COVID-19 Prognosis from Chest X-ray Images by using Deep Learning Approaches: A Next Generation Diagnostic Tool

Global public health is overwhelmed due to the ongoing Corona Virus Disease (COVID-19). As of October 2022, the causative virus SARS-CoV-2 and its multiple variants have infected more than 600 million confirmed cases and nearly 6.5 million fatalities globally. The main objective of this reported study is to understand the COVID-19 infection better from the chest X-ray (CXR) image database of COVID-19 cases from the dataset of CXR of normal, pneumonia and COVID-19 patients. Deep learning approaches like VGG-16 and LSTM models were used to classify images as normal, pneumonia and COVID-19 impacted by extracting the features. It has been observed during the COVID-19 pandemic peaks that large number of patients could not avail medical beds and were seen stranded outdoors. To address such health emergency situations with limited available bed and scarcity of expert physicians, computer-aided analysis could save precious lives through early screening and appropriate care. Such computer-based deep-learning strategy could help during future pandemics, especially when the available health resources and the need for preventive measures to take do not match the burden of a disease. [ FROM AUTHOR] Copyright of Journal of Pure & Applied Microbiology is the property of Dr. M. N. Khan 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.)

Digital Disease Surveillance for Emerging Infectious Diseases: An Early Warning System Using the Internet and Social Media Data for COVID-19 Forecasting in Canada.

BACKGROUND: Emerging Infectious Diseases (EID) are a significant threat to population health globally. We aimed to examine the relationship between internet search engine queries and social media data on COVID-19 and determine if they can predict COVID-19 cases in Canada. METHODS: We analyzed Google Trends (GT) and Twitter data from 1/1/2020 to 3/31/2020 in Canada and used various signal-processing techniques to remove noise from the data. Data on COVID-19 cases was obtained from the COVID-19 Canada Open Data Working Group. We conducted time-lagged cross-correlation analyses and developed the long short-term memory model for forecasting daily COVID-19 cases. RESULTS: Among symptom keywords, "cough," "runny nose," and "anosmia" were strong signals with high cross-correlation coefficients >0.8 ( rCough = 0.825, t - 9; rRunnyNose = 0.816, t - 11; rAnosmia = 0.812, t - 3 ), showing that searching for "cough," "runny nose," and "anosmia" on GT correlated with the incidence of COVID-19 and peaked 9, 11, and 3 days earlier than the incidence peak, respectively. For symptoms- and COVID-related Tweet counts, the cross-correlations of Tweet signals and daily cases were rTweetSymptoms = 0.868, t - 11 and tTweetCOVID = 0.840, t - 10, respectively. The LSTM forecasting model achieved the best performance (MSE = 124.78, R2 = 0.88, adjusted R2 = 0.87) using GT signals with cross-correlation coefficients >0.75. Combining GT and Tweet signals did not improve the model performance. CONCLUSION: Internet search engine queries and social media data can be used as early warning signals for creating a real-time surveillance system for COVID-19 forecasting, but challenges remain in modelling.

Model for movement of individuals in society after the corona pandemic using deep learning algorithms

The health crisis that attributed to the rapid spread of the COVID-19 has impacted the globe negatively in terms of economy, education and transport and led to the global lockdown. The risk of the COVID-19 infection has been increased due to a lack of successful cure for the disease. Thus, social distancing is considered as the most appropriate precaution measureto control the viral spread throughout the world. Social distancing means that physical contact between individuals can be prevented to reduce the viral transmission effectively. The purpose of this work is to provide a deep learning model capable of predicting the movement of people in the pandemic to take precautions and control the COVID-19 infection. This model is based on twoLSTMand GRU algorithms. The results show that the GRU is better than LSTM in terms of prediction error rate and duration. © 2023 Author(s).

