artificial neural network
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Examination of semen characteristics is routinely performed for fertility status investigation of the male partner of an infertile couple as well as for evaluation of the sperm donor candidate. A useful tool for preliminary assessment of semen characteristics might be an artificial neural network. Thus, the aim of the present study was to construct an artificial neural network, which could be used for predicting the result of semen analysis based on the basic questionnaire data. On the basis of eleven survey questions two models of artificial neural networks to predict semen parameters were developed. The first model aims to predict the overall performance and profile of semen. The second network was developed to predict the concentration of sperm. The network to evaluate sperm concentration proved to be the most efficient. 92.93% of the patients in the learning process were properly qualified for the group with a correct or incorrect result, while the result for the test set was 85.71%. This study suggests that an artificial neural network based on eleven survey questions might be a valuable tool for preliminary evaluation and prediction of the semen profile.
- MeSH
- analýza spermatu * metody přístrojové vybavení MeSH
- lidé MeSH
- motilita spermií MeSH
- mužská infertilita MeSH
- neuronové sítě * MeSH
- počet spermií metody přístrojové vybavení MeSH
- průzkumy a dotazníky MeSH
- sperma * MeSH
- spermabanky MeSH
- spermie * abnormality růst a vývoj ultrastruktura MeSH
- Check Tag
- lidé MeSH
- MeSH
- kognice MeSH
- lidé MeSH
- neuronové sítě MeSH
- neuroplasticita MeSH
- percepce MeSH
- teoretické modely MeSH
- Check Tag
- lidé MeSH
Využití umělé inteligence jako asistenční detekční metody v endoskopii se v uplynulých letech těší zvyšujícímu se zájmu. Algoritmy strojového učení slibují zefektivnění detekce polypů, a dokonce optickou lokalizaci nálezů, to vše s minimálním zaškolením endoskopisty. Praktickým cílem této studie je analýza CAD softwaru (computer-aided diagnosis) Carebot pro detekci kolorektálních polypů s využitím konvoluční neuronové sítě. Navržený binární klasifikátor pro detekci polypů dosahuje přesnosti až 98 %, specificity 0,99 a preciznosti 0,96. Současně je diskutována nezbytnost dostupnosti rozsáhlých klinických dat pro vývoj modelů na bázi umělé inteligence pro automatickou detekci adenomů a benigních neoplastických lézí.
The use of artificial intelligence as an assistive detection method in endoscopy has attracted increasing interest in recent years. Machine learning algorithms promise to improve the efficiency of polyp detection and even optical localization of findings, all with minimal training of the endoscopist. The practical goal of this study is to analyse the CAD software (computer-aided diagnosis) Carebot for colorectal polyp detection using a convolutional neural network. The proposed binary classifier for polyp detection achieves accuracy of up to 98%, specificity of 0.99 and precision of 0.96. At the same time, the need for the availability of large-scale clinical data for the development of artificial--intelligence-based models for the automatic detection of adenomas and benign neoplastic lesions is discussed.
- Klíčová slova
- prostorová lokalizace,
- MeSH
- diagnóza počítačová * MeSH
- lidé MeSH
- neuronové sítě MeSH
- polypy střeva * diagnóza MeSH
- umělá inteligence MeSH
- Check Tag
- lidé MeSH
Optimization of neural network topology, weights and neuron transfer functions for given data set and problem is not an easy task. In this article, we focus primarily on building optimal feed-forward neural network classifier for i.i.d. data sets. We apply meta-learning principles to the neural network structure and function optimization. We show that diversity promotion, ensembling, self-organization and induction are beneficial for the problem. We combine several different neuron types trained by various optimization algorithms to build a supervised feed-forward neural network called Group of Adaptive Models Evolution (GAME). The approach was tested on a large number of benchmark data sets. The experiments show that the combination of different optimization algorithms in the network is the best choice when the performance is averaged over several real-world problems.
