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Computers have enabled diverse and precise data processing and analysis for decades. Researchers of humanities and social sciences are increasingly adopting computational tools such as artificial intelligence (AI) and machine learning (ML) to analyse human behaviour in society by identifying patterns within data. In this regard, this paper presents the modelling of teachers and students' perceptions regarding gender bias in text books through AI. The data was collected from 470 respondents through a questionnaire using five different themes. The data was analysed with support vector machines (SVM), decision trees (DT), random forest (RF) and artificial neural networks (ANN). The experimental results show that the prediction of perceptions regarding gender varies according to the theme and leads to the different performances of the AI techniques. However, it is observed that when data from all the themes are combined, the best results are obtained. The experimental results show that ANN, on average, demonstrates the best performance by achieving an accuracy of 87.2%, followed by RF and SVM, which demonstrate an accuracy of 84% and 80%, respectively. This paper is significant in modelling human behaviour in society through AI, which is a significant contribution to the field. [ABSTRACT FROM AUTHOR]

Motor vehicle crashes can lead to traumatic experiences. High-impact collisions usually cause severe injuries or fatalities. A collision warning system that analyzes driving behaviors and warns drivers of impending crashes can prevent road collisions and save lives, so increasing traffic safety. An application of the Naïve Bayes classifier model to determine the potential for rear-end collisions between highway vehicles is presented. The Naïve Bayes classifier is a supervised machine-learning model based on Bayes's theorem. Two vehicles are utilized, with one vehicle following the other. The parameters studied are speed, distance, acceleration, and deceleration. Training examples involving over 100 potential collision scenarios have been evaluated. Simulation results show that the model successfully responds to and correctly predicts potential collisions. [ABSTRACT FROM AUTHOR]

Simple Summary: Soft tissue sarcomas are relatively rare malignant diseases. Part of the diagnosis and follow-up includes medical imaging of the thorax for detection of lung metastases. A Python script was created and trained using a set of lung X-rays and concordant CT scans from a high-volume German-speaking sarcoma center. It is capable of detecting malignant metastasis in the lung with a precision of 71.2%, specificity of 90.5%, sensitivity of 94% and accuracy of 91.2%. Furthermore, the program was able to detect even small nodules with a size <1 cm in conventional X-rays of the thorax. This algorithm was implemented into our daily clinical practice alongside with the radiologists' findings. With this tool we aim to improve the quality of our service and reduce the expenditure of time. Introduction: soft tissue sarcomas are a subset of malignant tumors that are relatively rare and make up 1% of all malignant tumors in adulthood. Due to the rarity of these tumors, there are significant differences in quality in the diagnosis and treatment of these tumors. One paramount aspect is the diagnosis of hematogenous metastases in the lungs. Guidelines recommend routine lung imaging by means of X-rays. With the ever advancing AI-based diagnostic support, there has so far been no implementation for sarcomas. The aim of the study was to utilize AI to obtain analyzes regarding metastasis on lung X-rays in the most possible sensitive and specific manner in sarcoma patients. Methods: a Python script was created and trained using a set of lung X-rays with sarcoma metastases from a high-volume German-speaking sarcoma center. 26 patients with lung metastasis were included. For all patients chest X-ray with corresponding lung CT scans, and histological biopsies were available. The number of trainable images were expanded to 600. In order to evaluate the biological sensitivity and specificity, the script was tested on lung X-rays with a lung CT as control. Results: in this study we present a new type of convolutional neural network-based system with a precision of 71.2%, specificity of 90.5%, sensitivity of 94%, recall of 94% and accuracy of 91.2%. A good detection of even small findings was determined. Discussion: the created script establishes the option to check lung X-rays for metastases at a safe level, especially given this rare tumor entity. [ABSTRACT FROM AUTHOR]