Artificial neural networks for prediction of COVID-19 in India by using backpropagation.

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.

Modified Convolutional Neural Network Architecture with XGBoost for Mucormycosis Detection and compare performance with XGBoost

Patients with coronavirus illness 2019, especially those in India, are more likely to see an increase in rhino-orbital mucormycosis. A well-known risk factor during COVID-19 infection and mucormycosis is diabetes mellitus (DM). With roughly 0.15 instances per 1000 people, mucormycosis is almost 82 times more common in India than it is in Western nations. Additionally, this fungus expanded quickly across numerous states, leading some of them to designate this illness an epidemic. Finding a solution for this potentially fatal fungal infection requires the aid of modern technologies, including artificial intelligence and data learning. In this paper, we combine a modified convolutional learning neural network with an XGBoost classifier to propose a unique black fungus detection method. Under the right circumstances, the CNNXGB model is made simpler by lowering the no of attributes since it is not essential to re-adjust the weight values throughout a back propagation cycle. On testing data, the mean classification performance is 98.26%, far better than current approaches. © 2023 IEEE.

Mucormycosis Detection using Hybrid Convolutional Neural Network with Support Vector Machine and Compare the performance with Support Vector Machine

Based on the patient's underlying condition, mucormycosis, often known as a black fungus illness, is an uncommon but severe disease with a high fatality rate. The large second wave of the COVID-19 epidemic has presented a challenge for the Indian healthcare system from this life-threatening powerful threat. The fungus family Mucorales causes mucormycosis, which affects numerous bodily organs. This fungal opportunistic illness spreads quickly. Recently, this unusual fungus has been infecting covid sufferers in India at greater rates than before. In India, the frequency of this black fungus illness amongst covid-19 as well as post-covid-19 patients is now on the rise. Finding a solution for this potentially fatal fungal infection requires the aid of modern technologies, including artificial intelligence and data learning. In this article, we present a unique hybrid model for black fungus identification that combines support vector machine classifier and convolutional learning network. Under the proper circumstances, the CNNSVM model is made simpler by minimizing the amount of variables because it is not important to constantly the weighting factors in a back propagation cycle. Additionally, it was shown that the SVM classifier was the best merging equivalent when the CNN was employed as a feature extractor, offering the highest accuracy-related synergy effect. On testing data, the mean classification performance was 99.3%, which is a significant improvement over current techniques. © 2023 IEEE.

Supervised Machine Learning Strategy for detection of covid19 patients

The current severe acute respiratory syndrome corona virus 2 (SARS-CoV-2) public health catastrophe, both human lives have been lost and the economy has disrupted severely the current scenario. In this paper, we develop a detection module using a series of steps that involves pre-processing, feature extraction and detection of covid-19 patients based on the images collected from the computerized tomography (CT) images. The images are initially pre-processed and then the features are extracted using Gray Level Co-occurrence Matrix (GLCM) and then finally classified using back propagation neural network (BPNN). The simulation is conducted to test the efficacy of the model against various CT image datasets of numerous patients. The results of simulation shows that the proposed method achieves higher detection rate, and reduced mean average percentage error (MAPE) than other existing methodologies. © 2023 IEEE.

COVID-19 prediction based on GLCM features of radiography image using SVM, KNN, and backpropagation ANN classifiers

COVID-19 is a type of disease that transmits a new variant of virus known as Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-COV-2) in the same novel coronavirus family as SARS-CoV and Middle East Respiratory Syndrome Coronovirus (MERS-COV). A fast method to detect the disease is essential to prevent larger transmission and to look after the infected patients. The Chest X-ray, one of the detection methods of COVID-19 can be used in the examination process of suspected cases. In this paper, a COVID-19 detection model through chest x-ray images is proposed by using Grey Level Co-occurrence Matrix (GLCM) with Support Vector Machine (SVM), K-Nearest Neighbor (KNN), and Backpropagation Artificial Neural Network (BP-ANN) classifiers. In this case, Principal Component Analysis (PCA) will be added as a mean to optimize features extraction process. The aim of this work is to find the best classifier for predicting chest x-ray images as normal, pneumonia, or COVID-19 suspect. The BP-ANN emerged as the best classifier with 85,5% accuracy, 85,8% precision, and 86,1% recall. © 2023 Author(s).

