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Concerns about the health effects of frequent exposure to electromagnetic fields (EMF) emitted from mobile towers and handsets have been raised because of the gradual increase in usage of cell phones and frequent setting up of mobile towers. Present study is targeted to detrimental effects of EMF radiation on various biological systems mainly due to online teaching and learning process by suppressing the immune system. During COVID-19 pandemic the increased usage of internet due to online education and online office leads to more detrimental effects of EMF radiation. Further inculcation of soft computing techniques in EMF radiation has been presented. A literature review focusing on the usage of soft computing techniques in the domain of EMF radiation has been presented in the article. An online survey has been conducted targeting Indian academic stakeholders' (Specially Teachers, Students and Parents termed as population in paper) for analyzing the awareness towards the bio hazards of EMF exposure.

The COVID-19 has affected and threatened the world health system very critically throughout the globe. In order to take preventive actions by the agencies in dealing with such a pandemic situation, it becomes very necessary to develop a system to analyze the impact of environmental parameters on the spread of this virus. Machine learning algorithms and artificial Intelligence may play an important role in the detection and analysis of the spread of COVID-19. This paper proposed a twinned gradient boosting machine (GBM) to analyze the impact of environmental parameters on the spread, recovery, and mortality rate of this virus in India. The proposed paper exploited the four weather parameters (temperature, humidity, atmospheric pressure, and wind speed) and two air pollution parameters (PM2.5 and PM10) as input to predict the infection, recovery, and mortality rate of its spread. The algorithm of the GBM model has been optimized in its four distributions for best performance by tuning its parameters. The performance of the GBM is reported as excellent (where R2 = 0.99) in training for the combined dataset comprises all three outcomes i.e. infection, recovery and mortality rates. The proposed approach achieved the best prediction results for the state, which is worst affected and highest variation in the atmospheric factors and air pollution level. [ABSTRACT FROM AUTHOR]

Over 2.52Cr covid-19 cases in India. Using data and algorithm route block rapid progress of covid-19.AI & ML to solve real world problem like the corona virus outbreal.AI used powerful complex algorithm with them process data which them immolated human cognation to interpretation and compression of complicated medical and health care data for any artificial intelligence, need to be big data needed. This paper mostly focuses on how to manage spread of infection using Artificial Intelligence. Also focuses on the topic and the challenges a developing country such as India faces and overcome. The condition is troubling and new scientific, environmental and infrastructure needs to play a crucial role in removing this important problem globally (including India). It focused in this report on how India, a developing country is trying to stop corona spreading, and how artificial intelligence (AI) plays an essential role in controlling and monitoring the disease. [ABSTRACT FROM AUTHOR]

COVID-19 preventive measures have been a hindrance to millions of people over the globe not only affecting their daily routine but also affecting the mental stability. Among several preventive measures for COVID-19 spread, the lockdown is an important measure which helps considerably reduce the number of cases. The updated news about the COVID-19 is drastically spread in social media. Particularly, Twitter is widely used to share posts and opinions about the COVID-19 pandemic. Sentiment analysis (SA) on tweets can be used to determine different emotions such as anger, disgust, sadness, joy, and trust. But transparence is needed to understand how a given sentiment is evaluated with the black-box machine learning models. With this motivation, this paper presents a new explainable artificial intelligence (XAI)-based hybrid approach to analyze the sentiments of the tweets during different COVID-19 lockdowns. The proposed model attempted to understand the public's emotions during the first, second, and third lockdowns in India by analyzing tweets on social media, and demonstrates the novelty of the work. A new hybrid model is derived by integrating surrogate model and local interpretable model-agnostic explanation (LIME) model to categorize and predict different human emotions. At the same time, the TopjSimilarity evaluation metric is employed to determine the similarity between the original and surrogate models. Furthermore, top words using the feature importance are identified. Finally, the overall emotions during the first, second, and third lockdowns are also estimated. For validating the enhanced outcomes of the proposed method, a series of experimental analysis was performed on the IEEE port and Twitter API dataset. The simulation results highlighted the supremacy of the proposed model with higher average precision, recall, F-score, and accuracy of 95.69%, 96.80%, 95.04%, and 96.76%, respectively. The outcome of the study reported that the public initially had a negative feeling and then started experiencing positive emotions during the third lockdown. [ABSTRACT FROM AUTHOR]

