ABSTRACT
On a global scale, most of the nations are looking forward toward implementing the Industry 4.0 solutions across all the sectors. It is imperative to understand how the fourth industrial revolution also known as the digital revolution is going affect the Human Resources of an organization. Industry 4.0 components assist the humans in their day-to-day tasks and reduce the physical strain arising out of repetitive jobs and help them in seamless decision-making. This manuscript aims at highlighting the effect Industry 4.0 shall have on the future of work especially in the post pandemic world. It also proposes a framework which represents the future of work when integrated with Industry 4.0 solutions. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.
ABSTRACT
Machine learning (ML) uses statistical theory to create models from data samples. Using the predictive and statistical models, computers can clean and curate the data, interpret and predict the outcomes of certainties (or uncertainties) with precise accuracy. Of course, the interpretation of the produced results and algorithmic solution designed for each problem needs to be fine-tuned and proficient for the target problem. Biomedical images relevant to different diseases are recorded from a body and are generally employed to diagnose precise physiological or pathological conditions. The objective of biomedical image analysis is exact modeling by using pattern recognition and computer vision to diagnose diseases by employing ML techniques. This chapter explains how artificial intelligence (AI) and ML techniques are utilized in disease diagnosis. An automated COVID-19 diagnosis approach based on deep feature extraction is also presented. After extracting features using deep transfer learning (DTL), the X-ray images are fed into the shallow ML model to diagnose COVID-19 from X-ray images. With chest X-ray, a patient can be identified as a potential COVID-19 patient and can be quarantined. X-ray equipment are already accessible in most hospitals, and already digitized. Since X-ray images are high dimensional data, a Convolutional Neural Network based feature extraction via transfer learning models are appropriate for the diagnosis of COVID-19. It may help an inpatient environment where the existing programs find it difficult to determine whether to keep the patient inward with other patients or separate them. This technique will also help classify patients with high COVID-19 risk who need to repeat testing with a false negative RT-PCR. © 2022 Elsevier Inc. All rights reserved.
ABSTRACT
Epidemiological models are a system of partial differential equations that model the spread of any epidemics in a closed population. These models are crucial tools for public health policy makers and medical practitioners. Reliable model descriptions often demand optimal parameter estimations. The model parameters are often estimated using numerical methods and traditional optimization algorithms. The inherent stochasticity in real-world outbreaks demand powerful optimizers for parameter estimation. Such ill-defined problems have been potential candidates for meta-heuristic optimization algorithms. The objectives of the proposed study include formulating parameter estimation as an optimization problem and finding optimal/near-optimal parameters for existing COVID models and to analyze the COVID epidemiological models (with optimal model parameters) based on their prediction efficacy. Using the parameters, forecasts for upcoming days can be produced. This paper compares epidemiological models with different machine learning models based on evaluation techniques. The top-five heavily affected states of India having the highest number of cases are considered for the study. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.