IoE-Based Predictive Oxygen Inventory Management

The COVID-19 pandemic highlighted a major flaw in the current medical oxygen supply chain and inventory management system. This shortcoming caused the deaths of several patients which could have been avoided by accurate prediction of the oxygen demand and the distribution of oxygen cylinders. To avoid such calamities in the future, this paper proposes an Internet of Everything (IoE) based solution which forecasts the demand for oxygen with 80-85% accuracy. The predicted variable of expected patients enables the system to calculate the requirement of oxygen up to the next 30 days from the initiation of data collection. The system is scalable and if implemented on a city or district level, will help in the fair distribution of medical oxygen resources and will save human lives during extreme load on the supply chain. © 2023 IEEE.

Application Of Neural Network For The Detection Of Covid-19 Or Viral Pneumonia

A bacterial infection in the lungs can cause viral pneumonia, a disease. Later the middle of December 2019, there have been multiple episodes of pneumonia in Wuhan City, China, with no known cause;it has since been discovered that this pneumonia is actually a new respiratory condition brought on by coronavirus infection. Humans who have lung abnormalities are more likely to develop high-risk conditions;this risk can be decreased with much quicker and more effective therapy. The symptoms of Covid-19 pneumonia are similar to those of viral pneumonia;they are not distinctive. X-ray or Computed Tomography (CT) scan images are used to identify lung abnormalities. Even for a skilled radiologist, it might be challenging to identify Covid-19/Viral pneumonia by looking at the X-ray images. For prompt and effective treatment, accurate diagnosis is essential. In this epidemic condition, delayed diagnosis can cause the number of cases to double, hence a suitable tool is required is necessary for the early identification of Covid-19. This paper highlights various AI techniques as a part of our contribution to swift identification and curie Covid-19 to front-line corona. The safety of Covid-19 people who have viral pneumonia is a concern. Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN), two AI technologies from Deep Learning (DL), were utilized to identify Covid-19/Viral pneumonia. The Algorithm is taught utilizing non-public local hospitals or Covid-19 wards, as well as X-ray images of healthy lungs, fake lungs from viral pneumonia, and ostentatious lungs from Covid-19 that are all publicly available. The model is also validated over a lengthy period of time using the transfer learning technique. The results correspond with clinically tested positive Covid-19 patients who underwent Swap testing conducted by medical professionals, giving us an accuracy of 78 to 82 percent. We discovered that each DL model has a unique expertise after testing the various models. [ FROM AUTHOR] Copyright of Journal of Pharmaceutical Negative Results is the property of ResearchTrentz and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)

Covid-19 or Viral Pneumonia Detection Using AI Tools

Viral pneumonia is a disease which occurs in lungs due to bacterial infection. Since middle of December 2019, many cases of pneumonia with unknown cause were found in Wuhan City, China;at present, it has been confirmed that it is a new respiratory disorder caused due to coronavirus infection. Lungs abnormality is highly risky condition in humans;the reduction of the risk is done by enabling quick and efficient treatment. The Covid-19 pneumonia is mimicking viral pneumonia, that is, their symptoms are undistinguished. Lung's abnormality is detected by Computed Tomography (CT) scan images or X-ray images. By viewing the X-rays or CT scan images, even for a well-trained radiologist, it is difficult to detect Covid-19/viral pneumonia. For quick and efficient treatment, it is necessary that proper detection must take place and during this epidemic situation, late detection can lead to doubling of cases;hence, there is a need of proper tool for quick detection of Covid-19/viral pneumonia. This chapter is discussing various AI tools for quick detection as a part of our contribution for quick detection and cure of Covid-19 to front line corona worriers and safety of viral pneumonia patients from Covid-19. The two AI tools are from deep learning (DL), that is, Convolutional Neural Networks (CNN) and Recurrent Neural Network (RNN), which are used for the detection of Covid-19/viral pneumonia. The algorithm is trained using available X-ray images of health lungs, viral pneumonia-affected lungs, and Covid-19-affected lungs available through Kaggle and nondisclosed local hospitals or Covid-19 wards. Also transfer learning method is also used for long-lasting validation of the model. The results give us an accuracy for CNN 83.2 to 94.1% results which are also matched with practically tested positive Covid-19 patients using swab tests by doctors. After testing the various models, we also came through that every model of DL has its own specialty. © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2022.

