A hybrid approach based on linguistic analysis and fuzzy logic to ensure the surveillance of people having paranoid personality disorder towards Covid-19 on social media

This paper presents a supervised learning method for paranoid detection in French tweets. A classifier uses four groups of features (textual, linguistic, meta-data, timeline) that exploit a hybrid approach. This approach uses information obtained from the text of tweets by applying Natural Language Processing (NLP) techniques to analyse them, such as morphological analysis, syntactic analysis and sentence embedding. Thus, information about the user such as the number of followers and the number of shared posts. Besides, information about tweets such as the number of symbols and the number of hashtags. Moreover, information about the publication date of tweets such as the number of postings in the morning. Finally, statistical techniques to combine and filter the different types of features extracted from the previous steps in order to calculate the distance between the training corpus (the labelled data) and the test corpus (unlabelled data). In addition, the state mentioned statistical techniques are used for detecting the writing style of patients. In general, our method benefits from different types of features and preserves the principle of relativity by taking advantage of fuzzy logic. Our results are encouraging with an accuracy of 78% for the detection of paranoid people and 70% for the detection of the behaviour of these people towards Covid-19. © 2023 Informa UK Limited, trading as Taylor & Francis Group.

Machine Learning-Based Predictors for ICU Admission of COVID-19 Patients

The burden on the health sector has increased when covid-19 was declared as a critical pandemic, making the decision-taking more crucial. This study aimed mainly to build predictors to aid in making decisions for severe patients to predict whether a patient has to be admitted to the intensive care unit (ICU) based only on the vital records. Statistical techniques were used on the electrical health records (EHR) that were accessible for the covid-19 patients. Samples were processed and then extracted based on criteria that support data imputation. Then, several feature selection techniques were utilized based on the field knowledge, Pearson correlation coefficient, and finally by taking the permutation importance of a hypothetical model to retain features that have the highest relationship with the target variable. Then two versions of data were obtained as stateless and grouped data with and without feature selection which were used to build models with various machine learning algorithms;logistic regression, linear support vector machine SVM, SVM with radial basis function RBF, and artificial neural network ANN. In this respect, the models reached an accuracy of more than 95% in most of the used classifiers and the best one scored is RBF-SVM with accuracy up to 98% and achieve 0.95 areas under curve (AUC) performance. These results indicate that trustworthy models were built to fulfill the high demand for accuracy that is more or less commensurate with the cost of accuracy in the health sector relying only on vital information. © 2022, ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering.

Investigating Novel Coronavirus Through Statistical Methods for Biomedical Applications: An Engineering Student Perspective

The World of today is suffering from novel coronavirus (nCOV2). This is a respiratory infectious disease that has affected the entire globe. This respiratory infection is first originated in Wuhan, China. Today, it has many variants like the “United Kingdom (UK) variant called B.1.1.7,” “South African variant is called B.1.351,” “Brazilian variant is known as P.1,” etc. In this research work, we will discuss the Indian scenario to tackle nCOV2. We will also present an engineering student’s perspective to detect changes developed in the patient’s chest suffering from nCOV2 employing statistical methods. Among all the statistical techniques, GLCM-based texture analysis-based technique has gained popularity due to its diverse applications. It is used in many applications like remote sensing, image processing, biomedical applications, seismic data analysis. Thus in this research work, this methodology is used various changes in the before and after images of the patient suffering from the novel coronavirus. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.