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This research presents a smart AI-driven microscope system designed for the high-precision characterization of 2D materials, particularly transition-metal dichalcogenides (TMDs). This transformative system introduces a generative deep learning-based image-to-image translation method, enabling high-throughput and automated TMD characterization. By integrating deep learning algorithms, it can analyze and interpret data from multiple imaging and spectroscopic techniques, including optical microscopy, Raman spectroscopy, and photoluminescence spectroscopy, without the need for extensive manual analysis. This invention bridges the fields of quantum technology and computer science by adopting an AI-based approach to characterize ultra-thin 2D materials, typically just one or two atoms thick. It paves the way for the development of the next generation of ultra-thin electronic devices, advanced sensors, and energy-efficient gadgets.

Novel Features of the AI-Driven Smart Microscope

This invention introduces several cutting-edge advancements in the automation of 2D material characterization, making it a game-changer in materials science and nanotechnology.

✅ Advanced Handling of Complex Structures – Accurately analyzes heterostructures and multi-layered 2D materials.

✅ Seamless Experimental Data Integration – Combines optical microscopy, Raman, and photoluminescence spectroscopy for a comprehensive material analysis.

✅ Ultra-Fast Processing Speed – Processes 100 optical images in just 30 seconds, delivering high-resolution insights in real time.

✅ “Super-Smart” Microscope Capabilities – Functions as an AI-powered microscope that enhances precision without manual intervention.

✅ Optimized for Small Data Sets – Unlike traditional deep convolutional networks, this model effectively generalizes even with limited training data.

✅ Layer Classification & Material Adaptability – Categorizes 2D material layers into five distinct classes, demonstrating adaptability across diverse material compositions.

A novel integrated approach has been proposed to enhance controlled architectural design rendering using diffusion models. The main goal of this study is to develop a hybrid diffusion model tailored to architectural design, which provides controlled, high-fidelity image generation for architectural elements while addressing limitations in existing generative models. Our method begins with a comparative analysis of various diffusion models, merging the top-performing models to create a hybrid ArchVisMix model. We employ ControlNet alongside this hybrid model as a base and optimize the parameters to achieve precise
control over architectural image generation. Additionally, we utilize the Flux model with ControlNet, which is particularly sensitive to parameter changes, allowing us to define a standardized set of parameters for generation. This ensures consistency and reliability in our results. Finally, we fine-tune our hybrid model using Low-Rank Adaptation (LoRA), allowing for more efficient training and better adaptation to architecture-specific prompts using a limited dataset. By integrating LoRA and ControlNet, our model not only overcomes the data limitation but also ensures more reliable and precise control over architectural outputs, allowing designers to generate high-quality, context-specific designs. This breakthrough has the potential to redefine how generative models are used in architectural practice, providing more tailored and effective tools for architects and designers.

Hybrid Diffusion Model: Developed a specialized diffusion model (ArchVisMix) by integrating top-performing generative models for architectural rendering.

ControlNet Integration: Utilized ControlNet to refine image generation, ensuring precise control over architectural elements.

Flux Model Optimization: Incorporated the Flux model with ControlNet to fine-tune parameter sensitivity for standardized image generation.

LoRA Fine-Tuning: Applied Low-Rank Adaptation (LoRA) to enhance model adaptability with limited training data.

Comparative Analysis: Conducted performance benchmarking of various diffusion models to select the most effective components.

Data-Efficient Training: Overcame data scarcity by optimizing generative capabilities with LoRA, ensuring high-quality outputs.

Context-Specific Design Generation: Enabled architects to generate controlled, high-fidelity designs tailored to specific requirements.

Generative AI for Architecture: Pioneered the use of hybrid diffusion models in architectural practice, enhancing design precision.

Unit Finder empowers architects by reducing manual search efforts, enabling data-driven design decisions, and accelerating the iterative design process. This tool enhances spatial layout optimization by leveraging advanced filtering mechanisms, similarity detection, and real-time data visualization. It integrates seamlessly with Revit and Rhino to analyze unit floor plans and retrieve similar designs based on spatial features like boundaries, enclosures, and door locations. This enables architects and designers to explore optimal layouts efficiently.

⚙️ Technical Details

✅ Data Processing & Feature Extraction

• Extracts corner points, enclosures, and door locations from unit plans.
• Uses geometric transformations to standardize layouts.
• Employs nearest neighbor algorithms (k-NN) to find the most similar unit plans from a database of 1350+ preprocessed plans.

