Pattern Recognition System using MLP Neural Networks

Chauhan, Sarvda

doi:10.9790/3021-0205990993

Cited by 7 publications

(5 citation statements)

References 9 publications

(1 reference statement)

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“…It is challenging to manually find the most suitable hyperparameter combination for the dataset. At present, hyperparameter optimization mainly depends on the mathematical understanding of the algorithm, empirical judgment, and trial and error (Chauhan and Dhingra, 2012;Assi et al, 2018;Itano et al, 2018;Feurer et al, 2019). However, these empirical methods have a high chance of missing the optimal hyperparameter combination to match the dataset and building a bloated network structure that requires too many computing BO is suitable for multidimensional, high-cost valuation problems and has been widely used in maximum likelihood hyperparameter optimization (Feurer et al, 2019).…”

Section: Tuning the Dnn Hyperparameters Using Bayesian Optimization A...mentioning

confidence: 99%

“…However, no one dataset ensures that the site structures of different regions are covered. Researchers (Chauhan and Dhingra, 2012;Assi et al, 2018;Itano et al, 2018) rely on the mathematical understanding of the algorithm, empirical judgment, and trial and error to determine the network architectures, which incurs high computational costs. Researchers have proposed a data-driven method to limit the initial calculation space and utilized a population-based algorithm to enhance the computational efficiency of nonlinear parameter combinations (Guo et al, 2021;Luo et al, 2022).…”

Section: Introductionmentioning

confidence: 99%

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Rapid construction of Rayleigh wave dispersion curve based on deep learning

2023

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Introduction: The dispersion curve of the Rayleigh-wave phase velocity (VR) is widely utilized to determine site shear-wave velocity (Vs) structures from a distance of a few metres to hundreds of metres, even on a ten-kilometre crustal scale. However, the traditional theoretical-analytical methods for calculating VRs of a wide frequency range are time-consuming because numerous extensive matrix multiplications, transfer matrix iterations and the root searching of the secular dispersion equation are involved. It is very difficult to model site structures with many layers and apply them to a population-based inversion algorithm for which many populations of multilayers forward modelling and many generations of iterations are essential.Method: In this study, we propose a deep learning method for constructing the VR dispersion curve in a horizontally layered site with great efficiency. A deep neural network (DNN) based on the fully connected dense neural network is designed and trained to directly learn the relationships between Vs structures and dispersion curves. First, the training and validation sets are generated randomly according to a truncated Gaussian distribution, in which the mean and variance of the Vs models are statistically analysed from different regions’ empirical relationships between soil Vs and its depth. To be the supervised dataset, the corresponding VRs are calculated by the generalized reflection-transmission (R/T) coefficient method. Then, the Bayesian optimization (BO) is designed and trained to seek the optimal architecture of the deep neural network, such as the number of neurons and hidden layers and their combinations. Once the network is trained, the dispersion curve of VR can be constructed instantaneously without building and solving the secular equation.Results and Discussion: The results show that the DNN-BO achieves a coefficient of determination (R2) and MAE for the training and validation sets of 0.98 and 8.30 and 0.97 and 8.94, respectively, which suggests that the rapid method has satisfactory generalizability and stability. The DNN-BO method accelerates the dispersion curve calculation by at least 400 times, and there is almost no increase in computation expense with an increase in soil layers.

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Section: Tuning the Dnn Hyperparameters Using Bayesian Optimization A...mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Rapid construction of Rayleigh wave dispersion curve based on deep learning

2023

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“…Here, we exploit the obtained classes, from original-by-original SI and intra-layer UPL tasks, as ground truths of the fingerprinted smartphones to classify "original" or "shared images" into m classes. Generally, ANNs, inspired by the biological form of the human neural system, have proven their effectiveness in classification tasks [57]. They are very flexible in learning features and can solve non-linear problems.…”

Section: Social-by-original Smartphone Identification and Inter-layermentioning

confidence: 99%

No Matter What Images You Share, You Can Probably Be Fingerprinted Anyway

2021

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The popularity of social networks (SNs), amplified by the ever-increasing use of smartphones, has intensified online cybercrimes. This trend has accelerated digital forensics through SNs. One of the areas that has received lots of attention is camera fingerprinting, through which each smartphone is uniquely characterized. Hence, in this paper, we compare classification-based methods to achieve smartphone identification (SI) and user profile linking (UPL) within the same or across different SNs, which can provide investigators with significant clues. We validate the proposed methods by two datasets, our dataset and the VISION dataset, both including original and shared images on the SN platforms such as Google Currents, Facebook, WhatsApp, and Telegram. The obtained results show that k-medoids achieves the best results compared with k-means, hierarchical approaches, and different models of convolutional neural network (CNN) in the classification of the images. The results show that k-medoids provides the values of F1-measure up to 0.91% for SI and UPL tasks. Moreover, the results prove the effectiveness of the methods which tackle the loss of image details through the compression process on the SNs, even for the images from the same model of smartphones. An important outcome of our work is presenting the inter-layer UPL task, which is more desirable in digital investigations as it can link user profiles on different SNs.

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“…The most often applicable in practice for solving the problem of neural network pattern recognition is a multilayer perceptron [10,11] due to its relatively simple algorithmic implementation, the availability of advanced training methods, parallel computing capabilities. ANN type perceptron is also called Multilayer Feed-forward neural network because there are no feedback signals propagate from ANN input to its output through the unification of the neural layers with usually sigmoidal activation functions.…”

Section: Problem Of Pattern Recognition By Artificial Neural Networkmentioning

confidence: 99%

Evaluation of Neural Network Output Results Reliability in Pattern Recognition

Marshakov

Galushka

Fathi

et al. 2018

Advances in Intelligent Systems and Computing

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Artificial neural networks (ANN) are well-known effective parallel systems which have successfully approved themselves in solving of complicated artificial intelligence problems. The practice of the widespread ANN application due to their high efficiency in solving non-formalized or hard-formalized problems associated with the need for ANN training on experimental material particularly in pattern recognition. In solving problems of pattern recognition the feedforward neural network is usually used due to the simplicity of algorithmic implementation, the availability of advanced training methods, the possibilities of multi-parallel computations. When neural network classifier implementing within decision support systems, it is necessary to assess the ANN results reliability based upon interpretation of the output signals to establish trust between users and the neural network algorithm. In this paper the ANN output results reliability evaluation method in terms of the degree of belonging of the recognized patterns to the originally specified classes is considered. The proposed method is based on the computation of Euclidean distance between the actual ANN output vector characterizing the class of the object recognition and a set of sample vectors defining a priori known classes at the training stage followed by its evaluation by the curve construction of the normal probability distribution law coinciding with the Gaussian function. A feature of this method is the construction of an individual probability distribution curve computed for each ANN output vector. An experimental research of the proposed method in MATLAB is presented on the example of solving the known Fisher's Iris Database classification for input data without noise and with noise. The obtained results confirm the adequacy of the proposed method which can be used both in independent neural network pattern recognition systems and within decision support complexes.

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Pattern Recognition System using MLP Neural Networks

Cited by 7 publications

References 9 publications

Rapid construction of Rayleigh wave dispersion curve based on deep learning

Rapid construction of Rayleigh wave dispersion curve based on deep learning

No Matter What Images You Share, You Can Probably Be Fingerprinted Anyway

Evaluation of Neural Network Output Results Reliability in Pattern Recognition

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