Residential Load Forecasting based on Deep Neural Network

This paper presents residential load forecasting using multivariate multi-step Deep Neural Networks (DNN) such as LSTM, CNN, Stacked LSTM, and Hybrid CNN-LSTM. A preliminary Exploratory Data Analysis (EDA) is conducted, and the decision variables are identified. An elbowing method is used to determine the number of clusters. Data is categorized based on weekdays, weekends, vacations, and Covid-Lockdown. Dimensionality-reduction using principal component analysis (PCA) is conducted. Seasonality-based clustering is found to improve the DNN model prediction accuracy further. A comparative analysis employs error metrics such as RMSE, MSE, MAPE, and MAE. The multivariate LSTM model with feedback is found to be the best fit model with the better performance indices. © 2023 IEEE.

Accelerometer-Based Bed Occupancy Detection for Automatic, Non-Invasive Long-Term Cough Monitoring

We present a new machine learning based bed occupancy detection system that uses only the accelerometer signal captured by a bed-attached consumer smartphone. Automatic bed occupancy detection is necessary for automatic long-term cough monitoring since the time that the monitored patient occupies the bed is required to accurately calculate a cough rate. Accelerometer measurements are more cost-effective and less intrusive than alternatives such as video monitoring or pressure sensors. A 249-hour dataset of manually-labelled acceleration signals gathered from seven patients undergoing treatment for tuberculosis (TB) was compiled for experimentation. These signals are characterised by brief activity bursts interspersed with long periods of little or no activity, even when the bed is occupied. To process them effectively, we propose an architecture consisting of three interconnected components. An occupancy-change detector locates instances at which bed occupancy is likely to have changed, an occupancy-interval detector classifies periods between detected occupancy changes and an occupancy-state detector corrects falsely-identified occupancy changes. Using long short-term memory (LSTM) networks, this architecture achieved an AUC of 0.94. To demonstrate the application of this bed occupancy detection system to a complete cough monitoring system, the daily cough rates along with the corresponding laboratory indicators of a patient undergoing TB treatment were estimated over a period of 14 days. This provides a preliminary indication that automatic cough monitoring based on bed-mounted accelerometer measurements may present a non-invasive, non-intrusive and cost-effective means of monitoring the long-term recovery of patients suffering from respiratory diseases such as TB and COVID-19. © 2013 IEEE.

Business Intelligence and Analytics from Big Data to Healthcare

Healthcare is one of the largest and complex sectors in the stock market. It comprises a broad range of companies including hospitals, healthcare providers, selling of medical devices, drugs, and insurance. When the coronavirus unexpectedly comes into sight, the entire world economy has stagger. This has decreased the surgeries, outpatient department footfall, international patients, medical device pharmaceutical, and healthcare commodities. Medical device industry has worst affected the export of medical devices and the critical raw materials are disturbed due to the restriction on movement, social distancing, travel, and transport. Healthcare industry challenged a burden such as 100% of alertness for the protection in hospitals and further investment of manpower, equipment, consumables, etc. Health and wealth are the two main components of well-being in life. Almost everything requires money from food to education to health services. During this pandemic situation, we have to take care of both health and wealth. Healthcare industry is one of the world's major and fastest emergent enterprises. Nowadays, with the advancement of expertise like analytics, business intelligence (BI), and artificial intelligence (AI), the prediction of stock value has improved and benefits the investors to make the right decisions. This study aimed to predict the hospital and healthcare services stocks in the Indian Stock Market Index (Nifty). Companies like Apollo Hospitals Enterprise Limited, Cadila Healthcare Ltd., Dr. Reddy's Laboratories, Fortis Healthcare Limited, Max Healthcare InstituteLimited, Opto Circuits Limited, Panacea Biotec, Poly Medicure Ltd., Thyrocare Technologies Limited, and Zydus Wellness Ltd. were used in this study to predict healthcare stocks. Hospital and healthcare service stocks were predicted using linear regression (LR), autoregressive integrated moving average (ARIMA), and long short-term memory (LSTM). © 2022 Scrivener Publishing LLC.