A Novel Explainable CNN Model for Screening COVID-19 on X-ray Images

Due to the rapid propagation characteristic of the Coronavirus (COV-ID-19) disease, manual diagnostic methods cannot handle the large number of infected individuals to prevent the spread of infection. Despite, new automated diagnostic methods have been brought on board, particularly methods based on artificial intelligence using different medical data such as X-ray imaging. Thoracic imaging, for example, produces several image types that can be processed and analyzed by machine and deep learning methods. X-ray imaging materials widely exist in most hospitals and health institutes since they are affordable compared to other imaging machines. Through this paper, we propose a novel Convolutional Neural Network (CNN) model (COV2Net) that can detect COVID-19 virus by analyzing the X-ray images of suspected patients. This model is trained on a dataset containing thousands of X-ray images collected from different sources. The model was tested and evaluated on an independent dataset. In order to approve the performance of the proposed model, three CNN models namely MobileNet, Residential Energy Services Network (Res-Net), and Visual Geometry Group 16 (VGG-16) have been implemented using transfer learning technique. This experiment consists of a multi-label classification task based on X-ray images for normal patients, patients infected by COVID-19 virus and other patients infected with pneumonia. This proposed model is empowered with Gradient-weighted Class Activation Mapping (Grad-CAM) and Grad-Cam++ techniques for a visual explanation and methodology debugging goal. The finding results show that the proposed model COV2Net outperforms the state-of-the-art methods. © 2023 CRL Publishing. All rights reserved.

Forecasting Emerging Pandemics with Transfer Learning and Location-aware News Analysis

Monitoring and forecasting epidemic diseases are of prime importance to public health organizations and policymakers in taking proper measures and adjusting prevention tactics. Early prediction is especially important to restrict the spread of emerging pandemics such as COVID-19. However, despite increasing research and development for various epidemics, several challenges remain unresolved. On the one hand, early-stage epidemic prediction for emerging new diseases is difficult because of data paucity and lack of experience. On the other hand, many existing studies ignore or fail to leverage the contribution of social factors such as news, geolocations, and climate. Even though some researchers have recognized the profound impact of social features, capturing the dynamic correlation between these features and pandemics requires an extensive understanding of heterogeneous formats of data and mechanisms. In this paper, we design TLSS, a neural transfer learning architecture for learning and transferring general characteristics of existing epidemic diseases to predict a new pandemic. We propose a new feature module to learn the impact of news sentiment and semantic information on epidemic transmission. We then combine this information with historical time-series features to forecast future infection cases in a dynamic propagation process. We compare the proposed model with several state-of-the-art statistics approaches and deep learning methods in epidemic prediction with different lead times of ground truth. We conducted extensive experiments on three stages of COVID-19 development in the United States. Our experiment demonstrates that our approach has strong predictive performance for COVID infection cases, especially with longer lead times. © 2022 IEEE.

Prediction of Survival COVID-19 Patients by Using Backpropagation Neural Network Algorithm

COVID-19 is a disease caused by a virus and increasing in cases every day. This is because the large number of patients makes it difficult to be treated at the hospital. This is behind the need for survival prediction of COVID-19 patients within 48 days so that the medical team can prioritize patients who are predicted to not survive on that period. In this research, the firefly algorithm is used which aims to select attributes and will perform comparisons for data that is balance or imbalance and combined with data that do feature selection and does not feature selection. The data that will be used are age, asthma, diabetes, gender, COPD, pregnancy, hypertension, obesity, ICU, chronic kidney disease, smoking, heart disease, immune deficiency, pneumonia, and other medical history. In this research, the selected attributes were gender, type of patient, intubation, pneumonia, age, pregnancy, diabetes, COPD (Chronic Obstructive Pulmonary Disease), asthma, hypertension, other diseases, obesity, chronic kidney disease, smokers, contact with COVID patients, and ICU. The prediction model with the highest level of performance is a model with balanced data with a recall value of 0.79, then a precision value of 0.93, then an f score of 0.85, then an accuracy value of 0.86, then a specificity 0,93, then a NPV 0,82 and a geometric mean value of 0.87 © 2022 IEEE.

