Jay Dev

Expert in Machine Learning and AI, with a solid Computer Science background. Pursuing a Master’s in Computer Applications at Lovely Professional University, focusing on AI-driven solutions. Projects include automated rice plant disease detection and customer segmentation analysis using machine learning, computer vision, NLP, and generative AI.

Key achievements include QUALIFIED FOR ASSISTANT PROFESSOR UGC NET (Lectureship)

98.44 %tile | Computer Science and Applications

Developed an image recognition architecture using PCA+CNN reducing the model size by 96% than traditional CNN.

Freelance QA Tester with a 5-star rating, recognized for attention to detail, problem-solving, and creativity. Eager to leverage AI and automation expertise to drive impactful solutions.

Machine Learning Intern

SkillCraft Technology (Sep’24 – Oct’24)

• Developed a CNN-based Hand Gesture Recognition model using edge detection and contour analysis.
• Implemented SVM for Binary Classification of dog and cat images, optimizing performance with precision and recall metrics.
• Built a House Price Prediction model using Linear Regression, focusing on data cleaning, feature selection, and evaluation.
• Strengthened skills in data preprocessing, model evaluation, and visualization.
• Recognition: Received a letter of recommendation for outstanding performance and problem-solving abilities.

Generative AI Intern

Prodigy InfoTech (Aug’24 – Sep’24)

• Fine-tuned GPT-2 on a jokes dataset to enhance creative text generation and NLP expertise.
• Developed an AI-powered image generation model using Stable Diffusion for generative art projects.
• Applied Neural Style Transfer techniques to create visually compelling images.
• Used Markov Chains for coherent text generation and creative storytelling.
• Recognition: Received a letter of recommendation for outstanding performance and problem-solving abilities.

Master of Computer Applications (MCA)

Specialization: Machine Learning & Data Science

Projects

• Rice Plant Disease Detection – Developed an AI-based model to detect diseases in rice plants using image processing and deep learning.
• Customer Segmentation – Implemented clustering algorithms to analyze customer behavior and segment them for targeted marketing.
• Side Face Recognition – Designed a hybrid CNN-PCA approach for recognizing side faces with improved accuracy and reduced training time.
• Sub-Object Detection – Built a hierarchical object detection system for real-time video and image retrieval applications.
• Stress Analysis – Worked on analyzing stress patterns using machine learning for predictive maintenance and safety applications.