KNN-Based Machine Learning Classifier Used on Deep Learned Spatial Motion Features for Human Action Recognition

Noninvasive prediction of lymph node metastasis in pancreatic cancer using an ultrasound-based clinicoradiomics machine learning model

OBJECTIVES

This study was designed to explore and validate the value of different machine learning models based on ultrasound image-omics features in the preoperative diagnosis of lymph node metastasis in pancreatic cancer (PC).

METHODS

This research involved 189 individuals diagnosed with PC confirmed by surgical pathology (training cohort: n = 151; test cohort: n = 38), including 50 cases of lymph node metastasis. Image-omics features were extracted from ultrasound images. After dimensionality reduction and screening, eight machine learning algorithms, including logistic regression (LR), support vector machine (SVM), K-nearest neighbors (KNN), random forest (RF), extra trees (ET), extreme gradient boosting (XGBoost), light gradient boosting machine (LightGBM), and multilayer perceptron (MLP), were used to establish image-omics models to predict lymph node metastasis in PC. The best omics prediction model was selected through ROC curve analysis. Machine learning models were used to analyze clinical features and determine variables to establish a clinical model. A combined model was constructed by combining ultrasound image-omics and clinical features. Decision curve analysis (DCA) and a nomogram were used to evaluate the clinical application value of the model.

RESULTS

A total of 1561 image-omics features were extracted from ultrasound images. 15 valuable image-omics features were determined by regularization, dimension reduction, and algorithm selection. In the image-omics model, the LR model showed higher prediction efficiency and robustness, with an area under the ROC curve (AUC) of 0.773 in the training set and an AUC of 0.850 in the test set. The clinical model constructed by the boundary of lesions in ultrasound images and the clinical feature CA199 (AUC = 0.875). The combined model had the best prediction performance, with an AUC of 0.872 in the training set and 0.918 in the test set. The combined model showed better clinical benefit according to DCA, and the nomogram score provided clinical prediction solutions.

CONCLUSION

The combined model established with clinical features has good diagnostic ability and can be used to predict lymph node metastasis in patients with PC. It is expected to provide an effective noninvasive method for clinical decision-making, thereby improving the diagnosis and treatment of PC.

Design and Development of an Imitation Detection System for Human Action Recognition Using Deep Learning

Human action recognition (HAR) is a rapidly growing field with numerous applications in various domains. HAR involves the development of algorithms and techniques to automatically identify and classify human actions from video data. Accurate recognition of human actions has significant implications in fields such as surveillance and sports analysis and in the health care domain. This paper presents a study on the design and development of an imitation detection system using an HAR algorithm based on deep learning. This study explores the use of deep learning models, such as a single-frame convolutional neural network (CNN) and pretrained VGG-16, for the accurate classification of human actions. The proposed models were evaluated using a benchmark dataset, KTH. The performance of these models was compared with that of classical classifiers, including K-Nearest Neighbors, Support Vector Machine, and Random Forest. The results showed that the VGG-16 model achieved higher accuracy than the single-frame CNN, with a 98% accuracy rate.

Control Method of Cold and Hot Shock Test of Sensors in Medium

In order to meet the latest requirements for sensor quality test in the industry, the sample sensor needs to be placed in the medium for the cold and hot shock test. However, the existing environmental test chamber cannot effectively control the temperature of the sample in the medium. This paper designs a control method based on the support vector machine (SVM) classification algorithm and K-means clustering combined with neural network correction. When testing sensors in a medium, the clustering SVM classification algorithm is used to distribute the control voltage corresponding to temperature conditions. At the same time, the neural network is used to constantly correct the temperature to reduce overshoot during the temperature-holding phase. Eventually, overheating or overcooling of the basket space indirectly controls the rapid rise or decrease in the temperature of the sensor in the medium. The test results show that this method can effectively control the temperature of the sensor in the medium to reach the target temperature within 15 min and stabilize when the target temperature is between 145 °C and -40 °C. The steady-state error is less than 0.31 °C in the high-temperature area and less than 0.39 °C in the low-temperature area, which well solves the dilemma of the current cold and hot shock test.