Utilizing Machine Learning to Reassess the Predictability of Bank Stocks

Objectives: Accurate prediction of stock market returns is a very challenging task due to the volatile and non-linear nature of the financial stock markets. In this work, we consider conventional time series analysis techniques with additional information from the Google Trend website to predict stock price returns. We further utilize a machine learning algorithm, namely Random Forest, to predict the next day closing price of four Greek systemic banks. Methods/Analysis: The financial data considered in this work comprise Open, Close prices of stocks and Trading Volume. In the context of our analysis, these data are further used to create new variables that serve as additional inputs to the proposed machine learning based model. Specifically, we consider variables for each of the banks in the dataset, such as 7 DAYS MA,14 DAYS MA, 21 DAYS MA, 7 DAYS STD DEV and Volume. One step ahead out of sample prediction following the rolling window approach has been applied. Performance evaluation of the proposed model has been done using standard strategic indicators: RMSE and MAPE. Findings: Our results depict that the proposed models effectively predict the stock market prices, providing insight about the applicability of the proposed methodology scheme to various stock market price predictions. Novelty /Improvement: The originality of this study is that Machine Learning Methods highlighted by the Random Forest Technique were used to forecast the closing price of each stock in the Banking Sector for the following trading session. Doi: 10.28991/ESJ-2023-07-03-04 Full Text: PD

Utilizing a Restricted Access e-Learning Platform for Reform, Equity, and Self-development in Correctional Facilities

Objectives: The goal of this paper is to address the issues that arose because of the exclusion of law offenders in the Greek Correctional Institutions from second chance education during the COVID-19 pandemic. During this period, the offenders were deprived of their right to equal access to second-chance education since the pandemics blocked mobility and close contact with teaching personnel. Methods/Analysis: In this paper, we propose a framework based on the Technology Acceptance Model (TAM) that will be deployed to evaluate the acceptance of the CILMS by the learners in Correctional Institutions. We describe a methodology and a set of hypotheses that can reveal the intention of learners to use the system based on several factors, such as trust, perception of privacy, perception of usefulness, and perception of self-efficacy. Findings: We suggest that eLearning and limited Internet access should be added to the list of fundamental human rights for CI detainees as well, in order to counteract their separation from physical society. Inmates are still individuals. In fact, they should be placed in solitary confinement as prescribed by the law. Novelty/Improvement:This viewpoint has been demonstrated with the development and evaluation of acceptance by inmates through the TAM technology acceptance methodology, as well as the proposal of a generic privacy-preserving Web information and services access model for CIs that can, at the same time, provide sufficient information access freedom while respecting the restrictions that should be imposed on such an access for CI inmates. Doi: 10.28991/ESJ-2022-SIED-017 Full Text: PD

Online reservation systems in e-Business: Analyzing decision making in e-Tourism

Tourism is one of the fastest growing industries worldwide and in general, the Internet continues to gain importance in the tourism sector. The study focuses on exploration of knowledge of online booking systems and on the views of local students-users concerning the booking rate based on these online systems. Another perspective of this project is to investigate the decision-making process (emotion-focused) that they follow in order to choose a tourist destination via online booking systems. For the purposes of this study, three scales were administered E-WOM and Accommodation Scale, Emotion-Based Decision-Making Scale and Trait Emotional Intelligence Scale. Then, survey data were collected, preprocessed and analyzed based on Data Mining techniques evaluating the results. More specifically, classification and association algorithms were utilized to manage to describe hidden patterns. E-Tourism will continue to be oriented towards the consumers and the technology that surrounds them, providing dynamic communication in electronic business

Secure Distributed Cloud Storage based on the Blockchain Technology and Smart Contracts

Objectives: This paper addresses the problem of secure data storage and sharing over cloud storage infrastructures. A secure, distributed cloud storage structure incorporating the blockchain structure is proposed that supports confidentiality, integrity, and availability. Methods/Analysis: The proposed structure combines two well-known technologies: one of them is the Ethereum Blockchain and its Smart Contracts and the other is the RSA encryption and authentication scheme. The Ethereum Blockchain is used as a data structure, which ensures data availability and integrity while RSA provides sensitive data confidentiality and source authentication. Findings: As a result, users of the proposed structure can trust it and be certain that they can securely exchange information through a publicly accessible and shared cloud storage. The application can be used either through a user interface (UI) or a command-line interface (CLI). Novelty /Improvement:The novelty of this work is that the system that is proposed could be used for secure data storage on the cloud as well as for file sharing and authentication verification. Also, secure data storage and file sharing are already offered by the proposed system. Doi: 10.28991/ESJ-2023-07-02-012 Full Text: PD

