Abstract Automatic text classification, in which textual data is categorized into specified categories based on its content, is a classic issue in the science of Natural Language Processing. In recent years, there has been a notable surge in research on medical text classification due to the increasing availability of medical data like patient medical records and medical literature. Machine learning and statistical methods, such as those used in medical text classification, have proven to be highly efficient for these tasks. However, a significant amount of manual labor is still required to categorize the extensive dataset utilized for training. Recent research have demonstrated the effectiveness of pretrained language models, including machine learning models, in reducing the time and effort required for feature engineering by medical experts. However, there is no statistically significant enhancement in performance when directly applying the machine learning model to the classification task. In this paper, we present a hybrid machine learning model that combines individual traditional algorithms augmented by a genetic algorithm. However, the improved model is designed to enhance performance by optimizing the weight parameter. In this context, the best single model demonstrated commendable accuracy. In addition, when applying the hybridization approach and optimizing the weight parameters, the results were substantially enhanced. The results underscore the superiority of our augmented hybrid model over individual traditional algorithms. We conduct experiments using two distinct types of datasets: one comprising medical records, such as the Heart Failure Clinical Record and another consisting of medical literature, such as PubMed 20k RCT. So, the objective is to clearly showcase the effectiveness of our approach by highlighting the significant enhancements in accuracy, precision, F1-score and Recall achieved through our improved model.

The last two years witnessed a rapid outbreak of the COVID-19 virus with an exponential increase of critical cases leading to death of many infected people, the social distancing remains the common technique adopted by all countries to reduce the contamination risk and is also recommended by WHO (World Health Organization). According to previous research studies in virology, it is reasonable to maintain a distance between people in public areas. In this paper, we propose a method based on wireless localization and distance between wireless users using both machine learning techniques and correlation-distance model elaboration. We collected indoor RSSI data of WIFI stations using smartphones, perform data pre-processing RSSI and feature selection, we apply classification techniques to determine the location, and finally use the correlation between measurements to estimate distance using a proposed model. Results are assessed in terms of classification and fitting accuracy. Our model is able to determine user's location with an accuracy of 91.1 % using classification and distance discrimination method is proposed to detect any breach of social distancing standard.