Research on the Improvement of Big Data Feature Investment Analysis Algorithm for Abnormal Trading in the Financial Securities Market

Traditional investment analysis algorithms usually only analyze the similarity between financial time series and financial data, which leads to inaccurate and inefficient analysis of investment characteristics. In addition, the trading volume of financial securities market is huge, the amount of investment data is also very large, and the detection of abnormal transactions is difficult. The aim of feature extraction is to obtain mathematical features that can be recognized by machine. Different from the traditional methods, this paper studies and improves the big data investment analysis algorithm of abnormal transactions in financial securities market. After processing the captured trading data of financial securities market, the big data feature of abnormal trading is extracted. Combined with the abnormal trading and the financial securities market, the investment strategy is determined. The optimization objective function is set and the genetic algorithm is used to improve the investment analysis algorithm. The simulation experiment verifies the improved investment analysis algorithm, and the average Accuracy of investment analysis is increased by at least 11.24%, the ROI is significantly improved, and the efficiency is higher, which indicates that the proposed algorithm has ideal application performance.

Electroencephalogram Image under Complex Domain Analysis Algorithm to Analyze Neurological Status Epilepticus and Poor Prognostic Factors of Children

This study was to adopt the electroencephalogram (EEG) image to analyze the neurological status epilepticus (SE) and adverse prognostic factors of children using the complex domain analysis algorithm, aiming at providing a theoretical basis for the clinical treatment of children with SE. 24-hour EEG was adopted to diagnose 197 children with SE. The patients were divided into an experimental group (100 cases) and a control group (97 cases) using a random number table method. The EEGs of children in the experimental group were analyzed using the compound domain analysis algorithm, and those in the control group were diagnosed by a professional doctor. The indicators of children in two groups were compared to analyze the effect of the compound domain analysis algorithm in diagnosing diseases through EEG. The prognostic scores of 197 children were scored one month after they were diagnosed, treated, and discharged, and the adverse prognostic factors were analyzed. As a result, EEG can accurately and effectively analyze the brain diseases in children. The sensitivity and specificity of the complex domain analysis algorithm for the detection of epilepsy EEG were much higher than those of the EEG automatic detection algorithm based on time-domain waveform similarity and the EEG automatic detection algorithm based on convolutional neural network (CNN), and the average running time was opposite, showing obvious difference ( P < 0.05 ).The average accuracy, sensitivity, and specificity of children in the experimental group were 96.11%, 97.10%, and 95.19%, respectively; and those in the control group were 88.83%, 90.14%, and 87.82%, respectively, so there was an obvious difference in accuracy between two groups ( P < 0.05 ). There were 57 cases with good prognosis and 140 cases with poor prognosis; there were 70 males with good prognosis and 19 poor prognoses and 69 women with good prognosis and 19 poor prognoses. Among 121 patients with infections, 84 cases had good prognosis and 37 cases had poor prognosis; 39 cases of irregular medication had good prognosis in 31 cases and a poor prognosis in 8 cases; and 37 cases had no obvious cause, including 25 cases with good prognosis and 12 cases with poor prognosis. In short, the EEG diagnosis and treatment effect of the compound domain analysis algorithm were better than those of professional doctors; the gender of the patient had no effect on the poor prognosis, and the pathogenic factors had an impact on the poor prognosis of the patient.