Machine Learning

In medical applications, machine learning often grapples with limited training data. Classical self-supervised deep learning techniques have been helpful in this domain, but these algorithms have yet to achieve the required accuracy for medical use. Recently quantum algorithms show promise in handling complex patterns with small datasets. To address this challenge, this study presents a novel solution that combines self-supervised learning with Variational Quantum Classifiers (VQC) and utilizes Principal Component Analysis (PCA) as the dimensionality reduction technique.

The choice of the dataset is the key for OCR systems. Unfortunately, there are very few works on Telugu character datasets. The work by Pramod et al has 500 words and an average of 50 images with 50 fonts in four styles for training data each image of size 48x48 per category. They used the most frequently occurring words in Telugu but were unable to cover all the words in Telugu. Later works were based on character level. The dataset by Hastie has 460 classes and 160 samples per class which is made up of 500 images.

We define personal risk detection as the timely identification of when someone is in the midst of a dangerous situation, for example, a health crisis or a car accident, events that may jeopardize a person’s physical integrity. We work under the hypothesis that a risk-prone situation produces sudden and significant deviations in standard physiological and behavioural user patterns. These changes can be captured by a group of sensors, such as the accelerometer, gyroscope, and heart rate.

The Internet Graphs (IGraphs) dataset is a substantial collection of real intra-AS (Autonomous System) graphs sourced from the Internet Topology Data Kit (ITDK) project. Comprising a total of 90,326 graphs, each ranging from 12 to 250 nodes, this dataset provides a diverse and extensive resource for the exploration and analysis of network structures within autonomous systems.