N-Gram-Codon and Recurrent Neural Network (RNN) to Update Pfizer-BioNTech mRNA Vaccine

In the fight against SARS-CoV-2, Pfizer BioNTech based on synthetic messenger RNA (mRNA) proved to be quicker and more effective even with a small dose of micrograms per injection. Unfortunately, such a vaccine requires very low temperatures to prevent degradation of mRNA. In this paper, the authors have developed three new models of recurrent neural network (1-simple LSTM 2-BDLSTM 3-BERT) using n-gram-codon technique for the codification of mRNA. The primary aim is to analyse the mRNA sequence and predict the stability/reactivity rates at various codon positions. The results of the predictions will be presented in the form of recommendations to support laboratories in updating Pfizer's BioNTech vaccine. The obtained results were validated by the Stanford OpenVaccine dataset and the evaluation measures recall, precision, f1-score, accuracy, and loss.

Centralized CNN-GRU Model by Federated Learning for COVID-19 Prediction in India

In 2019, the corona virus was found in Wuhan, China. The corona virus has traveled several countries in the world from the beginning of 2020. The early estimation of COVID-19 cases is one of the efficient approaches to control the pandemic. Many researchers had proposed the deep learning model for the efficient estimation of COVID-19 cases for different provinces in the world. The research work had not focused on the discussion of robustness in the model. In this study, centralized federated-convolutional neural network–gated recurrent unit (Fed-CNN–GRU) model is proposed for the estimation of active cases per day in different provinces of India. In India, the uneven transmission of COVID-19 virus was seen in 36 provinces due to the different geographical areas and population densities. So, the methodology of this study had focused on the development of single deep learning algorithm, which is robust and reliable to estimate the active cases of COVID-19 in different provinces of India. The concept of transfer and federated learning is involved to enhance the estimation of active cases of COVID-19 by the CNN–GRU model. The study had considered the active cases per day dataset for 36 provinces in India from 12 March, 2020 to 17 January, 2022. Based on the study, it is proven that the centralized CNN–GRU model by federated learning had captured the transmission dynamics of COVID-19 in different provinces with an enhanced result. IEEE

Rapid Detection of SARS-CoV-2 RNA in Human Nasopharyngeal Specimens Using Surface-Enhanced Raman Spectroscopy and Deep Learning Algorithms.

A rapid and cost-effective method to detect the infection of SARS-CoV-2 is fundamental to mitigating the current COVID-19 pandemic. Herein, a surface-enhanced Raman spectroscopy (SERS) sensor with a deep learning algorithm has been developed for the rapid detection of SARS-CoV-2 RNA in human nasopharyngeal swab (HNS) specimens. The SERS sensor was prepared using a silver nanorod array (AgNR) substrate by assembling DNA probes to capture SARS-CoV-2 RNA. The SERS spectra of HNS specimens were collected after RNA hybridization, and the corresponding SERS peaks were identified. The RNA detection range was determined to be 103-109 copies/mL in saline sodium citrate buffer. A recurrent neural network (RNN)-based deep learning model was developed to classify 40 positive and 120 negative specimens with an overall accuracy of 98.9%. For the blind test of 72 specimens, the RNN model gave a 97.2% accuracy prediction for positive specimens and a 100% accuracy for negative specimens. All the detections were performed in 25 min. These results suggest that the DNA-functionalized AgNR array SERS sensor combined with a deep learning algorithm could serve as a potential rapid point-of-care COVID-19 diagnostic platform.

