Constitution identification model in traditional Chinese medicine based on multiple features

Objective To construct a precise model for identifying traditional Chinese medicine(TCM)constitutions;thereby offering optimized guidance for clinical diagnosis and treatment plan-ning;and ultimately enhancing medical efficiency and treatment outcomes.Methods First;TCM full-body inspection data acquisition equipment was employed to col-lect full-body standing images of healthy people;from which the constitutions were labelled and defined in accordance with the Constitution in Chinese Medicine Questionnaire(CCMQ);and a dataset encompassing labelled constitutions was constructed.Second;heat-suppres-sion valve(HSV)color space and improved local binary patterns(LBP)algorithm were lever-aged for the extraction of features such as facial complexion and body shape.In addition;a dual-branch deep network was employed to collect deep features from the full-body standing images.Last;the random forest(RF)algorithm was utilized to learn the extracted multifea-tures;which were subsequently employed to establish a TCM constitution identification mod-el.Accuracy;precision;and F1 score were the three measures selected to assess the perfor-mance of the model.Results It was found that the accuracy;precision;and F1 score of the proposed model based on multifeatures for identifying TCM constitutions were 0.842;0.868;and 0.790;respectively.In comparison with the identification models that encompass a single feature;either a single facial complexion feature;a body shape feature;or deep features;the accuracy of the model that incorporating all the aforementioned features was elevated by 0.105;0.105;and 0.079;the precision increased by 0.164;0.164;and 0.211;and the F1 score rose by 0.071;0.071;and 0.084;respectively.Conclusion The research findings affirmed the viability of the proposed model;which incor-porated multifeatures;including the facial complexion feature;the body shape feature;and the deep feature.In addition;by employing the proposed model;the objectification and intel-ligence of identifying constitutions in TCM practices could be optimized.

Clinical Features of Renal Insufficiency due to Multiple Myeloma and Related Risk Factors

Objective: To study clinical features of the patients with multiple myeloma(MM) accompanied by renal insufficiency and investigate the related risk factors of renalimpairment. Methods: A control study of clinical characteristics was performed between 91 patientswith renal insufficiency due to MM and 165 patients with normal renal function in MM during the sameperiod. The data were statistically analyzed by chi-square test and logistic regression analysis.Results: Renal insufficiency was the initial presentation in 48 (52.7%) of the 91 patients, and 30(62.5%) of the 48 patients were misdiagnosed. The prognosis of group with renal insufficiency wassignificantly poorer than that of group with normal renal function: mortality in 3 months, 3months-1 year was 26/91 vs 14/165 (P 【 0.0001), 14/91 vs 12/165 (P 【 0.05) respectively, andpatients survived 】 1 year was 18/91 vs 95/165 (P 【 0.0001). The incidence of hypercalcemia,hyperuricemic, severe anemia, high serum M-protein concentration and lytic bone lesions weresignificantly higher in renal insufficiency group than those in control group (P 【 0.05). Logisticregression analysis identified 5 risk factors of renal impairment, including, severe anemia(Exp(β)=13.819, P 【 0.0001), use of nephrotoxic drugs (Exp(β)=6.217, P = 0.001), high serumM-protein concentration (Exp(β) = 5.026, P = 0.001), male (Exp(β)=3.745, P=0.006), andhypercalcemia (Exp(β)=3A72, P=0.006), but age, serum density of uric acid, type of serum M-protein,and Bence Jones proteinuria were not significantly associated with renal insufficiency. Conclusion:Renal insufficiency was a common early complication of MM, which often resulted in misdiagnosis.The status of these patients tended to be very bad, with many other complications, when MM wasdiagnosed, so their prognosis was poor. The occurrence of renal insufficiency in patients with MMand hypercalcemia, severe anemia, high serum M-protein concentration, especially use of nephrotoxicdrugs should be alert.

