scholarly journals MEYE: Web-app for translational and real-time pupillometry

2021 ◽  
Author(s):  
Raffaele Mazziotti ◽  
Fabio Carrara ◽  
Aurelia Viglione ◽  
Leonardo Lupori ◽  
Luca Lo Verde ◽  
...  
Keyword(s):  
Neural Network ◽  
Mouse Models ◽  
State Of The Art ◽  
Web Browser ◽  
Non Invasive ◽  
Temporal Specificity ◽  
On Line ◽  
Web App ◽  
Set Up ◽  
Neuropsychiatric Conditions

AbstractPupil dynamics alterations have been found in patients affected by a variety of neuropsychiatric conditions, including autism. Studies in mouse models have used pupillometry for phenotypic assessment and as a proxy for arousal. Both in mice and humans, pupillometry is non-invasive and allows for longitudinal experiments supporting temporal specificity, however its measure requires dedicated setups. Here, we introduce a Convolutional Neural Network that performs on-line pupillometry in both mice and humans in a web app format. This solution dramatically simplifies the usage of the tool for non-specialist and non-technical operators. Because a modern web browser is the only software requirement, this choice is of great interest given its easy deployment and set-up time reduction. The tested model performances indicate that the tool is sensitive enough to detect both spontaneous and evoked pupillary changes, and its output is comparable with state-of-the-art commercial devices.

scholarly journals DANNP: an efficient artificial neural network pruning tool

2017 ◽  
Vol 3 ◽  
pp. e137 ◽  
Author(s):  
Mona Alshahrani ◽  
Othman Soufan ◽  
Arturo Magana-Mora ◽  
Vladimir B. Bajic
Keyword(s):  
Neural Network ◽  
State Of The Art ◽  
Model Performance ◽  
Training Data ◽  
Classification Problems ◽  
Link Type ◽  
On Line ◽  
Pruning Algorithms ◽  
Artificial Neural ◽  
The Impact

Background Artificial neural networks (ANNs) are a robust class of machine learning models and are a frequent choice for solving classification problems. However, determining the structure of the ANNs is not trivial as a large number of weights (connection links) may lead to overfitting the training data. Although several ANN pruning algorithms have been proposed for the simplification of ANNs, these algorithms are not able to efficiently cope with intricate ANN structures required for complex classification problems. Methods We developed DANNP, a web-based tool, that implements parallelized versions of several ANN pruning algorithms. The DANNP tool uses a modified version of the Fast Compressed Neural Network software implemented in C++ to considerably enhance the running time of the ANN pruning algorithms we implemented. In addition to the performance evaluation of the pruned ANNs, we systematically compared the set of features that remained in the pruned ANN with those obtained by different state-of-the-art feature selection (FS) methods. Results Although the ANN pruning algorithms are not entirely parallelizable, DANNP was able to speed up the ANN pruning up to eight times on a 32-core machine, compared to the serial implementations. To assess the impact of the ANN pruning by DANNP tool, we used 16 datasets from different domains. In eight out of the 16 datasets, DANNP significantly reduced the number of weights by 70%–99%, while maintaining a competitive or better model performance compared to the unpruned ANN. Finally, we used a naïve Bayes classifier derived with the features selected as a byproduct of the ANN pruning and demonstrated that its accuracy is comparable to those obtained by the classifiers trained with the features selected by several state-of-the-art FS methods. The FS ranking methodology proposed in this study allows the users to identify the most discriminant features of the problem at hand. To the best of our knowledge, DANNP (publicly available at www.cbrc.kaust.edu.sa/dannp) is the only available and on-line accessible tool that provides multiple parallelized ANN pruning options. Datasets and DANNP code can be obtained at www.cbrc.kaust.edu.sa/dannp/data.php and https://doi.org/10.5281/zenodo.1001086.

scholarly journals Fast and Deep Graph Neural Networks

2020 ◽  
Vol 34 (04) ◽  
pp. 3898-3905 ◽  
Author(s):  
Claudio Gallicchio ◽  
Alessio Micheli
Keyword(s):  
Neural Network ◽  
Dynamical System ◽  
Neural Networks ◽  
State Of The Art ◽  
Input Graph ◽  
Sparse Networks ◽  
Set Up ◽  
The Stability ◽  
Graph Neural Networks ◽  
Architectural Organization

