A K GOKUL

AudioShield:An AI-Enabled Fake Audio Detection

This project is an advanced AI-Enabled Fake Audio Detection System designed to help identify manipulated audio content. The system utilizes a **hybrid CNN-RCNN model** (Convolutional Neural Networks and Recurrent Convolutional Neural Networks) to accurately classify audio files as either real or fake.

Audio manipulation is becoming increasingly sophisticated, and with the rise of deepfakes, it's crucial to have reliable systems to detect such frauds. This detection system is built to analyze various features of audio files, including waveform patterns, spectral information, and signal anomalies that indicate manipulation.

Core Features

• User Features:
  • Audio Upload & Detection: Users can upload audio files to the platform, and the system will process the file to detect whether the audio is real or fake.
  • Fake Detection History: Users can view a history of previously uploaded files and their detection results, enabling easy tracking of suspicious content.
  • Profile Management: Users can create accounts, update profiles, and manage settings, ensuring a personalized experience.
  • Authentication & Security: The platform provides secure login/signup features with OTP-based authentication for account recovery, enhancing user data safety.
• Admin Features:
  • User Account Management: Admins can monitor and manage user accounts, approve or deny requests, and track user activity.
  • Block/Unblock Users: Admins have the ability to block or unblock users based on their activity, ensuring that only legitimate users are allowed to access the platform.
  • Data & Analytics: Admins can access detailed statistics on the number of uploaded files, detection results, and user engagement, helping to improve the system over time.

Technical Stack & Development

• Frontend: The user-facing mobile application is built with Flutter, ensuring a cross-platform experience for both Android and iOS users.
• Backend: The backend is powered by Django, providing a robust framework for user authentication, database management, and handling complex logic for audio detection processing.
• AI Model: The hybrid CNN-RNN model is trained on a large dataset of real and fake audio samples. This deep learning model efficiently analyzes the frequency domain and temporal features of the audio to make accurate predictions.
• Web Admin Panel: The web-based admin panel is built using HTML & CSS, offering a clean and easy-to-navigate interface for administrators to manage users and access detailed reports.

How it Works

When a user uploads an audio file, the backend processes the file by extracting its features, such as waveform data and spectral representation. The CNN model analyzes spatial features, while the RNN captures temporal dependencies in the audio signal. By combining both approaches, the system achieves high accuracy in identifying audio manipulations.

The audio is then classified as either real or fake. The result, along with additional metadata about the audio (e.g., detection confidence, processing time), is displayed to the user. This real-time analysis helps individuals and organizations identify potentially manipulated audio and take appropriate action.

Use Cases & Applications

• Media & Journalism: Detect fake interviews, speeches, or audio recordings used in fake news.
• Legal Industry: Verify the authenticity of recorded evidence used in legal cases.
• Security & Surveillance: Detect fabricated audio in security recordings or surveillance footage.
• Social Media Platforms: Identify deepfake audio used in malicious content on social media and other platforms.

This project demonstrates how AI and machine learning can be used to combat the growing issue of audio manipulation, helping protect the integrity of digital media and ensuring that the public can trust what they hear.

AI-Enabled Weapon Detection for Security Applications

The **AI-Enabled Weapon Detection System** utilizes state-of-the-art computer vision models to identify weapons in surveillance footage, enhancing security in public spaces and critical infrastructure. Powered by the **YOLOv5** (You Only Look Once version 5) object detection model, this system enables real-time detection of various types of weapons such as guns, knives, and explosives, among others.

With the increasing need for enhanced security, particularly in high-risk areas, this weapon detection system provides an automated and efficient solution to alert security personnel about potential threats. The system processes live video streams from cameras, analyzes the footage in real-time, and raises an alarm if any suspicious objects are detected.

Core Features

• Real-time Weapon Detection: The system can process live video streams, instantly detecting weapons as soon as they appear in the footage. It can detect a variety of weapons such as firearms, knives, and even explosive devices.
• Automated Threat Alerts: Once a weapon is detected, the system sends real-time alerts to security personnel via email, SMS, or mobile application, allowing for immediate action.
• Scalability: The system can be integrated with multiple cameras across various locations, providing scalable solutions for both small and large security networks.
• High Accuracy: Thanks to YOLOv5’s fast processing and accurate detection, the system minimizes false positives and ensures that only real threats are flagged.
• Historical Data Tracking: Security teams can access a historical log of weapon detections, allowing them to review past incidents and improve security measures.

