Rishinathh K.S

To seek and maintain full-time position that offers professional challenges utilizing interpersonal skills, excellent time management and problem-solving skills. Organized and dependable candidate successful at managing multiple priorities with a positive attitude. Willingness to take on added responsibilities to meet team goals.

• NSS(National Service Scheme)- conducted and volunteered in events Ayudh- Volunteered for teaching kids Volunteer in the Sahaya club(social service)

• Machine Learning Open Online Course Certification by Stanford University(Coursera)
• Neural Network and Deep Learning Certification from deeplearning.ai(Coursera)
• Introduction to TensorFlow for Artificial Intelligence, Machine Learning, and Deep Learning from deeplearning.ai(Coursera)

Automate Electrical Appliance's Usage using Computer Vision, 01/2019 - 07/2019, To reduce the wastage of electricity by automating control over the electrical appliances in the industries (as a part of Cisco's ThingQbator)., Computer Vision, Deep Learning, Arduino, We were able to deploy the product developed in a confined area successfully. Multi-class Currency Classification using Convolutional Neural Networks and Cloud Computing Systems for visually challenged people, 08/2019 - 11/2019, To identify currency denomination using Deep Learning Image Classification Algorithm for the visually challenged people., Keras, Tensorflow, Google Cloud Platform, CNN, Android Studio, Our paper got published at Springer International Conference on Advances in Distributed Computing and Machine Learning(ICADCML-2020). Integrated Deep learning-based Driver Management System using only cameras, 05/2020 - present, To develop a model that authorizes the driver(face recognition) and also monitors the driver's activities and alerts immediately when detected unsafe actions like drinking, smoking, mobile phone usage, drowsiness while driving., Object detection algorithms, Google Colab for training(GPU), OpenCV, Tensorflow, Our custom trained model to detect cigar, phone, drinks achieved 70.74%mAP(mean average precision)