Detection of artificial pornographic pictures based on multiple features and tree mode

It is easy for teenagers to view pornographic pictures on social networks. Many researchers have studied the detection of real pornographic pictures, but there are few studies on those that are artificial. In this work, we studied how to detect artificial pornographic pictures, especially when they are on social networks. The whole detection process can be divided into two stages： feature selection and picture detection. In the feature selection stage, seven types of features that favour picture detection were selected. In the picture detection stage, three steps were included. 1） In order to alleviate the imbalance in the number of artificial pornographic pictures and normal ones, the training dataset of artificial pornographic pictures was expanded. Therefore, the features which were extracted from the training dataset can also be expanded too. 2） In order to reduce the time of feature extraction, a fast method which extracted features based on the proportionally scaled picture rather than the original one was proposed. 3） Three tree models were compared and a gradient boost decision tree （GBDT） was selected for the final picture detection. Three sets of experimental results show that the proposed method can achieve better recognition precision and drastically reduce the time cost of the method.

Clinical and pathological features of Devic’s neuromyelitis optica and multiple sclerosis in Chinese patients

BACKGROUND：Devic＇s neuromyelitis optica （DNMO） and multiple sclerosis in Asian populations have been considered to be the same disease. However, there is an increasing number of studies suggesting that DNMO and multiple sclerosis are different diseases.OBJECTIVE：Little information is available regarding comparisons of DNMO patients between China and other countries, as well as clinical manifestations of Chinese patients with DNMO and multiple sclerosis. The present study performed a multi-center, pathological, retrospective analysis.DESIGN, TIME AND SETTING：A retrospective analysis of clinical data from seven patients with DNMO diagnosed between 1957 and 1998.PARTICIPANTS：Data from Chinese DNMO patients was provided by the Shanghai Second Medical University, Sun Yat-sen University of Medical Sciences and the First Affiliated Hospital of Harbin Medical University in China.METHODS：Clinical and pathological data from Chinese patients with DNMO were retrospectively analyzed. The clinical characteristics of DNMO were compared between Chinese and Caucasian patients. In addition, clinical and pathological differences between DNMO and multiple sclerosis Chinese patients were compared.MAIN OUTCOME MEASURES：Clinical and pathological features of Chinese patients with DNMO.RESULTS：All seven Chinese patients with DNMO exhibited abrupt onset of vision disturbance, with a disease course of 3 clays to 9 years. DNMO recurred in two of the patients. Demyelinating lesions were observed in all patients, with necrotic lesions and gitter cells in five patients, collagenous hyperplasia in one patient, and perivascular inflammatory cell infiltration in six patients. Comparison between Chinese and Caucasian DNMO patients revealed no significant differences in age at onset, clinical onset, duration, or interval between optic neuritis and myelitis. Compared with Chinese multiple sclerosis patients, Chinese DNMO patients presented with fewer recurrences, higher occurrence of necrosis, perivascular inflammatory cell infiltration and gitter cells, and a lower occurrence of collagenous hyperplasia.CONCLUSION：There was no difference in DNMO clinical features between Chinese and Caucasian patients. However, the clinical and pathological features of DNMO were different compared with multiple sclerosis in Chinese patients. Results suggested that the characteristics of DNMO in Chinese patients were significantly different than multiple sclerosis.

Clinical features of multiple myeloma patients with isolated extramedullary relapse

This study sought to analyze the clinical features and prognosis of multiple myeloma with isolated extramedullary relapse and with the absence of systemic progression.The clinical features and outcome were retrospectively analyzed in six multiple myeloma patients.These patients had secretory multiple myeloma at diagnosis.When relapsed,the dissociation between medullary and extramedullary response was detected.The serum or urine monoclonal component was extremely low or absent.The plasma cells in bone marrow were 〈5%.All patients received new targeted therapies（thalidomide or bortezomib） before extramedullary relapse.It is difficult to achieve second remission for them.Even in those showing response,the duration of response was extremely short.The median of overall survival from diagnosis and from extramedullary relapse was 19 months and 6 months,respectively.The overall survival was significantly shorter compared to the patients without extramedullary involvement（84 months,P= 0.001）.These patients exhibited a special and rare relapse pattern.Patients with this relapse pattern were resistant to current therapies,including novel targeted agents and associated with poor prognosis.

