A Novel Approach for Identification of Tomato Flu using Deep Learning

The study proposes a novel deep learning-based model for early and accurate detection of the Tomato Flu virus, also known as tomato fever, which has recently emerged in children under the age of five in the Indian state of Kerala. The model utilizes a deep learning method to classify skin pictures and check whether a person is suffering from the virus or not, with an accuracy of 100% and a validation loss of 0.2463. Additionally, an API is developed for easy integration into various web/app frameworks. The authors highlight the importance of careful management of rare viral diseases, especially in the context of the ongoing COVID-19 pandemic. © 2023 Bharati Vidyapeeth, New Delhi.

Coupling Machine Learning and Agent-Based Modeling to Characterize Contamination Sources in Water Distribution Systems for Changing Demand Regimes

Water distribution systems (WDSs) deliver clean, safe drinking water to consumers, providing an essential service to constituents. WDSs are increasingly at risk of contamination due to aging infrastructure and intentional acts that are possible through cyber-physical vulnerabilities. Identifying the source of a contamination event is challenging due to limited system-wide water quality monitoring and non-uniqueness present in solving inverse problems to identify source characteristics. In addition, changes in the expected demand patterns that are caused by, for example, social distancing during a pandemic, adoption of water conservation behaviors, or use of decentralized water sources can change the anticipated propagation of contaminant plumes in a network. This research develops a computational framework to characterize contamination sources using machine learning (ML) techniques and simulate water demands and human exposure to a contaminant using agent-based modeling (ABM). An ABM framework is developed to simulate demand changes during the COVID-19 pandemic. The ABM simulates population movement dynamics, transmission of COVID-19 within a community, decisions to social distance, and changes in demands that occur due to social distancing decisions. The ABM is coupled with a hydraulic simulation model, which calculates flows in the network to simulate the movement of a contaminant plume in the network for several contamination event scenarios. ML algorithms are applied to determine the location of source nodes. Research results demonstrate that ML using random forests can identify source nodes based on inline and mobile sensor data. Sensitivity analysis is conducted to explore the number of mobile sensors that are needed to accurately identify the source node. Rapidly identifying contamination source nodes can increase the speed of response to a contamination event, reducing the impact to the community and increasing the resiliency of WDSs during periods of changing demands. © World Environmental and Water Resources Congress 2023.All rights reserved

Deep Learning-and Transfer Learning-based Models for COVID-19 Detection using Radiography Images

Image classification and segmentation techniques are still very popular in the medical field (for healthcare), in which the medical image plays an important role in the detection and screening of diseases. Recently, the spread of new viral diseases, namely Covid-19, requires powerful computer models and rich resources (datasets) to fight this phenomenon. In this study, we propose to examine the CNN Deep Learning algorithm and two Transfer Learning models, namely RestNet50 and MobileNetV2 using the pretrained model of the ImageNet database, experimented on the new dataset (COVID-QU-Ex Dataset 2022) offered by the University of Qatar. These models are tested to classify radiography images into two classes (Covid19 and Normal). The results achieved by CNN (Acc =95.97%), ResNet50 (Acc =95.53%) and MobileNetV2 (Acc =97.32%) show that these algorithms are promising in order to combat this Covid-19 disease by detecting it through thoracic images (Chest X-ray type). © 2023 IEEE.

Masked Facial Recognition Using Deep Metric Learning

Due to the Covid-19 disease, masked faces identification has become the current challenge. This type of identification is difficult because masks cover noses and mouths, obscuring important features for facial recognition. A deep learning-based model for recognizing masked faces is presented in this paper. We tested our system on a dataset of 2113 images collected from 179 people with and without masks. The obtained results are analysed using various metrics and appear to be motivating. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

COVID-19 Detection by Medical Images with Pretrained Transfer Learning-based Model using CNN: A Systematic Review

