{"version":"1.0","provider_name":"JITA -Journal of Information Technology and Application","provider_url":"https:\/\/jita-au.com","author_name":"admin","author_url":"https:\/\/jita-au.com\/index.php\/author\/jita-au-com\/","title":"INTELLIGENT DISTANCE LEARNING SYSTEMS - JITA -Journal of Information Technology and Application","type":"rich","width":600,"height":338,"html":"<blockquote class=\"wp-embedded-content\" data-secret=\"Zx9lri1Rbe\"><a href=\"https:\/\/jita-au.com\/index.php\/2024\/04\/02\/intelligent-distance-learning-systems\/\">INTELLIGENT DISTANCE LEARNING SYSTEMS<\/a><\/blockquote><iframe sandbox=\"allow-scripts\" security=\"restricted\" src=\"https:\/\/jita-au.com\/index.php\/2024\/04\/02\/intelligent-distance-learning-systems\/embed\/#?secret=Zx9lri1Rbe\" width=\"600\" height=\"338\" title=\"&#8220;INTELLIGENT DISTANCE LEARNING SYSTEMS&#8221; &#8212; JITA -Journal of Information Technology and Application\" data-secret=\"Zx9lri1Rbe\" frameborder=\"0\" marginwidth=\"0\" marginheight=\"0\" scrolling=\"no\" class=\"wp-embedded-content\"><\/iframe><script>\n\/*! This file is auto-generated *\/\n!function(d,l){\"use strict\";l.querySelector&&d.addEventListener&&\"undefined\"!=typeof URL&&(d.wp=d.wp||{},d.wp.receiveEmbedMessage||(d.wp.receiveEmbedMessage=function(e){var t=e.data;if((t||t.secret||t.message||t.value)&&!\/[^a-zA-Z0-9]\/.test(t.secret)){for(var s,r,n,a=l.querySelectorAll('iframe[data-secret=\"'+t.secret+'\"]'),o=l.querySelectorAll('blockquote[data-secret=\"'+t.secret+'\"]'),c=new RegExp(\"^https?:$\",\"i\"),i=0;i<o.length;i++)o[i].style.display=\"none\";for(i=0;i<a.length;i++)s=a[i],e.source===s.contentWindow&&(s.removeAttribute(\"style\"),\"height\"===t.message?(1e3<(r=parseInt(t.value,10))?r=1e3:~~r<200&&(r=200),s.height=r):\"link\"===t.message&&(r=new URL(s.getAttribute(\"src\")),n=new URL(t.value),c.test(n.protocol))&&n.host===r.host&&l.activeElement===s&&(d.top.location.href=t.value))}},d.addEventListener(\"message\",d.wp.receiveEmbedMessage,!1),l.addEventListener(\"DOMContentLoaded\",function(){for(var e,t,s=l.querySelectorAll(\"iframe.wp-embedded-content\"),r=0;r<s.length;r++)(t=(e=s[r]).getAttribute(\"data-secret\"))||(t=Math.random().toString(36).substring(2,12),e.src+=\"#?secret=\"+t,e.setAttribute(\"data-secret\",t)),e.contentWindow.postMessage({message:\"ready\",secret:t},\"*\")},!1)))}(window,document);\n\/\/# sourceURL=https:\/\/jita-au.com\/wp-includes\/js\/wp-embed.min.js\n<\/script>\n","thumbnail_url":"https:\/\/jita-au.com\/wp-content\/uploads\/2024\/03\/cover_issue_949_en_US.jpg","thumbnail_width":595,"thumbnail_height":793,"description":"Vol. 10 No. 1 (2020): JITA &#8211; APEIRON Dragan Vasiljevi\u0107, Branko Latinovi\u0107 INTELLIGENT DISTANCE LEARNING SYSTEMS Original scientific paper DOI:https:\/\/doi.org\/10.7251\/JIT2001044V Download Article PDF Abstract Models used for creating intelligent systems based on artificial non-chromic networks indicate to the teachers which educational as well as teaching activities should be corrected. Activities that require to be corrected are performed at established distance learning systems and thus can be: lectures, assignments, tests, grading, competitions, directed leisure activities, and case studies. Results regarding data processing in artificial neural networks specifically indicate a specific activity that needs to be maintained, promoted, or changed in order to improve students\u2019 abilities and achievements. The developed models are also very useful to students who can understand their achievements much better as well as to develop their skills for future competencies. These models indicate that students\u2019 abilities are far more developed in those who use some of the mentioned distance learning systems in comparison with the students who learn due to the traditional classes system. Keywords: neural networks, distance learning system, achievements, competencies. Vol. 26 No. 2 (2023): JITA &#8211; APEIRON Igor Shubinsky, Alexey Ozerov Application of Artificial Intelligence Methods for the Prediction of Hazardous Failures Original scientific paper DOI: https:\/\/doi.org\/10.7251\/JIT2302061S Download Article PDF Abstract The availability of real-time data on the state of railway facilities and the state-of-the art technologies for data collection and analysis allow transition to the fourth generation maintenance. It is based on the prediction of the facility functional safety and dependability and the risk-oriented facility management. The article describes an approach to assessing the risks of hazardous facility failures using the latest digital data processing methods. The implementation of this approach will help set maintenance objectives and contribute to the efficient use of resources and the reduction of railway facility managers\u2019 expenditures. Keywords: predictive analysis, maintenance, functional safety, Big Data, Data Science, risk indicators. Vol. 26 No. 2 (2023): JITA &#8211; APEIRON Igor Shubinsky, Alexey Ozerov Application of Artificial Intelligence Methods for the Prediction of Hazardous Failures Original scientific paper DOI: https:\/\/doi.org\/10.7251\/JIT2302061S Download Article PDF Abstract The availability of real-time data on the state of railway facilities and the state-of-the art technologies for data collection and analysis allow transition to the fourth generation maintenance. It is based on the prediction of the facility functional safety and dependability and the risk-oriented facility management. The article describes an approach to assessing the risks of hazardous facility failures using the latest digital data processing methods. The implementation of this approach will help set maintenance objectives and contribute to the efficient use of resources and the reduction of railway facility managers\u2019 expenditures. Keywords: predictive analysis, maintenance, functional safety, Big Data, Data Science, risk indicators."}