Masked Face Images Based Gender Classification using Hybrid Bat Algorithm Optimized Bagging

The face is one of the biometrics utilized to learn information from a person, such as gender. Gender classification study is expanding daily as a result of how important it is and how many other sectors, like forensics, security, business, and others, employ it. However, in order to protect themselves and stop the spread of Covid-19 during this epidemic, everyone must wear a face mask. Because many crucial facial features that help determine a person's gender are obscured by masks, using one creates an issue for the gender classification system. To obtain optimal performance outcomes, suitable hyperparameters are also required. As a result, the objective of this study is to develop a gender categorization system based on mask-covered faces utilizing a novel technique that combines several features in the Gray Level Co-occurrence Matrix (GLCM), which is then fed into the Bagging classifier.A Hybrid Bat Algorithm (HBA) is used to optimize the bagging hyperparameters. With 97% accuracy, precision, recall, and f1-score values, the suggested model is demonstrated to have greater performance than before the hyperparameters were tuned using HBA. © 2023 IEEE.

Identification and Detecting COVID-19 from X-Ray Images Using Convolutional Neural Networks

The sudden increase of COVID-19 patients is alarming, and it requires quick diagnosis in a quick time. PCR testing is one of the most used methods to test and diagnose COVID, which is time-consuming. In this paper, we present an end-to-end technique that can detect COVID-19 using chest X-ray scans. We have trained and optimized a convolutional neural network (ConvNet), which was trained on a large COVID-19 dataset. We have performed a series of experiments on a number of different architectures. We have chosen the best performing network architecture and then carried on a series of additional experiments to find the optimal set of hyper-parameters and show and justify a number of data augmentation strategies that have allowed us to enhance our performance on the test set greatly. Our final trained ConvNet has managed to obtain a test accuracy of 97.89%. This high accuracy and very fast test speed can be beneficial to get quick COVID test results for further treatment. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Robustness of Support Vector Regression and Random Forest Models: A Simulation Study

Classical statistics are usually based on parametric models, where the performance depends heavily on assumptions and is not robust in the presence of outliers in the data. Due to the COVID-19 pandemic, our daily lives have changed significantly, including slowing economic growth. These extreme changes can manifest as an outlier in time series studies and adversely affect the results of data analysis. Many classical methods of official statistics are prone to outliers. In this work, we evaluate machine learning methods: Support Vector Regression (SVR) and Random Forest (RF) and compare it with ARIMA to determine the robustness through simulation studies. Robustness is measured by the sensitivity of the SVR and Random Forest hyperparameter and the model's error in the presence of outliers. Simulations show that more outliers lead to higher RMSE values, and conversely, more samples lead to lower RMSE values. The type of outliers significantly impacts the RMSE value of the ARIMA model, where additional outliers (AO) have a worse impact than temporary change (TC). Consecutive outliers produce a smaller RMSE mean than non-consecutive outliers. Based on the sensitivity of hyperparameters, SVR and Random Forest models are relatively robust to the presence of outliers in the data. Based on the simulation results of 100 iterations, we find that SVR is more robust than ARIMA and Random Forest in modeling time series data with outliers. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

COVID 19 Prediction Using Chest X-Ray And CNN Hyperparameter Optimizer

According to mid-June 2020, the abrupt escalation of coronavirus reported widespread fear and crossed 16 million confirmed cases. To fight against this growth, clinical imaging is recommended, and for illustration, X-Ray images can be applied for opinion. This paper categorizes chest X-ray images into three classes- COVID-19 positive, normal, and pneumonia affected. We have used a CNN model for analysis, and hyperparameters are used to train and optimize the CNN layers. Swarm-based artificial intelligent algorithm - Grey Wolf Optimizer algorithm has been used for further analysis. We have tested our proposed methodology, and comparative analysis has been done with two openly accessible dataset containing COVID- 19 affected, pneumonia affected, and normal images. The optimized CNN model features delicacy, insight, values of F1 scores of 97.77, 97.74, 96.24 to 92.86, uniqueness, and perfection, which are better than models at the leading edge of technology. © 2022 IEEE.

