Polynomial Series FLANN for Nonlinear Equalization

Haweel, Mohammad T.; El‐Samie, Fathi E. Abd; Zahran, O.

doi:10.21608/mjeer.2019.76768

Cited by 3 publications

(2 citation statements)

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“…where the adaptation constant is μ. The operation of the time-consuming multiplication has been replaced by the less time-consuming sign change operation using the signed LMS [20][21][22]. In the APM method, the update equation for the k th weight is:…”

Section: Proposed Adaptive Polynomial Methodsmentioning

confidence: 99%

Adaptive Polynomial Method for Solving Third-Order ODE With Application in Thin Film Flow

2021

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Differential equations are commonly used to model several engineering, science, and biological applications. Unfortunately, finding analytical solutions for solving higher-order Ordinary Differential Equations (ODEs) is a challenge. Numerical methods represent a leading candidate for solving such ODEs. This work presents an innovated adaptive technique that uses polynomials to solve linear or nonlinear third-order ODEs. The proposed technique adapts the coefficients of the polynomial to obtain an explicit analytical solution. A signed least mean square algorithm is exploited to enhance the adaptation process and to decrease both computational requirements and time. The efficiency of the proposed Adaptive Polynomial Method (APM) is illustrated through six well-known examples. The proposed technique is compared with recent analytical and numerical methods to validate its effectiveness in terms of Mean Square Error (MSE) and computation time. An application in a thin film flow system is modeled to a third-order ODE. The proposed technique is compared with recent numerical and analytical methods in solving the thin film flow equation, and the proposed technique has achieved better results. Furthermore, the proposed technique provides an analytical solution with an increased dynamic range and much lower computational time than those of the conventional numerical methods.

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Section: Proposed Adaptive Polynomial Methodsmentioning

confidence: 99%

Adaptive Polynomial Method for Solving Third-Order ODE With Application in Thin Film Flow

2021

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“…More research to apply DL and ML is encouraged. Examples of such networks include generalized probabilistic neural network, neural dynamic classification, Polynomial Series functional-link artificial neural network (FLANN)[77,78], Legendre FLANN[79] and deep reinforcement networks. The performance of current research cannot contribute to a reliable clinical establishment.…”

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confidence: 99%

A Review on Autism Spectrum Disorder Diagnosis Using Task-Based Functional Mri

Haweel

Seada

Ghoniemy

et al. 2021

IJICIS

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Autism spectrum disorder (ASD) is a neurodevelopmental disorder associated with impairments in social and lingual abilities. The current gold standard for diagnosis is the autism diagnostic observation schedule (ADOS) combined with expert clinical judgement. The actual cause for autism is still unknown. Early ASD diagnosis is critical for conducting personalized treatment plans and can lead to significant development enhancements. Machine learning techniques, especially deep learning, have been widely incorporated in attempts to develop objective computer-aided technologies to diagnose autism with brain imaging modalities. Task-based functional magnetic resonance imaging (TfMRI) is a brain imaging modality that reveals functional activity of the brain in response to different experiments to study the effects of a brain disease or disorder. This study provides a comprehensive review of research that deploys traditional machine learning and deep learning techniques in diagnosing ASD based on TfMRI. Classification results manifest that TfMRI holds early autism biomarkers and suggests future research to establish multi-modal studies that integrate TfMRI with structural, functional, clinical and gnomic data with higher number of participating subjects.

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