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Ansar Siddique
Department of Software Engineering, University of Gujrat, Punjab 50700, Pakistan

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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 January 2021 in Intelligent Automation & Soft Computing
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ACS Style

Sarah Chaudhry; Fakhra Batool; Abdul Hafeez Muhammad; Ansar Siddique. Designing an Online Appointment System for Semiliterate Users. Intelligent Automation & Soft Computing 2021, 28, 379 -395.

AMA Style

Sarah Chaudhry, Fakhra Batool, Abdul Hafeez Muhammad, Ansar Siddique. Designing an Online Appointment System for Semiliterate Users. Intelligent Automation & Soft Computing. 2021; 28 (2):379-395.

Chicago/Turabian Style

Sarah Chaudhry; Fakhra Batool; Abdul Hafeez Muhammad; Ansar Siddique. 2021. "Designing an Online Appointment System for Semiliterate Users." Intelligent Automation & Soft Computing 28, no. 2: 379-395.

Journal article
Published: 01 January 2021 in Intelligent Automation & Soft Computing
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ACS Style

Quadri Noorulhasan Naveed; Ali M. Aseere; Abdulhafeez Muhammad; Saiful Islam; Mohamed Rafik N. Qureshi; Ansar Siddique; Mohammad Rashid Hussain; Samreen Shahwar. Evaluating and Ranking Mobile Learning Factors Using a Multi-criterion Decision-making (MCDM) Approach. Intelligent Automation & Soft Computing 2021, 29, 111 -129.

AMA Style

Quadri Noorulhasan Naveed, Ali M. Aseere, Abdulhafeez Muhammad, Saiful Islam, Mohamed Rafik N. Qureshi, Ansar Siddique, Mohammad Rashid Hussain, Samreen Shahwar. Evaluating and Ranking Mobile Learning Factors Using a Multi-criterion Decision-making (MCDM) Approach. Intelligent Automation & Soft Computing. 2021; 29 (1):111-129.

Chicago/Turabian Style

Quadri Noorulhasan Naveed; Ali M. Aseere; Abdulhafeez Muhammad; Saiful Islam; Mohamed Rafik N. Qureshi; Ansar Siddique; Mohammad Rashid Hussain; Samreen Shahwar. 2021. "Evaluating and Ranking Mobile Learning Factors Using a Multi-criterion Decision-making (MCDM) Approach." Intelligent Automation & Soft Computing 29, no. 1: 111-129.

Journal article
Published: 01 January 2021 in Intelligent Automation & Soft Computing
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ACS Style

Abdulhafeez Muhammad; Ansar Siddique; Quadri Noorulhasan Naveed; Usman Saleem; Mohd Abul Hasan; Basit Shahzad. Investigating Crucial Factors of Agile Software Development Through Composite Approach. Intelligent Automation & Soft Computing 2021, 27, 15 -34.

AMA Style

Abdulhafeez Muhammad, Ansar Siddique, Quadri Noorulhasan Naveed, Usman Saleem, Mohd Abul Hasan, Basit Shahzad. Investigating Crucial Factors of Agile Software Development Through Composite Approach. Intelligent Automation & Soft Computing. 2021; 27 (1):15-34.

Chicago/Turabian Style

Abdulhafeez Muhammad; Ansar Siddique; Quadri Noorulhasan Naveed; Usman Saleem; Mohd Abul Hasan; Basit Shahzad. 2021. "Investigating Crucial Factors of Agile Software Development Through Composite Approach." Intelligent Automation & Soft Computing 27, no. 1: 15-34.

Article
Published: 01 June 2020 in IET Software
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The field of software development is growing rapidly and prevailing in every walk of life. The role of software developers in such a challenging and complex activity is very much important. The allocation of right software developers (i.e. who possesses appropriate coding skills) to projects is one of the crucial factors for successful software development. The problem is that it is very difficult for a client, project manager, as well as for software development organisations to find out an appropriate developer and assign him/her to a particular project. To achieve this, there is a need for such a sound mechanism that could detect the level of software developer coding expertise. This study has formulated criteria for novice and expert developers and carried out such criteria to discover the level of coding expertise of software developers using three different models of deep learning. These models include long short-term memory (LSTM), convolution 1D and hybrid (a combination of LSTM and convolution 1D). The deep learning models have analysed software developers’ previously written source code collected from the GitHub repository. An experiment was conducted to evaluate the performance of models. The results showed that the LSTM model performed better in comparison to other models by achieving 96.25% accuracy.

ACS Style

Farooq Javeed; Ansar Siddique; Akhtar Munir; Basit Shehzad; Muhammad I.U. Lali. Discovering software developer's coding expertise through deep learning. IET Software 2020, 14, 213 -220.

AMA Style

Farooq Javeed, Ansar Siddique, Akhtar Munir, Basit Shehzad, Muhammad I.U. Lali. Discovering software developer's coding expertise through deep learning. IET Software. 2020; 14 (3):213-220.

Chicago/Turabian Style

Farooq Javeed; Ansar Siddique; Akhtar Munir; Basit Shehzad; Muhammad I.U. Lali. 2020. "Discovering software developer's coding expertise through deep learning." IET Software 14, no. 3: 213-220.

Journal article
Published: 15 May 2020 in Sustainability
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The rapid growth of Information and Communication Technologies (ICT)—specifically, the Internet—has given emergence to e-learning. Resultantly, web-based e-learning systems are being increasingly developed to enhance the learning process. However, the utilization of such systems is low, mainly owing to poor quality content and overall design problems. To improve usage, it is imperative to identify the factors with the most significant impact on the quality of these systems so that the e-learning industry keeps these factors in consideration while developing e-learning systems. This study focused on the identification and prioritization of factors related to the design quality of e-learning systems through a hierarchical quality model. Thus, firstly, an extensive literature review was conducted to identify the factors that most affect the quality of web-based e-learning systems. Secondly, among the identified factors, only those with the most significant effect were considered. To identify the most important quality criteria, a survey was conducted. An instrument was deployed among 157 subjects, including e-learning designers, developers, students, teachers, and educational administrators. Finally, a second instrument was distributed among 51 participants to make a pairwise comparison among the criteria and rank them according to their relative importance. The identified and prioritized factors were classified into four main categories. Among these four factors, content was identified as the most important factor, whereas design was found to be the least important factor.

ACS Style

Abdul Hafeez Muhammad; Ansar Siddique; Ahmed E. Youssef; Kashif Saleem; Basit Shahzad; Adnan Akram; Al-Batool Saleh Al-Thnian. A Hierarchical Model to Evaluate the Quality of Web-Based E-Learning Systems. Sustainability 2020, 12, 4071 .

AMA Style

Abdul Hafeez Muhammad, Ansar Siddique, Ahmed E. Youssef, Kashif Saleem, Basit Shahzad, Adnan Akram, Al-Batool Saleh Al-Thnian. A Hierarchical Model to Evaluate the Quality of Web-Based E-Learning Systems. Sustainability. 2020; 12 (10):4071.

Chicago/Turabian Style

Abdul Hafeez Muhammad; Ansar Siddique; Ahmed E. Youssef; Kashif Saleem; Basit Shahzad; Adnan Akram; Al-Batool Saleh Al-Thnian. 2020. "A Hierarchical Model to Evaluate the Quality of Web-Based E-Learning Systems." Sustainability 12, no. 10: 4071.