scholarly journals Blood Glucose Prediction Using Artificial Neural Networks Trained with the AIDA Diabetes Simulator: A Proof-of-Concept Pilot Study

2011 ◽  
Vol 2011 ◽  
pp. 1-11 ◽  
Author(s):  
Gavin Robertson ◽  
Eldon D. Lehmann ◽  
William Sandham ◽  
David Hamilton
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Blood Glucose ◽  
Pilot Study ◽  
Single Case ◽  
Future Research ◽  
Case Scenario ◽  
Real Patient ◽  
Glucose Levels ◽  
Artificial Neural

Diabetes mellitus is a major, and increasing, global problem. However, it has been shown that, through good management of blood glucose levels (BGLs), the associated and costly complications can be reduced significantly. In this pilot study, Elman recurrent artificial neural networks (ANNs) were used to make BGL predictions based on a history of BGLs, meal intake, and insulin injections. Twenty-eight datasets (from a single case scenario) were compiled from the freeware mathematical diabetes simulator, AIDA. It was found that the most accurate predictions were made during the nocturnal period of the 24 hour daily cycle. The accuracy of the nocturnal predictions was measured as the root mean square error over five test days (RMSE5 day) not used during ANN training. For BGL predictions of up to 1 hour aRMSE5 dayof (±SD)0.15±0.04 mmol/L was observed. For BGL predictions up to 10 hours, aRMSE5  dayof (±SD)0.14±0.16 mmol/L was observed. Future research will investigate a wider range of AIDA case scenarios, real-patient data, and data relating to other factors influencing BGLs. ANN paradigms based on real-time recurrent learning will also be explored to accommodate dynamic physiology in diabetes.

Forecasting Stock Returns with Artificial Neural Networks

2011 ◽  
pp. 47-79 ◽  
Author(s):  
Suraphan Thawornwong ◽  
David Enke
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Network ◽  
Artificial Neural Networks ◽  
Stock Returns ◽  
Future Research ◽  
Network Architectures ◽  
Network Applications ◽  
Artificial Neural ◽  
Neural Network Applications

During the last few years there has been growing literature on applications of artificial neural networks to business and financial domains. In fact, a great deal of attention has been placed in the area of stock return forecasting. This is due to the fact that once artificial neural network applications are successful, monetary rewards will be substantial. Many studies have reported promising results in successfully applying various types of artificial neural network architectures for predicting stock returns. This chapter reviews and discusses various neural network research methodologies used in 45 journal articles that attempted to forecast stock returns. Modeling techniques and suggestions from the literature are also compiled and addressed. The results show that artificial neural networks are an emerging and promising computational technology that will continue to be a challenging tool for future research.

scholarly journals Computational Principles of Supervised Learning in the Cerebellum

2018 ◽  
Vol 41 (1) ◽  
pp. 233-253 ◽  
Author(s):  
Jennifer L. Raymond ◽  
Javier F. Medina
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Supervised Learning ◽  
Brain Area ◽  
Future Research ◽  
Brain Areas ◽  
Adaptive Mechanisms ◽  
Networks Analysis ◽  
Artificial Neural ◽  
Organizational Principles

Supervised learning plays a key role in the operation of many biological and artificial neural networks. Analysis of the computations underlying supervised learning is facilitated by the relatively simple and uniform architecture of the cerebellum, a brain area that supports numerous motor, sensory, and cognitive functions. We highlight recent discoveries indicating that the cerebellum implements supervised learning using the following organizational principles: ( a) extensive preprocessing of input representations (i.e., feature engineering), ( b) massively recurrent circuit architecture, ( c) linear input–output computations, ( d) sophisticated instructive signals that can be regulated and are predictive, ( e) adaptive mechanisms of plasticity with multiple timescales, and ( f) task-specific hardware specializations. The principles emerging from studies of the cerebellum have striking parallels with those in other brain areas and in artificial neural networks, as well as some notable differences, which can inform future research on supervised learning and inspire next-generation machine-based algorithms.

scholarly journals Measuring Gait-Event-Related Brain Potentials (gERPs) during Instructed and Spontaneous Treadmill Walking: Technical Solutions and Automated Classification through Artificial Neural Networks

2020 ◽  
Vol 10 (16) ◽  
pp. 5405
Author(s):  
Cornelia Herbert ◽  
Michael Munz
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Pilot Study ◽  
Treadmill Walking ◽  
Human Gait ◽  
Brain Potentials ◽  
Eeg Data ◽  
Continuous Eeg ◽  
Artificial Neural ◽  
Technical Solutions

