Wavelet Neural Network Model for Yield Spread Forecasting

Shah, Firdous A.; Debnath, Lokenath

doi:10.3390/math5040072

Cited by 7 publications

(5 citation statements)

References 34 publications

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“…The primary aim of an ANN is to find an optimal solution in an infinite space. Thus, we believe that the output of this study widened the ability of the conventional neural network in various mathematical perspectives, such as complex analysis [65], stability analysis [66] and forecasting analysis [67][68][69][70]. In this article, the core solution diversification principle of the EDA was found to be beneficial for ANN optimization tasks.…”

Section: Discussionmentioning

confidence: 86%

Discrete Mutation Hopfield Neural Network in Propositional Satisfiability

et al. 2019

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The dynamic behaviours of an artificial neural network (ANN) system are strongly dependent on its network structure. Thus, the output of ANNs has long suffered from a lack of interpretability and variation. This has severely limited the practical usability of the logical rule in the ANN. The work presents an integrated representation of k-satisfiability (kSAT) in a mutation hopfield neural network (MHNN). Neuron states of the hopfield neural network converge to minimum energy, but the solution produced is confined to the limited number of solution spaces. The MHNN is incorporated with the global search capability of the estimation of distribution algorithms (EDAs), which typically explore various solution spaces. The main purpose is to estimate other possible neuron states that lead to global minimum energy through available output measurements. Furthermore, it is shown that the MHNN can retrieve various neuron states with the lowest minimum energy. Subsequent simulations performed on the MHNN reveal that the approach yields a result that surpasses the conventional hybrid HNN. Furthermore, this study provides a new paradigm in the field of neural networks by overcoming the overfitting issue.

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Section: Discussionmentioning

confidence: 86%

Discrete Mutation Hopfield Neural Network in Propositional Satisfiability

et al. 2019

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“…Meanwhile, an RBF algorithm and SPSS Clementine technique were also combined to support the wavelet transform sequences for the prediction process. Shah et al 54 forecast output growth using wavelet transforms and Levenberg–Marquardt (LM) ANN models.…”

Section: Related Workmentioning

confidence: 99%

A novel wavelet decomposition and transformation convolutional neural network with data augmentation for breast cancer detection using digital mammogram

Oyelade

Ezugwu

2022

Sci Rep

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Research in deep learning (DL) has continued to provide significant solutions to the challenges of detecting breast cancer in digital images. Image preprocessing methods and architecture enhancement techniques have been proposed to improve the performance of DL models such as convolutional neural networks (CNNs). For instance, the wavelet decomposition function has been used for image feature extraction in CNNs due to its strong compactness. Additionally, CNN architectures have been optimized to improve the process of feature detection to support the classification process. However, these approaches still lack completeness, as no mechanism exists to discriminate features to be enhanced and features to be eliminated for feature enhancement. More so, no studies have approached the use of wavelet transform to restructure CNN architectures to improve the detection of discriminant features in digital mammography for increased classification accuracy. Therefore, this study addresses these problems through wavelet-CNN-wavelet architecture. The approach presented in this paper combines seam carving and wavelet decomposition algorithms for image preprocessing to find discriminative features. These features are passed as input to a CNN-wavelet structure that uses the new wavelet transformation function proposed in this paper. The CNN-wavelet architecture applied layers of wavelet transform and reduced feature maps to obtain features suggestive of abnormalities that support the classification process. Meanwhile, we synthesized image samples with architectural distortion using a generative adversarial network (GAN) model to argue for their training datasets' insufficiency. Experimentation of the proposed method was carried out using DDSM + CBIS and MIAS datasets. The results obtained showed that the new method improved the classification accuracy and lowered the loss function values. The study's findings demonstrate the usefulness of the wavelet transform function in restructuring CNN architectures for performance enhancement in detecting abnormalities leading to breast cancer in digital mammography.

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“…In this section, the authors present the Te-transform and its properties. Equation ( 11) and Equation (12) show the short-time Fourier transform (STFT) and the dyadic Wavelet transform (DWT), respectively, for an 𝑓 (𝑡) ∈ 𝐿² (ℝ) [1][2][3]18,[22][23][24].…”

Section: Dyadic Te-transformmentioning

confidence: 99%

A High-Resolution Dyadic Transform for Non-Stationary Signal Analysis

2021

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This article shows a new Te-transform and its periodogram for applications that mainly exhibit stochastic behavior with a signal-to-noise ratio lower than −30 dB. The Te-transform is a dyadic transform that combines the properties of the dyadic Wavelet transform and the Fourier transform. This paper also provides another contribution, a corollary on the energy relationship between the untransformed signal and the transformed one using the Te-transform. This transform is compared with other methods used for the analysis in the frequency domain, reported in literature. To perform the validation, the authors created two synthetic scenarios: a noise-free signal scenario and another signal scenario with a signal-to-noise ratio equal to −69 dB. The results show that the Te-transform improves the sensitivity in the frequency spectrum with respect to previously reported methods.

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Wavelet Neural Network Model for Yield Spread Forecasting

Cited by 7 publications

References 34 publications

Discrete Mutation Hopfield Neural Network in Propositional Satisfiability

Discrete Mutation Hopfield Neural Network in Propositional Satisfiability

A novel wavelet decomposition and transformation convolutional neural network with data augmentation for breast cancer detection using digital mammogram

A High-Resolution Dyadic Transform for Non-Stationary Signal Analysis

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