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

Thematic Analysis: A Corpus-Based Method for Understanding Themes/Topics of a Corpus through a Classification Process Using Long Short-Term Memory (LSTM)

Using advanced algorithms to conduct a thematic analysis reduces the time taken and increases the efficiency of the analysis. Long short-term memory (LSTM) is effective in the field of text classification and natural language processing (NLP). In this study, we adopt LSTM for text classification in order to perform a thematic analysis using concordance lines that are taken from a corpora of news articles. However, the statistical and quantitative analyses of corpus linguistics are not enough to fully identify the semantic shift of terms and concepts. Therefore, we suggest that a corpus should be classified from a linguistic theoretical perspective, as this would help to determine the level of the linguistic patterns that should be applied in the experiment of the classification process. We suggest investigating the concordance lines of the articles rather than only the relationship between collocates, as this has been a limitation for many studies. The findings of this research work highlight the effectiveness of the proposed methodology for the thematic analysis of media coverage, reaching 84% accuracy. This method provides a deeper thematic analysis than only applying the classification process through the collocational analysis.

Deep Learning Architecture for UAV Traffic-Density Prediction

The research community has paid great attention to the prediction of air traffic flows. Nonetheless, research examining the prediction of air traffic patterns for unmanned aircraft traffic management (UTM) is relatively sparse at present. Thus, this paper proposes a one-dimensional convolutional neural network and encoder-decoder LSTM framework to integrate air traffic flow prediction with the intrinsic complexity metric. This adapted complexity metric takes into account the important differences between ATM and UTM operations, such as dynamic flow structures and airspace density. Additionally, the proposed methodology has been evaluated and verified in a simulation scenario environment, in which a drone delivery system that is considered essential in the delivery of COVID-19 sample tests, package delivery services from multiple post offices, an inspection of the railway infrastructure and fire-surveillance tasks. Moreover, the prediction model also considers the impacts of other significant factors, including emergency UTM operations, static no-fly zones (NFZs), and variations in weather conditions. The results show that the proposed model achieves the smallest RMSE value in all scenarios compared to other approaches. Specifically, the prediction error of the proposed model is 8.34% lower than the shallow neural network (on average) and 19.87% lower than the regression model on average. © 2023 by the authors.

Improved LSTM-based deep learning model for COVID-19 prediction using optimized approach.

Individuals in any country are badly impacted both economically and physically whenever an epidemic of infectious illnesses breaks out. A novel coronavirus strain was responsible for the outbreak of the coronavirus sickness in 2019. Corona Virus Disease 2019 (COVID-19) is the name that the World Health Organization (WHO) officially gave to the pneumonia that was caused by the novel coronavirus on February 11, 2020. The use of models that are informed by machine learning is currently a major focus of study in the field of improved forecasting. By displaying annual trends, forecasting models can be of use in performing impact assessments of potential outcomes. In this paper, proposed forecast models consisting of time series models such as long short-term memory (LSTM), bidirectional long short-term memory (Bi-LSTM), generalized regression unit (GRU), and dense-LSTM have been evaluated for time series prediction of confirmed cases, deaths, and recoveries in 12 major countries that have been affected by COVID-19. Tensorflow1.0 was used for programming. Indices known as mean absolute error (MAE), root means square error (RMSE), Median Absolute Error (MEDAE) and r2 score are utilized in the process of evaluating the performance of models. We presented various ways to time-series forecasting by making use of LSTM models (LSTM, BiLSTM), and we compared these proposed methods to other machine learning models to evaluate the performance of the models. Our study suggests that LSTM based models are among the most advanced models to forecast time series data.