How It Works

The system uses YOLOv5, a cutting-edge deep learning model for object detection. YOLOv5 is a real-time detection algorithm that divides an image into a grid and predicts bounding boxes around objects, in this case, weapons. Once the system detects a weapon, it marks the object with a bounding box and provides a confidence score, indicating the likelihood that the detected object is indeed a weapon.

YOLOv5 is trained on a large dataset of labeled weapon images, which allows it to accurately classify and detect different types of weapons in various environments. The model's ability to perform object detection at high speeds makes it ideal for security applications where real-time detection is critical.

Training the YOLOv5 Model

The YOLOv5 model is trained using a large, annotated dataset containing images of various weapons in different scenarios. The training process involves feeding these images into the model, allowing it to learn to recognize patterns and characteristics that distinguish weapons from other objects.

• Dataset: The dataset includes thousands of labeled images of weapons such as firearms, knives, and explosives, captured in various angles, lighting conditions, and environments. This ensures the model is robust and can handle diverse real-world situations.
• Training Process: The model is fine-tuned using transfer learning, starting with a pre-trained YOLOv5 model and adapting it for weapon detection using the custom dataset. This reduces the amount of training data required and speeds up the model's learning process.
• Optimization: The model is optimized for accuracy and speed using techniques like data augmentation, hyperparameter tuning, and batch normalization to improve performance in detecting weapons.

Integration with Security Systems

The weapon detection system can be seamlessly integrated with existing security infrastructure. Once the YOLOv5 model is trained and deployed, it can be embedded into surveillance cameras, security dashboards, or mobile applications, making it a flexible solution for various use cases. Integration with cloud platforms ensures that the system can scale and provide remote monitoring capabilities for security teams.

Technologies & Tools

• YOLOv5 (Object Detection): YOLOv5 is the backbone of the weapon detection system, providing fast and accurate real-time detection of objects in video streams.
• OpenCV: Used for video processing and manipulation, OpenCV helps in capturing video from cameras and processing each frame for weapon detection.
• TensorFlow/PyTorch: The machine learning libraries used for training and deploying the YOLOv5 model, making the integration of deep learning into security systems seamless.
• Flask/Django (Backend): For building a web-based dashboard that displays alerts, logs, and historical data, Flask or Django can be used to create a robust backend.
• Twilio/Email API: Used for sending real-time alerts and notifications to security personnel via SMS, email, or push notifications.
• Cloud Platforms: The system can be deployed and scaled on cloud platforms like AWS, Azure, or Google Cloud for storage, processing, and monitoring across multiple locations.

Use Cases & Applications

• Public Safety: Detect weapons in crowded public spaces, ensuring quick intervention before any harm is caused.
• Airport and Border Security: Monitor security footage for weapons detection to protect critical infrastructure from security threats.
• Schools and Universities: Enhance campus security by monitoring surveillance cameras for weapons and providing immediate alerts to authorities.
• Corporate Offices: Detect potential threats in corporate environments, ensuring a safer workplace for employees and visitors.
• Event Security: Monitor large-scale events like concerts or sports games to ensure that no weapons enter the venue unnoticed.

By implementing this AI-powered weapon detection system, security teams can stay one step ahead of potential threats, improving safety and efficiency in high-risk areas.

Bacteria Detection System using YOLOv5

This project demonstrates the use of **YOLOv5** for real-time detection of bacterial colonies in microscopic images. YOLOv5 is a state-of-the-art object detection model known for its speed and accuracy in detecting objects in images. This system leverages deep learning to accurately identify and count bacterial colonies, making it useful in microbiological research, healthcare, and diagnostics.

Project Overview

The primary goal of this system is to assist researchers and healthcare professionals by automating the identification and counting of bacterial colonies in microscopic images. This is crucial in areas like infection diagnosis, antibiotic testing, and microbiological analysis.

How It Works

The system takes as input images of bacterial growth on agar plates captured through a microscope. YOLOv5, a deep learning model trained specifically for object detection, processes these images to detect the presence of bacterial colonies. The model identifies each colony as an object, draws bounding boxes around them, and classifies them accordingly.

Key Features

• Real-time Detection: YOLOv5 processes images in real-time, providing fast results, which is important for dynamic laboratory environments.
• High Accuracy: With fine-tuning, the system can achieve high precision and recall, reducing the chances of false positives or negatives in bacterial detection.
• Automated Counting: The system automatically counts the number of bacterial colonies, eliminating the need for manual counting, saving time and reducing human error.
• Easy to Use: The system is designed for ease of use, with an intuitive interface where users can upload images and get results in seconds.

