Agent-based models (ABM) and differential equations (DE) are two commonly used methods for immune system simulation. However, it is difficult for ABM to estimate key parameters of the model by incorporating experimental data, whereas the differential equation model is incapable of describing the complicated immune system in detail. To overcome these problems, we developed an integrated ABM regression model (IABMR). It can combine the advantages of ABM and DE by employing ABM to mimic the multi-scale immune system with various phenotypes and types of cells as well as using the input and output of ABM to build up the Loess regression for key parameter estimation. Next, we employed the greedy algorithm to estimate the key parameters of the ABM with respect to the same experimental data set and used ABM to describe a 3D immune system similar to previous studies that employed the DE model. These results indicate that IABMR not only has the potential to simulate the immune system at various scales, phenotypes and cell types, but can also accurately infer the key parameters like DE model. Therefore, this study innovatively developed a complex system development mechanism that could simulate the complicated immune system in detail like ABM and validate the reliability and efficiency of model like DE by fitting the experimental data.
The challenges of the aging population is becoming more and more prominent worldwide. Among them, in the face of the elderly fall phenomenon, human fall detection technology research and development has practical application value. Because of a large number of network parameters in the field of fall detection and the limited computing power of embedded devices, which makes it difficult to run on the embedded platform, this paper proposes an OT-YOLOV3 (OpenCV+Tiny-YOLOV3) fall detection method. In this method, Gaussian processing and other operations are used to preprocess the fallen image to avoid the influence of the angle change of the image on the recognition result. Then, the feature extraction network in Tiny-YOLOV3 was replaced by the MobileNet network to increase the number of network layers and reduce the number of parameters and calculations in the model. At the same time, the multi-scale prediction method was used to improve detection accuracy. Experimental results show that the accuracy of the proposed model is 10% higher than that of the YOLOV3 (You Only Look Once Version three) model, 4% higher than that of the Tiny-YOLOV3 model, 3% higher than that of the YOLOV3 model, 3% higher than that of Tiny-YOLOV3 model, and the model size is only 45% of that of YOLOV3 model and 65% of Tiny-YOLOV3. Compared with YOLOV3 and Tiny-YOLOV3 processing methods, the drop recognition effect is significantly improved and the model memory is reduced, which meets the requirements of real-time and efficient detection for embedded devices.
With the development of scientific research techniques, drug discovery has shifted from the serendipitous approach of the past to more targeted models based on an understanding of the underlying biological mechanisms of disease. However, there are hundreds or more of mechanism of action (MoA) data in the known drugs, which makes this process faced with complicated multi-label classification of text data. Traditional multi-label text classification algorithms will increase the complexity of the model and reduce the accuracy as the number of labels increases. Although deep learning algorithms can solve the problem of model complexity, they are currently only suitable for processing image format data. To overcome these problems, this study proposes a multi-label classification method based on Bayesian deep learning, which can convert non-image data format into image data, making it suitable for Convolutional neural network algorithm requirements. Then in the PyTorch environment, the Bayesian deep learning algorithm and the EfficientNet convolutional neural network are perfectly combined using the BLiTZ library to construct the Bayesian convolutional neural network model which named BCNNM. Not only improves the classification efficiency, this method also solves the problem of imbalanced classification of multi-label data, and fully considers the uncertainty in the neural network. In the process of drug development, this method has important practical significance for processing the multi-label classification of MoA data.
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