artificial neural network
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A lower-extremity exoskeleton can facilitate the lower limbs' rehabilitation by providing additional structural support and strength. This article discusses the design and implementation of a functional prototype of lower extremity brace actuation and its wireless communication control system. The design provides supportive torque and increases the range of motion after complications reducing muscular strength. The control system prototype facilitates elevating a leg, gradually followed by standing and slow walking. The main control modalities are based on an Artificial Neural Network (ANN). The prototype's functionality was tested by time-angle graphs. The final prototype demonstrates the potential application of the ANN in the control system of exoskeletons for joint impairment therapy.
- Klíčová slova
- Keras, Lower-extremity, control system, exoskeleton, neural network, walking,
- MeSH
- chůze MeSH
- dolní končetina MeSH
- exoskeleton * MeSH
- neuronové sítě MeSH
- točivý moment MeSH
- Publikační typ
- časopisecké články MeSH
Introduction: Artificial neural networks are becoming an essential technology in data analysis, and their influence is starting to permeate the field of medicine. Experimental surgery has been a long-term subject of study of our lab; this is naturally reflected in our interest in other areas of modern technologies including artificial neural networks and their advancements. In the current issue, we would like to explore this aspect of technical progress. The main goal is to critically evaluate the strengths and weaknesses of artificial neural network technology concerning its use in clinical and experimental surgery. Methods: The article is focused on in-silico modeling, particularly on the potential of neural networks in terms of image data processing in medicine. The text briefly summarizes the historical development of deep learning neural networks and their basic principles. Furthermore, basic taxonomy tasks are presented. Finally, potential learning problems and possible solutions are also mentioned. Results: The article points out various possible uses of artificial neural networks in biological applications. Several biomedical applications of artificial neural networks are used to describe the division and principles of the most common tasks of machine learning and deep learning such as classification, detection, and segmentation. Conclusion: The application of artificial neural network methods in medicine and surgery offers a considerable potential; by learning directly from the data, they make it possible to avoid lengthy and subjective setting of parameters by an expert engineer. Nevertheless, the use of an unbalanced dataset can lead to unexpected, although traceable errors. The solution is to collect a dataset large enough to enable both learning and verification of proper functionality.
- Klíčová slova
- artificial neural network, dataset, deep learning, machine learning,
- MeSH
- lidé MeSH
- neuronové sítě * MeSH
- počítače MeSH
- počítačové zpracování obrazu metody MeSH
- strojové učení * MeSH
- Check Tag
- lidé MeSH
- Publikační typ
- časopisecké články MeSH
Artificial neural network based modeling is a generic approach to understand and correlate different complex parameters of biological systems for improving the desired output. In addition, some new inferences can also be predicted in a shorter time with less cost and labor. As terpenoid indole alkaloid pathway in Vinca minor is very less investigated or elucidated, a strategy of elicitation with hydroxylase and acetyltransferase along with incorporation of various precursors from primary shikimate and secoiridoid pools via simultaneous employment of cyclooxygenase inhibitor was performed in the hairy roots of V. minor. This led to the increment in biomass accumulation, total alkaloid concentration, and vincamine production in selected treatments. The resultant experimental values were correlated with algorithm approaches of artificial neural network that assisted in finding the yield of vincamine, alkaloids, and growth kinetics using number of elicits. The inputs were the hydroxylase/acetyltransferase elicitors and cyclooxygenase inhibitor along with various precursors from shikimate and secoiridoid pools and the outputs were growth index (GI), alkaloids, and vincamine. The approach incorporates two MATLAB codes; GRNN and FFBPNN. Growth kinetic studies revealed that shikimate and tryptophan supplementation triggers biomass accumulation (GI = 440.2 to 540.5); while maximum alkaloid (3.7 % dry wt.) and vincamine production (0.017 ± 0.001 % dry wt.) was obtained on supplementation of secologanin along with tryptophan, naproxen, hydrogen peroxide, and acetic anhydride. The study shows that experimental and predicted values strongly correlate each other. The correlation coefficient for growth index (GI), alkaloids, and vincamine was found to be 0.9997, 0.9980, 0.9511 in GRNN and 0.9725, 0.9444, 0.9422 in FFBPNN, respectively. GRNN provided greater similarity between the target and predicted dataset in comparison to FFBPNN. The findings can provide future insights to calculate growth index, alkaloids, and vincamine in combination to different elicits.
