Name: Harsh Pathak
Type: User
Company: Delhi Technological University
Bio: I am a fourth year undergrad from Delhi Technological University, with a keen interest in the field of machine learning, AI, and software development. r
Location: Delhi
Harsh Pathak's Projects
A Brain tumor is considered as one of the aggressive diseases, among children and adults. Brain tumors account for 85 to 90 percent of all primary Central Nervous System(CNS) tumors. Every year, around 11,700 people are diagnosed with a brain tumor. The 5-year survival rate for people with a cancerous brain or CNS tumor is approximately 34 percent for men and36 percent for women. Brain Tumors are classified as: Benign Tumor, Malignant Tumor, Pituitary Tumor, etc. Proper treatment, planning, and accurate diagnostics should be implemented to improve the life expectancy of the patients. The best technique to detect brain tumors is Magnetic Resonance Imaging (MRI). A huge amount of image data is generated through the scans. These images are examined by the radiologist. A manual examination can be error-prone due to the level of complexities involved in brain tumors and their properties. Application of automated classification techniques using Machine Learning(ML) and Artificial Intelligence(AI)has consistently shown higher accuracy than manual classification. Hence, proposing a system performing detection and classification by using Deep Learning Algorithms using ConvolutionNeural Network (CNN), Artificial Neural Network (ANN), and TransferLearning (TL) would be helpful to doctors all around the world.
Building Low Latency Applications with CPP by Packt Publishing
Context This dataset contains 100k dielectron events in the invariant mass range 2-110 GeV for use in outreach and education. These data were selected for use in education and outreach and contain a subset of the total event information. The selection criteria may be different from that used in CMS physics results.
Config files for my GitHub profile.
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The Unified AI Framework
Repository for OpenCV's extra modules
A lightweight javascript timezone library
Built an adjacency matrix for a graph representing different cities at different nodes. Assigned weights to the edges which represented the distance or time taken for the vehicle to go from one city to another. This framework can also be implemented on supply chain optimization and Travelling salesman problem.