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• LSI with Support Vector Machine for Text Categorization – a practical example with Python

  18-29

  Joao Henrique Gomes Da Costa Cavalcanti

  József Menyhárt

  Views:

  401

  Artificial intelligence is becoming a powerful tool of modernity science, there is even a science consensus about how our society is turning to a data-driven society. Machine learning is a branch of Artificial intelligence that has the ability to learn from data and understand its behavers. Python programming language aiming the challenges of this new era is becoming one of the most popular languages for general programming and scientific computing. Keeping all this new era circumstances in mind, this article has as a goal to show one example of how to use one supervised machine learning method, Support Vector Machine, and to predict movie’s genre according to its description using the programming language of the moment, python. Firstly, Omdb official API was used to gather data about movies, then tuned Support Vector Machine model for Latent semantic indexing capable of predicting movies genres according to its plot was coded. The performance of the model occurred to be satisfactory considering the small dataset used and the occurrence of movies with hybrid genres. Testing the model with larger dataset and using multi-label classification models were purposed to improve the model.

• Manufacturing Process Optimization and Tool Condition Monitoring in Mechanical Engineering

  72-89

  Krisztián Deák

  József Menyhárt

  Views:

  194

  The optimization of manufacturing and production processes with various computer software is essential these days. Solutions on the market allow us to optimize and improve our manufacturing and production processes; one of the most popular software is called Tecnomatrix, which is described in this paper. Tool condition monitoring is a vital part of the manufacturing process in the industry. It requires continuous measurement of the wear of the cutting tool edges to improve the surface quality of the work piece and maintain productivity. Multiple methods are available for the determination of the actual condition of the cutting tool. Vibration diagnostics and acoustic methods are included in this paper. These methods are simple, it requires only high sensitive sensors, microphones, and data acquisition unit to gather the vibration signal and make signal improvement. Extended Taylor equation is applied for tool edge wear ratio. Labview and Matlab software are applied for the measurement and the digital signal processing. Machine learning method with artificial neural network is for the detection and prediction of the edge wear to estimate the remaining useful lifetime (RUL) of the tool.

• Labour Economics - From the Technological Development Perspective

  98-108

  József Boros

  Views:

  72

  The impact of technological advancements on the labor market and innovation processes is a critically important research area. The aim of this study is to examine the emergence and frequency of technological innovations in scientific publications, with a particular focus on the Journal of Labour Economics from 2000 to 2020. The research employs content analysis methods, searching for eight different terms and expressions related to technological development (e.g., technology, artificial intelligence, machine learning) across 1405 articles. The study also analyzes the number of occurrences and annual publication trends of these terms. A total of 9469 instances were identified, indicating that in 64,7% of the cases, at least one technological term appeared. An analysis of annual trends reveals an increase in the usage of certain keywords (technology, artificial intelligence, and machine learning). In a smaller subset of articles, only 1%, technological terms were mentioned at least 50 times. The results suggest that although the topic of technological development plays a significant role in labor market research, the frequency of its appearance and the depth of analysis vary considerably. The increase in the appearance of technological terms is predominantly observed in the fields of artificial intelligence and machine learning. These findings are specific to a single journal, indicating the need for further research involving other labor market journals to ensure representativeness.

• The Impact of Optical Character Recognition Artificial Intelligence on the Labour Market

  9-16

  Zsolt Krutilla

  Zsófia Riczu

  Views:

  374

  Because of present day information technology, there is neither need to plant complicated computers for more millions price if we would like to process and store big amounts of data, nor modelling them. The microprocessors and CPUs produced nowadays by that kind of technology and calculating capacity could not have been imagined 10 years before. We can store, process and display more and more data. In addition to this level of data processing capacity, programs and applications using machine learning are also gaining ground. During machine learning, biologically inspired simulations are performed by using artificial neural networks to able to solve any kind of problems that can be solved by computers. The development of information technology is causing rapid and radical changes in technology, which require not only the digital adaptation of users, but also the adaptation of certain employment policy and labour market solutions. Artificial intelligence can fundamentally question individual labour law relations: in addition to reducing the living workforce, it forces new employee competencies. This is also indicated by the Supiot report published in 1998, the basic assumption of which was that the social and economic regulatory model on which labour law is based is in crisis.

• Maintenance Strategies and Life Cycle Costs of Renewable Energy Systems

  106-116

  Imre Kocsis

  Judit T. Kiss

  Krisztián Deák

  Views:

  141

  Life cycle costs are important factors in decisions on renewable energy investments. Since maintenance costs generally constitute a high portion of the life cycle costs, the maintenance strategy applied in a project can affect the bottom line significantly. The effective maintenance tools used in the production industry (e.g., diagnostics, condition monitoring, data management, integrated information systems, machine learning, and automated decision making) can be involved in planning and maintenance of renewable energy systems to gain the benefits of these approaches. In this paper the effects of maintenance strategies on life cycle costs are investigated and the benefits of up-to-date condition monitoring techniques are presented through case studies.