LSTM Model based Time Series Modelling and Prognosis of Foreign Exchange Rates against US Dollar

In today's global economy, precision in projecting macroeconomic characteristics such as the foreign exchange rate, or at the very least properly gauging the trend, is critical for any prospective investment. In recent time, application of artificial intelligence-based forecasting models for macroeconomic variables has been extremely fruitful. The global currency rate changed dramatically during the Covid-19 incident. This study examines the behaviour of the Australian dollar's (AUD) daily exchange rates against the US dollar's (USD) daily exchange rates from January 2016 to December 2020 and makes LSTM RNN-based predictions for the 2021 exchange rate. There are different sorts of performance metrics used in exchange rate forecasting to compute the accuracy of the projected result. This research investigates six performance metrics and discovers that the accuracy of the anticipated outcomes is satisfactory when compared to the actual data. © 2022 IEEE.

Human Emotion Prediction Analysis on Post-COVID-19 Crisis in Digital Media Using Deep Learning

The COVID-19 pandemic has produced a significant impact on society. Apart from its deadliest attack on human health and economy, it has also been affecting the mental stability of human being at a larger scale. Though vaccination has been partially successful to prevent further virus outreach, it is leaving behind typical health-related complications even after surviving from the disease. This research work mainly focuses on human emotion prediction analysis in post-COVID-19 period. In this work, a considerable amount of data collection has been performed from various digital sources, viz. Facebook, e-newspapers, and digital news houses. Three distinct classes of emotion, i.e., analytical, depressed, and angry, have been considered. Finally, the predictive analysis is performed using four deep learning models, viz. CNN, RNN, LSTM, and Bi-LSTM, based on digital media responses. Maximum accuracy of 97% is obtained from LSTM model. It has been observed that the post-COVID-19 crisis has mostly depressed the human being. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Stock Price Prediction Using News Sentiment Analysis

In this era of digitization, tasks can be performed from anywhere in the world that previously required manual movement. It is the same for investing and trading in stocks. With the ease of investing and trading in stocks via the internet, a more extensive segment of society has started investing. The stock price depends on multiple factors such as politics, economics, war, society, and news sentiment. Therefore stocks are really hard to predict due to such vast dependencies. Stock markets are an important issue in the financial world. Prediction of stock prices during the global pandemic of Novel Coronavirus 2019 (COVID-19) can be very helpful to stakeholders. The attempt of predicting the stock prices have been made by previous researchers using sentimental news analysis through Support Vector Machine (SVM), Neural Network, and Naive Bayes. However, they have low accuracy, and some even claim that news is not a crucial governing factor for the stock price. This paper aims to predict the stock market prices through news sentimental analysis using techniques such as Long Short Term Memory and Artificial Neural Network against classifier models like Natural Language Toolkit, Valence Aware Dictionary for Sentiment Reasoning, Recurrent Neural Network for price prediction. S.Mohan [1] MAPE scores came out to be 1.17, 2.43 for RNN and RNN with news polarity for Facebook stock prices. Our results came out to be 1.21 and 1.94, slightly better results, thus showing optimism in the dependence of stock prices on the news. © 2022 IEEE.

Multi-Output Gaussian Process-Based Data Augmentation for Multi-Building and Multi-Floor Indoor Localization

