RESEARCH INFORMATION SYSTEM OF IMAGE RECOGNITION

Data classification in presence of noise can lead to much worse results than expected for pure patterns. In paper was investigated problem of the research is the process of user recognition and identification in the video sequence. The main contributions presented in this paper are experimental examination of influence of different types of noise and to the increase the security of an IT company by developing a computer system for recognizing and identifying users in the video sequence. Based on the study of methods and algorithms for finding faces in images, the Viola-Jones method, wavelet transform and the method of principal components were chosen. These methods are among the best in terms of the ratio of recognition efficiency and work speed. However, the training of classifiers is very slow, but the face search results are very fast.

Citrus Huanglongbing Recognition Algorithm Based on CKMOPSO

In view of the similarity of characteristics between the features of the disease images and the large dimension, and the features correlation of the disease images, this will lead to the generation of feature redundancy, and will introduce a serious impact on the recognition efficiency and accuracy of citrus Huanglongbing. In addition, they have the defects of high cost of detection algorithms and low detection accuracy. This will occur in the image cutting feature extraction stage, so this paper uses the citrus Huanglongbing recognition algorithm based on kriging model simplex crossover local based search Multi-objective particle swarm optimization algorithm(CKMOPSO) selects feature vectors with strong classification capabilities from the original disease image features, experimental results show that this is an effective recognition method.

Bayesian Recognition Procedures with Independent Signs of Inflammatory Processes in Gliomas, Metastases and Meningiomas by Indicators of Erythrocyte Sedimentation Rate

Introduction. The article discusses the application of Bayesian recognition procedures with independent signs in relation to the data of the modified erythrocyte sedimentation rate, which were taken from patients with gliomas, metastases, meningiomas, craniocerebral concussion and from a group of healthy people. Purpose of the article. Improving the efficiency of recognition of inflammatory processes in gliomas, metastases and meningiomas by indicators of erythrocyte sedimentation rate using optimal recognition procedures with independent signs. Results. In previous articles by the authors, an attempt was made to recognize inflammatory processes by indicators of the modified erythrocyte sedimentation rate caused by brain cancer using Bayesian recognition procedures based on a single substance. In this work, a new model was built using several independent signs (different substances) at once. The results obtained on the basis of the new model significantly increased their efficiency in relation to the models that were used earlier. Such an increase in all comparisons ranged from 3 to 12 %, and up to almost 94 %. If earlier it was possible to recognize only combinations of diagnoses in which there were no more than two diagnoses, then in this work for the first time it was possible to recognize three diagnoses at once. At the same time, the recognition efficiency became slightly more than 70 %. An attempt was also made to recognize more than three diagnoses, but the new model did not give significant results, slightly exceeding 50 % when recognizing four diagnoses at once. Conclusions. Thanks to the use of Bayesian recognition procedures with independent signs, it was possible to significantly increase the recognition of inflammatory processes caused by brain cancer. The modified erythrocyte sedimentation rate, which is an auxiliary tool in the diagnosis of gliomas, allows one or another pathology to be determined in the preoperative period, since the pathology is finally determined only when studying a surgically removed tumor. In the postoperative period, such a modification is an indicator of repeated recurrence of gliomas. It was also possible to significantly increase the recognition of inflammatory processes caused by non-oncological disease (traumatic brain injury) in relation to oncological processes in gliomas, metastases and meningiomas. Keywords: Bayesian recognition procedure, independent signs, gliomas, metastases, meningiomas, modified erythrocyte sedimentation rate, complex parameter.

