Papers by Suha K . Assayed
People and Behavior Analysis, 2024
Students in schools and colleges face some challenges, including rigorous academic schedules, aca... more Students in schools and colleges face some challenges, including rigorous academic schedules, academic work load, standardized tests, and assignments deadline, which can contribute to significant stress and mental health risks. Thus, numerous studies have been conducted to control stress between students, such as self-guided stress management programs. This paper reviews several studies published between 2019 and 2023 exploring the impacts of deploying state-of-the-art artificial intelligence chatbots which are used to boost and manage psychological disorders and mental health symptoms such as anxiety, depression, fear, and worry between prospective and current undergraduate students. This study outlines the key phases of the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA). It begins with screening (115) articles and subsequently includes (13) articles for intensive review, which all focus on psychological disorders and mental health conditions that can be influenced by the conversational agents and chatbots. However, the results revealed that anxiety and depression are the main psychological disorders that can be influenced by conversational agents and chatbots. Moreover, this review revealed multiple cases of universities and schools, emphasizing the implementation of chatbots for both learning and advising functions. The Systematic Literature Review (SLR) in this study is constrained to the specific databases, and the search keywords were limited to the article title fields only; which excluded the abstracts. This could have caused some missing relevant studies. In future work, other essential databases will be included, such as Scopus and WoS. In addition, we will include more information about the technique and the complexities of using the chatbot as it may have a significant impact on student behaviors and emotions.
Lecture notes in civil engineering, 2024
Research Square (Research Square), Dec 10, 2023
Lecture notes in civil engineering, 2024
Purpose-This paper aims to develop a novel chatbot to improve student services in high school by ... more Purpose-This paper aims to develop a novel chatbot to improve student services in high school by transferring students' enquiries to a particular agent, based on the enquiry type. In accordance to that, comparison between machine learning and neural network is conducted in order to identify the most accurate model to classify students' requests. Methodology-In this study we selected the data from high school students, since high school is one of the most essential stages in students' lives, as in this stage, students have the option to select their academic streams and advanced courses that can shape their careers according to their passions and interests. A new corpus is created with (1004) enquiries. The data is annotated manually based on the type of request. The label high-school-courses is assigned to the requests that are related to elective courses and standardized tests during high school. On the other hand, the label majors & universities is assigned to the questions that are related to applying to universities along with selecting the majors. Two novel classifier chatbots are developed and evaluated, where the first chatbot is developed by using a Naive Bayes Machine Learning Algorithm, while the other is developed by using Recurrent Neural Networks (RNN)-LSTM. Findings-Some features and techniques are used in both models in order to improve the performance. However, both models have conveyed a high accuracy score which exceeds (91%). The models have been validated as a pilot testing by using high school students as well as experts in education and six questions and enquiries are presented to the chatbots for the evaluation. Implications and future work-This study can add value to the team of researchers and developers to integrate such classifiers into different applications. As a result, this improves the users' services, in particular, those implemented in educational institutions. In the future, it is certain that intent recognition will be developed with the addition of a voice recognition feature which can successfully integrated into smartphones.
BUiD Doctoral Research Conference 2023, 2024
Purpose-This paper aims to develop a novel chatbot to improve student services in high school by ... more Purpose-This paper aims to develop a novel chatbot to improve student services in high school by transferring students' enquiries to a particular agent, based on the enquiry type. In accordance to that, comparison between machine learning and neural network is conducted in order to identify the most accurate model to classify students' requests. Methodology-In this study we selected the data from high school students, since high school is one of the most essential stages in students' lives, as in this stage, students have the option to select their academic streams and advanced courses that can shape their careers according to their passions and interests. A new corpus is created with (1004) enquiries. The data is annotated manually based on the type of request. The label high-school-courses is assigned to the requests that are related to elective courses and standardized tests during high school. On the other hand, the label majors & universities is assigned to the questions that are related to applying to universities along with selecting the majors. Two novel classifier chatbots are developed and evaluated, where the first chatbot is developed by using a Naive Bayes Machine Learning Algorithm, while the other is developed by using Recurrent Neural Networks (RNN)-LSTM. Findings-Some features and techniques are used in both models in order to improve the performance. However, both models have conveyed a high accuracy score which exceeds (91%). The models have been validated as a pilot testing by using high school students as well as experts in education and six questions and enquiries are presented to the chatbots for the evaluation. Implications and future work-This study can add value to the team of researchers and developers to integrate such classifiers into different applications. As a result, this improves the users' services, in particular, those implemented in educational institutions. In the future, it is certain that intent recognition will be developed with the addition of a voice recognition feature which can successfully integrated into smartphones.
