A Driving Performance Forecasting System Based on Brain Dynamic State Analysis Using 4-D Convolutional Neural Networks.

Vehicle accidents are the primary cause of fatalities worldwide. Most often, experiencing fatigue on the road leads to operator errors and behavioral lapses. Thus, there is a need to predict the cognitive state of drivers, particularly their fatigue level. Electroencephalography (EEG) has been demonstrated to be effective for monitoring changes in the human brain state and behavior. Thirty-seven subjects participated in this driving experiment and performed a perform lane-keeping task in a visual-reality environment. Three domains, namely, frequency, temporal, and 2-D spatial information, of the EEG channel location were comprehensively considered. A 4-D convolutional neural-network (4-D CNN) algorithm was then proposed to associate all information from the EEG signals and the changes in the human state and behavioral performance. A 4-D CNN achieves superior forecasting performance over 2-D CNN, 3-D CNN, and shallow networks. The results showed a 3.82% improvement in the root mean-square error, a 3.45% improvement in the error rate, and a 11.98% improvement in the correlation coefficient with 4-D CNN compared with 3-D CNN. The 4-D CNN algorithm extracts the significant θ and alpha activations in the frontal and posterior cingulate cortices under distinct fatigue levels. This work contributes to enhancing our understanding of deep learning methods in the analysis of EEG signals. We even envision that deep learning might serve as a bridge between translation neuroscience and further real-world applications

Classification of Arrhythmia by Using Deep Learning with 2-D ECG Spectral Image Representation

The electrocardiogram (ECG) is one of the most extensively employed signals used in the diagnosis and prediction of cardiovascular diseases (CVDs). The ECG signals can capture the heart's rhythmic irregularities, commonly known as arrhythmias. A careful study of ECG signals is crucial for precise diagnoses of patients' acute and chronic heart conditions. In this study, we propose a two-dimensional (2-D) convolutional neural network (CNN) model for the classification of ECG signals into eight classes; namely, normal beat, premature ventricular contraction beat, paced beat, right bundle branch block beat, left bundle branch block beat, atrial premature contraction beat, ventricular flutter wave beat, and ventricular escape beat. The one-dimensional ECG time series signals are transformed into 2-D spectrograms through short-time Fourier transform. The 2-D CNN model consisting of four convolutional layers and four pooling layers is designed for extracting robust features from the input spectrograms. Our proposed methodology is evaluated on a publicly available MIT-BIH arrhythmia dataset. We achieved a state-of-the-art average classification accuracy of 99.11\%, which is better than those of recently reported results in classifying similar types of arrhythmias. The performance is significant in other indices as well, including sensitivity and specificity, which indicates the success of the proposed method.Comment: 14 pages, 5 figures, accepted for future publication in Remote Sensing MDPI Journa

One-dimensional discrete-time CNN with multiplexed template-hardware

This paper presents a novel discrete-time and fully programmable cellular neural network (CNN) suitable for processing one-dimensional (1-D) signals. As 1-D signals are typically very long sequences, the system consists of a linear analog shift register for data I/O coupled to a 1×n CNN array. In addition to the 1-D CNN architecture, a unique feature of our implementation is that the number of multipliers needed to implement both CNN templates has been minimized. This is conceivable because the multipliers are multiplexed between the A*y and B*u products during alternating phases of the controlling clock. The CNN system has been implemented in current mode based on the S2I technique using MOSIS Orbit 2 µm CMOS technology. The paper presents a thorough behavioral analysis of the new architecture, circuit-level implementations, and corresponding measured experimental result

