Improved Predictive Sparse Decomposition Method With Densenet for Prediction of Lung Cancer

Mienye, Ibomoiye Domor; Sun, Yuqing; Wang, Zenghui

doi:10.47839/ijc.19.4.1986

Cited by 19 publications

(2 citation statements)

References 34 publications

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“…Hence, deep learning techniques such as RNNs that consider the client's shopping behaviour and transaction sequences can be vital in detecting important fraud patterns [8], [47], [48]. For example, Benchaji et al [8] proposed a credit card fraud detection method using an LSTM network to achieve sequential modelling and ensure improved fraud detection.…”

Section: Related Workmentioning

confidence: 99%

A Deep Learning Ensemble With Data Resampling for Credit Card Fraud Detection

Mienye

Sun

2023

IEEE Access

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Credit cards play an essential role in today's digital economy, and their usage has recently grown tremendously, accompanied by a corresponding increase in credit card fraud. Machine learning (ML) algorithms have been utilized for credit card fraud detection. However, the dynamic shopping patterns of credit card holders and the class imbalance problem have made it difficult for ML classifiers to achieve optimal performance. In order to solve this problem, this paper proposes a robust deep-learning approach that consists of long short-term memory (LSTM) and gated recurrent unit (GRU) neural networks as base learners in a stacking ensemble framework, with a multilayer perceptron (MLP) as the meta-learner. Meanwhile, the hybrid synthetic minority oversampling technique and edited nearest neighbor (SMOTE-ENN) method is employed to balance the class distribution in the dataset. The experimental results showed that combining the proposed deep learning ensemble with the SMOTE-ENN method achieved a sensitivity and specificity of 1.000 and 0.997, respectively, which is superior to other widely used ML classifiers and methods in the literature.

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Section: Related Workmentioning

confidence: 99%

A Deep Learning Ensemble With Data Resampling for Credit Card Fraud Detection

Mienye

Sun

2023

IEEE Access

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“…The network learns a hidden correlation between the input features and reconstructs it at the output. Furthermore, a sparsity constraint can be imposed on the hidden units that enable the network to better represent the input [19,21,22], thereby allowing the supervised learning algorithm to perform better classification. Meanwhile, determining the optimal parameters to train the feature learning algorithm is essential in achieving excellent deep feature learning [23,24].…”

Section: Introductionmentioning

confidence: 99%

Improved Heart Disease Prediction Using Particle Swarm Optimization Based Stacked Sparse Autoencoder

Mienye

Sun

2021

Electronics

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Heart disease is the leading cause of death globally. The most common type of heart disease is coronary heart disease, which occurs when there is a build-up of plaque inside the arteries that supply blood to the heart, making blood circulation difficult. The prediction of heart disease is a challenge in clinical machine learning. Early detection of people at risk of the disease is vital in preventing its progression. This paper proposes a deep learning approach to achieve improved prediction of heart disease. An enhanced stacked sparse autoencoder network (SSAE) is developed to achieve efficient feature learning. The network consists of multiple sparse autoencoders and a softmax classifier. Additionally, in deep learning models, the algorithm’s parameters need to be optimized appropriately to obtain efficient performance. Hence, we propose a particle swarm optimization (PSO) based technique to tune the parameters of the stacked sparse autoencoder. The optimization by the PSO improves the feature learning and classification performance of the SSAE. Meanwhile, the multilayer architecture of autoencoders usually leads to internal covariate shift, a problem that affects the generalization ability of the network; hence, batch normalization is introduced to prevent this problem. The experimental results show that the proposed method effectively predicts heart disease by obtaining a classification accuracy of 0.973 and 0.961 on the Framingham and Cleveland heart disease datasets, respectively, thereby outperforming other machine learning methods and similar studies.

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