The education world has moved from analogue mediums to digital. This offers teachers the opportunity to take advantage of tools and features that can decrease the load. By using new technologies, we increase the avail...
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The present paper introduces a mathematical model for the cross-talking between microRNA and Protein. Studying the qualitative properties of the proposed model, we infer that the microRNA is an inhibitor for the Prote...
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In modern numerical simulations, in order to be reliable, a computer code needs to be accurate and fast. The Framework for Combining Optimization and Simulation Software (COSMOS) is a tool which allows for the seamles...
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As global digitization continues to grow, technology becomes more affordable and easier to use, and social media platforms thrive, becoming the new means of spreading information and news. Communities are built around...
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As global digitization continues to grow, technology becomes more affordable and easier to use, and social media platforms thrive, becoming the new means of spreading information and news. Communities are built around sharing and discussing current events. Within these communities, users are enabled to share their opinions about each event. Using Sentiment Analysis to understand the polarity of each message belonging to an event, as well as the entire event, can help to better understand the general and individual feelings of significant trends and the dynamics on online social networks. In this context, we propose a new ensemble architecture, EDSAEnsemble (Event Detection Sentiment Analysis Ensemble), that uses Event Detection and Sentiment Analysis to improve the detection of the polarity for current events from Social Media. For Event Detection, we use techniques based on Information Diffusion taking into account both the time span and the topics. To detect the polarity of each event, we preprocess the text and employ several Machine and Deep Learning models to create an ensemble model. The preprocessing step includes several word representation models: raw frequency, TFIDF, Word2Vec, and Transformers. The proposed EDSA-Ensemble architecture improves the event sentiment classification over the individual Machine and Deep Learning models. Authors
This paper discusses various applications of fractals in neurosciences and presents a methodology for their investigation and modeling with appropriate software. It is presented how to use multifractal analysis for ch...
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Phase Contrast X-ray Imaging represents a technique that has shown remarkable potential in the research field, by providing better visualization of soft tissue, high-contrast images, and high spatial resolution. In th...
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In this paper, we introduce new discourse quality metrics and an evaluation method, and furthermore, we provide a pilot implementation and evaluate it in a specific use case – that of public speaking. Voice analysis ...
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Face emotion recognition is an important subject in fields such as psychology and cognitive sciences, as well as in applications that use machine learning methods to create intelligent models capable of understanding ...
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This paper presents a signal processing framework for automatic anxiety level classification in a virtual reality exposure therapy system. Two types of biophysical data (heart rate and electrodermal activity) were rec...
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In this paper, we propose a genetic algorithm based on behavioral psychology developed by Carl Gustav Jung (16 Personalities model), in which we describe the person's behavioral features related to his personality...
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