1.0JITA -Journal of Information Technology and Applicationhttps://jita-au.comadminhttps://jita-au.com/index.php/author/jita-au-com/rich600338<blockquote class="wp-embedded-content" data-secret="qwLiqv9t2P"><a href="https://jita-au.com/index.php/2024/04/02/ambient-intelligence-and-e-learning/">AMBIENT INTELLIGENCE AND E-LEARNING</a></blockquote><iframe sandbox="allow-scripts" security="restricted" src="https://jita-au.com/index.php/2024/04/02/ambient-intelligence-and-e-learning/embed/#?secret=qwLiqv9t2P" width="600" height="338" title="“AMBIENT INTELLIGENCE AND E-LEARNING” — JITA -Journal of Information Technology and Application" data-secret="qwLiqv9t2P" frameborder="0" marginwidth="0" marginheight="0" scrolling="no" class="wp-embedded-content"></iframe><script> /*! This file is auto-generated */ !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); //# sourceURL=https://jita-au.com/wp-includes/js/wp-embed.min.js </script> https://jita-au.com/wp-content/uploads/2024/03/cover_issue_949_en_US.jpg595793Vol. 10 No. 1 (2020): JITA – APEIRON Julijana Vasiljević, Željko Stanković AMBIENT INTELLIGENCE AND E-LEARNING Original scientific paper DOI:https://doi.org/ 10.7251/JIT2001035V Download Article PDF Abstract The use of ambient intelligence knowledge inevitably leads to a new education concept particularly in creating an environment towards the implementation of teaching as well as the process of education. The process of teaching and education, besides conventional and physical elements of the environment, will be enriched with elements regarding modern information technology. Ambient intelligence will be presented in this paper as a result of the artificial algorithm neural networks, through the following contexts: e-learning environment, identification, and security. The key role in raising students’ achievements as well as competency levels belongs to modern information technology which works towards creating ambient intelligence. It is also executed through the concept of e-learning onto one of the convenient learning management platforms. Survey results indicate that with the use of ambient intelligence, better results are achieved, especially in mathematics taught at the elementary school level. Furthermore, learned lessons are memorized by students for a long period, which is proved by higher levels of students’ knowledge and skill acquisition in terms of general progress. Keywords: ambient intelligence, e-learning, neural networks. Vol. 26 No. 2 (2023): JITA – 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’ expenditures. Keywords: predictive analysis, maintenance, functional safety, Big Data, Data Science, risk indicators. Vol. 26 No. 2 (2023): JITA – 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’ expenditures. Keywords: predictive analysis, maintenance, functional safety, Big Data, Data Science, risk indicators.