Background. Deep and organ space surgical site infections (SSI) require more intensive treatment, may result in more severe clinical disease and may have different risk factors when compared to superficial SSIs. Machine learning (ML) algorithms provide the opportunity to analyze multiple factors to predict of the type and time of development of SSI. Therefore, we developed a ML model to predict type and postoperative week of SSI. Methodology. A case-control study was conducted among patients who developed a SSI after undergoing general surgery procedures at a tertiary care hospital between 2019 to 2020. Patients were followed for 30 days. Six ML algorithms were trained as predictors of type of infection (superficial vs deep/organ space) and time of infection, and tested using area under the receiver operating characteristic curve (AUC-ROC). Results. Data for 113 patients with SSIs was available. Of these 62 (54.8%) had superficial and 51 had (45.2%) deep/organ space infections. Compared with other ML algorithms, the XG boost univariate model had highest AUC-ROC (.84) for prediction of type of SSI and Stochastic gradient boosting univariate, logistic regression univariate, XG boost univariate, and random forest classification univariate model had the highest AUC-ROC (.74) for prediction of week of infection. Conclusions. ML models offer reasonable accuracy in prediction of superficial vs deep SSI and time of developing infection. Follow-up duration and allocation of treatment strategies can be informed by ML predictions.
End-to-end learning for autonomous driving uses a convolutional neural network (CNN) to predict the steering angle from a raw image input. Most of the solutions available for end-to-end autonomous driving are computationally too expensive, which increases the inference of autonomous driving in real time. Therefore, in this paper, CNN architecture has been trained which is lightweight and achieves comparable results to Nvidia’s PilotNet. The data used to train and evaluate the network is collected from the Car Learning to Act (CARLA) simulator. To evaluate the proposed architecture, the MSE (mean squared error) is used as the performance metric. Results of the experiment shows that the proposed model is 4x lighter than Nvidia’s PilotNet in term of parameters but still attains comparable results to PilotNet. The proposed model has achieved 5.1 × 10 − 4 MSE on testing data while PilotNet MSE was 4.7 × 10 − 4 .
Weed management is becoming increasingly important for sustainable crop production. Weeds cause an average yield loss of 11.5% billion in Pakistan, which is more than PKR 65 billion per year. A real-time laser weeding robot can increase the crop’s yield by efficiently removing weeds. Therefore, it helps decrease the environmental risks associated with traditional weed management approaches. However, to work efficiently and accurately, the weeding robot must have a robust weed detection mechanism to avoid physical damage to the targeted crops. This work focuses on developing a lightweight weed detection mechanism to assist laser weeding robots. The weed images were collected from six different agriculture farms in Pakistan. The dataset consisted of 9000 images of three crops: okra, bitter gourd, sponge gourd, and four weed species (horseweed, herb paris, grasses, and small weeds). We chose a single-shot object detection model, YOLO5. The selected model achieved a mAP of 0.88@IOU 0.5, indicating that the model predicted a large number of true positive (TP) with much less prediction of false positive (FP) and false negative (FN). While SSD-ResNet50 achieved a mAP of 0.53@IOU 0.5, the model predicted fewer TP with significant outcomes as FP or FN. The superior performance of the YOLOv5 model made it suitable for detecting and classifying weeds and crops within fields. Furthermore, the model was ported to an Nvidia Xavier AGX standalone device to make it a high-performance and low-power computation detection system. The model achieved an FPS rate of 27. Therefore, it is highly compatible with the laser weeding robot, which takes approximately 22.04 h at a velocity of 0.25 feet per second to remove weeds from a one-acre plot.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.