New Business Model: EWOM (Electronic Word of Mouth) platform Improvements through the Elements lead to success in E commerce platforms

Electronic commerce (e-commerce) is an emerging field that applies technology in commerce. One of the concepts used in electronic commerce is an electronic word of mouth EWOM . Technically, the concept is used to evaluate electronic commerce platforms, services/products, and behaviours. Sometimes EWOM is used as a government initiative to sensitize the citizens. The goal of this research is to improve the methods and activities used in Electronic word of mouth platforms for evaluating e-commerce platforms. Therefore, this research: 1) proposes a framework for Electronic word of mouth; 2) chooses MAROOF.SA as the system to be improved by the new EWOM business model based on the proposed framework; 3) verifies the new EWOM business model through two justifications plans: an experiment and survey. The proposed framework was derived from Starbucks experiment of using electronic commerce , which highlighted three main elements that can be used in the proposed framework. In this paper, co-creations value, usability and payment methods were the three elements considered when improving the working of the MAROOF EWOM Platform

Finding the Needle in a Haystack: On the Automatic Identification of Accessibility User Reviews

In recent years, mobile accessibility has become an important trend with the goal of allowing all users the possibility of using any app without many limitations. User reviews include insights that are useful for app evolution. However, with the increase in the amount of received reviews, manually analyzing them is tedious and time-consuming, especially when searching for accessibility reviews. The goal of this paper is to support the automated identification of accessibility in user reviews, to help technology professionals in prioritizing their handling, and thus, creating more inclusive apps. Particularly, we design a model that takes as input accessibility user reviews, learns their keyword-based features, in order to make a binary decision, for a given review, on whether it is about accessibility or not. The model is evaluated using a total of 5,326 mobile app reviews. The findings show that (1) our model can accurately identify accessibility reviews, outperforming two baselines, namely keyword-based detector and a random classifier; (2) our model achieves an accuracy of 85% with relatively small training dataset; however, the accuracy improves as we increase the size of the training dataset

Predicting Pulsars from Imbalanced Dataset with Hybrid Resampling Approach

Pulsar stars, usually neutron stars, are spherical and compact objects containing a large quantity of mass. Each pulsar star possesses a magnetic field and emits a slightly different pattern of electromagnetic radiation which is used to identify the potential candidates for a real pulsar star. Pulsar stars are considered an important cosmic phenomenon, and scientists use them to study nuclear physics, gravitational waves, and collisions between black holes. Defining the process of automatic detection of pulsar stars can accelerate the study of pulsar stars by scientists. This study contrives an accurate and efficient approach for true pulsar detection using supervised machine learning. For experiments, the high time-resolution (HTRU2) dataset is used in this study. To resolve the data imbalance problem and overcome model overfitting, a hybrid resampling approach is presented in this study. Experiments are performed with imbalanced and balanced datasets using well-known machine learning algorithms. Results demonstrate that the proposed hybrid resampling approach proves highly influential to avoid model overfitting and increase the prediction accuracy. With the proposed hybrid resampling approach, the extra tree classifier achieves a 0.993 accuracy score for true pulsar star prediction

Big Data: Learning, Analytics, and Applications

With the rise of autonomous systems, the automation of faults detection and localization becomes critical to their reliability. An automated strategy that can provide a ranked list of faulty modules or files with respect to how likely they contain the root cause of the problem would help in the automation bug localization. Learning from the history if previously located bugs in general, and extracting the dependencies between these bugs in particular, helps in building models to accurately localize any potentially detected bugs. In this study, we propose a novel fault localization solution based on a learning-to-rank strategy, using the history of previously localized bugs and their dependencies as features, to rank files in terms of their likelihood of being a root cause of a bug. The evaluation of our approach has shown its efficiency in localizing dependent bugs

Electroencephalogram Signals for Detecting Confused Students in Online Education Platforms with Probability-Based Features

Article discusses how despite the advantages of online education, it lacks face-to-face settings, which makes it very difficult to analyze the students’ level of interaction, understanding, and confusion. This study proposes a novel engineering approach that uses probability-based features (PBF) for increasing the efficacy of machine learning models

Toward Leveraging Artificial Intelligence to Support the Identification of Accessibility Challenges

The goal of this thesis is to support the automated identification of accessibility in user reviews or bug reports, to help technology professionals prioritize their handling, and, thus, to create more inclusive apps. Particularly, we propose a model that takes as input accessibility user reviews or bug reports and learns their keyword-based features to make a classification decision, for a given review, on whether it is about accessibility or not. Our empirically driven study follows a mixture of qualitative and quantitative methods. We introduced models that can accurately identify accessibility reviews and bug reports and automate detecting them. Our models can automatically classify app reviews and bug reports as accessibility-related or not so developers can easily detect accessibility issues with their products and improve them to more accessible and inclusive apps utilizing the users' input. Our goal is to create a sustainable change by including a model in the developer's software maintenance pipeline and raising awareness of existing errors that hinder the accessibility of mobile apps, which is a pressing need. In light of our findings from the Blackboard case study, Blackboard and the course material are not easily accessible to deaf students and hard of hearing. Thus, deaf students find that learning is extremely stressful during the pandemic

I Cannot See You—The Perspectives of Deaf Students to Online Learning during COVID-19 Pandemic: Saudi Arabia Case Study

The COVID-19 pandemic brought about many challenges to course delivery methods, which have forced institutions to rapidly change and adopt innovative approaches to provide remote instruction as effectively as possible. Creating and preparing content that ensures the success of all students, including those who are deaf and hard-of-hearing has certainly been an all-around challenge. This study aims to investigate the e-learning experiences of deaf students, focusing on the college of the Technical and Vocational Training Corporation (TVTC) in the Kingdom of Saudi Arabia (KSA). Particularly, we study the challenges and concerns faced by deaf students during the sudden shift to online learning. We used a mixed-methods approach by conducting a survey as well as interviews to obtain the information we needed. Our study delivers several important findings. Our results report problems with internet access, inadequate support, inaccessibility of content from learning systems, among other issues. Considering our findings, we argue that institutions should consider a procedure to create more accessible technology that is adaptable during the pandemic to serve individuals with diverse needs

Electroencephalogram Signals for Detecting Confused Students in Online Education Platforms with Probability-Based Features

Online education has emerged as an important educational medium during the COVID-19 pandemic. Despite the advantages of online education, it lacks face-to-face settings, which makes it very difficult to analyze the students’ level of interaction, understanding, and confusion. This study makes use of electroencephalogram (EEG) data for student confusion detection for the massive open online course (MOOC) platform. Existing approaches for confusion detection predominantly focus on model optimization and feature engineering is not very well studied. This study proposes a novel engineering approach that uses probability-based features (PBF) for increasing the efficacy of machine learning models. The PBF approach utilizes the probabilistic output from the random forest (RF) and gradient-boosting machine (GBM) as a feature vector to train machine learning models. Extensive experiments are performed by using the original features and PBF approach through several machine learning models with EEG data. Experimental results suggest that by using the PBF approach on EEG data, a 100% accuracy can be obtained for detecting confused students. K-fold cross-validation and performance comparison with existing approaches further corroborates the results