Online reservation systems in e-Business: analyzing decision making in e-Tourism

Tourism is one of the fastest growing industries worldwide and in general, the Internet continues to gain importance in the tourism sector. The study focuses on exploration of knowledge of online booking systems and on the views of local students-users concerning the booking rate based on these online systems. Another perspective of this project is to investigate the decision-making process (emotion-focused) that they follow in order to choose a tourist destination via online booking systems. For the purposes of this study, three scales were administered E-WOM and Accommodation Scale, Emotion-Based Decision-Making Scale and Trait Emotional Intelligence Scale. Then, survey data were collected, preprocessed and analyzed based on Data Mining techniques evaluating the results. More specifically, classification and association algorithms were utilized to manage to describe hidden patterns. E-Tourism will continue to be oriented towards the consumers and the technology that surrounds them, providing dynamic communication in electronic business

Automatic interactive music improvisation based on data mining

One of the main challenges in music improvisation is interactive improvisation between a human and a system. In this thesis we present a musical interactive system (called polyhymnia) acting as melody continuator. For each musical pattern given by the user, it recalls a similar general pattern stored in its memory and reforms it. The proposed system addresses music representation and musical pattern similarity using data mining. We propose a scheme for monophonic music representation as traditional data sets suitable for common data mining algorithms and investigate the application of clustering similarity measures to musical pattern similarity. Data Mining is an emerging machine learning process of extracting previously unknown, actionable information from very large scientific and commercial databases. Machine learning has played a crucial role in the computer music almost since its beginning. Recently, research in the field has focused on music mining. We also present experimental results for testing and evaluating the efficiency and accuracy of the proposed system “polyhymnia”.Μία από τις βασικές προκλήσεις στο μουσικό αυτοσχεδιασμό είναι ο διαδραστικός αυτοσχεδιασμός μεταξύ ενός ανθρώπου και ενός συστήματος. Στη παρούσα ενότητα παρουσιάζουμε ένα μουσικό διαδραστικό σύστημα (Πολύμνια) ως συνεχιστή της μελωδίας (as melody continuator). Για κάθε μουσικό πρότυπο (pattern) που έχει δοθεί από το χρήστη, το ευφυές σύστημα ανακαλεί ένα όμοιο (similar) γενικό πρότυπο που είναι αποθηκευμένο στη βάση του (database) και το οποίο το αναμορφώνει ανάλογα (reform). Το προτεινόμενο σύστημα κατευθύνει τη μουσική αναπαράσταση και την ομοιότητα του μουσικού προτύπου (musical pattern similarity) στη χρήση της εξόρυξης δεδομένων (data mining). Προτείνουμε ένα σχήμα μουσικής αναπαράστασης το οποίο μπορεί να χρησιμοποιηθεί για ανάλυση εξόρυξης δεδομένων (data mining analysis) η οποία στοχεύει στη μάθηση γενικών προτύπων και για τη συχνότητα και για τη διάρκεια σε συγκεκριμένα είδη μουσικής (music styles). Η εξόρυξη δεδομένων είναι μια αναδυόμενη διαδικασία μηχανικής μάθησης με την εξαγωγή προηγουμένως άγνωστων, αγώγιμων (actionable) πληροφοριών από πολύ μεγάλες επιστημονικές και εμπορικές βάσεις δεδομένων. Η μηχανική μάθηση (machine learning) έχει παίξει έναν κρίσιμο ρόλο στη υπολογιστική μουσική (computer music) σχεδόν από την αρχή της. Πρόσφατα η έρευνα στο πεδίο έχει εστιαστεί στην εξόρυξη μουσικής (music mining). Παρουσιάζουμε επίσης πειραματικά αποτελέσματα για έλεγχο και αξιολόγηση της αποδοτικότητας (efficiency) και της ακρίβειας του προτεινόμενου συστήματος «Πολύμνια»