Texture classification using convolutional neural network optimized with whale optimization algorithm

Dixit, Ujjawal; Mishra, Apoorva; Shukla, Anupam; Tiwari, Ritu

doi:10.1007/s42452-019-0678-y

Cited by 44 publications

(20 citation statements)

References 23 publications

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“…The CNN has its origin in the biological science domain for purpose classification tasks [6]. It is considered to be a special neural network because of its kind of arrangement in terms of full weight sharing.…”

Section: Overview Of Convolutional Neural Networkmentioning

confidence: 99%

Comparative Analysis of Performance of Deep Learning Classification Approach based on LSTM-RNN for Textual and Image Datasets

Gaafar

Dahr

Hamoud

2022

IJCAI

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Deep learning approaches can be applied to a large amount of data for the purpose of simplifying and improving the engineering practice of automated decision-making activities rather than relying on human encoded heuristics. The need for generating faster and effective decisions about systems, processes, and applications gave rise to many artificial intelligences motivated approaches such as convolutional neural networks (CNNs), recurrent neural networks (RNNs), fuzzy analytics, etc. Deep learning deploys diverse multiple layers of cascaded processing elements to enable features extraction and transformations. These deep learning approaches conduct multiple levels of depiction corresponding to distinct abstraction levels. There are several applications of deep learning algorithms including weather forecasting, object recognition, stock market performance forecasts, medical diagnosis, and emergency warning systems. This paper investigates the performance of the deep learning approach on the basis of processing components, data representation, and data types. To achieve this, a deep learning algorithm based on a long short-term memory-recurrent neural network (LSTM-RNN) was utilized to learn hidden patterns and features in the textual and image datasets respectively. The outcomes reveal that the performance of the image-based deep learning model was better in terms of speed due to well-defined patterns of data representation against the data with sentiments-based deep learning by 3.49 mins. to 18.25 mins. While the LSTM-RNN with images offered better classification accuracy by 96.50% to 85.69% due to complex network architecture, processing elements, and features of the underlying datasets. Povzetek: .

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Section: Overview Of Convolutional Neural Networkmentioning

confidence: 99%

Comparative Analysis of Performance of Deep Learning Classification Approach based on LSTM-RNN for Textual and Image Datasets

Gaafar

Dahr

Hamoud

2022

IJCAI

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“…The meta-heuristic optimized CNN classifier was then used for classifying skin cancer images and confirmed that the procedure improved the accuracy of the CNN. Dixit et al 72 optimized CNN through WOA at the convolutional and fully connected layers to recognize texture. This approach aimed to optimize the values of the filters and the values of the weights and biases.…”

Section: Metaheuristic Algorithms and Related Workmentioning

confidence: 99%

Characterization of abnormalities in breast cancer images using nature‐inspired metaheuristic optimized convolutional neural networks model

Oyelade

Ezugwu

2021

Concurrency and Computation

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Convolutional neural networks (CNN) are deep learning models widely reported as performing well in several image classification tasks. Training the networks for optimal performance is considered an NP-hard problem. The combination of a metaheuristic algorithm with CNN has been proposed to address this problem because the metaheuristic algorithms have efficient performance, parameter tuning, and stagnation prevention methods. Moreover, there is now an increasing need to apply the best performing metaheuristic algorithm to optimize the parameters, training and learning rate of the CNN models. This study aims to investigate the best performing metaheuristic algorithm for fine-tuning the weights, biases, and hyperparameters of CNN networks for solving the problem of characterization of abnormalities in breast images. Furthermore, hybrid models consisting of a CNN architecture and five representative metaheuristic algorithms to efficiently detect breast cancer abnormalities are presented. The adopted approach involves training a CNN network using genetic algorithm (GA), whale optimization algorithm (WOA), multiverse optimizer (MVO), satin bower optimization (SBO), and life choice-based optimization (LCBO) algorithms to optimize only weights and bias of the model. Two categories of experiments were carried out: the first involved training of the CNN model, while the second involved optimizing the training of CNN model with metaheuristic algorithms. A comparative analysis of the impact of the proposed GA, WOA, MVO, SBO, and LCBO metaheuristic algorithms on the performance of the CNN architecture was carried out. Empirical and statistical analyses are presented to validate further the findings obtained in the study. Results obtained showed that MVO, SBO and LCBO outperformed GA, WOA. The classification accuracy obtained for CNN-GA, CNN-WOA, CNN-MVO, CNN-SBO, and CNN-LCBO at the fifth epoch was 0.76, 0.75, 0.84, 0.80, and 0.86, respectively. At the same time, the traditional CNN achieved an accuracy of 0.66. The outcome of this study showed that contrary to widespread practice, physics-based, biology-based, and human-based optimization algorithms promise better performance parameter tuning for CNN as compared with evolutionary-based and swarm-based metaheuristic algorithms.

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“…Among many applications already using WOA there are some that utilize it in other deep learning field. Dixit et al proposed applying Whale Optimization Algorithm in convolutional neural network architecture in the texture classification task [ 19 ]. WOA has been employed for optimizing values of the filters in convolution layers as well as for weights and biases optimization in fully-connected ones.…”

Section: Introductionmentioning

confidence: 99%

The Whale Optimization Algorithm Approach for Deep Neural Networks

Brodzicki

Piekarski

Jaworek-Korjakowska

2021

Sensors

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One of the biggest challenge in the field of deep learning is the parameter selection and optimization process. In recent years different algorithms have been proposed including bio-inspired solutions to solve this problem, however, there are many challenges including local minima, saddle points, and vanishing gradients. In this paper, we introduce the Whale Optimisation Algorithm (WOA) based on the swarm foraging behavior of humpback whales to optimise neural network hyperparameters. We wish to stress that to the best of our knowledge this is the first attempt that uses Whale Optimisation Algorithm for the optimisation task of hyperparameters. After a detailed description of the WOA algorithm we formulate and explain the application in deep learning, present the implementation, and compare the proposed algorithm with other well-known algorithms including widely used Grid and Random Search methods. Additionally, we have implemented a third dimension feature analysis to the original WOA algorithm to utilize 3D search space (3D-WOA). Simulations show that the proposed algorithm can be successfully used for hyperparameters optimization, achieving accuracy of 89.85% and 80.60% for Fashion MNIST and Reuters datasets, respectively.

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Texture classification using convolutional neural network optimized with whale optimization algorithm

Cited by 44 publications

References 23 publications

Comparative Analysis of Performance of Deep Learning Classification Approach based on LSTM-RNN for Textual and Image Datasets

Comparative Analysis of Performance of Deep Learning Classification Approach based on LSTM-RNN for Textual and Image Datasets

Characterization of abnormalities in breast cancer images using nature‐inspired metaheuristic optimized convolutional neural networks model

The Whale Optimization Algorithm Approach for Deep Neural Networks

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