SENTIMENT ANALYSIS AND PREDICTION OF POLARITY VACCINES BASED ON TWITTER DATA USING DEEP NLP TECHNIQUES

The global impact of COVID-19 has been significant and several vaccines have been developed to combat this virus. However, these vaccines have varying levels of efficacy and effectiveness in preventing illness and providing immunity. As the world continues to grapple with the ongoing pandemic, the development and distribution of effective vaccines remains a top priority, making monitoring prevention strategies mandatory and necessary to mitigate the spread of the disease. These vaccines have raised a huge debate on social networks and in the media about their effectiveness and secondary effects. This has generated big data, requiring intelligent tools capable of analyzing these data in depth and extracting the underlying knowledge and feelings. There is a scarcity of works that analyze feelings and the prediction of these feelings based on their estimated polarities at the same time. In this work, first, we use big data and Natural Language Processing (NLP) tools to extract the entities expressed in tweets about AstraZeneca and Pfizer and estimate their polarities;second, we use a Long Short-Term Memory (LSTM) neural network to predict the polarities of these two vaccines in the future. To ensure parallel data treatment for large-scale processing via clustered systems, we use the Apache Spark Framework (ASF) which enables the treatment of massive amounts of data in a distributed way. Results showed that the Pfizer vaccine is more popular and trustworthy than AstraZeneca. Additionally, according to the predictions generated by Long Short-Term Memory (LSTM) model, it is likely that Pfizer will continue to maintain its strong market position in the foreseeable future. These predictive analytics, which uses advanced machine learning techniques, have proven to be accurate in forecasting trends and identifying patterns in data. As such, we have confidence in the LSTM's prediction of Pfizer's ongoing dominance in the industry. © Hassan Badi, Imad Badi, Karim El Moutaouakil, Aziz Khamjane, Abdelkhalek Bahri 2022

Evaluation of e-learners' concentration using recurrent neural networks.

Recently, interest in e-learning has increased rapidly owing to the lockdowns imposed by COVID-19. A major disadvantage of e-learning is the difficulty in maintaining concentration because of the limited interaction between teachers and students. The objective of this paper is to develop a methodology to predict e-learners' concentration by applying recurrent neural network models to eye gaze and facial landmark data extracted from e-learners' video data. One hundred eighty-four video data of ninety-two e-learners were obtained, and their frame data were extracted using the OpenFace 2.0 toolkit. Recurrent neural networks, long short-term memory, and gated recurrent units were utilized to predict the concentration of e-learners. A set of comparative experiments was conducted. As a result, gated recurrent units exhibited the best performance. The main contribution of this paper is to present a methodology to predict e-learners' concentration in a natural e-learning environment.

Multicommodity Prices Prediction Using Multivariate Data-Driven Modeling: Indonesia Case

One of the problems experienced by micro, small, and medium enterprises (MSMEs) during this pandemic is that most MSME actors do not understand plan-making during a crisis. This situation was exacerbated by erratic commodity prices, which resulted in several MSME players choosing to temporarily close because their turnover got a drastic decline. To help MSME actors maintain their business by knowing commodity price predictions, we propose a deep learning model using the long short-term memory (LSTM) method to predict commodity prices in Indonesia. LSTM is a type of recurrent neural network (RNN) with a memory cell to store information and solve the vanishing gradient problem in RNN. Furthermore, multivariate LSTM leverages the model to predict datasets with more than one feature. This study used a dataset collected from the Pusat Informasi Harga Pangan Strategis Nasional (PIHPS Nasional) managed by the Indonesian Ministry of Finance and Bank Indonesia consisting of significantly contributed food commodities to the formation of (strategic) inflation rates in Indonesia. The time range of commodity prices is from August 1, 2017, to July 30, 2021. There are 11 commodity price features in the dataset, namely, rice, chicken meat, eggs, onions, garlic, large red chilies, curly red chilies, red chilies, green chilies, cooking oil, and sugar. The lowest mean absolute error (MAE) on prediction is up to 255.998 obtained by the attention multivariate LSTM model with the Adam optimizer, adding batch normalization (Batchnorm) layer, reducing LSTM layer, hidden size, and grouped features. It makes the prediction more accurate and avoids overfitting and underfitting in this case. IEEE

