Genetic Artificial Neural Network Research Articles

Optimization of green extraction process with natural deep eutectic solvent and comparative in vivo pharmacokinetics of bioactive compounds from Astragalus-Safflower pair

IntroductionNatural deep eutectic solvents (NaDESs) are promising not only in componential extraction, but also in drug delivery system due to their green and safe features. In this work, NaDESs were applied to extract bioactive components from Astragalus-Safflower pair, a classic traditional Chinese medicine combination. Furthermore, ready-to-use crude extracts were administrated to SD rats. MethodsTotal 9 NaDESs composed of food grade ingredients were screened for the extraction of representative 9 components (hydroxysafflor yellow A, anhydrosafflor yellow B, eleutheroside B, calycosin-7-O-glucoside, kaempferol-3-O-rutinoside, ononin, calycosin, astraganoside, and carthamin) from Astragalus-Safflower pair. Afterwards, genetic artificial neural network (GNN) was adopted for optimizing the ultrasound-assisted extraction process. After SD rats were orally administrated with the ready-to-use crude extracts extracted under the optimized conditions, the in vivo pharmacokinetic characteristics of 5 components were evaluated comprehensively from weight, gender, solvent and modeling surgery, under a well-established UPLC-MS/MS method. ResultsBetaine-Lactic acid (Bet-Lac) was eventually determined as the optimal extraction solvent for subsequent experiments. The optimal ultrasound assisted extraction process was as follows: 90 min of extraction time, 65 °C of temperature, 80% of Bet-Lac content and 50 mg/ml of solid-liquid ratio. Bet-Lac enhanced to varying degrees the bioavailability of analytes in normal and cerebral ischemia/reperfusion injured (CI/RI) rats in contrast with corresponding rats administrated with water extract groups (p < 0.05). Besides, the bioavailability of active components in CI/RI rat plasma was significantly lower than that in normal rats (p < 0.05), indicating pathological damage of CI/R had a significant impact on pharmacokinetic profile of compounds in rats. However, gender and body weight had no significant effects on the pharmacokinetic profile of bioactive components. ConclusionsNaDESs exhibited higher extraction efficiency than conventional solvents. And GNN is reliable to optimize the ultrasound assisted extraction process. This study supported the potential of non-toxic NaDESs as solvents for extraction process and drug delivery systems at the same time.

Retraction Note: Risk analysis method of bank microfinance based on multiple genetic artificial neural networks

Retraction Note: Risk analysis method of bank microfinance based on multiple genetic artificial neural networks

Short-term Traffic Flow Prediction Based on Genetic Artificial Neural Network and Exponential Smoothing

In order to improve the accuracy of short-term traffic flow prediction, a combined model composed of artificial neural network optimized by using Genetic Algorithm (GA) and Exponential Smoothing (ES) has been proposed. By using the metaheuristic optimal search ability of GA, the connection weight and threshold of the feedforward neural network trained by a backpropagation algorithm are optimized to avoid the feedforward neural network falling into local optimum, and the prediction model of Genetic Artificial Neural Network (GANN) is established. An ES prediction model is presented then. In order to take the advantages of the two models, the combined model is composed of a weighted average, while the weight of the combined model is determined according to the prediction mean square error of the single model. The road traffic flow data of Xuancheng, Anhui Province with an observation interval of 5 min are used for experimental verification. Additionally, the feedforward neural network model, GANN model, ES model and combined model are compared and analysed, respectively. The results show that the prediction accuracy of the optimized feedforward neural network is much higher than that before the optimization. The prediction accuracy of the combined model is higher than that of the two single models, which verifies the feasibility and effectiveness of the combined model.

RETRACTED ARTICLE: Risk analysis method of bank microfinance based on multiple genetic artificial neural networks

As a supplement to the financing of small- and medium-sized enterprises, bank microfinance companies are nonbank financial institutions, and it has played an active role in maintaining the stability of financial markets. However, in the course of the operation of microfinance companies, due to the lack of careful management and risk control, the problem of risk management has become increasingly prominent. The purpose of this paper is to study the microfinance risk based on polygenic artificial neural network, and it provides theory and practice application for risk management of microcredit enterprises. Taking the risk management of China’s microfinance companies as the research object, on the basis of previous studies, this paper analyzes the risks of bank microfinance companies. Secondly, the basic theory of neural network model and its transformation function are introduced, and the learning method of neural network. At the same time, the learning algorithm of neural network and its improved algorithm are mainly introduced. It lays a theoretical foundation for the follow-up empirical research. Then, through the empirical study of data-based risk assessment of microcredit of farmers, the sample data are divided into training samples and test samples for comparison. Then, we use MATLAB software to establish a neural network model for farmers’ microcredit risk assessment. Finally, in order to make the neural network model of farmers’ credit risk assessment better popularize and apply, and effectively reduce the credit risk of farmers’ microcredit. The corresponding policy suggestions are put forward, which proves the validity and applicability of the neural network in the field of farmers’ microcredit risk assessment. It provides a good basis for rural credit cooperatives to identify the credit risk of farmers.

