A Study of The Convolutional Neural Networks Applications

Shamsaldin, Ahmed S.; Fattah, Polla; Rashid, Tarik A.; Al-Salihi, Nawzad K.

doi:10.25079/ukhjse.v3n2y2019.pp31-40

Cited by 25 publications

(14 citation statements)

References 20 publications

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“…The algorithms of the most popular ancestor CNN "LeNet", and deep learning CNN "AlexNet" are the important models to study [43,44]. The difference between deep learning CNN's and conventional MLP's are the weight sharing and limited connection that is not present in conventional MLP's [45]. Otherwise, both models follow the homogeneous linear neural network.…”

Section: Image Classificationmentioning

confidence: 99%

Hybrid Feature Extractions and CNN for Enhanced Periocular Identification During Covid-19

Wazirali¹,

Ahmed²

2022

Computer Systems Science and Engineering

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The global pandemic of novel coronavirus that started in 2019 has seriously affected daily lives and placed everyone in a panic condition. Widespread coronavirus led to the adoption of social distancing and people avoiding unnecessary physical contact with each other. The present situation advocates the requirement of a contactless biometric system that could be used in future authentication systems which makes fingerprint-based person identification ineffective. Periocular biometric is the solution because it does not require physical contact and is able to identify people wearing face masks. However, the periocular biometric region is a small area, and extraction of the required feature is the point of concern. This paper has proposed adopted multiple features and emphasis on the periocular region. In the proposed approach, combination of local binary pattern (LBP), color histogram and features in frequency domain have been used with deep learning algorithms for classification. Hence, we extract three types of features for the classification of periocular regions for biometric. The LBP represents the textual features of the iris while the color histogram represents the frequencies of pixel values in the RGB channel. In order to extract the frequency domain features, the wavelet transformation is obtained. By learning from these features, a convolutional neural network (CNN) becomes able to discriminate the features and can provide better recognition results. The proposed approach achieved the highest accuracy rates with the lowest false person identification.

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Section: Image Classificationmentioning

confidence: 99%

Hybrid Feature Extractions and CNN for Enhanced Periocular Identification During Covid-19

Wazirali¹,

Ahmed²

2022

Computer Systems Science and Engineering

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“…More complex neural networks like Recurrent neural networks were introduced after that. [14]. Another approach makes use of a residual network for epilepsy seizure prediction [15].…”

Section: Literature Surveymentioning

confidence: 99%

“…Network is a kind of artificial neural network that works on principles of forward propagation and backward propagation [14]. Our proposed model makes use of a 1 D convolutional network.…”

Section: Convolutional Neural Network Convolutional Neuralmentioning

confidence: 99%

Comparative analysis of multiple machine learning algorithms for epileptic seizure prediction

Lekshmy

Panickar

Harikumar

2022

J. Phys.: Conf. Ser.

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Epilepsy is a common neurological disease that affects more than 2 percent of the population globally. An imbalance in brain electrical activities causes unpredictable seizures, which eventually leads to epilepsy. Neurostimulators have the power to intervene in advance and avoid the occurrence of seizures. Its efficiency can be increased with the help of heuristics like advanced seizure prediction. Early identification of preictal state will help easy activation of neurostimulator on time. This research concentrates on the performance analysis of various machine learning algorithms on recorded EEG data. Through this study, we aim to find the best model, which can be used to create an ensemble model for better learning. This involves modeling and simulation of classical machine learning technique like Logistic regression, Naive Bayes model, K nearest neighbors Random Forest, and deep learning techniques like an Artificial neural network, Convolutional neural networks, Long short term memory, and Autoencoders. In this analysis, Random Forest and Long Short-Term Memory performed well among all models in terms of sensitivity and specificity.

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“…They are widely used for image recognition, classification, regression and much more. Among all the back propagation neural network structures there are several subgroups, such as artificial neural networks [19], convolutional neural networks [20], recurrent neural networks [21], and others. For the purpose of anomaly detection, one network architecture, i.e., autoencoder [22,23] is widely used.…”

Section: Autoencoder Neural Networkmentioning

confidence: 99%

Level Crossing Barrier Machine Faults and Anomaly Detection with the Use of Motor Current Waveform Analysis

2021

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Barrier machines are a key component of automatic level crossing systems ensuring safety on railroad crossings. Their failure results not only in delayed railway transportation, but also puts human life at risk. To prevent faults in this critical safety element of automatic level crossing systems, it is recommended that fault and anomaly detection algorithms be implemented. Both algorithms are important in terms of safety (information on whether a barrier boom has been lifted/lowered as required) and predictive maintenance (information about the condition of the mechanical components). Here, the authors propose fault models for barrier machine fault and anomaly detection procedures based on current waveform observation. Several algorithms were applied and then assessed such as self-organising maps (SOM), autoencoder artificial neural network, local outlier factor (LOF) and isolation forest. The advantage of the proposed solution is there is no change of hardware, which is already homologated, and the use of the existing sensors (in a current measurement module). The methods under evaluation demonstrated acceptable rates of detection accuracy of the simulated faults, thereby enabling a practical application at the test stage.

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A Study of The Convolutional Neural Networks Applications

Cited by 25 publications

References 20 publications

Hybrid Feature Extractions and CNN for Enhanced Periocular Identification During Covid-19

Hybrid Feature Extractions and CNN for Enhanced Periocular Identification During Covid-19

Comparative analysis of multiple machine learning algorithms for epileptic seizure prediction

Level Crossing Barrier Machine Faults and Anomaly Detection with the Use of Motor Current Waveform Analysis

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