The book is a collection of best selected research papers presented at the Third World Conference on Internet of Things: Applications & Future (ITAF 2023) organized by Global Knowledge Research Foundation in Cairo...
详细信息
ISBN:
(数字)9789819947645
ISBN:
(纸本)9789819947638
The book is a collection of best selected research papers presented at the Third World Conference on Internet of Things: Applications & Future (ITAF 2023) organized by Global Knowledge Research Foundation in Cairo during February 4–5, 2023. It includes innovative works from researchers, leading innovators, business executives, and industry professionals to examine the latest advances and applications for commercial and industrial end users across sectors within the emerging Internet of things ecosphere. It shares state-of-the-art as well as emerging topics related to Internet of things such as big data research, emerging services and analytics, Internet of things (IoT) fundamentals, electronic computation and analysis, big data for multi-discipline services, security, privacy and trust, IoT technologies, and open and cloud technologies.
The proliferation of fake news poses a substantial threat to information integrity, prompting the need for robust detection mechanisms. This study advances the research on Arabic fake news detection and overcomes the ...
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The proliferation of fake news poses a substantial threat to information integrity, prompting the need for robust detection mechanisms. This study advances the research on Arabic fake news detection and overcomes the limitation of lower accuracy for fake news detection. This research addresses Arabic fake news detection using word embedding and a powerful stacking classifier. The proposed model combines bagging, boosting, and baseline classifiers, harnessing the strengths of each to create a robust ensemble. Extensive experiments are carried out to evaluate the proposed approach indicating remarkable results, with recall, F1 score, accuracy, and precision reaching 99%. The utilization of advanced stacking techniques, coupled with appropriate textual feature extraction, empowers the model to effectively detect Arabic fake news. Study results make a valuable contribution to fake news detection, particularly in the Arabic context, providing a valuable tool for enhancing information veracity and fostering a more informed public discourse. Furthermore, the proposed model’s accuracy is compared with other cutting-edge models from the existing literature to showcase its superior performance.
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