A model to predict ammonia emission using a modified genetic artificial neural network: Analyzing cement mixed with fly ash from a coal-fired power plant

Selective catalytic reduction (SCR) and selected non-catalytic reduction (SNCR) use ammonia as a catalyst for removing nitrogen oxides in power plants. However, unreacted ammonia in the flue gas denitrification system reacts chemically with SO3 and is adsorbed on the coal fly ash. Unfortunately, fly ash is commonly mixed with concrete because of its technological and economic benefits, without consideration of secondary environmental contamination. We measured the ammonia emissions from different types of fly ash mortar and found they have a strong correlation with the mixing ratio of fly ash, the mortar age, and the size. In order to find out this correlation and thus predict the ammonia concentration under different conditions, we constructed artificial neural network (ANN) models. The comparison results show that the ANN model initialized with the genetic ANN (GANN) algorithm has the smallest root-mean-square error (RMSE) between given and predicted outputs.

A hybrid genetic artificial neural network (G-ANN) algorithm for optimization of energy component in a wireless mesh network toward green computing

Wireless mesh networks are a special class of wireless networks that are implemented as a collection of radio nodes in a mesh pattern or topology. Unlike MANETs, the mobility of nodes is very less in the topology. Quality of service is an essential metric in the performance of mesh networks which are attributed to several parameters including optimal routing through shortest path, ability for other nodes to communicate even if a particular node in the mesh fails, minimization of packet loss and time delay, computational complexity and cost, energy. This research paper is focused toward minimization of energy taken as the objective function and a five-stage neural network is used and trained after optimizing with a genetic algorithm. The experiments have been conducted in NS2 and Qualnet environment with a varying number of mesh routers and energy computed. The performance of energy savings has been compared against conventional routing techniques like AODV and a bee colony optimization technique presented in the literature. An energy savings of 51% have been reported in the paper justifying the superiority of the hybrid G-ANN algorithm.

Vibration signal analysis and fault diagnosis of bogies of the high-speed train based on deep neural networks

Bogie is the most important component in a running gear of a high-speed train. Advantages and disadvantages of its mechanical performance can directly influence safety and comfort of train running. Failure of key components on the bogie always leads to serious vibration and performance decrease of each train component, and can even cause serious accidents such as de-railing and overturning. Therefore, it is very necessary to adopt an efficient and accurate intelligent diagnosis method. Based on the previous researches, the paper proposed a deep neural network method to make systematic and complete diagnosis of bogie faults. Firstly, a deep neural network was used to diagnose standard bearing system faults, and the results were compared with those obtained by traditional neural networks such as FANN (Firefly Artificial Neural Network), PSONN (Particle Swarm Optimization Neural Network) and GANN (Genetic Artificial Neural Network) to highlight advantages of the deep neural network. Then, based on vibration data extracted by a multi-body dynamic model of the high-speed train, the deep neural network was used to diagnose 8 conditions of the bogie systematically. The fault diagnosis was repeated for 10 times. Results obtained for the 10 times present obvious advantages of the deep neural network which could obtain the average diagnosis rate of 98.3 % and had the diagnosis standard deviation of 0.71. Finally, training process of fault diagnosis was extracted. Results show that the deep neural network could converge to a critical value at the quickest speed. All the above analysis results indicate that the deep neural network has irreplaceable advantages in intelligent diagnosis of bogies.

Creation of an effective colorectal anastomotic leak early detection tool using an artificial neural network

Anastomotic leaks greatly increase both morbidity and mortality amongst colorectal patients. Earlier detection of leaks leads to improved patient outcomes; however, diagnosis often proves difficult due to heterogeneous presentation and varied differential diagnosis. The purpose of the study was to create an artificial neural network (ANN) capable of accurately identifying patients at risk of developing a post-operative colorectal anastomotic leak. A genetic ANN was trained and validated on a retrospective patient cohort. Two comparative groups were identified: those with anastomotic leaks confirmed at re-operation with a control group of patients with a post-operative delayed recovery, but in whom leak was excluded and no re-operation required. Seventy-six patients were identified: 20 confirmed leaks and 56 controls. No significant difference in the baseline features between leak and control groups in terms of age (leaks 65.9 years [SD 9.29] controls 58.3 years [SD 17.0)], P = 0.054). Utilising backwards variable selection, ANN maintained 19 input variables. Internal validation of the ANN produced a sensitivity of 85.0 %, specificity of 82.1 %, and AUC of 0.89 for correct identification of clinical anastomotic leaks. Of the 20 confirmed leaks, the model correctly identified 17 and misclassified 10 control patients in the clinical leak category. External validation on 12 consecutive pilot prospective patients produced a specificity of 83.3 %. ANNs can be created to accurately detect clinical anastomotic leaks in the early post-operative period using routinely available clinical data. Further prospective ANN testing is required to confirm generalisability. ANNs may provide useful real-world tools for improving patient safety and outcomes.

