Forecasting Natural Events Using Axonal Delay

The ability to forecast natural phenomena relies on understanding causality. By definition this understanding must include a temporal component. In this paper, we consider the ability of an emerging class of neural network, which encode temporal information into the network, to perform the difficult task of Natural Event Forecasting. The Axonal Delay Network (ADN) models axonal delay in order to make predictions about sunspot activity, the Auroral Electrojet (AE) index and daily temperatures during a heatwave. The performance of this network is benchmarked against older types of neural networks; including the Multi-Layer Perceptron (MLP) network and Functional Link Neural Network (FLNN). The results indicate that the inherent temporal characteristics of the Axonal Delay Network make it well suited to the processing and prediction of natural phenomena

Financial time series prediction using spiking neural networks

In this paper a novel application of a particular type of spiking neural network, a Polychronous Spiking Network, was used for financial time series prediction. It is argued that the inherent temporal capabilities of this type of network are suited to non-stationary data such as this. The performance of the spiking neural network was benchmarked against three systems: two "traditional", rate-encoded, neural networks; a Multi-Layer Perceptron neural network and a Dynamic Ridge Polynomial neural network, and a standard Linear Predictor Coefficients model. For this comparison three non-stationary and noisy time series were used: IBM stock data; US/Euro exchange rate data, and the price of Brent crude oil. The experiments demonstrated favourable prediction results for the Spiking Neural Network in terms of Annualised Return and prediction error for 5-Step ahead predictions. These results were also supported by other relevant metrics such as Maximum Drawdown and Signal-To-Noise ratio. This work demonstrated the applicability of the Polychronous Spiking Network to financial data forecasting and this in turn indicates the potential of using such networks over traditional systems in difficult to manage non-stationary environments. © 2014 Reid et al

A Spiking Neural Network for Financial Prediction

In this paper a Polychronous Spiking Network was applied to financial time series prediction with the aim of exploiting the inherent temporal capabilities of the spiking neural model. The performance of this network was benchmarked against two “traditional”, rate-encoded, neural networks; a Multi-Layer Perceptron network and a Functional Link Neural Network. Three non-stationary datasets were used to test these simulations: IBM stock data; US/Euro exchange rate data, and the price of Brent crude oil. The experiments demonstrated favourable prediction results for the spiking neural network in terms of Annualised Return, for both 1-Step and 5-Step ahead predictions. These results were also supported by other relevant metrics such as Maximum Drawdown, Signal-To-Noise ratio, and Normalised Mean Square Error. The results suggest that the inherent temporal characteristics of the polychronous spiking network make it a more suited architecture than traditional neural networks for use in non-stationary financial data prediction environments

A deep gated recurrent neural network for petroleum production forecasting

Forecasting of oil production plays a vital role in petroleum engineering and contributes to supporting engineers in the management of petroleum reservoirs. However, reliable production forecasting is difficult to achieve, particularly in view of the increase in digital oil big data. Although a significant amount of work has been reported in the literature in relation to the use of machine learning in the oil and gas domain, traditional forecasting approaches have limited potential in terms of representing the complex features of time series data. More specifically, in a high-dimensional nonlinear multivariate time series dataset, a shallow machine is incapable of inferring the dependencies between past and future values. In this context, a novel forecasting model for petroleum production is proposed in this work. The model is a deep-gated recurrent neural network consisting of multiple hidden layers, where each layer has a number of nodes. The proposed model has a low-complexity architecture and the capacity to track long-interval time-series datasets. To evaluate the robustness of our model, the proposed technique was benchmarked with various standard approaches. The extensive empirical results demonstrate that the proposed model outperforms existing approaches

Deep Learning Algorithms for Human Fighting Action Recognition

— Human action recognition using skeletons has been employed in various applications, including healthcare robots, human-computer interaction, and surveillance systems. Recently, deep learning systems have been used in various applications, such as object classification. In contrast to conventional techniques, one of the most prominent convolutional neural network deep learning algorithms extracts image features from its operations. Machine learning in computer vision applications faces many challenges, including human action recognition in real time. Despite significant improvements, videos are typically shot with at least 24 frames per second, meaning that the fastest classification technologies take time. Object detection algorithms must correctly identify and locate essential items, but they must also be speedy at prediction time to meet the real-time requirements of video processing. The fundamental goal of this research paper is to recognize the real-time state of human fighting to provide security in organizations by discovering and identifying problems through video surveillance. First, the images in the videos are investigated to locate human fight scenes using the YOLOv3 algorithm, which has been updated in this work. Our improvements to the YOLOv3 algorithm allowed us to accelerate the exploration of a group of humans in the images. The center locator feature in this algorithm was adopted as an essential indicator for measuring the safety distance between two persons. If it is less than a specific value specified in the code, they are tracked. Then, a deep sorting algorithm is used to track people. This framework is filtered to process and classify whether these two people continue to exceed the programmatically defined minimum safety distance. Finally, the content of the filter frame is categorized as combat scenes using the OpenPose technology and a trained VGG-16 algorithm, which classifies the situation as walking, hugging, or fighting. A dataset was created to train these algorithms in the three categories of walking, hugging, and fighting. The proposed methodology proved successful, exhibiting a classification accuracy for walking, hugging, and fighting of 95.0%, 87.4%, and 90.1%, respectively

Human Fall Down Recognition Using Coordinates Key Points Skeleton

Falls pose a substantial threat to human safety and can quickly result in disastrous repercussions. This threat is particularly true for the elderly, where falls are the leading cause of hospitalization and injury-related death. A fall that is detected and responded to quickly has a lower danger and long-term impact. Many real-time fall detection solutions are available; however, these solutions have specific privacy, maintenance, and proper use issues. Vision-based fall event detection has the benefit of being completely private and straightforward to use and maintain. However, in real-world scenarios, falls are diverse and result in high detection instability. This study proposes a novel vision-based technique for fall detection and analyzes an extracted skeleton to define human postures. OpenPose can be used to get skeletal information about the human body. It identifies a fall using three critical parameters: the center of the value of the head and shoulder coordinates, the critical points of the shoulder coordinates, and the distance between the center of the skeleton's head and the floor with the angle between the center of the shoulders and the ground. Our proposed methodology was effective, with a classification accuracy of 97.7%

An investigation into the development of a mobile multimedia application to support the learning process for speech and language therapy

International audienc

A machine learning system for automatic detection of preterm activity using artificial neural networks and uterine electromyography data

International audienc

Forecasting Weather Signals Using a Polychronous Spiking Neural Network

Due to its inherently complex and chaotic nature predicting various weather phenomena over non trivial periods of time is extremely difficult. In this paper, we consider the ability of an emerging class of temporally encoded neural network to address the challenge of weather forecasting. The Polychronous Spiking Neural Network (PSNN) uses axonal delay to encode temporal information into the network in order to make predictions about weather signals. The performance of this network is benchmarked against the Multi-Layer Perceptron network as well as Linear Predictor. The results indicate that the inherent characteristics of the Polychronous Spiking Network make it well suited to the processing and prediction of complex weather signals