Detecting COVID-19 on CT Images with Impulsive-Backpropagation Neural Networks

This paper presents an impulsive-backpropagation neural network (IBNN) based learning algorithm for detecting Coronavirus Disease 2019 (COVID-19), by classifying chest computed tomography (CT) images. Inspired by the nerve impulses in brain networks, the IBNN algorithm consists of two parts: a multi-layered network of impulsive neurons and a gradient decent backpropagation mechanism. The effectiveness of the IBNN algorithm is validated on clinical COVID-19 database, and a classification accuracy of 98.19% is achieved. It is further demonstrated by comparative studies that the IBNN may outperform some other learning algorithms through the integration of nerve impulses and backpropagation. Considering the intricate attributes of the chest CT scan images, the IBNN algorithm also exhibits a potential capacity of pattern recognition on complicated samples. © 2022 IEEE.

Survey of explainable artificial intelligence techniques for biomedical imaging with deep neural networks.

Artificial Intelligence (AI) techniques of deep learning have revolutionized the disease diagnosis with their outstanding image classification performance. In spite of the outstanding results, the widespread adoption of these techniques in clinical practice is still taking place at a moderate pace. One of the major hindrance is that a trained Deep Neural Networks (DNN) model provides a prediction, but questions about why and how that prediction was made remain unanswered. This linkage is of utmost importance for the regulated healthcare domain to increase the trust in the automated diagnosis system by the practitioners, patients and other stakeholders. The application of deep learning for medical imaging has to be interpreted with caution due to the health and safety concerns similar to blame attribution in the case of an accident involving autonomous cars. The consequences of both a false positive and false negative cases are far reaching for patients' welfare and cannot be ignored. This is exacerbated by the fact that the state-of-the-art deep learning algorithms comprise of complex interconnected structures, millions of parameters, and a 'black box' nature, offering little understanding of their inner working unlike the traditional machine learning algorithms. Explainable AI (XAI) techniques help to understand model predictions which help develop trust in the system, accelerate the disease diagnosis, and meet adherence to regulatory requirements. This survey provides a comprehensive review of the promising field of XAI for biomedical imaging diagnostics. We also provide a categorization of the XAI techniques, discuss the open challenges, and provide future directions for XAI which would be of interest to clinicians, regulators and model developers.

Research on Prediction of Medical Training Effect Based on PSO-BP Neural Network

Today, the world is still suffering from Coronavirus disease 2019(COVID-19) and other disasters. Therefore, it is critical to improve medical emergency professional training, and ensuring the training effect has become the top priority. As a result, this paper builds a Particle Swarm Optimization Back Propagation(PSO-BP) neural network model using training data from the National Disaster Life Support(NDLS) course to predict NDLS training outcomes. The PSO algorithm is used to calculate the initial weights of the BP network, and the model is then trained using error back propagation to obtain the predicted value of the training effect. When compared to the standard BP neural network prediction results, experimental analysis shows that the prediction model's accuracy reaches 93.24 percentage, and the prediction accuracy is improved by 11.71 percentage. It is also better in terms of convergence speed, minimum error, global search ability, and learning smoothness. This approach is suitable for medical training effect prediction and additionally to assist the training providers in grasping trainees' learning effects in advance to improve training quality. © 2022 IEEE.

Detecting COVID-19 from Lung Computed Tomography Images: A Swarm Optimised Artificial Neural Network Approach

COVID-19 has affected many people across the globe. Though vaccines are available now, early detection of the disease plays a vital role in the better management of COVID-19 patients. An Artificial Neural Network (ANN) powered Computer Aided Diagnosis (CAD) system can automate the detection pipeline accounting for accurate diagnosis, overcoming the limitations of manual methods. This work proposes a CAD system for COVID-19 that detects and classifies abnormalities in lung CT images using Artificial Bee Colony (ABC) optimised ANN (ABCNN). The proposed ABCNN approach works by segmenting the suspicious regions from the CT images of non-COVID and COVID patients using an ABC optimised region growing process and extracting the texture and intensity features from those suspicious regions. Further, an optimised ANN model whose input features, initial weights and hidden nodes are optimised using ABC optimisation classifies those abnormal regions into COVID and non-COVID classes. The proposed ABCNN approach is evaluated using the lung CT images collected from the public datasets. In comparison to other available techniques, the proposed ABCNN approach achieved a high classification accuracy of 92.37% when evaluated using a set of 470 lung CT images. Author

