Explainable artificial intelligence approach towards classifying educational android app reviews using deep learning

Mobile application developers rely largely on user reviews for identifying issues in mobile applications and meeting the users' expectations. User reviews are unstructured, unorganized and very informal. Identifying and classifying issues by extracting required information from reviews is difficult due to a large number of reviews. To automate the process of classifying reviews many researchers have adopted machine learning approaches. Keeping in view, the rising demand for educational applications, especially during COVID-19, this research aims to automate Android application education reviews' classification and sentiment analysis using natural language processing and machine learning techniques. A baseline corpus comprising 13,000 records has been built by collecting reviews of more than 20 educational applications. The reviews were then manually labelled with respect to sentiment and issue types mentioned in each review. User reviews are classified into eight categories and various machine learning algorithms are applied to classify users' sentiments and issues of applications. The results demonstrate that our proposed framework achieved an accuracy of 97% for sentiment identification and an accuracy of 94% in classifying the most significant issues. Moreover, the interpretability of the model is verified by using the explainable artificial intelligence technique of local interpretable model-agnostic explanations. (PsycInfo Database Record (c) 2023 APA, all rights reserved)

Prediction of Covid-19 transmission in India using deep learning neural network

Time series forecasting is a decisive step in data modeling and a significant area in machine learning. This paper presents Long short-term memory (LSTM) network, a deep learning neural network for predicting Covid-19 cases in India. The neural network models are trained and tested with Covid-19 case data sets obtained from PRS Legislative Research database. Further, the parameter optimization is carried out for choosing the optimal network. The parameters considered for evaluating the performance of LSTM network are RMSE, number of epochs, accuracy and loss. The results are compared with various recurrent neural network models and autoregressive model. The results revealed an improved accuracy of 92.8% for LSTM network in predicting the transmission of Covid-19 in India. © 2023 Author(s).

Detection of COVID-19 patient based on attention segmental recurrent neural network (ASRNN) Archimedes optimization algorithm using ultra-low-dose CT images

In this article, the detection of COVID-19 patient based on attention segmental recurrent neural network (ASRNN) with Archimedes optimization algorithm (AOA) using ultra-low-dose CT (ULDCT) images is proposed. Here, the ultra-low-dose CT images are gathered via real time dataset. The input images are preprocessed with the help of convolutional auto-encoder to recover the ULDCT images quality by removing noises. The preprocessed images are given to generalized additive models with structured interactions (GAMI) for extracting the radiomic features. The radiomic features, such as morphologic, gray scale statistic, Haralick texture are extracted using GAMI-Net. The ASRNN classifier, whose weight parameters optimized with Archimedes optimization algorithm enables COVID-19 ULDCT images classification as COVID-19 or normal. The proposed approach is activated in MATLAB platform. The proposed ASRNN-AOA-ULDCT attains accuracy 22.08%, 24.03%, 34.76%, 34.65%, 26.89%, 45.86%, and 32.14%;precision 23.34%, 26.45%, 34.98%, 27.06%, 35.87%, 34.44%, and 22.36% better than the existing methods, such as DenseNet-HHO-ULDCT, ELM-DNN-ULDCT, EDL-ULDCT, ResNet 50-ULDCT, SDL-ULDCT, CNN-ULDCT, and DRNN-ULDCT, respectively. © 2023 John Wiley & Sons, Ltd.

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.

Assessment of humoral and cellular immunity after bivalent BNT162b2 vaccination and potential association with reactogenicity

