The evaluation criteria in the road transported with fuzzy logic support

This paper addresses the issue of critical infrastructure protection regarding prevention and response when an incident of road transport. Critical infrastructure is necessary for each country. Security functionality and security of the individual components or system components. When an incident occurs always, distort proper operation. As a result of the incident sends requests to the criteria just before the onset but also in the development we must take into account. Evaluation criteria are important parts not only for prevention of occurrence of the incident as well as reaction. Implications of evaluation criteria are to minimize the risks and consequences of the incident. © 2018, Danube Adria Association for Automation and Manufacturing, DAAAM. All rights reserved

The proposal of security and safety management system with fuzzy logic support

This paper presents the implementation of the fuzzy logic into the security and safety solution in the soft targets. The primary structure of safety and security tool is described in the first part. This tool has been implemented into specific object; for example, for object without security and safety solution (soft targets). In the next part, the primary rules for the use of fuzzy logic are defined. The aim of this paper is to define the primary layout for software which could help operators with decision-making process. The article also proposes and describes the solution for system application of security requirements by understanding the soft targets threats. The system solution is different for each organization and object; however, the main structure is the same

Conditions for testing effects of radio-frequency electromagnetic fields on electronic devices

In the last years, a large growth of radio-frequency electromagnetic interference was detected, especially in the frequency bands from 0.8 GHz up to 6 GHz. Tests of immunity of electrical devices must be carried out, because this type of interference can significantly affect their functionality. The aim of this paper is to explain the issue of electromagnetic susceptibility and describe the conditions of testing the immunity of electronic devices against the radio-frequency electromagnetic field according to the relevant electromagnetic compatibility standards. Also, sample electromagnetic immunity tests on the basic set of the intrusion and hold-up alarm system will be presented. © 2018, World Scientific and Engineering Academy and Society. All rights reserved.IGA / CebiaTech / 2017/006; 2017-2019, Ministry of Education of the Republic of Belarus; MSMT-7778/2014, Novartis Foundation for Sustainable Development; LO1303, Novartis Foundation for Sustainable Development; MEYS, Ministry of Education, Youth and Science; FEDER, European Regional Development Fund; CZ .1.05 / 2.1.00 / 03.0089; VI2017201905

Immunity of electronic devices against radio-frequency electromagnetic fields

One of the major types of electromagnetic interference, which affect electronic devices in their normal operation, is the interference with radio-frequency electromagnetic fields. This interference is generated by the mainly radio and television transmitters, industrial equipment and other transmitters and receivers in general use for communication. Testing of electromagnetic susceptibility of electronic devices on radiated radio-frequency electromagnetic field is governed by the basic standard IEC 61000-4-3 and the equipment under test are exposed to test electromagnetic fields with an intensity from 1 V/m to 30 V/m, the most often in the frequency range from 80 MHz to 2 GHz. The aim of this paper is to explain the issue of electromagnetic susceptibility and to present sample the electromagnetic immunity tests of the basic set of the intrusion and hold-up alarm system against the radio-frequency electromagnetic field according to the relevant electromagnetic compatibility standards. © The Authors, published by EDP Sciences, 2017

Web page content adjustment for search engines using machine learning and natural language processing

Optimizacija mrežnih stranica za tražilice (engl. Search engine optimization, SEO) podrazumijeva tehnike pomoću kojih autor mrežnih stranica provodi nad svojim stranicama kako bi one što bolje rangirale u organskim (prirodnim) rezultatima pretraživanja na internetskim tražilicama za odabrane ključne riječi. Taj proces između ostalog uključuje i optimizaciju sadržaja, odnosno prilagodbu sadržaja mrežnih stranica prema preporukama za optimizaciju mrežnih stranica za tražilice (u daljem tekstu SEO preporukama). Ovim istraživanjem ispituje se mogućnost upotrebe strojnog učenja za klasifikaciju mrežnih stranica u tri predefinirane klase s obzirom na stupanj prilagodbe sadržaja SEO preporukama. Pomoću strojnoga učenja izgrađeni su klasifikatori koji su naučili svrstati nepoznati uzorak (mrežnu stranicu) u predefinirane klase, te utvrditi značajne faktore (varijable) koje utječu na stupanj prilagodbe. Također izgrađen je sustav ispravka „neprilagođenih“ stranica upotrebom tehnika iz domene obrade prirodnog jezika. Rezultati su pokazali da se pomoću strojnog učenja može ocijeniti stupanj prilagođenosti stranice SEO preporukama, da se strojno učenje može koristiti za utvrđivanje značajnih faktora, te da se izgrađeni sustav prilagodbe može koristiti za ispravak tj. poboljšanje mrežnih stranica koje su u prethodnim fazama klasificirane kao "neprilagođene".Search engine optimization (SEO) involves techniques by which the author of the website customizes the website so that it ranks higher in organic (natural) search results on popular Internet search engines for selected keywords. This process includes, among others, the optimization of content (text) to fit SEO recommendations. This study examines the possibility of using machine learning tecniques to classify web pages into three predefined classes related to the degree of content adjustment to the SEO recommendations. Using machine learning algorithms, classifiers are built and trained to classify an unknown sample (web page) in the predefined classes and to identify important factors that affect the degree of adjustment. In addition, using algorithms from the domain of natural language processing a system for correction is built and tested. Results show that machine learning can be used to predict the degree of adjustments of web pages to SEO recommendations, for identifying important SEO factors and that the proposed correction system can be used to correct pages which were classified as "misfits" in prior stages

