Medical diagnosis of lung nodules by fusing CT image feature extraction and improved SVM algorithm / 医疗卫生装备

Objective To propose a lung nodule diagnosis method based on CT image feature extraction and improved support vector machine(SVM)algorithm to enhance the accuracy and efficiency of automatic identification of lung nodules.Methods A cascade feature extraction method combining deep learning-based feature extraction and traditional manual extraction was used for CT image feature extraction,and the extracted features were input into an improved SVM algorithm to complete automated identification of lung nodules,using a multiple kernel learning support vector machine(MKL-SVM)algorithm and a particle swarm optimization(PSO)algorithm that integrated simulated annealing(SA)algorithm for parameter optimizing.The performance of cascade features was tested by comparing traditional feature extraction,deep learning-based feature extraction and cascade feature extraction.Comparison tests were performed using single kernel functions(RBF kernel,Sigmoid kernel and polynomial kernel functions)to validate the performance of the MKL-SVM algorithm.Tests were carried out using SVM functions with Sigmoid kernel to compare the fitness curves of the PSO algorithm and the PSO-SA algorithm for optimization to validate the effectiveness of the PSO-SA algorithm.Comparison analyses were conducted with the existing computer aided diagnosis(CAD)models of lung under the same dataset to verify the diagnostic efficacy of the proposed model of cascade features combined with improved MKL-SVM(cascade features with improved MKL-SVM,CF with MKL-SVM).Results The performance test results showed that cascade feature extraction had the F value with a mean value of 0.934 1,a maximum value of 0.957 3,a minimum value of 0.919 5 and a median value of 0.939 7,which behaved better in accuracy than manual feature extraction and deep learning-based feature extraction.The kernel function comparison test results indicated that the MKL-SVM algorithm had the best diagnostic performance with the mean value of F value of 0.924 3,the maximum value of 0.935 0 and the AUC value of 0.987 3.The Sigmoid kernel comparison test results found that PSO-SA al-gorithm had the best fitness value of 0.943 7,which gained advantages over the PSO algorithm.The model comparison test revealed that compared with the lung CAD model,the CF+MKL-SVM model had advantages in generalization ability,AUC value(0.9845),the values of all the indexes(all higher than 0.9),specificity and precision.Conclusion The proposed method can be used for automatic recognition of lung cancer and enhances the accuracy for detecting lung cancer.

Continuous Motion Estimation of Elbow Joint Based on Multi-Modal Information Fusion / 医用生物力学

Objective Aiming at the problems of lacking initiative in upper limb rehabilitation training equipment, single training mode, and low active participation of patients, an upper limb continuous motion estimation algorithm model based on multi-modal information fusion was proposed, so to realize accurate estimation of elbow joint torque. Methods Firstly, the surface electromyography (sEMG) signal and posture signal of participants were collected at four angular velocities, and the time domain characteristics of the signal were extracted. The principal component analysis was adopted to multi-feature fusion. The back propagation neural network (BPNN) was optimized through the additional momentum and the adaptive learning rate method. The particle swarm optimization (PSO) algorithm was used to optimize the neural network and a continuous motion estimation model based on PSO-BPNN was constructed. Finally, the joint torque calculated by the second type of Lagrangian equation was used as the accurate value to train the model. The performance of the model was compared with the traditional BP neural network model. Results The root mean square error (RMSE) of the traditional BP neural network model was 558.9 N·m, and the R2 coefficient was 77.19%, Whereas the RMSE and the R2 coefficient of the optimized model were 113.6 mN·m and 99.12%, respectively.Thereby, the accuracy of torque estimation was improved apparently. Conclusions The method for continuous motion estimation of the elbow joint proposed in this study can estimate the motion intention accurately, and provide a practical scheme for the active control of upper exoskeleton rehabilitation robot.