Prior research in judgment and decision making (JDM) has investigated the effect of problem framing on human preferences. Furthermore, research in JDM documented the absence of such reversal of preferences when making decisions from experience. However, little is known about the effect of context on preferences under the combined influence of problem framing and problem format. Also, little is known about how cognitive models would account for human choices in different problem frames and types (general/specific) in the experience format. One of the primary objectives of this research is to investigate the presence of preference reversals under the influence of problem framing (gain/loss), problem format (experience/description), and problem type (general/specific). Another objective of this research is to develop cognitive models to account for human choices across different problem frames and types in the experience format. A total of 320 participants from India were randomly assigned to one of eight between‐subjects conditions that differed in problem frame, format, and type. Results revealed preference reversals in the description condition; however, they were absent in the experience condition. Moreover, preference reversals were less pronounced in the general problem framing compared to the specific problem framing. Furthermore, specific problems influenced risk‐seeking behavior among participants. We developed cognitive and heuristics models using instance‐based learning theory and natural mean heuristic. Results reveal models' dependency on recent and frequent observations during information sampling. These experience‐based cognitive models could help build artificial intelligence models with fewer preference reversals. This paper focuses on investigating the presence of preference reversals under the influence of problem framing (gain/loss), problem format (experience/description), and problem type (general/specific). Furthermore, Uttrani et al. develop cognitive models using instance‐based learning theory and natural mean heuristic to account for human choices across different problem frames and types in the experience format. [ABSTRACT FROM AUTHOR]

Infectious diseases are highly contagious due to rapid transmission and very challenging to diagnose in the early stage. Artificial Intelligence and Machine Learning now become a strategic weapon in assisting infectious disease prevention, rapid-response in diagnosis, surveillance, and management. In this paper, a bifold COVID_SCREENET architecture is introduced for providing COVID-19 screening solutions using Chest Radiography (CR) images. Transfer learning using nine pre-trained ImageNet models to extract the features of Normal, Pneumonia, and COVID-19 images is adapted in the first fold and classified using baseline Convolutional Neural Network (CNN). A Modified Stacked Ensemble Learning (MSEL) is proposed in the second fold by stacking the top five pre-trained models, and then the predictions resulted. Experimentation is carried out in two folds: In first fold, open-source samples are considered and in second fold 2216 real-time samples collected from Tamilnadu Government Hospitals, India, and the screening results for COVID data is 100% accurate in both the cases. The proposed approach is also validated and blind reviewed with the help of two radiologists at Thanjavur Medical College & Hospitals by collecting 2216 chest X-ray images between the month of April and May. Based on the reports, the measures are calculated for COVID_SCREENET and it showed 100% accuracy in performing multi-class classification. [ABSTRACT FROM AUTHOR]

The COVID‐19 pandemic has affected thousands of people around the world. In this study, we used artificial neural network (ANN) models to forecast the COVID‐19 outbreak for policymakers based on 1st January to 31st October 2021 of positive cases in India. In the confirmed cases of COVID‐19 in India, it's critical to use an estimating model with a high degree of accuracy to get a clear understanding of the situation. Two explicit mathematical prediction models were used in this work to anticipate the COVID‐19 epidemic in India. A Boltzmann Function‐based model and Beesham's prediction model are among these methods and also estimated using the advanced ANN‐BP models. The COVID‐19 information was partitioned into two sections: training and testing. The former was utilized for training the ANN‐BP models, and the latter was used to test them. The information examination uncovers critical day‐by‐day affirmed case changes, yet additionally unmistakable scopes of absolute affirmed cases revealed across the time span considered. The ANN‐BP model that takes into consideration the preceding 14‐days outperforms the others based on the archived results. In forecasting the COVID‐19 pandemic, this comparison provides the maximum incubation period, in India. Mean square error, and mean absolute percent error have been treated as the forecast model performs more accurately and gets good results. In view of the findings, the ANN‐BP model that considers the past 14‐days for the forecast is proposed to predict everyday affirmed cases, especially in India that have encountered the main pinnacle of the COVID‐19 outbreak. This work has not just demonstrated the relevance of the ANN‐BP techniques for the expectation of the COVID‐19 outbreak yet additionally showed that considering the incubation time of COVID‐19 in forecast models might produce more accurate assessments. [ABSTRACT FROM AUTHOR]

