Abstract Building Energy Management System (BEMS) has been a substantial topic
nowadays due to its importance in reducing energy wastage. However, the performance of …

大数据时代, 数据爆炸式的增长, 数据获取变得更容易的同时数据缺失现象也更加普遍.
数据的缺失极大地降低了数据的实用性. 数据缺失问题的处理成为大数据处理的热点研究课题 …

As the importance of data-based predictive maintenance frameworks is rising, one of the
emerging issues is the missing values in industrial data. Data with various missing values …

Increasing the production of the three major food crops (MFCs), maize (Zea mays), rice
(Oryza sativa), and wheat (Triticum aestivum), is essential to fulfilling the food demand for …

The adoption of Artificial Intelligence (AI) in high-stakes domains such as healthcare, wildlife
preservation, autonomous driving and criminal justice system calls for a data-centric …

A significant advancement that occurs during the data cleaning stage is estimating missing
data. Studies have shown that improper data handling leads to inaccurate analysis …

Interactions between individuals and recommender systems can be viewed as a continuous
feedback loop, consisting of pre-consumption and post-consumption phases. Pre …

A missing value is one of the factors that often cause incomplete data in almost all studies,
even those that are well-designed and controlled. It can also decrease a study's statistical …

One of the most common causes of incompleteness is missing data, which occurs when no
data value for the variables in observation is stored. An adaptive approach model …

Ensuring that Machine Learning (ML) models make correct and meaningful inferences is
necessary for the broader adoption of such models into high-stakes decision-making …