About
I'm a Full-Stack Developer skilled in the MERN stack, AWS Cloud, and AI-based solutions, with a strong grasp of Data Structures, C/C++, and Python. I've built impactful projects like AgriSens, an AI-powered smart farming assistant, and published research in IEEE Xplore and IGI Global. Alongside coding, I enjoy content writing, video editing, and exploring new technologies to build scalable, real-world solutions.
Work Experience
Skills
Check out my latest work
I've worked on a variety of projects, from simple websites to complex web applications. Here are a few of my favorites.
SkillN
The next-generation platform transforming technical interview preparation through AI-driven insights and personalized learning
XploreWorld
A React-based application designed for seamless travel tracking and exploration, offering interactive maps and comprehensive city insights for enriched travel experiences.
Research & Publications
Published research papers contributing to the field of AI and agricultural technology.
Smart Crop Recommendation System with Plant Disease Identification
This paper introduces a smart web-based system that combines crop recommendation with plant disease detection to support farmers in making better agricultural decisions. Using machine learning models like Random Forest and a CNN-based image classification method, the system suggests the best crops based on environmental factors (soil nutrients, rainfall, pH, etc.) and identifies plant diseases from leaf images. A user-friendly interface allows farmers to interact with the system easily, helping improve crop yield, reduce losses, and promote sustainable farming practices.
Optimizing Load Distribution in Big Data Ecosystems: A Comprehensive Survey
This publication presents a comprehensive survey on optimizing load distribution in big data ecosystems, focusing on the challenges of managing large-scale data in cloud environments. It examines various load balancing strategiesโsuch as Round Robin, Least Connection, Resource-Based, and Dynamic approachesโhighlighting their strengths and limitations. The study also proposes a model for efficient load balancing, tested in a simulated environment, to demonstrate improvements in performance, scalability, and resource utilization. Key findings and future research directions are also discussed.
Get in Touch
Feel free to reach out to me via email at diwakarr956@gmail.com and I'll respond whenever I can.