SC⁃BPSO: A Filter Fused BPSO Feature Selection Method in Hepatocellular Carcinoma Classification / 中国生物化学与分子生物学报

Early diagnosis of cancer can significantly improve the survival rate of cancer patients, especially in patients with hepatocellular carcinoma (HCC). Machine learning is an effective tool in cancer classification. How to select high⁃classification accuracy feature subsets with low dimension in complex and high⁃dimensional cancer datasets is a difficult problem in cancer classification. In this paper, we propose a novel feature selection method, SC⁃BPSO: a two⁃stage feature selection method implemented by combining the Spearman correlation coefficient, chi⁃square independent test⁃based filter method, and binary particle swarm optimal (BPSO) based wrapper method. It has been applied to the cancer classification of high⁃dimensional data to classify normal samples and HCC samples. The dataset in this paper is obtained from 130 liver tissue microRNA sequence data (64 hepatocellular carcinoma, 66 normal liver tissue) from National Center for Bioinformatics (NCBI) and European Bioinformatics Institute (EBI). First, the liver tissue microRNA sequence data was preprocessed to extract the three types of features of microRNA expression, editing level and post⁃editing expression. Then, the parameters of the SC⁃BPSO algorithm in the liver cancer classification were adjusted to select a subset of key features. Finally, classifiers were used to establish classification models, predict the results, and compare the classification results with the feature subset selected by the information gain filter, the information gain ratio filter and the BPSO wrapper feature selection algorithm using the same classifier. Using the feature subset selected by the SC⁃BPSO algorithm, the classification accuracy is up to 98. 4%. The experimental results showed that compared with the other three feature selection algorithms, the SC⁃ BPSO algorithm can effectively find feature subsets with relatively small size and higher accuracy. This may have important implications for cancer classification with a small number of samples and high⁃ dimension features.

Research on gait recognition and prediction based on optimized machine learning algorithm / 生物医学工程学杂志

Aiming at the problems of individual differences in the asynchrony process of human lower limbs and random changes in stride during walking, this paper proposes a method for gait recognition and prediction using motion posture signals. The research adopts an optimized gated recurrent unit (GRU) network algorithm based on immune particle swarm optimization (IPSO) to establish a network model that takes human body posture change data as the input, and the posture change data and accuracy of the next stage as the output, to realize the prediction of human body posture changes. This paper first clearly outlines the process of IPSO's optimization of the GRU algorithm. It collects human body posture change data of multiple subjects performing flat-land walking, squatting, and sitting leg flexion and extension movements. Then, through comparative analysis of IPSO optimized recurrent neural network (RNN), long short-term memory (LSTM) network, GRU network classification and prediction, the effectiveness of the built model is verified. The test results show that the optimized algorithm can better predict the changes in human posture. Among them, the root mean square error (RMSE) of flat-land walking and squatting can reach the accuracy of 10 -3, and the RMSE of sitting leg flexion and extension can reach the accuracy of 10 -2. The R 2 value of various actions can reach above 0.966. The above research results show that the optimized algorithm can be applied to realize human gait movement evaluation and gait trend prediction in rehabilitation treatment, as well as in the design of artificial limbs and lower limb rehabilitation equipment, which provide a reference for future research to improve patients' limb function, activity level, and life independence ability.

Study on noninvasive blood glucose detection method using the near-infrared light based on particle swarm optimization and back propagation neural network / 生物医学工程学杂志

Most of the existing near-infrared noninvasive blood glucose detection models focus on the relationship between near-infrared absorbance and blood glucose concentration, but do not consider the impact of human physiological state on blood glucose concentration. In order to improve the performance of prediction model, particle swarm optimization (PSO) algorithm was used to train the structure paramters of back propagation (BP) neural network. Moreover, systolic blood pressure, pulse rate, body temperature and 1 550 nm absorbance were introduced as input variables of blood glucose concentration prediction model, and BP neural network was used as prediction model. In order to solve the problem that traditional BP neural network is easy to fall into local optimization, a hybrid model based on PSO-BP was introduced in this paper. The results showed that the prediction effect of PSO-BP model was better than that of traditional BP neural network. The prediction root mean square error and correlation coefficient of ten-fold cross-validation were 0.95 mmol/L and 0.74, respectively. The Clarke error grid analysis results showed that the proportion of model prediction results falling into region A was 84.39%, and the proportion falling into region B was 15.61%, which met the clinical requirements. The model can quickly measure the blood glucose concentration of the subject, and has relatively high accuracy.

