In Luxmikant
Contact Information:
- Email: luxmikant@outlook.com
- Phone: +91 7018209392
- Address: VIT Hostel, Sehore, 466114, MP
Passionate about Data Science, I am drawn to its analytical core and transformative potential. With a keen interest in machine learning and AI advancements, particularly large language models and transformer architectures, I am eager to explore their practical applications. Dedicated to extracting insights from data, I embrace the problem-solving nature of the field. Engaging with the Data Science community, I value continuous learning and knowledge exchange. I strive to contribute to the data-driven landscape, applying my skills to real-world challenges and making a meaningful impact.
- Programming Languages: Python, C++
- Machine Learning Frameworks: TensorFlow, PyTorch, Keras, Scikit-learn
- Data Analysis and Visualization: Pandas, NumPy, Matplotlib, Seaborn, Plotly
- Computer Vision: OpenCV, Image Processing, Object Detection, Image Recognition
- Observation
- Decision making
- Communication
- Multi-tasking
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VIT Bhopal University
- B.Tech in CSE (Specialization in Health Informatics)
- CGPA: 8/10 (Ongoing)
- Expected Graduation: May 2026
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Govt Sen Sec School Garsa, Distt Kullu
- Intermediate School
- Percentage: 74%
- Completed in 2021
- Date: January 2024
- Developed an automatic number plate recognition (ANPR) system using the YOLOV8 object detection algorithm.
- Trained a custom YOLOV8 model on a labeled dataset of license plates, achieving high accuracy in identifying and locating license plates in images and videos.
- Implemented real-time processing capabilities for video feeds from traffic cameras, enabling rapid and precise detection of moving vehicles' license plates.
- Utilized optical character recognition (OCR) techniques to extract text from cropped license plates, resulting in highly accurate recognition rates.
- Date: March 2024
- Designed and implemented a spiking neural network (SNN) architecture for classification of the MNIST dataset, achieving a test set accuracy of 98.68%.
- Employed the Leaky Integrate-and-Fire (LIF) neuron model to create a multi-layer SNN, consisting of input, hidden, and output layers.
- Used surrogate gradient descent optimization techniques to optimize the weights and biases of the network, taking advantage of the spiking nature of the LIF model.
- Date: March 2024
- Built a transformer-based architecture for end-to-end crop disease classification, leveraging self-attention mechanisms to capture long-range dependencies among pixels and spectral bands using vision and swin transformer.
- Prepared a large-scale dataset of multispectral images of healthy and diseased crops, employing data augmentation techniques to increase variability and mitigate overfitting.
- Engineered input representations tailored to the unique characteristics of the transformer architecture, accounting for differences in scale, orientation, and modality.
- 2024-2025: Achieved 98.48% accuracy in MNIST Dataset using SNN (Spiking Neural Network)
- 2023-2024: Selected for Smart India Hackathon (College Round)
Feel free to reach out to me via email or phone for any collaboration or queries!