Intelligent Telework Internet Cost Requirement Modeling Using Optimizable Machine Learning Algorithms

The COVID-19 pandemic has caused disruption to the economy due to the increasing infection that affects the workforce in different sectors. The Philippine government has imposed lockdowns to control the spread of infection. This urged the different sectors to implement flexible work schedules or work from home setup. A work-from-home (WFH) setup burdens both the employee and employer by installing different equipment set-ups such as WiFi-equipped laptops, computers, tablets, or smartphones. However, the internet stability in some of the areas in the Philippines is not yet reliable. In this study, an application is used collect survey information and provide an estimate of the telework internet cost requirement of a given government employee or a given government employee implementing a work-from-home set up in their respective household. This involves survey results from different respondents who are currently on a work-from-home setup and significant factors from the survey have been analyzed using machine learning (ML) algorithms. Among the machine learning algorithms used, the ensemble bagged trees model outperformed the other ML models. This work can be extended by incorporating a wider scope of datasets from different industry doing work from home set-up. In addition, in terms of education, it is also recommended to determine the WFH set up not just with the government employee and employer but to also extend this into the education side. © 2022 IEEE.

Efficient Centroid Based Distance Monitoring System Using Deep Learning

COVID-19 virus spread very rapidly if we come in contact to the other person who is infected, this was treated as acute pandemic. As per the data available at WHO more than 663 million infected cases reported and 6.7 million deaths are confirmed worldwide till Dec, 2022. On the basis of this big reported number, we can say that ignorance can cause harm to the people worldwide. Most of the people are vaccinated now but as per standard guideline of WHO social distancing is best practiced to avoid spreading of COVID-19 variants. This is difficult to monitor manually by analyzing the persons live cameras feed. Therefore, there is a need to develop an automated Artificial Intelligence based System that detects and track humans for monitoring. To accomplish this task, many deep learning models have been proposed to calculate distance among each pair of human objects detected in each frame. This paper presents an efficient deep learning monitoring system by considering distance as well as velocity of the object detected to avoid each frame processing to improve the computation complexity in term of frames/second. The detected human object closer to some allowed limit (1m) marked by red color and all other object marked with green color. The comparison of with and without direction consideration is presented and average efficiency found 20.08 FPS (frame/Second) and 22.98 FPS respectively, which is 14.44% faster as well as preserve the accuracy of detection. © 2023 IEEE.

Multi2Claim: Generating Scientific Claims from Multi-Choice Questions for Scientific Fact-Checking

In automated scientific fact-checking, machine learning models are trained to verify scientific claims given evidence. A major bottleneck of this task is the availability of large-scale training datasets on different domains, due to the required domain expertise for data annotation. However, multiple-choice question-answering datasets are readily available across many different domains, thanks to the modern online education and assessment systems. As one of the first steps towards addressing the fact-checking dataset scarcity problem in scientific domains, we propose a pipeline for automatically converting multiple-choice questions into fact-checking data, which we call Multi2Claim. By applying the proposed pipeline, we generated two large-scale datasets for scientific-fact-checking: Med-Fact and Gsci-Fact for the medical and general science domains, respectively. These two datasets are among the first examples of large-scale scientific-fact-checking datasets. We developed baseline models for the verdict prediction task using each dataset. Additionally, we demonstrated that the datasets could be used to improve performance measured by weighted F1 on existing fact-checking datasets such as SciFact, HEALTHVER, COVID-Fact, and CLIMATE-FEVER. In some cases, the improvement in performance was up to a 26% increase. The generated datasets are publicly available. © 2023 Association for Computational Linguistics.

Fake News Detection Using Transformer and Ensemble Learning Models

On social media, misinformation can spread quickly, posing serious problems. Understanding the content and sensitive nature of fake news and misinformation is critical to prevent the damage caused by them. To this end, the characteristics of information must first be discerned. In this paper, we propose a transformer-based hybrid ensemble model to detect misinformation on the Internet. First, false and true news on Covid-19 were analyzed, and various text classification tasks were performed to understand their content. The results were utilized in the proposed hybrid ensemble learning model. Our analysis revealed promising results, establishing the capability of the proposed system to detect misinformation on social media. The final model exhibited an excellent F1 score (0.98) and accuracy (0.97). The AUC (Area Under The Curve) score was also high at 0.98, and the ROC (Receiver Operating Characteristics) curve revealed that the true-positive rate of the data was close to one in this model. Thus, the proposed hybrid model was demonstrated to be successful in recognizing false information online. © 2022 IEEE.