Technical Stack

• Model: **YOLOv5**, pre-trained on a custom dataset of bacterial colonies, fine-tuned for high accuracy in detecting various bacteria types.
• Backend: Python with **PyTorch**, leveraging the powerful capabilities of YOLOv5 for object detection and image processing.
• Libraries: OpenCV for image processing, Matplotlib for visualizing detection results, and Flask (optional) for building a web-based interface for easy deployment.

Training the Model

The YOLOv5 model was trained on a dataset of labeled images containing various types of bacterial colonies. Data augmentation techniques were applied to improve model generalization. The training process involved optimizing the model to minimize the loss function and achieve high detection accuracy.

Applications

• Healthcare: Detecting bacterial growth in medical microbiology labs to help diagnose infections.
• Research: Analyzing bacterial growth patterns in laboratory experiments and testing the effects of antibiotics.
• Food Industry: Detecting bacterial contamination in food products to ensure safety and hygiene.
• Environmental Monitoring: Detecting and analyzing bacterial colonies in environmental samples, such as water or soil.

This project demonstrates how AI and deep learning can be applied in the field of microbiology, helping researchers and professionals automate time-consuming tasks like bacterial colony detection, thereby improving accuracy, speed, and efficiency.

ML Data Agent

ML Data Agent is an intelligent assistant that processes user-uploaded CSV files, automates data analysis tasks, and enables interactive model development.

Core Workflows

• Data Cleaning: Automatically detects and removes unwanted columns, handles missing values, and standardizes data formats.
• Visualization: Generates interactive charts and plots to help you explore and understand your dataset.
• Feature Engineering: Suggests and applies transformations to create new features for improved model performance.
• Target Selection & Modeling: Guides you to select the target column and trains multiple machine learning models (e.g., decision trees, random forests, logistic regression).
• Downloadable Outputs: Allows you to download the cleaned CSV and the trained model for future use.
• AI Chat Support: Chat with the agent using ChatGROQ's LLM models to ask questions, troubleshoot issues, and get recommendations.

Technologies & Integration

• Python, Pandas, Scikit-learn for data processing and modeling.
• Plotly and Matplotlib for visualizations.
• ChatGROQ LLM API for conversational support.
• Framework: LangChain for orchestrating LLM pipelines and custom tool development.
• Graph Management: LangGraph for constructing and visualizing tool graphs.
• Dropdown menu to select from multiple LLMs for tailored assistance.
• Flask or FastAPI backend for file handling and model serving.

This project streamlines the end-to-end data science workflow by combining automated data processing, model training, and AI-powered interactive support in a single agent.

AI Doctor Chatbot Assistant

An intelligent conversational assistant tailored for healthcare professionals, offering clinical decision support, patient data summarization, and interactive diagnostic tools.

Core Capabilities

• get_drug_info: Fetch detailed drug monographs and prescribing information.
• check_drug_interactions: Identify potential adverse interactions between multiple medications.
• get_symptom_info: Provide insights into symptom causes and associated conditions.
• retrieve_patient_summary: Summarize patient history and recent visits from stored records.
• explain_medical_abbreviation: Decode common and obscure medical acronyms.
• clinical_guidelines_tool: Access up-to-date clinical protocols and treatment guidelines.
• differential_diagnosis_tool: Generate ranked lists of possible diagnoses based on inputs.
• dosage_calculator_tool: Calculate weight- or age-based medication dosages.
• literature_summarization_tool: Condense recent research articles into key takeaways.
• lab_test_interpretation_tool: Interpret lab values and flag abnormal results.
• patient_education_tool: Generate easy-to-understand educational materials for patients.
  • Report Analysis: Parse structured reports to pull key findings.
  • Image Report Analysis: Analyze image-based reports (e.g., X-rays, MRIs) and highlight critical observations.

Technologies & Architecture

• Frontend: HTML, CSS, JavaScript for an interactive UI.
• Backend: Django REST framework for API endpoints and business logic.
• Framework: LangChain for orchestrating LLM pipelines and custom tool development.
• Data Storage: ChromaDB for vector embeddings of documents and clinical notes.
• Database: PostgreSQL to persist patient records, reports, and chat logs.
• LLM Integration: Connects to ChatGROQ or other selected LLMs via dropdown for flexible conversational models.

Built to streamline clinical workflows, this assistant combines robust backend services, a modular frontend, and advanced LLM-driven tools to support doctors in delivering precise, data-driven patient care.