Hybrid analysis of the learner's online behavior based on learning style

Since the covid pandemic, universities propose online education to ensure learning continuity. However, the insufficient preparation led to a major drop in the learner's performance and his/her dissatisfaction with the learning experience. This may be due to several reasons, including the insensitivity of the virtual learning environment to the learner's preferences. We propose to address the issue of student's dissatisfaction and lack of interaction, by integrating learning style theory into the analysis of the learner's online behavior. Our work differentiates itself from the rest of researches that employed learning style theory by its two step process. First, we classify the learning activities into learning categories based on learning style theory. Second, we define behavioral features that quantify the learner's behavior across the learning categories. The analysis of the learner's online behavior based on the behavioral features revealed new aspects of the learner's preferences. We consider these findings to be best useful for developing learning style-sensitive adaptive learning environments. Nevertheless, the behavioral features could be beneficial in different contexts. In fact, when applied to course outcome prediction, the behavioral features enhanced the results by 10%. The latter indicates that behavioral features reflected the correlation between behavior and academic performance. © 2023, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.

The Effect of the Pharmaceutical Marketing Mix Elements During Corona Virus Pandemic on the Decision of the Prescription for Foreign-Made Drugs

The study aimed to identify the effect of the pharmaceutical marketing mix elements during the corona virus pandemic on the decision to prescribe foreign-made medicines. They were based on the descriptive analytical method, using the questionnaire to collect study data. The study tool was distributed to (114) specialist physicians in Zarqa governorate in an intentional inspection manner after ensuring its validity for application. The study found a set of results and the most important are: there is an effect at the level (α ≤ 0.05) for the elements of the pharmaceutical marketing mix (product price, distribution, and promotion) combined and individually on the decision for prescribed foreign-made medicines for specialist doctors in Zarqa governorate. The study recommended that Jordanian pharmaceutical companies should take attention to developing marketing strategies. That include all elements of the pharmaceutical marketing mix, (pharmaceutical product, drug price, drug distribution, and drug promotion). Therefore enhancing its competition with foreign companies producing medicines, as this elements directly impact the prescription decision to consider demographic variables for physicians when preparing marketing plans, because of its impact on the prescription decision. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

TextBlob and BiLSTM for Sentiment analysis toward COVID-19 vaccines

Nowadays, social media like Twitter, play a vital role in our life since it is a source of swapping views, thoughts, and feelings towards many issues such as the global pandemic covid-19. Nevertheless, it can a source of diffusion of fake news which can affect negatively the opinions of many people and even change their thoughts behind a lot of sensitive situations such as the COVID-19 vaccines. In this context, it is crucial for public health agencies to understand and identify people's opinions and views toward COVID-19 vaccines. To this end, we propose our model to classify the tweets of people into three classes, negative, neutral, and positive. In fact, we considered a large dataset extracted from Twitter includes 174490 tweets. Tweet analysis was conducted by TextBlob to categorize the sentiment and the Bidirectional LSTM model to classify the sentiments. The proposed model was compared with other studied machine learning classifiers and deep learning algorithms. The aim of this work also is to select the best model between the studied model that is suitable for the sentiment analysis for COVID-19 vaccines. BiLSTM outperformed the other studied models with ahigh accuracy rate of 94.12%.

Deep Learning Approach for COVID-19 Diagnosis Using X-Ray Images

During the times of pandemics, faster diagnosis plays a key role in the response efforts to contain the disease as well as reducing its spread. Computer-aided detection would save time and increase the quality of diagnosis in comparison with manual human diagnosis. Artificial intelligence (AI) through deep learning is considered as a reliable method to design such systems. In this research paper, an AI-based diagnosis approach has been suggested to tackle the COVID-19 pandemic. The proposed system employs a deep learning algorithm on chest X-ray images to detect the infected subjects. An enhanced convolutional neural network (CNN) architecture has been designed with 22 layers which are then trained over a chest X-ray dataset. More after, a classification component has been introduced to classify the X-ray images into three categories. The system has been evaluated through a series of observations and experimentations. The experimental results have shown a promising performance in terms of accuracy. The system has diagnosed COVID-19 with accuracy of 0.9961% and normal subjects with accuracy of 0.96067 while it showed 0.9588 accuracy on Pneumonia. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.