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( Sahar Yassine ),( Seifedine Kadry ),( Miguel Angel Sicilia ) 한국인터넷정보학회 2020 KSII Transactions on Internet and Information Syst Vol.14 No.5
The rapid growth of instructional videos repositories and their widespread use as a tool to support education have raised the need of studies to assess the quality of those educational resources and their impact on the quality of learning process that depends on them. Khan Academy (KA) repository is one of the prominent educational videos’ repositories. It is famous and widely used by different types of learners, students and teachers. To better understand its characteristics and the impact of such repositories on education, we gathered a huge amount of KA data using its API and different web scraping techniques, then we analyzed them. This paper reports the first quantitative and descriptive analysis of Khan Academy repository (KA repository) of open video lessons. First, we described the structure of repository. Then, we demonstrated some analyses highlighting content-based growth and evolution. Those descriptive analyses spotted the main important findings in KA repository. Finally, we focused on users’ interactions with video lessons. Those interactions consisted of questions and answers posted on videos. We developed interaction profiles for those videos based on the number of users’ interactions. We conducted regression analysis and statistical tests to mine the relation between those profiles and some quality related proposed metrics. The results of analysis showed that all interaction profiles are highly affected by video length and reuse rate in different subjects. We believe that our study demonstrated in this paper provides valuable information in understanding the logic and the learning mechanism inside learning repositories, which can have major impacts on the education field in general, and particularly on the informal learning process and the instructional design process. This study can be considered as one of the first quantitative studies to shed the light on Khan Academy as an open educational resources (OER) repository. The results presented in this paper are crucial in understanding KA videos repository, its characteristics and its impact on education.
M. Ijaz Khan,Seifedine Kadry,Yu-Ming Chu,El Mostafa Kalmoun,Zulfiqar Ali 한국자기학회 2020 Journal of Magnetics Vol.25 No.2
The present communication develops the governing expressions that describe a steady incompressible two-dimensional flow of micropolar Ferrosoferric Oxide fluid towards a stretched surface under the impact of Lorentz force (magnetic field). Ferrofluids are made out of nanoscale ferromagnetic materials suspended in a base fluid (oil, kerosene, water). The distinction between the magnetorheological fluids (MRF) and ferrofluids (FF) is the size of the materials. The materials in a ferrofluid fundamentally comprise of nanomaterials, which are suspended by Brownian diffusion and generally under normal conditions will not settle. Here, Ferrosoferric Oxide (Fe₃O₄) is considered as nanoparticle and water as a base fluid. The governing equations are modeled by using Tiwari-Das nanofluid model with the help of appropriate similarity transformations. Furthermore, radiative heat flux and convective boundary condition is accounted. The numerical results of the governing equations are obtained through implementation of Built-in-Shooting technique. The impact of radiation parameter, stretching ratio parameter, magnetic parameter, thermal Biot number, micro-rotation parameter, velocity slip parameter and Darcy-Forchheimer number on the flow velocity and temperature are revealed graphically and discussed. The engineering curiosity like skin friction and Nusselt number are computationally computed and tabulated.
An Artificial Intelligence Approach for Word Semantic Similarity Measure of Hindi Language
( Farah Younas ),( Jumana Nadir ),( Muhammad Usman ),( Muhammad Attique Khan ),( Sajid Ali Khan ),( Seifedine Kadry ),( Yunyoung Nam ) 한국인터넷정보학회 2021 KSII Transactions on Internet and Information Syst Vol.15 No.6
AI combined with NLP techniques has promoted the use of Virtual Assistants and have made people rely on them for many diverse uses. Conversational Agents are the most promising technique that assists computer users through their operation. An important challenge in developing Conversational Agents globally is transferring the groundbreaking expertise obtained in English to other languages. AI is making it possible to transfer this learning. There is a dire need to develop systems that understand secular languages. One such difficult language is Hindi, which is the fourth most spoken language in the world. Semantic similarity is an important part of Natural Language Processing, which involves applications such as ontology learning and information extraction, for developing conversational agents. Most of the research is concentrated on English and other European languages. This paper presents a Corpus-based word semantic similarity measure for Hindi. An experiment involving the translation of the English benchmark dataset to Hindi is performed, investigating the incorporation of the corpus, with human and machine similarity ratings. A significant correlation to the human intuition and the algorithm ratings has been calculated for analyzing the accuracy of the proposed similarity measures. The method can be adapted in various applications of word semantic similarity or module for any other language.
Fuzzy Decision Based Energy-Evolutionary System for Sustainable Transport in Ubiquitous Fog Network
Lakhan Abdullah,Mohammed Mazin Abed,Abdulkareem Karrar Hameed,Jaber Mustafa Musa,Kadry Seifedine,Nedoma Jan,Martinek Radek 한국컴퓨터산업협회 2023 Human-centric Computing and Information Sciences Vol.13 No.-
These days, the usage of sustainable transport applications has been growing in practice and has minimized global environmental issues as fuel vehicles did. Sustainable transport applications are distributed and can access data from anywhere in the network. However, due to sustainable electrical transport, much digital data is offloaded to the server to obtain the electricity stations. Therefore, many factors challenge sustainable vehicle applications, such as battery power consumption, service searching cost, execution delay, and execution accuracy. Thus, the existing decision support methods, such as TOPSIS multi-criteria decision method (MCDM), only support the fixed and accurate. Therefore, the fuzzy-based strategy will be more optimal for sustainable transport. The study devises the fuzzy-based energy-efficient decision support system (FBEES), which minimizes energy consumption, delay, and cost and increases scheduling accuracy for sustainable applications. These vehicles are connecting to the ubiquitous fog servers at different data centers in the system and offload their data for their processing. Simulation results show that FBEES minimizes energy by 30%, cost by 29%, delay by 31%, and improves accuracy compared to existing methods for sustainable transport applications.