Hyperparameter Search for CT-Scan Classification Using Hyperparameter Tuning in Pre-Trained Model CNN With MLP

The common approach to find best hyperparameter in CNN training is grid search, by observing one set to another of hyperparameter for obtaining the best result. However, this approach is considered inefficient, time-consuming, and ineffectively computational. In this study, we are observing 2 hyperparameter tuning algorithms (bayesian optimization and random search) in search of the best hyperparameter for CT-Scan classification case. The used dataset is COVID-19 and non-COVID-19 lung CT-Scans. Several CNN architectures are also used such as: InceptionV4, MobileNetV3, and EfficientnetV2 with additional multi-layer perceptron on top layers. Based on the experiments, model EfficientnetV2-L architecture using hyperparameter from bayesian-optimization can outperform other models, with batch size of 32, learning rate of 0.01, dropout 0.5, Adam optimizer and SoftMax activation, resulting in the accuracy rate of 0.94% and a model training time of 50 minutes 40 seconds. © 2022 IEEE.

Medical Images Classification Using Hybrid Deep Learning CNN-PSO-GA

Convolutional Neural Network (CNN) has made outstanding achievements in image processing and detection. The recent research uses CNN to classify the medical images, but this performance depends on its hyperparameters chosen by the programmer. Choosing these parameters is a difficult process if done manually, so there is a need to find out alternative methods. To solve this problem, the researchers hybridized a CNN with particle swarm optimization (PSO) to find better values for these hyperparameters. PSO was hybridized using genetic algorithm to solve the retired particle problem. The purpose of this research is to take advantage of the achievements of deep learning in classifying medical images. The proposed model was tested with three datasets: malaria, COVID-19, and pneumonia. The model achieved 99.5%, 100%, and 99.7% accuracy for the above datasets respectively. These results were compared with the results of the standard CNN;the proposed model surpassed the standard CNN in overall performance. © 2022 IEEE.

A Transfer Learning Approach for Diagnosis of COVID-19 Cases from Chest Radiography Images

The new coronavirus disease 2019 (COVID-19) pandemic completely changed individuals' daily lives and created economic disruption across the world. Many countries are using movement restrictions and physical distancing as their measures to slow down this transmission. Effective screening of COVID-19 cases is needed to stop the spreading of these diseases. In the first phases of clinical assessment, it was seen that patients with deformities in chest X-ray images show the signs of COVID-19 infection. Inspired from this, in this study, a novel framework is designed to detect the COVID-19 cases from chest radiography images. Here, a pre-trained deep convolutional neural network VGG-16 is used to extract discriminating features from the radiography images. These extracted features are given as an input to the Logistic regression classifier for automatic detection of COVID-19 cases. The suggested framework obtained a remarkable accuracy of 99.1% with a 100% sensitivity rate in comparison with other state-of-the-art classifier. © 2022 IEEE.

IMPLEMENTATION OF MACHINE LEARNING ALGORITHM FOR COVID19 DETECTION USING X-RAY AND VOICE DATASET

Ever since the deadly corona virus came into existence the life of the people has been shattered both in terms of health and economic crisis. Even today its various variants are creating havoc among the people. The traditional way of testing the disease is time consuming and is also not cost efficient due to the requirement of PEP kits. In this paper various Machine Learning (ML) techniques have been implemented based on the cough samples and chest x-ray images of the individuals. A hybrid model with GUI interface is designed to predict covid-19 and to perform the comparative analysis of sequential model and ResNet50 for image dataset, CNN with hyper parameter tuning and CNN for voice dataset. From the experimental analysis, ResNet50 performed better when compared to sequential model for image dataset and CNN with hyper parameter tuning performed better when compared with CNN model for voice dataset. © 2022 IEEE.

An Optimized Deep Learning Model for Automatic Diagnosis of COVID-19 Using Chest X-Ray Images

COVID-19 has caused havoc throughout the world in the last two years by infecting over 455 million people. Development of automatic diagnosis software tools for rapid screening of COVID-19 via clinical imaging such as X-ray is vital to combat this pandemic. An optimized deep learning model is designed in this paper to perform automatic diagnosis on the chest X-ray (CXR) images of patients and classify them into normal, pneumonia and COVID-19 cases. A convolutional neural network (CNN) is employed in optimized deep learning model given its excellent performances in feature extraction and classification. A particle swarm optimization with multiple chaotic initialization scheme (PSOMCIS) is also designed to fine tune the hyperparameters of CNN, ensuring the proper training of network. The proposed deep learning model, namely PSOMCIS-CNN, is evaluated using a public database consists of the CXR images with normal, pneumonia and COVID-19 cases. The proposed PSOMCIS-CNN is revealed to have promising performances for automatic diagnosis of COVID-19 cases by producing the accuracy, sensitivity, specificity, precision and F1 score values of 97.78%, 97.77%, 98.8%, 97.77% and 97.77%, respectively. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

AutoMID: A Novel Framework For Automated Computer Aided Diagnosis Of Medical Images

