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<!DOCTYPE HTML>
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<head>
<title>Srivathsan Karthikeyan's Artificial Intelligence Projects Portfolio</title>
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<h1>Artificial Intelligence Projects Portfolio<br /></h1>
<h2 style="background: rgba(0, 0, 0, 0.5); color: inherit;">Author : Srivathsan Karthikeyan<br /></h2>
<h2 style="background: rgba(0, 0, 0, 0.5); color: inherit;">Includes Machine Learning, Deep Learning, Computer Vision, NLP<br /></h2>
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Continue
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Projects
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<h2>
<a href="https://github.com/sri-dsa/blob/main/machine_learning/NLP_supervised.ipynb"
style="display: inline-block; transition: transform 0.2s ease-in-out, opacity 0.3s ease-in-out;"
onmouseover="this.style.transform='scale(1.1)'; this.style.opacity='0.8';"
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AI-Powered SLAM for Autonomous Navigation
</a>
</h2>
</header>
<a href="https://github.com/sri-dsa/blob/main/machine_learning/NLP_supervised.ipynb" class="image fit"><img src="7.png" alt="" /></a>
<p>This project implements Simultaneous Localization and Mapping (SLAM) to enable autonomous robots to navigate unfamiliar environments. Using LiDAR, Visual SLAM (ORB-SLAM, RTAB-Map), and Kalman Filters, the system constructs real-time maps while tracking the robot’s position. Integrated with Reinforcement Learning (RL) and Sensor Fusion, it enhances navigation in forests, disaster zones, and extreme conditions. Runs efficiently on Jetson Nano for real-time edge processing.</p>
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<h2>
<a href="https://github.com/sri-dsa/blob/main/machine_learning/Feature_engineering.ipynb"
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onmouseover="this.style.transform='scale(1.1)'; this.style.opacity='0.8';"
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AI-Powered Surveillance & Object Detection
</a>
</h2>
</header>
<a href="https://github.com/sri-dsa/blob/main/machine_learning/Feature_engineering.ipynb" class="image fit"><img src="2.png" alt="" /></a>
<p>A real-time AI surveillance system using Facenet, YOLO, and DeepSORT for object and facial recognition. It can detect and track multiple objects, analyze human behavior, and provide security alerts. Works with thermal cameras for low-light conditions, making it suitable for forest and extreme environments.</p>
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<h2>
<a href="https://github.com/sri-dsa/blob/main/machine_learning/Hierarchical_clustering.ipynb"
style="display: inline-block; transition: transform 0.2s ease-in-out, opacity 0.3s ease-in-out;"
onmouseover="this.style.transform='scale(1.1)'; this.style.opacity='0.8';"
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Multimodal AI (Gesture + Voice Control)
</a>
</h2>
</header>
<a href="https://github.com/sri-dsa/blob/main/machine_learning/Hierarchical_clustering.ipynb" class="image fit"><img src="3.avif" alt="" /></a>
<p>A dual-control system combining NLP-based voice commands and gesture recognition. If voice commands fail, gestures take over, ensuring uninterrupted control. Uses BERT, GPT, and ASR models for speech processing, integrating seamlessly with robotics for hands-free interaction.</p>
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<h2>
<a href="https://github.com/sri-dsa/blob/main/machine_learning/linear_regression.ipynb"
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AI Navigation & Reinforcement Learning
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</h2>
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<a href="https://github.com/sri-dsa/blob/main/machine_learning/linear_regression.ipynb" class="image fit"><img src="4.jpg" alt="" /></a>
<p>This module optimizes path planning and obstacle avoidance using Reinforcement Learning (RL). Leveraging PPO, SAC, and DQN, it enables autonomous vehicles to learn from environments and improve navigation efficiency. Implemented with AirSim and SLAM for realistic simulation and deployment.</p>
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<a href="https://github.com/sri-dsa/blob/main/machine_learning/knn_regressor.ipynb"
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Edge AI Processing for Real-Time Inference
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<a href="https://github.com/sri-dsa/blob/main/machine_learning/knn_regressor.ipynb" class="image fit"><img src="5.png" alt="" /></a>
<p>Deploys lightweight AI models on Jetson Nano & Raspberry Pi for low-latency, high-performance inference. Uses TensorRT to optimize CNN-based detection models, allowing on-device AI processing without cloud dependency. Enhances real-time decision-making in constrained environments.</p>