Indoor Air Quality Assessment Based on Genetic Artificial Neural Network

A new method for indoor air quality assessment with training artificial neural network prediction model was presented. Genetic algorithm was used to train artificial neural network prediction model in this method. The train sample for artificial neural network learning model was grading standard. According to the weight and threshold after network study, this method could be used to make assessment on indoor air quality. The example of indoor air quality assessment demonstrated that this method improved prediction precision. This assessment method is more accurate than the traditional assessment methods. Moreover, it has the ability of global optimization, self-learning, self-adaption. This algorithm overcomes the shortcomings of traditional BP artificial neural network algorithm.

Improved Wavelet Neural Network Based on Hybrid Genetic Algorithm Applicationin on Fault Diagnosis of Railway Rolling Bearing

The method of improved wavelet transform neural network based on hybrid GA(genetic algorithm) is presented to diagnose rolling bearings faults in this paper. Genetic Artificial Neural Networks(GA-ANN) overcomes BP neural network’s fault of slow convergence, long hours of training, and falling into the local minimum point. And First, the signal is processed through the wavelet deoising, Then, three— layer wavelet packet is adopted to decompose the denoising signal of rolling beatings, and constructs the wavelet packet energy eigenvector, then takes those wavelet packet energy eigenvectors as fault samples to train BP neural network. Genetic algorithm is used to optimize the training process of BP network. The experimental results show that the optimized BP network by genetic algorithm can diagnose bearing faults, and it is superior to the BP network without optimization, the method has fair prospects of application for the rotary machine fault diagnosis.

Learning-based multi-pass adaptive scheduling for a dynamic manufacturing cell environment

Because the essential attributes are uncertain in a dynamic manufacturing cell environment, to select a near-optimal subset of manufacturing attributes to enhance the generalization ability of knowledge bases remains a critical, unresolved issue for classical artificial neural network-based (ANN-based) multi-pass adaptive scheduling (MPAS). To resolve this problem, this study develops a hybrid genetic /artificial neural network (GA/ANN) approach for ANN-based MPAS systems. The hybrid GA/ANN approach is used to evolve an optimal subset of system attributes from a large set of candidate manufacturing system attributes and, simultaneously, to determine configuration and learning parameters of the ANN according to various performance measures. In the GA/ANN-based MPAS approach, for a given feature subset and the corresponding topology and learning parameters of an ANN decoded by a GA, an ANN was applied to evaluate the fitness in the GA process and to generate the MPAS knowledge base used for adaptive scheduling control mechanisms. The results demonstrate that the proposed GA/ANN-based MPAS approach has, according to various performance criteria, a better system performance over a long period of time than those obtained with classical machine learning-based MPAS approaches and the heuristic individual dispatching rules.

ESP: A Method to Predict Toxicity and Pharmacological Properties of Chemicals Using Multiple MCASE Databases.

We describe here the development of a computer program which uses a new method called Expert System Prediction (ESP), to predict toxic end points and pharmacological properties of chemicals based on multiple modules created by the MCASE artificial intelligence system. The modules are generally based on different biological models measuring related end points. The purpose is to improve the decision making process regarding the overall activity or inactivity of the chemicals and also to enable rapid in silico screening. ESP evaluates the significance of the biophores from a different viewpoint and uses this information for predicting the activity of new chemicals. We have used a unique encoding system to represent relevant features of a chemical in the form of a pattern vector and a genetic artificial neural network (GA-ANN) to gain knowledge of the relationship between these patterns and the overall pharmacological property. The effectiveness of ESP is illustrated in the prediction of general carcinogenici...

ESP: A Method To Predict Toxicity and Pharmacological Properties of Chemicals Using Multiple MCASE Databases

We describe here the development of a computer program which uses a new method called Expert System Prediction (ESP), to predict toxic end points and pharmacological properties of chemicals based on multiple modules created by the MCASE artificial intelligence system. The modules are generally based on different biological models measuring related end points. The purpose is to improve the decision making process regarding the overall activity or inactivity of the chemicals and also to enable rapid in silico screening. ESP evaluates the significance of the biophores from a different viewpoint and uses this information for predicting the activity of new chemicals. We have used a unique encoding system to represent relevant features of a chemical in the form of a pattern vector and a genetic artificial neural network (GA-ANN) to gain knowledge of the relationship between these patterns and the overall pharmacological property. The effectiveness of ESP is illustrated in the prediction of general carcinogenicity of a diverse set of chemicals using MCASE male/female rats and mice carcinogenicity modules.

Use of genetic artificial neural networks and spectral imaging for defect detection on cherries

A machine vision system was created to identify different types of tissue characteristics on cherries. It consists of an enhanced NIR range vidicon black and white camera (sensing range 400–2000 nm), a monochrometer controlled light source, and a computer. Multiple spectral images of cherry samples were collected over the 680–1280 nm range at increments of 40 nm. Using the spectral signatures of different tissues on cherry images, artificial neural networks were applied to pixel-wise classification. An enhanced genetic algorithm was applied to design the topology and evolve the weights for multi-layer feed forward artificial neural networks. An average of 73% classification accuracy was achieved for correct identification as well as quantification of all types of cherry defects. No false positives or false negatives occurred, errors resulted only from misclassification of defect type or quantification of defect.