2-D Cnn for time series trend prediction

Tese de mestrado, Matemática Financeira, Universidade de Lisboa, Faculdade de Ciências, 2020Recentemente, Redes Neurais Artificiais (RNAs) têm sido desenvolvidas e aplicadas á pre visão e classificação de séries temporais devido á sua capacidade de modelação não linear. Redes Neurais Convolucionais ( CNNs do inglês Convolutional Neural Networks), um tipo de rede neural habitualmente usada para classificação de imagens, ganharam recentemente popularidade nos mercados financeiros. Em Gudeleke, Boluk e Ozbayoglu (2017), os autores apresentam um método para prever a tendência dos preços de fecho de fundos de investimento usando uma CNN bidimensional. Os autores usaram dados de dezassete fundos de investimento distintos (Financial Select Sector SPDR ETF, Utilities Select Sector SPDR ETF, Industrial Select Sector SPDR ETF, SPDR S&P 500 ETF, Consumer Staples Select Sector SPDR ETF, iShares MSCI Germany ETF, Materials Select Sector SPDR ETF, Technology Select Sector SPDR ETF, Health Care Select Sector SPDR ETF, iShares MSCI Hong Kong ETF, iShares MSCI Canada ETF, Consumer Discret Sel Sect SPDR ETF, iShares MSCI Mexico Capped ETF, SPDR Dow Jones Industrial Average ETF, Energy Select Sector SPDR ETF, iShares MSCI Australia ETF and iShares MSCI Japan ETF) para criar imagens de (28 × 28) pixeis em nível de cinza. Estas imagens contêm 28 dias de negociação e 28 séries temporais correspondentes ao preço de fecho, volume e alguns indicadores técnicos calculados para diferentes períodos. Essas imagens são então usadas para alimentadar uma CNN bidimensional que retorna a tendência do preço de fecho do dia seguinte. Neste trabalho, um primeiro modelo foi desenvolvido a fim de reproduzir os resultados obtidos em Gudeleke, Boluk e Ozbayoglu (2017). A exatidão de 58% foi alcançada para o modelo de classificação binário, significativamente inferior ao valor de referência de 78%. Por outro lado, o nosso modelo de classificação multinomial apresentou uma melhor performance com uma exatidão de 69% em comparação com o valor de referência de 63%. Um estudo secundário tentou melhorar o desempenho dos modelos alterando a aquitetura das CNNs. Ao remover a camada de agrupamento da CNN, foram alcançados melhores resul tados em ambas as classificações binária e multinomial. No caso do modelo de classificação binária, foi observado um aumento de 6% na exatidão ( correspondente a uma exatidão de 64%). Considerando o modelo classificação multinomial, não foi identificada uma melhoria na exatidão, no entando foram observadas melhorias na precisão e na revocação para as classes de compra e venda. Ao aumentar o tamanho das imagens geradas a partir dos dados de entrada, foi observado um aumento de 5% na exatidão ( correspondente a uma exatidão de 69%) parao modelo de classificação binária. Por outro lado, ao passarmos para um modelo de previsão da tendência dos retornos semanais, um aumento de 6% na exatidão ( correspondente a uma exatidão de 75%) foi observado para o modelo de classificação binária. Já no caso do modelo de classificação multinomial, foi observada uma diminuição de 4% na exatidão ( correspondente a uma exatidão de 65%). Para além disso, valores de precisão significativamente superiores foram obtidos para duas das três classes. Finalmente, foi desenvolvido um modelo para prever a tendência dos retornos mensais. Para tal, mais uma vez, foi necessário aumentar o tamanho das imagens geradas a partir dos dados de entrada. Um aumento de 15% na exatidão ( correspondente a uma exatidão de 90%) foi observado No caso do modelo de classificação binária. Já em relação ao modelo de classificação multinomial, um aumento de exatidão de 16% foi alcançado ( correspondente a uma exatidão de 81%) juntamente com um aumento na precisão e revocação para as classes de compra e venda.Convolution Neural Networks have recently gain popularity as time series forecasting and classification models due to their ability of non-linear modeling. In this work a classification model for predicting the trend of ETFs closing prices using a 2-D CNN was developed. The 2-D CNN was trained with labelled images generated out of the ETFs financial data. Our model were able to predict the next day, week and month price movements with 69%, 75% and 90% accuracy, respectively

Hybrid CNN Bi-LSTM neural network for Hyperspectral image classification

Hyper spectral images have drawn the attention of the researchers for its complexity to classify. It has nonlinear relation between the materials and the spectral information provided by the HSI image. Deep learning methods have shown superiority in learning this nonlinearity in comparison to traditional machine learning methods. Use of 3-D CNN along with 2-D CNN have shown great success for learning spatial and spectral features. However, it uses comparatively large number of parameters. Moreover, it is not effective to learn inter layer information. Hence, this paper proposes a neural network combining 3-D CNN, 2-D CNN and Bi-LSTM. The performance of this model has been tested on Indian Pines(IP) University of Pavia(PU) and Salinas Scene(SA) data sets. The results are compared with the state of-the-art deep learning-based models. This model performed better in all three datasets. It could achieve 99.83, 99.98 and 100 percent accuracy using only 30 percent trainable parameters of the state-of-art model in IP, PU and SA datasets respectively