Machine Learning, a subtype of AI, enables computers to mimic human behavior without explicit programming. Machine learning models aren't used very often in diagnostic imaging because there isn't enough knowledge and resources to do so. Hence, this study aims to apply automated machine learning to the diagnosis of medical images to make machine learning more accessible to non-experts. In this study, a dataset containing 2313 images each of covid-19, pneumonia and normal chest x-rays were selected and divided into testing, training, and validation datasets. The AutoGluon library was used to train and produce a model that would classify an input image and infer the probable diagnosis from the diseases it was trained upon. This study can prove that applying hyperparameter optimization and neural architecture search is able to produce high accuracy models for medical image diagnosis. © 2022 Association for Computing Machinery.

Topic Modeling on Covid-19 Vaccination in Indonesia Using LDA Model

According to data from covid19.go.id, there is a lot of hoax news about Covid-19 vaccinations spread across various social media in Indonesia. Meanwhile, the ability to monitor and track misinformation and trends regarding Covid-19 and its spread is an important part of the response process by the media and government to dealing with fake news about Covid-19. Twitter is a social media that is actively used in spreading issues. Twitter users in Indonesia reached 18.45 million users as of January 2022. To find out useful information on Twitter social media comments regarding the Covid-19 Vaccination, a method, namely Topic Modeling, can be used. This study aims to obtain the distribution of the Covid-19 Vaccination topic on Twitter Data in Indonesia to assist the government in knowing the trend of topics related to Covid-19 vaccination and the trend of changing the topic. The dataset on Twitter used is 10,140 pieces about Covid-19 vaccinations in Indonesia in the period August 2021 to April 2022. Based on the Latent Dirichlet Association (LDA), the 5 most popular topics were obtained for each model, and spread in the fields of health, religion, society. Based on the alpha and beta hyperparameter tuning, it was found that the topic with K=5, alpha=asymmetric, and beta=0.61 was a relevant LDA topic model for the research dataset because good in topic diversity and have coherence value=0.590. © 2022 IEEE.

Facial Mask Wearing Condition Detection Using SSD MobileNetV2

Wearing a facemask is one of the main ways to prevent the spread of respiratory diseases such as Covid-19, so it is helpful to monitor people's facemask-wearing status through vision-based systems. In this paper, a system has been developed that divides people's face mask-wearing conditions using image processing into three classes: without a facemask, correct facemask-wearing, and incorrect facemask-wearing. For this purpose, the SSD-MobileNetV2 neural network has been used, and several hyperparameter sets have been compared for the best possible accuracy. A lightweight custom convolutional neural networks (CNN) has also been used as the second stage to improve the classification accuracy, so this stage can be used in cases where higher accuracy is required. The proposed neural network was implemented on a Raspberry-Pi3, and this system can control an entrance gate using a servo motor automatically. © 2022 IEEE.

COVID-19 Mortality Prediction using Machine Learning: A Deep Forest Approach

COVID-19 is an extremely deadly disease which has wreaked havoc worldwide. Initially, the first case was reported in the wet markets of Wuhan, China in the early 2020's. Though the mortality rate is low compared to other dangerous diseases, a lot of people have already succumbed to this virus. Vaccines have been successfully rolled out and it seems effective in preventing the severe symptoms of the coronavirus. However, a section of people (the elderly and people with existing comorbidities) still continue to die. It is extremely important to predict the patient vulnerability using machine learning since appropriate medicines and treatments can be given in time and precious lives can be saved. In this research, the deep forest classifier is utilized to predict the COVID-19 casualty status. This classifier requires extremely low hyperparameter tuning and can easily compete with the deep learning classifiers. This algorithm performed better than the traditional machine learning classifiers with an accuracy of 92%. The positive results obtained signifies the potential use of deep forest to prevent unwanted COVID-19 deaths by effectively deploying them in various medical facilities. Further, it can reduce the extreme burden already existing on healthcare systems caused by the novel coronavirus. © 2022 IEEE.

