Nowadays, huge and accessible data is an ever increasing field of study. The change in technology is, in turn, increasing its degree of interactivity, configuring several scenarios of great complexity in which data is understood on the basis of our interaction with it at different levels. Data visualization involves presenting data in graphical or pictorial form which makes the information easy to take in. It helps to explain facts and determine courses of action. Criminology is an interesting application where data visualization plays an important role in terms of prediction and analysis. Crime analysis plays an important role in devising solutions to crime problems and formulating crime prevention strategies. The purpose of this paper is to evaluate the performance of machine learning algorithms, which can be used for analyzing data collected of the past crimes. We identified the most appropriate machine learning algorithm to analyze the collected data from sources specialized in crime prevention. This study helps the institutions against crime to better predict and classify it.
In today's data-driven world, data storage and information extraction are key processes where the focus of researchers and industries is centralized. While relational databases are widely used, knowledge graphs have emerged as a cutting-edge idea and have begun to compete with them as a result of utilization from renowned companies. Nevertheless, creating knowledge graphs is a time-consuming process that needs domain experts' support. This motivated us to research automatic or semi-automatic creation of knowledge graphs from structured or unstructured data. To do that and comprehend the newest advancements in this field, we have analyzed papers published in the recent five years in well-known libraries such as ACM, IEEE, Springer Link, and ScienceDirect. The analysis takes into account the process of building knowledge graphs, which encompasses the steps involved in managing unstructured texts, defining nodes and relationships between them, and developing ontologies. Moreover, widely used machine learning algorithms including support vector machines, neural networks, random forests, and logistic regression, and other algorithms such as K-Means, TF-IDF, BERT, and skip-gram, the usage of graph database platform Neo4j and Python scripting language, were considered. Conclusive to the study, there exist some semi-automatic approaches but the fully automatic ones remain as ideas.
During the last decade, educational institutions have made many steps forward, not only in the teaching of technology, but also using it, mainly in the development of new information systems, which are increasingly required in all sectors of life, including natural sciences. Nowadays ICT is becoming key elements on teaching of the new generation. In the study are presented different platforms that can be used for teaching purposes in pre-university cycles. Platforms are discussed for their accessibility, how they work, how user-friendly are, the benefits of using them, and comparing them.
In the fintech industry, credit scoring plays a vital role in helping lenders assess the creditworthiness of potential borrowers. While traditional methods rely on regression analysis, machine learning techniques have become a popular alternative due to their increased accuracy and efficiency. However, effective credit scoring involves more than just selecting the right method; it requires a robust risk management framework that considers various data sources. In this study, we evaluate the effectiveness and efficiency of regression analysis and machine learning techniques in predicting creditworthiness, while also considering their role in an overall risk management strategy.
With the development of information and communication technologies (ICT) and the increasing of online teaching materials, ICT is becoming an essential aspect of teachers’ education and competencies. This paper aims to investigate how and why Albanian biology student-teachers use ICT during their master studies. The study method involved a survey (n = 56) of second-year biology student-teachers studying at the Faculty of Natural Sciences in two consecutive academic years, 2021-2022 and 2022-2023. The survey explored several topics, including general characteristics (age, gender, academic performance), access to digital equipment, digital competence, and the use of ICTs during studies. Our results show that students have low access to digital equipment. Students with high and average academic performance spent more time using ICT for study purposes than those with low academic performance (p<0.05). More than 90% of students emphasize that access to digital equipment and training is crucial to help them integrate ICT in their classes as teachers.
