Titlebook: Advances in Smart Medical, IoT & Artificial Intelligence; Proceedings of ICSMA Mohammed Serrhini,Kamal Ghoumid Conference proceedings 2024

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悦耳

Conference proceedings 2024conference. With a focus on the intersection of the conference themes, this book provides a unique platform for researchers, practitioners, and innovators to share their cutting-edge research, innovations, and solutions...Get ready to dive into the latest advancements in Artificial Intelligence and

失望未来

alabaster

https://doi.org/10.1007/978-3-658-13574-4olutions to the problem. The MO k-MST dilemma arises in various real-world decision-making scenarios. Numerical experiments demonstrate that our proposed hybrid approach outperforms the standalone simulated annealing method, thus offering enhanced performance.

付出

Complementation in Finite Groups,utperforming other techniques such as zoning and profile projection. Subsequently, a study on the structure of the multilayer perceptron neural network is conducted, achieving a very high accuracy using Loci features. In contrast, the accuracy does not exceed 90% for zoning and profile projection.

豪华

Transfer Learning for Efficiency in Elderly Fall Detection with Limited Data Samplesled N×Subsampling and utilized transfer learning with MobileNetV2. Our study leveraged the public URFD database, and the obtained experimental results demonstrated a notable achievement: an accuracy range of 94.74% to 98.94%, using only a 15% training subset consisting of 732 images of activities of daily living and 369 images of fall scenarios.

Stable-Angina

A Dynamic Hybrid Approach Based on Ant Colony Optimization and Simulated Annealing to Solve the Multolutions to the problem. The MO k-MST dilemma arises in various real-world decision-making scenarios. Numerical experiments demonstrate that our proposed hybrid approach outperforms the standalone simulated annealing method, thus offering enhanced performance.

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脆弱带来

凹处

Transfer Learning for Efficiency in Elderly Fall Detection with Limited Data Samplestiveness in healthcare applications, is challenged by limited accessibility to substantial datasets, especially in the case of fall detection. Moreover, training deep learning models is both time-consuming and costly. To address these issues, in this paper, we implemented a sample size technique cal