Hi, my name is Akshay Kapoor

I'm a Software Developer who is transforming ideas into code and solutions into reality.

This project represents a major step towards accurately identifying various anomalies in health conditions digitally. It can be readily available for hospitals and its working staff to analyze the pathological trends using digital images of patients. One can upload the image and our model will capture the RGB pattern from it, based on which relevant outcome will be projected.

Implemented 2x2x2 Rubik's cube solver using BFS, IDS and A* search. For demonstration, I have used BFS in this video. When running the code, you need to provide the method with which you want the solution (BFS, IDS or A*) and the list of initial moves for shuffling. The agent then provides with final moves that can be applied after shuffling to get to the solution state.

For this project I developed an intelligent agent to play the game of Othello using the Minimax algorithm. The agent utilizes the OthelloState class, making use of functions such as gameOver(), score(), and generateMoves(). The evaluation function for the agent is based on the score function. Additionally, I designed the agent constructor to accept a search depth parameter. To validate the agent's performance, I tested it against the OthelloRandomPlayer and achieved consistent victories. I also implemented a second agent using the Alpha-Beta search algorithm, comparing the search times of both agents at different depths.

The Sentiment Analyzer is a Python-based project that utilizes a naive Bayes classifier to analyze the sentiment of text data. By training on labeled data, the classifier learns word probabilities associated with positive, negative, and neutral sentiments. The project offers an improved version of the classifier that includes preprocessing steps for enhanced accuracy. Users can input sentences or files to classify sentiment, and the system calculates probabilities to determine the sentiment expressed. The project provides an evaluation module to assess classifier performance, making it a valuable tool for sentiment analysis in various domains such as social media monitoring, customer feedback analysis, and opinion mining.

The Seam Carving project implements a content-aware image resizing algorithm called seam carving. It allows for resizing images while preserving important information by selectively removing or adding pixels with the least value. The project utilizes Python libraries like NumPy, SciPy, and Numba for implementation and optimization. It demonstrates features such as reducing height/width while maintaining content integrity and increasing image dimensions by adding pixels. The project aims to improve the algorithm by adding forward energy mapping and optimizing processing time for large images.

The Chat Protocol project involves designing and implementing a stateful and concurrent chat protocol with a user-friendly interface. The protocol ensures the correct state is maintained in both the server and client programs, allowing for concurrent handling of multiple clients. Communication is facilitated through a hardcoded port number, and the user interface is implemented using the Tkinter library. Robustness is demonstrated through error handling and credential verification, ensuring secure communication. The project provides a reliable and efficient platform for real-time chat conversations, showcasing the successful implementation of a chat protocol with state management and concurrent capabilities.