✅ Machine Learning & Computer Vision

• Trained a similarity model on architectural layouts to improve retrieval accuracy.
• Implements spatial clustering to group similar designs.
• Supports multi-modal filtering based on dimensions, adjacency conditions, and functional requirements.

✅ Integration & UI

• Fully integrated with Revit & Rhino for real-time analysis.
• User-friendly web interface for interactive exploration.
• Visualization of top-k similar plans (k=20) with intuitive filtering.

In my master’s thesis, I explored the intersection of Machine Learning (ML), Deep Learning (DL), and Computer Vision to enhance the tagging accuracy of artistic images. This research focused on Behance, an Adobe platform where artists worldwide showcase their work. Given the complexity of artistic styles and content, traditional tagging methods often fail to capture the semantic depth of these images. My work aimed to bridge this gap through semantic optimization and fine-tuned image tagging techniques.

🧠 Technical Details

✅ Dataset & Feature Extraction

• Collected a dataset of artistic images from Behance, including diverse styles such as digital art, illustrations, and photography.
• Extracted low-level (color, texture, shape) and high-level (context, subject) features using deep neural networks.

✅ Machine Learning Model & Optimization

• Fine-tuned a CNN-LSTM hybrid model to capture both spatial (CNN) and contextual (LSTM) information from images.
• Applied transfer learning with pre-trained models like ResNet and EfficientNet to improve feature extraction.
• Incorporated semantic ranking to enhance tag relevance by optimizing word embeddings.

✅ Tagging & Evaluation

• Developed a custom tagging algorithm that generates contextually meaningful tags rather than generic labels.
• Evaluated performance using precision, recall, and F1-score, achieving a significant improvement in accuracy over traditional methods.
• Conducted user studies to validate the relevance and usability of generated tags

Publication

In the ever-expanding world of e-commerce, efficiently categorizing products is crucial for improving searchability, recommendations, and user experience. Traditional methods rely on manual tagging or basic keyword matching, which often leads to inconsistent or inaccurate product classifications. To address this, I developed Smart Shopping Assistant, an NLP-based recommendation system that accurately predicts categories and subcategories from natural language product descriptions.

🧠 Technical Details

✅ Data Processing & Feature Engineering

• Collected and preprocessed e-commerce product data, including titles, descriptions, and existing tags.
• Applied text cleaning, tokenization, and lemmatization to standardize input.
• Utilized TF-IDF, word embeddings (Word2Vec, FastText), and contextual embeddings (BERT) to represent product descriptions.

✅ Machine Learning & Deep Learning Models

• Trained a multi-class classification model to predict categories using:
  • Traditional ML: Random Forest, SVM, XGBoost.
  • Deep Learning: BiLSTM, Transformer-based models (BERT).
• Fine-tuned a pre-trained BERT model for more accurate semantic understanding of product descriptions.
• Implemented a hierarchical classification approach to predict both category and subcategory dynamically.

✅ Performance Optimization & Deployment

• Optimized hyperparameters to improve accuracy and reduce overfitting.
• Achieved significant improvement in classification accuracy, surpassing rule-based and traditional keyword-matching approaches.
• Designed a real-time API that integrates with e-commerce platforms, enabling automated product categorization at scale.

As computational materials science evolves, simulating spintronic devices and 2D materials requires highly specialized AI models. My current project focuses on training BERT and GPT models to create:

1️⃣ A fine-tuned BERT-based model for understanding and processing scientific data
2️⃣ A GPT-based conversational assistant trained to assist researchers in providing insights, calculations, and relevant literature suggestions.

🧠 Technical Details

✅ Fine-Tuned BERT for 2D Materials

• Data Collection: Compiled a large corpus of scientific literature, equations, and device simulation results related to TMDs, spintronic devices, and quantum materials.
• Preprocessing & Tokenization:
  • Domain adaptation
  • Applied custom tokenization to recognize material-specific terms and equations.
• Fine-Tuning Approach:
  • Trained for relation extraction and context-aware tagging of materials properties.

✅ AI Chat Agent – GPT for Device Simulations

• Model Adaptation: Trained GPT-3.5/4 using scientific papers, simulation logs, and experimental data.
• Knowledge Integration