The significant difference between traditional machine learning and knowledge driven machine learning is information source for them to train a learning system. For traditional machine learning, data is the starting point of the whole pipeline and then a learning algorithm is designed to approximate the...
Our goal is to understand the characteristics and challenges of deep learning model reengineering. We conducted a mixed-methods case study of this phenomenon, focusing on the context of computer vision. Our results draw from two data sources: defects reported in open-source reeengineering projects,...
Our GFMs use multitask learning (MTL) to simultaneously learn graph-level and node-level properties of atomistic structures, such as energy and atomic forces. Using over 154 million atomistic structures for training, we illustrate the performance of our approach along with the lessons learned on ...
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2a), a pre-processing step (Step 2) is designed to calibrate raw NDVI images, so that intensity distribution can be normalised to correct overflowing pixels. At this step, an algorithm called contrast limited adaptive histogram equalisation (CLAHE)24 is applied to increase the contrast between ...
CNN—as well as analysis of their performance in comparison to a baseline model. A baseline model serves as a benchmark against which the performance of more sophisticated models is evaluated (Brownlee, 2019b). Typically, baseline models are straightforward to construct, quick to implement and ...
Then, the deep learning architectures implemented to automatically learn and extract deep features from raw data are introduced. Afterwards, it is described the proposed fusion mechanism to combine the aforementioned features. Lastly, it is introduced the machine learning algorithm trained to categorize ...
That is, we design and implement a UAV-based real-time target detection system (UAV-RTDS). The contributions of this study are as follows. In order to ensure accurate target detection, we use the YOLOv4 algorithm (Bochkovskiy, Wang, and Liao Citation2020), rather than a lightweight ...
Algorithm 2: clustering SARBOLD-LLM scheme is appropriate for any problem and solution domain. It can be used for use cases in many different fields. Although the second module of this study focuses on AI methods, this module can also evolve into other topics, such as which hardware to be...
Rendering 3D virtual scenarios has become a popular alternative for generating per-pixel-labeled image datasets, especially in fields like autonomous drivi