An effective approach to address processing time and computational complexity employing modified CCT for lung disease classification

Early identification and adequate treatment can help prevent lung disorders from becoming chronic, severe, and life-threatening. X-ray images are commonly used and an automated and effective method involving deep learning techniques can potentially contribute to quick and accurate diagnosis of lung disorders. However, in the study of medical imaging using deep learning, two obstacles limit interpretability. One is an insufficient and imbalanced number of training samples in most medical datasets. The other is excessive training time. Although training time can be reduced by decreasing the number of pixels in the images, training with low resolution images tends to result in poor performance. This study represents a solution to overcome these impediments by balancing the number of images and reducing overall processing time while preserving accuracy. The dataset used in this research contains an unequal number of images in the different classes. The quantity of data in the classes is balanced by creating synthetic images based on the patterns and characteristics of the original images, using a Deep Convolutional Generative Adversarial Network (DCGAN). Unwanted regions are removed from the X-ray images, the brightness and contrast of the images are enhanced, and the abnormalities are highlighted by using different artifact removal, noise reduction, and enhancement techniques. We propose a Modified Compact Convolutional Transformer (MCCT) model using 32 × 32 sized images for the categorization of lung disorders into four classes. An ablation study of eleven cases is employed to adjust several hyper parameters and layer topologies. This reduces training time while preserving accuracy. Six transfer learning models, VGG19, VGG16, ResNet152, ResNet50, ResNet50V2, and MobileNet are applied with the same image size the performance is compared with the proposed MCCT model. Our MCCT model records the greatest test accuracy of 95.37%, requiring a short training time, 10-12 s/epoch, whereas the other models only reach near-moderate performance with accuracies ranging from 43% to 79% and training times of 80-90 s/epoch. The robustness of the model with regards to the number of training samples is validated by training the model multiple times reducing the number of training images gradually from 49621 images to 6204 images. Results suggest that even with a smaller dataset, the performance is sustained. Our proposed approach may contribute to an effective CAD based diagnostic system by addressing the issues of insufficient and imbalanced numbers of medical images, excessive training times and low-resolution images. © 2022

Light Gradient Boosting with Hyper Parameter Tuning Optimization for COVID-19 Prediction

The 2019 coronavirus disease (COVID-19) caused pandemic and a huge number of deaths in the world. COVID-19 screening is needed to identify suspected positive COVID-19 or not and it can reduce the spread of COVID-19. The polymerase chain reaction (PCR) test for COVID-19 is a test that analyzes the respiratory specimen. The blood test also can be used to show people who have been infected with SARS-CoV-2. In addition, age parameters also contribute to the susceptibility of COVID-19 transmission. This paper presents the extra trees classification with random over-sampling by considering blood and age parameters for COVID-19 screening. This research proposes enhanced preprocessing data by using KNN Imputer to handle large missing values. The experiments evaluated the existing classification methods such as Random Forest, Extra Trees, Ada Boost, Gradient Boosting, and the proposed Light Gradient Boosting with hyperparameter tuning to measure the predictions of patients infected with SARS-CoV-2. The experiments used sample data from 559 patients with infected SARS-CoV-2. The experimental results show that the proposed scheme achieves an accuracy of about 98,58%, recall of 98,58%, the precision of 98,61%, F1-Score of 98,61%, and AUC of 0,9682.

Finding the needle in a haystack: Extraction of Informative COVID-19 Danish Tweets

Finding informative COVID-19 posts in a stream of tweets is very useful to monitor health-related updates. Prior work focused on a balanced data setup and on English, but informative tweets are rare, and English is only one of the many languages spoken in the world. In this work, we introduce a new dataset of 5,000 tweets for finding informative COVID-19 tweets for Danish. In contrast to prior work, which balances the label distribution, we model the problem by keeping its natural distribution. We examine how well a simple probabilistic model and a convolutional neural network (CNN) perform on this task. We find a weighted CNN to work well but it is sensitive to embedding and hyperparameter choices. We hope the contributed dataset is a starting point for further work in this direction. © 2021 Association for Computational Linguistics.

Hyper-parameter Tuning for Long Short-Term Memory (LSTM) Algorithm to Forecast a Disease Spreading

Artificial intelligence (AI), machine learning (ML), and deep learning (DL) are just a few of the technologies that can help healthcare organizations support real-time decision-making to control the spread of a pandemic. This study aims to investigate hyper-parameter tuning for Long Short-Term Memory (LSTM) to forecast SARSCoV2 daily infection cases in the Russian Federation by selecting the best loss function, activation function, number of epochs, number of neurons in a cell, and optimizer to minimize the error in addition to a good fit for the model on both the training and validation sets. In the meanwhile, we use LSTM, LSTMs (stacked LSTM), bidirectional LSTM, BILSTMs (stacked BILSTM), convolution neuron networks (Conv), ConvLSTMs and other forecasting models to forecast the daily SARSCoV2 infection cases one by one. We analyzed and compared the results obtained by these models. We discovered that BiLSTM can efficiently extract features from the data, which are the items from the previous day. With the extracted feature data, we used this model to forecast daily infection cases. We used BiLSTM to forecast SARSCoV2 spreading in the Russian Federation for one month. Hence, the Bidirectional LSTM can accurately predict daily SARSCoV2 infection cases in Russia, according to experiments. © 2022 IEEE.

