Proposed Convolution Architecture for Monkeypox Detection using Dermoscopy Images

Monkeypox is a skin disease that spreadsfrom animals to people and then people to people, the class of the monkeypox is zoonotic and its genus are othopoxvirus. There is no special treatment for monkeypox but the monkeypox and smallpox symptoms are almost similar, so the antiviral drug developed for prevent from smallpox virus may be used for monkeypox Infected person, the Prevention of monkeypox is just like COVID-19 proper hand wash, Smallpox vaccine, keep away from infected person, used PPE kits. In this paper Deep learning is use for detection of monkeypox with the help of CNN model, The Original Images contains a total number of 228 images, 102 belongs to the Monkeypox class and the remaining 126 represents the normal. But in deep learning greater amount of data required, data augmentation is also applied on it after this the total number of images are 3192. A variety of optimizers have been used to find out the best result in this paper, a comparison is usedbased on Loss, Accuracy, AUC, F1 score, Validation loss, Validation accuracy, validation AUC, Validation F1 score of each optimizer. after comparing alloptimizer, the Adam optimizer gives the best result its total testing accuracy is 92.21%, total number of epochs used for testing is 100. With the help of deep learning model Doctors are easily detect the monkeypox virus with the single image of infected person. © 2023 IEEE.

CovidXNet: An Ensemble of Deep Neural Networks for Non-invasive Binary Identification of COVID-19 through Chest Radiographs

This study compared five different image classification algorithms, namely VGG16, VGG19, AlexNet, DenseNet, and ConVNext, based on their ability to detect and classify COVID-19-related cases given chest X-ray images. Using performance metrics like accuracy, F1 score, precision, recall, and MCC compared these intelligent classification algorithms. Upon testing these algorithms, the accuracy for each model was quite unsatisfactory, ranging from 80.00% to 92.50%, provided it is for medical application. As such, an ensemble learning-based image classification model, made up of AlexNet and VGG19 called CovidXNet, was proposed to detect COVID-19 through chest X-ray images discriminating between health and pneumonic lung images. CovidXNet achieved an accuracy of 97.00%, which was significantly better considering past results. Further studies may be conducted to increase the accuracy, particularly for identifying and classifying chest radiographs for COVID-19-related cases, since the current model may still provide false negatives, which may be detrimental to the prevention of the spread of the virus. © 2022 IEEE.

Comparative Analysis of Linear Regression and Logistic Regression for the COVID-19 Detection from CT-Scans

The new emerging Coronavirus disease (COVID-19) is a pandemic disease due to its enormous infectious capability. Generally affecting the lungs, COVID-19 engenders fever, dry cough, and tiredness. However, some patients may not show symptoms. An imaging test, such as a chest X-ray or a chest CT scan, is therefore requested for reliable detection of this pneumonia type. Despite the decreasing trends both in the new and death reported cases, there is an extent need for quick, accurate, and inexpensive new methods for diagnosis. In this framework, we propose two machine learning (ML) algorithms: linear regression and logistic regression for effective COVID-19 detection in the abdominal Computed Tomography (CT) dataset. The ML methods proposed in this paper, effectively classify the data into COVID-19 and normal classes without recourse to image preprocessing or analysis. The effectiveness of these algorithms was shown through the use of the performance measures: accuracy, precision, recall, and F1-score. The best classification accuracy was obtained as 96% with logistic regression using the saga solver with no added penalty against 95.3% with linear regression. As for precision, recall, and F1-score the value of 0.89 was reached by logistic regression for all these metrics, as well as the value of 0.87 by linear regression. © 2022 IEEE.

CTC: COVID-19 Tweet Classification using CT-BERT

CTC is my submitted work to the Information Retrieval from Microblogs during Disasters (IRMiDis) Track at the Forum for Information Retrieval Evaluation (FIRE) 2022. Coronavirus disease (COVID-19) is an infectious disease caused by the SARS-CoV-2 virus. Most people infected with the virus experience a mild to moderate respiratory illness and recover without requiring special treatment. However, some become seriously ill and require medical attention. Vaccines against coronavirus and prompt reporting of symptoms saved many lives during the pandemic. The analysis of COVID-19-related tweets can provide valuable insights regarding the stance of people toward the new vaccine. It can also help the authorities to plan their strategies based on people's opinions about the vaccine and ensure the effectiveness of vaccination campaigns. Tweets describing symptoms can also aid in identifying high-alert zones and determining quarantine regulations. The IRMiDis track focuses on these COVID-19-related tweets that flooded Twitter. I developed an effective classifier for both Tasks 1 and 2. The evaluation score of my submitted run is reported in terms of accuracy and macro-F1 score. I achieved an accuracy of 0.770, a macro-F1 score of 0.773 in Task 1, and an accuracy of 0.820, a macro-F1 score of 0.746 in Task 2. I enjoyed the first rank among other submissions in both the tasks. © 2022 Copyright for this paper by its authors.

