Titlebook: Applied Intelligence and Informatics; Second International Mufti Mahmud,Cosimo Ieracitano,Francesco Carlo Mor Conference proceedings 2022 T

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https://doi.org/10.1007/978-3-319-47892-0 that reveal an appreciable robustness and open new scenarios for different applications of the methodology developed in this work, always in the context of optimal management of industrial and commercial spaces.

Prologue

https://doi.org/10.1007/978-3-319-47892-0andom Forest, XGBoost and Support Vector Machine algorithms were compared to find the best DCE-MRI instant for breast cancer classification: the pre-contrast and the third post-contrast instants resulted as the most informative items. Random Forest can be considered the optimal algorithm showing an

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Rosemond Boohene,Daniel AgyapongG signal and used to build channel.frequency.time volumes. A system based on a custom deep Convolutional Neural Network (CNN), named . was designed and developed to discriminate between pre-hand-opening, pre-hand-closing and resting. The proposed system outperformed a comparable method in the litera

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Shahamak Rezaei,Victoria Hill,Yipeng Liudel is proposed based on ML models. The proposed ensemble classifier achieved an accuracy of 94.92% which is approximately 5% accuracy increase compared to individual classifier approach. The source code used in this work are publicly available at:

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Ali Davari,Amer Dehghan Najmabadimodel that implements a pre-trained CNN model, namely, VGG-16. This model is used to classify the segmented images into ‘Good’ and ‘Bad’. Finally, the segmented images are entered into a pre-trained ResNet34 model that identifies the Crown and Rump regions. This can be used by obstetric practitioner

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ConDet2: An Improved Conjunctivitis Detection Portable Healthcare App Powered by Artificial Intelligctivitis. In this work, we present with .ConDet2 which provides an advanced solution than the earlier version of it. It is faster with a higher accuracy level (95%) than the previously released .ConDet.

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