Web page content adjustment for search engines using machine learning and natural language processing

Optimizacija mrežnih stranica za tražilice (engl. Search engine optimization, SEO) podrazumijeva tehnike pomoću kojih autor mrežnih stranica provodi nad svojim stranicama kako bi one što bolje rangirale u organskim (prirodnim) rezultatima pretraživanja na internetskim tražilicama za odabrane ključne riječi. Taj proces između ostalog uključuje i optimizaciju sadržaja, odnosno prilagodbu sadržaja mrežnih stranica prema preporukama za optimizaciju mrežnih stranica za tražilice (u daljem tekstu SEO preporukama). Ovim istraživanjem ispituje se mogućnost upotrebe strojnog učenja za klasifikaciju mrežnih stranica u tri predefinirane klase s obzirom na stupanj prilagodbe sadržaja SEO preporukama. Pomoću strojnoga učenja izgrađeni su klasifikatori koji su naučili svrstati nepoznati uzorak (mrežnu stranicu) u predefinirane klase, te utvrditi značajne faktore (varijable) koje utječu na stupanj prilagodbe. Također izgrađen je sustav ispravka „neprilagođenih“ stranica upotrebom tehnika iz domene obrade prirodnog jezika. Rezultati su pokazali da se pomoću strojnog učenja može ocijeniti stupanj prilagođenosti stranice SEO preporukama, da se strojno učenje može koristiti za utvrđivanje značajnih faktora, te da se izgrađeni sustav prilagodbe može koristiti za ispravak tj. poboljšanje mrežnih stranica koje su u prethodnim fazama klasificirane kao "neprilagođene".Search engine optimization (SEO) involves techniques by which the author of the website customizes the website so that it ranks higher in organic (natural) search results on popular Internet search engines for selected keywords. This process includes, among others, the optimization of content (text) to fit SEO recommendations. This study examines the possibility of using machine learning tecniques to classify web pages into three predefined classes related to the degree of content adjustment to the SEO recommendations. Using machine learning algorithms, classifiers are built and trained to classify an unknown sample (web page) in the predefined classes and to identify important factors that affect the degree of adjustment. In addition, using algorithms from the domain of natural language processing a system for correction is built and tested. Results show that machine learning can be used to predict the degree of adjustments of web pages to SEO recommendations, for identifying important SEO factors and that the proposed correction system can be used to correct pages which were classified as "misfits" in prior stages

Web page content adjustment for search engines using machine learning and natural language processing

Optimizacija mrežnih stranica za tražilice (engl. Search engine optimization, SEO) podrazumijeva tehnike pomoću kojih autor mrežnih stranica provodi nad svojim stranicama kako bi one što bolje rangirale u organskim (prirodnim) rezultatima pretraživanja na internetskim tražilicama za odabrane ključne riječi. Taj proces između ostalog uključuje i optimizaciju sadržaja, odnosno prilagodbu sadržaja mrežnih stranica prema preporukama za optimizaciju mrežnih stranica za tražilice (u daljem tekstu SEO preporukama). Ovim istraživanjem ispituje se mogućnost upotrebe strojnog učenja za klasifikaciju mrežnih stranica u tri predefinirane klase s obzirom na stupanj prilagodbe sadržaja SEO preporukama. Pomoću strojnoga učenja izgrađeni su klasifikatori koji su naučili svrstati nepoznati uzorak (mrežnu stranicu) u predefinirane klase, te utvrditi značajne faktore (varijable) koje utječu na stupanj prilagodbe. Također izgrađen je sustav ispravka „neprilagođenih“ stranica upotrebom tehnika iz domene obrade prirodnog jezika. Rezultati su pokazali da se pomoću strojnog učenja može ocijeniti stupanj prilagođenosti stranice SEO preporukama, da se strojno učenje može koristiti za utvrđivanje značajnih faktora, te da se izgrađeni sustav prilagodbe može koristiti za ispravak tj. poboljšanje mrežnih stranica koje su u prethodnim fazama klasificirane kao "neprilagođene".Search engine optimization (SEO) involves techniques by which the author of the website customizes the website so that it ranks higher in organic (natural) search results on popular Internet search engines for selected keywords. This process includes, among others, the optimization of content (text) to fit SEO recommendations. This study examines the possibility of using machine learning tecniques to classify web pages into three predefined classes related to the degree of content adjustment to the SEO recommendations. Using machine learning algorithms, classifiers are built and trained to classify an unknown sample (web page) in the predefined classes and to identify important factors that affect the degree of adjustment. In addition, using algorithms from the domain of natural language processing a system for correction is built and tested. Results show that machine learning can be used to predict the degree of adjustments of web pages to SEO recommendations, for identifying important SEO factors and that the proposed correction system can be used to correct pages which were classified as "misfits" in prior stages