AN INTELLIGENT CBMIR SYSTEM FOR DETECTION AND LOCALIZATION OF LUNG DISEASES

The diagnosis of lung diseases is a complicated and time-consuming task for radiologists. Often radiologists struggle with accurately diagnosing lung diseases, They use Commonly CT imaging signs (CISs) which common appear in CT lung nodules in the diagnosis of lung diseases. Computer-aided diagnosis systems (CAD) can automatically diagnose and detect these signs by analyzing CT scans, which will reduce radiologists workload. The diagnosis and recognition efficiency and accuracy can be improved by using content-based medical image retrieval (CBMIR). This paper proposes a new intelligent CBMIR method to retrieve CISs helping in diagnosing and recognize lung diseases by using deep Convolutional Neural Network (CNN). Fine-tuned YOLOv4 (You Only Look Once) object detector model are proposed to fast detect and efficiently localize signs in real-time. The proposed CBMIR system can be applied as a useful and accurate medical instrument for diagnostics. The experimental results show an average detection accuracy of CT signs lung diseases as high as 92% and a mean average precision (MAP) of 0.92 is achieved using the proposed technique. Also, it takes only 0.1 milliseconds for the retrieval process. The proposed system presents high improvement as compared to the other system. It achieved better precision of retrieval results and the fastest run of the retrieval time.

Stroke recognition efficiency by emergency medical dispatchers

Background: Delaying treatment for an acute stroke can typically lead to further severity and disastrous consequences. The majority of patients, however, did not arrive at the hospital in time to get thrombolysis. While the accuracy of stroke diagnosis by emergency medical dispatchers (EMDs) remains uncertain. The focus of this research was to assess the accuracy of the stroke detection (face-arm-speech-time tool) (FAST) utilized by EMDs for the management of acute stroke was as well as how it affected patients' time from door to CT and door-to-needle time.Methods: From 1 January 2020 to 31 December 2020, this research was performed retrospectively. We included all patients over the age of 18 who had an acute stroke identified by incident dispatch code 18. Data was gathered using pre-hospital forms and hospital records. The CT scan findings were used to predict the overall diagnosis.Results: Overall 244 patients, only 143 likely cases (62.4%) were identified by EMDs out of 181 final stroke diagnosis. Conversely, the specificity was 44.4 percent and the sensitivity was 63.5 percent. Only 19 patients (10.5%) obtained a CT scan within 60 minutes, per the data from acute stroke patients, whereas only 40 patients with acute ischemic stroke acquired r-TPA (22.1%).Conclusions: It remains undecided if the failure to use FAST, the inability to recognize positive FAST indicators, or the failure to report FAST findings involves issues with EMDs assessments.

Design and Implementation of Human Motion Recognition Information Processing System Based on LSTM Recurrent Neural Network Algorithm

With the comprehensive development of national fitness, men, women, young, and old in China have joined the ranks of fitness. In order to increase the understanding of human movement, many researches have designed a lot of software or hardware to realize the analysis of human movement state. However, the recognition efficiency of various systems or platforms is not high, and the reduction ability is poor, so the recognition information processing system based on LSTM recurrent neural network under deep learning is proposed to collect and recognize human motion data. The system realizes the collection, processing, recognition, storage, and display of human motion data by constructing a three-layer human motion recognition information processing system and introduces LSTM recurrent neural network to optimize the recognition efficiency of the system, simplify the recognition process, and reduce the data missing rate caused by dimension reduction. Finally, we use the known dataset to train the model and analyze the performance and application effect of the system through the actual motion state. The final results show that the performance of LSTM recurrent neural network is better than the traditional algorithm, the accuracy can reach 0.980, and the confusion matrix results show that the recognition of human motion by the system can reach 85 points to the greatest extent. The test shows that the system can recognize and process the human movement data well, which has great application significance for future physical education and daily physical exercise.

Recognition Method of Abnormal Power Consumption State of Power Users Based on Strategy Gradient Algorithm

Because the traditional method ignores the problem of power load data preprocessing, the accuracy of the recognition result of the power consumption status is not high, the recognition efficiency is not high, and the recognition effect is not good. For this reason, a method for identifying the abnormal power consumption status of power users based on the strategy gradient algorithm is proposed. The preprocessing of power load data mainly includes the completion of missing data and the feature extraction of power load data. Based on the results of the preprocessing, the abnormal increase in user power consumption is detected. Finally, the strategy gradient algorithm is used for initial training and training process testing to complete the identification of the abnormal state of power users. The experimental results show that the accuracy of the power status recognition result of the proposed method is higher, and the recognition time is always less than 2.0 s, indicating that the recognition effect of the method is better.