Application of an extreme learning machine network with particle swarm optimization in syndrome classification of primary liver cancer / 中西医结合学报

OBJECTIVE@#By optimizing the extreme learning machine network with particle swarm optimization, we established a syndrome classification and prediction model for primary liver cancer (PLC), classified and predicted the syndrome diagnosis of medical record data for PLC and compared and analyzed the prediction results with different algorithms and the clinical diagnosis results. This paper provides modern technical support for clinical diagnosis and treatment, and improves the objectivity, accuracy and rigor of the classification of traditional Chinese medicine (TCM) syndromes.@*METHODS@#From three top-level TCM hospitals in Nanchang, 10,602 electronic medical records from patients with PLC were collected, dating from January 2009 to May 2020. We removed the electronic medical records of 542 cases of syndromes and adopted the cross-validation method in the remaining 10,060 electronic medical records, which were randomly divided into a training set and a test set. Based on fuzzy mathematics theory, we quantified the syndrome-related factors of TCM symptoms and signs, and information from the TCM four diagnostic methods. Next, using an extreme learning machine network with particle swarm optimization, we constructed a neural network syndrome classification and prediction model that used "TCM symptoms + signs + tongue diagnosis information + pulse diagnosis information" as input, and PLC syndrome as output. This approach was used to mine the nonlinear relationship between clinical data in electronic medical records and different syndrome types. The accuracy rate of classification was used to compare this model to other machine learning classification models.@*RESULTS@#The classification accuracy rate of the model developed here was 86.26%. The classification accuracy rates of models using support vector machine and Bayesian networks were 82.79% and 85.84%, respectively. The classification accuracy rates of the models for all syndromes in this paper were between 82.15% and 93.82%.@*CONCLUSION@#Compared with the case of data processed using traditional binary inputs, the experiment shows that the medical record data processed by fuzzy mathematics was more accurate, and closer to clinical findings. In addition, the model developed here was more refined, more accurate, and quicker than other classification models. This model provides reliable diagnosis for clinical treatment of PLC and a method to study of the rules of syndrome differentiation and treatment in TCM.

Feature fusion of electrocardiogram and surface electromyography for estimating the fatigue states during lower limb rehabilitation / 生物医学工程学杂志

In the process of lower limb rehabilitation training, fatigue estimation is of great significance to improve the accuracy of intention recognition and avoid secondary injury. However, most of the existing methods only consider surface electromyography (sEMG) features but ignore electrocardiogram (ECG) features when performing in fatigue estimation, which leads to the low and unstable recognition efficiency. Aiming at this problem, a method that uses the fusion features of ECG and sEMG signal to estimate the fatigue during lower limb rehabilitation was proposed, and an improved particle swarm optimization-support vector machine classifier (improved PSO-SVM) was proposed and used to identify the fusion feature vector. Finally, the accurate recognition of the three states of relax, transition and fatigue was achieved, and the recognition rates were 98.5%, 93.5%, and 95.5%, respectively. Comparative experiments showed that the average recognition rate of this method was 4.50% higher than that of sEMG features alone, and 13.66% higher than that of the combined features of ECG and sEMG without feature fusion. It is proved that the feature fusion of ECG and sEMG signals in the process of lower limb rehabilitation training can be used for recognizing fatigue more accurately.

