A Hybrid Learning Algorithms for Wavelet Process Neural Network and Its Application

The advances in deep learning algorithms, exponential computing power, and availability of digital patient data like never before have led to the wave of interest and investment in artificial intelligence in health care. No radiology conference is complete without a substantial dedication to AI. Many radiology departments are keen to get involved but are unsure of where and how to begin. This short article provides a simple road map to aid departments to get involved with the technology, demystify key concepts, and pique an interest in the field. We have broken down the journey into seven steps; problem, team, data, kit, neural network, validation, and governance.

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Audio-Based Drone Detection and Identification Using Deep Learning Techniques with Dataset Enhancement through Generative Adversarial Networks

Sensors ◽

10.3390/s21154953 ◽

2021 ◽

Vol 21 (15) ◽

pp. 4953

Author(s):

Sara Al-Emadi ◽

Abdulla Al-Ali ◽

Abdulaziz Al-Ali

Keyword(s):

Neural Network ◽

Deep Learning ◽

Recurrent Neural Network ◽

Learning Algorithms ◽

Generative Adversarial Networks ◽

Generative Adversarial Network ◽

Adversarial Networks ◽

Detection And Identification ◽

Learning Techniques ◽

The Impact

Drones are becoming increasingly popular not only for recreational purposes but in day-to-day applications in engineering, medicine, logistics, security and others. In addition to their useful applications, an alarming concern in regard to the physical infrastructure security, safety and privacy has arisen due to the potential of their use in malicious activities. To address this problem, we propose a novel solution that automates the drone detection and identification processes using a drone’s acoustic features with different deep learning algorithms. However, the lack of acoustic drone datasets hinders the ability to implement an effective solution. In this paper, we aim to fill this gap by introducing a hybrid drone acoustic dataset composed of recorded drone audio clips and artificially generated drone audio samples using a state-of-the-art deep learning technique known as the Generative Adversarial Network. Furthermore, we examine the effectiveness of using drone audio with different deep learning algorithms, namely, the Convolutional Neural Network, the Recurrent Neural Network and the Convolutional Recurrent Neural Network in drone detection and identification. Moreover, we investigate the impact of our proposed hybrid dataset in drone detection. Our findings prove the advantage of using deep learning techniques for drone detection and identification while confirming our hypothesis on the benefits of using the Generative Adversarial Networks to generate real-like drone audio clips with an aim of enhancing the detection of new and unfamiliar drones.

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Fault-diagnosis of digital circuits using neural network of hybrid learning algorithm

FUZZ-IEEE'99. 1999 IEEE International Fuzzy Systems. Conference Proceedings (Cat. No.99CH36315) ◽

10.1109/fuzzy.1999.790161 ◽

1999 ◽

Author(s):

Yong-Hyun Cho ◽

Yong-Su Park

Keyword(s):

Neural Network ◽

Fault Diagnosis ◽

Digital Circuits ◽

Learning Algorithm ◽

Hybrid Learning ◽

Hybrid Learning Algorithm

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Learning algorithms for a neural network in FPGA

Proceedings of the 2002 International Joint Conference on Neural Networks. IJCNN'02 (Cat. No.02CH37290) ◽

10.1109/ijcnn.2002.1005613 ◽

2003 ◽

Cited By ~ 1

Author(s):

T. Aoyama ◽

Qianyi Wang ◽

R. Suematsu ◽

R. Shimizu ◽

U. Nagashima

Keyword(s):

Neural Network ◽

Learning Algorithms

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Modeling of Vehicle Dynamics From Real Vehicle Measurements Using a Neural Network With Two-Stage Hybrid Learning for Accurate Long-Term Prediction

IEEE Transactions on Vehicular Technology ◽

10.1109/tvt.2004.830145 ◽

2004 ◽

Vol 53 (4) ◽

pp. 1076-1084 ◽

Cited By ~ 20

Author(s):

Y.U. Yim ◽

S.-Y. Oh

Keyword(s):

Neural Network ◽

Vehicle Dynamics ◽

Hybrid Learning ◽

Two Stage ◽

Term Prediction ◽

Long Term Prediction ◽

Real Vehicle

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Evaluation Efficiency of Hybrid Deep Learning algorithms with Neural Network, Decision Tree and Boosting Methods for Predicting Groundwater Potential

Geocarto International ◽

10.1080/10106049.2021.1920635 ◽

2021 ◽

pp. 1-20

Author(s):

Yunzhi Chen ◽

Wei Chen ◽

Subodh Chandra Pal ◽

Asish Saha ◽

Indrajit Chowdhuri ◽

...

Keyword(s):

Neural Network ◽

Deep Learning ◽

Decision Tree ◽

Learning Algorithms ◽

Groundwater Potential

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EMOTIONS RECOGNITION IN HUMAN SPEECH USING DEEP NEURAL NETWORKS

Vestnik komp iuternykh i informatsionnykh tekhnologii ◽

10.14489/vkit.2021.01.pp.044-051 ◽

2021 ◽

pp. 44-51

Author(s):

E. Yu. Shchetinin

Keyword(s):

Neural Network ◽

Machine Learning ◽

Neural Networks ◽

Convolutional Neural Network ◽

Recurrent Neural Network ◽

Deep Neural Networks ◽

Learning Algorithms ◽

Machine Learning Algorithms ◽

Audio Recordings ◽

Computer Studies

The recognition of human emotions is one of the most relevant and dynamically developing areas of modern speech technologies, and the recognition of emotions in speech (RER) is the most demanded part of them. In this paper, we propose a computer model of emotion recognition based on an ensemble of bidirectional recurrent neural network with LSTM memory cell and deep convolutional neural network ResNet18. In this paper, computer studies of the RAVDESS database containing emotional speech of a person are carried out. RAVDESS-a data set containing 7356 files. Entries contain the following emotions: 0 – neutral, 1 – calm, 2 – happiness, 3 – sadness, 4 – anger, 5 – fear, 6 – disgust, 7 – surprise. In total, the database contains 16 classes (8 emotions divided into male and female) for a total of 1440 samples (speech only). To train machine learning algorithms and deep neural networks to recognize emotions, existing audio recordings must be pre-processed in such a way as to extract the main characteristic features of certain emotions. This was done using Mel-frequency cepstral coefficients, chroma coefficients, as well as the characteristics of the frequency spectrum of audio recordings. In this paper, computer studies of various models of neural networks for emotion recognition are carried out on the example of the data described above. In addition, machine learning algorithms were used for comparative analysis. Thus, the following models were trained during the experiments: logistic regression (LR), classifier based on the support vector machine (SVM), decision tree (DT), random forest (RF), gradient boosting over trees – XGBoost, convolutional neural network CNN, recurrent neural network RNN (ResNet18), as well as an ensemble of convolutional and recurrent networks Stacked CNN-RNN. The results show that neural networks showed much higher accuracy in recognizing and classifying emotions than the machine learning algorithms used. Of the three neural network models presented, the CNN + BLSTM ensemble showed higher accuracy.

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