A radiomics-based model for the outcome prediction in COVID-19 positive patients through deep learning with both longitudinal chest x-ray and chest computed tomography images

The coronavirus disease 2019 (COVID-19) pandemic had a major impact on global health and was associated with millions of deaths worldwide. During the pandemic, imaging characteristics of chest X-ray (CXR) and chest computed tomography (CT) played an important role in the screening, diagnosis and monitoring the disease progression. Various studies suggested that quantitative image analysis methods including artificial intelligence and radiomics can greatly boost the value of imaging in the management of COVID-19. However, few studies have explored the use of longitudinal multi-modal medical images with varying visit intervals for outcome prediction in COVID-19 patients. This study aims to explore the potential of longitudinal multimodal radiomics in predicting the outcome of COVID-19 patients by integrating both CXR and CT images with variable visit intervals through deep learning. 2274 patients who underwent CXR and/or CT scans during disease progression were selected for this study. Of these, 946 patients were treated at the University of Pennsylvania Health System (UPHS) and the remaining 1328 patients were acquired at Stony Brook University (SBU) and curated by the Medical Imaging and Data Resource Center (MIDRC). 532 radiomic features were extracted with the Cancer Imaging Phenomics Toolkit (CaPTk) from the lung regions in CXR and CT images at all visits. We employed two commonly used deep learning algorithms to analyze the longitudinal multimodal features, and evaluated the prediction results based on the area under the receiver operating characteristic curve (AUC). Our models achieved testing AUC scores of 0.816 and 0.836, respectively, for the prediction of mortality. © 2023 SPIE.

E-Learning Readiness Assessment Using Machine Learning Methods

Assessing e-learning readiness is crucial for educational institutions to identify areas in their e-learning systems needing improvement and to develop strategies to enhance students' readiness. This paper presents an effective approach for assessing e-learning readiness by combining the ADKAR model and machine learning-based feature importance identification methods. The motivation behind using machine learning approaches lies in their ability to capture nonlinearity in data and flexibility as data-driven models. This study surveyed faculty members and students in the Economics faculty at Tlemcen University, Algeria, to gather data based on the ADKAR model's five dimensions: awareness, desire, knowledge, ability, and reinforcement. Correlation analysis revealed a significant relationship between all dimensions. Specifically, the pairwise correlation coefficients between readiness and awareness, desire, knowledge, ability, and reinforcement are 0.5233, 0.5983, 0.6374, 0.6645, and 0.3693, respectively. Two machine learning algorithms, random forest (RF) and decision tree (DT), were used to identify the most important ADKAR factors influencing e-learning readiness. In the results, ability and knowledge were consistently identified as the most significant factors, with scores of ability (0.565, 0.514) and knowledge (0.170, 0.251) using RF and DT algorithms, respectively. Additionally, SHapley Additive exPlanations (SHAP) values were used to explore further the impact of each variable on the final prediction, highlighting ability as the most influential factor. These findings suggest that universities should focus on enhancing students' abilities and providing them with the necessary knowledge to increase their readiness for e-learning. This study provides valuable insights into the factors influencing university students' e-learning readiness.

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.

Covid-19 Vaccination Stance Detection Using Natural Language Processing and Machine-Learning Algorithms

The coronavirus outbreak has resulted in unprecedented measures, forcing authorities to make decisions related to establishing lockdowns in areas most affected by the pandemic. Social Media have supported people during this difficult time. On November 9, 2020, when the first vaccine with an efficacy rate over 90% was announced, social media reacted and people around the world began to express their feelings about this vaccination. This paper aims to analyze the dynamics of opinion on COVID-19 vaccination, in which the civil society is highly manifested in the vaccination process. We compared classical machine learning algorithms to select the best performing classifier. 4,392 tweets were collected and analyzed. The proposed approach can help governments create and evaluate appropriate communication tools to provide clear and relevant information to the general public, increasing public confidence in vaccination campaigns. © 2022 Copyright for this paper by its authors.

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.