PSO+: nonlinear fitting fluorescence data based on particle swarm optimizing combine with other iteration algorithm / 生物医学工程学杂志

The convective polymerase chain reaction (CCPCR) uses the principle of thermal convection to allow the reagent to flow in the test tube and achieve the purpose of amplification by the temperature difference between the upper and lower portions of the test tube. In order to detect the amplification effect in real time, we added a fluorophore to the reagent system to reflect the amplification in real time through the intensity of fluorescence. The experimental results show that the fluorescence curve conforms to the S-type trend of the amplification curve, but there is a certain jitter condition due to the instability of the thermal convection, which is not conducive to the calculation of the cycle threshold (CT value). In order to solve this problem, this paper uses the dynamic method, using the double S-type function model to fit the curve, so that the fluorescence curve is smooth and the initial concentration of the nucleic acid can be deduced better to achieve the quantitative purpose based on the curve. At the same time, the PSO+ algorithm is used to solve the double s-type function parameters, that is, particle swarm optimization (PSO) algorithm combined with Levenberg-Marquardt, Newton-CG and other algorithms for curve fitting. The proposed method effectively overcoms PSO randomness and the shortcoming of traditional algorithms such as Levenberg-Marquardt and Newton-CG which are easy to fall into the local optimal solution. The of the data fitting result can reach 0.999 8. This study is of guiding significance for the future quantitative detection of real-time fluorescent heat convection amplification.

Spatial epidemiological characteristics and prediction models of bacterial dysentery in Chongqing from 2009 to 2016 based on meteorological elements / 上海交通大学学报（医学版）

Objective: To analyze the spatial epidemiological characteristics of bacillary dysentery and its correlation with meteorological elements in Chongqing, and to construct its incidence prediction model, thus providing scientific basis for the prevention and control of bacterial dysentery. Methods: The data of bacterial dysentery cases and meteorological factors from 2009 to 2016 in Chongqing was collected in this study. Descriptive methods were employed to investigate the epidemiological distribution of bacillary dysentery. Spatiotemporal scanning statistics was used to analyze spatiotemporal characteristics of bacillary dysentery. DCCA coefficient method was used to quantify the correlation between the incidence of bacillary dysentery and meteorological elements. Both Boruta algorithm and particle swarm optimization algorithm (PSO) combined with support vector machine for regression model (SVR) were used to establish the prediction model for the incidence of bacterial dysentery. Results: ①The mean annual reported incidence of bacillary dysentery in Chongqing from 2009 to 2016 was 29.394/100 000. Children <5 years old had the highest incidence (295.892/100 000) among all age categories and scattered children had the highest proportion (50.335%) among all occupation categories. The seasonal incidence peak was from May to October. Bacterial dysentery showed a significant spatial-temporal aggregation that the most likely clusters for disease was found mainly in the main urban areas and main gathering time was from June to October. ②The most important meteorological elements associated with the incidence of bacterial dysentery were monthly mean atmospheric pressure (ρDCCA=-0.918), monthly mean maximum temperature (ρDCCA=0.875) and monthly mean temperature (ρDCCA=0.870). ③The mean squared error (MSE), mean absolute percentage error (MAPE) and square correlation coefficient (R2) of PSO_SVR model constructed based on meteorological elements were 0.055, 0.101 and 0.909, respectively. Conclusion: The main urban areas of Chongqing and the northeast of Chongqing should be regarded as the key areas for the prevention and control of bacillary dysentery. At the same time, according to the characteristics of bacillary dysentery, relevant health departments should take targeted measures to control the spread and prevalence of bacillary dysentery among children <5 years old, scattered children and farmers. The PSO_SVR model constructed based on meteorological elements has good predictive performance and can provide scientific theoretical support for the prevention and control of bacterial dysentery.

Utilização de modelos geométricos simplificados de uma córnea para fins de otimização / Use of simplified geometrical models of a cornea for optimization purposes

Resumo Otimização por Enxame de Partículas (PSO) é uma técnica de inteligência artificial (AI), que pode ser usada para encontrar soluções aproximadas para problemas numéricos de maximização e minimização extremamente difíceis. Neste trabalho, utilizou-se um algoritmo PSO para comparar os deslocamentos sofridos por uma amostra de córnea humana submetida à uma pressão interna de 45 mmHg com resultados de simulações numéricas e identificar valores otimizados para propriedades hiperelásticas da córnea (µ e α). Por meio dos resultados das simulações via análise inversa pelo Método dos Elementos Finitos (MEF), em conjunto com o algoritmo PSO, foram encontrados valores otimizados de µ = 0,047 e α = 106,7. Quando comparado com resultados otimizados por meio de um software comercial, foram encontrados erros de aproximadamente 0,15%. Por meio dos resultados obtidos, verificou-se ainda que, variando os valores dos coeficientes de inércia da partícula no algoritmo PSO, os resultados podem sofrer ligeira melhoria, o que demonstra potencial uso do PSO em conjunto com análise inversa do MEF para caracterização de materiais hiperelásticos, utilizando modelos geométricos simplificados