We address the efficiency issue for the construction of a deep graph neural network (GNN). The approach exploits the idea of representing each input graph as a fixed point of a dynamical system (implemented through a recurrent neural network), and leverages a deep architectural organization of the recurrent units. Efficiency is gained by many aspects, including the use of small and very sparse networks, where the weights of the recurrent units are left untrained under the stability condition introduced in this work. This can be viewed as a way to study the intrinsic power of the architecture of a deep GNN, and also to provide insights for the set-up of more complex fully-trained models. Through experimental results, we show that even without training of the recurrent connections, the architecture of small deep GNN is surprisingly able to achieve or improve the state-of-the-art performance on a significant set of tasks in the field of graphs classification.

scholarly journals A Siamese Neural Network for Non-Invasive Baggage Re-Identification

2020 ◽  
Vol 6 (11) ◽  
pp. 126
Author(s):  
Pier Luigi Mazzeo ◽  
Christian Libetta ◽  
Paolo Spagnolo ◽  
Cosimo Distante
Keyword(s):  
Neural Network ◽  
State Of The Art ◽  
Conveyor Belt ◽  
Handling System ◽  
Non Invasive ◽  
Training Images ◽  
Traffic Jams ◽  
Baggage Handling

Baggage travelling on a conveyor belt in the sterile area (the rear collector located after the check-in counters) often gets stuck due to traffic jams, mainly caused by incorrect entries from the check-in counters on the collector belt. Using suitcase appearance captured on the Baggage Handling System (BHS) and airport checkpoints and their re-identification allows for us to handle baggage safer and faster. In this paper, we propose a Siamese Neural Network-based model that is able to estimate the baggage similarity: given a set of training images of the same suitcase (taken in different conditions), the network predicts whether the two input images belong to the same baggage identity. The proposed network learns discriminative features in order to measure the similarity among two different images of the same baggage identity. It can be easily applied on different pre-trained backbones. We demonstrate our model in a publicly available suitcase dataset that outperforms the leading latest state-of-the-art architecture in terms of accuracy.

scholarly journals Gesture recognition system based on CNN-IndRNN and OpenBCI

2021 ◽  
Vol 336 ◽  
pp. 06003
Author(s):  
Na Wu ◽  
Hao JIN ◽  
Xiachuan Pei ◽  
Shurong Dong ◽  
Jikui Luo ◽  
...  
Keyword(s):  
Neural Network ◽  
Gesture Recognition ◽  
Recognition Accuracy ◽  
State Of The Art ◽  
Recognition System ◽  
Classification Performance ◽  
Non Invasive ◽  
Average Accuracy ◽  
Open Source Hardware ◽  
Semg Signals

Surface electromyography (sEMG), as a key technology of non-invasive muscle computer interface, is an important method of human-computer interaction. We proposed a CNN-IndRNN (Convolutional Neural Network-Independent Recurrent Neural Network) hybrid algorithm to analyse sEMG signals and classify hand gestures. Ninapro’s dataset of 10 volunteers was used to develop the model, and by using only one time-domain feature (root mean square of sEMG), an average accuracy of 87.43% on 18 gestures is achieved. The proposed algorithm obtains a state-of-the-art classification performance with a significantly reduced model. In order to verify the robustness of the CNN-IndRNN model, a compact real¬time recognition system was constructed. The system was based on open-source hardware (OpenBCI) and a custom Python-based software. Results show that the 10-subject rock-paper-scissors gesture recognition accuracy reaches 99.1%.

scholarly journals An Enhanced Image Segmentation From 3D to 2D by Using Modified Neural Network

2021 ◽  
Vol 9 (12) ◽  
pp. 1712-1716
Author(s):  
P. Pushpalatha
Keyword(s):  
Neural Network ◽  
Optical Coherence Tomography Angiography ◽  
State Of The Art ◽  
Vascular Networks ◽  
Clinical Routine ◽  
Non Invasive ◽  
Previous State ◽  
Visualization Techniques ◽  
High Pass ◽  
Data Acquisition Process