Nowadays, new challenges around increasing production quality and productivity, and decreasing energy consumption, are growing in the manufacturing industry. In order to tackle these challenges, it is of vital importance to monitor the health of critical components. In the machine tool sector, one of the main aspects is to monitor the wear of the cutting tools, as it affects directly to the fulfillment of tolerances, production of scrap, energy consumption, etc. Besides, the prediction of the remaining useful life (RUL) of the cutting tools, which is related to their wear level, is gaining more importance in the field of predictive maintenance, being that prediction is a crucial point for an improvement of the quality of the cutting process. Unlike monitoring the current health of the cutting tools in real time, as tool wear diagnosis does, RUL prediction allows to know when the tool will end its useful life. This is a key factor since it allows optimizing the planning of maintenance strategies. Moreover, a substantial number of signals can be captured from machine tools, but not all of them perform as optimum predictors for tool RUL. Thus, this paper focuses on RUL and has two main objectives. First, to evaluate the optimum signals for RUL prediction, a substantial number of them were captured in a turning process and investigated by using recursive feature elimination (RFE). Second, the use of bidirectional recurrent neural networks (BRNN) as regressive models to predict the RUL of cutting tools in machining operations using the investigated optimum signals is investigated. The results are compared to traditional machine learning (ML) models and convolutional neural networks (CNN). The results show that among all the signals captured, the root mean squared (RMS) parameter of the forward force ( F-y ) is the optimum for RUL prediction. As well, the bidirectional long-short term memory (BiLSTM) and bidirectional gated recurrent units (BiGRU), which are two types of BRNN, along with the RMS of F-y signal, achieved the lowest root mean squared error (RMSE) for tool RUL, being also computationally the most demanding ones.

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.

Sentiment Analysis and Topic Classification of Twitter-based Data Set on the Face-to-face Classes Resumption in the Philippines during the COVID-19 Pandemic

Due to the Corona Virus Disease 2019 (COVID-19) pandemic, there was a need for shift in pedagogy of education. Several delivery modes for educational materials and activities had to be implemented to adapt in the situation brought about by the pandemic. In the Philippines, there has been a call to fully transition to face-to-face classes expressed on social media. In this study, a data set was built consisting of tweets (Twitter data) regarding the resumption of face-to-face classes in the Philippines. This data set was subjected to training and testing to classify them in terms of topic and sentiment using Recurrent Neural Network Long Short-Term Memory (LSTM) and Multinomial Naïve Bayes. The LSTM sentiment classifier resulted to 78.33% accuracy and LSTM topic classifier produced 61.34% accuracy. The Multinomial Naïve Bayes classifier obtained 77.22% accuracy for classifying sentiment while 58.33% accuracy for topic classification. © 2022 IEEE.

A Hybrid Deep Neural Network Architecture for Day-Ahead Electricity Forecasting: Post-COVID Paradigm

Predicting energy demand in adverse scenarios, such as the COVID-19 pandemic, is critical to ensure the supply of electricity and the operation of essential services in metropolitan regions. In this paper, we propose a deep learning model to predict the demand for the next day using the "IEEE DataPort Competition Day-Ahead Electricity Demand Forecasting: Post-COVID Paradigm” database. The best model uses hybrid deep neural network architecture (convolutional network–recurrent network) to extract spatial-temporal features from the input data. A preliminary analysis of the input data was performed, excluding anomalous variables. A sliding window was applied for importing the data into the network input. The input data was normalized, using a higher weight for the demand variable. The proposed model's performance was better than the models that stood out in the competition, with a mean absolute error of 2361.84 kW. The high similarity between the actual demand curve and the predicted demand curve evidences the efficiency of the application of deep networks compared with the classical methods applied by other authors. In the pandemic scenario, the applied technique proved to be the best strategy to predict demand for the next day.

Learning the Protein Language Model of SARS-CoV-2 Spike Proteins

Ahstract-SARS-CoV-2 virus has long been evolving posing an increased risk in terms of infectivity and transmissibility which causes greater impact in communities worldwide. With the surge of collected SARS-CoV-2 sequences, studies found out that most of the emerging variants are linked to increased mutations in the spike (S) protein as observed in Alpha, Beta, Gamma, and Delta variants. Multiple approaches on genomic surveillance have been performed to monitor the mutational status and spread of the virus however most are heavily dependent on labels attributed to these sequences. Hence, this study features a system that has the capability to learn the protein language model of SARS-CoV-2 spike proteins, based on a bidirectional long-short term memory (BiLSTM) recurrent neural network, using sequence data alone. Upon obtaining the sequence embedding from the model, observed clusters are generated using the Leiden clustering algorithm and is visualized to monitor similarities between variants in terms of grammatical probability and semantic change. Additionally, the system measures the validity of a user-generated next-generation sequence capturing potential sequence mutations indicative of viral escape, particularly mutations by substitutions. Further studies on methods uncovering semantic rules that govern spike proteins are recommended to learn more about other viral characteristics conclusive of the future of the COVID-19 pandemic. © 2023 IEEE.