Performance of COVID Face Mask Detection Based on SVM Algorithm Using OpenCV DNN as Preprocessing Model

As COVID-19 became a pandemic in the world, wearing a mask has become one of the best measures to prevent the spread of the epidemic, so face mask recognition in public places has become a very important part of controlling the epidemic. This paper mainly tests the performance of the OpenCV DNN preprocessing model (OpenCV DNN + SVM) based on the SVM algorithm model in the face mask recognition dataset. The dataset I use is from Kaggle called COVID Face Mask Detection Dataset. This dataset contains 503 face images with masks and 503 face images without masks. I test the performance of using OpenCV DNN + SVM and using only the SVM algorithm to evaluate this study by setting a control experimental group. In this study, it was found that using OpenCV DNN + SVM, the accuracy of ROI parameters and SVM parameters can reach 93.06% and F1score can also reach 93.06% without a lot of adjustment. The accuracy rate can only reach 68.31%, and the F1score reaches 68.31%. Findings suggest that the method using OpenCV DNN + SVM can achieve slightly better results in the COVID Face Mask Detection Dataset, and can perform better than only using the SVM algorithm. In addition, using OpenCV DNN preprocessing model based on the SVM algorithm plays an important role in feature extraction in face mask recognition. If the developer does enough parameters tuning, the accuracy will also increase. © 2022 SPIE.

A drug recommender system Based on Collaborative Filtering for Covid-19 patients

The epidemic caused by a new mutation of the coronavirus family called Covid-19 has created a global crisis involving all the world's countries. This disease has become a severe danger to everyone due to its unknown nature, high spread, and inability to detect the infected. In this regard, one of the important issues facing patients with Covid-19 is the prescription of Drugs according to the severity of the disease and considering the records of underlying diseases in people. In recent years, recommender systems have been developed significantly along with the advancement in information technology and artificial intelligence, which is one of its applications in various fields of medical sciences. Among them, we can refer to recommending systems for the prevention, control, and treatment of diseases. In this research, using the collaborative filtering approach as one of the types of recommender systems as well as the K-means clustering algorithm, a Drug recommendation system for patients with Covid-19 in the treatment stage of the disease is presented. The results of this research show that this recommender system has an acceptable performance based on the evaluation criteria of precision, recall, and F1-score compared to the opinions of experts in this field. © 2023 IEEE.

A New Convolution Neural Layer Based on Weights Constraints

With the rapid development of artificial intelligence techniques, emerging deep neural networks (DNN) is one of the most effective ways to solve many challenges. Convolution neural networks (CNNs) are considered one of the most popular AI techniques used to extract and analyze meaningful features for image datasets, especially in the medical diagnosis field. In this paper, a proposed constrained convolution layer (COCL) for the CNN model is proposed. The new layer uses a constrained number of weights in each kernel trained in the phase of learning and excludes the others weights with zero values. The proposed method is introduced to extract a special type of feature considering the local shape of a sub-image (window) and topological relations between group pixels. The features extract according to a random distribution of weights in kernels that are determined considering a particular desired percentage. Furthermore, this paper proposed a CNN model architecture that uses COCL rather than the traditional CNN layer (TCL). The efficiency of the method is evaluated using three types of medical image datasets compared with the traditional convolution layer, pre-trained deep neural networks (pre-DNNs), and state-of-art methods. The proposed model outperforms other methods in terms of accuracy and F1 score metrics and exceeds more than 98%, 89%, and 93% for the three datasets used in the evaluation, respectively. © 2022 IEEE.