Digital modulation classification using multi-layer perceptron and time-frequency features

Considering that real communication signals corrupted by noise are generally nonstationary, and timefrequency distributions are especially suitable for the analysis of nonstationary signals, time-frequency distributions are introduced for the modulation classification of communication signals： The extracted time-frequency features have good classification information, and they are insensitive to signal to noise ratio （SNR） variation. According to good classification by the correct rate of a neural network classifier, a multilayer perceptron （MLP） classifier with better generalization, as well as, addition of time-frequency features set for classifying six different modulation types has been proposed. Computer simulations show that the MLP classifier outperforms the decision-theoretic classifier at low SNRs, and the classification experiments for real MPSK signals verify engineering significance of the MLP classifier.

A reliability-oriented genetic algorithm-levenberg marquardt model for leak risk assessment based on time-frequency features

Since leaks in high-pressure pipelines transporting crude oil can cause severe economic losses,a reliable leak risk assessment can assist in developing an effective pipeline maintenance plan and avoiding unexpected incidents.The fast and accurate leak detection methods are essential for maintaining pipeline safety in pipeline reliability engineering.Current oil pipeline leakage signals are insufficient for feature extraction,while the training time for traditional leakage prediction models is too long.A new leak detection method is proposed based on time-frequency features and the Genetic Algorithm-Levenberg Marquardt(GA-LM)classification model for predicting the leakage status of oil pipelines.The signal that has been processed is transformed to the time and frequency domain,allowing full expression of the original signal.The traditional Back Propagation(BP)neural network is optimized by the Genetic Algorithm(GA)and Levenberg Marquardt(LM)algorithms.The results show that the recognition effect of a combined feature parameter is superior to that of a single feature parameter.The Accuracy,Precision,Recall,and F1score of the GA-LM model is 95%,93.5%,96.7%,and 95.1%,respectively,which proves that the GA-LM model has a good predictive effect and excellent stability for positive and negative samples.The proposed GA-LM model can obviously reduce training time and improve recognition efficiency.In addition,considering that a large number of samples are required for model training,a wavelet threshold method is proposed to generate sample data with higher reliability.The research results can provide an effective theoretical and technical reference for the leakage risk assessment of the actual oil pipelines.

Expert ranking method based on ListNet with multiple features

The quality of expert ranking directly affects the expert retrieval precision.According to the characteristics of the expert entity,an expert ranking model based on the list with multiple features was proposed.Firstly,multiple features was selected through the analysis of expert pages;secondly,in order to learn parameters through gradient descent and construct expert ranking model,all features were integrated into ListNet ranking model;finally,expert ranking contrast experiment will be performed using the trained model.The experimental results show that the proposed method has a good effect,and the value of NDCG@1 increased14.2%comparing with the pairwise method with expert ranking.

Clinical features of multiple trauma patients combined with spine and spinal cord injuries

Objective To analyze the clinical features of the multiple trauma patients combined with spine and spinal cord injuries.Methods A retrospective study was performed in143multiple trauma patients combined with spine and spinal

Using Neural Networks to Combine Multiple Features in Remote Sensing Image Classification

Remote sensing image classification is the basis of remote sensing image analysis and understanding.It aims to assign each pixel an object class label.To achieve satisfactory classification accuracy,single feature is not enough.Multiple features are usually integrated in remote sensing image classification.In this paper,a method based on neural network to combine multiple features was proposed.A single network was used to perform the task instead of ensemble of neural networks.A special architecture of network was designed to fit the task.The method effectively avoids the problems in direct conjunction of multiple features.Experiments on Indian93 data set show that the method has obvious advantages over conjunction of features on both recognition rate and training time.

Novel feature fusion method for speech emotion recognition based on multiple kernel learning

In order to improve the performance of speech emotion recognition, a novel feature fusion method is proposed. Based on the global features, the local information of different kinds of features is utilized. Both the global and the local features are combined together. Moreover, the multiple kernel learning method is adopted. The global features and each kind of local feature are respectively associated with a kernel, and all these kernels are added together with different weights to obtain a mixed kernel for nonlinear mapping. In the reproducing kernel Hilbert space, different kinds of emotional features can be easily classified. In the experiments, the popular Berlin dataset is used, and the optimal parameters of the global and the local kernels are determined by cross-validation. After computing using multiple kernel learning, the weights of all the kernels are obtained, which shows that the formant and intensity features play a key role in speech emotion recognition. The classification results show that the recognition rate is 78. 74% by using the global kernel, and it is 81.10% by using the proposed method, which demonstrates the effectiveness of the proposed method.