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AI Performance Monitoring & Logging
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</h2>
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<a href="https://github.com/sri-dsa/blob/main/machine_learning/SVC.ipynb" class="image fit"><img src="6.png" alt="" /></a>
<p>A real-time AI monitoring system using Prometheus & Grafana for performance tracking. Logs latency, model accuracy, and resource utilization, providing alerts for system failures. Ensures reliability in critical deployments.</p>
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<a href="https://github.com/sri-dsa/blob/main/machine_learning/tree_ensemble.ipynb"
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Gesture-Controlled Autonomous Robot
</a>
</h2>
<p>This project enables touchless control of an autonomous vehicle using hand gestures. Utilizing MediaPipe, OpenCV, and deep learning models, it translates real-time gestures into movement commands. The system is designed for harsh environments, ensuring precise navigation without physical contact. Integrated with reinforcement learning, it adapts to dynamic terrains.</p>
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<h2>
<a href="https://github.com/sri-dsa/blob/main/machine_learning/PCA.ipynb"
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Dimensionality Reduction for Precision: Interactive PCA on Cancer Tumor Data Features
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<a href="https://github.com/sri-dsa/blob/main/machine_learning/PCA.ipynb" class="image fit"><img src="images/pca.jpeg" alt="" /></a>
<p>In this interactive exploration, we employ Principal Component Analysis (PCA) on the "cancer_tumor_data_features" dataset to achieve dimensionality reduction with precision. Through interactive manipulation of data, we aim to extract the most informative tumor features, aiding researchers in accurately characterizing tumor subtypes and advancing personalized cancer treatments. Join us on this transformative journey as we unravel the power of PCA in the fight against cancer.</p>
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<h2>
<a href="https://github.com/sri-dsa/blob/main/machine_learning/Kmeans_quantization.ipynb"
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Revitalizing Palmtrees.jpg: Interactive K-Means Color Quantization
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<a href="https://github.com/sri-dsa/blob/main/machine_learning/Kmeans_quantization.ipynb" class="image fit"><img src="images/color.png" alt="" /></a>
<p>Join us in an interactive journey of image transformation as we breathe new life into "palmtrees.jpg" using K-Means Color Quantization. Through dynamic color reduction, watch as the palm tree landscape undergoes a revitalizing change, preserving its essence while unleashing a captivating visual experience. Get ready to engage with the power of data-driven image processing, where creativity meets data science to create an artful masterpiece.</p>
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<a href="https://github.com/sri-dsa/blob/main/AWS_sagemaker/NLP.ipynb"
style="display: inline-block; transition: transform 0.2s ease-in-out, opacity 0.3s ease-in-out;"
onmouseover="this.style.transform='scale(1.1)'; this.style.opacity='0.8';"
onmouseout="this.style.transform='scale(1)'; this.style.opacity='1';">
Sentiment Analysis for Movie Reviews using SageMaker
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</h2>
</header>
<a href="https://github.com/sri-dsa/blob/main/AWS_sagemaker/NLP.ipynb" class="image fit"><img src="images/imdb.png" alt="" /></a>
<p>In this project, we will perform sentiment analysis for movie reviews using Amazon SageMaker. By leveraging machine learning algorithms, we aim to classify movie reviews as positive or negative, gaining valuable insights into audience opinions and sentiments. With SageMaker's powerful tools, we will create an efficient and accurate sentiment analysis model for enhanced decision-making in the entertainment industry.</p>
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<h2>
<a href="https://github.com/sri-dsa/blob/main/deep_learning/transfer_learning.ipynb"
style="display: inline-block; transition: transform 0.2s ease-in-out, opacity 0.3s ease-in-out;"
onmouseover="this.style.transform='scale(1.1)'; this.style.opacity='0.8';"
onmouseout="this.style.transform='scale(1)'; this.style.opacity='1';">
Empowering Disease Diagnosis: Interactive Transfer Learning with InceptionV3 on Custom Malaria Images
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</h2>
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<a href="https://github.com/sri-dsa/blob/main/deep_learning/transfer_learning.ipynb" class="image fit"><img src="images/malaria.webp" alt="" /></a>
