SaaS E-Commerce Platforms Web Accessibility Evaluation

Sohaib, Osama; Naderpour, Mohsen; Hussain, Walayat

doi:10.1109/fuzz-ieee.2018.8491621

Cited by 7 publications

(8 citation statements)

References 26 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Fourth, qualitative methods would provide supplemental findings. CSR is a multi-criteria decision-making (MCDM) real-world problem, thus, a fuzzy MCDM method (such as [47]- [49]) would likely provide deeper understanding of CSR activities.…”

Section: Discussion Implicatons and Conclusionmentioning

confidence: 99%

Corporate Social Responsibility in Higher Education: A PLS-SEM Neural Network Approach

Binsawad

2020

IEEE Access

View full text Add to dashboard Cite

Corporate social responsibility (CSR) has become vital to the competitiveness of organizations. However, there is very limited research on CSR in the context of higher education. Therefore, this study investigates the impact of CSR factors on the university competitiveness with other universities. The contribution of this study includes applying a multi-analytical methodology by applying Partial Least Squares-Structural Equation Modelling (PLS-SEM) and Artificial Neural Network (ANN). First, PLS-SEM analysis was applied to measure which CSR factor has the most significant influence on the increased competitiveness of Saudi Arabian universities. Then, an ANN model analysis was applied to rank the relative influence of the significant CSR factors attained from the PLS-SEM analysis. The findings show that the CSR factors such as market-oriented CSR activities, society-oriented CSR activities and workforce-oriented CSR activities have a significant positive impact on increased competitiveness of Saudi Arabian universities. However, based on the ANN findings, society-oriented CSR is the most significant predictor of a university's increased competitiveness, followed by workforce-oriented CSR and finally by market-oriented CSR. The practical implications of this study provide Saudi universities to focus on society-oriented CSR to maintain and sustain their competitive advantage over other universities.INDEX TERMS ANN, corporate social responsibility, education, PLS SEM, Saudi Arabia.

show abstract

Section: Discussion Implicatons and Conclusionmentioning

confidence: 99%

Corporate Social Responsibility in Higher Education: A PLS-SEM Neural Network Approach

Binsawad

2020

IEEE Access

View full text Add to dashboard Cite

show abstract

“…Pattern matching uses similar structural features ( Belkin & Croft, 1992 ; Gates, 2011 ), and ML for generating verbs, parts of idioms, or adverbs by ML techniques are applied by Curto (2010) . While POS and parse information have been used in some works for key selection ( Sohaib, Naderpour & Hussain, 2018 ), some used verbs, adjectives and prepositions ( Sohaib, Naderpour & Hussain, 2018 ) for the key. The proposed system also utilizes POS information for the selection of keys.…”

Section: Background and Literature Reviewmentioning

confidence: 99%

“…The approaches based on POS information for distractor generation (key and distractor both should be of the same POS) are proposed in Coniam (2013) . Frequency (occurrences of both key and distractor should be the same) used in Sohaib, Naderpour & Hussain (2018) , domain ontology (by using web ontology language ( Antoniou & Van Harmelen, 2004 )) proposed by Papasalouros, Kanaris & Kotis (2008) , distributional hypothesis (similar words in a similar context) by Celikyilmaz & Hakkani-Tur (2011) , pattern matching (by using parse information) used by Hoshino & Nakagawa (2007) , by semantic analysis based approach like ( Aldabe & Maritxalar, 2010 ) used latent semantic analysis, ( Aldabe, Maritxalar & Mitkov, 2009 ) used verbs similarity by distributional data, ( Belkin & Croft, 1992 ) used semantic similarity between two words using Patwardhan and Pedersen’s method, Kumar, Banchs & D’Haro (2015) used word2vec tool. Deep semantic analysis and neural embedding-based approaches can be used for sophisticated distractor generation.…”

Section: Background and Literature Reviewmentioning

confidence: 99%

Automatic computer science domain multiple-choice questions generation based on informative sentences