Adaptive target and jamming recognition for the pulse doppler radar fuze based on a time-frequency joint feature and an online-updated naive bayesian classifier with minimal risk

This paper considers the problem of target and jamming recognition for the pulse Doppler radar fuze(PDRF).To solve the problem,the matched filter outputs of the PDRF under the action of target and jamming are analyzed.Then,the frequency entropy and peak-to-peak ratio are extracted from the matched filter output of the PDRF,and the time-frequency joint feature is constructed.Based on the time-frequency joint feature,the naive Bayesian classifier(NBC)with minimal risk is established for target and jamming recognition.To improve the adaptability of the proposed method in complex environments,an online update process that adaptively modifies the classifier in the duration of the work of the PDRF is proposed.The experiments show that the PDRF can maintain high recognition accuracy when the signal-to-noise ratio(SNR)decreases and the jamming-to-signal ratio(JSR)increases.Moreover,the applicable analysis shows that he ONBCMR method has low computational complexity and can fully meet the real-time requirements of PDRF.

Advanced Feature Fusion Algorithm Based on Multiple Convolutional Neural Network for Scene Recognition

Scene recognition is a popular open problem in the computer vision field.Among lots of methods proposed in recent years,Convolutional Neural Network(CNN)based approaches achieve the best performance in scene recognition.We propose in this paper an advanced feature fusion algorithm using Multiple Convolutional Neural Network(Multi-CNN)for scene recognition.Unlike existing works that usually use individual convolutional neural network,a fusion of multiple different convolutional neural networks is applied for scene recognition.Firstly,we split training images in two directions and apply to three deep CNN model,and then extract features from the last full-connected(FC)layer and probabilistic layer on each model.Finally,feature vectors are fused with different fusion strategies in groups forwarded into SoftMax classifier.Our proposed algorithm is evaluated on three scene datasets for scene recognition.The experimental results demonstrate the effectiveness of proposed algorithm compared with other state-of-art approaches.

Tool Wear Monitoring in Drilling Using Multiple Feature Fusion of the Cutting Force

This paper presents a tool wear monitoring method in drilling process using cutting force signal. The kurtosis coefficient and the energy of a special frequency band of cutting force signals were taken as the signal features of tool wear as well as the mean value and the standard deviation from the time and frequency domain. The relationships between the signal feature and tool wear were discussed; then the vectors constituted of the signal features were input to the artificial neural network for fusion in order to realize intelligent identification of tool wear. The experimental results show that the artificial neural network can realize fusion of multiple features effectively, but the identification precision and the extending ability are not ideal owing to the relationship between the features and the tool wear being fuzzy and not certain.

Learning Multi-Modality Features for Scene Classification of High-Resolution Remote Sensing Images

Scene classification of high-resolution remote sensing (HRRS) image is an important research topic and has been applied broadly in many fields. Deep learning method has shown its high potential to in this domain, owing to its powerful learning ability of characterizing complex patterns. However the deep learning methods omit some global and local information of the HRRS image. To this end, in this article we show efforts to adopt explicit global and local information to provide complementary information to deep models. Specifically, we use a patch based MS-CLBP method to acquire global and local representations, and then we consider a pretrained CNN model as a feature extractor and extract deep hierarchical features from full-connection layers. After fisher vector (FV) encoding, we obtain the holistic visual representation of the scene image. We view the scene classification as a reconstruction procedure and train several class-specific stack denoising autoencoders (SDAEs) of corresponding class, i.e., one SDAE per class, and classify the test image according to the reconstruction error. Experimental results show that our combination method outperforms the state-of-the-art deep learning classification methods without employing fine-tuning.

A survey: which features are required for dynamic visual simultaneous localization and mapping?

