Khaled Shaalan | The British University in Dubai (original) (raw)
Papers by Khaled Shaalan
Procedia Computer Science, 2024
With the growing prominence of social media as a platform for expressing opinions and emotions, u... more With the growing prominence of social media as a platform for expressing opinions and emotions, understanding the emotional undercurrents in large volu mes of text data has become increasingly crucial. Tweets, often reflecting public sentiment, contain a rich tapestry of emotions that can be harnessed for diverse applications ranging from market analysis to mental health monitoring. The dataset comprises 40,000 tweet records, each tagged with one of thirteen distinct emotions, making it a challenging task to perform multiclass emotion classification due to the sheer volume of data and the nuanced spectrum of emotional expressions. Traditional classification models often struggle with such high-dimensional, multi-category data, leading to the need for a more sophisticated approach that ensures both accuracy and computational efficiency. To address the complexity of multiclass emotion classification, we propose a novel approach that combines text preprocessing, advanced feature extraction using TF-IDF, and dimensionality reduction via 2D Principal Component Analysis (PCA). We then apply K-means clustering to the reduced feature set to identify inherent groupings within the emotional content of the tweets. This method not only reduces computational demands but also logically consolidates the emotions into fewer categories, potentially enhancing the performance of subsequent classification models. The implementat ion of our method yielded distinct clusters that suggest a logical grouping of the emotions within the tweets. The 2D PCA visualization revealed clear separations among clusters, indicating that our approach successfully captured meaningful patterns in the dataset. The ability to effectively cluster complex emotional data opens the door to creating more nuanced and efficient multiclass classification models. By reducing the number of categories and focusing on clustered groups, we can streamline the classification process and enhance the interpretability of results. This has significant implications for real-world applications, including targeted marketing campaigns, public policy
Procedia Computer Science, 2024
In the realm of public health, the discourse surrounding diseases significantly impacts awareness... more In the realm of public health, the discourse surrounding diseases significantly impacts awareness, funding, and stigma.
Traditional methods of analyzing disease mentions often overlook the intricate relationships and sentiments expressed in text,
which can provide deeper insights into public perception and information dissemination. Existing literature on disease discourse
primarily focuses on quantitative metrics such as disease frequency counts or associations based on semantic similarity.
However, such analyses rarely account for the sentiment of the discourse, which can play a critical role in shaping public
perception and response to health information. Moreover, the complexity of textual data and the subtleties of language usage
present significant challenges for extracting meaningful patterns, especially when considering the emotional context of diseaserelated
discussions. This study introduces a novel approach that integrates sentiment analysis and network visualization to
examine the discourse on diseases within a comprehensive textual dataset. By applying the TextBlob library, we analyze
sentence-level sentiment and categorize it as positive or negative. Utilizing these sentiment scores, we construct two distinct
network graphs to depict the relationships between diseases based on their co-occurrence within sentiment-laden sentences. The
analysis reveals contrasting landscapes of disease discourse: the positive sentiment network highlights diseases frequently
mentioned in the context of successful treatment or optimistic outcomes, forming distinct clusters around well-managed
conditions. In contrast, the negative sentiment network elucidates diseases that coalesce around shared public concerns, fears, or
complications, marking out critical areas for public health intervention. The findings underscore the importance of sentiment in
understanding disease discourse, offering a novel perspective that can assist public health officials in tailoring communication
strategies. By recognizing patterns in disease associations within negative contexts, interventions can be more effectively targeted
to address misconceptions and alleviate public health anxieties. Conversely, identifying diseases with strong positive connections
can guide campaigns to bolster preventive measures and highlight medical advancements. This methodology serves as a blueprint
Procedia Computer Science
In this paper, we introduce a comprehensive approach to the classification of Arabic poetry using... more In this paper, we introduce a comprehensive approach to the classification of Arabic poetry using deep learning techniques. We utilized a dataset comprising nearly one million records of Arabic poetry verses, each labeled with nine different poets encompassing both classical and modern poetic styles. We explored various algorithms to identify the most effective models for accurately determining the poet's identity from the verses. Based on our analysis, we selected LSTM (Long Short-Term Memory) and Bi-LSTM (Bidirectional LSTM) as our primary baseline models, given the demonstrated efficacy of RNN (Recurrent Neural Network) variants in text classification. LSTM, in particular, has shown significant proficiency in analyzing sequential data across multiple languages. Our results indicate a promising average classification accuracy of 92.35%, highlighting the potential for automating the classification of texts in morphologically complex languages such as Arabic. Notably, Bi-LSTM marginally outperformed the standard LSTM, achieving average accuracy of 92.56%. We further discuss the implications of our findings for Arabic literature, particularly in the realm of poetry, and address the challenges encountered during the study. These findings represent a significant advancement in Arabic NLP, offering a powerful framework for poet identification and paving the way for future applications of deep learning in processing Arabic literary texts.