Abstract Particle Swarm Optimization (PSO) is an artificial intelligence technique (AI) that can be used to find approximate solutions to numerical problems of maximization and minimization. In this study, it was used a PSO algorithm to compare displacements from human cornea sample subjected to internal pressure of 45 mmHg with Results of numerical simulations were provided which identified optimized values for hyperelastic properties of the cornea (µ and α). By means of the results from numerical simulations via inverse analysis by the Finite Element Method (FEM), in conjunction with the PSO algorithm, optimized values of µ = 0.047 and α = 106.7 were found. When compared with optimized results from commercial software, errors around 0.15% were found. Results showed that, varying the values of particle inertia coefficients in the PSO algorithm, simulated displacements have improved when compared to experimental data. This demonstrates the potential use of PSO algorithm in conjunction with the FEM inverse analysis for hyperelastic materials characterization, using simplified geometrical models

Energy Efficient Clustering in Multi-hop Wireless Sensor Networks Using Differential Evolutionary MOPSO

ABSTRACT The primary challenge in organizing sensor networks is energy efficacy. This requisite for energy efficacy is because sensor nodes capacities are limited and replacing them is not viable. This restriction further decreases network lifetime. Node lifetime varies depending on the requisites expected of its battery. Hence, primary element in constructing sensor networks is resilience to deal with decreasing lifetime of all sensor nodes. Various network infrastructures as well as their routing protocols for reduction of power utilization as well as to prolong network lifetime are studied. After analysis, it is observed that network constructions that depend on clustering are the most effective methods in terms of power utilization. Clustering divides networks into inter-related clusters such that every cluster has several sensor nodes with a Cluster Head (CH) at its head. Sensor gathered information is transmitted to data processing centers through CH hierarchy in clustered environments. The current study utilizes Multi-Objective Particle Swarm Optimization (MOPSO)-Differential Evolution (DE) (MOPSO-DE) technique for optimizing clustering.

Application of least squares support vector machines and partial least squares in quantitation on purification of Gardenia jasminoides intermediate using NIR spectroscopy / 中草药

Objective: To establish the quantitative models for analyzing the content of critical quality indicators in the purification process of Gardenia jasminoides intermediate in Reduning Injection using near-infrared (NIR) spectroscopy. Methods: The contents of shanzhiside, geniposidic acid, deacetyl asperulosidic acid methyl ester, genipin-1-β-D-gentiobioside, geniposide, chlorogenic acid, and total acid were determined by the reference method and NIR spectra were acquired. After removing the outliers, selecting the optimal spectral preprocessing method and selecting the best spectral wavelength, partial least squares (PLS) and the least squares support vector machines (LS-SVM) were used to build the models for predicting the contents of the above quality indicators in 18 unknown samples. Results: For shanzhiside, geniposidic acid, deacetyl asperulosidic acid methyl ester, genipin-1-β-D-gentiobioside, geniposide, chlorogenic acid, and total acid, the relative standard errors of prediction (RSEP) was lower than 3% for PLS models and LS-SVM models, indicating both methods could exhibit the satisfactory fitting results and predictive abilities. However, the LS-SVM models of shanzhiside and total acid showed lower predictive errors than PLS models. For geniposidic acid, deacetyl asperulosidic acid methyl ester, genipin-1-β-D-gentiobioside, geniposide, and chlorogenic acid, both models have the closer predictive errors. Conclusion: S-SVM shows better predictive performance than PLS. The established NIR quantitative models can be used for rapidly measuring the content of critical quality indicators in the purification process of G. jasminoides intermediate in Reduning Injection.