Machine learning modeling techniques and statistical projections to predict the outbreak of COVID-19 with implication to India

The World Health Organization confirmed coronavirus as global pandemic on March 11, 2020. The first wave started during March–April 2020, followed by second wave during September–November 2020 and third wave during January–February 2021 in many parts of the world. In spite of vaccinations and herd immunity, the new mutating virus is continuously inducing new spikes and asymptotic death rates in several countries. Various prediction models are used to predict the outcomes of pandemic. Machine learning regression models such as Least Absolute Shrinkage and Selection Operator (Lasso), Linear Regression, Ridge, Elastic-Net, Random Forest, Adaptive Boosting (AdaBoost), Light Gradient Boosted Machine (LGBM), and Extreme Gradient Boosting (XGBoost) are considered to predict and study the exponential increase of mortality rate, number of confirmed cases, and recovery rate. Also, Facebook Prophet Model is used to predict the outbreak of COVID-19. To build these models, COVID-19 real-time dataset is extracted from Johns Hopkins University that considers the number of confirmed cases, total deaths, and number of recovered cases. Information such as country/region, confirmed cases, province/state, recovered cases, death rate, and last update is considered to make predictions. These models were trained, tested, and compared for their performances based on the parameters R-squared value, R-squared modified score, Mean Squared deviation and Root Mean Square Error. The results are tabulated to observe the best model for pandemic outbreak prediction. Based on the results of these models, the concerned officials can infer the necessary measure that has to be taken to control the outbreak of COVID-19 pandemic. © 2022 Elsevier Inc. All rights reserved.

Neural Architecture and Feature Search for Predicting the Ridership of Public Transportation Routes

Accurately predicting the ridership of public-transit routes provides substantial benefits to both transit agencies, who can dispatch additional vehicles proactively before the vehicles that serve a route become crowded, and to passengers, who can avoid crowded vehicles based on publicly available predictions. The spread of the coronavirus disease has further elevated the importance of ridership prediction as crowded vehicles now present not only an inconvenience but also a public-health risk. At the same time, accurately predicting ridership has become more challenging due to evolving ridership patterns, which may make all data except for the most recent records stale. One promising approach for improving prediction accuracy is to fine-tune the hyper-parameters of machine-learning models for each transit route based on the characteristics of the particular route, such as the number of records. However, manually designing a machine-learning model for each route is a labor-intensive process, which may require experts to spend a significant amount of their valuable time. To help experts with designing machine-learning models, we propose a neural-architecture and feature search approach, which optimizes the architecture and features of a deep neural network for predicting the ridership of a public-transit route. Our approach is based on a randomized local hyper-parameter search, which minimizes both prediction error as well as the complexity of the model. We evaluate our approach on real-world ridership data provided by the public transit agency of Chattanooga, TN, and we demonstrate that training neural networks whose architectures and features are optimized for each route provides significantly better performance than training neural networks whose architectures and features are generic. © 2022 IEEE.

A novel transfer deep learning model with reinforcement-learning-based hyperparameter optimization for short-term load forecasting during the COVID-19 pandemic

The coronavirus disease 2019 pandemic has significantly impacted the world. The sudden decline in electricity load demand caused by strict social distancing restrictions has made it difficult for traditional models to forecast the load demand during the pandemic. Therefore, in this study, a novel transfer deep learning model with reinforcement-learning-based hyperparameter optimization is proposed for short-term load forecasting during the pandemic. First, a knowledge base containing mobility data is constructed, which can reflect the changes in visitor volume in different regions and buildings based on mobile services. Therefore, the sudden decline in load can be analyzed according to the socioeconomic behavior changes during the pandemic. Furthermore, a new transfer deep learning model is proposed to address the problem of limited mobility data associated with the pandemic. Moreover, reinforcement learning is employed to optimize the hyperparameters of the proposed model automatically, which avoids the manual adjustment of the hyperparameters, thereby maximizing the forecasting accuracy. To enhance the hyperparameter optimization efficiency of the reinforcement-learning agents, a new advance forecasting method is proposed to forecast the state-action values of the state space that have not been traversed. The experimental results on 12 real-world datasets covering different countries and cities demonstrate that the proposed model achieves high forecasting accuracy during the coronavirus disease 2019 pandemic. © 2022 - IOS Press. All rights reserved.