Location fingerprinting based on Received Signal Strength Indicator (RSSI) has become a mainstream indoor localization technique due to its advantage of not requiring the installation of new infrastructure and the modification of existing devices, especially given the prevalence of Wi-Fi-enabled devices and the ubiquitous Wi-Fi access in modern buildings. The use of Artificial Intelligence (AI)/Machine Learning (ML) technologies like Deep Neural Networks (DNNs) makes location fingerprinting more accurate and reliable, especially for large-scale multi-building and multi-floor indoor localization. The application of DNNs for indoor localization, however, depends on a large amount of preprocessed and deliberately-labeled data for their training. Considering the difficulty of the data collection in an indoor environment, especially under the current epidemic situation of COVID-19, we investigate three different methods of RSSI data augmentation based on Multi-Output Gaussian Process (MOGP), i.e., by a single floor, by neighboring floors, and by a single building;unlike Single-Output Gaussian Process (SOGP), MOGP can take into account the correlation among RSSI observations from multiple Access Points (APs) deployed closely to each other (e.g., APs on the same floor of a building) by collectively handling them. The feasibility of the MOGP-based RSSI data augmentation is demonstrated through experiments using a recently-published work based on Recurrent Neural Network (RNN) indoor localization model and the UJIIndoorLoc, i.e., the most popular publicly-available multi-building and multi-floor indoor localization database;the RNN model trained with the UJIIndoorLoc database, augmented by using the whole RSSI data of a building in fitting an MOGP model (i.e., by a single building), outperforms the other two augmentation methods and reduces the mean three-dimensional positioning error from 8.62 m to 8.42 m in comparison to the RNN model trained with the original UJIIndoorLoc database. © 2022 IEEE.

On the domain aided performance boosting technique for deep predictive networks: A COVID-19 scenario

Deep learning models are one of the widely used techniques for forecasting time series data in various applications. It has already been established that the Recurrent Neural Networks (RNN) such as the Long Short-Term Memory (LSTM), Gated Recurrent Units (GRU), etc., perform well in analyzing sequence data for accurate time-series predictions. But, these specialized recurrent architectures suffer from certain drawbacks due to their computational complexity and also their dependency on short term historical data. Hence, there is a scope for further improvement. This paper analyzes the effects of various optimizers and hyper-parameter tuning, on the precision and time efficiency of different deep neural architectures. The analysis has been conducted on COVID-19 pandemic data. Since Convolutional Neural Networks (CNN) are known for their super-human ability in identifying patterns from images, the time-series data has been transformed into a slope-information domain for analyzing the slope patterns over time. The domain patterns have been projected on a 2D plane for further analysis using a restricted recursive CNN (RRCNN) algorithm. The experimental results reveal that the proposed methodology reduces the error over benchmarked sequence models by almost 20% and further reduces the training time by nearly 50%. The prediction models considered in this study have been evaluated using Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and Mean Absolute Percentage Error (MAPE%).

Tackling COVID-19 Infodemic Using Deep Learning

Humanity is battling one of the most deleterious virus in modern history, the COVID-19 pandemic, but along with the pandemic there’s an infodemic permeating the pupil and society with misinformation which exacerbates the current malady. We try to detect and classify fake news on online media to detect fake information relating to COVID-19 and coronavirus. The dataset contained fake posts, articles and news gathered from fact checking websites like politifact whereas real tweets were taken from verified twitter handles. We incorporated multiple conventional classification techniques like Naive Bayes, KNN, Gradient Boost and Random Forest along with Deep learning approaches, specifically CNN, RNN, DNN and the ensemble model RMDL. We analyzed these approaches with two feature extraction techniques, TF-IDF and GloVe Word Embeddings which would provide deeper insights into the dataset containing COVID-19 info on online media. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