BUiD Doctoral Research Conference 2023, 2024
Purpose-This paper aims to review several studies published between 2018 to 2022 about advising c... more Purpose-This paper aims to review several studies published between 2018 to 2022 about advising chatbots in schools and universities as well as evaluating the state-of-the-art machine learning models that are deployed into these models. Methodology-This paper follows the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA), it demonstrated the main phases of the systematic review, it starts with screening 128 articles and then including 11 articles for systematic review which focused on the current services of the advising chatbots in schools and universities, as well the artificial models that are embedded into the chatbots. Findings-Two main dimensions with other sub-dimensions are extracted from the 11 included studies as it shows the following: 1-Advising chatbots AI Architecture which includes other sub-dimensions on identifying the deep learning based chatbots, hybrid chatbots and other open-resources for customizing chatbots; 2-The goals of the advising chatbot as it includes both the admission advising and academic advising. Conclusion-Most of studies shows that advising chatbots are developed for admission and academic advising. Few researchers who study the chatbots in high schools, there is a lack of studies in developing chatbots for students advising in high schools. Limitations and future work-This study is constrained to review the studies from 2018-2022, and it is not exposed to the chatbots artifacts, even though, the human-chatbot interaction has an essential impact on students' experiences. Future research should include the impact of chatbots interactive design and students' experiences.
2023 8th International Conference on Smart and Sustainable Technologies (SpliTech)
A smart sustainable city is an innovative city that use a smart technology and smart education be... more A smart sustainable city is an innovative city that use a smart technology and smart education besides other ecosystem’s drivers in order to improve the quality of life. Several smart applications are developed by using different artificial intelligence models to enhance the education. A chatbot is considered as one of these advanced technologies that can be deployed in schools to enhance the education and learning processes in smart cities. However, high schools are the most crucial stage in student’s life as they have higher stress levels regarding their future compared to other students. They need advices more than others do, for example, they need help to find the best-fit universities and courses that can fit with their interests and goals, however, most previous studies focused on providing chatbots for supporting the matriculated and new incoming students at universities. Therefore, to fill this gap, this study aims to develop a novel chatbot for supporting students in high schools particularly by using the Multinomial Naive-Bayes and Random Forest algorithms, which can be, able to understand the natural language of students’ enquiries and accordingly, the intention class for each question will be predicted. The data in this study is comprised of 505 questions, which were obtained from multiple resources such as students' blogs, schools' counsellors, students, parents as well as from schools & universities' websites. The results in this study shows that the performance of the chatbot is improved by using the techniques of pre-processing and the feature extractions such as CountVectorizers and TF-IDF. Moreover, the results show that the Random Forest classifier performed better than Multinomial Naive-Bayes in all metrics. The performance of the models is checked by using different metrics such as accuracy, precision, recall and F1-score and all show high scores with exceeding 90%. However, the accuracy of Multinomial Naive-Bayes classifier achieved higher score when using CountVectorizers compared to using the TF-IDF. In the future work, this result will be investigated by re-evaluated the model with using a large corpus with students’ enquiries.
2023 4th International Conference on Intelligent Engineering and Management (ICIEM)
The sustainable development goal 4 (SDG4) aims to “ensure inclusive and equitable quality educati... more The sustainable development goal 4 (SDG4) aims to “ensure inclusive and equitable quality education and promote lifelong learning opportunities for all”, Therefore., researchers are inspired to study the fairness and equality in different aspects of education. Some studies are focused on social and academic initiatives and others on developing state-of-the-art technology to enhance the education between students equally. Henceforth., in this paper a novel affordable chatbot implemented by using a neural network model and natural language processing (NLP) to assist students in high schools particularly., since high school is one of the most essential stages in students” lives., as in this stage., students have the option to select their academic streams and advanced courses that can shape their career with their passions and interests. The dataset in this study collected from different academic resources such as schools & universities websites., schools” advisers., parents and students. It includes (968) pairs of enquiries and tags. The first column represents the student's enquiry; the second column indicates the tag or the class of each sentence. The model built by connecting the input data into embedding layer., and then the data fed into the LSTM layer with different number of neurons., then authors used sigmoid function for the output layer. The result in this study shows that the performance of the chatbot is improved by increasing the number of neurons from 5 to 8, the model achieved high accuracy ratio with score (96.5 % ). In future the model will be developed with stacked LSTM layers with using softmax activation function in the output layer, as different classes will be added as well in the dataset.
