The book presents the proceedings of the 12th International Conference on Frontiers of Intelligent computing: Theory and Applications (FICTA 2024), held at Intelligent Systems Research Group (ISRG), London Metropolita...
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ISBN:
(数字)9789819601431
ISBN:
(纸本)9789819601424;9789819601455
The book presents the proceedings of the 12th International Conference on Frontiers of Intelligent computing: Theory and Applications (FICTA 2024), held at Intelligent Systems Research Group (ISRG), London Metropolitan University, London, United Kingdom, during June 6–7, 2024. Researchers, scientists, engineers and practitioners exchange new ideas and experiences in the domain of intelligent computing theories with prospective applications in various engineering disciplines in the book. This book is divided into four volumes. It covers broad areas of information and decision sciences, with papers exploring both the theoretical and practical aspects of data-intensive computing, data mining, evolutionary computation, knowledge management and networks, sensor networks, signal processing, wireless networks, protocols and architectures. This book is a valuable resource for postgraduate students in various engineering disciplines.
EEG data classification plays a pivotal role in understanding brain activity and its applications in various domains. Deep learning has emerged as a powerful paradigm for automatically learning complex patterns from r...
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EEG data classification plays a pivotal role in understanding brain activity and its applications in various domains. Deep learning has emerged as a powerful paradigm for automatically learning complex patterns from raw data, eliminating the need for manual feature extraction. However, in the context of medical data, and in particular for EEG analysis, the use of deep learning approaching while having been very successful is not being included in medical diagnosis routines, yet. The aim of this survey is twofold. On one side, it provides a comprehensive overview of the current state-of-the-art in EEG data classification, with a specific focus on the use of deep learning techniques. On the other side, it also addresses the clinician community, explaining the power and trustfulness of such new approaches. The survey begins with an introduction highlighting the limitations of traditional model-based approaches and the potential of deep learning in EEG data classification. The fundamental principles and architectures of deep learning models are presented, including convolutional neural networks (CNNs), recurrent neural networks (RNNs) and Graph Convolution Neural Network (GCNNs) that have been successfully applied to EEG data classification tasks. A detailed review and analysis of existing literature on deep learning-based EEG data classification are provided, categorizing the studies based on the type of the input data, e.g., sequences, images, graphs or multi-modalities. We also discuss about the existing tools and technologies for EEG data classification and highlights the challenges and limitations associated with deep learning in EEG data classification, including limited data availability, interpretability of deep models and bias mitigation. Potential solutions and ongoing research efforts to overcome these challenges are explored, providing insights into the future directions of this field. This survey serves as a valuable resource for researchers, practitioners
The present book develops the mathematical and numerical analysis of linear, elliptic and parabolic partial differential equations (PDEs) with coefficients whose logarithms are modelled as Gaussian random fields (GRFs...
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ISBN:
(数字)9783031383847
ISBN:
(纸本)9783031383830
The present book develops the mathematical and numerical analysis of linear, elliptic and parabolic partial differential equations (PDEs) with coefficients whose logarithms are modelled as Gaussian random fields (GRFs), in polygonal and polyhedral physical domains. Both, forward and Bayesian inverse PDE problems subject to GRF priors are considered.;Adopting a pathwise, affine-parametric representation of the GRFs, turns the random PDEs into equivalent, countably-parametric, deterministic PDEs, with nonuniform ellipticity constants. A detailed sparsity analysis of Wiener-Hermite polynomial chaos expansions of the corresponding parametric PDE solution families by analytic continuation into the complex domain is developed, in corner- and edge-weighted function spaces on the physical domain.;The presented Algorithms and results are relevant for the mathematical analysis of many approximation methods for PDEs with GRF inputs, such as model order reduction, neural network and tensor-formatted surrogates of parametric solution families. They are expected to impact computational uncertainty quantification subject to GRF models of uncertainty in PDEs, and are of interest for researchers and graduate students in both, applied and computational mathematics, as well as in computational science and engineering.
We are pleased to present the proceedings of the workshops held in conjunction with ER 2005, the 24th International Conference on Conceptual Modeling. The objective of these workshops was to extend the spectrum of the...
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ISBN:
(数字)9783540322399
ISBN:
(纸本)9783540293958
We are pleased to present the proceedings of the workshops held in conjunction with ER 2005, the 24th International Conference on Conceptual Modeling. The objective of these workshops was to extend the spectrum of the main conferencebygivingparticipantsanopportunitytopresentanddiscussemerging hot topics related to conceptual modeling and to add new perspectives to this key mechanism for understanding and representing organizations, including the new “virtual” e-environments and the information systems that support them. To meet this objective, we selected 5 workshops: – AOIS 2005: 7th International Bi-conference Workshop on Agent-Oriented Information Systems – BP-UML 2005: 1st International Workshop on Best Practices of UML – CoMoGIS 2005: 2nd International Workshop on Conceptual Modeling for Geographic Information Systems – eCOMO 2005: 6th International Workshop on Conceptual Modeling - proaches for E-business – QoIS 2005: 1st International Workshop on Quality of Information Systems These 5 workshops attracted 18, 27, 31, 9, and 17 papers, respectively. F- lowing the ER workshopphilosophy, program committees selected contributions on the basis of strong peer reviews in order to maintain a high standard for accepted papers. The committees accepted 8, 9, 12, 4, and 7 papers, for acc- tance ratesof 44%,33%,39%,44%, and 41%,respectively. In total, 40 workshop papers were selected out of 102 submissions with a weighted averageacceptance rate of 40%.
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