An Effective Technique using MobileNet-V2 for Face Mask Detection

Recently, the widespread and extremely fatal disease known as the coronavirus spread throughout the entire world. China's Wuhan city served as its first hub for its spread. The COVID-19 outbreak has briefly disrupted our daily routines by affecting worldwide trade and travel. Precautions include hand washing, using hand sanitizer, keeping a safe distance, and most importantly wearing a mask. However, putting on a mask that prevents to some extent airborne droplet transmission will be helpful as a precautionary measure in this pandemic. In the near future, many public service providers will ask the customers to wear masks correctly to avail of their services. However, ensuring that everyone wears a face mask is a difficult chore. Many techniques such as Machine Learning, Deep learning models like CNN, RNN, MobileNet etc. are available to solve this problem. This paper presents a simplified approach using MobileNet-V2 for Face Mask Detection. The model is developed by utilizing TensorFlow, Keras, OpenCV, and Scikit-Learn. The face mask detection model's objective is to identify people's faces and determine whether they are wearing masks at the time they are recorded in the image. An alert will sound if there is a desecration on the scene or in public areas. The challenge with the model is to detect the face mask during motion of a person. Precision, recall, F1-score, support, and accuracy are used to evaluate the system's performance and show its practical pertinency. The system operates with a 99.9% F1 score. The currently developed model will be used in conjunction with embedded camera infrastructure which may then be used to a variety of verticals, including schools, universities, public spaces, airport terminals/gates, etc. © 2023 IEEE.

iCompass at WANLP 2022 Shared Task: ARBERT and MARBERT for Multilabel Propaganda Classification of Arabic Tweets

Propaganda content has seen massive spread in the biggest social media networks. Major global events such as Covid-19, presidential elections, and wars have all been infested with various propaganda techniques. In participation in the WANLP 2022 Shared Task(Alam et al., 2022), this paper provides a detailed overview of our machine learning system for propaganda techniques classification and its achieved results. The task was carried out using pre-trained transformer based models: ARBERT and MARBERT. The models were fine-tuned for the downstream task in hand: multilabel classification of Arabic tweets. According to the results, MARBERT and ARBERT attained 0.562 and 0.567 micro F1-score on the development set of subtask 1. The submitted model was MARBERT which attained a 0.597 micro F1-score and got the fifth rank. © 2022 Association for Computational Linguistics.

Fake News Detection System: An implementation of BERT and Boosting Algorithm

On social media, false information can proliferate quickly and cause big issues. To minimize the harm caused by false information, it is essential to comprehend its sensitive nature and content. To achieve this, it is necessary to first identify the characteristics of information. To identify false information on the internet, we suggest an ensemble model based on transformers in this paper. First, various text classification tasks were carried out to understand the content of false and true news on Covid-19. The proposed hybrid ensemble learning model used the results. The results of our analysis were encouraging, demonstrating that the suggested system can identify false information on social media. All the classification tasks were validated and shows outstanding results. The final model showed excellent accuracy (0.99) and F1 score (0.99). The Receiver Operating Characteristics (ROC) curve showed that the true-positive rate of the data in this model was close to one, and the AUC (Area Under The Curve) score was also very high at 0.99. Thus, it was shown that the suggested model was effective at identifying false information online. © 2023, EasyChair. All rights reserved.

Dense Feature Memory Augmented Transformers for COVID-19 Vaccination Search Classification

With the devastating outbreak of COVID-19, vaccines are one of the crucial lines of defense against mass infection in this global pandemic. Given the protection they provide, vaccines are becoming mandatory in certain social and professional settings. This paper presents a classification model for detecting COVID-19 vaccination related search queries, a machine learning model that is used to generate search insights for COVID-19 vaccinations. The proposed method combines and leverages advancements from modern state-of-the-art (SOTA) natural language understanding (NLU) techniques such as pretrained Transformers with traditional dense features. We propose a novel approach of considering dense features as memory tokens that the model can attend to. We show that this new modeling approach enables a significant improvement to the Vaccine Search Insights (VSI) task, improving a strong well-established gradient-boosting baseline by relative +15% improvement in F1 score and +14% in precision. © 2022 Association for Computational Linguistics.

Densely Connected Neural Network and Permutation Entropy in the Early Diagnostic in COVID Patients

The COVID-19 pandemic has been characterized by the high number of infected cases due to its rapid spread around the world, with more than 6 million of deaths. Given that we are all at risk of acquiring this disease and that vaccines do not completely stop its spread, it is necessary to continue proposing tools that help mitigate it. This is the reason why it is ideal to develop a method for early detection of the disease, for which this work uses the Stanford University database to classify patients with SARS-CoV-2, also commonly called as COVID-19, and healthy ones. In order to do that we used a densely connected neural network on a total of 77 statistical features, including permutation entropy, that were contrasted from two different time windows, extracted from the heart rate of 24 COVID patients and 24 healthy people. The results of the classification process reached an accuracy of 86.67% and 100% of precision with the additional parameters of recall and F1-score being 80% and 88.89% respectively. Finally, from the ROC curve for this classification model it could be calculated an AUC of 0.982. © 2022 Creative Commons.

