https://sabapub.com/index.php/jaai <p>Journal of Applied Artificial Intelligence (JAAI) is an international and interdisciplinary scholarly peer reviewed journal on artificial intelligence published by Saba Publishing.<br />JAAI devoted entirely to Artificial Intelligence and welcomes papers in the overall field including, but not limited to, machine learning and cognition, deep learning, supervised learning, unsupervised learning, classification, regression, clustering, big and streaming data, optimization algorithms, feature selection and extraction, pattern recognition, bio-informatics, uncertain information processes, recommender systems, E-service personalization, distributed and parallel processing, computer vision, neural networks, natural language processing, heuristic search, multi-objective optimization, multi-agent systems, advances in social network systems, reasoning under uncertainty, forecasting and predication models as well as other hot topics.</p> <p><strong>Editor in Chief: <a href="https://www.scopus.com/authid/detail.uri?authorId=55497463100" target="_blank" rel="noopener">Dr Nibras Abdullah</a></strong><br /><strong>ISSN (online)</strong>: <a href="https://portal.issn.org/resource/ISSN/2709-5908" target="_blank" rel="noopener">2709-5908</a><br /><strong>Frequency:</strong> Semiannual</p> Saba Publishing en-US Journal of Applied Artificial Intelligence 2709-5908 https://sabapub.com/index.php/jaai/article/view/1148 <p>One potential research problem related to clickbait data could be to investigate the impact of clickbait headlines on news consumption and perception of news credibility. The objective of using Machine Learning (ML) models to analyze clickbait data in this work is to determine an accurate model for identifying and classifying clickbait headlines, understand the features that make them successful, evaluate the model's performance in real-world scenarios, and compare the performance of different ML models to select the best one for clickbait classification. By achieving these objectives, the research could provide valuable insights into the mechanisms behind clickbait and the effectiveness of ML models in detecting and mitigating its impact. This research could inform the development of more effective algorithms and tools for combating clickbait and improving news literacy. The suggested methodology for detecting clickbait using machine learning involves collecting a large amount of clickbait and non-clickbait headlines, pre-processing and cleaning the data, identifying and extracting relevant features, selecting an appropriate ML algorithm, training and evaluating the model, making necessary adjustments, and deploying the final model in a production environment to detect clickbait in real-world data. The specific steps and details may vary depending on the task complexity and data availability.</p> Waleed Alanazi Abdulghani B. Alshaibani Badiea Abdulkarem Mohammed Al-Shaibani Zeyad Ghaleb Al-Mekhlafi Abdulraheem B. Alshaibani Anwar B. Alshaibani Copyright (c) 2025 Badiea Abdulkarem Mohammed Al-Shaibani, Waleed Alanazi, Abdulghani B. Alshaibani, Zeyad Ghaleb Al-Mekhlafi, Abdulraheem B. Alshaibani, Anwar B. Alshaibani https://creativecommons.org/licenses/by/4.0 2025-04-15 2025-04-15 6 1 1 17 10.48185/jaai.v6i1.1148 https://sabapub.com/index.php/jaai/article/view/1437 <h2>The use of information and communication technology (ICT) is rapidly expanding throughout society. Different ICTs are used by governments to communicate with their country's citizens and other e-government initiative stakeholders. The e-government initiative faces various internal and external challenges, including limited funding, rapid technological advancements, internet accessibility for the public, and concerns about privacy and security. To address these challenges, several frameworks have been proposed that help improve E-government performance. In order to measure the effectiveness of e-government, this paper aims to identify various constructs and their relationships by providing a summary of the proposed frameworks and models for electronic government development</h2> <p> </p> EHAB AL SHEIKH SALEH Mohd Fadzil Bin Abd Kadir Yousef Abubaker El-Ebiary Copyright (c) 2025 EHAB AL SHEIKH SALEH https://creativecommons.org/licenses/by/4.0 2025-04-15 2025-04-15 6 1 18 23 10.48185/jaai.v6i1.1437