An Artificial Approach for the Fractional Order Rape and Its Control Model

The current investigations provide the solutions of the nonlinear fractional order mathematical rape and its control model using the strength of artificial neural networks (ANNs) along with the Levenberg-Marquardt back -propagation approach (LMBA), i.e., artificial neural networks-Levenberg-Marquardt backpropagation approach (ANNs-LMBA). The fractional order investigations have been presented to find more realistic results of the mathe-matical form of the rape and its control model. The differential mathematical form of the nonlinear fractional order mathematical rape and its control model has six classes: susceptible native girls, infected immature girls, sus-ceptible knowledgeable girls, infected knowledgeable girls, susceptible rapist population and infective rapist population. The rape and its control differ-ential system using three different fractional order values is authenticated to perform the correctness of ANNs-LMBA. The data is used to present the rape and its control differential system is designated as 70% for training, 14% for authorization and 16% for testing. The obtained performances of the ANNs-LMBA are compared with the dataset of the Adams-Bashforth-Moulton scheme. To substantiate the consistency, aptitude, validity, exactness, and capability of the LMBA neural networks, the obtained numerical values are provided using the state transitions (STs), correlation, regression, mean square error (MSE) and error histograms (EHs).

Design of a machine learning to classify health beverages preferences for elderly people: an empirical study during COVID-19 in Thailand

This research designed a decision support system based upon a machine learning (DSS-ML) model for classifying health beverage preferences for elderly people. A neural network was designed involving training using particle swarm optimisation (PSO) in comparison with two ML models: logistic regression (LR) and a neural network (NN). The DSS-ML model was able to classify accurately and autonomously the preference complexities associated with the health beverage preferences for elderly people in accordance with the WHO's recommendation. In terms of contribution, the results demonstrated that NN training with PSO resulted in a higher ability to classify the preferences for health beverages than for the two ML models. Furthermore, NN training with PSO achieved faster convergence than NN. The benefits of this research can be separated into two parts. First, manufacturers can introduce beverages that satisfy elderly people's preferences. Second, elderly people can be made aware of appropriate health beverages. Copyright © 2022 Inderscience Enterprises Ltd.

Ensemble Deep Learning Approach with Attention Mechanism for COVID-19 Detection and Prediction

New coronavirus (COVID-19), which first appeared in Wuhan City and is now rapidly disseminating worldwide, may be predicted, diagnosed, and treated with the help of cutting-edge medical technology, such as artificial intelligence and machine learning algorithms. To detect COVID-19, we suggested an Ensemble deep learning method with an attention mechanism. The suggested approach uses an ensemble of RNN and CNN to extract features from data from diverse sources, such as CT scan pictures and blood test results. For image and video processing, CNNs are the most effective. RNNs, on the other hand, use text and speech data to extract features. Further, an attention mechanism is used to determine which features are most relevant for classification. Finally, the deep learning network utilizes the selected features for detection and prediction. As a result, data can be used to forecast future medical needs. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Computational Deep Learning Models for Detection of COVID-19 Using Chest X-Ray Images

Traditional deep learning architectures after the AlexNet have added more layers to achieve higher accuracy. However, with increasing number of layers, we are likely to encounter vanishing/exploding gradient problems in these architectures which significantly impact the training performance. This was solved by the introduction of residual networks which make use of "skip connections” by adding the output from the previous layer to the layer ahead. ResNets are often combined with the Inception v4 model and was first used by Google researchers as Inception-ResNet. Inception v4 aimed to reduce the complexity of Inception v3 model which gave the state-of-the-art accuracy on ILSVRC 2015 challenge. The initial set of layers before the Inception block in Inception v4, referred to as "stem of the architecture,” was modified to make it more uniform. This model can be trained without partition of replicas unlike the previous versions of inceptions which required different replica in order to fit in memory. This architecture uses memory optimization on back propagation to reduce the memory requirement. In this paper, we propose two approaches for detection of COVID-19 using chest X-ray images by implementing ResNet16 and Inception v4 and providing a comparison of their performances. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