Procedia Computer Science, 2024
The widespread use of social media has escalated concerns about cyberbullying. Traditional method... more The widespread use of social media has escalated concerns about cyberbullying. Traditional methods for detecting and managing
cyberbullying struggle with the sheer volume of electronic text data. This has led to the exploration of deep learning as a potential
solution. Researchers study focuses on implementing deep learning techniques to identify cyberbullying in Arabic social media,
specifically targeting three prevalent forms of Arabic: dialectal, Modern Standard, and Classical. The collected data corpus was
about 30, 0000 tweets. In this work, we first examined the sentiment analysis as cyberbullying, and No cyberbullying, then we
further classified the cyberbullying by labelling the data under six different cyberbullying categories. We implemented deep
learning models such as CNN, RNN, and a combination of CNN-RNN. The results that obtained from 2-classes classification
showed a superiority of LSTM in terms of accuracy with 95.59%, while the best accuracy in the 6-classes classification gained
from implementing CNN with 78.75%. Meanwhile the f1-score results were the highest in LSTM for the 2-lasses and 6-classes
classifications with 96.73%, and 89%, respectively. These findings emphasize the potential for deep learning techniques to be
applied in the development of automated systems for identifying and combating cyberbullying on social media and show how well
they work in detecting cyberbullying.
Procedia Computer Science
Spam detection in SMS communication is a crucial task for maintaining the quality of messaging se... more Spam detection in SMS communication is a crucial task for maintaining the quality of messaging services and protecting users from unwanted and potentially harmful messages. Arabic SMS spam detection poses unique challenges due to the rich morphology and complex structure of the Arabic language, which can significantly impact the performance of traditional text classification methods. To address these challenges, this paper presents a novel approach for Arabic SMS spam detection using a hybrid deep learning model that combines Convolutional Neural Networks (CNN) and Bidirectional Long Short-Term Memory (Bi-LSTM) networks. The proposed model leverages the strengths of CNNs in capturing local features and patterns in the text and the capability of Bi-LSTM networks to understand long-term dependencies and contextual information. This hybrid architecture is designed to effectively handle the complexities of the Arabic language and improve the accuracy of spam detection. The model was evaluated on a dataset of Arabic SMS messages, consisting of both ham (non-spam) and spam messages. The dataset underwent preprocessing steps, including text cleaning, tokenization, and padding, to prepare it for training the deep learning model. The hybrid model was trained using the Adam optimizer and evaluated using accuracy, precision, recall, and F1 score metrics. Early stopping was implemented to prevent overfitting during the training process. The results demonstrate that the hybrid model achieved high performance, with an accuracy of 0.9699, precision of 0.9739, recall of 0.9675, and an F1 score of 0.9707. These metrics indicate the model's effectiveness in accurately detecting spam in Arabic SMS messages. Additionally, the paper provides visualizations of the confusion matrix, ROC curve, and training-validation loss graph to illustrate the model's performance. The implications of this research are significant for the field of Arabic text classification and spam detection. The proposed hybrid model offers a robust solution for accurately classifying Arabic SMS messages, which can be integrated into messaging platforms to enhance spam detection capabilities and improve user experience. Future work could explore data augmentation techniques, transfer learning, and advanced hybrid architectures to further enhance the model's performance and applicability.
