https://digitalintelligentsiaconsultancyservices.com/ojs/index.php/iamecs2/issue/feed Informatica : Journal of Applied Machines Electrical Electronics Computer Science and Communication Systems 2021-06-13T07:49:05+00:00 Ms. P. Padmavathy M.A., M.Phil. digitalintelligentsiacservices@gmail.com Open Journal Systems <p><strong>Informatica : Journal of Applied Machines Electrical Electronics Computer Science and Communication Systems(IJAM-ECS<sup>2</sup>)</strong> is a peer-reviewed, Quarterly, Open Access online journal that publishes original research and review articles on both the research and development aspects of different Engineering subjects such as Mechanical, Electrical, Electronics, Computer Science and Communication Systems. It will also be publishing Conference Proceedings or Highly Specialised Books Volumes. It only receives manuscripts submitted to a Journal that has are submitted through the Journal website for publication. <strong>Informatica : Journal of Applied Machines Electrical Electronics Computer Science and Communication Systems</strong> is an open access publication focusing entirely on publishing high quality papers of all topics of Mechanical, Electrical, Electronics, Computer Science and Communications Systems. This enables fast dissemination so that authors can publish their papers/books chapters in an online issue. The Journal/Series is likely to be covered by Scopus, the largest abstract and citation database of peer-reviewed literature.</p> <p>Peer-review is under the responsibility of the authors/volume editors/conference organizers who according to the international peer-review standards may use single blind, double blind, or open peer review. All information regarding the Journal is given in the Journal Website. For details specific to the conference peer-review process, please contact the conference organizer or the guest editor of the conference or authors of highly specialized books.</p> https://digitalintelligentsiaconsultancyservices.com/ojs/index.php/iamecs2/article/view/56 Editorial Board 2021-06-05T16:45:35+00:00 Dr R Ponnusamy prof.r.ponnusamy@gmail.com <p>Editorial Board</p> 2021-06-13T00:00:00+00:00 Copyright (c) 2021 Copyright (c) 2021 Creative Commons Attribution 4.0 International License. https://digitalintelligentsiaconsultancyservices.com/ojs/index.php/iamecs2/article/view/57 Title Page 2021-06-05T16:54:46+00:00 Dr R Ponnusamy prof.r.ponnusamy@gmail.com <h1 class="page_title">Title Page</h1> 2021-06-13T00:00:00+00:00 Copyright (c) 2021 Creative Commons Attribution-NonCommercial 4.0 International License. https://digitalintelligentsiaconsultancyservices.com/ojs/index.php/iamecs2/article/view/58 Editorial Team 2021-06-05T16:59:16+00:00 Dr R Ponnusamy prof.r.ponnusamy@gmail.com <p>Editorial Team</p> 2021-06-13T00:00:00+00:00 Copyright (c) 2021 Creative Commons Attribution-NonCommercial 4.0 International License. https://digitalintelligentsiaconsultancyservices.com/ojs/index.php/iamecs2/article/view/59 Prediction of the network attacks using the ensemble learning method 2021-06-05T17:01:53+00:00 G. Rajasekaran rockranjith364@gmail.com NK. Rahul Basu rockranjith364@gmail.com S. Ranjih Kumar rockranjith364@gmail.com J Prasanth rockranjith364@gmail.com <p>Generally, to create information for the Intrusion Detection System (IDS), it is essential to set the actual running surroundings to discover all of the <br>opportunities of assaults, which is expensive. To advise a gadget gaining knowledge of a primarily based approach, expect the DOS, R2L, U2R, Probe <br>and ordinary assaults via prediction consequences inside the shape of quality accuracy from evaluating supervise class gadget gaining knowledge of <br>algorithms with vote casting classifiers. Additionally, to assess and talk about the overall performance of various gadget, gaining knowledge of algorithms <br>from the given dataset with assessment class report, become aware of the confusion matrix and categorizing information from precedence. The result <br>suggests that the effectiveness of the proposed gadget gaining knowledge of the set of rules approach may be compared with quality accuracy with <br>precision, Recall and F1 Score.</p> 2021-06-13T00:00:00+00:00 Copyright (c) 2021 Creative Commons Attribution-NonCommercial 4.0 International License. https://digitalintelligentsiaconsultancyservices.com/ojs/index.php/iamecs2/article/view/60 Detecting and Monitoring Students Engagement in MOOC and Virtual Environment using Deep Learning Technique 2021-06-05T17:05:06+00:00 A Ezil Sam Leni praveenraja125@gmail.com Rangesh B praveenraja125@gmail.com Praveen C praveenraja125@gmail.com Sandeep C praveenraja125@gmail.com <p>The Existing System uses various sensors such as, 1. Photoplethysmographic (PPG), 2. Galvanic Skin response.</p> <p>The use of these sensor data makes the whole project expensive. To overcome the costly approach, we have proposed a system that uses the primary laptop/mobile RGB camera to capture the video and extract frames and use a deep learning algorithm; the emotion and engagement of students can be detected and monitored. The algorithm that we use is CNN(Convolutional Neural Network). CNN's are famous records pushed system mastering technique that uses deep mastering to extract capabilities and classify photo records.</p> 2021-06-13T00:00:00+00:00 Copyright (c) 2021 Creative Commons Attribution-NonCommercial 4.0 International License. https://digitalintelligentsiaconsultancyservices.com/ojs/index.php/iamecs2/article/view/61 Real Time Prediction of Taxi Services Using Deep Learning 2021-06-13T07:49:05+00:00 F Mary Harin Fernandez mary.fherin@gmail.com S Moniga mary.fherin@gmail.com H Nandhini mary.fherin@gmail.com M Pavithra mary.fherin@gmail.com <p>Modelling travel requirement is an essential part of transportation organisation. Searching passenger hotspots, balancing requirement and supply <br>problems, and re-allocating taxis to help drivers locate customers can all be aided by analysing demand. Predicting the number of taxi needed around the <br>city will help the taxi owners to coordinate the taxi fleet and reduce passenger and driver wait time. In this project, we propose a sequence learning model <br>that, based on recent requirement and other similar data, can predict the estimated taxi demand in each area of a region. Since future taxi requests are <br>associated with knowledge about past behaviour, recalling the data from the past is basic. By separating the city into more modest regions and foreseeing <br>the interest in each area, we evaluate our approach on a data set of taxi needed. We show that this method outperforms feed-forward neural networks and other prediction methods. We also display how other related data, such as time, temperature and drop-offs, influences the outcomes.</p> 2021-06-01T00:00:00+00:00 Copyright (c) 2021 Creative Commons Attribution-NonCommercial 4.0 International License.