OBJECTIVE • Contributed to the development of an Augmenting Intelligence Computing that enhances knowledge acquisition, validation, and individualized learning by integrating human intelligence with machine learningSTRATEGY • Prepared a 5M row SQL dataset using SQLite, with data extracted and classified through a fine-tuned BERTmodel for concept classification and Flair NER for entity recognition• Used Graph Neural Networks and LLaMA model to tailor content based on user understanding, enhancingengagement and learning outcomes• Employed a bipartite GNN to model user-topic relationships, extract embeddings, and predict links, resultingin personalized and highly relevant topic recommendations• Implemented an AI validation process involving co-reference resolution, claim extraction, evidence retrieval,LLM analysis, and to ensure the accuracy and reliability of user contributions• Designed and implemented key metrics for AIC, including Concept Index, Domain Index, Correctness Score, Reasoning Score, Content Fitment Index, and Language Index, to tailor content based on user proficiency, domain expertise, accuracy, reasoning abilities, content relevance, and language comprehension

Mentors: Prof. Arnab Bhattacharya and Prof. Shivam TripathiObjective:• Classify fog in optical camera images using visibility data, advanced classification, and optimal featuresStrategy:• Employed sensor-gathered visibility values to classify optical camera images as fog and no-fog images• Categorized fog images into varying density levels: very dense, dense, medium, light, and no-fog• Mitigated class imbalance by visually representing fog and no-fog data across timeframes using bar plots• Utilized VGG16 model for feature extraction from Raw Images, followed by feature selection through PCA• Determined optimal feature count using Scree Plot and Explained Variance Ratio• Executed SVM, Random Forest, Logistic Regression, ANN models for Binary & Multiclass ClassificationImpact:• The NN model achieves the highest test accuracy (85.54% binary and 78.25% multiclass) among all models• Bayesian Neural Network used to forecast fog probability, yielding a mean CSI of 0.69 on the test dataset