Procedia Computer Science, 2024
In the digital age, the ubiquity of text messaging has unfortunately paved the way for SMS phishi... more In the digital age, the ubiquity of text messaging has unfortunately paved the way for SMS phishing, or 'smishing,' a deceptive practice where fraudsters dispatch fraudulent messages to extract sensitive information from unsuspecting recipients. This issue is not trivial. It represents a significant threat to both personal privacy and organizational security, leading to potential data breaches and financial repercussions. Against this backdrop, the imperative for advanced detection strategies is undeniable. This survey leverages a systematic review methodology to assess the effectiveness of Natural Language Processing (NLP) and Machine Learning (ML) techniques in the detection of SMS phishing, also known as smishing. By methodically analyzing research spanning various detection strategies, the study illuminates the evolution from basic rule-based frameworks to sophisticated ML algorithms, enriched with NLP for deep analysis. The findings underscore the superior efficacy of combining ML classifiers with NLP, particularly through the deployment of advanced deep learning models like Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs), which offer unprecedented accuracy in identifying and thwarting complex smishing attacks. The value of this study lies in its comprehensive synthesis of current methodologies and its contribution to the ongoing enhancement of cybersecurity defenses. It serves as a crucial guide for future research directions, emphasizing the necessity of adopting and innovating cutting-edge NLP and ML techniques to stay ahead of evolving digital threats.
Soft Computing, May 9, 2023
Education and Information Technologies, Mar 1, 2022
Advances in intelligent systems and computing, Aug 29, 2018
One of the important revolutionary tools widely used and globally implemented by educational inst... more One of the important revolutionary tools widely used and globally implemented by educational institutes and universities is none other than the electronic learning (E-learning system). The aim of this system is to deliver education. As a result, the users of an E-learning system can have enormous benefits. The developed countries are successfully implementing the E-learning system besides realization of its massive benefits. On the contrary, the developing countries have failed, either fully or partially, to implement the E-learning system. A main reason is that those countries do not have an absolute utilization and considered below the satisfactory level. For instance, in United Arab Emirate, one of the developing countries, a growing number of universities are investing for many years in E-learning systems in order to enhance the quality of student education. However, their utilization among students has not fulfilled the satisfactory level. Imagine the evidence that the behavior of user is mainly required for the successful use of these web-based tools, investigating the unified theory of acceptance and use of technology (UTAUT) of E-learning system used in practical education is the basic aim of this research study. A survey on E-learning usage among 280 students was conducted and by using the given responses, the assumptions of the research resulting from this model have been practically validated. The partial least square method was employed to examine these responses. In predicting a student’s intention to use E-learning, the UTAUT model was strongly corroborated by the obtained results. In addition, the findings reveal that all important factors of behavioral intention to use E-learning system were reportedly found as the social influence, performance expectancy and facilitating conditions of learning. Remarkably, a significant impact on student intention towards E-learning system was not suggested by the effort expectancy. Consequently, The three key factors leading to successful E-Learning system are thought to be the good perception and encouraging university policy.
Lecture notes in networks and systems, Dec 3, 2021
Lecture Notes in Computer Science, 2023
International journal of data and network science, 2023
2020 International Conference on Communications, Computing, Cybersecurity, and Informatics (CCCI)
Detection, and recommendation systems are widely used in many companies and organizations in favo... more Detection, and recommendation systems are widely used in many companies and organizations in favor of giving their employees or even customers a better service. The importance of the systems motivated the investigation of the existing approaches to test its efficiency and possible usage. In this article, the aim is to integrate both systems to create a system that is able to detect diabetes and then recommend a proper plan or medication to overcome diabetes. So, we conducted a study of different approaches of both detection and recommendation systems in order to select the appropriate one. Recommendations systems have different methods. In this paper, we focused on the content-based recommendation method, in which we evaluated using a movie recommender application and movie dataset. We also evaluated and tested four different detection approaches, in which we found that the most accurate approach is the random forest with an accuracy of 79.2% and F-measure of 0.787. A discussion of the usage of a collaborative recommendation system is also introduced to give a wide vision and comparison of the two methods to decide which one is better for a diabetes treatment recommendation system.
The combination of two technology model which are the Technology Acceptance Model (TAM) and Use o... more The combination of two technology model which are the Technology Acceptance Model (TAM) and Use of Gratifications Theory (U&G) to create an integrated model is the first step in predicting the importance of using emotional icons and the level of satisfaction behind this usage. The reason behind using these two theories into one integrated model is that U&G provides specific information and a complete understanding of usage, whereas TAM theory has proved its effectiveness with a variety of technological applications. A self-administered survey was conducted in University of Fujairah with college students to find out the social and cognitive factors that affect the usage of stickers in WhatsApp in the United Arab of Emirates. The hypothesized model is validated empirically using the responses received from an online survey of 372 respondents were analyzed using structural equation modeling (SEM-PLS). The results show that ease of use, perceived usefulness, cognition, hedonic and socia...
