The researchers identify two sites: one healthcare provider serves a large population, but its clinical information system allows queries only on past/current smoking and a certain number of genes; the other provider has more detailed behavioral data (including number of cigarettes per week) and ...
If the data size is limited, we can review the data carefully to detect outliers, but it can be difficult for large-scale data to identify outliers or degraded data. A commonly cited drawback of the probability approach is that it is not resilient to model distribution predictions or the ...
In this particular example we will recreate ["Write with Transformer"](https://transformer.huggingface.co/doc/gpt2-large) together. It's an application that lets you write anything using transformers like GPT-2 and XLNet.  ...
Research to solve engineering and science problems commonly require the collection and complex analysis of a vast amount of data. This makes them a natural exemplar of big data applications. For example, data from weather stations, high resolution images from CT scans, or data captured by astronom...
A novel physics-informed neural networks approach (PINN-MT) to solve mass transfer in plant cells during drying. Biosyst Eng. 2023;230:219–41. https://doi.org/10.1016/j.biosystemseng.2023.04.012. Second contribution of PIML in food engineering field; implementation of automatic differentiation...
are the rich source of information for monitoring the spread of disease. Identification of patterns through analysis of these data streams can help to comprehend shifts in disease hotspots and support surveillance actions. Scaling the use of analytical tools on such large volumes of data, for examin...
providers like Google, Microsoft, and other e-commerce industries have been utilizing both private and public cloud infrastructures assisting real-time applications [17,30]. The mentioned cloud services requires substantial internet-connected resources and large data storage facilities for the collection, ...
Using a single type of data storage can limit the system's ability to meet different user expectations. Some data storage platforms are designed for large batches of data processing, whereas others are optimized for real-time data processing. If a Big Data system only supports one type of data...
The likelihood inferences for specific models may be susceptible to data leakage outliers. If the data size is limited, we can review the data carefully to detect outliers, but it can be difficult for large-scale data to identify outliers or degraded data. A commonly cited drawback of the pr...