- Klíčová slova
- Artificial neural network, Generalized regression neural network, MATLAB, Vinca minor,
- MeSH
- alkaloidy biosyntéza MeSH
- kořeny rostlin metabolismus MeSH
- neuronové sítě * MeSH
- Vinca metabolismus MeSH
- Publikační typ
- časopisecké články MeSH
- práce podpořená grantem MeSH
- Názvy látek
- alkaloidy MeSH
Taking advantage of surface-enhanced Raman scattering (SERS) methodology with its unique ability to collect abundant intrinsic fingerprint information and noninvasive data acquisition we set up a SERS-based approach for recognition of physically induced DNA damage with further incorporation of artificial neural network (ANN). As a proof-of-concept application, we used the DNA molecules, where the one oligonucleotide (OND) was grafted to the plasmonic surface while complimentary OND was exposed to UV illumination with various exposure doses and further hybridized with the grafted counterpart. All SERS spectra of entrapped DNA were collected by several operators using the portable spectrometer, without any optimization of measurements procedure (e.g., optimization of acquisition time, laser intensity, finding of optimal place on substrate, manual baseline correction, etc.) which usually takes a significant amount of operator's time. The SERS spectra were employed as input data for ANN training, and the performance of the system was verified by predicting the class labels for SERS validation data, using a spectra dataset, which has not been involved in the training process. During that phase, accuracy higher than 98% was achieved with a level of confidence exceeding 95%. It should be noted that utilization of the proposed functional-SERS/ANN approach allows identifying even the minor DNA damage, almost invisible by control measurements, performed with common analytical procedures. Moreover, we introduce the advanced ANN design, which allows not only classifying the samples but also providing the ANN analysis feedback, which associates the spectral changes and chemical transformations of DNA structure.
- Klíčová slova
- Artificial neural network, DNA, Detection and recognition, Photo-damage, SERS,
- MeSH
- biosenzitivní techniky * MeSH
- DNA chemie izolace a purifikace MeSH
- kovové nanočástice chemie MeSH
- neuronové sítě MeSH
- oligonukleotidy chemie MeSH
- poškození DNA * MeSH
- Ramanova spektroskopie * MeSH
- zlato chemie MeSH
- Publikační typ
- časopisecké články MeSH
- Názvy látek
- DNA MeSH
- oligonukleotidy MeSH
- zlato MeSH
This work aims to synthesize akaganeite nanoparticles (AKNPs) by using microwave and use them to adsorb Congo red dye (CR) from the aqueous solution. The AKNPs with an average particle size of about 50 nm in width and 100 nm in length could be fabricated in 20 min. The effects of pH, CR initial concentration, adsorption time, and adsorbent dosage on the adsorption process were investigated and the artificial neural network (ANN) was used to analyze the adsorption data. The various ANN structures were examined in training the data to find the optimal model. The structure with training function, TRAINLM; adaptation learning function, LARNGDM; transfer function, LOGSIG (in hidden layer) and PURELIN (in output layer); and 10 neutrons in hidden layer having the highest correlation (R2 = 0.996) and the lowest MSE (4.405) is the optimal ANN structure. The consistency between the experimental data and the data predicted by the ANN model showed that the behavior of the adsorption process of CR onto AKNPs under different conditions can be estimated by the ANN model. The adsorption kinetics was studied by fitting the data into pseudo-first-order, pseudo-second-order, Elovich, and intraparticle diffusion models. The results showed that the adsorption kinetics obeyed the pseudo-second-order model and governed by several steps. The adsorption isotherms at the different temperatures were studied by fitting the data to Langmuir, Freundlich, and Temkin isotherm models. The R2 obtained from the Langmuir model was above 0.9 and the highest value in three of four temperatures, suggesting that the adsorption isotherms were the best fit to the Langmuir model and the maximum adsorption capacity was estimated to be more than 150 mg/g. Thermodynamic studies suggested that the adsorption of CR onto AKNPs was a spontaneous and endothermic process and physicochemical adsorption. The obtained results indicated the potential application of microwave-synthesize AKNPs for removing organic dyes from aqueous solutions.
