INFORMATICA ECONOMICA

JOURNAL

 

CONTENTS

Vol. 23 No. 1/2019

Issue Topic: Machine Learning Algorithms

An Analysis of the Most Used Machine Learning Algorithms for Online Fraud Detection
Elena-Adriana MINASTIREANU, Gabriela MESNITA 5
Today illegal activities regarding online financial transactions have become increasingly complex and borderless, resulting in huge financial losses for both sides, customers and organizations. Many techniques have been proposed to fraud prevention and detection in the online environment. However, all of these techniques besides having the same goal of identifying and combating fraudulent online transactions, they come with their own characteristics, advantages and disadvantages. In this context, this paper reviews the existing research done in fraud detection with the aim of identifying algorithms used and analyze each of these algorithms based on certain criteria. To analyze the research studies in the field of fraud detection, the systematic quantitative literature review methodology was applied. Based on the most called machine-learning algorithms in scientific articles and their characteristics, a hierarchical typology is made. Therefore, our paper highlights, in a new way, the most suitable techniques for detecting fraud by combining three selection criteria: accuracy, coverage and costs.
Keywords: Bank fraud, Detection algorithms, Machine-Learning algorithms, Online transactions

Evaluating Google Speech-to-Text API's Performance for Romanian e-Learning Resources
Bogdan IANCU 17
This paper presents a way of performing ASR on multimedia e-learning resources available in Romanian with the usage of the Google Cloud Speech-to-Text API. The material presents the history of ASR systems together with the main approaches used by the algorithms behind these systems. The cloud computing providers, that offer ASR solutions via SaaS, are analyzed as well. After performing a short literature review, the author focuses on applying the Google Cloud Speech-to-Text API on various video e-learning resources available online on YouTube. By doing this, the resources can be easily indexed and transformed into searchable materials. The WER score is used in order to measure the accuracy of the model and to compare it with similar works. The results are more than satisfying, thus the proposed model can be used as a method of automating the indexing of multimedia e-learning resources.
Keywords: ASR, Speech-to-text, Romanian, WER, E-learning

Data Mining Methods on Time Price Series for Algorithmic Trading Systems
Cristian PAUNA 26
Buy cheap and sell more expensive. This is the main principle to make a profit on capital markets for hundreds of years. The rule is simple but to apply it in practice has become a very difficult task nowadays, with very high price volatility in the financial markets. Once electronic trading was widespread released, reliable solutions can be found using algorithmic trading systems. This paper presents a data mining method applied to the time price series in order to generate buy and sell decisions using computational algorithms. It was found that an original data mining method based on the price cyclicality function gives us an important profit edge when it is about the capital investments on the short and medium term. The Cyclical Trading Method will be presented together with the main principles and practices to design and optimize trading software. Test results are also included in this article in order to compare the presented method with other known methodologies to trade the capital markets.
Keywords: Data mining, Time price series, Capital markets, Cyclical trading method, Trading algorithms, Trading software

Financial Banking Dataset for Supervised Machine Learning Classification
Irina RAICU 37
Social media has opened new avenues and opportunities for financial banking institutions to improve the quality of their products and services and to understand and to adapt to their customers' needs. By directly analyzing the feedback of its customers, financial banking institutions can provide personalized products and services tailored to their customer needs. This paper presents a research framework for creation of a financial banking dataset in order to be used for Sentiment Classification using various Machine Learning methods and techniques. The dataset contains 2234 financial banking comments from Romanian financial banking social media collected via web scraping technique.
Keywords: Dataset, Financial banking, Web scraping, Opinion mining, Machine learning

The Kullback-Leibler Divergence Class in Decoding the Chest Sound Pattern
Antonio CLIM, Razvan Daniel ZOTA 50
Kullback-Leibler Divergence Class or relative entropy is a special case of broader divergence. It represents a calculation of how one probability distribution diverges from another one, expected probability distribution. Kullback-Leibler divergence has a lot of real-time applications. Even though there is a good progress in the field of medicine, there is a need for a statistical analysis for supporting the emerging requirements. In this paper, we are discussing the application of Kullback-Leibler divergence as a possible method for predicting hypertension by using chest sound recordings and machine learning algorithms. It would have a major out-reached benefit in emergency health care systems. Decoding the chest sound pattern has a wide degree in distinguishing different irregularities and wellbeing states of a person in the medicinal field. The proposed method for the estimation of blood pressure is chest sound analysis using a method that creates a record of sounds delivered by the contracting heart, coming about because of valves and related vessels vibration and analyzing it with the help of Kullback-Leibler divergence and machine algorithm. An analysis using the Kullback-Leibler divergence method will allow finding the difference in chest sound recordings which can be evaluated by a machine learning algorithm. The report also proposes the method for analysis of chest sound recordings in Kullback-Leibler divergence class.
Keywords: Relative entropy, k-nearest neighbors algorithm, Bolster vector machines, Gaussian blend display, Chest sound pattern

The Impact of Information Society and Cyber-Culture in Greek Tourism Phenomenon
Maria MANOLΑ 61
First, in this paper we will investigates the influence of one of the most important Greek film directors, Angelopoulos, in all national, European and worldwide tourism thru the eyes of cinema camera and Greek cyber-culture. Initially, his award-winning action and the content of his most important work, has always been united with Greece promotion and touristic development. The paper traces how, in the context of information society, his filmography can be an example of good practice for touristic promotion and development and how this kind of cinematography can be a more dynamic section of Greek touristic economy. Second, this paper aims to investigate how ancient drama which flourished in Greek antiquity still represents a portal of touristic attraction and development. Ancient Greek tragedians as well as Aristophanes’ comedies magnetize and attract tourists and students from Europe and the whole world. In addition, touristic destinations where tragedy flourished have a huge number of views from tourists. Finally, we present a case study that analyzes the meaning of literary tourism and examines the prospects of its development in Greece. Through conceptual analyzes in two examples it attempts to present the wealth of literature in regard to the style of writing, values and meanings. The aim of the study is to examine the ways that will help the development of tourism through literature as well as to attract potential tourists to these island destinations.
Keywords: Information society, Cyber-Culture, Filmography, Touristic economy, Publicity, literary tourism.

The 18th International Conference on Informatics in Economy, IE 2019 72

Publishing Guide for Authors 73

INFOREC Association 75





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