An optimization approach and a model for Job Shop Scheduling Problem with Linear Programming

Optimization approaches and models are developed for job shop scheduling problems over the last decades, particularly the most attempts have been done in industry and considerable progress has been made on an academic line. The Job-shop scheduling considered the most significant industrial activities, mostly in manufacturing. The JSSP (Job Shop Scheduling Problems) is typical NP-hard problem. To solve this problem, we have used the linear programming approach. Real data have been taken from the company of the metalworking industry. The model has been created, then it was analyzed using Spreadsheet – Excel Solver. The appropriate sequence has been obtained and the results shown that it is possible to achieve the minimum completion time compared to other sequence combination

Development of the Infocommunication System for the Intelligent Rail Transport System of Dangerous Goods in Hungary

Through the examples of recent accidents in Hungary and abroad, we will reveal the possible reasons for the release of dangerous goods into the environment, and, considering these accidents, we will draw conclusions for the safety of rail transport of dangerous goods. While examining the situation of the rail transport of dangerous goods in Hungary, we will present the fundamental concepts and regulations of the transport of dangerous goods in Europe. Today the system of the transport of dangerous goods greatly relies on manual recording and identification methods. Our aim is to provide the ICT bases for tracking the transport of such goods and monitoring their storage conditions, by making suggestions for the development of a monitoring and tracking system to increase the safety of the rail transport system, provide protection for the critical rail infrastructure and ensure the safety of the transport of dangerous goods

BRAIN. Broad Research in Artificial Intelligence and Neuroscience-Review of Recent Trends in Measuring the Computing Systems Intelligence

<p>Many difficult problems, from the philosophy of computation point of view, could require<br> computing systems that have some kind of intelligence in order to be solved. Recently, we have<br> seen a large number of artificial intelligent systems used in a number of scientific, technical and<br> social domains. Usage of such an approach often has a focus on healthcare. These systems can<br> provide solutions to a very large set of problems such as, but not limited to: elder patient care;<br> medical diagnosis; medical decision support; out-of-hospital emergency care; drug classification<br> among others. A recent key focus is that most of these developed intelligent systems are agent-based<br> approaches, or in other words, they can be considered as agent-based intelligent systems (ABISs).<br> ABISs are formally based on a set of interacting intelligent agents (IAs) in addition to the use of<br> intelligent cooperative approaches namely forming intelligent cooperative multiagent systems<br> (ICMASs). The main direction of study consists in the possibility to measure the artificial systems<br> intelligence, frequently called machine intelligence quotient (MIQ). Recently, we performed some<br> research related to the measuring of the machine intelligence. There is presented a comprehensive<br> review of the scientific literature related to the measuring of the MIQ. We consider that the<br> measuring of the machine intelligence is very actual and important, which could allow the<br> differentiation of ABISs based on their intelligence, choosing of the agent-based systems able to<br> solve the most intelligently specific problems. As the main conclusion of the performed study, we<br> mention that cannot be given a unanimous definition of the ABISs intelligence. Even if the machine<br> intelligence cannot be defined, it could be measured. We discuss this affirmation more in-depth in<br> the paper. This is similar to the human intelligence that is not understood very well but can be<br> measured using human intelligence tests.</p

BRAIN. Broad Research in Artificial Intelligence and Neuroscience-Review of Recent Trends in Measuring the Computing Systems Intelligence

Many difficult problems, from the philosophy of computation point of view, could require computing systems that have some kind of intelligence in order to be solved. Recently, we have seen a large number of artificial intelligent systems used in a number of scientific, technical and social domains. Usage of such an approach often has a focus on healthcare. These systems can provide solutions to a very large set of problems such as, but not limited to: elder patient care; medical diagnosis; medical decision support; out-of-hospital emergency care; drug classification among others. A recent key focus is that most of these developed intelligent systems are agent-based approaches, or in other words, they can be considered as agent-based intelligent systems (ABISs). ABISs are formally based on a set of interacting intelligent agents (IAs) in addition to the use of intelligent cooperative approaches namely forming intelligent cooperative multiagent systems (ICMASs). The main direction of study consists in the possibility to measure the artificial systems intelligence, frequently called machine intelligence quotient (MIQ). Recently, we performed some research related to the measuring of the machine intelligence. There is presented a comprehensive review of the scientific literature related to the measuring of the MIQ. We consider that the measuring of the machine intelligence is very actual and important, which could allow the differentiation of ABISs based on their intelligence, choosing of the agent-based systems able to solve the most intelligently specific problems. As the main conclusion of the performed study, we mention that cannot be given a unanimous definition of the ABISs intelligence. Even if the machine intelligence cannot be defined, it could be measured. We discuss this affirmation more in-depth in the paper. This is similar to the human intelligence that is not understood very well but can be measured using human intelligence tests