Intelligent Methods and Models for Assessing Level of Student Adaptation to Online Learning

The problem of introducing online learning is becoming more and more popular in our society. Due to COVID-19 and the war in Ukraine, there is an urgent need for the transition of educational institutions to online learning, so this paper will help people not make mistakes in the process and afterward. The paper's primary purpose is to investigate the effectiveness of machine learning tools that can solve the problem of assessing student adaptation to online learning. These tools include intelligent methods and models, such as classification techniques and neural networks. This work uses data from an online survey of students at different levels: school, college, and university. The survey consists of questions such as gender, age, level of education, whether the student is in the city, class duration, quality of Internet connection, government/non-government educational institution, availability of virtual learning environment, whether the student is familiar with IT, financial conditions, type of Internet connection, a device used for studying, etc. To obtain the results on the effectiveness of online education were used the following machine learning algorithms and models: Random Forest (RF), Extra Trees (ET), Extreme, Light, and Simple Gradient Boosting (GB), Decision Trees (DT), K-neighbors (K-mean), Logistic Regression (LR), Support Vector Machine (SVM), Naїve Bayes (NB) classifier and others. An intelligent neural network model (NNM) was built to address the main issue. © 2023 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0). CEUR Workshop Proceedings (CEUR-WS.org)

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.

Towards Detection of Cyberbullying in Hinglish Code Mixed Data

Covid-19 eruption and lockdown situation have increased the usages of online platforms which have impacted the users. Cyberbullying is one of the negative outcomes of using social media platforms which leads to mental and physical distress. This study proposes a machine learning-based approach for the detection of cyberbullying in Hinglish text. We use the Hinglish Code-Mixed Corpus, which consists of over 6,000 tweets, for our experiments. We use various machine learning algorithms, including Logistic regression (LR), Multinomial Naive Bayes (MNB), Support vector machine (SVM), Random Forest (RF), to train our models. We evaluate the performance of the models using standard evaluation metrics such as precision, recall, and F1-score. Our experiments show that the LR with Term Frequency-Inverse Document Frequency (TFIDF) outperforms the other models, achieving 92% accuracy. Our study demonstrates that machine learning models can be effective for cyberbullying detection in Hinglish text, and the proposed approach can help identify and prevent cyberbullying on social media platforms. © 2023 Bharati Vidyapeeth, New Delhi.

Accurate Machine Learning Algorithm for Monkey Pox Based on Covid-19

The Covid-19 virus is still marching all over the world. Many people are getting infected and a few are fatal to death. This research paper expressed that supervised learning has revealed supreme results than unsupervised learning in machine learning. Within supervised learning, random forest regression outplays all other algorithms like logistic regression (LR), support vector machine (SVM), decision tree (DT), etc. Now monkeypox is escalating in other countries at present. This virus is allied to human orthopox viruses. It can expand from one to one through contact person having rash or body fluids etc. The symptoms of monkeypox are much similar to covid19 virus-like fever, cold, fatigue, and body pains. Herewith we concluded that random forest regression shows possible foremost (97.15%) accuracy. © 2023 IEEE.

A Machine Learning-driven IoT Architecture for Predicting the Growth and Trend of Covid-19 Epidemic Outbreaks to Identify High-risk Locations

Covid-19 has had a destructive influence on global economics, social life, education, and technologies. The rise of the Covid-19 pandemic has increased the use of digital tools and technologies for epidemic control. This research uses machine learning (ML) models to identify populated areas and predict the disease's risk and impact. The proposed system requires only details about mask utilization, temperature, and distance between individuals, which helps protect the individual's privacy. The gathered data is transferred to an ML engine in the cloud to determine the risk probability of public areas concerning Covid-19. Extracted data are input for multiple ML techniques such as Random Forest (RF), Decision tree (DT), Naive Bayes classifier(NBC), Neural network(NN), and Support vector machine (SVM). Expectation maximization (EM), K-means, Density, Filtered, and Farthest first (FF) clustering algorithms are applied for clustering. Compared to other algorithms, the K-means produces better superior accuracy. The regression technique is utilized for prediction. The outcomes of several methods are compared, and the most suitable ML algorithms utilized in this study are used to identify high-risk locations. In comparison to other identical architectures, the suggested architecture retains excellent accuracies. It is observed that the time taken to build the model using locally weighted learning(LWL) was 0.02 seconds, and the NN took more time to build, which is 0.90 seconds. To test the model, an LWL algorithm took more time which is 1.73 seconds, and the NN took less time to test, which is 0.02 seconds. The NBC has a 99.38 percent accuracy, the RF classifier has a 97.33 percent accuracy, and the DT has a 94.51 percent accuracy for the same data set. These algorithms have significant possibilities for predicting the likelihood of crowd risks of Covid-19 in a public space. This approach generates automatic notifications to concerned government authorities in any aberrant detection. This study is likely to aid researchers in modeling healthcare systems and spur additional research into innovative technology. © 2023 IEEE.