In recent years,simultaneous localization and mapping in dynamic environments(dynamic SLAM)has attracted significant attention from both academia and industry.Some pioneering work on this technique has expanded the potential of robotic applications.Compared to standard SLAM under the static world assumption,dynamic SLAM divides features into static and dynamic categories and leverages each type of feature properly.Therefore,dynamic SLAM can provide more robust localization for intelligent robots that operate in complex dynamic environments.Additionally,to meet the demands of some high-level tasks,dynamic SLAM can be integrated with multiple object tracking.This article presents a survey on dynamic SLAM from the perspective of feature choices.A discussion of the advantages and disadvantages of different visual features is provided in this article.

Association Study of Two Polymorphisms in Vitamin D Pathway with Multiple Sclerosis in the Moroccan Population

Background: Hypovitaminosis D is reported through the literature to be involved in autoimmune diseases such as multiple sclerosis (MS). In the last decade, numerous studies have investigated the association of single nucleotide polymorphisms (SNPs) with MS, including rs2248359 (CYP24A1) and rs703842 (CYP27B1) that are involved in vitamin D metabolic pathway. However, results were conflicting, probably due to ethnic differences between the studied populations. In this context, the present study aimed to analyze the association between these two SNPs and MS within the Moroccan population. Methods: rs2248359 and rs703842 were genotyped in 113 patients and 146 healthy controls. To assess their association with the disease risk, we compared the genotypic and allelic frequencies between the study groups. We also explored their possible influence on certain clinical features (age at onset, type, disability status and severity score) and with vitamin D3 serum level (DSL) by comparing mean values of these variables between the different genotypes. Results: No statistically significant differences in the distribution of both SNPs were found between patients and controls. A trend has emerged concerning the minor G allele of rs703842 which appears to have a protective effect on developing MS, but this result remained slightly below significance. Also, the two polymorphisms had no impact on the clinical features tested and the DSL. Conclusion: There is no convincing evidence that rs2248359 and rs703842 are associated with MS risk, its clinical features or vitamin D level in Moroccans. Further larger investigations are needed to confirm these findings.

Review of the diagnosis and clinical features of multiple sclerosis in China

This review focused on the diagnosis and clinical features of multiple sclerosis(MS) in China. We have identified the published researching information from 1976 to 2008 in China. The key issues related to the diagnosis and clinical features of MS in China were summarized. The first patient with MS in China was reported in 1926 from Xiehe hospital. Case reports on MS has been increasing during recent decades. Almost all the patients with MS were confirmed by the McDonald criteria(1977) before1984. After the...

基于机器学习的农田土壤抗剪强度参数检测方法研究

土壤抗剪强度参数包括粘聚力和内摩擦角,是评价土壤侵蚀敏感性和反映耕层耕作性能的重要指标。为实现农田土壤抗剪切强度参数的快速检测,提出了一种基于机器学习的土壤抗剪切强度参数检测方法。以STM32单片机为核心处理器,采用圆锥杆、滚珠丝杆滑台、三角支架等构建土壤数据采集装置,利用DYMH-103柱式压力传感器和FlexiForce薄膜传感器分别检测圆锥杆贯入土壤的锥尖阻力和锥侧压力,采用CSF11土壤水分传感器获取土壤含水率信息,通过多传感器数据特征向量提取构建建模数据集。数据集相关性分析结果表明:土壤抗剪强度参数与锥尖阻力、锥侧压力和土壤含水率之间具有明显相关性。利用蒙特卡罗交叉验证(Monte Carlo Cross Validation,MCCV)剔除了数据集中的4个异常样本;同时,提出了一种ELM-PLSR组合建模算法,以决定系数R^(2)和RPD为评价指标,对比评估了ELM、PLSR和ELM-PLSR 3种不同机器学习模型,结果表明:ELM-PLSR模型预测性能优于ELM模型和PLSR模型;检测粘聚力时,对应的R^(2)、RPD分别为0.919和3.475;检测内摩擦角时,对应的R 2和RPD分别为0.910和3.304。研究结果可为土壤抗剪强度参数快速测量提供参考。