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Ali Aseere
College of Computer Science, King Khalid University, Abha 62529, Saudi Arabia

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Journal article
Published: 14 February 2021 in Sustainability
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In the higher education sector, there is a growing trend to offer academic information to users through websites. Contemporarily, the users (i.e., students/teachers, parents, and administrative staff) greatly rely on these websites to perform various academic tasks, including admission, access to learning management systems (LMS), and links to other relevant resources. These users vary from each other in terms of their technological competence, objectives, and frequency of use. Therefore, academic websites should be designed considering different dimensions, so that everybody can be accommodated. Knowing the different dimensions with respect to the usability of academic websites is a multi-criteria decision-making (MCDM) problem. The fuzzy analytic hierarchy process (FAHP) approach has been considered to be a significant method to deal with the uncertainty that is involved in subjective judgment. Although a wide range of usability factors for academic websites have already been identified, most of them are based on the judgment of experts who have never used these websites. This study identified important factors through a detailed literature review, classified them, and prioritized the most critical among them through the FAHP methodology, involving relevant users to propose a usability evaluation framework for academic websites. To validate the proposed framework, five websites of renowned higher educational institutes (HEIs) were evaluated and ranked according to the usability criteria. As the proposed framework was created methodically, the authors believe that it would be helpful for detecting real usability issues that currently exist in academic websites.

ACS Style

Abdulhafeez Muhammad; Ansar Siddique; Quadri Naveed; Uzma Khaliq; Ali Aseere; Mohd Hasan; Mohamed Qureshi; Basit Shehzad. Evaluating Usability of Academic Websites through a Fuzzy Analytical Hierarchical Process. Sustainability 2021, 13, 2040 .

AMA Style

Abdulhafeez Muhammad, Ansar Siddique, Quadri Naveed, Uzma Khaliq, Ali Aseere, Mohd Hasan, Mohamed Qureshi, Basit Shehzad. Evaluating Usability of Academic Websites through a Fuzzy Analytical Hierarchical Process. Sustainability. 2021; 13 (4):2040.

Chicago/Turabian Style

Abdulhafeez Muhammad; Ansar Siddique; Quadri Naveed; Uzma Khaliq; Ali Aseere; Mohd Hasan; Mohamed Qureshi; Basit Shehzad. 2021. "Evaluating Usability of Academic Websites through a Fuzzy Analytical Hierarchical Process." Sustainability 13, no. 4: 2040.

Journal article
Published: 01 April 2019 in Technologies
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Energy is considered the most costly and scarce resource, and demand for it is increasing daily. Globally, a significant amount of energy is consumed in residential buildings, i.e., 30–40% of total energy consumption. An active energy prediction system is highly desirable for efficient energy production and utilization. In this paper, we have proposed a methodology to predict short-term energy consumption in a residential building. The proposed methodology consisted of four different layers, namely data acquisition, preprocessing, prediction, and performance evaluation. For experimental analysis, real data collected from 4 multi-storied buildings situated in Seoul, South Korea, has been used. The collected data is provided as input to the data acquisition layer. In the pre-processing layer afterwards, several data cleaning and preprocessing schemes are applied to the input data for the removal of abnormalities. Preprocessing further consisted of two processes, namely the computation of statistical moments (mean, variance, skewness, and kurtosis) and data normalization. In the prediction layer, the feed forward back propagation neural network has been used on normalized data and data with statistical moments. In the performance evaluation layer, the mean absolute error (MAE), mean absolute percentage error (MAPE), and root mean squared error (RMSE) have been used to measure the performance of the proposed approach. The average values for data with statistical moments of MAE, MAPE, and RMSE are 4.3266, 11.9617, and 5.4625 respectively. These values of the statistical measures for data with statistical moments are less as compared to simple data and normalized data which indicates that the performance of the feed forward back propagation neural network (FFBPNN) on data with statistical moments is better when compared to simple data and normalized data.

ACS Style

Muhammad Fayaz; Habib Shah; Ali Mohammad Aseere; Wali Khan Mashwani; Abdul Salam Shah. A Framework for Prediction of Household Energy Consumption Using Feed Forward Back Propagation Neural Network. Technologies 2019, 7, 30 .

AMA Style

Muhammad Fayaz, Habib Shah, Ali Mohammad Aseere, Wali Khan Mashwani, Abdul Salam Shah. A Framework for Prediction of Household Energy Consumption Using Feed Forward Back Propagation Neural Network. Technologies. 2019; 7 (2):30.

Chicago/Turabian Style

Muhammad Fayaz; Habib Shah; Ali Mohammad Aseere; Wali Khan Mashwani; Abdul Salam Shah. 2019. "A Framework for Prediction of Household Energy Consumption Using Feed Forward Back Propagation Neural Network." Technologies 7, no. 2: 30.