P.019 A machine learning approach to asymmetric burst suppression and refractory status epilepticus outcome

Background: Treatment of refractory status epilepticus (RSE) is often titrated to achieve EEG burst suppression. However, optimal burst suppression characteristics are largely unknown. We used an unsupervised machine learning algorithm to predict RSE outcome based on the quantitative burst suppression ratio (QBSR). Methods: We conducted principal component analysis (PCA) as a linear combination of 22 QBSR features from non-anoxic adult RSE patients at the Winnipeg Health Sciences Centre. We also determined the most predictive components that significantly differed between survivors and non-survivors. Results: Using 135,765 QBSRs from 7 survivors and 10 non-survivors, PCA identified a predominantly non-survivor cluster of 8 patients (75% non-survivors). The first 2 PCA components comprised 75% data variance. The most important first component feature was skewness of QBSR distribution in the right or left hemisphere (0.52 each). The most important second component feature was third QBSR quantile of the left hemisphere (0.49). Only right hemispheric QBSR features significantly differed between groups: QBSR skewness for the first component (Benjamini-Hochberg adjusted p=0.038) and average QBSR for the second component (0.32, Benjamini-Hochberg adjusted p=0.046). Conclusions: Our pilot study shows that RSE patient survival may be impacted by QBSR, with differential hemispheric EEG burst suppression characteristics predicting poor RSE outcome.

Detecting Web Attacks Based on Clustering Algorithm and Multi-branch CNN

Abstract—This paper proposes and develops a web attack detection model that combines a clustering algorithm and a multi-branch convolutional neural network (CNN). The original feature set was clustered into clusters of similar features. Each cluster of similar features was generalized in a convolutional structure of a branch of the CNN. The component feature vectors are assembled into a synthetic feature vector and included in a fully connected layer for classification. Using K-fold cross-validation, the accuracy of the proposed method 98.8%, F1-score is 98.9% and the improvement rate of accuracy is 1.479%.Tóm tắt—Bài báo đề xuất và phát triển mô hình phát hiện tấn công Web dựa trên kết hợp thuật toán phân cụm và mạng nơ-ron tích chập (CNN) đa nhánh. Tập đặc trưng ban đầu được phân cụm thành các nhóm đặc trưng tương ứng. Mỗi nhóm đặc trưng được khái quát hoá trong một nhánh của mạng CNN đa nhánh để tạo thành một vector đặc trưng thành phần. Các vector đặc trưng thành phần được ghép lại thành một vector đặc trưng tổng hợp và đưa vào lớp liên kết đầy đủ để phân lớp. Sử dụng phương pháp kiểm thử chéo trên mô hình đề xuất, độ chính xác đạt 98,8%, F1-score đạt 98,8% và tỉ lệ cải tiến độ chính xác là 1,479%.

GINGIVAL FIBROMATOSIS WITH HEARING LOSS: A JONES SYNDROME CASE REPORT

Introduction: Hereditary Gingival Enlargement (HGF), a rare entity, is also known as familial elephantiasis, elephantiasis gingivae, diffuse fibromatosis. It is a benign, non-haemorrhagic fibrous enlargement of gingival tissue. It is frequently a component feature of many syndromes. Jones syndrome is one such syndrome, characterized by gingival overgrowth and progressive deafness. Case report: A 27-year-old male patient reported to the Department of Oral Medicine and Radiology with the chief complaint of swollen gums for the past 7 years. The patient also complained of bleeding gums on brushing. The medical history stated a concurrent gradual hearing loss 7 years back. An incisional biopsy was done to confirm the fibrous nature of the diagnosis. Conclusion: This reporting is of unique case that remained undiagnosed for almost a decade. It also emphasises the need of a multidisciplinary approach during diagnosis and treatment.

Clustering analysis of the distribution characteristics of unobserved economic regions in China——Based on multi-indicator panel data

The existence of unobserved economy is one of the important factors affecting GDP calculation. This paper uses the provincial panel data from 2010 to 2019 in China, and adopts the method of principal component feature extraction to carry out cluster analysis on the multi-indicator panel data. This method preserves the dynamic characteristics of the panel data, calculates the comprehensive score of each eigenvalue, and gives weight to the eigenvalue by using the entropy method, so as to optimize the clustering results representing the eight indicators of the unobserved economy. Through the analysis, it is found that the regional development of China’s unobserved economy is obviously different, and each type has different influencing factors. This result has important practical significance for different regions in China to formulate differentiated unobserved economic governance policies. This also helps to make better use of resources and develop an energy-saving economy.

Fault Location Method Based on SVM and Similarity Model Matching

To locate the fault location accurately and solve the problem quickly is the key to improve the power supply capacity of power grid. This paper presents a fault location method based on SVM fault branch selection algorithm and similarity matching. Firstly, an SVM-based fault branch filter classifier was constructed based on the positive sequence component feature matrix data of each monitoring point, which can accurately select the branch where the current fault is located. Then, based on the positive sequence voltage distribution characteristics, the Euclidean distance and Pearson correlation coefficient (PCC) are used to establish the similarity objective function of fault location. And then, the fault is accurately located by the objective function. Finally, the proposed method is validated by using an IEEE-14 node network. The results show that the proposed method is effective and accurate.

Image Identification Algorithm of Deep Compensation Transformation Matrix based on Main Component Feature Dimensionality Reduction

In order to reconstruct and identify three-dimensional (3D) images, an image identification algorithm based on a deep learning compensation transformation matrix of main component feature dimensionality reduction is proposed, including line matching with point matching as the base, 3D reconstruction of point and line integration, parallelization automatic differentiation applied to bundle adjustment, parallelization positive definite matrix system solution applied to bundle adjustment, and an improved classifier based on a deep compensation transformation matrix. Based on the INRIA database, the performance and reconstruction effect of the algorithm are verified. The accuracy rate and success rate are compared with L1APG, VTD, CT, MT, etc. The results show that random transformation and re-sampling of samples during training can improve the performance of the classifier prediction algorithm under the condition that the training time is short. The reconstructed image obtained by the algorithm described in this study has a low correlation with the original image, with high number of pixels change rate (NPCR) and unified average changing intensity (UACI) values and low peak signal to noise ratio (PSNR) values. Image reconstruction effect is better with image capacity advantage. Compared with other algorithms, the proposed algorithm has certain advantages in accuracy and success rate with stable performance and good robustness. Therefore, it can be concluded that image recognition based on the dimension reduction of principal component features provides good recognition effect, which is of guiding significance for research in the image recognition field.

Bayesian Discriminant Analysis of Yogurt Products Based on Raman Spectroscopy

Background The quality discrimination of dairy products is an important basis on which to achieve quality assurance. Objective Taking the discriminant analysis of brand yogurt products as an example, a new rapid discriminant method can be constructed. Method The first three principal components were selected as the pattern vectors of the samples. Then, at random, 75% of the samples were collected as a training set, and their mean values and covariance matrices were calculated to construct a Gauss Bayesian discriminant model. The remaining 25% of samples were employed as a test set, and the pattern vectors of each sample were input into the above model. Next, the posterior probability of each sample in relation to each category could be obtained. Results: The category corresponding to the maximum posterior probability as the brand classification of each sample was defined. Conclusions We constructed a Gauss Bayesian discriminant model to discriminate these different yogurt products after the principal component feature extraction of Raman properties. The results indicate the rationality and wide application prospects of this approach. Highlights A fast dairy product discriminant method based on Gauss Bayesian model and Raman spectroscopy was established.