Abstract: Optical coherence tomography angiography (OCTA) is an imaging which can applied in ophthalmology to provide detailed visualization of the perfusion of vascular networks in the eye. compared to previous state of the art dye-based imaging, such as fluorescein angiography. OCTA is non-invasive, time efficient, and it allows for the examination of retinal vascular in 3D. These advantage of the technique combined with the good usability in commercial devices led to a quick adoption of the new modality in the clinical routine. However, the interpretation of OCTA data is not without problems commonly observed image artifacts and the quite involved algorithmic details of OCTA signal construction can make the clinical assessment of OCTA exams challenging. In this paper we describe the technical background of OCTA and discuss the data acquisition process, common image visualization techniques, as well as 3D to 2D projection using high pass filtering, relu function and convolution neural network (CNN) for more accuracy and segmentation results.

scholarly journals Automated and Formal Synthesis of Neural Barrier Certificates for Dynamical Models

2021 ◽  
pp. 370-388
Author(s):  
Andrea Peruffo ◽  
Daniele Ahmed ◽  
Alessandro Abate
Keyword(s):  
Neural Network ◽  
State Of The Art ◽  
The State ◽  
Dynamical Models ◽  
Safety Verification ◽  
Data Set ◽  
New Approach ◽  
Formal Synthesis ◽  
Numerical Robustness ◽  
Set Up

AbstractWe introduce an automated, formal, counterexample-based approach to synthesise Barrier Certificates (BC) for the safety verification of continuous and hybrid dynamical models. The approach is underpinned by an inductive framework: this is structured as a sequential loop between a learner, which manipulates a candidate BC structured as a neural network, and a sound verifier, which either certifies the candidate’s validity or generates counter-examples to further guide the learner. We compare the approach against state-of-the-art techniques, over polynomial and non-polynomial dynamical models: the outcomes show that we can synthesise sound BCs up to two orders of magnitude faster, with in particular a stark speedup on the verification engine (up to three orders less), whilst needing a far smaller data set (up to three orders less) for the learning part. Beyond improvements over the state of the art, we further challenge the new approach on a hybrid dynamical model and on larger-dimensional models, and showcase the numerical robustness of our algorithms and codebase.

Prediction of Thermally Induced Time-Variant Machine Tool Error Maps Using a Fuzzy Artmap Neural Network

1997 ◽  
Vol 119 (4A) ◽  
pp. 623-630 ◽  
Author(s):  
N. Srinivasa ◽  
J. C. Ziegert
Keyword(s):  
Neural Network ◽  
Machine Tool ◽  
Duty Cycle ◽  
Machine Tools ◽  
Cutting Tool ◽  
Continuous Process ◽  
Accurate Method ◽  
Fuzzy Artmap ◽  
On Line ◽  
Set Up

A novel approach to on-line learning and prediction of time-variant machine tool error maps is proposed. These error maps are measured using a fast calibration device called the laser ball-bar (LBB) that directly measures the total positioning errors at the cutting tool using trilateration. The learning and prediction of these error maps is achieved using a Fuzzy ARTMAP neural network by treating the problem as an incremental approximation of a functional mapping between thermal sensor readings and the associated positional errors at each location of the cutting tool. Experimental measurements of the positional errors for a two axis turning center were performed using the LBB over two separate thermal duty cycles. The Fuzzy ARTMAP was trained on-line using the data collected over the first thermal duty cycle, which simulated machining of large workpieces with several hours of machining, inspection and set-up time. The network was made to predict the error map of the machine for a new thermal duty cycle that simulated machining of a range of short and long workpieces with shorter machining and set-up times. Results of these predictions show that the LBB and Fuzzy ARTMAP combination is a fast and accurate method for real-time error compensation in machine tools. This method overcomes drawbacks in currently methodologies including high cost and excessive downtime to calibrate machine tools. Application of the Fuzzy ARTMAP to continuous process improvement is discussed.

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