Eye State Identification Utilizing EEG Signals: A Combined Method Using Self-Organizing Map and Deep Belief Network

Ahmadi, Neda; Nilashi, Mehrbakhsh; Minaei‐Bidgoli, Behrouz; Farooque, Murtaza M. Junaid; Samad, Sarminah; Aljehane, Nojood O.; Zogaan, Waleed; Ahmadi, Hossein

doi:10.1155/2022/4439189

Cited by 7 publications

(2 citation statements)

References 65 publications

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“…The objective of SOM is to transfer all input data objects with n dimensions to the output in a manner in which the objects are related to each other [55,56]. The SOM is able to perform unsupervised training for the datasets, in which the target is clustering the data [14,15,[57][58][59][60][61]. The Euclidean distance [62,63] from the input vector to all nodes is computed when the input vector is given to the network.…”

Section: Sommentioning

confidence: 99%

A Combined Method for Diabetes Mellitus Diagnosis Using Deep Learning, Singular Value Decomposition, and Self-Organizing Map Approaches

et al. 2023

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Diabetes in humans is a rapidly expanding chronic disease and a major crisis in modern societies. The classification of diabetics is a challenging and important procedure that allows the interpretation of diabetic data and diagnosis. Missing values in datasets can impact the prediction accuracy of the methods for the diagnosis. Due to this, a variety of machine learning techniques has been studied in the past. This research has developed a new method using machine learning techniques for diabetes risk prediction. The method was developed through the use of clustering and prediction learning techniques. The method uses Singular Value Decomposition for missing value predictions, a Self-Organizing Map for clustering the data, STEPDISC for feature selection, and an ensemble of Deep Belief Network classifiers for diabetes mellitus prediction. The performance of the proposed method is compared with the previous prediction methods developed by machine learning techniques. The results reveal that the deployed method can accurately predict diabetes mellitus for a set of real-world datasets.

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Section: Sommentioning

confidence: 99%

A Combined Method for Diabetes Mellitus Diagnosis Using Deep Learning, Singular Value Decomposition, and Self-Organizing Map Approaches

et al. 2023

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“…A Deep Belief Network (DBN) is a deep learning architecture consisting of multiple layers of Restricted Boltzmann Machines (RBMs). Restricted Boltzmann Machines (RBMs) utilize stochastic generative neural networks as a means of unsupervised learning and feature extraction [41], [42]. It is imperative to provide clear definitions for Restricted Boltzmann Machines (RBMs) and the associated training algorithms in order to formulate the mathematical representation of a Deep Belief Network (DBN), which is depicted in equation 2 below.…”

Section: B Machine Learning/ Deep Learning Modelsmentioning

confidence: 99%

A Comparison of Re-Sampling Techniques for Detection of Multi-Step Attacks on Deep Learning Models

Jamal,

Naz,

Khattak

et al. 2023

IEEE Access

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The increasing dependence on data analytics and artificial intelligence (AI) methodologies across various domains has prompted the emergence of apprehensions over data security and integrity. There exists a consensus among scholars and experts that the identification and mitigation of Multi-step attacks pose significant challenges due to the intricate nature of the diverse approaches utilized. This study aims to address the issue of imbalanced datasets within the domain of Multi-step attack detection. To achieve this objective, the research explores three distinct re-sampling strategies, namely over-sampling, undersampling, and hybrid re-sampling techniques. The study offers a comprehensive assessment of several re-sampling techniques utilized in the detection of Multi-step attacks on deep learning (DL) models. The efficacy of the solution is evaluated using a Multi-step cyber attack dataset that emulates attacks across six attack classes. Furthermore, the performance of several re-sampling approaches with numerous traditional machine learning (ML) and deep learning (DL) models are compared, based on performance metrics such as accuracy, precision, recall, F-1 score, and G-mean. In contrast to preliminary studies, the research focuses on Multi-step attack detection. The results indicate that the combination of Convolutional Neural Networks (CNN) with Deep Belief Networks (DBN), Long Short-Term Memory (LSTM), and Recurrent Neural Networks (RNN) provides optimal results as compared to standalone ML/DL models. Moreover, the results also depict that SMOTEENN, a hybrid re-sampling technique, demonstrates superior effectiveness in enhancing detection performance across various models and evaluation metrics. The findings indicate the significance of appropriate re-sampling techniques to improve the efficacy of Multistep attack detection on DL models.

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A novel approach to iris recognition at-a-distance: leveraging BW-CNN framework

Shirke,

Midhunchakkaravarthy,

Deshpande

2024

Eng. Res. Express

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A novel approach for iris recognition by integrating the Black Widow Optimization (BWO) algorithm with Convolutional Neural Networks (CNNs), forming the innovative Black Widow-CNN (BW-CNN) framework, is presented in this paper. The expanding field of biometrics has ushered in a transformative era of personal identification technologies, with iris recognition standing at the forefront due to its unparalleled security and reliability. The BW-CNN methodology is distinguished by its preprocessing steps, which include image normalization and segmentation, feature extraction through the ScaT-LOOP method, and optimized classification using CNNs fine-tuned with the BWO algorithm. This approach ensures high precision in iris recognition, making it suitable for various applications, from national security to public health. Experimental results, evaluated using comprehensive metrics such as sensitivity, specificity, accuracy, precision, F-measure, and others, demonstrate the superiority of the BW-CNN framework over existing methods. This research not only advances the capabilities of iris recognition systems but also contributes to the broader narrative of innovation in digital identity management, promising more secure and reliable biometric identification systems.

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Eye State Identification Utilizing EEG Signals: A Combined Method Using Self-Organizing Map and Deep Belief Network

Cited by 7 publications

References 65 publications

A Combined Method for Diabetes Mellitus Diagnosis Using Deep Learning, Singular Value Decomposition, and Self-Organizing Map Approaches

A Combined Method for Diabetes Mellitus Diagnosis Using Deep Learning, Singular Value Decomposition, and Self-Organizing Map Approaches

A Comparison of Re-Sampling Techniques for Detection of Multi-Step Attacks on Deep Learning Models

A novel approach to iris recognition at-a-distance: leveraging BW-CNN framework

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