International Journal of Advanced Computer Science and Applications, 2021
Procedia Computer Science, 2024
With the growing prominence of social media as a platform for expressing opinions and emotions, u... more With the growing prominence of social media as a platform for expressing opinions and emotions, understanding the emotional undercurrents in large volu mes of text data has become increasingly crucial. Tweets, often reflecting public sentiment, contain a rich tapestry of emotions that can be harnessed for diverse applications ranging from market analysis to mental health monitoring. The dataset comprises 40,000 tweet records, each tagged with one of thirteen distinct emotions, making it a challenging task to perform multiclass emotion classification due to the sheer volume of data and the nuanced spectrum of emotional expressions. Traditional classification models often struggle with such high-dimensional, multi-category data, leading to the need for a more sophisticated approach that ensures both accuracy and computational efficiency. To address the complexity of multiclass emotion classification, we propose a novel approach that combines text preprocessing, advanced feature extraction using TF-IDF, and dimensionality reduction via 2D Principal Component Analysis (PCA). We then apply K-means clustering to the reduced feature set to identify inherent groupings within the emotional content of the tweets. This method not only reduces computational demands but also logically consolidates the emotions into fewer categories, potentially enhancing the performance of subsequent classification models. The implementat ion of our method yielded distinct clusters that suggest a logical grouping of the emotions within the tweets. The 2D PCA visualization revealed clear separations among clusters, indicating that our approach successfully captured meaningful patterns in the dataset. The ability to effectively cluster complex emotional data opens the door to creating more nuanced and efficient multiclass classification models. By reducing the number of categories and focusing on clustered groups, we can streamline the classification process and enhance the interpretability of results. This has significant implications for real-world applications, including targeted marketing campaigns, public policy
Procedia Computer Science, 2024
In the realm of public health, the discourse surrounding diseases significantly impacts awareness... more In the realm of public health, the discourse surrounding diseases significantly impacts awareness, funding, and stigma.
Traditional methods of analyzing disease mentions often overlook the intricate relationships and sentiments expressed in text,
which can provide deeper insights into public perception and information dissemination. Existing literature on disease discourse
primarily focuses on quantitative metrics such as disease frequency counts or associations based on semantic similarity.
However, such analyses rarely account for the sentiment of the discourse, which can play a critical role in shaping public
perception and response to health information. Moreover, the complexity of textual data and the subtleties of language usage
present significant challenges for extracting meaningful patterns, especially when considering the emotional context of diseaserelated
discussions. This study introduces a novel approach that integrates sentiment analysis and network visualization to
examine the discourse on diseases within a comprehensive textual dataset. By applying the TextBlob library, we analyze
sentence-level sentiment and categorize it as positive or negative. Utilizing these sentiment scores, we construct two distinct
network graphs to depict the relationships between diseases based on their co-occurrence within sentiment-laden sentences. The
analysis reveals contrasting landscapes of disease discourse: the positive sentiment network highlights diseases frequently
mentioned in the context of successful treatment or optimistic outcomes, forming distinct clusters around well-managed
conditions. In contrast, the negative sentiment network elucidates diseases that coalesce around shared public concerns, fears, or
complications, marking out critical areas for public health intervention. The findings underscore the importance of sentiment in
understanding disease discourse, offering a novel perspective that can assist public health officials in tailoring communication
strategies. By recognizing patterns in disease associations within negative contexts, interventions can be more effectively targeted
to address misconceptions and alleviate public health anxieties. Conversely, identifying diseases with strong positive connections
can guide campaigns to bolster preventive measures and highlight medical advancements. This methodology serves as a blueprint
Procedia Computer Science
In this paper, we introduce a comprehensive approach to the classification of Arabic poetry using... more In this paper, we introduce a comprehensive approach to the classification of Arabic poetry using deep learning techniques. We utilized a dataset comprising nearly one million records of Arabic poetry verses, each labeled with nine different poets encompassing both classical and modern poetic styles. We explored various algorithms to identify the most effective models for accurately determining the poet's identity from the verses. Based on our analysis, we selected LSTM (Long Short-Term Memory) and Bi-LSTM (Bidirectional LSTM) as our primary baseline models, given the demonstrated efficacy of RNN (Recurrent Neural Network) variants in text classification. LSTM, in particular, has shown significant proficiency in analyzing sequential data across multiple languages. Our results indicate a promising average classification accuracy of 92.35%, highlighting the potential for automating the classification of texts in morphologically complex languages such as Arabic. Notably, Bi-LSTM marginally outperformed the standard LSTM, achieving average accuracy of 92.56%. We further discuss the implications of our findings for Arabic literature, particularly in the realm of poetry, and address the challenges encountered during the study. These findings represent a significant advancement in Arabic NLP, offering a powerful framework for poet identification and paving the way for future applications of deep learning in processing Arabic literary texts.
