In the computer network, any one computer can access the database or external equipment (such as printer) in other computer if it is authorized the right of access. Therefore, the manager of scientific research and managers of hospital can collect timely and correctly the information from which can make the correct evaluations and decisions
Neural Networks (Computer) ; hospitals

Human chromosomes karyotyping is an important means to diagnose genetic diseases. Chromosome image type recognition is a key step in the karyotyping process. Accurate and efficient identification is of great significance for automatic chromosome karyotyping. In this paper, we propose a model named segmentally recalibrated dense convolutional network (SR-DenseNet). In each stage of the model, the dense connected network layers is used to extract the features of different abstract levels of chromosomes automatically, and then the concatenation of all the layers which extract different local features is recalibrated with squeeze-and-excitation (SE) block. SE blocks explicitly construct learnable structures for importance of the features. Then a model fusion method is proposed and an expert group of chromosome recognition models is constructed. On the public available Copenhagen chromosome recognition dataset (G-bands) the proposed model achieves error rate of only 1.60%, and with model fusion the error further drops to 0.99%. On the Padova chromosome dataset (Q-bands) the model gets the corresponding error rate of 6.67%, and with model fusion the error further drops to 5.98%. The experimental results show that the method proposed in this paper is effective and has the potential to realize the automation of chromosome type recognition.
Chromosomes ; Humans ; Neural Networks, Computer

Vietnam officially integrated into the Internet in 19th November 1997. The health sector actively prepared for connecting the Internet via National gate, currently set up the local area network (LAN) to establish the Intranet of the sector. The Internet the central institute for medical science information would be major institute for setting up this tool.
Neural Networks (Computer) ; Health Care Sector

AIMTo set up an artificial neural network system and optimize by genetic algorithm (GA) to predict drug bioavailability.

METHODSGenetic algorithm was used to optimize weights of the artificial neural network. The optimal solution of the artificial neural network model at a specific condition was obtained using the good search ability of genetic algorithm in order to predict drug bioavailability. Volume, refractivity, lgP(c), hydration, polarizability, E(HOMO) and E(LUMO) are inputs of the drug bioavailability prediction neural network, and its output is average drug bioavailability.

RESULTSThe prediction precision of average drug bioavailability of the GA- neural network model is 95.9%.

CONCLUSIONThis model can be used in the forecasting of drug bioavailability.

A new method based on convolution kernel compensation (CKC) for decomposing multi-channel surface electromyogram (sEMG) signals is proposed in this paper. Unsupervised learning and clustering function of self-organizing map (SOM) neural network are employed in this method. An initial innervations pulse train (IPT) is firstly estimated, some time instants corresponding to the highest peaks from the initial IPT are clustered by SOM neural network. Then the final IPT can be obtained from the observations corresponding to these time instants. In this paper, the proposed method was tested on the simulated signal, the influence of signal to noise ratio (SNR), the number of groups clustered by SOM and the number of highest peaks selected from the initial pulse train on the number of reconstructed sources and the pulse accuracy were studied, and the results show that the proposed approach is effective in decomposing multi-channel sEMG signals.
Algorithms ; Cluster Analysis ; Electromyography ; Neural Networks (Computer)

Multi-layer perceptron (MLP) neural network belongs to multi-layer feedforward neural network, and has the ability and characteristics of high intelligence. It can realize the complex nonlinear mapping by its own learning through the network. Bipolar disorder is a serious mental illness with high recurrence rate, high self-harm rate and high suicide rate. Most of the onset of the bipolar disorder starts with depressive episode, which can be easily misdiagnosed as unipolar depression and lead to a delayed treatment so as to influence the prognosis. The early identifica- tion of bipolar disorder is of great importance for patients with bipolar disorder. Due to the fact that the process of early identification of bipolar disorder is nonlinear, we in this paper discuss the MLP neural network application in early identification of bipolar disorder. This study covered 250 cases, including 143 cases with recurrent depression and 107 cases with bipolar disorder, and clinical features were statistically analyzed between the two groups. A total of 42 variables with significant differences were screened as the input variables of the neural network. Part of the samples were randomly selected as the learning sample, and the other as the test sample. By choosing different neu- ral network structures, all results of the identification of bipolar disorder were relatively good, which showed that MLP neural network could be used in the early identification of bipolar disorder.
Bipolar Disorder ; diagnosis ; Humans ; Neural Networks (Computer)