Efficient Machine Learning Model For Covid-19 Spread Prediction

Machine learning models have shown great performance in prediction and detection of many diseases such as cancer, heart attack, liver infection, and kidney infection. COVID-19 emerged as one of the deadly disease. Its cases grownin unpredictable manner. Regression is the mathematical technique in machine learning that can used to find relation between outcome variable with independent variable. In the present manuscript, regression has been used to predict COVID-19 growth. It has been found that the model is highly accurate in the COVID case prediction. © 2023 IEEE.

Assessing Robustness of a Deep Learning Model for COVID-19 Classification on Chest Radiographs

The COVID-19 pandemic has made a dramatic impact on human life, medical systems, and financial resources. Due to the disease's pervasive nature, many different and interdisciplinary fields of research pivoted to study the disease. For example, deep learning (DL) techniques were employed early to assess patient diagnosis and prognosis from chest radiographs (CXRs) and computed tomography (CT) scans. While the use of artificial intelligence (AI) in the medical sector has displayed promising results, DL may suffer from lack of reproducibility and generalizability. In this study, the robustness of a pre-trained DL model utilizing the DenseNet-121 architecture was evaluated by using a larger collection of CXRs from the same institution that provided the original model with its test and training datasets. The current test set contained a larger span of dates, incorporated different strains of the virus, and included different immunization statuses. Considering differences in these factors, model performance between the original and current test sets was evaluated using area under the receiver operating characteristic curve (ROC AUC) [95% CI]. Statistical comparisons were performed using the Delong, Kolmogorov-Smirnov, and Wilcoxon rank-sum tests. Uniform manifold approximation and projection (UMAP) was used to help visualize whether underlying causes were responsible for differences in performance between test sets. In the task of classifying between COVID-positive and COVID-negative patients, the DL model achieved an AUC of 0.67 [0.65, 0.70], compared with the original performance of 0.76 [0.73, 0.79]. The results of this study suggest that underlying biases or overfitting may hinder performance when generalizing the model. © 2023 SPIE.

Deep-learning models for Covid-19 Detection Using Chest X-Ray Images

Deep Convolutional Neural Networks are a form of neural network that can categorize, recognize, or separate images. The problem of COVID-19 detection has become the world's most complex challenge since 2019. In this research work, Chest X-Ray images are used to detect patients' Covid Positive or Negative with the help of pre-trained models: VGG16, InceptionV3, ResNet50, and InceptionResNetV2. In this paper, 821 samples are used for training, 186 samples for validation, and 184 samples are used for testing. Hybrid model InceptionResNetV2 has achieved overall maximum accuracy of 94.56% with a Recall value of 96% for normal CXR images, and a precision of 95.12% for Covid Positive images. The lowest accuracy was achieved by the ResNet50 model of 92.93% on the testing dataset, and a Recall of 93.93% was achieved for the normal images. Throughout the implementation process, it was discovered that factors like epoch had a considerable impact on the model's accuracy. Consequently, it is advised that the model be trained with a sufficient number of epochs to provide reliable classification results. The study's findings suggest that deep learning models have an excellent potential for correctly identifying the covid positive or covid negative using CXR images. © 2023 IEEE.

Multi-objective Evolutionary Discretization of Gene Expression Profiles: Application to COVID-19 Severity Prediction

Machine learning models can use information from gene expressions in patients to efficiently predict the severity of symptoms for several diseases. Medical experts, however, still need to understand the reasoning behind the predictions before trusting them. In their day-to-day practice, physicians prefer using gene expression profiles, consisting of a discretized subset of all data from gene expressions: in these profiles, genes are typically reported as either over-expressed or under-expressed, using discretization thresholds computed on data from a healthy control group. A discretized profile allows medical experts to quickly categorize patients at a glance. Building on previous works related to the automatic discretization of patient profiles, we present a novel approach that frames the problem as a multi-objective optimization task: on the one hand, after discretization, the medical expert would prefer to have as few different profiles as possible, to be able to classify patients in an intuitive way;on the other hand, the loss of information has to be minimized. Loss of information can be estimated using the performance of a classifier trained on the discretized gene expression levels. We apply one common state-of-the-art evolutionary multi-objective algorithm, NSGA-II, to the discretization of a dataset of COVID-19 patients that developed either mild or severe symptoms. The results show not only that the solutions found by the approach dominate traditional discretization based on statistical analysis and are more generally valid than those obtained through single-objective optimization, but that the candidate Pareto-optimal solutions preserve the sense-making that practitioners find necessary to trust the results. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Time series prediction of the COVID-19 outbreak in India using LSTM based deep learning models