Customer Sentiments Toward Saudi Banks During the Covid-19 Pandemic

In view of the recent interest of Saudi banks in customers' opinions through social media, our research aims to capture the sentiments of bank users on Twitter. Thus, we collected and manually annotated more than 12, 000 Saudi dialect tweets, and then we conducted experiments on machine learning models including: Support Vector Machine (SVM), Random Forest (RF), and Logistic Regression (RL) as well as state-of-the-art language models (i.e. MarBERT) to provide baselines. Results show that the accuracy in SVM, LR, RF, and MarBERT achieved 82.4%, 82%, 81%, and 82.1% respectively. Our models code and dataset will be made publicly available on GitHub. © ICNLSP 2022.All rights reserved

Evaluation of AI Techniques for Detecting Deceptive Reviews in Cyberspace: A Study of Pre- and Post-COVID-19 Trends

With cyberspace's continuous evolution, online reviews play a crucial role in determining business success in various sectors, ranging from restaurants and hotels to e-commerce applications. Typically, a favorable review for a specific product draws in more consumers and results in a significant boost in sales. Unfortunately, a few businesses are using deceptive methods to improve their online reputation by using fake reviews of competitors. As a result, detecting fake reviews has become a difficult and ever-changing research field. Verbal characteristics extracted from review text, as well as nonverbal features such as the reviewer's engagement metrics, the IP address of the device, and so on, play an important role in detecting fake reviews. This article examines and compares various machine learning techniques for detecting deceptive reviews on various online platforms such as e-commerce websites such as Amazon and online review websites such as Yelp, among others. © 2023 IEEE.

Classification of Chest X-Ray Images for COVID-19 Positive Patients Using Transfer Learning

The Coronavirus pandemic COVID-19 which has been declared as a pandemic by the World Health Organization has infected more than 212,165,567 and fatality figure of 4,436,957 as of 22nd August 2021. This infection develops into pneumonia which causes breathing problem;this can be detected using chest x-rays or CT scan. This work aims to produce an automated way of detecting the presence of COVID-19 infection using chest X-rays as a part of transfer learning strategy to extract numerical features out of an image using pre trained models as feature extractors. Then construct a secondary data set out of these features, and use these features which are simple numerical vectors represented in tabular form as an input to simple machine learning classifiers that work well with numerical data in tabular form such as SVM, KNN, Logistic regression and Naive Bayes. This work also aims to extract features using texture-based techniques such as GLCM and use the GLCM to obtain 2nd order statistical features and construct another secondary data set based on texture-based feature extraction techniques on images. These features are again fed into simple machine learning classifiers mentioned above. A comparison is done, between deep learning feature extraction strategies and texture-based feature extraction strategies and the results are compared and analyzed. Considering the deep learning strategies Mobile Net with SVM perform the best with 0.98 test accuracy, followed by logistic regression, KNN and Naive Bayes algorithm. With respect to GLCM feature extraction strategy, KNN with test accuracy with 0.96 performed the best, followed by logistic regression, SVM and naive Bayes. Overall performance wise deep learning strategies proved to be effective but in terms of calculation time and number of features, texture-based strategy of GLCM proved effective. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

An Improved Accuracy in Anticipating the User Nature using a novel logistic regression algorithm throughout the pandemic across online social media based on Indian metrics over Support Vector Clustering Algorithm

This study's primary goal is to apply machine learning classifier techniques to raise the intensity percentage of user nature detection in order to detect the impact of coronavirus on Twitter users by comparing Novel Logistic Regression and Support Vector Clustering algorithms. Materials and Methods: The accuracy percentage with a confidence interval of 95% and G-power (value =0.8) was determined many times using the LR method with test size =10 and the SVC algorithm with test size =10. The likelihood that an item belongs to one category or another is predicted using a LR model. Support Vector Clustering algorithm generates a line or hyperplane that divides the data into categories. Results and Discussion: LR model has greater efficiency (91%) when compared to Support Vector Clustering (59%). Two groups are numerically unimportant, according to the data obtained with a coefficient of determination of p=0.121 (p>0.05). Conclusion: LR performs substantially better than the Support Vector Clustering. © 2022 IEEE.