A new coronavirus that caused the Covid-19 sickness, has already elevated the threat to humans. The virus is quickly spreading around the planet. Therefore, in order to detect sick individuals and stop the infection from spreading, it is vital that we develop fast diagnostic tests. The advancement of machine learning would make it possible to implement pre- ventative actions as soon as possible by enabling early detection of Covid19. However, insufficient sample sizes, particularly chestX-ray pictures, has made it more challenging to diagnose this ailment. In this study, we examined a number of these recently created transfer learning-based CNN models that can identify COVID-19 in lung CT or images of X-ray to diagnose Covid-19 using images of X-ray. We gathered data on the research resources that are readily available. We looked into and examined datasets, pre-processing methods, segmentation approaches, extraction of features, classification, and experimentation outcomes that could be useful for determining future research paths in the area of applying transfer learning based CNN models to diagnose COVID-19 disease. We have analyzed various models such as ResNet50, DenseNet-21, VGG-16, ImageNet, and some hybrid models and evaluated their performance matrix with a particular set of data used in their research work. Additionally, in orderfor a model to perform at its best, it is observed that there aren't enough data sets of COVID-19-infected individuals. This calls for augmentation, segmentation, and domain adaptation in transfer learning. © 2022 IEEE.

A prognostic model for classification of COVID-19 severity based on clinical and laboratory testing data

This study aimed to build an machine learning based model to predict the COVID-19 severity and reveal risk factors related to COVID-19 severity based on laboratory testing and clinical data for 420 participants, using tree-based models such as XGBoost, LightGBM, random forest. We calculated the Odds Ratios (OR) to investigate whether the top-ranked features were statistically significant for severity classification, turning out that high sensitivity C-reactive protein (hs-CRP) was the most important feature for determining of COVID-19 severity and XGBoost model showed the highest performance in classifying COVID-19 severity and healthy controls with F1score (0.84) and AUC (0.87). We expect that our results are of considerable significance for early screening for diagnosing COVID-19 severity, which, in turn, assist in further retrospective research for uncommon infectious diseases. © 2023 IEEE.

A Model to Detect Face Mask Using Deep Learning

Coronavirus pandemic started spreading in 2019 and is still spreading until now in 2021 all over the world. Due to this the healthcare sectors are going on crisis all over the world. One basic protective measure that we can implement in our daily life is wearing a face mask. Wearing a mask properly can control the spread of this virus to a great extent. Various regions have made wearing face mask mandatory to prevent spread of this virus. In this paper we have proposed a deep learning-based model to detect face mask using python, OpenCV, TensorFlow and it can be used in our health care sectors. © 2022 IEEE.

Forecasting COVID-19 Cases Using n-SARS-CoV-2 Variants

This work proposes a novel Deep Learning-based model to forecast the total number of confirmed COVID-19 cases in four of the worst-hit states of India. Along with statewide restrictions and public holidays, a novel parameter is introduced for training the proposed model, which considers the Alpha, Beta, Delta, and Omicron variants and the degree of their prevalence in each of the four states. Recurrent Neural Network-based Long-Short Term Memory is applied to the custom dataset, with the lowest Mean Absolute Percentage Error being 0.77% for the state of Maharashtra. SHapley Additive exPlanations values are used to examine the significance of the various parameters. The proposed model can be applied to other countries and can include newer variants of the novel coronavirus discovered in the future. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Detection of Covid-19 using Transfer Learning Technique

The whole world is suffering from the wave of the novel coronavirus that causes the large-scale death of a population and is proclaimed a pandemic by WHO. As RT-PCR tests to detect Coronavirus are costly and time taking. So now these days, the purpose of the researcher is to detect these diseases with the help of Artificial Intelligence or Machine learning-based models using CT scan images and X-rays images. So the testing cost, time taken and the number of data required could be minimized. In this paper, transfer learning based on three fine-tuned models has been proposed for Covid detection. The performance of these proposed fine-tuned models has been also compared with other competing models to check the accuracy and other matrices. © 2022 IEEE.