The epidemic caused by COVID-19 presents a significant risk to the continuation of human civilisation and has already done irreparable damage to society. In this paper, forecasting of Coronavirus outbreak in India is performed by LSTM and CovnLSTM deep neural network techniques. COVID-19 data of confirmed cases of India is used. It was taken from John Hopkins University. The loss rate of ConvLSTM is lower than LSTM and RMSE of ConvLSTM is lower than LSTM. For training Covn-LSTM shows 0.069% and testing ConvLSTM shows 0.32% improvement over LSTM model. Therefore, ConvLSTM outperformed over LSTM model. Further wise selection of hyper-parameters could increase the accuracy of the models. © 2023 IEEE.

A Classification Approach to Detect Public Sentiments towards COVID-19 Vaccines

In this paper, team University of Botswana Computer Science (UBCS) investigate the opinions of Twitter users towards vaccine uptake. In particular, we build three different text classifiers to detect people's opinions and classify them as provax-for opinions that are for vaccination, antivax for opinions against vaccination and neutral-for opinions that are neither for or against vaccination. Two different datasets obtained from Twitter, 1 by Cotfas and the other by Fire2022 Organizing team were merged to and used for this study. The dataset contained 4392 tweets. Our first classifier was based on the basic BERT model and the other 2 were machine learning models, Random Forest and Multinomial Naive Bayes models. Naive Bayes classifier outperformed other classifiers with a macro-F1 score of 0.319. © 2022 Copyright for this paper by its authors.

Comparative Study of VGG-13, AlexNet, MobileNet and Modified-DarkCovidNet for Chest X-Ray Classification

Convolutional neural network (CNN) is the most widely used structure-building technique for deep learning models. In order to classify chest x-ray pictures, this study examines a number of models, including VGG-13, AlexN ct, MobileNet, and Modified-DarkCovidNet, using both segmented image datasets and regular image datasets. Four types of chest X- images: normal chest image, Covid-19, pneumonia, and tuberculosis are used for classification. The experimental results demonstrate that the VGG offers the highest accuracy for segmented pictures and Modified Dark CovidN et performs best for multi class classification on segmented images. © 2023 Bharati Vidyapeeth, New Delhi.

CovidMis20: COVID-19 Misinformation Detection System on Twitter Tweets Using Deep Learning Models

Online news and information sources are convenient and accessible ways to learn about current issues. For instance, more than 300 million people engage with posts on Twitter globally, which provides the possibility to disseminate misleading information. There are numerous cases where violent crimes have been committed due to fake news. This research presents the CovidMis20 dataset (COVID-19 Misinformation 2020 dataset), which consists of 1,375,592 tweets collected from February to July 2020. CovidMis20 can be automatically updated to fetch the latest news and is publicly available at: https://github.com/everythingguy/CovidMis20. This research was conducted using Bi-LSTM deep learning and an ensemble CNN+Bi-GRU for fake news detection. The results showed that, with testing accuracy of 92.23% and 90.56%, respectively, the ensemble CNN+Bi-GRU model consistently provided higher accuracy than the Bi-LSTM model. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

An Investigation of Deep-Learned Features for Classifying Radiographic Images of COVID-19