UltrasonicG: Highly Robust Gesture Recognition on Ultrasonic Devices

In the current critical situation of novel coronavirus, the use of contactless gesture recognition method can reduce human contact and decrease the probability of virus transmission. In this context, ultrasound-based sensing has been widely concerned for its slow propagation speed, low sampling rate, and easy access to devices. However, limited by the complexity of gestural movements and insufficient training data, the accuracy and robustness of gesture recognition are low. To solve this problem, we propose UltrasonicG, a system for highly robust gesture recognition on ultrasonic devices. The system first converts a single audio signal into a Doppler shift and subsequently extracts the feature values using the Residual Neural Network (ResNet34) and uses Bi-directional Long Short-Term Memory (Bi-LSTM) for gesture recognition. The method effectively improves the accuracy of gesture recognition by combining the information of feature dimension with time dimension. To overcome the challenge of insufficient dataset, we use data extension to expand the dataset. We have conducted extensive experiments and evaluations on UltrasonicG in a variety of real scenarios. The experimental results show that UltrasonicG can recognize 15 kinds of gestures with a recognition distance of 0.5 m. And it has a high accuracy and robustness with a comprehensive recognition rate of 98.8% under different environments and influencing factors. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

COMPARISON ARTIFICIAL NEURAL NETWORK METHODS OF BACKPROPAGATION AND LEARNING VECTOR QUANTIZATION FOR FORECASTING STOCK PRICES

During the COVID-19 pandemic stock trading is a hot topic of discussion and encourages new investors which positive impact on the market modal. Shares of PT. XL Axiata, Tbk. (EXCL) was sluggish despite reporting a surge profit in 2021, this prompted research on how to predict the stock price of EXCL for attract investors and encourage company to be more active in carrying out business strategies. In recent years, Artificial Neural Networks (ANN) are quite used in macroeconomics forecasting, because of their ability to detect and relate linear and non-linear functions. In this study, two ANN methods were used to predict the stock price of EXCL with backpropagation (BP) and Learning Vector Quantization (LVQ). In the prediction results, model evaluation is needed to measure the forecasting model from both methods, resulting in the confusion matrix with the accuracy, sensitivity, and specificity are provided. This research is given some value to stock action suggestions at EXCL. © 2022 Little Lion Scientific.

Estimation of Closed Hotels and Restaurants in Jakarta as Impact of Corona Virus Disease (Covid-19) Spread Using Backpropagation Neural Network

Corona Virus Disease (Covid-19) has become the focus of world attention because it attacked many people in the world and many people died. The effect of Covid-19 is not only on the health of people, it is negatively affecting all aspects of life including the social area, economy, sport, and tourism. Hotels and restaurants that are an important part of the tourism industry have got a big negative impact from Covid-19. Since this disease has spreaded in many countries including Indonesia, the Indonesian goverment adopted regulations to close the hotels and restaurants to prevent the spread of Covid-19. This research comes from the need to find out the estimated number of hotels and restaurants to be closed due to Covid-19. The estimation method will involve the Backpropagation Neural Network. The Backpropagation Neural Network can make estimation of the number of closed hotels and restaurants approaching the target. Simulations are applied by splitting the dataset into training data (80%) and testing data (20%). From Backpropagation Neural Network simulations, the Backpropagation Neural Network can make estimation of the number of closed hotels and restaurants in training data with optimal RMSE being 9.2422 and testing data with optimal RMSE being 8.9419. © 2022 InforMath Publishing Group.

Forecasting the stock price of vaccine manufacturers in China Using Machine Learning and Econometrics Model

Due to the sudden outbreak of COVID-19, there is a high volatility in stock price of vaccine manufacturers in China (Between December 15, 2020 and December 13, 2021, average monthly volatility of these companies is 986). The aim of this paper is to compare the price prediction result of four algorithms: Multivariable Regression Model (MLR), Auto Regressive Integrated Moving Average Model (ARIMA), Back Propagation Neural Network Model (BP-NN), Random Forest Regression (RF), and decide which one has a better performance. Data from December 2020 to December 2021 is collected from Wind and the 8 stocks is selected in leading companies in vaccine industry. It turns out that BP-NN Model gives the best result in predicting stock price of vaccine manufacturers, measured using commonly used indicator, i.e., root-mean-square error (RMSE) and R Square (R2). So next time in the similar situation, BP-NN can be seen as a powerful tool to help us make decision. This paper would help investors build an optimal strategy in selecting stocks in terms of pharmaceutical industry. © 2022 SPIE.