Artificial Intelligence-Based Smart Tele-Assisting Technology for First-Year Engineering Students

Engineering programs emphasize students career advancement by ensuring that engineering students gain technical and professional capabilities during their four-year study. In a traditional engineering laboratory, students "learn by doing", and laboratory equipment facilitates their discipline-specific knowledge acquisition. Unfortunately, there were significant educational uncertainties, such as COVID-19, which halted laboratory activities for an extended period, causing challenges for students to perform and obtain practical experiments on campus. To overcome these challenges, this research proposes and develops an Artificial Intelligence-based smart tele-assisting technology application to digitalize first-year engineering students practical experience by incorporating Augmented Reality (AR) and Machine Learning (ML) algorithms using the HoloLens 2. This application improves virtual procedural demonstrations and assists first-year engineering students in conducting practical activities remotely. This research also applies various machine learning algorithms to identify and classify different images of electronic components and detect the positions of each component on the breadboard (using the HoloLens 2). Based on a comparative analysis of machine learning algorithms, a hybrid CNN-SVM (Convolutional Neural Network - Support Vector Machine) model is developed and is observed that a hybrid model provides the highest average prediction accuracy compared to other machine learning algorithms. With the help of AR (HoloLens 2) and the hybrid CNN-SVM model, this research allows students to reduce component placement errors on a breadboard and increases students competencies, decision-making abilities, and technical skills to conduct simple laboratory practices remotely. © 2023 IEEE.

An Exploratory Study of Masked Face Recognition with Machine Learning Algorithms

Automated face recognition is a widely adopted machine learning technology for contactless identification of people in various processes such as automated border control, secure login to electronic devices, community surveillance, tracking school attendance, workplace clock in and clock out. Using face masks have become crucial in our daily life with the recent world-wide COVID-19 pandemic. The use of face masks causes the performance of conventional face recognition technologies to degrade considerably. The effect of mask-wearing in face recognition is yet an understudied issue. In this paper, we address this issue by evaluating the performance of a number of face recognition models which are tested by identifying masked and unmasked face images. We use six conventional machine learning algorithms, which are SVC, KNN, LDA, DT, LR and NB, to find out the ones which perform best, besides the ones which poorly perform, in the presence of masked face images. Local Binary Pattern (LBP) is utilized as the feature extraction operator. We generated and used synthesized masked face images. We prepared unmasked, masked, and half-masked training datasets and evaluated the face recognition performance against both masked and unmasked images to present a broad view of this crucial problem. We believe that our study is unique in elaborating the mask-aware facial recognition with almost all possible scenarios including half_masked-to-masked and half_masked-to-unmasked besides evaluating a larger number of conventional machine learning algorithms compared the other studies in the literature. © 2023 IEEE.

A Survey on Machine Learning Techniques for Multimodal Biomedical Signal Processing

In recent years, there has been a significant growth in the development of machine learning algorithms towards better experience in patient care. In this paper, a contemporary survey on the deep learning and machine learning techniques used in multimodal signal processing for biomedical applications is presented. Specifically, an overview of the preprocessing approaches and the algorithms proposed for five major biomedical applications are presented, namely detection of cardiovascular diseases, retinal disease detection, stress detection, cancer detection and COVID-19 detection. In each case, processing on each multimodal data type, such as an image or a text is discussed in detail. A list of various publicly available datasets for each of these applications is also presented. © 2023 IEEE.

Forecasting COVID-19 Pandemic using Prophet, LSTM, hybrid GRU-LSTM, CNN-LSTM, Bi-LSTM and Stacked-LSTM for India

The COVID-19 Pandemic has been around for four years and remains a health concern for everyone. Although things are somewhat returning to normal, increased incidence of COVID-19 cases in some regions of the world (such as China, Japan, France, South Korea, etc.) has bred worry and anxiety in world, including India. The scientific community, which includes governmental organizations and healthcare facilities, was eager to learn how the COVID-19 Pandemic would develop. The current work makes an attempt to address this question by employing cutting-edge machine learning and Deep Learning algorithms to anticipate the daily incidence of COVID-19 for India over the course of the next six months. For the purpose famous timeseries algorithms were implemented including LSTM, Bi-Directional LSTM and Stacked LSTM and Prophet. Owing to success of hybrid algorithms in specific problem domains- the present study also focuses on such algorithms like GRU-LSTM, CNN-LSTM and LSTM with Attention. All these models have been trained on timeseries dataset of COVID-19 for India and performance metrics are recorded. Of all the models, the simplistic algorithms have performed better than complex and hybrid ones. Owing to this best result was obtained with Prophet, Bidirectional LSTM and Vanilla LSTM. The forecast reveals flat nature of COVID-19 case load for India in future six months. . © 2023 IEEE.