Purpose: This article aims to model the challenges of implementing artificial intelligence and machine earning (AI-ML) for moderating the impacts of COVID-19, considering the agricultural supply chain (ASC) in the Indian context. Design/methodology/approach: 20 critical challenges were modeled based on a comprehensive literature review and consultation with experts. The hybrid approach of "Delphi interpretive structural modeling (ISM)-Fuzzy Matrice d' Impacts Croises Multiplication Applique'e à un Classement (MICMAC) − analytical network process (ANP)" was used. Findings: The study's outcome indicates that "lack of central and state regulations and rules" and "lack of data security and privacy" are the crucial challenges of AI-ML implementation in the ASC. Furthermore, AI-ML in the ASC is a powerful enabler of accurate prediction to minimize uncertainties. Research limitations/implications: This study will help stakeholders, policymakers, government and service providers understand and formulate appropriate strategies to enhance AI-ML implementation in ASCs. Also, it provides valuable insights into the COVID-19 impacts from an ASC perspective. Besides, as the study was conducted in India, decision-makers and practitioners from other geographies and economies must extrapolate the results with due care. Originality/value: This study is one of the first that investigates the potential of AI-ML in the ASC during COVID-19 by employing a hybrid approach using Delphi-ISM-Fuzzy-MICMAC-ANP. [ABSTRACT FROM AUTHOR]

The COVID-19 outbreak has led to a substantial loss of human life throughout the world and has a tremendous impact on healthcare services. Industry 4.0 technologies have established effective supply chain management towards the fulfillment of customized demands in the healthcare field. In addition, the internet of things, artificial intelligence, big data analytics, and 3D printing have been extensively used to combat the COVID-19 pandemic and assist in providing value-added services in the healthcare sector. Henceforth, this paper presents a scientometric analysis on the literature of aforementioned Industry 4.0 technologies in the context of COVID-19. It provides extensive insights into co-citation and co-occurrence analysis of high cited publications, participating countries, influential authors, prolific journals, and keywords using the CiteSpace tool. The analyses reveal that China has produced the highest research outputs, although India is the most collaborative country in this field. The current research hotspots include supply chain, 4D printing, and social distancing technologies. Furthermore, it explores emerging trends, intellectual structure of publications, research frontiers, and potential research directions for further work in the Industry 4.0 assisted healthcare domain. [Display omitted] • Industry 4.0 technologies have emerged in the healthcare domain as an area of growing interest in academia and industry discipline. • Artificial intelligence, big data analytics, internet of things and 3D printing have been extensively used to control the COVID-19 spread and assist in providing value-added services in the healthcare sector. • It explores emerging trends, intellectual structure of publications, and potential research directions for further work in the Industry 4.0 assisted healthcare domain. [ABSTRACT FROM AUTHOR]

The purpose of the present study was to explicate the factors determining customers' intention to use budgeting apps since the outbreak of COVID-19 pandemic. A cross-sectional survey in South India was conducted to collect data from 285 FinTech users. The data were analyzed using partial least square regression to estimate path coefficients and the PROCESS macro technique to identify moderation effects. Firstly, app engagement and self-efficacy were found to have a positive effect on the intention to use budgeting apps. Secondly, individuals who use FinTech services less frequently and those who use it to pay for a variety of expenses were found to have a greater effect on usage intentions of customer engagement, perceived trust, and perceived ease of use. Therefore, customization, real-time suggestions, providing tools for data visualization, smart data insights, and artificial intelligence-based recommendations and advice would assist customers in prudence money management. [ABSTRACT FROM AUTHOR]