Diagnosis of Pulmonary Diseases from Chest X-ray Using Deep Learning Approaches

In recent years, a severe pandemic has struck worldwide with the utmost shutter, enforcing a lot of stress in the medical industry. Moreover, the increasing population has brought to light that the work bestowed upon the healthcare specialists needs to be reduced. Medical images like chest X-rays are of utmost importance for the diagnosis of diseases such as pneumonia, COVID-19, thorax, and many more. Various manual image analysis techniques are time-consuming and not always efficient. Deep learning models for neural networks are capable of finding hidden patterns, assisting the experts in specified fields. Therefore, collaborating these medical images with deep learning techniques has paved the path for enormous applications leading to the reduction of pressure embarked upon the health industry. This paper demonstrates an approach for automatic lung diagnosing of COVID-19 (coronavirus) and thorax diseases from given CXR images, using deep learning techniques. The previously proposed model uses the concept of ResNet-18, ResNet-50, and Xception algorithms. This model gives the highest accuracy of 98% without segmentation and 95% with segmentation. Whereas, the proposed model uses CNN and CLAHE algorithms which achieves an accuracy of 99.22% without segmentation and 98.39% with segmentation. Therefore, this model will be able to provide assistance to health workforces and minimize manual errors precisely. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

EnsembleNet: An improved COVID19 Prediction Model using Chest X-Ray Images

This paper presents an improved COVID19 prediction model using chest X-Ray images with evolutionary algorithm based ensemble learning. The proposed model uses the transfer learning approach with state-of-the-art pre-trained models for training in isolation. Following the fine-tuning of the models, ensemble of the models is used for inferencing. The weight of the ensemble models are learned by the Differential Evolutional (DE) algorithm. The proposed model exploits the importance of each model in COVID19 inferencing. The proposed model is experimented on COVIDx-CXR2 dataset. Our study shows that the proposed EnsembleNet model outperforms the individual state-of-the-art models in terms of generalization accuracy. © 2022 IEEE.

A Risk Assessment Model for Passenger´s Journey in the Air Transportation System

The global pandemic of SARS-CoV 2 required a rapid response of the air travel industry, to provide guidance and evaluate mitigating measures. There is a clear void in the literature related to air travel infection in terms of analysis of the whole passenger journey. Some effort in the past focused in the aircraft environment, but even is these cases, it is hard to determine that the infection itself took place onboard. The present paper presents the combination of previous studies and models in a original way to produce a method which can be used to analyze the whole passenger’s journey, from arrival to the airport of origin, to leaving the baggage claim on the destination. The model uses a modified mechanistic transmission model adapted from epidemiology studies and previous air travel applications to generalize the concept for the whole journey. The journey is represented by a series of compartmens where each passenger can be exposed to infection by different routes (airborne and fomite) with a certain probability. Model parameters are calibrated model based on the SARS CoV outbreak of 2003. An internal study, based on an focus group, for regional aircraft operation shows that the flight phase risk is not predominant when compared to check-in and security screening phases. Also the effect of the common health indicated measures has shown that the risk is significantly reduced. © 2022, American Institute of Aeronautics and Astronautics Inc, AIAA. All rights reserved.

Music Recommendation Systum Based on Emotion

With the onset of lockdown in the COVID-19 scenario, people were forced to confine themselves within the four walls of their rooms which in the meantime invited mood disorders like depression, anxiety etc. Music has proven to be a potential empathetic companion in this tough time for all. The proposed emotion-based music recommendation system uses aser emotion as an input to recommend songs that are-ascertained using faciai expression or using direct inputs from the user. The model uses a Random Forest classifier and XGBoost algorithm to identify the song's emotion considering various features like instruineiitainess, energy, acoustics, liveness, etc, and lyrical similarity among songs with the help of Term-Frequency times Inverse Document-Frequency (TF-IBF). The results of comprehensive experiments on reai data confirm the accuracy of the proposed emotion classification system that can be integrated into any recommendation engine. © 2021 IEEE.