- Klíčová slova
- Adsorption, Akaganeite nanoparticles, Artificial neural network, Congo red, Microwave synthesized, Modeling,
- MeSH
- adsorpce MeSH
- chemické látky znečišťující vodu * MeSH
- kinetika MeSH
- koncentrace vodíkových iontů MeSH
- Kongo červeň analýza MeSH
- mikrovlny MeSH
- nanočástice * MeSH
- neuronové sítě MeSH
- termodynamika MeSH
- železité sloučeniny MeSH
- Publikační typ
- časopisecké články MeSH
- Názvy látek
- akaganeite MeSH Prohlížeč
- chemické látky znečišťující vodu * MeSH
- Kongo červeň MeSH
- železité sloučeniny 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 study presents an application of the self-organizing migrating algorithm (SOMA) to train artificial neural networks for skin segmentation tasks. We compare the performance of SOMA with popular gradient-based optimization methods such as ADAM and SGDM, as well as with another evolutionary algorithm, differential evolution (DE). Experiments are conducted on the skin dataset, which consists of 245,057 samples with skin and non-skin labels. The results show that the neural network trained by SOMA achieves the highest accuracy (93.18%), outperforming ADAM (84.87%), SGDM (84.79%), and DE (91.32%). The visual evaluation also reveals the SOMA-trained neural network's accurate and reliable segmentation capabilities in most cases. These findings highlight the potential of incorporating evolutionary optimization algorithms like SOMA into the training process of artificial neural networks, significantly improving performance in image segmentation tasks.
- Klíčová slova
- Artificial neural networks, Computer vision, Optimization algorithm, SOMA, Skin segmentation, Swarm intelligence,
- MeSH
- algoritmy * MeSH
- kůže * diagnostické zobrazování MeSH
- lidé MeSH
- neuronové sítě * MeSH
- počítačové zpracování obrazu metody MeSH
- Check Tag
- lidé MeSH
- Publikační typ
- časopisecké články MeSH
A back-propagation neural network was used as a pattern recognition tool for LAMMA mass spectral data. Standard EPA source profiles were used as training and test data of the net. The elemental patterns (10 elements) of the sum of 100 mass spectra of fine dust particles were presented to the trained nets and satisfactory recognition (> 50%) was obtained.
- MeSH
- lasery MeSH
- látky znečišťující vzduch * MeSH
- neuronové sítě * MeSH
- prach * MeSH
- rozpoznávání automatizované * MeSH
- Publikační typ
- časopisecké články MeSH
- Názvy látek
- látky znečišťující vzduch * MeSH
- prach * MeSH
Bacteria are an active and diverse component of pelagic communities. The identification of main factors governing microbial diversity and spatial distribution requires advanced mathematical analyses. Here, the bacterial community composition was analysed, along with a depth profile, in the open Adriatic Sea using amplicon sequencing of bacterial 16S rRNA and the Neural gas algorithm. The performed analysis classified the sample into four best matching units representing heterogenic patterns of the bacterial community composition. The observed parameters were more differentiated by depth than by area, with temperature and identified salinity as important environmental variables. The highest diversity was observed at the deep chlorophyll maximum, while bacterial abundance and production peaked in the upper layers. The most of the identified genera belonged to Proteobacteria, with uncultured AEGEAN-169 and SAR116 lineages being dominant Alphaproteobacteria, and OM60 (NOR5) and SAR86 being dominant Gammaproteobacteria. Marine Synechococcus and Cyanobium-related species were predominant in the shallow layer, while Prochlorococcus MIT 9313 formed a higher portion below 50 m depth. Bacteroidota were represented mostly by uncultured lineages (NS4, NS5 and NS9 marine lineages). In contrast, Actinobacteriota were dominated by a candidatus genus Ca. Actinomarina. A large contribution of Nitrospinae was evident at the deepest investigated layer. Our results document that neural network analysis of environmental data may provide a novel insight into factors affecting picoplankton in the open sea environment.
- MeSH
- biodiverzita * MeSH
- mikrobiota * MeSH
- neuronové sítě * MeSH
- Publikační typ
- časopisecké články MeSH
- práce podpořená grantem MeSH
- Geografické názvy
- Středozemní moře MeSH
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