Received Trail Blazers Award for delivering 10 automation fixes on time as a team to support and maintain Managed File Transfer system

Quantum teleportation of images has been a long-awaited and necessary operation in the domain of Quantum Information Processing. Various Quantum Image Representations have been proposed to suit the different requirements of Quantum Image processing, such as edge detection, inversion, color operation, geometric transformation, etc. To meet the Quantum Teleportation of an Image, the existing algorithm is not capable of teleporting the Quantum Image. Instead, the algorithm can only transport classical bits via quantum channels, making it a superdense coding algorithm rather than actual quantum teleportation. In this paper, we demonstrate a simulation of quantum image teleportation to overcome the challenge mentioned above. To do this, we have presented a new Quantum Image Representation technique so that Quantum Teleportation can be simulated using Quantum Simulator.

Pneumonia is a disease that threat humanity to this day. Even though we have developed vaccines and medicines, many lives are still lost every year due to this disease. So we have made an effort to develop an algorithm which would be able to detect this disease in an early stage to help people with the diagnosis. We have developed a Convolutional Neural Network (CNN) based Deep Learning (DL) model that detects pneumonia from the X-ray image of patients. This helps in classifying a given image, which, in turn, helps the physicians and other medical persons for easier diagnosis. In this study we have compared the outputs from two CNN based DL models: a 3 layered model and another 10 layered model. We have taken the chest x-ray images of different patients for developing and testing the proposed algorithm in Python platform.

The advent of Deep Learning models like VGG-16 and Resnet-50 has considerably revolutionized the field of image classification, and by using these Convolutional Neural Networks (CNN) architectures, one can get a high classification accuracy on a wide variety of image datasets. However, these Deep Learning models have a very high computational complexity and so incur a high computational cost of running these algorithms as well as make it hard to interpret the results. In this paper, we present a shallow CNN architecture which gives the same classification accuracy as the VGG-16 and Resnet-50 models for thin blood smear RBC slide images for detection of malaria, while decreasing the computational run time by an order of magnitude. This can offer a significant advantage for commercial deployment of these algorithms, especially in poorer countries in Africa and some parts of the Indian subcontinent, where the menace of malaria is quite severe.