Qualidade de investimentos no uso de água pluvial: Particles Swarm Optimization para a maximização do valor presente líquido / Quality of investments in rainwater harvesting systems: Particles Swarm Optimization for maximization of net present value

A análise da qualidade dos investimentos deve fazer parte da tomada de decisão pela implantação dos sistemas de aproveitamento de água pluvial, de modo a avaliar se os investimentos na redução dos impactos ambientais são atrativos também sob o ponto de vista dos investidores. Considerando-se os métodos baseados no fluxo de caixa descontado, o valor presente líquido é o melhor indicador para tal avaliação, pois permite decidir qual é o melhor entre dois projetos excludentes, além de apresentar uma rotina de cálculo simples e de fácil entendimento pelos usuários. Este trabalho teve como objetivo propor uma ferramenta para a análise da qualidade dos investimentos em um sistema de aproveitamento de água pluvial, baseada na otimização do valor presente líquido, por meio da técnica de Particles Swarm Optimization. Para demonstrar a utilização da ferramenta proposta, desenvolveu-se um estudo de caso para uma edificação pública existente. Foram considerados três cenários para a tarifa água: sem reajuste e com valores limite e máximo do reajuste histórico das tarifas da concessionária local: 5,69 e 19,63% ao ano. Em todos os cenários foram encontradas duas alternativas: existência de cobrança da tarifa de esgoto correspondente ao volume de água pluvial utilizado e inexistência dela. Os resultados obtidos com o uso da ferramenta proposta podem auxiliar na tomada de decisão para uso dos sistemas de aproveitamento da água pluvial.

Analysis of the quality of investments should be part of decision-making for implementing rainwater-harvesting systems in order to evaluate whether investments in environmental impacts mitigation are also attractive from the point of view of investors. Considering discounted cash flow methods, net present value is the best indicator for this analysis, because it allows deciding which is the best between two mutually exclusive projects, and it also presents a simple calculation routine and is easily understood by users. This work aimed at presenting a decision-making analysis tool for rainwater-harvesting systems, which is based on the maximization of net present value, using the Particles Swarm Optimization technique. The decision tool was used to evaluate the quality of the investments for implementing a rainwater-harvesting system, considering a building of the public tax category. Three scenarios of the water bill were considered: current rate structure and increases of 100%, 150% and 200%. All scenarios contemplate two alternatives: with sewage rate corresponding to the rainwater use and no fee. Results illustrate opportunities for investments in rainwater-harvesting systems.

Multimodal Medical Image Registration Based on Gradient Vector Flow and Particle Swarm Optimization / 航天医学与医学工程

Objective To study the method based on gradient vector flow (GVF) and particle swarm optimization (PSO) for realizing multimodal medical image registration and improving its accuracy. Methods In view of three major components of image registration, i.e. the feature space, the similarity metric and the search strategy, a novel method was proposed with three improvements. Firstly, the GVF field was employed as the feature space. Then three similarity metrics were proposed based on GVF field. Finally, an improved PSO combined with crossover mechanism of genetic algorithm was utilized to search for the optimal transformation of two images. Results With 54 times of experiments on both simulated and real medical images, it was demonstrated that this method accurately registered the multimodal medical images to be superior to the method based on PSO of pixels, and the Walsh transform method. Conclusion The method based on GVF and PSO is effective for multimodal medical image registration.

NEURAL NETWORK TRAINING WITH PARALLEL PARTICLE SWARM OPTIMIZER / 药物分析学报

Objective To reduce the execution time of neural network training. Methods Parallel particle swarm optimization algorithm based on master-slave model is proposed to train radial basis function neural networks, which is implemented on a cluster using MPI libraries for inter-process communication. Results High speed-up factor is achieved and execution time is reduced greatly. On the other hand, the resulting neural network has good classification accuracy not only on training sets but also on test sets. Conclusion Since the fitness evaluation is intensive, parallel particle swarm optimization shows great advantages to speed up neural network training.