. Jibhakate, P. Parnerkar, S. Mondal, V. Bharambe and S. Mantri, "Skin Lesion Classification using Deep Learning and Image Processing," 2020 3rd International Conference on Intelligent Sustainable Systems (ICISS), Thoothukudi, India, 2020, pp. 333-340, doi: 10.1109/ICISS49785.2020.9316092. https://ieeexplore.ieee.org/document/9316092

Hyper Localization of Leaks in Piping and Cabling Systems using Reinforcement Learning

Published in Deep Learning DevCon 2021/Lattice - The Machine Learning Journal, 2021

Leaks have undoubtedly been one of the biggest problems plaguing piping and cabling systems across industries like electricity and power, building and smart cities, oil and gas, etc. Addressing these leaks in time becomes paramount as failure leads to a complete standstill of the transportation chain. Most AI based leak detection systems have failed to reach the deployment state as these systems are prone to output false positives. It is imperative to observe that these leaks don’t occur every day or in other words they are rare events. But when they do occur, these leaks more often than not go unnoticed. Due to the insufficient number of identified leak points, it becomes difficult to build an AI based model for the same. In an attempt to aid/replace rule-based and physics-based leak detection systems, this paper proposes a novel AI based leak detection solution using reinforcement learning which not only reduces false positives but also extends itself to multi armed bandit-based leak localization. By using this methodology, we model the latent behavior of any piping or cabling systems and provide a Q-learning based shortest path recommendation in order to help the maintenance team reach the leak node in a short amount of time.

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talks

teaching

Teaching experience 1

Undergraduate course, University 1, Department, 2014

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Teaching experience 2

Workshop, University 1, Department, 2015

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