Procedia Computer Science, 2024
The widespread use of social media has escalated concerns about cyberbullying. Traditional method... more The widespread use of social media has escalated concerns about cyberbullying. Traditional methods for detecting and managing
cyberbullying struggle with the sheer volume of electronic text data. This has led to the exploration of deep learning as a potential
solution. Researchers study focuses on implementing deep learning techniques to identify cyberbullying in Arabic social media,
specifically targeting three prevalent forms of Arabic: dialectal, Modern Standard, and Classical. The collected data corpus was
about 30, 0000 tweets. In this work, we first examined the sentiment analysis as cyberbullying, and No cyberbullying, then we
further classified the cyberbullying by labelling the data under six different cyberbullying categories. We implemented deep
learning models such as CNN, RNN, and a combination of CNN-RNN. The results that obtained from 2-classes classification
showed a superiority of LSTM in terms of accuracy with 95.59%, while the best accuracy in the 6-classes classification gained
from implementing CNN with 78.75%. Meanwhile the f1-score results were the highest in LSTM for the 2-lasses and 6-classes
classifications with 96.73%, and 89%, respectively. These findings emphasize the potential for deep learning techniques to be
applied in the development of automated systems for identifying and combating cyberbullying on social media and show how well
they work in detecting cyberbullying.
Procedia Computer Science
Spam detection in SMS communication is a crucial task for maintaining the quality of messaging se... more Spam detection in SMS communication is a crucial task for maintaining the quality of messaging services and protecting users from unwanted and potentially harmful messages. Arabic SMS spam detection poses unique challenges due to the rich morphology and complex structure of the Arabic language, which can significantly impact the performance of traditional text classification methods. To address these challenges, this paper presents a novel approach for Arabic SMS spam detection using a hybrid deep learning model that combines Convolutional Neural Networks (CNN) and Bidirectional Long Short-Term Memory (Bi-LSTM) networks. The proposed model leverages the strengths of CNNs in capturing local features and patterns in the text and the capability of Bi-LSTM networks to understand long-term dependencies and contextual information. This hybrid architecture is designed to effectively handle the complexities of the Arabic language and improve the accuracy of spam detection. The model was evaluated on a dataset of Arabic SMS messages, consisting of both ham (non-spam) and spam messages. The dataset underwent preprocessing steps, including text cleaning, tokenization, and padding, to prepare it for training the deep learning model. The hybrid model was trained using the Adam optimizer and evaluated using accuracy, precision, recall, and F1 score metrics. Early stopping was implemented to prevent overfitting during the training process. The results demonstrate that the hybrid model achieved high performance, with an accuracy of 0.9699, precision of 0.9739, recall of 0.9675, and an F1 score of 0.9707. These metrics indicate the model's effectiveness in accurately detecting spam in Arabic SMS messages. Additionally, the paper provides visualizations of the confusion matrix, ROC curve, and training-validation loss graph to illustrate the model's performance. The implications of this research are significant for the field of Arabic text classification and spam detection. The proposed hybrid model offers a robust solution for accurately classifying Arabic SMS messages, which can be integrated into messaging platforms to enhance spam detection capabilities and improve user experience. Future work could explore data augmentation techniques, transfer learning, and advanced hybrid architectures to further enhance the model's performance and applicability.