Maheen

Asif

Ahmad

et al. 2022

PeerJ Computer Science

View full text Add to dashboard Cite

Students require continuous feedback for effective learning. Multiple choice questions (MCQs) are extensively used among various assessment methods to provide such feedback. However, manual MCQ generation is a tedious task that requires significant effort, time, and domain knowledge. Therefore, a system must be present that can automatically generate MCQs from the given text. The automatic generation of MCQs can be carried out by following three sequential steps: extracting informative sentences from the textual data, identifying the key, and determining distractors. The dataset comprising of various topics from the 9th and 11th-grade computer science course books are used in this work. Moreover, TF-IDF, Jaccard similarity, quality phrase mining, K-means, and bidirectional encoder representation from transformers techniques are utilized for automatic MCQs generation. Domain experts validated the generated MCQs with 83%, 77%, and 80% accuracy, key generation, and distractor generation, respectively. The overall MCQ generation achieved 80% accuracy through this system by the experts. Finally, a desktop app was developed that takes the contents in textual form as input, processes it at the backend, and visualizes the generated MCQs on the interface. The presented solution may help teachers, students, and other stakeholders with automatic MCQ generation.

show abstract

“…Cloud computing is an on-demand, expandable, costeffective, virtualized, and all-time available model. It has been known as an effective technology in parallel computing, which offers a range of services such as virtualized resources, metered resource usage, on-demand computing resources access, dynamic and elastic scaling, and ubiquitous computing that can be released and provisioned without effort [8]. In this regard, cloud resources and services are facing significant uncertainty during provisioning.…”

Section: A Cloud Computing Characteristicsmentioning

confidence: 99%

“…In SaaS, the applications are deployed in the cloud and the cloud clients are allowed to access them through the Internet [7]. IaaS provides virtual private network hardware and software, hardware and software firewalls, storage, processing power, and network infrastructure [8]. PaaS permits clients to use predefined tools prepared by cloud providers to deploy web applications and other programming software [9].…”

Section: Introductionmentioning

confidence: 99%

Fault-Tolerant Load Balancing in Cloud Computing: A Systematic Literature Review

et al. 2022

View full text Add to dashboard Cite

Nowadays, cloud computing is growing daily and has been developed as an effective and flexible paradigm in solving large-scale problems. It has been known as an Internet-based computing model in which computing and virtual resources, such as services, applications, storage, servers, and networks, are shared among numerous cloud users. Since the number of cloud users and their requests are increasing rapidly, the loads on the cloud systems may be underloaded or overloaded. These situations cause different problems, such as high response time and power consumption. To handle the mentioned problems and improve the performance of cloud servers, load balancing methods have a significant impact. Generally, a load balancing method aims to detect under-loaded and overloaded nodes and balance the load among them. In the recent decade, this problem has attracted a lot of interest among researchers, and several solutions have been proposed. Considering the important role of fault-tolerant in load balancing algorithms, there is a lack of an organized and in-depth study in this field yet. This gap prompted us to provide the current study aimed to collect and review the available papers in the field of fault tolerance load balancing methods in cloud computing. The existing algorithms are divided into two categories, namely, centralized and distributed, and reviewed based on vital qualitative parameters, such as scalability, response time, reliability, availability, throughput, and overhead. In this regard, other criteria such as the type of detected faults and adopted simulation tools are taken into account.

show abstract

SaaS E-Commerce Platforms Web Accessibility Evaluation

Cited by 7 publications

References 26 publications

Corporate Social Responsibility in Higher Education: A PLS-SEM Neural Network Approach

Corporate Social Responsibility in Higher Education: A PLS-SEM Neural Network Approach

Automatic computer science domain multiple-choice questions generation based on informative sentences

Fault-Tolerant Load Balancing in Cloud Computing: A Systematic Literature Review

Contact Info

Product

Resources

About