Effat University is a leading private non-profit institution of higher education for women in Saudi Arabia, operating under the umbrella of King Faisal Charitable Foundation. Effat University is strengthened by the legacy of its founder, Effat Al Thunayyan, wife of the late King Faisal.On 30 January 2009, Effat College became Effat University. The inauguration of its three colleges, the establishment of the Research and Consultancy Institute, and success achieved on the academic, education and social levels, paved the way to becoming a university.In 2011, Effat University obtained approval for its first graduate program. Wikipedia.
Asghar H.M.,Effat University
Computers in Human Behavior | Year: 2015
Abstract The popularity of Facebook as a source of information has generated a need for reliable and valid information seeking instruments. Current approaches to measure individual's information seeking behaviors and other motives (e.g., socialization, entertainment, self-seeking, diversion) behind Facebook usage have proved to be problematic as they use theorized variables, fail to measure information seeking, and exclusively take into account uses and gratifications theory (UGT) in social media. In the present study, a 23-item scale of Information Seeking in Facebook (ISFS) reflecting the core information seeking behaviors was developed to measure the information seeking in Facebook usage. The ISFS was administered to Facebook users (N = 150) in order to obtain item analysis and reliability estimates which resulted in a refined 21-item scale. Several self-report measures (General Social Media Usage, Online Friendships, Facebook Friendships, and Social Media Use Integration) were used to obtain construct validity evidence. Strong reliability evidence was found in the data collected with the scale (α =.89) and the ISFS scores converged with scores for other scales of Facebook activities. Given the reliability and validity results with good factor loadings, the ISFS scale was suggested as a method of measuring information seeking in Facebook. Implications for future research and practice are discussed in the light of information seeking in Facebook usage. © 2015 Elsevier Ltd.
Mir N.,Effat University |
Hussain S.A.,Riphah International University
Procedia Computer Science | Year: 2011
With the increase in Internet Technologies, great amount of information is following electronically everyday over the network. Information security is a way to protect information against its confidentiality, reliability and availability. Hiding exchange of information is an important factor in the field of security. Cryptography and Steganography are two very important methods for this purpose and are both used to ensure data confidentiality. In Steganography a cover media is used to hide the existence of data where cryptography is used to protect information by transferring plain text into cipher text. Different methods have been studied for multimedia objects but there are very few methods for hiding information into text without altering its integrity. Web based attacks have been a very common practice in recent years and hence need strong security mechanisms for the sake of secret communication. Many robust algorithms can be developed using text Steganography for web pages as they contain a wide amount of bandwidth. A few techniques using web tools like HTML and XML have been proposed but they do not make use of features of these languages very well. This paper discusses some proposed methods, implementations of different embedding techniques and two different ways for hiding data and also a comparative analysis is made based upon some security variables. Text Steganography is applied on XML files and is further encrypted using a cryptographic algorithm. © 2010 Published by Elsevier Ltd.
Qaisar S.M.,Effat University
International Journal of Circuits, Systems and Signal Processing | Year: 2014
This work is a contribution to enhance the signal processing chain required in remote systems like mobiles, biomedical implants, satellites, etc. The system is powered by a battery therefore it must be power efficient. Filtering is a basic operation, almost required in every signal processing system. The classical filtering is time-invariant, the sampling frequency and the filter order remains unique. Therefore it can render a useless increase of the processing activity, especially in the case of sporadic signals. In this context an adaptive rate filtering technique, based on an event driven sampling is devised. It adapts the sampling frequency and the filter order by analysing the input signal characteristics. It correlates the processing activity to the signal variations. The computational complexity and the output quality of the proposed technique are compared to the classical one for a speech signal. Results show a drastic computational gain, of the proposed technique compared to the classical one, along with a comparable output quality.
Rashid T.,Effat University |
Asghar H.M.,Ludwig Maximilians University of Munich
Computers in Human Behavior | Year: 2016
The widespread technology use among current college and university students has made higher educational institutions worldwide acknowledge the need of incorporating it in teaching and learning for explicit reasons. But does access and usage of technology enhance academic performance and foster student engagement in reality? Researches in the last over two decades have conjectured both the positive and negative outcomes of the students' continuous interface with technology. Student engagement and self-directed learning (SDL) are the two other themes that have independently attracted considerable interest of researchers, ascribable to the explicit and implicit assertions that both are related to the academic success. Additionally, the relationship of technology use with these two academic behaviors have also been investigated although not very extensively. The current study aimed to inspect a path model with technology use, student engagement, self-directed learning and academic performance among undergraduate students. 761 students responded to an online survey comprising three scales: Media and Technology Usage and Attitude Scale (MTUAS), Self-Rating Scale of Self-Directed Learning (SRSSDL), and student version of Utrecht's Work Engagement Scale (UWES-S). The results showed that use of technology has a direct positive relationship with students' engagement and self-directed learning, however, no significant direct effect was found between technology use and academic performance. The findings point towards the complex interchange of relationships of the students' technology use with student engagement, self-directed learning and academic performance. The implications and future research directions are discussed. © 2016 Elsevier Ltd. All rights reserved.
Masetic Z.,International BURCH University |
Subasi A.,Effat University
Computer Methods and Programs in Biomedicine | Year: 2016
Background and objectives: Automatic electrocardiogram (ECG) heartbeat classification is substantial for diagnosing heart failure. The aim of this paper is to evaluate the effect of machine learning methods in creating the model which classifies normal and congestive heart failure (CHF) on the long-term ECG time series. Methods: The study was performed in two phases: feature extraction and classification phase. In feature extraction phase, autoregressive (AR) Burg method is applied for extracting features. In classification phase, five different classifiers are examined namely, C4.5 decision tree, k-nearest neighbor, support vector machine, artificial neural networks and random forest classifier. The ECG signals were acquired from BIDMC Congestive Heart Failure and PTB Diagnostic ECG databases and classified by applying various experiments. Results: The experimental results are evaluated in several statistical measures (sensitivity, specificity, accuracy, F-measure and ROC curve) and showed that the random forest method gives 100% classification accuracy. Conclusions: Impressive performance of random forest method proves that it plays significant role in detecting congestive heart failure (CHF) and can be valuable in expressing knowledge useful in medicine. © 2016 Elsevier Ireland Ltd.