Human Activity Recognition Using LSTM Architecture

The covid 19 pandemic has made everything virtual, including education. It is difficult to tell if students are focused or not due to online education. To help teachers, we are developing a framework for recognizing and assessing student focus. By using the concept of brain-computer communication, we can find the student's concentration level. The data obtained from the electroencephalogram (EEG) signals is used as a data set to predict concentration levels. A four-channel device is used to capture brain waves. The data were preprocessed and feature extraction was performed to determine the concentration level as active or inactive. In this method, we use a multiclass approach to develop a deep learning model that uses LSTM to classify concentration into low, or high concentration levels with accuracy of 88%. © 2022 IEEE.

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.

Advancement of management information system for discovering fraud in master card based intelligent supervised machine learning and deep learning during SARS-CoV2.

During coronavirus (SARS-CoV2) the number of fraudulent transactions is expanding at a rate of alarming (7,352,421 online transaction records). Additionally, the Master Card (MC) usage is increasing. To avoid massive losses, companies of finance must constantly improve their management information systems for discovering fraud in MC. In this paper, an approach of advancement management information system for discovering of MC fraud was developed using sequential modeling of data depend on intelligent forecasting methods such as deep Learning and intelligent supervised machine learning (ISML). The Long Short-Term Memory Network (LSTM), Logistic Regression (LR), and Random Forest (RF) were used. The dataset is separated into two parts: the training and testing data, with a ratio of 8:2. Also, the advancement of management information system has been evaluated using 10-fold cross validation depend on recall, f1-score, precision, Mean Absolute Error (MAE), Receiver Operating Curve (ROC), and Root Mean Square Error (RMSE). Finally various techniques of resampling used to forecast if a transaction of MC is genuine/fraudulent. Performance for without re-sampling, with under-sampling, and with over-sampling is measured for each Algorithm. Highest performance of without re-sampling was 0.829 for RF algorithm-F score. While for under-sampling, it was 0.871 for LSTM algorithm-RMSE. Further, for over-sampling, it was 0.921 for both RF algorithm-Precision and LSTM algorithm-F score. The results from running advancement of management information system revealed that using resampling technique with deep learning LSTM generated the best results than intelligent supervised machine learning.

Prediction model of COVID-19 based on spatiotemporal attention mechanism

The continuous spread of the COVID-19 has brought profound impacts on human society. For the prevention and control of virus spreading, it is critical to predict the future trend of epidemic situation. Existing studies on COVID-19 spread prediction, based on classic SEIR models or naive time-series prediction models, are rarely considering the characteristics of complex regional correlation and strong time series dependence in the process of epidemic spread, which limits the performance of epidemic prediction. To this end, we propose a COVID-19 prediction model based on auto-encoder and spatiotemporal attention mechanism. The proposed model estimates the trend of COVID-19 by capturing the dynamic spatiotemporal dependence between the epidemic situation sequences of different regions. In particular, a spatial attention mechanism is implemented in the encoder section for every given region to capture the dynamic correlation between the epidemic situation time-series of the region and those of the related regions. Based on the leant correlation, an long short-term memory (LSTM) network is then applied to extract the epidemic sequential features for the given region by combining the recent epidemic situations of the region and the related regions. On the other hand, to better predict the dynamic of the future epidemic situation, temporal attention is introduced into an LSTM network-based decoder to capture the temporal dependence of the epidemic situation sequence. We evaluate the proposed model on several open datasets of COVID-19, and experimental results show that the proposed model outperforms the state-of-the-art models. The metrics of RMSE and MAE of the proposed model on the COVID-19 epidemic dataset of some European countries decreased 22. 3% and 25. 0%. The metrics of RMSE and MAE of the proposed model on the COVID-19 epidemic dataset of some Chinese provinces decreased 10. 1% and 10. 4%. © 2022 Beijing University of Aeronautics and Astronautics (BUAA). All rights reserved.

Sputum deposition classification for mechanically ventilated patients using LSTM method based on airflow signals.

