Deep Learning-Based Real-Time AI Virtual Mouse System Using Computer Vision to Avoid COVID-19 Spread

Shriram, S.; Nagaraj, B.; Jain, Jaya; Shankar, S.; Ajay, P.

doi:10.1155/2021/8133076

Cited by 126 publications

(83 citation statements)

References 8 publications

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“…Distant metastasis (M) is divided into three criteria: Mx, M0, and M1. Mx means no metastasis: metastasis, M0 means 3 metastasis values, and M1 means >12 metastasis values [ 19 ].…”

Section: Methodsmentioning

confidence: 99%

Diagnostic Value of Spiral CT and Magnetic Resonance Imaging Scanning in Gastric Cancer and Precancerous Lesions

2022

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In order to explore the diagnostic value of spiral CT and magnetic resonance imaging scanning in gastric cancer and precancerous lesions, the author selected 56 gastric cancer patients treated in a medical center (group) as the experimental subjects, and all patients underwent MRI, multislice spiral CT scan, and enhanced CT scan two weeks before surgery; at the same time, the gastric cancer staging results of patients were diagnosed according to surgical pathology. All patients were examined under the condition of knowledge and performed breath-holding exercise before examination. The status, location, and extent of tumor lesions were evaluated. In comparison of T staging of gastric cancer patients with magnetic resonance scanning imaging and postoperative pathological examination results, among them, T staging of 3 patients did not match the results of pathological examination, the accuracy rates of T staging on magnetic resonance scanning imaging were T 1 = 94.7 % , T 2 = 87.6 % , T 3 = 91.2 % , and T 4 = 94.7 % , and the total accuracy was 92.1%. Comparison of helical CT scan imaging and postoperative pathological examination results is as follows. Among them, T staging of 8 patients did not match the results of pathological examination, 3 patients were ulcerative, 5 were protuberance, the accuracy rates of T staging in spiral CT scan imaging were T 1 = 90.8 % , T 2 = 82.2 % , T 3 = 76.9 % , and T 4 = 87.6 % , and the total accuracy was 84.5%. The total accuracy rate of imaging N staging was 77.5%, and the total accuracy rate of spiral CT scanning imaging N staging was 80.8%. The accuracy rates of M0 and M1 staging in magnetic resonance imaging were 100.0% and 96.5%, respectively, and the accuracy rates of M0 and M1 staging in spiral CT scan imaging were 96.5% and 96.5%, respectively. The accuracy rate of magnetic resonance imaging for the diagnosis of gastric cancer N staging was 77.4%, and the reason for the low accuracy rate was mainly because the enhancement effect was not obvious due to insufficient enhancement. The accuracy rate of spiral CT in diagnosing N staging of gastric cancer was 80.9%, which was mainly due to the small diameter of the middle celiac artery lymph nodes. Magnetic resonance and spiral CT scans have high value in the early diagnosis and staging of gastric cancer.

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“…Distant metastasis (M) is divided into three criteria: Mx, M0, and M1. Mx means no metastasis: metastasis, M0 means 3 metastasis values, and M1 means >12 metastasis values [ 19 ].…”

Section: Methodsmentioning

confidence: 99%

Diagnostic Value of Spiral CT and Magnetic Resonance Imaging Scanning in Gastric Cancer and Precancerous Lesions

2022

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“…e essence of an image recognition system is that the computer imitates the process of human recognizing and recognizing objects. Figure 7 shows the block diagram of the image recognition system of stored grain pests [22,23]. It mainly consists of three parts: image preprocessing, feature extraction, and recognition and classification.…”

Section: Design Of the Stored Grain Pest Identificationmentioning

confidence: 99%

Intelligent Storage Data Classification System Based on the BP Neural Network

Li¹

2022

Journal of Control Science and Engineering

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In order to solve the problem of multifeature recognition and classification of many kinds of pests, this study puts forward a method of pest feature classification using the BP neural network. Through the preprocessing of stored grain pest images, five characteristic parameters are obtained and optimized and input into the BP network for training. The experimental results show that sample 3 of flat grain thief and sample 4 of bark beetle are not well recognized. Because these two kinds of pests have small bodies and thin legs, some detailed features are eliminated after image processing, resulting in a low recognition rate. But the overall recognition rate can reach 95%. Conclusion. The experiment has obtained good recognition results. This method is accurate and effective for the classification and recognition of stored grain pests and provides a scientific basis for the scientific decision-making of controlling stored grain pests.

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“…Air pollution is affected by pollution sources, meteorological conditions, and other factors. Significant changes in pollution sources and meteorological conditions will lead to changes in the sources of receptor pollutants, resulting in changes in the receptor composition spectrum at the same sampling point and even the sources of pollutants at the same sampling point at different times of the same pollution period [23].…”

Section: Classification Analysis To Reduce Receptor Datamentioning

confidence: 99%

Human Health Risk Prediction Method of Regional Atmospheric Environmental Pollution Sources Based on PMF and PCA Analysis under Artificial Intelligence Cloud Model

Zhang

Sun

Liu

et al. 2022

International Journal of Analytical Chemistry

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In order to solve the problem that atmospheric particulate matter has become the primary pollutant with serious harm and complex sources in recent years, this paper proposes an accurate identification method of pollution sources based on a receptor model to obtain the contribution rate of each pollution source category. This method takes the 75-day measured environmental receptor data of an area under the artificial intelligence cloud model as the basic data, uses the normrnd () function to expand the receptor data, and uses the positive definite matrix factor analysis (PMF) and principal component analysis (PCA) models to verify the rationality of the data expansion. The results are as follows: the number of extended simulated receptor component spectra has a certain effect on the PCA analysis results, but the effect is smaller than the extended range. All relative errors are less than 14%, and the relative error is the smallest when the six simulated receptor component spectra are expanded, that is, the PCA analysis results of the expanded data are most consistent with the measured data; the number of expanded simulated receptor component spectra has a certain influence on the PMF analysis results. But the relative error is less than 40%. When extending the spectrum of six simulated receptor components, the relative error is the smallest, that is, the PMF analysis results of the extended data are most consistent with the measured data. It is proven that this method provides a more direct basis for the targeted treatment of pollution sources that are more harmful to human health.

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Deep Learning-Based Real-Time AI Virtual Mouse System Using Computer Vision to Avoid COVID-19 Spread

Cited by 126 publications

References 8 publications

Diagnostic Value of Spiral CT and Magnetic Resonance Imaging Scanning in Gastric Cancer and Precancerous Lesions

Diagnostic Value of Spiral CT and Magnetic Resonance Imaging Scanning in Gastric Cancer and Precancerous Lesions

Intelligent Storage Data Classification System Based on the BP Neural Network

Human Health Risk Prediction Method of Regional Atmospheric Environmental Pollution Sources Based on PMF and PCA Analysis under Artificial Intelligence Cloud Model

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