Dramatic Increase in Fear-Related Discussion on Twitter during COVID-19: Analysis, Topic Modeling and Tweets Classification

COVID-19 is one the most lethal virus, causing millions of death to date. It was initially detected in Wuhan, China. It then spread rapidly around the globe, which resultantly created major setbacks in the public health sector. The reason of millions of deaths is not only due to the virus itself but it is also linked to peoples' mental state, and sentiments triggered by the fear of the virus. These sentiments are predominantly available on posts/tweets on social media. This paper presents a novel approach for exploratory data analysis of twitter to understand the emotions of general public;country wise, and user wise. Firstly K-Means clustering is employed for topic modeling to categorize the emotions in each tweet. Further supervised machine learning techniques are used to categorize the multi-label tweets. This research concluded that Fear was the most common emotion in twitter discussion. Furthermore, we classified the dataset by performing decision tree (DT), logistic regression (LR), and support vector machine (SVM), finally this paper concluded the results of classification, which shows that SVM can attain better classification accuracy (99%) for COVID-19 text classification. © 2022 IEEE.

Detection of COVID-19 using ResNet50, VGG19, MobileNet, and Forecasting, using Logistic Regression, Prophet, and SEIRD Model

Artificial Intelligence can quickly identify hazardous viral strains in humans. To detect COVID-19 symptoms, AI algorithms can be used to train to examine medical images like X-rays and CT scans. This can help healthcare providers to diagnose the disease more accurately and quickly. AI helps examine data on the spread of COVID-19 andmake predictions about how it will likely spread in the future. Machine learning algorithms known as Convolutional Neural Networks (CNN) are highly effective at evaluating images. As a result, CNN could assist in the early detection of COVID-19 by evaluating medical images like X-rays and CT scans to spot the disease's symptoms. This article's main aim is to provide brief information on some of the CNN models to detect and forecast COVID-19. The models were purely trained with Chest X-ray images of different categorized patients. The COVID-19 prediction models like ResNet50, VGG19, and MobileNet give accuracies of 98.50%, 97.68%, and 93.94%, respectively. On the other hand, forecasting also plays a vital role in reducing the pandemic because it helps us to analyze the risk and plan a solution to avoid it. The model is trained with some forecasting techniques like Prophet, LogisticRegression, and S EIRD model based on a text-based dataset that contains parameters such as the number of people infected per day recovered per day an d many more for visualizing the trends in forecasting, which help in decision-making to analyze risks and plan solutions to prevent the further spread of the disease. © 2023 IEEE.

The Effect of CoViD-19 Pandemic on the Hospitalization of a Department of Oncology of an Italian Hospital

In the last years, the entire world has been affected by the SARS-COV-2 pandemic, that represents the etiologic agent of Coronavirus disease 2019 (CoViD-19), which degenerated into a global pandemic in 2020. CoViD-19 has also had a strong impact on cancer patients. Our analysis has been performed at the Department of Oncology of the AORN "Cardarelli” in Naples, collecting data from all patients who had access in 2019–2020. We aim to understand how CoViD-19 affected hospital admissions. The statistical analysis showed that between 2019 and 2020 there was an increase in urgent hospitalizations and a decrease in scheduled hospitalization, probably to decrease the risk of infection, particularly in this category of susceptible patients. Indeed, as recommended by the European Society of Medical Oncology, during the pandemic, it was necessary to reorganize healthcare activities, ensure adequate care for patients infected with CoViD-19. Therefore telemedicine services were implemented and clinic visits were reduced. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Covid-19: The Effect on Hospitalization Patient of Ophthalmology Department in "Antonio Cardarelli” Hospital

A pneumonia outbreak of unknown origin was reported in Wuhan, China in late December. This virus, called coronavirus-2, has an impact on the respiratory tract, leading to acute respiratory syndromes. In 2020, this virus was declared a pandemic by the World Health Organization since it caused a high number of deaths worldwide. In addition, this pandemic has had a negative impact on the world economy, focusing the attention of the practitioners on the resource management in health structures. This work was carried out to evaluate the effects of the pandemic on the ordinary hospitalization activities of the Department of Ophthalmology at "A. Cardarelli” based in Naples (Italy). The dataset was evaluated using statistical analysis techniques and logistic regression. The results, for this department, did not show significant differences when comparing the health variables of the pre-pandemic year (2019) with the pandemic year (2020). © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Comparison Between Two Hospitals to Study the Impact of COVID-19 on Emergency Medicine Activities

Beginning in December 2019, a new epidemic, called COVID-19 has disrupted our lives. Tt started from the city of Wuhan in China to affect the whole world. This epidemic has changed the health care systems around the world, revealing their shortcomings and bringing attention to effective and efficient management of wards. In this paper, our aim is to investigate how COVID-19 pandemic affecting the Emergency Medicine ward of "San Giovanni di Dio and Ruggi d'Aragona,” also comparing the obtained outcome with respect to the same sample of Cardarelli for unveiling and analyze possible similarities and differences in procedures and suggested possible future directions. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