Procedia Computer Science, 2024
In the digital age, the ubiquity of text messaging has unfortunately paved the way for SMS phishi... more In the digital age, the ubiquity of text messaging has unfortunately paved the way for SMS phishing, or 'smishing,' a deceptive practice where fraudsters dispatch fraudulent messages to extract sensitive information from unsuspecting recipients. This issue is not trivial. It represents a significant threat to both personal privacy and organizational security, leading to potential data breaches and financial repercussions. Against this backdrop, the imperative for advanced detection strategies is undeniable. This survey leverages a systematic review methodology to assess the effectiveness of Natural Language Processing (NLP) and Machine Learning (ML) techniques in the detection of SMS phishing, also known as smishing. By methodically analyzing research spanning various detection strategies, the study illuminates the evolution from basic rule-based frameworks to sophisticated ML algorithms, enriched with NLP for deep analysis. The findings underscore the superior efficacy of combining ML classifiers with NLP, particularly through the deployment of advanced deep learning models like Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs), which offer unprecedented accuracy in identifying and thwarting complex smishing attacks. The value of this study lies in its comprehensive synthesis of current methodologies and its contribution to the ongoing enhancement of cybersecurity defenses. It serves as a crucial guide for future research directions, emphasizing the necessity of adopting and innovating cutting-edge NLP and ML techniques to stay ahead of evolving digital threats.
Soft Computing, May 9, 2023
Education and Information Technologies, Mar 1, 2022
Advances in intelligent systems and computing, Aug 29, 2018
One of the important revolutionary tools widely used and globally implemented by educational inst... more One of the important revolutionary tools widely used and globally implemented by educational institutes and universities is none other than the electronic learning (E-learning system). The aim of this system is to deliver education. As a result, the users of an E-learning system can have enormous benefits. The developed countries are successfully implementing the E-learning system besides realization of its massive benefits. On the contrary, the developing countries have failed, either fully or partially, to implement the E-learning system. A main reason is that those countries do not have an absolute utilization and considered below the satisfactory level. For instance, in United Arab Emirate, one of the developing countries, a growing number of universities are investing for many years in E-learning systems in order to enhance the quality of student education. However, their utilization among students has not fulfilled the satisfactory level. Imagine the evidence that the behavior of user is mainly required for the successful use of these web-based tools, investigating the unified theory of acceptance and use of technology (UTAUT) of E-learning system used in practical education is the basic aim of this research study. A survey on E-learning usage among 280 students was conducted and by using the given responses, the assumptions of the research resulting from this model have been practically validated. The partial least square method was employed to examine these responses. In predicting a student’s intention to use E-learning, the UTAUT model was strongly corroborated by the obtained results. In addition, the findings reveal that all important factors of behavioral intention to use E-learning system were reportedly found as the social influence, performance expectancy and facilitating conditions of learning. Remarkably, a significant impact on student intention towards E-learning system was not suggested by the effort expectancy. Consequently, The three key factors leading to successful E-Learning system are thought to be the good perception and encouraging university policy.
Lecture notes in networks and systems, Dec 3, 2021
Lecture Notes in Computer Science, 2023
International journal of data and network science, 2023
2020 International Conference on Communications, Computing, Cybersecurity, and Informatics (CCCI)
Detection, and recommendation systems are widely used in many companies and organizations in favo... more Detection, and recommendation systems are widely used in many companies and organizations in favor of giving their employees or even customers a better service. The importance of the systems motivated the investigation of the existing approaches to test its efficiency and possible usage. In this article, the aim is to integrate both systems to create a system that is able to detect diabetes and then recommend a proper plan or medication to overcome diabetes. So, we conducted a study of different approaches of both detection and recommendation systems in order to select the appropriate one. Recommendations systems have different methods. In this paper, we focused on the content-based recommendation method, in which we evaluated using a movie recommender application and movie dataset. We also evaluated and tested four different detection approaches, in which we found that the most accurate approach is the random forest with an accuracy of 79.2% and F-measure of 0.787. A discussion of the usage of a collaborative recommendation system is also introduced to give a wide vision and comparison of the two methods to decide which one is better for a diabetes treatment recommendation system.
The combination of two technology model which are the Technology Acceptance Model (TAM) and Use o... more The combination of two technology model which are the Technology Acceptance Model (TAM) and Use of Gratifications Theory (U&G) to create an integrated model is the first step in predicting the importance of using emotional icons and the level of satisfaction behind this usage. The reason behind using these two theories into one integrated model is that U&G provides specific information and a complete understanding of usage, whereas TAM theory has proved its effectiveness with a variety of technological applications. A self-administered survey was conducted in University of Fujairah with college students to find out the social and cognitive factors that affect the usage of stickers in WhatsApp in the United Arab of Emirates. The hypothesized model is validated empirically using the responses received from an online survey of 372 respondents were analyzed using structural equation modeling (SEM-PLS). The results show that ease of use, perceived usefulness, cognition, hedonic and socia...
International Journal of Advanced Computer Science and Applications, 2021