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Transfer learning models have proven superior to classical machine learning approaches in various text classification tasks, such as sentiment analysis, question answering, news categorization, and natural language inference. Recently, these models have shown exceptional results in natural language understanding (NLU). Advanced attention‐based language models like BERT and XLNet excel at handling complex tasks across diverse contexts. However, they encounter difficulties when applied to specific domains. Platforms like Facebook, characterized by continually evolving casual and sophisticated language, demand meticulous context analysis even from human users. The literature has proposed numerous solutions using statistical and machine learning techniques to predict the sentiment (positive or negative) of online customer reviews, but most of them rely on various business, review, and reviewer features, which leads to generalizability issues. Furthermore, there have been very few studies investigating the effectiveness of state‐of‐the‐art pre‐trained language models for sentiment classification in reviews. Therefore, this study aims to assess the effectiveness of BERT, RoBERTa, ALBERT, DistilBERT, and XLNet in sentiment classification using the Yelp reviews dataset. The models were fine‐tuned, and the results obtained with the same hyperparameters are as follows: 98.30 for RoBERTa, 98.20 for XLNet, 97.40 for BERT, 97.20 for ALBERT, and 96.00 for DistilBERT. [ABSTRACT FROM AUTHOR]

During the last few decades, the utilization of unmanned aerial vehicles has grown in military and has seen new civil applications because of their reduced cost and hovering capabilities. This article presents a visual servoing system for detecting and tracking a moving object using an unmanned aerial vehicle. The system consists of two sequential components. The first component addresses the detection of a moving object, using the unmanned aerial vehicle based on color features. The second component addresses the visual servoing control that is designed to guide the unmanned aerial vehicle according to the target position and inclination. Three fuzzy logic modules are implemented in order to control the unmanned aerial vehicle in tracking a moving object. The proposed method is validated on a real flight using an AR.Drone 2.0 quadcopter. The obtained results show that the performance of the proposed method is suitable for object-following tasks in surveillance applications. This research investigates the use of unmanned aerial vehicle technology for crowd monitoring during Hajj and it could also be used for border surveillance to monitor Saudi Arabia's borders. [ABSTRACT FROM AUTHOR]

We studied the impact of 2D and 3D educational contents on learning and memory recall using electroencephalography (EEG) brain signals. For this purpose, we adopted a classification approach that predicts true and false memories in case of both short term memory (STM) and long term memory (LTM) and helps to decide whether there is a difference between the impact of 2D and 3D educational contents. In this approach, EEG brain signals are converted into topomaps and then discriminative features are extracted from them and finally support vector machine (SVM) which is employed to predict brain states. For data collection, half of sixty-eight healthy individuals watched the learning material in 2D format whereas the rest watched the same material in 3D format. After learning task, memory recall tasks were performed after 30 minutes (STM) and two months (LTM), and EEG signals were recorded. In case of STM, 97.5% prediction accuracy was achieved for 3D and 96.6% for 2D and, in case of LTM, it was 100% for both 2D and 3D. The statistical analysis of the results suggested that for learning and memory recall both 2D and 3D materials do not have much difference in case of STM and LTM. [ABSTRACT FROM AUTHOR]

ABSTRACT Service-oriented computing has emerged as the state-of-the-art distributed computing model for loosely coupled service-centric business applications. Specifically, service-oriented computing techniques allow organizations to compose Web services. Indeed, the organizational capacity to compose Web services has received much interest, particularly with respect to supporting business-to-business or enterprise application integration. In this paper, we present a system that proposes to adopt service composition as a new approach to integrate data from various data sources. We specify the business process description language as well as the service composition engine that supports the proposed language. We also present different strategies for executing a composite data service call. Copyright © 2013 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]