Gated Memory Unit: A Novel Recurrent Neural Network Architecture for Sequential Analysis

The predominant models used to analyze sequential data today are recurrent neural networks, specifically Long-Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) models, which utilize a temporal value known as the hidden state. These recurrent neural networks process sequential data by storing and modifying a hidden state through the use of mathematical functions known as gates. However, these networks hold many flaws such as limited temporal vision, insufficient memory capacity, and ineffective training times. In response, we propose a simple architecture, the Gated Memory Unit, which utilizes a new element, the hidden stack, a data stack implementation of the hidden state, as well as novel gates. This, along with a parameterized bounded activation function (PBA), allows the Gated Memory Unit (GMU) to outperform existing recurrent models effectively and efficiently. Trials on three datasets were used to display the new architecture's superior performance and reduced training time as well as the utility of the novel hidden stack compared to existing recurrent networks. On data which measures the daily death rate of SARS-Cov-2, the GMU was able to reduce losses to half that of comparable models and did so in nearly half the training time. Additionally, through the use of a generated spiking dataset, the GMU depicted its ability to use its hidden stack to store information past directly observable time steps. We prove that the Gated Memory Unit performs well on a variety of tasks and can outperform existing recurrent architectures. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Outbreak prediction of COVID-19 using recurrent neural network with gated recurrent units.

Respiratory infections corona virus 2-caused inflammatory disorders are CORONAVIRUS DISEASE 2019 (COVID-19) (SARS-CoV-2). A serious corona virus acute disease arose in 2019. Wuhan, China, was the first location to find the virus in December 2019, which has now been spreading all over the world. Recurrent neural networks, together with the use of LSTMs, fail to provide solutions to numerous issues (RNNs). So this paper has proposed RNN with Gated Recurrent Units for the COVID-19 prediction. This paper utilizes system, which was developed to assist nations (the Czech Republic, the United States, India, and Russia) combat the early stages of a newly emerging infection. For instance, the system tracks confirmed and reported cases, and monitors cures and deaths on a daily basis. This was done to allow the relevant parties to have an early grasp of the disastrous damage the lethal virus will bring. The implemented is an ensemble approach of RNN and GRU that work has computed the RMSE value for the different cases such as infected, cure and death across the four different countries.

Design of ear-contactless stethoscope and improvement in the performance of deep learning based on CNN to classify the heart sound.

Cardiac-related disorders are rapidly growing throughout the world. Accurate classification of cardiovascular diseases is an important research topic in healthcare. During COVID-19, auscultating heart sounds was challenging as health workers and doctors wear protective clothing, and direct contact with patients can spread the outbreak. Thus, contactless auscultation of heart sound is necessary. In this paper, a low-cost ear contactless stethoscope is designed where auscultation is done with the help of a bluetooth-enabled micro speaker instead of an earpiece. The PCG recordings are further compared with other standard electronic stethoscopes like Littman 3 M. This work is made to improve the performance of deep learning-based classifiers like recurrent neural networks (RNN) and convolutional neural networks (CNN) for different valvular heart problems using tuning of hyperparameters like learning rate of optimizers, dropout rate, and hidden layer. Hyper-parameter tuning is used to optimize the performances of various deep learning models and their learning curves for real-time analysis. The acoustic, time, and frequency domain features are used in this research. The investigation is made on the heart sounds of normal and diseased patients available from the standard data repository to train the software models. The proposed CNN-based inception network model achieved an accuracy of 99.65 ± 0.06% on the test dataset with a sensitivity of 98.8 ± 0.05% and specificity of 98.2 ± 0.19%. The proposed hybrid CNN-RNN architecture attained 91.17 ± 0.03% accuracy on test data after hyperparameter optimization, whereas the LSTM-based RNN model achieved 82.32 ± 0.11% accuracy. Finally, the evaluated results were compared with machine learning algorithms, and the improved CNN-based Inception Net model is the most effective among others.