Computer Science & Engineering: An International Journal, 2023
Digitalization is not limited merely to business companies and high-tech industries; it has incre... more 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.
IEEE, 2023
A smart sustainable city is an innovative city that use a smart technology and smart education be... more A smart sustainable city is an innovative city that use a smart technology and smart education besides other ecosystem’s drivers in order to improve the quality of life. Several smart applications are developed by using different artificial intelligence models to enhance the education. A chatbot is considered as one of these advanced technologies that can be deployed in schools to enhance the education and learning processes in smart cities. However, high schools are the most crucial stage in student’s life as they have higher stress levels regarding their future compared to other students. They need advices more than others do, for example, they need help to find the best-fit universities and courses that can fit with their interests and goals, however, most previous studies focused on providing chatbots for supporting the matriculated and new incoming students at universities. Therefore, to fill this gap, this study aims to develop a novel chatbot for supporting students in high schools particularly by using the Multinomial Naive-Bayes and Random Forest algorithms, which can be, able to understand the natural language of students’ enquiries and accordingly, the intention class for each question will be predicted. The data in this study is comprised of 505 questions, which were obtained from multiple resources such as students' blogs, schools' counsellors, students, parents as well as from schools & universities' websites. The results in this study shows that the performance of the chatbot is improved by using the techniques of pre-processing and the feature extractions such as CountVectorizers and TF-IDF. Moreover, the results show that the Random Forest classifier performed better than Multinomial Naive-Bayes in all metrics. The performance of the models is checked by using different metrics such as accuracy, precision, recall and F1-score and all show high scores with exceeding 90%. However, the accuracy of Multinomial Naive-Bayes classifier achieved higher score when using CountVectorizers compared to using the TF-IDF. In the future work, this result will be investigated by re-evaluated the model with using a large corpus with students’ enquiries.
2023 4th International Conference on Intelligent Engineering and Management (ICIEM)-IEEE, 2023
The sustainable development goal 4 (SDG4) aims to “ensure inclusive and equitable quality educati... more The sustainable development goal 4 (SDG4) aims to “ensure inclusive and equitable quality education and promote lifelong learning opportunities for all”, Therefore., researchers are inspired to study the fairness and equality in different aspects of education. Some studies are focused on social and academic initiatives and others on developing state-of-the-art technology to enhance the education between students equally. Henceforth., in this paper a novel affordable chatbot implemented by using a neural network model and natural language processing (NLP) to assist students in high schools particularly., since high school is one of the most essential stages in students” lives., as in this stage., students have the option to select their academic streams and advanced courses that can shape their career with their passions and interests. The dataset in this study collected from different academic resources such as schools & universities websites., schools” advisers., parents and students. It includes (968) pairs of enquiries and tags. The first column represents the student's enquiry; the second column indicates the tag or the class of each sentence. The model built by connecting the input data into embedding layer., and then the data fed into the LSTM layer with different number of neurons., then authors used sigmoid function for the output layer. The result in this study shows that the performance of the chatbot is improved by increasing the number of neurons from 5 to 8, the model achieved high accuracy ratio with score (96.5 % ). In future the model will be developed with stacked LSTM layers with using softmax activation function in the output layer, as different classes will be added as well in the dataset.
Computer Science & Engineering: An International Journal, 2023
Multiple factors influence college selection and admission behaviors. Most researchers focused on... more Multiple factors influence college selection and admission behaviors. Most researchers focused on the academic and socioeconomic factors; the academic factors are high school GPA, SAT, admission tests, etc. On the other hand, the socioeconomic factors could be family income and first-generation students, which means parents did not complete their bachelor's degrees. However, some universities admission policies do not pay any attention to the race or to the minorities even though some of them might be from the lowincome students which could not afford any admission tests, and they might lose their chance to get admitted into their preferred universities. Therefore, most universities want a fairness admission system that include both the disadvantaged students along with other high-score achievement students. Thus, several simulations have been developed by using the agent-based models in order to simulate a real world system by considering other factors and domains that are vari...
