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pro vyhledávání: '"Altahhan, Abdulrahman"'
Autor:
Raieli, Salvatore, Altahhan, Abdulrahman, Jeanray, Nathalie, Gerart, Stéphane, Vachenc, Sebastien
Tabular datasets are widely used in scientific disciplines such as biology. While these disciplines have already adopted AI methods to enhance their findings and analysis, they mainly use tree-based methods due to their interpretability. At the same
Externí odkaz:
http://arxiv.org/abs/2410.17758
Autor:
Miskow, Andrzej, Altahhan, Abdulrahman
In this paper, we study the effect of introducing channel and spatial attention mechanisms, namely SEN-Net, ECA-Net, and CBAM, to existing CNN vision-based models such as VGGNet, ResNet, and ResNetV2 to perform the Facial Emotion Recognition task. We
Externí odkaz:
http://arxiv.org/abs/2410.17740
Accurately predicting the state of charge of Lithium-ion batteries is essential to the performance of battery management systems of electric vehicles. One of the main reasons for the slow global adoption of electric cars is driving range anxiety. The
Externí odkaz:
http://arxiv.org/abs/2410.17049
Autor:
Sahu, Siddharth, Altahhan, Abdulrahman
Capsule Networks outperform Convolutional Neural Networks in learning the part-whole relationships with viewpoint invariance, and the credit goes to their multidimensional capsules. It was assumed that increasing the number of capsule layers in the c
Externí odkaz:
http://arxiv.org/abs/2410.16908
Autor:
Amine Naimi, Altahhan Abdulrahman, Nils Bausch, Victor M. Becerra, Vineet Vajpayee, Jiamei Deng
Publikováno v:
Naimi, A, Deng, J M, Abdulrahman, A, Vajpayee, V, Becerra, V & Bausch, N 2020, Dynamic neural network-based system identification of a pressurized water reactor . in Proceedings of the 8th International Conference on Control, Mechatronics and Automation, (ICCMA 2020) . Institute of Electrical and Electronics Engineers, pp. 100-104, The 8th International Conference on Control, Mechatronics and Automation, Moscow, Russian Federation, 6/11/20 . https://doi.org/10.1109/ICCMA51325.2020.9301483
ICCMA
ICCMA
This work presents a dynamic neural network-based (DNN) system identification approach for a pressurized water nuclear reactor. The presented empirical modelling approach describes the DNN structure using differential equations. Local optimization al
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::679e5a554e8a48a63adfd4441d431f40
https://eprints.leedsbeckett.ac.uk/id/eprint/7192/7/DynamicNeuralNetwork-basedSystemIdentificationOfAPressurizedWaterReactorAM-DENG.pdf
https://eprints.leedsbeckett.ac.uk/id/eprint/7192/7/DynamicNeuralNetwork-basedSystemIdentificationOfAPressurizedWaterReactorAM-DENG.pdf
Akademický článek
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Publikováno v:
Artificial Neural Networks & Machine Learning - ICANN 2016: 25th International Conference on Artificial Neural Networks, Barcelona, Spain, September 6-9, 2016, Proceedings, Part II; 2016, p38-46, 9p
Autor:
Altahhan, Abdulrahman
Publikováno v:
2016 International Joint Conference on Neural Networks (IJCNN); 2016, p4565-4570, 6p
Publikováno v:
2016 International Joint Conference on Neural Networks (IJCNN); 2016, p3264-3271, 8p