<p>Step into the realm of interactive data exploration as we employ transfer learning with the InceptionV3 model to revolutionize malaria detection using custom images. Experience the power of pre-trained features fused with cutting-edge deep learning techniques to create a robust and accurate malaria classification system. Join us on this transformative journey to empower healthcare professionals and researchers in early disease diagnosis and improve patient outcomes worldwide.</p>
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<h2>
<a href="https://github.com/sri-dsa/blob/main/deep_learning/autoencoders.ipynb"
style="display: inline-block; transition: transform 0.2s ease-in-out, opacity 0.3s ease-in-out;"
onmouseover="this.style.transform='scale(1.1)'; this.style.opacity='0.8';"
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Interactive Autoencoders: Unraveling MNIST's Hidden Representations
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</h2>
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<a href="https://github.com/sri-dsa/blob/main/deep_learning/autoencoders.ipynb" class="image fit"><img src="images/auto.png" alt="" /></a>
<p>Join us on an interactive exploration as we delve into the world of autoencoders applied to the MNIST dataset. By leveraging this powerful unsupervised learning technique, we aim to unravel the hidden representations of MNIST images, compressing and reconstructing them with astonishing accuracy. Prepare to engage with cutting-edge deep learning as we unveil the transformative capabilities of autoencoders in image processing and data compression.</p>
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<h2>
<a href="https://github.com/sri-dsa/blob/main/deep_learning/RNN_TIMESERIES.ipynb"
style="display: inline-block; transition: transform 0.2s ease-in-out, opacity 0.3s ease-in-out;"
onmouseover="this.style.transform='scale(1.1)'; this.style.opacity='0.8';"
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Empowering Time Series Forecasting: Interactive RNN Analysis with RSCCASN Dataset
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</h2>
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<a href="https://github.com/sri-dsa/blob/main/deep_learning/RNN_TIMESERIES.ipynb" class="image fit"><img src="images/time.png" alt="" /></a>
<p>Step into the world of interactive data exploration as we harness the power of Recurrent Neural Networks (RNN) for time series forecasting using the RSCCASN dataset. Through dynamic analysis, we aim to empower businesses and researchers with accurate predictions of future trends and patterns. Join us on this transformative journey as we revolutionize time series forecasting using RNN, bringing precision and foresight to decision-making processes.</p>
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<header>
<h2>
<a href="https://github.com/sri-dsa/blob/main/deep_learning/NLP_RNN.ipynb"
style="display: inline-block; transition: transform 0.2s ease-in-out, opacity 0.3s ease-in-out;"
onmouseover="this.style.transform='scale(1.1)'; this.style.opacity='0.8';"
onmouseout="this.style.transform='scale(1)'; this.style.opacity='1';">
Experience the Magic of NLP: Interactive Text Generation from "shakespeare.txt"
</a>
</h2>
</header>
<a href="https://github.com/sri-dsa/blob/main/deep_learning/NLP_RNN.ipynb" class="image fit"><img src="images/nlp.jpeg" alt="" /></a>
<p>Prepare for an immersive adventure as we delve into the fascinating realm of Natural Language Processing (NLP) and embark on text generation using the esteemed "shakespeare.txt" dataset. Through interactive analysis, we endeavor to revive Shakespearean prose and poetry, tapping into the prowess of language models to craft captivating and evocative text. Join us in this enchanting journey to witness the artistry of NLP as we breathe new life into Shakespeare's words, creating an interactive experience that celebrates the essence of his literary brilliance.</p>
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<header>
<h2>
<a href="https://github.com/sri-dsa/blob/main/deep_learning/DCGAN.ipynb"
style="display: inline-block; transition: transform 0.2s ease-in-out, opacity 0.3s ease-in-out;"
onmouseover="this.style.transform='scale(1.1)'; this.style.opacity='0.8';"
onmouseout="this.style.transform='scale(1)'; this.style.opacity='1';">
Interactive DCGAN: Unleashing Creativity with MNIST
</a>
</h2>
</header>
<a href="https://github.com/sri-dsa/blob/main/deep_learning/DCGAN.ipynb" class="image fit"><img src="images/mnist-demo.webp" alt="" /></a>
<p>In this captivating exploration, we dive into the world of Deep Convolutional Generative Adversarial Networks (DCGAN) applied to the MNIST dataset. Through this interactive journey, we aim to unleash creativity by generating realistic and captivating images of handwritten digits, taking generative AI to new heights. Prepare to witness the magic of DCGAN as we bridge the gap between human creativity and AI, producing stunning visual art with an innovative deep learning approach.</p>
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