INTRODUCTION: An intent classification is a challenged task in Natural Language Processing (NLP) as we are asking the machine to understand our language by categorizing the users’ requests. As a result, the intent classification plays an essential role in having a chatbot conversation that understand students’ requests. OBJECTIVES: In this study, we developed a novel chatbot called “HSchatbot” for predicting the intent classifications from high school students’ enquiries. Evidently, students in high schools are the most concerned among all students about their future; thus, in this stage they need an instant support in order to prepare them to take the right decision for their career choice. METHODS: The authors in this study used the Multinomial Naive-Bayes and Random Forest classifiers for predicting the students’ enquiries, which in turn improved the performance of the classifiers by using the feature’s extractions. RESULTS: The results show that the random forest classifier performed better than Multinomial Naive-Bayes since the performance of this model is checked by using different metrics like accuracy, precision, recall and F1 score. Moreover, all showed high accuracy scores exceeding 90% in all metrics. However, the accuracy of Multinomial Naive-Bayes classifier performed much better when using CountVectorizers compared to using the TF-IDF. CONCLUSION: In the future work, the results will be analysed and investigated in order to figure out the main factors that affect the performance of Multinomial Naive-Bayes classifier, as well as evaluating the model with using a large corpus of students’ questions and enquiries.
The various types of social media were increased rapidly, as people’s need to share knowledge between others. In fact, there are various types of social media apps and platforms such as Facebook, Twitter, Reddit, Instagram, and others. Twitter remains one of the most popular social application that people use for sharing their emotional states. However, this has increased particularly during the COVID-19 pandemic. In this paper, we proposed a chatbot for evaluating the sentiment analysis by using machine learning algorithms. The authors used a dataset of tweets from Kaggle’s website, and that includes 41157 tweets that are related to the COVID-19. These tweets were classified and labelled to four categories: Extremely positive, positive, neutral, negative, and extremely negative. In this study, we applied Machine Learning algorithms, Support Vector Machines (SVM), and the Naïve Bayes (NB) algorithms and accordingly, we compared the accuracy between them. In addition to that, the classifiers were evaluated and compared after changing the test split ratio. The result shows that the accuracy performance of SVM algorithm is better than Naïve Bayes algorithm, even though Naïve Bayes perform poorly with low accuracy, but it trained the data faster comparing to SVM.
Digitalization is not limited merely to business companies and high-tech industries; it has increasingly changed families' behaviors and attitudes as they are exposed to the digital world using different technological aspects. Therefore, numerous risks can be raised between all members of the family. For example, if IoT devices in a smart home are not embedded with high-security standards, they would be vulnerable to being attacked by hackers. Cyberattacks will not be limited to attacking virtually, but also they could unlock the home's door from the phone, and accordingly, the criminal will enter the home, and they can lose much more than credit cards. In this paper we identified various types of risks, with providing an analysis about the vulnerabilities and protecting families from digital attackers.
Medical colleges are considered one of the most competitive schools compared to other university departments. Most countries adopted the particular application process to ensure maximum fairness between students. For example, in UK students apply through the UCAS system, and most of USA universities use either Coalition App or Common App, on the other hand, some universities use their own websites. In fact, a Unified Admission Application process is adopted in Jordan for allocating the students to the public universities. However, the universities and colleges in Jordan are evaluating the applicants by using merely the centralized system without considering the socioeconomics factor, as the high school GPA is the essential player their selection mechanism. In this paper, the authors will use an Agent Based model (ABM) to simulate different scenarios by using Netlogo software (v. 6.3). The authors used different parameters such as the family-income and the high school GPA in order to maximize the utilities of the fairness and equalities of universities admission. The model is simulated into different scenarios. For instance, students with low family income and high GPA given them the priority in studying medicine comparing with same high school GPA and higher family-income, as a results, after several rotations of the simulation the reputation of medical schools are identified based on students’ preferences and seats’ allocated as it shows that high ranking universities are mainly allocated with have high cut-off GPA score.
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