Detection of COVID-19 for image dataset by using Deep learning algorithm

This COVID-19 study uses a new way of looking at data to shed light on important topics and societal problems. After digesting specific interpretations, experts' points of view are looked at: We'll study and categorize these subfields based on their importance and influence in the academic world. Web-based education, cutting-edge technologies, AI, dashboards, social networking, network security, industry titans (including blockchain), safety, and inventions will be discussed. By combining chest X-ray images with machine learning, the article views provide element breadth, ideal understanding, critical issue detection, and hypothesis and practice concepts. We've used machine learning techniques in COVID-19 to help manage the pandemic flow and stop infections. Statistics show that the hybrid strategy is better than traditional ones. © 2022 IEEE.

WHAT DO USERS OF RHEUMATOID ARTHRITIS ONLINE FORUMS TALK ABOUT? APPLYING A DEEP LEARNING APPROACH TO UNCOVER COMMON THEMES

BackgroundSocial media platforms have become a vital resource for individuals seeking information and support regarding health issues, including rheumatoid arthritis (RA). As such, the content generated on these platforms represents a valuable source of data for gaining insight into patients' perspectives on RA. However, previous research in this area has primarily relied on qualitative analyses of small sample sizes, limiting the ability to extract meaningful insights from social media content related to RA. With the advancement of machine learning techniques, it is now possible to analyze and extract insights from large volumes of social media posts related to RA.ObjectivesThe purpose of this study was to identify the most common topics discussed in a large dataset of submissions about RA on Reddit, one of the world's largest online forums.MethodsThe data for this study was collected from the two largest Reddit forums ("subreddits”) dedicated to RA, r/rheumatoid arthritis and r/rheumatoid, which have 18.9k and 7.6k members respectively. We retrieved all submissions but excluded responses in our analyses. All deleted or duplicate submissions and those with fewer than 10 words were removed, retaining 11,094 submissions from over 5,000 users for the analysis. To identify common themes, we applied topic modeling, a technique in natural language processing that identifies underlying themes or topics in a collection of documents. We used the Bertopic Python package (Grootendorst, 2022), which employs deep learning techniques to perform the topic modeling.ResultsThe data indicates a significant increase in submissions to the two subreddits, rising from 113 in 2014 to 2892 in 2021 and 1928 in the first 8 months of 2022. Upon analysis, 65 topics were identified, with 4162 submissions (37.5%) remaining unclassified. A topic specifically dedicated to requests to participate in surveys was removed as it did not pertain to the experiences of forum users. Among the remaining topics, the top 10 accounted for 44.90% of all submissions. To better understand each topic, a sample of 10 submissions with the highest probability for that topic were examined (Table 1).Table 1.Top 10 most frequent topicsTopicn of submissionsShare of total*Side effects of methotrexate5268.02%COVID & vaccines4627.04%Mental health4386.68%RF and anti CCP test results3315.04%RA of friends, partners, and close relatives2623.99%Complaints about rheumatologist2123.23%Questions about Humira1882.87%Questions about prednisone1822.77%Diets and RA1752.67%Early symptoms of possible RA1702.59%Exercise and RA1682.56%* After excluding unclassified topicsThree of the ten topics pertained to specific medications - methotrexate, Humira, and prednisone, accounting for 12.71% of the total. The most prevalent topic, at 8.02%, focused on the side effects of methotrexate, with many submissions inquiring about symptoms such as nausea. The second most common topic, at 7.04%, primarily revolved around COVID-19 and related issues, with some pre-COVID vaccine discussions also included. In 2021, COVID-related discussions were the most prevalent topic. The third most frequent topic (6.68% of total), dealt with mental health and the emotional struggles faced by those living with RA.ConclusionThe surge in submissions on Reddit demonstrates its growing popularity as an online forum for discussing topics related to RA. Utilizing deep learning-based topic modeling has proven to be an effective method for extracting meaningful topics from the questions and experiences shared by users. The vast amount of data generated by Reddit, in combination with advanced machine learning techniques, enables both an overview of the various topics discussed and a detailed examination of specific topics. This makes the use of social media data a valuable source of insight into the concerns of RA platform users.Reference[1]Grootendorst, M. (2022). BERTopic: Neural topic modeling with a class-based TF-IDF procedure. arXiv preprint arXiv:2203.05794.Acknowledgements:NIL.Disclosure of InterestsNone Decla ed.

Security Vulnerability in Face Mask Monitoring System

The COVID-19 pandemic has increased demand for face mask detection systems that utilize deep learning and machine learning algorithms. However, these systems are susceptible to adversarial attacks, where an attacker can manipulate the system to make incorrect predictions. This study aimed to test the vulnerability of a deep learning-based face mask detection model to a specific type of attack called a black box adversarial attack in which the attacker possesses only partial information about the target model. The study's findings showed that the attack successfully reduced the model's accuracy from 96.48% to 49.25%. This emphasizes the need for more robust defense mechanisms in face mask detection systems to ensure their reliability. © 2023 Bharati Vidyapeeth, New Delhi.

