Digit Recognition Applied to Reconstructed Audio Signals Using Deep Learning

2021 ◽  
Author(s):  
Anastasia-Sotiria Toufa ◽  
Constantine Kotropoulos
Keyword(s):  
Deep Learning ◽  
Audio Signals ◽  
Digit Recognition

Multi-Modal Emotion Recognition Based On deep Learning Of EEG And Audio Signals

2021 ◽  
Author(s):  
Zhongjie Li ◽  
Gaoyan Zhang ◽  
Jianwu Dang ◽  
Longbiao Wang ◽  
Jianguo Wei
Keyword(s):  
Deep Learning ◽  
Emotion Recognition ◽  
Audio Signals

scholarly journals Characterization of Symbolic Rules Embedded in Deep DIMLP Networks: A Challenge to Transparency of Deep Learning

2017 ◽  
Vol 7 (4) ◽  
pp. 265-286 ◽  
Author(s):  
Guido Bologna ◽  
Yoichi Hayashi
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Predictive Accuracy ◽  
Binary Classification ◽  
Classification Problem ◽  
Rule Extraction ◽  
Breast Cancer Dataset ◽  
Cancer Dataset ◽  
Digit Recognition ◽  
Rule Complexity

AbstractRule extraction from neural networks is a fervent research topic. In the last 20 years many authors presented a number of techniques showing how to extract symbolic rules from Multi Layer Perceptrons (MLPs). Nevertheless, very few were related to ensembles of neural networks and even less for networks trained by deep learning. On several datasets we performed rule extraction from ensembles of Discretized Interpretable Multi Layer Perceptrons (DIMLP), and DIMLPs trained by deep learning. The results obtained on the Thyroid dataset and the Wisconsin Breast Cancer dataset show that the predictive accuracy of the extracted rules compare very favorably with respect to state of the art results. Finally, in the last classification problem on digit recognition, generated rules from the MNIST dataset can be viewed as discriminatory features in particular digit areas. Qualitatively, with respect to rule complexity in terms of number of generated rules and number of antecedents per rule, deep DIMLPs and DIMLPs trained by arcing give similar results on a binary classification problem involving digits 5 and 8. On the whole MNIST problem we showed that it is possible to determine the feature detectors created by neural networks and also that the complexity of the extracted rulesets can be well balanced between accuracy and interpretability.

scholarly journals Spec2Mol: An end-to-end deep learning framework for translating MS/MS Spectra to de-novo molecules

2021 ◽  
Author(s):  
Eleni Litsa ◽  
Vijil Chenthamarakshan ◽  
Payel Das ◽  
Lydia Kavraki
Keyword(s):  
Deep Learning ◽  
Mass Spectrum ◽  
Chemical Compound ◽  
Biomarker Discovery ◽  
De Novo ◽  
Reference Database ◽  
Original Structure ◽  
Audio Signals ◽  
Chemical Structures ◽  
Fragmentation Tree

Elucidating the structure of a chemical compound is a fundamental task in chemistry with application in multiple domains including the emerging field of metabolomics, with promising applications in drug discovery, precision medicine, and biomarker discovery. The common practice for elucidating the structure of a chemical compound is to obtain a mass spectrum and subsequently retrieve its structure from spectral databases. However, database retrieval methods fail to identify novel molecules that are not present in the reference database. In this work, we propose Spec2Mol, a deep learning architecture for molecular structure recommendation given mass spectra alone. Spec2Mol is inspired by the Speech2Text deep learning architectures for translating audio signals into text. Our approach is based on an encoder-decoder architecture. The encoder learns the spectra embeddings, while the decoder, pre-trained on a massive dataset of chemical structures for translating between different molecular representations, reconstructs SMILES sequences of the recommended chemical structures. We have evaluated Spec2Mol by assessing the molecular similarity between the recommended structures and the original structure. Our analysis showed that Spec2Mol is able to identify the presence of key substructures in the molecule from its mass spectrum, and shows on par performance, when compared to existing fragmentation tree based methods, in recommending molecules for a given mass spectrum.

scholarly journals Deep Learning for Identifying Malicious Firmware

2021 ◽  
Author(s):  
David Noever ◽  
Samantha E. Miller Noever
Keyword(s):  
Deep Learning ◽  
Operating Systems ◽  
Deep Neural Network ◽  
Forensic Analysis ◽  
Embedded Devices ◽  
Learning Methods ◽  
Digit Recognition ◽  
Image Conversion ◽  
Thumbnail Images ◽  
The Internet Of Things

A malicious firmware update may prove devastating to the embedded devices both that make up the Internet of Things (IoT) and alsothat typically lack the same security verifications now applied to full operating systems. This work converts the binary headers of 40,000 firmware examples from bytes into 1024-pixel thumbnail images to train a deep neural network. The aim is to distinguish benign and malicious variants using modern deep learning methods without needing detailed functional or forensic analysis tools. One outcome of this image conversion enables contact with the vast machine learning literature already applied to handle digit recognition (MNIST). Another result indicates that greater than 90% accurate classifications prove possible using image-based convolutional neural networks (CNN) when combined with transfer learning methods. The envisioned CNN application would intercept firmware updates before their distribution to IoT networks and score their likelihood of containing malicious variants.

scholarly journals Handwritten Digit Recognition using Convolutional Neural Network in Python with Tensorflow and Observe the Variation of Accuracies for Various Hidden Layers

2019 ◽  
Author(s):  
Fathma Siddique ◽  
Shadman Sakib ◽  
Md. Abu Bakr Siddique
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Handwritten Digit Recognition ◽  
Diverse Range ◽  
Digit Recognition ◽  
Handwritten Digit ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

In recent times, with the increase of Artificial Neural Network (ANN), deep learning has brought a dramatic twist in the field of machine learning by making it more Artificial Intelligence (AI). Deep learning is used remarkably used in vast ranges of fields because of its diverse range of applications such as surveillance, health, medicine, sports, robotics, drones etc. In deep learning, Convolutional Neural Network (CNN) is at the center of spectacular advances that mixes Artificial Neural Network (ANN) and up to date deep learning strategies. It has been used broadly in pattern recognition, sentence classification, speech recognition, face recognition, text categorization, document analysis, scene, and handwritten digit recognition. The goal of this paper is to observe the variation of accuracies of CNN to classify handwritten digits using various numbers of hidden layer and epochs and to make the comparison between the accuracies. For this performance evaluation of CNN, we performed our experiment using Modified National Institute of Standards and Technology (MNIST) dataset. Further, the network is trained using stochastic gradient descent and the backpropagation algorithm.

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