A Bicentric Study to Investigate the Impact of COVID-19 on Urological Patients

In December 2019, SARS-CoV-2 broke out, which raised great attention worldwide. In fact, it was essential to reorganize the management of economic, infrastructural and medical resources to deal with the inadequate preparation of medical practitioners for this emergency. It was evident that the global health, medical and scientific communities were not adequately prepared for this emergency, so during the pandemic. In this paper, data extracted from hospital discharge records of the Department of Urology of the A.O.R.N "Cardarelli” in Naples, Italy, were used. This work is an extension of a previous work, whose goal concerned how admission procedure in the Urology department of the "San Giovanni di Dio and Ruggi d'Aragona” hospital has been affected by COVID-19 pandemic. In this work we compare the results obtained for the patients of the University Hospital "San Giovanni di Dio and Ruggi d'Aragona” of Salerno and the patients of the A.O.R.N. "Antonio Cardarelli” of Naples (Italy). Data have been extracted from both hospitals discharge records of the Departments of Urology. Experimental analysis performed comparing pre-pandemic data with those collected during the epidemic showed an in-crease in the number of emergency hospitalizations and a decrease in planned pre-admission hospitalizations. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Analysis of the Reorganisation of Skin Transplantation Surgeries During the COVID-19 Pandemic

Coronavirus disease has spread throughout the world rapidly and has changed the world health scenario. Each hospital department was faced with an emergency and then reorganized services. The aim of the present work is to assess the impact of the Covid-19 epidemic on the activity of the transplant center in the A.O.R.N. "Antonio Cardarelli” of Naples (Italy). This study was conducted considering all patients undergoing skin transplantation in the years 2019 (in the absence of Covid-19) and 2020 (in the pandemic emergency). In the work, the logistical regression was used to analyze the tie among hospitalization year (as a dependent variable) and the following independent variables: gender, age, Length of stay (LOS), relative weight DRG, discharge mode and admission procedure. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Analysis of Hospital Admissions of Neurological Patients in the COVID-19 Era: Comparison Between Hospitals

In the last few years, the COVID-19 pandemic has strongly affected different hospital departments, revealing their major weaknesses. For this reason, this emergency has been a driver for healthcare transformation in a very short interval of time in order to optimize the resources, minimize costs and simultaneously increase the caring services, also limiting over-occupancy in wards, especially emergency ones. One of the main factors for assessing the efficiency of a department is associated with how long a patient stays in the facility (LOS). This bicentric study investigated how COVID-19 has modified the activity of the Complex Operative Unit (C.O.U.) of Neurology and Stroke of the University Hospital "San Giovanni di Dio e Ruggi d'Aragona” of Salerno (Italy) and the hospital A.O.R.N. "Antonio Cardarelli” of Naples (Italy). In the work data for the year 2019 (in the absence of Covid-19) and in the year of Covid-19 pandemic 2020 were considered. This work used the logistic regression technique to study the following variables: age, gender, length of stay (LOS), relative weight of DRG and mode of discharge. The analysis shows that in 2020 there was a greater adequacy of admissions, with an increase in the relative weight of DRG. And the statistical analysis obtained the following significant variables: gender, age, relative weight of DRG and discharge mode. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

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.

Analysis of the impact of COVID-19 on the organization of liver transplantation

The Coronavirus 2 (SARS-CoV-2), causing severe acute respiratory syndrome, is the source of the global pandemic known as Coronavirus Disease-2019 (COVID-19). The comorbidities, such as diabetes mellitus, cardiovascular disorders such hypertension, kidney disease, lung disease, and age, affect COVID-19 effects severity (1-3). This new disease has changed surgery practice in most countries around the world. (4) In order to maintain social distance, surgery departments may take a variety of steps, such as canceling face-To-face outpatient and nonurgent appointments, screening scheduled clinic visits, conducting telephone consultations with patients who have nonurgent conditions, and rescheduling appointments for a few months. (5) The focus of this work is to analyze the activity of the Department of liver transplant surgery in "A.O.R.N. Antonio Cardarelli"of Naples (Italy) was analyzed. In particular, the data-set obtained in the year 2019 (pre-pandemic) was compared with that in the following year 2020 (pandemic). The data refers to patients undergoing liver transplantation. © 2022 ACM.

A New Hybrid Artificial Intelligence Model for Diseases Identification

Artificial intelligence is increasingly applied in many fields, specially in medicine to assist patients and physicians. Growing datasets provide a sound basis to adapt machine learning methods to identify and detect some diseases. These later, are often very similar which make difficult their identification by chest X-ray images. In this paper, we introduce a diagnostic AI model that allow to separate, diagnose and classify three various diseases: tuberculosis, covid19 and Pneumonia. The proposed model is based on a combination of Deep Learning using the deep SqueezeNet model and Machine Learning: SVM, KNN, Logistic Regression, decision tree and Naive Bayes. The model is applied to a chest X-ray dataset containing images for each type of disease. To train and test our model, we split the image dataset into two training and test subsets in order to differentiate between different disease types. The accuracy show clearly that our model provides better results of diagnosis and identification. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.