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INFORMATIONAL WARFARE – INFLUENCE ON INFORMATIONAL STRUCTURES

2019, Grabusts, Pēteris, Zorins, Aleksejs, Teilāns, Artis

The concept of information warfare encompasses the use of information and communication technologies to gain an advantage over a potential opponent. The information warfare is the manipulation with the information that trusts the goal, so that the goal should make decisions about its interests in the interests of opponents. Information structures are treated as systems that process different types of information, provide storage and access to users. Such structures may enclose neural networks, self-learning systems etc. They need to be ready to learn, respond to threats and ensure their safety, which is topical in today's information warfare. This paper will address aspects related to the security of information systems from a system theory point of view. The knowledge base of information structures can be elements of artificial intelligence, which security must be protected against various threats. The authors considers artificial neural networks to be one of the potential threats in the context of information warfare.

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METHOD FOR COLOR LASER MARKING PROCESS OPTIMIZATION WITH THE USE OF GENETIC ALGORITHMS

2017, Pavels Narica, Lazov, Lyubomir, Teilāns, Artis, Grabusts, Pēteris, Teirumnieks, Edmunds, Pavels Cacivkins

Optimization of color laser marking process mostly depends on effective identification of optimal values of laser marking parameters. This is a difficult combinatorial optimization problem, which is still essential for companies that use laser marking systems. The study proposes a new approach to the process optimization through the use of genetic algorithms, carrying out preliminary experimental investigation, analyzing the laser marking results, and presenting possible improvements to the current implementation of genetic algorithms.

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E-LEARNING: DEVELOPING TOMORROW'S EDUCATION

2021, Rohit, Grabusts, Pēteris, Teilāns, Artis

E-learning refers to the term to deliver education or training using digital resources. Computer-based learning, which is considered the keystone of today's E-learning concept, was born in the 80s. Earlier E-learning provides education using only text as with the development in technology it allows adding various forms, i.e., Graphical Text, Images, Video Conferencing etc. In today's time, the concept of is E-learning growing at a rapid pace. Improved bandwidth and growing technology helped in pushing the expansion of E-learning. Along with the university, large corporate companies are also resorting to E-learning. E-learning provides many advantages as compare to Instructor-led training (ILT). E-learning saves the times of travel as physical presence is not required. Education can be provided from anywhere at any time. E-learning is cost-effective also as the course, once developed, can be modified easily. There can some concern which can be faced by the trainer and leaners in future. Adopting E-learning will be a step towards saving the environment. It will be environmentally friendly as tablets will replace books; paper notes will be replaced with digital messages. Digital tools will help to reduce the burden of a student. Artificial Intelligence is a prevalent concept in computer science. A branch of AI, known as a Neural Network, is based on the human brain. The research's main aim is to review existing methods and analyse further possibilities of E-learning systems with neural networks.

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CORAS for Threat and Risk Modeling in Social Networks

2015, Aleksandrs Larionovs, Teilāns, Artis, Grabusts, Pēteris

As more users joining social networks possibility of threats is growing, as the information can be reached by expanding number of individuals that increases the possibility that the information “package” will find way to subjects with the appropriate degree of sensitivity to the information – vulnerability. Therefore, the risk management process and, in particular, the risk identification and analysis of key characteristics should be performed. Presented paper describes usage of CORAS methodology for modelling of social network risks.