This paper presents the use of an artificial neural network (NN) approach for predicting the muscle forces around the elbow joint. The main goal was to create an artificial NN which could predict the musculotendon forces for any general muscle without significant errors. The input parameters for the network were morphological and anatomical musculotendon parameters, plus an activation level experimentally measured during a flexion/extension movement in the elbow. The muscle forces calculated by the 'Virtual Muscle System' provide the output. The cross-correlation coefficient expressing the ability of an artificial NN to predict the "true" force was in the range 0.97-0.98. A sensitivity analysis was used to eliminate the less sensitive inputs, and the final number of inputs for a sufficient prediction was nine. A variant of an artificial NN for a single specific muscle was also studied. The artificial NN for one specific muscle gives better results than a network for general muscles. This method is a good alternative to other approaches to calculation of muscle force.
- MeSH
- algoritmy * MeSH
- biologické modely * MeSH
- kosterní svaly fyziologie MeSH
- lidé MeSH
- loketní kloub fyziologie MeSH
- mechanický stres MeSH
- neuronové sítě * MeSH
- počítačová simulace MeSH
- pohyb fyziologie MeSH
- reprodukovatelnost výsledků MeSH
- rozpoznávání automatizované metody MeSH
- rozsah kloubních pohybů MeSH
- senzitivita a specificita MeSH
- svalová kontrakce fyziologie MeSH
- svalová síla fyziologie MeSH
- Check Tag
- lidé MeSH
- Publikační typ
- časopisecké články MeSH
- práce podpořená grantem MeSH
This paper introduces a novel technique to evaluate comfort properties of zinc oxide nanoparticles (ZnO NPs) coated woven fabrics. The proposed technique combines artificial neural network (ANN) and golden eagle optimizer (GEO) to ameliorate the training process of ANN. Neural networks are state-of-the-art machine learning models used for optimal state prediction of complex problems. Recent studies showed that the use of metaheuristic algorithms improve the prediction accuracy of ANN. GEO is the most advanced methaheurstic algorithm inspired by golden eagles and their intelligence for hunting by tuning their speed according to spiral trajectory. From application point of view, this study is a very first attempt where GEO is applied along with ANN to improve the training process of ANN for any textiles and composites application. Furthermore, the proposed algorithm ANN with GEO (ANN-GEO) was applied to map out the complex input-output conditions for optimal results. Coated amount of ZnO NPs, fabric mass and fabric thickness were selected as input variables and comfort properties were evaluated as output results. The obtained results reveal that ANN-GEO model provides high performance accuracy than standard ANN model, ANN models trained with latest metaheuristic algorithms including particle swarm optimizer and crow search optimizer, and conventional multiple linear regression.
- MeSH
- Accipitridae * MeSH
- algoritmy MeSH
- neuronové sítě MeSH
- oxid zinečnatý * MeSH
- propylaminy MeSH
- sulfidy MeSH
- textilie MeSH
- zvířata MeSH
- Check Tag
- zvířata MeSH
- Publikační typ
- časopisecké články MeSH
- práce podpořená grantem MeSH
Rapid development of computer technologies leads to the intensive automation of many different processes traditionally performed by human experts. One of the spheres characterized by the introduction of new high intelligence technologies substituting analysis performed by humans is sleep scoring. This refers to the classification task and can be solved - next to other classification methods - by use of artificial neural networks (ANN). ANNs are parallel adaptive systems suitable for solving of non-linear problems. Using ANN for automatic sleep scoring is especially promising because of new ANN learning algorithms allowing faster classification without decreasing the performance. Both appropriate preparation of training data as well as selection of the ANN model make it possible to perform effective and correct recognizing of relevant sleep stages. Such an approach is highly topical, taking into consideration the fact that there is no automatic scorer utilizing ANN technology available at present.
- MeSH
- elektroencefalografie metody MeSH
- lidé MeSH
- neuronové sítě MeSH
- polysomnografie metody MeSH
- spánek fyziologie MeSH
- stadia spánku fyziologie MeSH
- Check Tag
- lidé MeSH
- Publikační typ
- časopisecké články MeSH
- práce podpořená grantem MeSH
- přehledy MeSH