A novel sputum deposition classification method for mechanically ventilated patients based on the long-short-term memory network (LSTM) method was proposed in this study. A wireless ventilation airflow signals collection system was designed and used in this study. The ventilation airflow signals were collected wirelessly and used for sputum deposition classification. Two hundred sixty data groups from 15 patients in the intensive care unit were compiled and analyzed. A two-layer LSTM framework and 11 features extracted from the airflow signals were used for the model training. The cross-validations were adopted to test the classification performance. The sensitivity, specificity, precision, accuracy, F1 score, and G score were calculated. The proposed method has an accuracy of 84.7 ± 4.1% for sputum and non-sputum deposition classification. Moreover, compared with other classifiers (logistic regression, random forest, naive Bayes, support vector machine, and K-nearest neighbor), the proposed LSTM method is superior. In addition, the other advantages of using ventilation airflow signals for classification are its convenience and low complexity. Intelligent devices such as phones, laptops, or ventilators can be used for data processing and reminding medical staff to perform sputum suction. The proposed method could significantly reduce the workload of medical staff and increase the automation and efficiency of medical care, especially during the COVID-19 pandemic.

Stock Market Prediction using Artificial Intelligence

This research focuses on predicting stock closing prices for one day or the future in specific economic conditions. Today, Sri Lanka faces a financial crisis due to the COVID-19 pandemic. Therefore, lots of investors are bankrupt due to unpredictable stock prices. This work mainly focuses on predicting stock prices in banking sector shares such as Commercial Bank (COMB.N), Hatton National Bank (HNB.N), Seylan Bank (SEYB.N), and Sampath Bank (SAMP.N) on Colombo Stock Exchange (CSE) in Sri Lanka. According to the hypothesis, All Share Price Index (ASPI) and Banking Sector indices have been taken as a numerical sentiment parameter other than the historical prices from each bank. Since ASPI shows overall market performance and Banking sector indices show banking sector capitalization changed over time. There can be a positive and negative sentiment when the ASPI and Sector Indices increase and decrease, respectively. Finally, a dataset is divided into 70% for training and 30% for testing. This study has used Recurrent Neural Networks (RNNs) such as Long short-term memory (LSTM) and Gated Recurrent Unit (GRU) using 25, 50, 100, 150, and 200 epochs. LSTM model has given the lowest Mean Squared Error (MSE) and Root Mean Square Error (RMSE). According to the LSTM model, COMB.N, HNB.N, and SAMP.N were given the lowest MSE, and RMSE for 100 epochs, and SEYB.N was given the lowest MSE and RMSE value for the 150 epochs. © 2022 IEEE.

A Convolutional Neural Network Incorporated Long Short-Term Memory with Autoencoder for Covid-19 Intensity Levels Detection

The intelligent machine assisted diagnostics for the reliable and rapid identification of coronavirus disease (COVID-19) has become a most demanded approach to prevent the novel coronavirus spread during the pandemic and to relieve the strain on the healthcare system. The need for speedy diagnosis necessitates deep learning approaches for predicting the patient's health, and disease severity assessment using Lung Ultrasound (LUS) is the secure, radiation-free, adaptable, and advantageous choice in prediction and detection of novel coronavirus. The suggested model is the convolutional neural network deep layers integrated with recurrent neural networks autoencoder block used to indicate disease intensity ranges from lung ultrasound (LUS) images. The evaluation metric for the proposed model used is the fivefold cross-validation approach. Experimental results for novel proposed model depict through confusion matrix and accuracy-validation curve compared between the traditional convolutional neural network model and united training model consisting of convolutional neural network and long short-term memory (LSTM) based convex probe and linear probe evident that accuracy rate has increased in predicting the intensity levels than the former model. The memory unit incorporated in the training model enables to store, modify, update the temporal features including both of training data and testing data. Convolutional Neural Network (CNN) incorporates an autoencoder block to provide a robust, noise-free classification model in predicting intensity levels. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.