Explore Sanjeeb's diverse range of projects that showcase my passion for creativity and problem-solving. From web development and design to innovative solutions, each project reflects my dedication to delivering excellence and exceeding expectations.
I'm thrilled to have you join this exciting journey with us.
his project is a testament to our shared dedication, creativity, and the pursuit of excellence.
Whether you're a team member, collaborator, or simply interested in what we're creating, your presence here is greatly valued.
The Jarvis voice assistant is designed to work with a variety of devices, including smartphones, smart home appliances, and personal computers. It uses artificial intelligence and natural language processing to understand and respond to voice commands.
Here we vow to analyze datasets related to diseases, study and predict the outcomes of it, using different ML models. This would ensure us to understand the accuracy of our choice of model we implement to visualize the data.
This project is part of our efforts in solving problems in densely crowded environments analysis. Following our previous topics in classifying crowd states, segmenting videos into components, estimating crowd size and tracking objects in crowds, the goal here is to detect the deviations from normal crowd behaviors, which is motivated by the ubiquity of camera surveillance systems, the challenges in modeling crowd behaviors, and the importance of automatic crowd monitoring for various applications.
This research delves into the realm of predicting waiting times in real-life banking queues, a ubiquitous aspect of daily life. Accurate waiting time prediction is essential for efficient queue management, and this paper explores various machine learning algorithms to achieve this goal. Our findings demonstrate the efficacy of SVR in predicting waiting times for everyone in the queue, making it a viable and robust solution for queue analysis in the context of developing countries' banking systems.
Human Activity Recognition system using OpenCV and deep learning. The system uses a pre-trained model to recognize human actions in real-time video captured from a camera. The code is written in Python and can be run from the command line.
To create a single-page web application and some machine-learning models which will show company information (logo, registered name and description) and stock plots based on the stock code given by the user. Also, the ML model will enable the user to get predicted stock prices for the data inputted by the user