A Multimodal Framework for the Identification of Vaccine Critical Memes on Twitter

Memes can be a useful way to spread information because they are funny, easy to share, and can spread quickly and reach further than other forms. With increased interest in COVID-19 vaccines, vaccination-related memes have grown in number and reach. Memes analysis can be difficult because they use sarcasm and often require contextual understanding. Previous research has shown promising results but could be improved by capturing global and local representations within memes to model contextual information. Further, the limited public availability of annotated vaccine critical memes datasets limit our ability to design computational methods to help design targeted interventions and boost vaccine uptake. To address these gaps, we present VaxMeme, which consists of 10,244 manually labelled memes. With VaxMeme, we propose a new multimodal framework designed to improve the memes' representation by learning the global and local representations of memes. The improved memes' representations are then fed to an attentive representation learning module to capture contextual information for classification using an optimised loss function. Experimental results show that our framework outperformed state-of-the-art methods with an F1-Score of 84.2%. We further analyse the transferability and generalisability of our framework and show that understanding both modalities is important to identify vaccine critical memes on Twitter. Finally, we discuss how understanding memes can be useful in designing shareable vaccination promotion, myth debunking memes and monitoring their uptake on social media platforms. © 2023 ACM.

RBTM: Prediction of COVID-19 from Random Blood Test with Machine Learning Algorithms

Recently, the ongoing global pandemic of novel coronavirus infection had a devastating impact worldwide. We develop an efficient classification model that effectively produces the predictive values of infected patients with suspicious symptoms and epidemiological history to defeat this. The research aims to use the Traditional technique to compare clinical blood tests of positive and negative cases. The diagnostic Machine Learning model incorporates 551random blood samples with the following parameters of the patient's demographic features, Platelet, Hemoglobin, Lymphocyte, Neutrophil, Leukocyte (WBC), Turbidimetric, Troponin-I of COVID positive and negative cases. The prediction model can achieve the classification report of Accuracy, Precision, Recall, and F1 score values. In this analysis, considering seven different algorithms for the prediction and the observation's estimation, the data is 5-fold cross-validated. Finally, investigational outcomes attain accurate predictions. Logistic Regression predicted 0.83% of accuracy. The Receiver Operator Characteristic (ROC) metrics for Logistic Regression, the Precision was 0.78%, Recall was 0.85%, and F1-score was 0.82%, Specificity was 0.58%, and Sensitivity was 0.41%. © 2022 IEEE.

Stress Analysis Using Machine Learning

Stress is one of the major health issues of the world and one of the major reasons for committing suicide. Also, it leads to other mental health issues such as depression, anxiety etc., and damage to organs related to respiratory, cardiovascular and nervous systems. In recent years, stress has impacted many individuals due to the pandemic situation. Since the governments across the globe had started to impose lockdowns, the levels of stress significantly raised because of the disturbances led by covid infections, losing loved ones, continuous engagement with laptops and mobiles etc. It is also found that stress has not only disturbed the health condition but also disturbed the relationships and became a self-destruction component. This project is aimed to help those people to understand their stress and consult a psychologist at right time to overcome the situation. Though stress is an active area of research and achieved high performance of models, those were based on signal and speech which were computationally costlier and text-based research work using a state-of-the-art model called the BERT has achieved an f1-score i.e. 80.65%. This project focuses on text-domain and uses open-sourced Stress Analysis on Social Media dataset available on Kaggle which contains 3.6 K samples. In this project, both Machine Learning and Deep Learning Models were trained with 80% of the data and validated with 20% of the data. After, optimization and evaluation of several models, the best model has achieved a benchmark result of 83.74% f1-score on test data using a new network architecture i.e. combination of stacked Transformer Encoder layers with stacked Bi-directional-LSTM. In addition to this, an explainable AI has been implemented for an embedding layer to inspect input attributions in predicting the results. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Medical Diagnosis and Identification of Covid -19 by Intelligent IoT System and Resnet 18 Bilinear Deep Greedy Network