Detecting Ransomware Attacks in Cloud Environment Using Machine Learning-Based Intelligence System in COVID-19 Chaos

2020-2022 provided nearly ideal circumstances for cybercriminals, with confusion and uncertainty dominating the planet due to COVID-19. Our way of life was altered by the COVID-19 pandemic, which also sparked a widespread shift to digital media. However, this change also increased people's susceptibility to cybercrime. As a result, taking advantage of the COVID-19 events' exceedingly unusual circumstances, cybercriminals launched widespread Phishing, Identity theft, Spyware, Trojan-horse, and Ransomware attacks. Attackers choose their victims with the intention of stealing their information, money, or both. Therefore, if we wish to safeguard people from these frauds at a time when millions have already fallen into poverty and the remaining are trying to survive, it is imperative that we put an end to these attacks and assailants. This manuscript proposes an intelligence system for identifying ransomware attacks using nature-inspired and machine-learning algorithms. To classify the network traffic in less time and with enhanced accuracy, Genetic Algorithm (GA) and Particle Swarm Optimization (PSO), two widely used algorithms are coupled in the proposed approach for Feature Selection (FS). Random Forest (RF) approach is used for classification. The system's effectiveness is assessed using the latest ransomware-oriented dataset of CIC-MalMem-2022. The performance is evaluated in terms of accuracy, model building, and testing time and it is found that the proposed method is a suitable solution to detect ransomware attacks. © 2022 IEEE.

Fine-Tuning ResNet-152V2 for COVID-19 Recognition in Chest Computed Tomography Images

The epidemic Covid-19 has extended to majority of nations. This pandemic is due to a contagious condition 'SARS-CoV-2', was identified by the the International Health association. In order to diagnosis this virus from 2D chest computed tomography (CT) images, we applied three different transfer learning algorithms: $VGG-19, ResNet-152V2$ and a Fine-Tuned version of $ResNet-152V2$. The different transfer learning models are used on three hundred and four exams where 74 are normal cases, 60 are community-acquired pneumonia (CAP) cases and 169 were confirmed corona-virus cases. The best accuracy value is reached by the fine-tuned $ResNet-152v2$ by 75% against 70% for the basic $ResNet-152v2$ and 66% for the $VGG-19$. © 2022 IEEE.

Assistance tools for the evaluation of machine learning algorithm performance: The decision tree-based tools developed by the Medical Imaging and Data Resource Center (MIDRC) Technology Development Project (TDP) 3c effort

MIDRC was created to facilitate machine learning research for tasks including early detection, diagnosis, prognosis, and assessment of treatment response related to the COVID-19 pandemic and beyond. The purpose of the Technology Development Project (TDP) 3c is to create resources to assist researchers in evaluating the performance of their machine learning algorithms. An interactive decision tree has been developed, organized by the type of task that the machine learning algorithm is being trained to perform. The user can select information such as: (a) the type of task, (b) the nature of the reference standard, and (c) the type of the algorithm output. Based on the user responses, they can obtain recommendations regarding appropriate performance evaluation approaches and metrics, including literature references, short video tutorials, and links to available software. Five tasks have been identified for the decision tree: (a) classification, (b) detection/localization, (c) segmentation, (d) time-to-event analysis, and (e) estimation. As an example, the classification branch of the decision tree includes binary and multi-class classification tasks and provides suggestions for methods and metrics as well as software recommendations, and literature references for situations where the algorithm produces either binary or non-binary (e.g., continuous) output and for reference standards with negligible or non-negligible variability and unreliability. The decision tree has been made publicly available on the MIDRC website to assist researchers in conducting task-specific performance evaluations, including classification, detection/localization, segmentation, estimation, and time-to-event tasks. © COPYRIGHT SPIE. Downloading of the is permitted for personal use only.

Ischemic Heart Disease Prediction Using Machine Learning and Deep Learning Techniques

To have control over heart patient health, we need a capable detector which finds out based onhealth records. The idea is to work on coronary artery disease (CAD), which has been the majorhealth issue at present. We took a data set to train our system (machine learning algorithm) towork on the CAD and identify the user's health stage and provide the required information. Asper previous analysis, we got accuracy of 96% now with a minor modification we are trying to impact the accuracy. CAD has been the major health disease that is leading to death in world at present after COVID19, it is causing 33% of death rate by a survey by WHO. So, it is essentialto overcome the disease with proper analysis and prevention, which is all about our project. We are trying to make healthcare handy such that a person that analyze and know about his/her health condition from anywhere and at any time regardless of working hours. © 2023 IEEE.

