HealthFog: An ensemble deep learning based Smart Healthcare System for Automatic Diagnosis of Heart Diseases in integrated IoT and fog computing environments

Cloud computing provides resources over the Internet and allows a plethora of applications to be deployed to provide services for different industries. The major bottleneck being faced currently in these cloud frameworks is their limited scalability and hence inability to cater to the requirements of centralized Internet of Things (IoT) based compute environments. The main reason for this is that latency-sensitive applications like health monitoring and surveillance systems now require computation over large amounts of data (Big Data) transferred to centralized database and from database to cloud data centers which leads to drop in performance of such systems. The new paradigms of fog and edge computing provide innovative solutions by bringing resources closer to the user and provide low latency and energy-efficient solutions for data processing compared to cloud domains. Still, the current fog models have many limitations and focus from a limited perspective on either accuracy of results or reduced response time but not both. We proposed a novel framework called HealthFog for integrating ensemble deep learning in Edge computing devices and deployed it for a real-life application of automatic Heart Disease analysis. HealthFog delivers healthcare as a fog service using IoT devices and efficiently manages the data of heart patients, which comes as user requests. Fog-enabled cloud framework, FogBus is used to deploy and test the performance of the proposed model in terms of power consumption, network bandwidth, latency, jitter, accuracy and execution time. HealthFog is configurable to various operation modes that provide the best Quality of Service or prediction accuracy, as required, in diverse fog computation scenarios and for different user requirements

Blood component therapy: Which, when and how much

Blood transfusion refers to the perioperative administration of blood and blood components. Adherence to proper indications for blood component therapy is essential because of its potential adverse effects and costs of transfusion. Over the years, the significance of blood components in treating certain diseases or conditions has been recognized. In this article, the most commonly used blood components along with the new developments in component therapy have been discussed. Recommendations by different academic and clinical trials and studies have been presented for quick reference. The individual coagulation factors are discussed in brief

Benefit of transesophageal echocardiography monitoring during cesarean section in a patient with complete atrioventricular canal defect

Anesthesia is a challenge in patients with congenital heart disease, especially during pregnancy and surgical delivery. A 23-year-old with a 34-week gestation, primigravida with atrioventricular (AV) canal defect was scheduled for a cesarean section. Preoperative transthoracic echocardiography (TTE) revealed a complete AV canal defect (Rastelli type II) with left-to-right shunt. Ventricular functions were normal. The patient was administered general anesthesia with endotracheal intubation, and a transesophageal echocardiography (TEE) probe was placed to monitor cardiac functions. The volume status of the parturient and the shunt fraction were continuously monitored with the echocardiography probe during the surgery. Minimal shunting at ventricular septal defect (VSD) was observed, as it was covered by the AV valve leaflets. The patient tolerated the procedure well and the trachea was extubated once she fulfilled the extubation criteria. Intraoperative TEE monitoring was a useful tool to understand and manage hemodynamic variations during cesarean section in the parturient with a complex cardiac lesion

CoviDetector: A transfer learning-based semi supervised approach to detect Covid-19 using CXR images

COVID-19 was one of the deadliest and most infectious illnesses of this century. Research has been done to decrease pandemic deaths and slow down its spread. COVID-19 detection investigations have utilised Chest X-ray (CXR) images with deep learning techniques with its sensitivity in identifying pneumonic alterations. However, CXR images are not publicly available due to users’ privacy concerns, resulting in a challenge to train a highly accurate deep learning model from scratch. Therefore, we proposed CoviDetector, a new semi-supervised approach based on transfer learning and clustering, which displays improved performance and requires less training data. CXR images are given as input to this model, and individuals are categorised into three classes: (1) COVID-19 positive; (2) Viral pneumonia; and (3) Normal. The performance of CoviDetector has been evaluated on four different datasets, achieving over 99% accuracy on them. Additionally, we generate heatmaps utilising Grad-CAM and overlay them on the CXR images to present the highlighted areas that were deciding factors in detecting COVID-19. Finally, we developed an Android app to offer a user-friendly interface. We release the code, datasets and results’ scripts of CoviDetector for reproducibility purposes; they are available at: https://github.com/dasanik2001/CoviDetecto

Abstracts of National Conference on Research and Developments in Material Processing, Modelling and Characterization 2020

This book presents the abstracts of the papers presented to the Online National Conference on Research and Developments in Material Processing, Modelling and Characterization 2020 (RDMPMC-2020) held on 26th and 27th August 2020 organized by the Department of Metallurgical and Materials Science in Association with the Department of Production and Industrial Engineering, National Institute of Technology Jamshedpur, Jharkhand, India. Conference Title: National Conference on Research and Developments in Material Processing, Modelling and Characterization 2020Conference Acronym: RDMPMC-2020Conference Date: 26–27 August 2020Conference Location: Online (Virtual Mode)Conference Organizer: Department of Metallurgical and Materials Engineering, National Institute of Technology JamshedpurCo-organizer: Department of Production and Industrial Engineering, National Institute of Technology Jamshedpur, Jharkhand, IndiaConference Sponsor: TEQIP-