Immuno-Computing-based Neural Learning for Data Classification

Bataineh, Ali Al; Kaur, Devinder

doi:10.14569/ijacsa.2019.0100632

Cited by 5 publications

(3 citation statements)

References 17 publications

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“…Therefore, this research proposes PSO and GWO as alternatives to backpropagation to train FFNN for the EEG classification problem. These algorithms proved their efficiency in discovering a global solution when the search space is large and challenging (Bataineh and Kaur, 2019) Furthermore, unlike gradient descent based backpropagation, there is no need for the cost function to be differentiable. They are a straightforward implementation.…”

Section: Output W X B Imentioning

confidence: 99%

High Performance Implementation of Neural Networks Learning Using Swarm Optimization Algorithms for EEG Classification Based on Brain Wave Data

Bataineh

Jarrah

2022

International Journal of Applied Metaheuristic Computing

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EEG analysis aims to help scientists better understand the brain, help physicians diagnose and treatment choices of the brain-computer interface. Artificial neural networks are among the most effective learning algorithms to perform computing tasks similar to biological neurons in the human brain. In some problems, the neural network model's performance might significantly degrade and overfit due to some irrelevant features that negatively influence the model performance. Swarm optimization algorithms are robust techniques that can be implemented to find optimal solutions to such problems. In this paper, Grey Wolf Optimizer (GWO) and Particle Swarm Optimization (PSO) algorithms are applied for the feature selection and the training of a Feed-forward Neural Network (FFNN). The performance of the FFNN in terms of test accuracy, precision, recall, and F1_score is investigated. Furthermore, this research has implemented other five machine learning algorithms for the purpose of comparison. Experimental results prove that the neural network model outperforms all other algorithms via GWO.

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Section: Output W X B Imentioning

confidence: 99%

High Performance Implementation of Neural Networks Learning Using Swarm Optimization Algorithms for EEG Classification Based on Brain Wave Data

Bataineh

Jarrah

2022

International Journal of Applied Metaheuristic Computing

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“…2 shows the workflow diagram of the CNN hyperparameters tuning for the EMNIST data classification using CSA. It consists of the following steps [24].…”

Section: Clonal Selection Algorithmmentioning

confidence: 99%

Optimal Convolutional Neural Network Architecture Design Using Clonal Selection Algorithm

Bataineh¹,

Kaur²

2019

IJMLC

Self Cite

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In this paper, the Clonal Selection Algorithm (CSA) is implemented to determine the optimal architecture of a convolutional neural network (CNN) for the classification of handwritten character digits. The efficacy of CNN in image recognition is one of the central motives why the world has woken up to the effectiveness of deep learning. During training, an optimal CNN architecture can extract complex features from the data that is being trained; however, the ideal architecture of a CNN for a specific problem cannot be determined by some standard procedure. In practice, CNN architectures are generally designed using human expertise and domain knowledge. By using CSA, optimal architecture of CNN can be determined autonomously through evolution of hyperparameters of the architecture for a given dataset. In this work, proposed methodology is tested on EMNIST dataset which is an enhanced version of MNIST dataset. The results have proven that the CSA based tuning is capable of generating optimal CNN architectures. Through this proposed technique, the best architecture of CNN for a given problem can be selfdetermined without any human intervention.

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“…CSA is an effective technique fairly easy to search for optimal LSTM topologies automatically. CSA has shown to deliver a more robust and effective approach to solving optimization problems [18], [19]. It is capable of finding a global solution by employing biologically inspired operators such as selection, cloning, and hypermutation [20], [21].…”

Section: Introductionmentioning

confidence: 99%

Immunocomputing-Based Approach for Optimizing the Topologies of LSTM Networks

Bataineh

Kaur

2021

IEEE Access

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This paper aims to automatically design optimal LSTM topologies using the clonal selection algorithm (CSA) to solve text classification tasks such as sentiment analysis and SMS spam classification. Designing optimal topologies involves determining the best configuration of hyperparameters that will give the best performance. The current state-of-the-art LSTM topologies are often designed using trial and error approaches which are incredibly time-consuming and require domain experts. Our proposed method, referred to as CSA-LSTM, is evaluated using the Large Movie Review Dataset (IMDB). Furthermore, to verify the robustness of the hyperparameters discovered by CSA for the IMDB dataset, we have used them for the other datasets, viz. the Twitter US Airline Sentiment and the SMS Spam Collection. Additionally, the discovered hyperparameters for the LSTM are combined with pre-determined convolutional neural network (CNN) layers to achieve the same or better results to fast the training time and fewer trainable parameters. For further verification and evaluation of the generalization ability and effectiveness of the proposed approach, it is compared with four machine learning algorithms widely used for text classification tasks: (1) random forest (RF), (2) logistic regression (LR), (3) support vector machine (SVM), and (4) multinomial naive Bayes (NB). The results of our experiments show that the LSTM topologies automatically designed by our CSA method are less expensive, reusable and outperform the machine learning algorithms and other models in the literature evaluated on the same three datasets. Through our proposed method, LSTM's best topology can be self-determined without any human intervention, making CSA-based algorithms a promising approach to automatically design optimal LSTM topologies that provide the best performance for a given task.INDEX TERMS clonal selection algorithm, deep learning, hyperparameters optimization, IMDB, immunocomputing, long short-term memory.

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Immuno-Computing-based Neural Learning for Data Classification

Cited by 5 publications

References 17 publications

High Performance Implementation of Neural Networks Learning Using Swarm Optimization Algorithms for EEG Classification Based on Brain Wave Data

High Performance Implementation of Neural Networks Learning Using Swarm Optimization Algorithms for EEG Classification Based on Brain Wave Data

Optimal Convolutional Neural Network Architecture Design Using Clonal Selection Algorithm

Immunocomputing-Based Approach for Optimizing the Topologies of LSTM Networks

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