Applications of Machine Learning in Viral Disease Diagnosis

Viral diseases are common and natural in human it spreads from animals and other humans. It seeks to identify the proper, reliable, and effective disease detection as quickly as possible so that patients can receive the right care. It becomes vital for medical field searches to have assistance from other disciplines like statistics and computer science because this detection is frequently a challenging process. These fields must overcome the difficulty of learning novel, non-traditional methodologies. Because so many new techniques are being developed, a thorough overview must be given while avoiding some specifics. In order to do this, we suggest a thorough analysis of machine learning which is used for the diagnosis of viral diseases caused in humans as well as plans. Predictions are made which is not obvious at the first glance does machine learning will be more helpful in making decisions. The study focuses on the machine learning algorithms for diagnosis of viral diseases for early diagnosis and treatment of viral diseases with greater accuracy. The work helps the researchers and medical professionals for learning and to give treatment for determining the applications of different machine learning techniques run to evaluate the parameters. Through examination of various parameters new machine learning model is proposed understanding the applications of machine learning in viral disease diagnosis like imaging techniques, plant virus diagnosis and the solution for the problem, Covid 19 diagnosis. © 2023 Bharati Vidyapeeth, New Delhi.

Decision region analysis to deconstruct the subgroup influence on AI/ML predictions

Assessing the generalizability of deep learning algorithms based on the size and diversity of the training data is not trivial. This study uses the mapping of samples in the image data space to the decision regions in the prediction space to understand how different subgroups in the data impact the neural network learning process and affect model generalizability. Using vicinal distribution-based linear interpolation, a plane of the decision region space spanned by the random 'triplet' of three images can be constructed. Analyzing these decision regions for many random triplets can provide insight into the relationships between distinct subgroups. In this study, a contrastive self-supervised approach is used to develop a 'base' classification model trained on a large chest x-ray (CXR) dataset. The base model is fine-tuned on COVID-19 CXR data to predict image acquisition technology (computed radiography (CR) or digital radiography (DX) and patient sex (male (M) or female (F)). Decision region analysis shows that the model's image acquisition technology decision space is dominated by CR, regardless of the acquisition technology for the base images. Similarly, the Female class dominates the decision space. This study shows that decision region analysis has the potential to provide insights into subgroup diversity, sources of imbalances in the data, and model generalizability. © 2023 SPIE.

Application of Machine Learning Techniques to Predict Visitors to the Tourist Attractions of the Moche Route in Peru

Due to the COVID-19 pandemic, the tourism sector has been one of the most affected sectors and requires management entities to develop urgent measures to reactivate and achieve digital transformation using emerging disruptive technologies. The objective of this research is to apply machine learning techniques to predict visitors to tourist attractions on the Moche Route in northern Peru, for which a methodology based on four main stages was applied: (1) data collection, (2) model analysis, (3) model development, and (4) model evaluation. Public data from official sources and internet data (TripAdvisor and Google Trends) during the period from January 2011 to May 2022 are used. Four algorithms are evaluated: linear regression, KNN regression, decision tree, and random forest. In conclusion, for both the prediction of national and foreign tourists, the best algorithm is linear regression, and the results allow for taking the necessary actions to achieve the digital transformation to promote the Moche Route and, thus, reactivate tourism and the economy in the north of Peru.

Predicting Intensive Care Unit Admission Using Machine and Deep Learning: COVID-19 Case Study

The COVID-19 pandemic has affected the entire world by causing widespread panic and disrupting normal life. Since the outbreak began in December 2019, the virus has killed thousands of people and infected millions more. Hospitals are struggling to keep up with large patient flows. In some situations, hospitals are lacking enough beds and ventilators to accommodate all of their patients or are running low on supplies such as masks and gloves. Predicting intensive care unit (ICU) admission of patients with COVID-19 could help clinicians better allocate scarce ICU resources. In this study, many machine and deep learning algorithms are tested over predicting ICU admission of patients with COVID-19. Most of the algorithms we studied are extremely accurate toward this goal. With the convolutional neural network (CNN), we reach the highest results on our metrics (90.09% accuracy and 93.08% ROC-AUC), which demonstrates the usability of these learning models to identify patients who are likely to require ICU admission and assist hospitals in optimizing their resource management and allocation during the COVID-19 pandemic or others. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.