Sri Ramakrishna Engineering College

About: Sri Ramakrishna Engineering College is a based out in . It is known for research contribution in the topics: Computer science & Control theory. The organization has 1030 authors who have published 843 publications receiving 3822 citations.

An Early Alert System For Sleep Apnea Disorder Using IoT

Abstract: SLEEP APNEA is a potentially serious disorder associated with pausing or stopping of breathing repeatedly. The monitoring of sleep apnea and its detection is very important for the society as it aids in improvement of health and also causes decrease in mortality rate. The current technologies in order to diagnose OSA requires the patients to undergo Polysomnography (PSG), a very complicated and invasive test method to be performed in a specialized center which involves many sensors and wires. Accordingly, each patient is required to stay in the same position throughout the duration of one night, thus restricting their movements and causing disturbance in sleep patterns. This paper proposes an easy, very cheap, and portable approach for the monitoring of patients with OSA using IoT (Internet of Things). The project concludes with highlighting the pros and cons of the current technologies of the current technologies which can set a map for researchers and clinicians in this developing field of study.

CNN-Based Vehicle Classification Using Transfer Learning

Abstract: This paper focuses on the classification of vehicles using Convolutional Neural Network (CNN) which is a class of deep learning neural network. This work makes use of transfer learning using the pre-trained networks to extract powerful and informative features and apply that to the classification task. In the proposed method, the pre-trained networks are trained on two vehicle datasets consisting of real-time images. The classifier performance along with the performance metrics such as accuracy, precision, false discovery rate, recall rate, and false negative rate is estimated for the following pre-trained networks: AlexNet, GoogLeNet, SqueezeNet, and ResNet18. The classification model is implemented on the standard vehicle dataset and also on a created dataset. The model is further used for the detection of the different vehicles using Regions with a Convolutional Neural Networks (RCNN) object detector on a smaller dataset. This paper focuses on finding the perfect network suitable for the classification problems which have only a limited amount of non-labeled data. The model makes use of limited pre-processing and achieves greater accuracy on continuous training of the networks on the vehicle images.

Interpretation of Artificial Intelligence Algorithms in the Prediction of Sepsis

Abstract: Despite the rise of Artificial Intelligence (AI) algorithms and their applications in various fields, their utilizations in high-risk fields like healthcare and finance is limited because of the lack of interpretability of their inner workings. Some algorithms are interpretable, but not accurate, whereas some produce accurate results and not decipherable. Research is underway to explore the possibilities to interrogate an AI system, and ask why it makes certain decisions. This paper aims to investigate the decision-making process by AI algorithms in the prediction of sepsis based on patients’ clinical records.We were ranked 59 in the PhysioNet/Computing in Cardiology Challenge 2019 and the utility score obtained on the full test set is 0.131, and our team name was ARUL.

A proficient vocal training system with pitch detection using SHR

Abstract: This paper insinuates a vocal training system that helps in proficient vocal training. There are two foremost objectives involved. The crucial objective is to estimate and detect pitch from a vocal. The secondary objective is to create an efficient vocal training system that perceives error in pitch. The purpose of this paper is to develop an adept vocal training system that perceives and appraises the pitches of both the amateur vocalist and the professional vocalist and displays inaccuracy of the amateur's voice.