Neural networks for data classification

Kumaraswamy, Balachandra

doi:10.1016/b978-0-12-820601-0.00011-2

Cited by 17 publications

(10 citation statements)

References 12 publications

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“…Numerically, minimisation is solved by means of the Levenberg–Marquardt algorithm [ 18 ], an iterative method that implements the strengths of the gradient descent algorithm (slow but converging) and the speed of the Gauss–Newton solver.…”

Section: Validation Of the Modelmentioning

confidence: 99%

Temperature Influence on Brake Pad Friction Coefficient Modelisation

Bellini,

Di Cocco,

Iacoviello

et al. 2023

Materials

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Brake pad linings are an essential part of the correct functioning of braking systems based on the use of pads and discs. Generally, the compounds used to make the gaskets are characterised by the use of over 20 sintered components, which allow friction coefficients that vary between 0.2 and 0.6 at temperatures up to 200 °C. In this work, a traditional compound was investigated under close-to-real conditions in order to evaluate the tribological behaviour at different temperatures. Finally, a model based on the proportionality between temperature increase and relative variation of the friction coefficient was proposed. From the experimental test, it was evident that the friction coefficient increased with the temperature, passing from 0.4 to 0.6 in the temperature range of 100 °C to 180 °C; however, a further temperature increment until 350 °C caused a reduction in the friction coefficient to 0.2. The proposed model was able to anticipate the abovementioned trend, especially at high temperatures.

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Section: Validation Of the Modelmentioning

confidence: 99%

Temperature Influence on Brake Pad Friction Coefficient Modelisation

Bellini,

Di Cocco,

Iacoviello

et al. 2023

Materials

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“…The BPNN is trained by updating the weight and bias values according to the Levenberg-Marquardt optimization algorithm (Kumaraswamy, 2021) [32]. The model was constructed using an input layer containing the 14 independent variables (C1-C14) reported in Table 1, hidden layers, and an output layer, with only one output corresponding to the C15 or C16 variables.…”

Section: Models and Their Performancementioning

confidence: 99%

A Crash Data Analysis through a Comparative Application of Regression and Neural Network Models

Mussone

Meinagh

2023

Safety

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One way to reduce road crashes is to determine the main influential factors among a long list that are attributable to driver behavior, environmental conditions, vehicle features, road type, and traffic signs. Hence, selecting the best modelling tool for extracting the relations between crash factors and their outcomes is a crucial task. To analyze the road crash data of Milan City, Italy, gathered between 2014–2017, this study used artificial neural networks (ANNs), generalized linear mixed-effects (GLME), multinomial regression (MNR), and general nonlinear regression (NLM), as the modelling tools. The data set contained 35,182 records of road crashes with injuries or fatalities. The findings showed that unbalanced and incomplete data sets had an impact on outcome performance, and data treatment methods could help overcome this problem. Age and gender were the most influential recurrent factors in crashes. Additionally, ANNs demonstrated a superior capability to approximate complicated relationships between an input and output better than the other regression models. However, they cannot provide an analytical formulation, but can be used as a baseline for other regression models. Due to this, GLME and MNR were utilized to gather information regarding the analytical framework of the model, that aimed to construct a particular NLM.

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“…Voxel-wise processing of DW data requires us to have a mathematical representation or biophysical model, an optimization algorithm, and an objective function that fundamentally designs the goal of the optimizer. Before the advent of DL tools, gradient descent, Newton's method, and the Levenberg-Marquardt algorithm were popular for solving inverse problems (55). These algorithms have been used with objective functions that closely mimic the noise distribution of the data.…”

Section: Maximum Likelihood Framework For Aimentioning

confidence: 99%

Artificial intelligence for diffusion MRI-based tissue microstructure estimation in the human brain: an overview

et al. 2023

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Artificial intelligence (AI) has made significant advances in the field of diffusion magnetic resonance imaging (dMRI) and other neuroimaging modalities. These techniques have been applied to various areas such as image reconstruction, denoising, detecting and removing artifacts, segmentation, tissue microstructure modeling, brain connectivity analysis, and diagnosis support. State-of-the-art AI algorithms have the potential to leverage optimization techniques in dMRI to advance sensitivity and inference through biophysical models. While the use of AI in brain microstructures has the potential to revolutionize the way we study the brain and understand brain disorders, we need to be aware of the pitfalls and emerging best practices that can further advance this field. Additionally, since dMRI scans rely on sampling of the q-space geometry, it leaves room for creativity in data engineering in such a way that it maximizes the prior inference. Utilization of the inherent geometry has been shown to improve general inference quality and might be more reliable in identifying pathological differences. We acknowledge and classify AI-based approaches for dMRI using these unifying characteristics. This article also highlighted and reviewed general practices and pitfalls involving tissue microstructure estimation through data-driven techniques and provided directions for building on them.

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Neural networks for data classification

Cited by 17 publications

References 12 publications

Temperature Influence on Brake Pad Friction Coefficient Modelisation

Temperature Influence on Brake Pad Friction Coefficient Modelisation

A Crash Data Analysis through a Comparative Application of Regression and Neural Network Models

Artificial intelligence for diffusion MRI-based tissue microstructure estimation in the human brain: an overview

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