The purpose of this paper is to achieve a measurement of temperature prediction for artificial heart without sensor, for which the research briefly describes the application of back propagation neural network as well as the optimized, by genetic algorithm, BP network. Owing to the limit of environment after the artificial heart implanted, detectable parameters out of body are taken advantage of to predict the working temperature of the pump. Lastly, contrast is made to demonstrate the prediction result between BP neural network and genetically optimized BP network, by which indicates that the probability is 1.84% with the margin of error more than 1%.
Heart, Artificial ; Neural Networks (Computer) ; Temperature

Artificial neural networks (ANNs) are powerful computational tools that are designed to replicate the human brain and adopted to solve a variety of problems in many different fields. Fault tolerance (FT), an important property of ANNs, ensures their reliability when significant portions of a network are lost. In this paper, a fault/noise injection-based (FIB) genetic algorithm (GA) is proposed to construct fault-tolerant ANNs. The FT performance of an FIB-GA was compared with that of a common genetic algorithm, the back-propagation algorithm, and the modification of weights algorithm. The FIB-GA showed a slower fitting speed when solving the exclusive OR (XOR) problem and the overlapping classification problem, but it significantly reduced the errors in cases of single or multiple faults in ANN weights or nodes. Further analysis revealed that the fit weights showed no correlation with the fitting errors in the ANNs constructed with the FIB-GA, suggesting a relatively even distribution of the various fitting parameters. In contrast, the output weights in the training of ANNs implemented with the use the other three algorithms demonstrated a positive correlation with the errors. Our findings therefore indicate that a combination of the fault/noise injection-based method and a GA is capable of introducing FT to ANNs and imply that the distributed ANNs demonstrate superior FT performance.
Algorithms ; Humans ; Models, Genetic ; Neural Networks (Computer)

Identification of Chinese medicinal materials is a fundamental part and an important premise of the modern Chinese medicinal materials industry. As for the traditional Chinese medicinal materials that imitate wild cultivation, due to their scattered, irregular, and fine-grained planting characteristics, the fine classification using traditional classification methods is not accurate. Therefore, a deep convolution neural network model is used for imitating wild planting. Identification of Chinese herbal medicines. This study takes Lonicera japonica remote sensing recognition as an example, and proposes a method for fine classification of L. japonica based on a deep convolutional neural network model. The GoogLeNet network model is used to learn a large number of training samples to extract L. japonica characteristics from drone remote sensing images. Parameters, further optimize the network structure, and obtain a L. japonica recognition model. The research results show that the deep convolutional neural network based on GoogLeNet can effectively extract the L. japonica information that is relatively fragmented in the image, and realize the fine classification of L. japonica. After training and optimization, the overall classification accuracy of L. japonica can reach 97.5%, and total area accuracy is 94.6%, which can provide a reference for the application of deep convolutional neural network method in remote sensing classification of Chinese medicinal materials.
Lonicera ; Neural Networks, Computer ; Remote Sensing Technology

With the advent of the era of big data, artificial intelligence based on machine learning, especially artificial neural network has rapidly developed and applicated in the field of stomatology, owning huge potential in segmentation and labelling of three-dimensional intraoral anatomical features. Automatic segmentation, labelling and diagnosis can assist dentists and technicians to complete heavy and repeat work, change stomatology from subjective perception to objective science, and help to make diagnosis and treatment plan efficiently and accurately. This review briefly summarized related knowledge and development of machine learning and artificial neural network, its application status and existing problems in the field of segmentation and labelling of three-dimensional intraoral anatomical features, and provided an outlook of its future development.
Artificial Intelligence ; Machine Learning ; Neural Networks, Computer