Hardware implementation of deep neural network for seizure prediction

Massoud, Yasmin M.; Ahmad, Ahmad A.; Abdelzaher, Mennatallah; Kuhlmann, Levin; Abd El Ghany, Mohamed A.

doi:10.1016/j.aeue.2023.154961

Cited by 1 publication

(1 citation statement)

References 19 publications

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“…The power of optimal techniques and strength of neural learning are utilized in artificial intelligence computing approaches to address a variety of challenging real-world scenarios [25,26]. Numerous divisions of applied sciences, including space science [27], plasma science [28], quantum mechanics [29], thermal physics [30], hydrodynamic [31], electrical motors and generators [32,33], electromagnetism [34], weather science [35], optical science [36], algebraic expressions [37], neuroinformatics [38], and microengineering [39], are finding extensive use for neural intelligence computing frameworks. These soft computing approaches outperformed all conventional methodologies and performed remarkably well in the aforementioned applications [40].…”

Section: Introductionmentioning

confidence: 99%

Scaled Conjugate Gradient Neural Intelligence for Motion Parameters Prediction of Markov Chain Underwater Maneuvering Target

Ali,

Zuberi,

Qing

et al. 2024

JMSE

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This study proposes a novel application of neural computing based on deep learning for the real-time prediction of motion parameters for underwater maneuvering object. The intelligent strategy utilizes the capabilities of Scaled Conjugate Gradient Neural Intelligence (SCGNI) to estimate the dynamics of underwater target that adhere to discrete-time Markov chain. Following a state-space methodology in which target dynamics are combined with noisy passive bearings, nonlinear probabilistic computational algorithms are frequently used for motion parameters prediction applications in underwater acoustics. The precision and robustness of SCGNI are examined here for effective motion parameter prediction of a highly dynamic Markov chain underwater passive vehicle. For investigating the effectiveness of the soft computing strategy, a steady supervised maneuvering route of undersea passive object is designed. In the framework of bearings-only tracking technology, system modeling for parameters prediction is built, and the effectiveness of the SCGNI is examined in ideal and cluttered marine atmospheres simultaneously. The real-time location, velocity, and turn rate of dynamic target are analyzed for five distinct scenarios by varying the standard deviation of white Gaussian observed noise in the context of mean square error (MSE) between real and estimated values. For the given motion parameters prediction problem, sufficient Monte Carlo simulation results support SCGNI’s superiority over typical generalized pseudo-Bayesian filtering strategies such as Interacting Multiple Model Extended Kalman Filter (IMMEKF) and Interacting Multiple Model Unscented Kalman Filter (IMMUKF).

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