- ItemOpen AccessFundamental research questions and proposals on predicting cryptocurrency prices using DNNsIn last decade, cryptocurrency has emerged in financial area as a key factor in businesses and financial market opportunities. Accurate predictions can assist cryptocurrency investors towards right investing decisions and lead to potential increased profits. Additionally, they can also support policy makers and financial researchers in studying cryptocurrency markets behavior. Nevertheless, cryptocurrency price prediction is considered a very challenging task, due to its chaotic and very complex nature. In this study we investigate three major research questions: i) Can deep learning efficiently predict cryptocurrency prices? ii) Are cryptocurrency prices a random walk process? iii) Is there a proper validation method of cryptocurrency price prediction models? To this end, we evaluate some of the most successful and widely used in bibliography deep learning algorithms forecasting cryptocurrency prices. The results obtained, provide significant evidence that deep learning models are not able to solve this problem efficiently and effectively. Following detailed experimentation and results analysis, we conclude that it is essential to invent and incorporate new techniques, strategies and alternative approaches such as more sophisticated prediction algorithms, advanced ensemble methods, feature engineering techniques and other validation metrics.
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- ItemOpen AccessReviewing machine learning techniques for predicting anxiety disorders.Anxiety disorders are a type of mental disorders characterized by important feelings of fear and anxiety. In recent years, the evolution of machine learning techniques has helped greatly to develop tools assisting doctors to predict mental disorders and support patient care. In this work, a comparative literature search was conducted on research for the prediction of specific types of anxiety disorders and suicide tendency, using machine learning techniques. Eighteen (18) studies were examined, revealing that machine learning techniques can be used for predicting anxiety disorders and two (2) additional studies were examined for predicting suicide tendencies. The accuracy of the results varies according to the type of anxiety disorder and the type of methods utilized for predicting the disorder. We can deduce that significant work has been done on the prediction of anxiety using machine learning techniques. However, in the future we may achieve higher accuracy scores and that could lead to a better treatment support for patients.
- ItemOpen AccessΠροσεγγιστική λύση μιας μη γραμμικής επαναληπτικής εξίσωσης(2014-11-10)Μια απλή μη γραμμική επαναληπτική εξίσωση, η οποία όμως παρουσιάζει πλούσια συμπεριφορά, είναι η y_(i+1)= y_i^2 + c, i=0,1,2,3,… και c πραγματικός αριθμός. Για τους λόγους αυτούς, η εξίσωση αυτή συγκέντρωσε το ενδιαφέρον αρκετών ερευνητών. Αναλυτική λύση έχει μόνο για κάποιες τιμές της παραμέτρου c. Στην εργασία αυτή δίνουμε προσεγγιστική λύση για τις τιμές της παραμέτρου c που είναι αυτό δυνατό.
- ItemOpen Accessjcropeditor - Editor for CROP learning objects written in java(2014-07-30)Concept, Resource, Order, Product (CROP) is a reference architecture for adaptive Learning Objects owned by Semantic Learning Services developed by the second author. According to CROP, composite Objects are essentially recursive, and adaptively is an emergent property of Learning Service communication and collaboration. CROP is formally represented as an OWL ontology consisting of the framework's concepts and its definitions. In this report we present a free, open-source, java-based graphical editor that populates CROP ontology with instances of Learning Objects at runtime through a (guided) graph-like interface. While developing this tool we proceeded to some adjustments on CROP ontology and further clarifications on the architecture. Our ultimate vision is to design Semantic Learning Domains where repositories of such ontologies exist and Services collaborate for delivering adaptive Objects to custom Learners’ needs.