Advance IoT Intelligent Healthcare System for Lung Disease Classification Using Ensemble Techniques

In healthcare systems, the Internet of Things (IoT) innovation and development approached new ways to evaluate patient data. A cloud-based platform tends to process data generated by IoT medical devices instead of high storage, and computational hardware. In this paper, an intelligent healthcare system has been proposed for the prediction and severity analysis of lung disease from chest computer tomography (CT) images of patients with pneumonia, Covid-19, tuberculosis (TB), and cancer. Firstly, the CT images are captured and transmitted to the fog node through IoT devices. In the fog node, the image gets modified into a convenient and efficient format for further processing. advanced encryption Standard (AES) algorithm serves a substantial role in IoT and fog nodes for preventing data from being accessed by other operating systems. Finally, the preprocessed image can be classified automatically in the cloud by using various transfer and ensemble learning models. Herein different pre-trained deep learning architectures (Inception-ResNet-v2, VGG-19, ResNet-50) used transfer learning is adopted for feature extraction. The softmax of heterogeneous base classifiers assists to make individual predictions. As a meta-classifier, the ensemble approach is employed to obtain final optimal results. Disease predicted image is consigned to the recurrent neural network with long short-term memory (RNN-LSTM) for severity analysis, and the patient is directed to seek therapy based on the outcome. The proposed method achieved 98.6% accuracy, 0.978 precision, 0.982 recalls, and 0.974 F1-score on five class classifications. The experimental findings reveal that the proposed framework assists medical experts with lung disease screening and provides a valuable second perspective. © 2023 CRL Publishing. All rights reserved.

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

An Early Detection Of NIPAH Infectious Disease Based On Integrated Medical Features For Human Using Ensemble RBM Techniques

Background: The importance of early diagnosis of a hazardous illness cannot be overstated. The transmission rate is extremely high, especially in the current pandemic condition. The ability to predict epidemics will aid public health in reducing mortality and morbidity. Machine Learning (ML) approaches are used in the construction of an effective disease prognosis model. Furthermore, only if the model learns good associated features from the data is it possible to generate a speedy outcome. As a result, selecting features is also necessary before beginning the forecasting process. Objective(s): However, because of the virus's dynamic structure, it's difficult to predict Nipah disease and/or zoonotic infection. Furthermore, there is no clinical treatment for Nipah. The major goal of this research is to develop a prognostic model for early diagnosis of Nipah disease using a combination of several clinical factors such as symptoms, disease incubation information, and routine blood test results confirmed by a lab technician.Proposed System: The healthcare application and data are more complex to handle than other ML applications since various clinical features are assessed throughout disease manifestation. As a result, selecting the most relevant variables is critical when designing a prognosis model for any viral disease. To deal with clinical features from a vast number of features, we proposed a Restricted Boltzmann Machine (RBM) method in this research. Additionally, we employed a hybrid ensemble learning method to predict if the patient was infected with NiV after choosing features using the RBM. Data Collection: The proposed system is being implemented using the NiV infection dataset that erupted in Kozhikode, Kerala in 2018 and 2019. Result(s): The developed stacking-based ensemble Meta classifier was successfully implemented using the python programming language, and its performance was evaluated using a variety of metrics includingaccuracy, precision, recall, f1-score, log loss, AUROC and MCC. Our proposed Stacking Ensemble Meta Classifier (SEMC) model achieved an accuracy rate of 88.3% with a log loss of 0.36. Model precision, recall, f1-score, AUROC, and MCC value were 92.5%, 89.2%, 90.9%, 92.1%, and 0.74 respectively. In addition, we calculated the gravitational pull of each feature using the SHAP approach and discovered that altered sensorium, fever, headache, and cough were the most critical clinical indicators that distinguished NiVD infection from our dataset. Therefore, this classification may assist the pathologist in diagnosing NiVD with symptoms before performing the RT-PCR medical test. Conclusion(s): Using our proposed SEMC technique, we developed a prognostic model for the diagnosis of Nipah in humans. The proposed technique's discriminatory efficiency exhibited good NiVD diagnosis efficacy. We anticipate that this model will aid medics in determining a prognosis more quickly during future epidemics. However, to achieve maximum accuracy, the model requires more unique samples.Copyright © 2022 Wolters Kluwer Medknow Publications. All rights reserved.

