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Titlebook: Recent Innovations in Artificial Intelligence and Smart Applications; Mostafa Al-Emran,Khaled Shaalan Book 2022 The Editor(s) (if applicab

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發(fā)表于 2025-3-30 10:37:53 | 只看該作者
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發(fā)表于 2025-3-30 15:27:11 | 只看該作者
Markov Switching Model for Driver Behavior Prediction: Use Cases on Smartphones,e driver actions, sensitivity, distraction, and response time. As the data collection is one of the major concerns for learning and validating different driving situations, we present a driver behavior switching model validated by a low-cost data collection solution using smartphones. The proposed m
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發(fā)表于 2025-3-30 19:55:47 | 只看該作者
Understanding the Impact of the Ontology of Semantic Web in Knowledge Representation: A Systematic ations of this relationship on knowledge representation and real-life solutions in various industries. PRISMA guidelines were used to select the papers and the authors focused on the most important 10 resources papers to investigate the research questions. It is concluded that semantic-web ontologie
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發(fā)表于 2025-3-30 22:27:21 | 只看該作者
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發(fā)表于 2025-3-31 03:28:56 | 只看該作者
Robotics and AI in Healthcare: A Systematic Review,timated to climb to 9 billion. By 2037, with a growth rate getting lower each year, we expect to have many older adults by then. With the increase in the price of caregivers and medication year by year, it is getting harder to maintain their longevity. The research papers reviewed will include the l
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發(fā)表于 2025-3-31 07:16:12 | 只看該作者
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發(fā)表于 2025-3-31 11:27:47 | 只看該作者
Book 2022art cities, telemedicine, and robotics. It sheds light on the recent AI innovations in classical machine learning, deep learning, Internet of Things (IoT), Blockchain, knowledge representation, knowledge management, big data, and natural language processing (NLP). The edited book covers empirical an
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發(fā)表于 2025-3-31 13:43:52 | 只看該作者
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發(fā)表于 2025-3-31 18:06:52 | 只看該作者
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發(fā)表于 2025-3-31 22:35:40 | 只看該作者
Exploring the Hidden Patterns in Maintenance Data to Predict Failures of Heavy Vehicles,acy, 68.09 and 41.24% Prediction of Issue Accuracy. So fleet managers should look into the historical data they have and let AI algorithms find the hidden patterns and employ them for better predictive maintenance schedules.
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