The aim of this study was to evaluate the diagnostic value of artificial intelligence algorithm combined with ultrasound endoscopy in early esophageal cancer and precancerous lesions by comparing the examination of conventional endoscopy and artificial intelligence algorithm combined with ultrasound endoscopy, and by comparing the real-time diagnosis of endoscopy and the ultrasonic image characteristics of artificial intelligence algorithm combined with endoscopic detection and pathological results. 120 cases were selected. According to the inclusion and exclusion criteria, 80 patients who met the criteria were selected and randomly divided into two groups: endoscopic examination combined with ultrasound imaging based on intelligent algorithm processing (cascade region-convolutional neural network (Cascade RCNN) model algorithm group) and simple use of endoscopy group (control group). This study shows that the ultrasonic image of artificial intelligence algorithm is effective, and the detection performance is better than that of endoscopic detection. The results are close to the gold standard of doctor recognition, and the detection time is greatly shortened, and the recognition time is shortened by 71 frames per second. Compared with the traditional convolutional neural network (CNN) algorithm, the accuracy and recall of image analysis and segmentation using feature pyramid network are increased. The detection rates of CNN model, Cascade RCNN model, and endoscopic detection alone in early esophageal cancer and precancerous lesions are 56.3% (45/80), 88.8% (71/80), and 44.1% (35/80), respectively. The detection rate of Cascade RCNN model and CNN model was higher than that of endoscopy alone, and the difference was statistically significant ( P < 0.05 ). The sensitivity, specificity, positive predictive value, and negative predictive value of Cascade RCNN model were higher than those of CNN model, which was close to the gold standard for physician identification. This provided a reference basis for endoscopic ultrasound identification of early upper gastrointestinal cancer or other gastrointestinal cancers.

To suppress noise in signals, a denoising method called AIC–SVD is proposed on the basis of the singular value decomposition (SVD) and the Akaike information criterion (AIC). First, the Hankel matrix is chosen as the trajectory matrix of the signals, and its optimal number of rows and columns is selected according to the maximum energy of the singular values. On the basis of the improved AIC, the valid order of the optimal matrix is determined for the vibration signals mixed with Gaussian white noise and colored noise. Subsequently, the denoised signals are reconstructed by inverse operation of SVD and the averaging method. To verify the effectiveness of AIC–SVD, it is compared with wavelet threshold denoising (WTD) and empirical mode decomposition with Savitzky–Golay filter (EMD–SG). Furthermore, a comprehensive indicator of denoising (CID) is introduced to describe the denoising performance. The results show that the denoising effect of AIC–SVD is significantly better than those of WTD and EMD–SG. On applying AIC–SVD to the micro-vibration signals of reaction wheels, the weak harmonic parameters can be successfully extracted during pre-processing. The proposed method is self-adaptable and robust while avoiding the occurrence of over-denoising.

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Current researches of incremental classification learning algorithms mainly focus on learning from data in a stationary environment. The incremental learning in a non-stationary environment (NSE), where the underlying data probability distribution changes over time, however, has received much less attentions despite the abundant real applications have generated the long-term and cumulative big data in NSE. Thus, the incremental learning in NSE has gradually received extensive attentions. Nevertheless, the popular incremental classification learning algorithms currently for NSE such as SEA and DWM generally place strict restrictions on the changes. These algorithms can only deal with gradual drift and noncyclical and no new category situations. Therefore, it is highly necessary to develop a novel efficient incremental classification learning algorithm for the gradually cumulative big data in complex NSE. The recently proposed Learn++.NSE algorithm is an important research achievement in this field. However, the vote weight of each base-classifier of the Learn++.NSE depends on its whole error rates in the environments experienced. Therefore, the classification learning efficiency of the Learn++.NSE should be further improved. A novel fast Learn++.NSE algorithm based on weighted moving average (WMA-Learn++.NSE) is presented in this paper, which computes the weighted average of error rates using the sliding window technology to optimize the weight calculation. By only using the recent classification error rates of each base-classifier inside the sliding window to calculate the vote weight, the WMA-Learn++.NSE accelerates the compute of vote weight and improves the efficiency of classification learning. The verification experiments and performance analyses on both synthetic and real data set are presented in this paper. The experimental results show that the WMA-Learn++.NSE can achieve a higher execution efficiency compared to the Learn++.NSE in getting the equivalent classification correct rate.

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