A simple and available measurement of onco-exosome dsDNA to protein ratio as a potential tumor marker

Sun, Yifan; Li, Miao; Zhang, Xiaoshan; Xu, Dongjie; Wu, Jie; Gu, Xinrui; Khan, Adeel; Shen, Han; Li, Zhiyang

doi:10.21203/rs.3.rs-2484220/v1

Cited by 1 publication

(1 citation statement)

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“…As a result, a larger model file size may reduce the inference time while using the model on a smartphone or a weaker device. If the model were to be integrated into a mobile application, the model would have to be shipped together with the application, which takes up storage space [72]. As shown in Figure 6, the file size of the MobileNet model was the smallest, as it was made to develop mobile-friendly models whereby file size and architectural complexity were of utmost concern.…”

Section: Resultsmentioning

confidence: 99%

Green Space Quality Analysis Using Machine Learning Approaches

Rustamov

Zaki

2023

Sustainability

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Green space is any green infrastructure consisting of vegetation. Green space is linked with improving mental and physical health, providing opportunities for social interactions and physical activities, and aiding the environment. The quality of green space refers to the condition of the green space. Past machine learning-based studies have emphasized that littering, lack of maintenance, and dirtiness negatively impact the perceived quality of green space. These methods assess green spaces and their qualities without considering the human perception of green spaces. Domain-based methods, on the other hand, are labour-intensive, time-consuming, and challenging to apply to large-scale areas. This research proposes to build, evaluate, and deploy a machine learning methodology for assessing the quality of green space at a human-perception level using transfer learning on pre-trained models. The results indicated that the developed models achieved high scores across six performance metrics: accuracy, precision, recall, F1-score, Cohen’s Kappa, and Average ROC-AUC. Moreover, the models were evaluated for their file size and inference time to ensure practical implementation and usage. The research also implemented Grad-CAM as means of evaluating the learning performance of the models using heat maps. The best-performing model, ResNet50, achieved 98.98% accuracy, 98.98% precision, 98.98% recall, 99.00% F1-score, a Cohen’s Kappa score of 0.98, and an Average ROC-AUC of 1.00. The ResNet50 model has a relatively moderate file size and was the second quickest to predict. Grad-CAM visualizations show that ResNet50 can precisely identify areas most important for its learning. Finally, the ResNet50 model was deployed on the Streamlit cloud-based platform as an interactive web application.

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Section: Resultsmentioning

confidence: 99%