Masked Face Recognition Using MobileNet V2 with Transfer Learning

Corona virus (COVID-19) is once in a life time calamity that has resulted in thousands of deaths and security concerns. People are using face masks on a regular basis to protect themselves and to help reduce corona virus transmission. During the on-going coronavirus outbreak, one of the major priorities for researchers is to discover effective solution. As important parts of the face are obscured, face identification and verification becomes exceedingly difficult. The suggested method is a transfer learning using MobileNet V2 based technology that uses deep feature such as feature extraction and deep learning model, to identify the problem of face masked identification. In the first stage, we are applying face mask detector to identify the face mask. Then, the proposed approach is applying to the datasets from Canadian Institute for Advanced Research10 (CIFAR10), Modified National Institute of Standards and Technology Database (MNIST), Real World Masked Face Recognition Database (RMFRD), and Stimulated Masked Face Recognition Database (SMFRD). The proposed model is achieving recognition accuracy 99.82% with proposed dataset. This article employs the four pre-programmed models VGG16, VGG19, ResNet50 and ResNet101. To extract the deep features of faces with VGG16 is achieving 99.30% accuracy, VGG19 is achieving 99.54% accuracy, ResNet50 is achieving 78.70% accuracy and ResNet101 is achieving 98.64% accuracy with own dataset. The comparative analysis shows, that our proposed model performs better result in all four previous existing models. The fundamental contribution of this study is to monitor with face mask and without face mask to decreases the pace of corona virus and to detect persons using wearing face masks. © 2023 CRL Publishing. All rights reserved.

Realistic Face Masks Generation Using Generative Adversarial Networks

Understanding facial expressions is important for the interactions among humans as it conveys a lot about the person's identity and emotions. Research in human emotion recognition has become more popular nowadays due to the advances in the machine learning and deep learning techniques. However, the spread of COVID-19, and the need for wearing masks in the public has impacted the current emotion recognition models' performance. Therefore, improving the performance of these models requires datasets with masked faces. In this paper, we propose a model to generate realistic face masks using generative adversarial network models, in particular image inpainting. The MAFA dataset was used to train the generative image inpainting model. In addition, a face detection model was proposed to identify the mask area. The model was evaluated using the MAFA and CelebA datasets, and promising results were obtained. © 2022 IEEE.

DCBC_DeepL: Detection and Counting of Blood Cells Employing Deep Learning and YOLOv5 Model

Blood cell identification and counting is critical for doctors and physicians nowadays in order to diagnose and treat a variety of disorders. Platelet identification and counting are frequently performed in the context of many types of sickness such as COVID-19 and others. However, it is frequently costly and time intensive. Additionally, it is not widely available. From this vantage point, it is necessary to develop an efficient technical model capable of detecting and counting three fundamental types of blood cells: platelets, red blood cells, and white blood cells. Thus, this study proposes a deep learning-based model based on the YOLOv5 model with a precision of 0.799. The model consists of thre different layers such as backbone, neck and output layer The model is extremely capable of detecting and counting individual blood cells. Doctors, physicians, and other professionals will be able to detect and count blood cells using real-time images. It will significantly minimise the cost and time associated with detecting and counting blood cells by utilizing real-time blood images. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Masked Face Recognition Using MobileNet V2 with Transfer Learning