In this proposal, a study based on deep-learned features via transfer learning was developed to obtain a set of features and techniques for pattern recognition in the context of COVID-19 images. The proposal was based on the ResNet-50, DenseNet-201 and EfficientNet-b0 deep-learning models. In this work, the chosen layer for analysis was the avg pool layer from each model, with 2048 features from the ResNet-50, 1920 features from the DenseNet0201 and 1280 obtained features from the EfficientNet-b0. The most relevant descriptors were defined for the classification process, applying the ReliefF algorithm and two classification strategies: individually applied classifiers and employed an ensemble of classifiers using the score-level fusion approach. Thus, the two best combinations were identified, both using the DenseNet-201 model with the same subset of features. The first combination was defined via the SMO classifier (accuracy of 98.38%) and the second via the ensemble strategy (accuracy of 97.89%). The feature subset was composed of only 210 descriptors, representing only 10% of the original set. The strategies and information presented here are relevant contributions for the specialists interested in the study and development of computer-aided diagnosis in COVID-19 images. Copyright © 2023 by SCITEPRESS - Science and Technology Publications, Lda. Under CC license (CC BY-NC-ND 4.0)

Covid19 Disease Assessment Using CNN Architecture

Recently, the COVID-19 pandemic has emerged as one of the world's most critical public health concerns. One of the biggest problems in the present COVID-19 outbreak is the difficulty of accurately separating COVID-19 cases from non-COVID-19 cases at an affordable price and in the initial stages. Besides the use of antigen Rapid Test Kit (RTK) and Reverse Transcription Polymerase Chain Reaction (RT-PCR), chest x-rays (CXR) can also be used to identify COVID-19 patients. Unfortunately, manual checks may produce inaccurate results, delay treatment or even be fatal. Because of differences in perception and experience, the manual method can be chaotic and imprecise. Technology has progressed to the point where we can solve this problem by training a Deep Learning (DL) model to distinguish the normal and COVID-19 X-rays. In this work, we choose the Convolutional Neural Network (CNN) as our DL model and train it using Kaggle datasets that include both COVID-19 and normal CXR data. The developed CNN model is then deployed on the website after going through a training and validation process. The website layout is straightforward to navigate. A CXR can be uploaded and a prediction made with minimal effort from the patient. The website assists in determining whether they have been exposed to COVID-19 or not. © 2023 IEEE.

Comparative study of Deep Learning Models for Covid 19 Diagnosis

The Covid 19 pandemic that started a couple of years ago has had a devastating effect on mankind across the globe. The disease had no known treatment. Early detection and prevention was very important to curtail the effects of the Pandemic. In this work two deep learning models the RestNet and the models are proposed for diagnosing Corona from chest X-rays and CT scans. The models were trained with publicly available data sets of covid and non covid images. It has been found that Inception V3 performs better than ResNet for chest x-rays and RestNet performs better for CT Scans. The performance of the RestNet is found to be similar for both the chest x-rays and CT scans datasets. © 2023 IEEE.

Transfer Learning Model in Deep Neural Network for COVID-19 Prediction from CT Images

Deep learning models are often used to process radi-ological images automatically and can accurately train networks' weights on appropriate datasets. One of the significant benefits of the network is that it is possible to use the weight of a pre-trained network for other applications by fine-tuning the current weight. The primary purpose of this work is to employ a pre-trained deep neural framework known as transfer learning to detect and diagnose COVID-19 in CT images automatically. This paper uses a popular deep neural model, ResNet152, as a neural transfer approach. The presented framework uses the weight obtained from the ImageNet dataset, fine-tuned by the dataset used in the work. The effectiveness of the suggested COVID-19 prediction system is evaluated experimentally and compared with DenseNet, another transfer learning model. The recommended ResNet152 transfer learning model exhibits improved performance and has a 99% accuracy when analogized with the DenseNet201 transfer learning model. © 2022 IEEE.

A CNN Transfer Learning -Electrocardiogram (ECG) Signal Approach to Predict COVID-19

Deep learning is increasingly gaining traction in cutting-edge medical sciences such as image classification, and genomics due to the high computational performance and accuracy in evaluating medical data. In this study, we investigate the cardiac properties of ECG Images and predict COVID-19 in a binary classification of patients who tested positive for COVID-19 and Normal Persons who tested negative. We analyzed the electrocardiogram (ECG) images by preprocessing the ECG data and building an ECG- Deep Learning- COVID-19 (ECG-DL-COVID) classifier to predict disease. The deep learning models in our experiments constituted CNN, Multi-Layer Perceptron (MLP), and Transfer Learning. Performance evaluation was done to compare the effectiveness of the proposed methodologies with other COVID-19 deep learning-related works. In the three experiments, we achieved an 87% prediction accuracy for MLP, a 90% prediction for CNN and a 93.8% prediction for Transfer Learning. Experimental results and performance evaluation show that the proposed models outperformed previous deep-learning models in the prediction of COVID-19 by a considerable margin. © 2023 IEEE.