A Real Time Face Mask Detection and Health Status Monitoring using Deep Learning after Pandemic

Over the past two years, COVID-19 has spread over 200 countries, leading to pandemic proportions. Still, the virus is rapidly changing, the variants spreading through direct and indirect contact in places irrespective of the vaccine distribution. Many infectious diseases spread through droplets, and micro-droplets require face masks and other social distancing measures. This article proposed for pandemic circumstances called real-time face mask with health screening (RTFMHS) with two screening methods: (i) Internet of Things (IoT) node monitors a real-time intelligent face mask detection using multi-level high-speed augmented convolution neural network (MLHS-CNN), (ii) Individuals health indicators such as non-contact body temper-ature sensor and blood oxygen saturation are sending to fog cloud to manipulate and display the user health status. A face mask detection technique based on MLHS-CNN is proposed to determine whether a person is wearing a mask properly. User health is computed in real-time by an RNN running on the fog server to estimate the risk of infection spreading. The proposed method uses a lightweight IoT node and fog-based Deep Learning (DL) tools for data analysis and diagnosis. The experimental results outperformed previous research in terms of detection accuracy, precision, recall, and time complexity. © 2022 IEEE.

Engagement Estimation using Time-series Facial and Body Features in an Unstable Dataset

The global education sector has been deeply shaken by COVID-19 and forced to shift to an online teaching model. However, the lack of face-to-face communication and interaction in online learning is critical to high-quality teaching and learning. Research on engagement is a crucial part of solving this problem. Because engagement is of time-series data with an ongoing change, research datasets used for engagement analysis need a certain preprocessing method to capture time-series related engagement features. This research proposed a novel deep learning preprocessing method for improving engagement estimation using time-series facial and body information to restore traditional scenes in online learning environments. Such information includes head pose, mouth shape, eye movement, and body distance from the screen. We conducted a preliminary experiment on the DAiSEE dataset for engagement estimation. We applied skipped moving average in data preprocessing to reduce the influence of the extracted noises and oversampled the low engagement level data to balance the engaged/unengaged data. Since engagement is continuous and cannot be captured at a particular instant in time or single images, temporal video classification generally performs better than static classifiers. Therefore, we adopted long short-term memory (LSTM) and Quasi-recurrent neural networks (QRNNs)sequence models to train models and achieved the correct rate of 55.7% (LSTM) and 51.1% (QRNN) using the original key points extracted from OpenPose. Finally, we proposed the optimization structure network achieved the engagement estimation correct rate of 68.5% in proposed LSTM models and 64.2% in QRNN models. The achieved correct rate is 10% higher than the baseline in the DAiSEE dataset. © 30th International Conference on Computers in Education Conference, ICCE 2022 - Proceedings.

Forecasting of Novel Corona Cases in India Using LSTM-Based Recurrent Neural Networks

Novel corona disease is spreading all over the world. The cases of the corona virus are increasing drastically day by day. Therefore, it is necessary to predict the cases in advance to handle the condition. Recently, machine learning comes into the picture of researchers to solve the problem in engineering. The present study is focused to the application of LSTM recurrent neural network to predict the Novel corona cases on the daily basis in India. Various RNN models are used in this study, and performance evaluation of each model is carried out using different statistical parameters such as mean absolute error (MAE), mean absolute percentage error (MAPE), route mean square error (RMSE), and coefficient of determination (r2-score) for regression problems. From the study, it is concluded that the LSTM RNN model can be utilized for the prediction of the novel corona cases. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.