An international health crisis has been caused by the widespread COVID-19 epidemic. COVID-19 patient diagnoses are made using deep learning, although this necessitates a massive radiography data collection in order to efficiently deliver an optimum result. This paper presents a novel Intelligent System with IoT sensors for covid 19 and "Bilinear Resnet 18 Deep Greedy Network,"which is effective with a limited amount of datasets. Despite peculiarities brought on by a small dataset, the suggested approach could successfully combat the anomalies of over fitting and under fitting. The suggested architecture ensures a successful conclusion when the trained model is correctly evaluated using the provided X-ray datasets of COVID-19 cases. The recommended model offers accuracy of 97%, which is superior to existing methodologies. Better precision, recall, and F1 score are provided;which are 98%, 96%, and 96.94% respectively, which is better than other existing methodology. © 2022 IEEE.

Using a 3D ResNet for Detecting the Presence and Severity of COVID-19 from CT Scans

Deep learning has been used to assist in the analysis of medical imaging. One use is the classification of Computed Tomography (CT) scans for detecting COVID-19 in subjects. This paper presents Cov3d, a three dimensional convolutional neural network for detecting the presence and severity of COVID-19 from chest CT scans. Trained on the COV19-CT-DB dataset with human expert annotations, it achieves a macro f1 score of 87.87 on the test set for the task of detecting the presence of COVID-19. This was the ‘runner-up' for this task in the ‘AI-enabled Medical Image Analysis Workshop and Covid-19 Diagnosis Competition' (MIA-COV19D). It achieved a macro f1 score of 46.00 for the task of classifying the severity of COVID-19 and was ranked in fourth place. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Enabling low-resource transfer learning across COVID-19 corpora by combining event-extraction and co-training

Social-science investigations can benefit from a direct comparison of heterogenous corpora: in this work, we compare U.S. state-level COVID-19 policy announcements with policy discussions on Twitter. To perform this task, we require classifiers with high transfer accuracy to both (1) classify policy announcements and (2) classify tweets. We find that co-training using event-extraction views significantly improves the transfer accuracy of our RoBERTa classifier by 3% above a RoBERTa baseline and 11% above other baselines. The same improvements are not observed for baseline views. With a set of 576 COVID-19 policy announcements, hand-labeled into 1 of 6 categories, our classifier observes a maximum transfer accuracy of .77 f1-score on a hand-validated set of tweets. This work represents the first known application of these techniques to an NLP transfer learning task and facilitates cross-corpora comparisons necessary for studies of social science phenomena. © ACL 2020.All right reserved.

Lung CT image segmentation algorithm based on improved U-net structure

Accurate segmentation of medical images can help doctors diagnose and treat diseases. In the face of the complex COVID-19 image, this paper proposes an improved U-net network segmentation model, which uses the residual network structure to deepen the network level, and adds the attention module to integrate different receptive field, global, local and spatial features to enhance the detail segmentation effect of the network. For the COVID-19 CT data set, the F1-Score, Accuracy, SE, SP and Precision of the U-Net network are 0.9176, 0.9578, 0.9669, 0.9487 and 0.8574 respectively. Compared with U-Net, our model proposed in this paper increased by 6.43%, 3.36%, 0.85%, 4.78% and 13.11% on F1-Score, Accuracy, SE, SP and Precision, respectively. The automatic and effective segmentation of COVID-19 lung CT image is realized. © 2022 IEEE.

Alpha Fusion Adversarial Attack Analysis Using Deep Learning

The deep learning model encompasses a powerful learning ability that integrates the feature extraction, and classification method to improve accuracy. Convolutional Neural Networks (CNN) perform well in machine learning and image processing tasks like segmentation, classification, detection, identification, etc. The CNN models are still sensitive to noise and attack. The smallest change in training images as in an adversarial attack can greatly decrease the accuracy of the CNN model. This paper presents an alpha fusion attack analysis and generates defense against adversarial attacks. The proposed work is divided into three phases: firstly, an MLSTM-based CNN classification model is developed for classifying COVID-CT images. Secondly, an alpha fusion attack is generated to fool the classification model. The alpha fusion attack is tested in the last phase on a modified LSTM-based CNN (CNN-MLSTM) model and other pre-trained models. The results of CNN models show that the accuracy of these models dropped greatly after the alpha-fusion attack. The highest F1 score before the attack was achieved is 97.45 And after the attack lowest F1 score recorded is 22%. Results elucidate the performance in terms of accuracy, precision, F1 score and Recall. © 2023 CRL Publishing. All rights reserved.