scholarly journals Artificial neural networks for the definition of kinetic subpopulations in electroejaculated and epididymal spermatozoa in the domestic cat

Reproduction ◽  
2012 ◽  
Vol 144 (3) ◽  
pp. 339-347 ◽  
Author(s):  
Alberto Contri ◽  
Daniele Zambelli ◽  
Massimo Faustini ◽  
Marco Cunto ◽  
Alessia Gloria ◽  
...  
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Sperm Motility ◽  
Principal Component ◽  
Domestic Cat ◽  
Computer Assisted ◽  
Multivariate Approach ◽  
Kinetic Characteristics ◽  
Artificial Neural ◽  
Definition Of

This study was designed for the identification of different sperm kinetic subpopulations in feline semen using artificial neural networks (ANNs) and for the evaluation of the effect of ejaculation on motility patterns of these subpopulations. Seven tomcats presented for routine orchiectomy were electroejaculated, and after 5 days, orchiectomized and epididymal tail sperms were collected. Sperm motility characteristics were evaluated using a computer-assisted sperm analyzer that provided individual kinetic characteristics of each spermatozoon. A total of 23 400 spermatozoa for electroejaculated and 9200 for epididymal tail samples were evaluated using a multivariate approach, comprising principal component analysis and ANN classification. The multivariate approach allowed the identification and characterization of three different and well-defined sperm subpopulations. There were significant differences before (epididymal tail spermatozoa) and after (electroejaculated sperm) ejaculation in sperm kinetic subpopulation characteristics. In both epididymal and ejaculated samples, the majority of subpopulation was characterized by high velocity and progressiveness; however, the electroejaculated samples showed significantly higher values, suggesting that the microenvironment of the epididymal tail could affect the sperm motility or, alternatively, seminal plasma could increase the kinetic characteristics of the spermatozoa, indicating that only after ejaculation, the spermatozoa express their motility potential. Nevertheless, further studies are required to clarify the functional significance of each kinetic subpopulation.

Definition of an Architecture to Configure Artificial Neural Networks Topologies Using Partial Reconfiguraton in FPGA

2015 ◽  
Vol 13 (7) ◽  
pp. 2094-2100 ◽  
Author(s):  
Carlos Alberto de Albuquerque Silva ◽  
Adriao Duarte Doria Neto ◽  
Jose Alberto Nicolau Oliveira ◽  
Jorge Dantas Melo ◽  
David Simonetti Barbalho ◽  
...  
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Artificial Neural ◽  
Definition Of

scholarly journals Artificial Neural Networks for Automated Cell Quantification in Lensless LED Imaging Systems

Proceedings ◽  
2018 ◽  
Vol 2 (13) ◽  
pp. 989 ◽  
Author(s):  
Agus Budi Dharmawan ◽  
Gregor Scholz ◽  
Shinta Mariana ◽  
Philipp Hörmann ◽  
Igi Ardiyanto ◽  
...  
Keyword(s):  
Neural Networks ◽  
Principal Component Analysis ◽  
Artificial Neural Networks ◽  
Light Emitting Diode ◽  
Principal Component ◽  
Component Analysis ◽  
Detection Accuracy ◽  
Cell Detection ◽  
Artificial Neural ◽  
Feature Values

Cell registration by artificial neural networks (ANNs) in combination with principal component analysis (PCA) has been demonstrated for cell images acquired by light emitting diode (LED)-based compact holographic microscopy. In this approach, principal component analysis was used to find the feature values from cells and background, which would be subsequently employed as neural inputs into the artificial neural networks. Image datasets were acquired from multiple cell cultures using a lensless microscope, where the reference data was generated by a manually analyzed recording. To evaluate the developed automatic cell counter, the trained system was assessed on different data sets to detect immortalized mouse astrocytes, exhibiting a detection accuracy of ~81% compared with manual analysis. The results show that the feature values from principal component analysis and feature learning by artificial neural networks are able to provide an automatic approach on the cell detection and registration in lensless holographic imaging.

scholarly journals Classifying Parkinson’s Disease Based on Acoustic Measures Using Artificial Neural Networks

Sensors ◽  
2018 ◽  
Vol 19 (1) ◽  
pp. 16 ◽  
Author(s):  
Lucijano Berus ◽  
Simon Klancnik ◽  
Miran Brezocnik ◽  
Mirko Ficko
Keyword(s):  
Neural Networks ◽  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Feature Selection ◽  
Artificial Neural Networks ◽  
Correlation Coefficient ◽  
Principal Component ◽  
Test Accuracy ◽  
Artificial Neural ◽  
Prediction Problems

In recent years, neural networks have become very popular in all kinds of prediction problems. In this paper, multiple feed-forward artificial neural networks (ANNs) with various configurations are used in the prediction of Parkinson’s disease (PD) of tested individuals, based on extracted features from 26 different voice samples per individual. Results are validated via the leave-one-subject-out (LOSO) scheme. Few feature selection procedures based on Pearson’s correlation coefficient, Kendall’s correlation coefficient, principal component analysis, and self-organizing maps, have been used for boosting the performance of algorithms and for data reduction. The best test accuracy result has been achieved with Kendall’s correlation coefficient-based feature selection, and the most relevant voice samples are recognized. Multiple ANNs have proven to be the best classification technique for diagnosis of PD without usage of the feature selection procedure (on raw data). Finally, a neural network is fine-tuned, and a test accuracy of 86.47% was achieved.

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