A Review of Deep Learning Algorithms and Their Applications in Healthcare

Deep learning uses artificial neural networks to recognize patterns and learn from them to make decisions. Deep learning is a type of machine learning that uses artificial neural networks to mimic the human brain. It uses machine learning methods such as supervised, semi-supervised, or unsupervised learning strategies to learn automatically in deep architectures and has gained much popularity due to its superior ability to learn from huge amounts of data. It was found that deep learning approaches can be used for big data analysis successfully. Applications include virtual assistants such as Alexa and Siri, facial recognition, personalization, natural language processing, autonomous cars, automatic handwriting generation, news aggregation, the colorization of black and white images, the addition of sound to silent films, pixel restoration, and deep dreaming. As a review, this paper aims to categorically cover several widely used deep learning algorithms along with their architectures and their practical applications: backpropagation, autoencoders, variational autoencoders, restricted Boltzmann machines, deep belief networks, convolutional neural networks, recurrent neural networks, generative adversarial networks, capsnets, transformer, embeddings from language models, bidirectional encoder representations from transformers, and attention in natural language processing. In addition, challenges of deep learning are also presented in this paper, such as AutoML-Zero, neural architecture search, evolutionary deep learning, and others. The pros and cons of these algorithms and their applications in healthcare are explored, alongside the future direction of this domain. This paper presents a review and a checkpoint to systemize the popular algorithms and to encourage further innovation regarding their applications. For new researchers in the field of deep learning, this review can help them to obtain many details about the advantages, disadvantages, applications, and working mechanisms of a number of deep learning algorithms. In addition, we introduce detailed information on how to apply several deep learning algorithms in healthcare, such as in relation to the COVID-19 pandemic. By presenting many challenges of deep learning in one section, we hope to increase awareness of these challenges, and how they can be dealt with. This could also motivate researchers to find solutions for these challenges.

A Deep Learning-Based Comparative Study to Track Mental Depression from EEG Data

Background Modern day's society is engaged in commitment-based and time-bound jobs. This invites tension and mental depression among many people who are not able to cope up with this type of working environment. Cases of mental depression are increasing day by day all over the world. Recently, the onset of the COVID-19 pandemic has added further fuel to the fire. In many countries, the ratio between patients with mental depression and psychiatrists or psychologists is remarkably poor. Under such a situation, the design, and development of an expert system by exploiting the hidden power of various deep learning (DL) and machine learning (ML) techniques can solve the problem up to a greater extent. Methodology Each deep learning and machine learning technique has got its advantages and disadvantages to handle different classification problems. In this article four neural network-based deep learning architectures namely MLP, CNN, RNN, RNN with LSTM, and two Supervised Machine Learning Techniques such as SVM and LR are implemented to investigate and compare their suitability to track the mental depression from EEG Data. Result Among Neural Network-Based Deep Learning techniques RNN model has achieved the highest accuracy with 97.50 % in Training Set and 96.50 % in the Testing set respectively. It has been followed with RNN with LSTM model when there were 40% data in the Testing Set. Whereas both the Supervised Machine Learning Models namely SVM and LR have outperformed with 100.00 % accuracies in Training Phase and approximately 97.25 % accuracies in Testing Phase respectively. Conclusion This investigation and comparison-oriented study establish the suitability of RNN, RNN with LSTM, SVM and LR model to track mental depression from EEG data. This type of comparative research using Machine Learning and Deep learning architectures must be framed out on this topic to finalize the design and development of an expert system for the automatic detection of depression from EEG data.

Analysis, visualization and forecasting of COVID-19 outbreak using LSTM model

The COVID-19 outbreak has been treated as a pandemic disease by the World Health Organization (WHO). Severe diseases like Middle East respiratory syndrome (MERS) and severe acute respiratory syndrome (SARS) are caused by members of a large family of viruses called coronavirus (CoV). A new strain was identified in humans in December 2019, named coronavirus (COVID-19). The highest affected countries are unable to predict the pace of the outbreak of COVID-19. So, AI is helpful to analyze the COVID-19 outbreak in the world. We have used the LSTM model to predict the outbreak of COVID-19 all over the world with limited epidemiological data. A variant of recurrent neural network (RNN) which has the capability of learning long-term dependencies with feedback connections, also known as long short-term memory (LSTM), is used in resolving the problems related to time series in deep learning. LSTM is capable of processing a single data point and an entire sequence of data related to any field. We observe that the LSTM model is useful to predict the ongoing outbreak so that authorities can take preventive action earlier. The LSTM model result shows that the growth rate of infected cases of COVID-19 increased exponentially every week. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021.