In the emerging context of immersive technologies, using Virtual Reality (VR) in education provides innovative ways to experience and understand complex concepts such as chemical reactions. This project demonstrates how VR can revolutionize how students interact with reactants and reaction products, allowing them to observe and manipulate chemical reactions in a controlled and fully immersive environment. By creating a virtual laboratory, participants can directly experience the effects and dynamics of various chemical reactions without the risks of handling real substances. This approach not only increases student engagement and curiosity but also improves information retention and a deep understanding of chemical processes. This project illustrates how the combination of Artificial Intelligence and Virtual Reality can transform STEAM education by providing a more interactive, safe, and accessible educational method. Our solutions present significant advances in educational practices, with the technologies used increasing student engagement exponentially. In other words, by incorporating Artificial Intelligence algorithms within virtual environments, we can take the field of eLearning to a higher level.
This study analyzed EEG signals from patients with spinal cord injuries by examining five different hand-wrist movements. The signal processing steps designed for an automatic and robust EEG-based BCI system were applied sequentially. Initially, 37 different features in the time, frequency, and time-frequency domains were extracted from the preprocessed signal. After trying widely used Manifold Learning (ML) methods in the literature, including t-Distributed Stochastic Neighbor Embedding (t-SNE), Local Linear Embedding (LLE), Multi-Dimensional Scaling (MDS), and ISOmetric Mapping (ISOMAP), we attempted the Spectral Embedding method, which has not yet been utilized in EEG signal analysis. The signals were then classified using three different machine-learning algorithms. The study compared classification performance using the accuracy metric. A multi-class classification method was employed specifically the triple classification method. The most successful performance was achieved by using the ISOMAP machine learning method and kNN classifier for the Pronation-Palmar Grasp-Hand Open combination, with an accuracy of 0.993 +/- 0.016. Other methods used were t-SNE, MDS, LLE, and Spectral Embedding, respectively. Regarding classifiers, the kNN, SVM, and Naive Bayes algorithms were found to be successful in that order. Based on these results, we propose a suitable methodology for designing a robust BCI system.
After a natural disaster, it is very important to be able to make a building damage assessment quickly and accurately. Considering real-world scenarios, one of the most important issues related to the deep learning based computer vision models used in this field is their generalization ability. Ideally, we would like to have a global model which is capable of making damage assessment successfully in all disasters in the future. However, this is a difficult task as images of one disaster varies greatly from images of another due to reasons like different geographical region structure, structure of houses, position of clouds, etc. On the other hand, many damage assessment models are evaluated on an unrealistic in-domain (ID) test set, which contains data from the same disasters in the training and test sets. In this study, we focus on the generalization problem and compare the model performance in an ID test setting with that in an out-of-domain (OOD) test setting, which is more realistic since the test data is formed from disasters not in the training set. We show that the performance of the model is comparatively lower for the OOD test setting and there is a large generalization gap in this case. We conclude that the models should be evaluated by using the OOD test setting. Then, we propose to generate post-disaster images and masks by using generative adversarial network (GAN) models and use the generated data to fine-tune the damage assessment model. It is seen that the generalization ability of the damage assessment model is increased by using this approach.
Finding a job these days is challenging because of the size, diversity, and goals of the market in a society impacted by pandemics, economic crises, or military hostilities. Trust is the most crucial factor in the job domain after performance expectations. It is particularly significant for women, less active job seekers, and people who did not experience job recommendations. Since recommender systems (RS) are one of the most frequently encountered human-centered and online applications in our daily lives, it is important to note that sound principles of trusting the environment of Artificial Intelligence (AI) systems are also required to characterize the trustworthiness of recommender systems. Otherwise, inadequate advice, high expectations and bad interpretations could lead to making bad choices or to demotivating job seekers. This paper expands on previous research, highlighting the point of view of trustworthiness in job recommender systems (JRS) and providing an overview of the dimensions of AI trustworthiness for the job domain. The purpose of this study is to investigate how trustworthy and suggestive outputs can improve the communication between a job mediator and a job seeker by enhancing the credibility of the information provided to job applicants and increasing customer satisfaction.
In this work we have experimented with the use of genetic programming as a feature selection method as well as a classifier to obtain business failure prediction models with different prediction temporal horizons. In the prediction models, a wide set of explanatory variables has been used, all of them based on the annual accounts of the company. In addition, an extended set of explanatory variables incorporating variables from the economic environment has been considered. Comparison of the prediction results between these alternatives shows a trend towards better results using the feature selection process, while there is no trend towards better results using economic environment variables.
