Immunocomputing-Based Approach for Optimizing the Topologies of LSTM Networks

Bataineh, Ali Al; Kaur, Devinder

doi:10.1109/access.2021.3084131

Cited by 30 publications

(14 citation statements)

References 43 publications

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“…Nature has always been a source of inspiration for researchers to develop new computing methodologies by observing how naturally occurring phenomena behave in various environmental situations to solve complex real-world problems [ 45 , 46 ]. This has led to groundbreaking analysis that has given rise to new fields, such as artificial immune systems [ 47 ], evolutionary computation [ 48 ], and swarm intelligence [ 49 ].…”

Section: Methodsmentioning

confidence: 99%

MLP-PSO Hybrid Algorithm for Heart Disease Prediction

Bataineh

Manacek

2022

JPM

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Background: Machine Learning (ML) is becoming increasingly popular in healthcare, particularly for improving the timing and accuracy of diagnosis. ML can provide disease prediction by analyzing vast amounts of healthcare data, thereby, empowering patients and healthcare providers with information to make informed decisions about disease prevention. Due to the rising cost of treatment, one of the most important topics in clinical data analysis is the prediction and prevention of cardiovascular disease. It is difficult to manually calculate the chances of developing heart disease due to a myriad of contributing factors. Objective: The aim of this paper is to develop and compare various intelligent systems built with ML algorithms for predicting whether a person is likely to develop heart disease using the publicly available Cleveland Heart Disease dataset. This paper describes an alternative multilayer perceptron (MLP) training technique that utilizes a particle swarm optimization (PSO) algorithm for heart disease detection. Methods: The proposed MLP-PSO hybrid algorithm and ten different ML algorithms are used in this study to predict heart disease. Various classification metrics are used to evaluate the performance of the algorithms. Results: The proposed MLP-PSO outperforms all other algorithms, obtaining an accuracy of 84.61%. Conclusions: According to our findings, the current MLP-PSO classifier enables practitioners to diagnose heart disease earlier, more accurately, and more effectively.

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Section: Methodsmentioning

confidence: 99%

MLP-PSO Hybrid Algorithm for Heart Disease Prediction

Bataineh

Manacek

2022

JPM

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“…Similarly to how the human body generates several antibodies to combat an antigen, antibodies are generated as candidate solutions to combat an antigen in the CSA algorithm [73]. Antibodies and antigens have the same format.…”

Section: ) Antibodies Initializationmentioning

confidence: 99%

Multi-Layer Perceptron Training Optimization Using Nature Inspired Computing

2022

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Although the multi-layer perceptron (MLP) neural networks provide a lot of flexibility and have proven useful and reliable in a wide range of classification and regression problems, they still have limitations. One of the most common is associated with the optimization algorithm used to train them. The most commonly used training method is stochastic gradient descent with backpropagation (or backpropagation for short) because it is mathematically tractable (given that the activation functions are differentiable). However, backpropagation is not guaranteed to find the globally optimal set of weights and biases. As a result, the MLP is often incapable of obtaining a desirable solution to the problem. Clonal selection algorithms (CSA) are optimization procedures that effectively explore a complex and large space to find values near the global optimum. Consequently, CSA can be used to solve the problem of training MLP networks. This paper presents a novel implementation of CSA for training MLP architectures to solve real-world problems such as breast cancer diagnosis, active sonar target classification, wheat classification, and flower classification. The CSA is used to find the optimal weights and biases that will significantly increase the classification accuracy of the MLP. The performance of our proposed approach is compared with other popular training methods: genetic algorithm (GA), ant colony optimization (ACO), particle swarm optimization (PSO), Harris hawks optimization (HHO), moth-flame optimization (MFO), flower pollination algorithm (FPA), and backpropagation (BP). The comparison is benchmarked using five classification datasets: Iris Flower, Sonar, Wheat Seeds, Breast Cancer Wisconsin, and Haberman's Survival. Comparative study results illustrate the improvements in MLP performance gained by using CSA over other training methods, and hence it can be considered a competitive approach to training MLP networks when solving real-world applications in various disciplines.

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“…Authors presented the application of deep learning techniques for SMS spam detection using LSTM in Gadde et al 46 and Al-Bataineh and Kaur. 47 Authors in the former also applied three different word embedding techniques based on the count, TF-IDF, and hashing vectorizer. The experimental results for LSTM were compared with some of the state-of-the-art ML techniques.…”

Section: Literature Reviewmentioning

confidence: 99%

A deep learning method for automatic SMS spam classification: Performance of learning algorithms on indigenous dataset

Abayomi‐Alli

Misra

Abayomi‐Alli

2022

Concurrency and Computation

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SMS, one of the most popular and fast‐growing GSM value‐added services worldwide, has attracted unwanted SMS, also known as SMS spam. The effects of SMS spam are significant as it affects both the users and the service providers, causing a massive gap in trust among both parties. This article presents a deep learning model based on BiLSTM. Further, it compares our results with some of the states of the art machine learning (ML) algorithm on two datasets: our newly collected dataset and the popular UCI SMS dataset. This study aims to evaluate the performance of diverse learning models and compare the result of the new dataset expanded (ExAIS_SMS) using the following metrics the true positive (TP), false positive (FP), F‐measure, recall, precision, and overall accuracy. The average accuracy for the BiLSTSM model achieved moderately improved results compared to some of the ML classifiers. The experimental results achieved significant improvement from the ground truth results after effective fine‐tuning of some of the parameters. The BiLSTM model using the ExAIS_SMS dataset attained an accuracy of 93.4% and 98.6% for UCI datasets. Further comparison of the two datasets on the state‐of‐the‐art ML classifiers gave an accuracy of Naive Bayes, BayesNet, SOM, decision tree, C4.5, J48 is 89.64%, 91.11%, 88.24%, 75.76%, 80.24%, and 79.2% respectively for ExAIS_SMS datasets. In conclusion, our proposed BiLSTM model showed significant improvement over traditional ML classifiers. To further validate the robustness of our model, we applied the UCI datasets, and our results showed optimal performance while classifying SMS spam messages based on some metrics: accuracy, precision, recall, and F‐measure.

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Immunocomputing-Based Approach for Optimizing the Topologies of LSTM Networks

Cited by 30 publications

References 43 publications

MLP-PSO Hybrid Algorithm for Heart Disease Prediction

MLP-PSO Hybrid Algorithm for Heart Disease Prediction

Multi-Layer Perceptron Training Optimization Using Nature Inspired Computing

A deep learning method for automatic SMS spam classification: Performance of learning algorithms on indigenous dataset

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