Corona virus (COVID-19) is once in a life time calamity that has resulted in thousands of deaths and security concerns. People are using face masks on a regular basis to protect themselves and to help reduce corona virus transmission. During the on-going coronavirus outbreak, one of the major priorities for researchers is to discover effective solution. As important parts of the face are obscured, face identification and verification becomes exceedingly difficult. The suggested method is a transfer learning using MobileNet V2 based technology that uses deep feature such as feature extraction and deep learning model, to identify the problem of face masked identification. In the first stage, we are applying face mask detector to identify the face mask. Then, the proposed approach is applying to the datasets from Canadian Institute for Advanced Research10 (CIFAR10), Modified National Institute of Standards and Technology Database (MNIST), Real World Masked Face Recognition Database (RMFRD), and Stimulated Masked Face Recognition Database (SMFRD). The proposed model is achieving recognition accuracy 99.82% with proposed dataset. This article employs the four pre-programmed models VGG16, VGG19, ResNet50 and ResNet101. To extract the deep features of faces with VGG16 is achieving 99.30% accuracy, VGG19 is achieving 99.54% accuracy, ResNet50 is achieving 78.70% accuracy and ResNet101 is achieving 98.64% accuracy with own dataset. The comparative analysis shows, that our proposed model performs better result in all four previous existing models. The fundamental contribution of this study is to monitor with face mask and without face mask to decreases the pace of corona virus and to detect persons using wearing face masks. © 2023 CRL Publishing. All rights reserved.

Analysis and Observations of Associated Factors of Cardiovascular Disease

Machine learning contributes into gamut of domains starting from industry automation to healthcare services. It is a field of Artificial Intelligence using which machine can make decision without human intervention. There are many predominant machine learning algorithms which have proven their excellence in the field of regression and classification problem. Machine learning now a day is used in large scale in field of disease prediction. The acceptability of a machine learning based model depends on dataset used for training the model. Analysis of dataset is very important to identify the importance of individual attributes contribute to make decision. In this paper a cardiovascular disease dataset collected from UCI has been analyzed in detail to identify the distribution and impact of them in decision making. © Published under licence by IOP Publishing Ltd.

Deep Learning Based Intelligent Classification of Covid-19 Pneumonia Using Cough Auscultations

The World Health Organization has designated COVID-19 a pandemic because its emergence has influenced more than 50 million world's population. Around 14 million deaths have been reported worldwide from COVID-19. In this research work, we have presented a method for autonomous screening of COVID-19 and Pneumonia subjects from cough auscultation analysis. Deep learning-based model (MobileNet v2) is used to analyze a 6757 self-collected cough dataset. The experimentation has demonstrated the efficiency of the proposed technique in distinguishing between COVID-19 and Pneumonia. The results have demonstrated the cumulative accuracy of 99.98%, learning rate of 0.0005 and validation loss of 0.0028. Furthermore, cough analysis can be performed for other patients screening of other pulmonary abnormalities. © 2021 IEEE.

Machine Learning based model for predicting Stress Level in Online Education Due to Coronavirus Pandemic: A Case Study of Bangladeshi Students

During the pre-pandemic era online education in Bangladesh was not popular and certificates achieved from online education were often discouraged by organizations. However, the scenario has changed a lot within the last one and half years. The covid-19 pandemic force almost all the countries to adapt to new norms in almost every aspect of life and that happened in Bangladesh also, especially in the education sector. Undoubtedly this caused psychological stress to almost every stakeholder of this system. Our paper aims to predict this stress level of students in the context of Bangladesh using machine learning techniques. To conduct the research primary data were collected using google form and after preprocessing the data several prominent supervised classifiers were applied to predict the stress levels of students due to online education. Among these classifiers, the proximity of the Random Forest algorithm was found to play the greatest role in predicting the stress level detection in online classes and the accuracy was 73.91%. © 2021 IEEE.

Automatic Classification of COVID-19 from Chest X-Ray Image using Convolutional Neural Network

