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Recent developments in the field of deep learning have led to the widespread integration of artificial neural networks in various domains of application. Prominent contemporary artificial neural network training techniques are based on first-order gradient computation. The emphasis on algorithmic performance has driven the emergence of variant artificial neural network training methodologies. Estimation theory, traditionally considered a sub-field of statistics and signal processing, has been explored by various researchers for the development of non-gradient based training methods. Articles published with the aim of utilizing estimation-based artificial neural network training techniques have shown promising results. We identify the integration of estimation theory within the artificial neural network training procedure as intelligent estimation. In this paper, the field of intelligent estimation is analyzed in greater depth with emphasis on the algorithmic performance of novel implementations. Intelligent estimation with applications in the professional domain is also considered, and will help lay the foundation for future research in the literature. [ABSTRACT FROM AUTHOR]

A comprehensive literature review on vessel behaviour pattern recognition in maritime and littoral environments, spanning a period of three decades, is presented. A vast majority of research focuses on anomalous vessel behaviour detection from Automated Identification System (AIS) data, using artificial intelligence (AI), Bayesian networks and machine learning based methods. AIS is a cooperative system that employs VHF transceivers to share vessel information through terrestrial and satellite communication networks. To track small boats not fitted with AIS, uncooperative surveillance systems are employed. These systems have been studied in a relatively small number of publications. In vessel behaviour pattern recognition, target classification is critically important, particularly, in asymmetric warfare situations. The paper concludes with a discussion of future outlook in vessel behaviour pattern recognition research. • Vessel behaviour pattern recognition in maritime and littoral domains is reviewed. • Most of the research considers the use of Automated Identification System (AIS). • Uncooperative surveillance that does not solely rely on AIS is more challenging. • Future research will focus on persistent/high-density sensing and big data analysis. [ABSTRACT FROM AUTHOR]

In recent years, elliptic partial differential equations (PDEs) have been challenging in numerical mathematics, engineering, and physics. In this paper, we proposed a novel approach called Multilayer Neural Network for Partial Differential Equations (MLNPDE) based on deep learning algorithms by exploring the elliptic PDE family under certain conditions such as initial conditions and boundary conditions to govern approximation solutions of PDEs. In the proposed model, we include a multilayer neural network using a densely connected network. Moreover, the approximation solution underlying PDEs can be expressed in two terms: the first term satisfies the boundary conditions, and the second term is a function of the unknown parameters that were estimated by our proposed model. Furthermore, we derive a normal form of the boundary conditions on the first term of the approximation solution for two-dimensional elliptic partial differential equation problems with an arbitrary domain. The numerical results show that our method achieved tremendous state-of-the-art performance in accuracy and efficiency compared to the classical methods. [ABSTRACT FROM AUTHOR]