1.Create problems around data structures and algorithms ,maths accross various difficulty level.2.respond to queries/suggestions/issues from candidates solving problems on cs for all platform.3.Solved query in various complex topic segment tree,disjoint set union, cycle detection, binary index tree,tries,tree traversal,digit dp,dp on trees, binary search trees,complex binary trees.

Our research project solved some complex real world problems and have some future impact 1.The paper proposes using chest X-ray images and deep learning techniques as an alternative, fast, and automated screening mechanism for detecting COVID-19 cases. This is important as the traditional PCR tests have high false negatives.2.The use of chest X-rays is advantageous as they are widely available, relatively low-cost, and less invasive compared to nasopharyngeal swabs required for PCR testing.3.The paper highlights the need to distinguish COVID-19 pneumonia from other types of bacterial and viral pneumonia based on the distinctive radiological features.4.In this research project we use "Domain Extension Transfer Learning" approach to leverage a larger related dataset (NIH Chest X-ray Dataset) to improve the performance on the COVID-19 classification task.5.The paper utilizes Gradient Class Activation Mapping (Grad-CAM) to provide visual explanations of the deep learning model's decision-making process.6.The paper emphasizes the urgent need for fast and automated COVID-19 screening as healthcare systems worldwide get overwhelmed by the influx of patients.7.The proposed deep learning-based approach can potentially assist medical professionals in handling the pandemic more effectively.8.The paper experiments with various popular CNN architectures like AlexNet, VGGNet, and ResNet for the COVID-19 classification task.9.The paper utilizes Grad-CAM, a deep learning-based visualization technique, to understand the decision-making process of the trained CNN models.10.The deep learning-based approach allows for end-to-end learning, where the model can directly take the raw X-ray image as input and output the COVID-19 classification prediction.11.The deep learning models trained in this work can potentially be generalized to other medical imaging tasks, beyond just COVID-19 detection, by further fine-tuning or adapting the pre-trained models.

portfolio Allocation project (tech stack node js,Java script)1.contributed design to help constricting financial portfolios made of several assets :bonds,commodities,cryptocurrencies,currencies, exchange traded funds, mutual funds,stocks etc.2.By optimizing asset allocation, this project can potentially increase portfolio returns by up to 5% while reducing risk by 2%. 3.This problem generally solves problem using mathematical optimization algorithms example linear programming.As the integration of a Web API inside Google Sheets is much easier than the integration of a third party JavaScript library, this proved a wise choice.I noticed two issues:1.The integration of PortfolioAllocation inside a Google Sheet requires stronger IT skills than I anticipated.2.The execution of PortfolioAllocation algorithms on Google Sheets servers isa.slowb.severely time constrainedFor these reasons we decided to stop developing the "vanilla" PortfolioAllocation library and replaced it with Portfolio Optimizer, a Web API to analyze and optimize financial portfolios.several features in Portfolio Optimizer1.Compatible with Google sheets.2.Compatible with any browser supporting ECMAScript 5 for front-end development3.Compatible with Node.js for back-end development.

This project generally is development of a comprehensive testing module application for a school e-governance system. To tackle the immense task of testing over 8,700 modules – a process that would have been incredibly time-consuming with manual methods – I contributed a powerful automated testing strategy using Java and Selenium.1.Leveraged Selenium to meticulously test each of the 8,700+ modules, achieving a significant 80% reduction in testing time.2.Developed robust test scripts that comprehensively covered 80% of core functionalities,10% of user workflows,10% of potential edge cases.3.This automation approach resulted ina.Enhanced test coverage (greater number of modules tested).b.Improved test consistency (reduced human error in repetitive tasks).c.Increased test execution speed (faster feedback loop).4.Additional Impact:1.The automated testing process identified a substantial number of bugs (provide a specific number if possible, e.g., "over 1,200 defects"). This early detection significantly improved the overall software quality and reduced post-release issues.2.The automation framework facilitated smoother maintenance and regression testing, ensuring the system's continued functionality with future updates.