COVID-19 has become one of the most virulent, acute, and life-Threatening diseases in recent times. No clinically approved drug is available till now for its treatment. Therefore, early and swift detection is very essential for reducing overall mortality. The chest x-ray image is one of the possible alternative methods for detecting COVID-19. Researchers are exploring image processing techniques along with deep learning-based models like AlexNet, VGGNet, SqueezeNet, GoogleNet, etc.To detect COVID-19. This study aims to formulate, implement and investigate deep learning-based models and their probable hyperparameters tuning for obtaining the best results when identifying COVID-19 using chest x-ray images. To meet this objective, images from different publicly available databases were collected. In this paper, ResNet18, ResNet50V2, DenseNet121, DenseNet201, modified DenseNet201 and VGG16 were used to detect COVID-19. From the experimental results, modified DenseNet201 showed the best performance with 99.5% mean accuracy, 99.5% mean F1 score and 100% mean sensitivity in binary (COVID-19 and normal) classification and 98.33% mean accuracy, 98.34 mean F1 score, and 98.34% mean sensitivity (98% sensitivity for COVID-19) in 3-class (COVID-19, pneumonia, normal) classification. This may contribute to the process of designing and implementing a system that can detect COVID-19 automatically in the near future and enhance the quality of healthcare services. © 2021 IEEE.

Automated Detection of COVID-19 from X-ray Images using Deep Convolutional Neural Networks

Coronavirus, also known as COVID19, is a dangerous disease that has put many people's lives in jeopardy around the world by damaging the lungs directly. The detection of coronavirus is a challenging medical procedure due to its increasing cases. Currently, the use of x-ray images for coronavirus diagnosis is commonly used. Recently, various deep learning based models have been used for image classification. These models have generated competitive results in terms of feature selection and classification. In this article, we proposed a set of seven pretrained neural network models (VGG16, VGG19, InceptionV3, ResNet50, Xception, DenseNet121 and InceptionResNetV2) for the detection of coronavirus infection using chest X-ray images collected from an open source. It was observed that out of these models, pretrained DenseNet121 yielded highest classification accuracy of 97% for the particular dataset. © 2021 IEEE.

Deep Learning Based Model for COVID-19 Pneumonia Prediction with Pulmonary CT Images

COVID-19 pneumonia prediction from computed tomography (CT) images has recently become a crucial part of the computer vision field. Computed tomography images can be used to predict COVID-19 pneumonia as an alternative to traditional testing such as RT-PCR mechanisms that helps to consume time and save more lives as well. The previous researcher faces the overfitting problem while working on a small dataset and deeper convolutional neural networks (CNNs). To overcome this problem, in this work, we consider a large dataset named Corona Hack-Chest X-Ray. And perform a comparative analysis based on fine-tuned CNN models, i.e., VGG16 and miniVGGNet. These models are used based on the transfer learning. For that, the models are initially trained with the ImageNet weights and re-trained with the dataset. The comparative analysis of the models for predicting COVID-19 from CT image is evaluated by the Corona Hack-Chest X-Ray dataset. We trained and test the models with 6368 CT images and evaluate the networks performance in terms of various machine learning metrics. This dataset shows 93% accuracy for the miniVGGNet model while showing 89% accuracy for the VGG16 model. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Am I Being Bullied on Social Media? An Ensemble Approach to Categorize Cyberbullying

People can easily reveal their aggressive remarks on social media platforms using the anonymity it provides. During the COVID-19 pandemic, the usage of social media has been increased several times according to surveys and people are vulnerable to cyber attacks now more than ever. Prevention of cyberbullying needs careful monitoring and identification. Most of the existing works on cyberbullying detection employed traditional machine learning classifiers with handcrafted fea-tures, and deep learning-based models have made their way in this domain very recently. Categorizing cyberbullying based on traits is a complex task and needs contextual consideration. In this work, we have proposed a new approach to detect cyberbullying on social media platforms using a neural ensemble method of transformer-based architectures with attention mechanism. Our proposed architecture is trained on one balanced and one imbalanced dataset and outperforms the given ML and DNN baselines by a significant margin in both cases. We achieved an average F1-score of 95.59% for five classes and 90.65% for six classes on the Fine-Grained Cyberbullying Dataset (FGCD), and 87.28% on Twitter parsed dataset. Our in-depth results provide great insights into the effectiveness of transformer-based models in cyberbullying detection and paves the way for future researches to combat this serious online issue. We have released our models and code.1 © 2021 IEEE.