International Journal of Artificial Intelligence & Applications, Mar 30, 2023
Anxiety and depression can have a significant impact on students' academic performance, h... more Anxiety and depression can have a significant impact on students' academic performance, however, these mental health impacts were increased during the Covid-19 pandemic, and accordingly students and parents need some people to share their feelings together; however, there are different types of social media apps and platforms such as Facebook, Twitter, Reddit, Instagram, and others. Twitter is one of the most popular social application that people prefer to share their emotional states. Interestingly, the psychologist and computer scientists are inspired to study these emotions. In this paper, we propose a chatbot for detecting the students feeling by using machine-learning algorithms. The authors used a dataset of tweets from Kaggle's paltform, and it includes 41157 tweets that are all related to the COVID-19. The tweets are classified into categories based on the feeling: Positive and negative. The authors applied Machine Learning algorithms, Support Vector Machines (SVM) and the Naïve Bayes (NB) and accordingly they 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, but the speed is extremely slow compared to Naive Bayes model. In future, other neural network algorithms such as the RNN, LSTM will be implemented, and Arabic tweets will be included in the future.
Digitalization is not limited merely to business companies and high-tech industries; it has incre... more 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.
Computer Science & Engineering: An International Journal (CSEIJ),
Multiple factors influence college selection and admission behaviors. Most researchers focused on... more Multiple factors influence college selection and admission behaviors. Most researchers focused on the academic and socioeconomic factors; the academic factors are high school GPA, SAT, admission tests, etc. On the other hand, the socioeconomic factors could be family income and first-generation students, which means parents did not complete their bachelor's degrees. However, some universities admission policies do not pay any attention to the race or to the minorities even though some of them might be from the lowincome students which could not afford any admission tests, and they might lose their chance to get admitted into their preferred universities. Therefore, most universities want a fairness admission system that include both the disadvantaged students along with other high-score achievement students. Thus, several simulations have been developed by using the agent-based models in order to simulate a real world system by considering other factors and domains that are varied in the complexities. This paper aimed to review several Agent-Based Models that are deployed by different admission offices from several international universities and colleges around the world, which is classified based on the main contribution of the simulations including the level of educational attainment as well as the universities selection behaviors.
Computer Science & Engineering: An International Journal , 2023
Digitalization is not limited merely to business companies and high-tech industries; it has incre... more 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.
Computer Networks & Communications
The various types of social media were increased rapidly, as people’s need to share knowledge bet... more 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 cla...
Computer Science & Engineering: An International Journal
Medical colleges are considered one of the most competitive schools compared to other university ... more 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...
International JJournal of Artificial Intelligence and Applications, 2023
Anxiety and depression can have a significant impact on students' academic performance, however, ... more Anxiety and depression can have a significant impact on students' academic performance, however, these mental health impacts were increased during the Covid-19 pandemic, and accordingly students and parents need some people to share their feelings together; however, there are different types of social media apps and platforms such as Facebook, Twitter, Reddit, Instagram, and others. Twitter is one of the most popular social application that people prefer to share their emotional states. Interestingly, the psychologist and computer scientists are inspired to study these emotions. In this paper, we propose a chatbot for detecting the students feeling by using machine-learning algorithms. The authors used a dataset of tweets from Kaggle's paltform, and it includes 41157 tweets that are all related to the COVID-19. The tweets are classified into categories based on the feeling: Positive and negative. The authors applied Machine Learning algorithms, Support Vector Machines (SVM) and the Naïve Bayes (NB) and accordingly they 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, but the speed is extremely slow compared to Naive Bayes model. In future, other neural network algorithms such as the RNN, LSTM will be implemented, and Arabic tweets will be included in the future.
David C. Wyld et al. (Eds): CCNET, AIMLA, CICS, IOTBS, NLTM, COIT , 2023
The various types of social media were increased rapidly, as people's need to share knowledge bet... more 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.
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Papers by Suha K . Assayed
This study outlines the key phases of the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA), it begins with screening (115) articles and subsequently including (13) articles for intensive review which all focus on psychological disorders and mental health conditions that can be influenced by the conversational agents and chatbots. However, the results revealed that anxiety and depression are the main psychological disorders that can be influenced by conversational agents and chatbots. Moreover, this review revealed multiple cases of universities and schools, emphasizing the implementation of chatbots for both learning and advising functions.
The Systematic Literature Review (SLR) in this study is constrained to the specific databases, and the search keywords were limited to the article title fields only; which did not include the abstracts. This could cause some missing relevant studies. In future work, other essential databases will be included such as Scopus and WoS in addition to expanding the search keywords to the abstracts.