The investigation of the neural correlates of human gait, as measured by means of non-invasive electroencephalography (EEG), is of central importance for the understanding of human gait and for novel developments in gait rehabilitation. Particularly, gait-event-related brain potentials (gERPs) may provide information about the functional role of cortical brain regions in human gait control. The purpose of this paper is to explore possible experimental and technical solutions for time-sensitive analysis of human gait ERPs during spontaneous and instructed treadmill walking. A solution (hardware/software) for synchronous recording of gait and EEG data was developed, tested and piloted. The solution consists of a custom-made USB synchronization interface, a time-synchronization module, and a data-merging module, allowing the temporal synchronization of recording devices, time-sensitive extraction of gait markers for the analysis of gERPs, and the training of artificial neural networks. In the present manuscript, the hardware and software components were tested with the following devices: A treadmill with an integrated pressure plate for gait analysis (zebris FDM-T) and an Acticap non-wireless 32-channel EEG system (Brain Products GmbH). The usability and validity of the developed solution was investigated in a pilot study (n = 3 healthy participants, n = 3 females, mean age = 22.75 years). The recorded continuous EEG data were segmented into epochs according to the detected gait markers for the analysis of gERPs. Finally, the EEG epochs were used to train a deep learning artificial neural network as classifier of gait phases. The results obtained in this pilot study, although preliminary, support the feasibility of the solution for the application of gait-related EEG analysis.

FORECASTING FOREIGN EXCHANGE RATES WITH ARTIFICIAL NEURAL NETWORKS: A REVIEW

2004 ◽  
Vol 03 (01) ◽  
pp. 145-165 ◽  
Author(s):  
WEI HUANG ◽  
K. K. LAI ◽  
Y. NAKAMORI ◽  
SHOUYANG WANG
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Exchange Rates ◽  
Network Architecture ◽  
Future Research ◽  
Promising Alternative ◽  
Practical Applications ◽  
Future Research Directions ◽  
Artificial Neural ◽  
Input Variables

Forecasting exchange rates is an important financial problem that is receiving increasing attention especially because of its difficulty and practical applications. Artificial neural networks (ANNs) have been widely used as a promising alternative approach for a forecasting task because of several distinguished features. Research efforts on ANNs for forecasting exchange rates are considerable. In this paper, we attempt to provide a survey of research in this area. Several design factors significantly impact the accuracy of neural network forecasts. These factors include the selection of input variables, preparing data, and network architecture. There is no consensus about the factors. In different cases, various decisions have their own effectiveness. We also describe the integration of ANNs with other methods and report the comparison between performances of ANNs and those of other forecasting methods, and finding mixed results. Finally, the future research directions in this area are discussed.

scholarly journals Tutorial: Artificial Neural Networks and Deep Learning to Analyze Single-Case Designs

2020 ◽  
Author(s):  
Marc J Lanovaz ◽  
Jordan D Bailey
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Visual Analysis ◽  
Clinical Medicine ◽  
Single Case ◽  
Simulated Data ◽  
Fundamental Physics ◽  
Promising Area ◽  
Artificial Neural ◽  
Single Case Designs

Since the start of the 21st century, few advances have had as far reaching consequences in science as the widespread adoption of artificial neural networks in fields as diverse as fundamental physics, clinical medicine, and social networking. In behavior analysis, one promising area for the adoption of neural networks involves the analysis of single-case graphs. However, few behavior analysts have any training on the use of these methods, which may limit progress in this area. The purpose of our tutorial is to address this issue by providing a step-by-step description on using artificial neural networks to improve the analysis of single-case graphs. To this end, we trained a new model using simulated data to analyze multiple baseline graphs and compared its outcomes to those of visual analysis on a previously published dataset. In addition to showing that artificial neural networks may outperform visual analysis, the tutorial provides information to facilitate the replication and extension of this line of work to other datasets and designs.

scholarly journals 4216 Application of artificial neural networks for image analysis of organ culture preserved donor corneas: A pilot study

1995 ◽  
Vol 35 ◽  
pp. S160
Author(s):  
T. Barisani-Asenbauer ◽  
St. Kaminski ◽  
G. Krenn ◽  
M. Budil ◽  
A. Dietrich ◽  
...  
Keyword(s):  
Neural Networks ◽  
Image Analysis ◽  
Artificial Neural Networks ◽  
Pilot Study ◽  
Organ Culture ◽  
Artificial Neural ◽  
Donor Corneas
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