scholarly journals The use of Artificial Neural Networks to classify primate vocalizations: a pilot study on black lemurs

2009 ◽  
pp. n/a-n/a ◽  
Author(s):  
Luca Pozzi ◽  
Marco Gamba ◽  
Cristina Giacoma
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Pilot Study ◽  
Artificial Neural ◽  
Black Lemurs ◽  
Primate Vocalizations

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.

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

scholarly journals Pregnancy risk factors in autism: a pilot study with artificial neural networks

2015 ◽  
Vol 79 (2) ◽  
pp. 339-347 ◽  
Author(s):  
Enzo Grossi ◽  
Federica Veggo ◽  
Antonio Narzisi ◽  
Angelo Compare ◽  
Filippo Muratori
Keyword(s):  
Risk Factors ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Pilot Study ◽  
Pregnancy Risk ◽  
Artificial Neural

scholarly journals Artificial Neural Networks to Predict the Apparent Degree of Supersaturation in Supersaturated Lipid-Based Formulations: A Pilot Study

Pharmaceutics ◽  
2021 ◽  
Vol 13 (9) ◽  
pp. 1398
Author(s):  
Harriet Bennett-Lenane ◽  
Joseph P. O’Shea ◽  
Jack D. Murray ◽  
Alexandra-Roxana Ilie ◽  
René Holm ◽  
...  
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Pilot Study ◽  
Water Soluble ◽  
Poorly Water Soluble Drugs ◽  
Water Soluble Drugs ◽  
Poorly Water Soluble ◽  
Artificial Neural ◽  
Capmul Mcm ◽  
Mean Square Errors

In response to the increasing application of machine learning (ML) across many facets of pharmaceutical development, this pilot study investigated if ML, using artificial neural networks (ANNs), could predict the apparent degree of supersaturation (aDS) from two supersaturated LBFs (sLBFs). Accuracy was compared to partial least squares (PLS) regression models. Equilibrium solubility in Capmul MCM and Maisine CC was obtained for 21 poorly water-soluble drugs at ambient temperature and 60 °C to calculate the aDS ratio. These aDS ratios and drug descriptors were used to train the ML models. When compared, the ANNs outperformed PLS for both sLBFCapmulMC (r2 0.90 vs. 0.56) and sLBFMaisineLC (r2 0.83 vs. 0.62), displaying smaller root mean square errors (RMSEs) and residuals upon training and testing. Across all the models, the descriptors involving reactivity and electron density were most important for prediction. This pilot study showed that ML can be employed to predict the propensity for supersaturation in LBFs, but even larger datasets need to be evaluated to draw final conclusions.

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.

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