In any classification tasks, the metrics are required to evaluate the DL models. It is worth mentioning that various metrics can be used in different fields of studies. It means that the metrics which are used in medical analysis are mostly different with other domains, such as cybersecurity o...
Other metrics that are more specific to deep learning applications are the number of model parameters (Guo, Wu, et al., 2021, Wang et al., 2022) and time to train models or run the detection (ER-3) (Cheansunan and Phunchongharn, 2019, Decker et al., 2020, Gu et al., 2021, ...
284 Accesses Explore all metrics Abstract In this research, authors give a literature analysis of the methods used to detect and anticipate security risks in software testing by using a number of deep learning models. The purpose of this study is to conduct a literature review on the use of ...
Evaluating a model is a critical step in developing an efficient deep-learning model. Following image pre-processing, training, and validation, the test images are input into the trained model for classification to evaluate its performance. There are various evaluation metrics, such as the confusion...
4.1. Evaluation Metrics The deep learning-based object detection methods were evaluated on our custom dataset. The analysis of the models was based on five metrics—detection accuracy (DA), precision (P), average precision (AP), mean average precision (mAP), and recall (R). These are present...
we present a unified theoretical view of relational machine learning models which can address these tasks. We provide fundamental definitions, compare existing model architectures and discuss performance metrics, datasets and evaluation protocols. In addition, we emphasize possible high impact applications an...
This paper aims to explore the application of deep learning in smart contract vulnerabilities detection. Smart contracts are an essential part of blockchain technology and are crucial for developing decentralized applications. However, smart contract vul
Reproducibility: Two model training runs using the same code and data will result in exactly the same metrics. All sources of randomness are controlled for. Cost reduction: Using AzureML, all compute resources (virtual machines, VMs) are requested at the time of starting the training job and ...
Deep learning (DL) has the potential to transform medical diagnostics. However, the diagnostic accuracy of DL is uncertain. Our aim was to evaluate the diagnostic accuracy of DL algorithms to identify pathology in medical imaging. Searches were conducted
These metrics are formulated and described in greater detail in Attar et al. (2022). The metrics were computed across all geometries in the training and testing datasets. This computation resulted in metric values for each geometry subclass and these values were collected as distributions and visual...