Ensemble Deep Learning Framework for Situational Aspects-Based Annotation and Classification of International Student's Tweets during COVID-19

As the COVID-19 pandemic swept the globe, social media plat-forms became an essential source of information and communication for many. International students, particularly, turned to Twitter to express their struggles and hardships during this difficult time. To better understand the sentiments and experiences of these international students, we developed the Situational Aspect-Based Annotation and Classification (SABAC) text mining framework. This framework uses a three-layer approach, combining baseline Deep Learning (DL) models with Machine Learning (ML) models as meta-classifiers to accurately predict the sentiments and aspects expressed in tweets from our collected Student-COVID-19 dataset. Using the pro-posed aspect2class annotation algorithm, we labeled bulk unlabeled tweets according to their contained aspect terms. However, we also recognized the challenges of reducing data's high dimensionality and sparsity to improve performance and annotation on unlabeled datasets. To address this issue, we proposed the Volatile Stopwords Filtering (VSF) technique to reduce sparsity and enhance classifier performance. The resulting Student-COVID Twitter dataset achieved a sophisticated accuracy of 93.21% when using the random forest as a meta-classifier. Through testing on three benchmark datasets, we found that the SABAC ensemble framework performed exceptionally well. Our findings showed that international students during the pandemic faced various issues, including stress, uncertainty, health concerns, financial stress, and difficulties with online classes and returning to school. By analyzing and summarizing these annotated tweets, decision-makers can better understand and address the real-time problems international students face during the ongoing pandemic.

Assessing an AI-based Smart Imagery Framing and Truthing (SIFT) system to assist radiologists annotating lung abnormalities on chest X-ray images for development of deep learning models

To assess a Smart Imagery Framing and Truthing (SIFT) system in automatically labeling and annotating chest X-ray (CXR) images with multiple diseases as an assist to radiologists on multi-disease CXRs. SIFT system was developed by integrating a convolutional neural network based-augmented MaskR-CNN and a multi-layer perceptron neural network. It is trained with images containing 307,415 ROIs representing 69 different abnormalities and 67,071 normal CXRs. SIFT automatically labels ROIs with a specific type of abnormality, annotates fine-grained boundary, gives confidence score, and recommends other possible types of abnormality. An independent set of 178 CXRs containing 272 ROIs depicting five different abnormalities including pulmonary tuberculosis, pulmonary nodule, pneumonia, COVID-19, and fibrogenesis was used to evaluate radiologists' performance based on three radiologists in a double-blinded study. The radiologist first manually annotated each ROI without SIFT. Two weeks later, the radiologist annotated the same ROIs with SIFT aid to generate final results. Evaluation of consistency, efficiency and accuracy for radiologists with and without SIFT was conducted. After using SIFT, radiologists accept 93% SIFT annotated area, and variation across annotated area reduce by 28.23%. Inter-observer variation improves by 25.27% on averaged IOU. The consensus true positive rate increases by 5.00% (p=0.16), and false positive rate decreases by 27.70% (p<0.001). The radiologist's time to annotate these cases decreases by 42.30%. Performance in labelling abnormalities statistically remains the same. Independent observer study showed that SIFT is a promising step toward improving the consistency and efficiency of annotation, which is important for improving clinical X-ray diagnostic and monitoring efficiency. © 2023 SPIE.

Performance Analysis of VGG-16 Deep Learning Model for COVID-19 Detection using Chest X-Ray Images

COVID-19 is an infectious disease caused by newly discovered coronavirus. Currently, RT-PCR and Rapid Testing are used to test a person against COVID-19. These methods do not produce immediate results. Hence, we propose a solution to detect COVID-19 from chest X-ray images for immediate results. The solution is developed using a convolutional neural network architecture (VGG-16) model to extract features by transfer learning and a classification model to classify an input chest X-ray image as COVID-19 positive or negative. We introduced various parameters and computed the impact on the performance of the model to identify the parameters with high impact on the model's performance. The proposed solution is observed to provide best results compared to the existing ones. © 2023 Bharati Vidyapeeth, New Delhi.