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Haneul Lee and Seokheon Yun
Accurately predicting construction costs during the initial planning stages is crucial for the successful completion of construction projects. Recent advancements have introduced various machine learning-based methods to enhance cost estimation precision...
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Fan Zhang, Melissa Petersen, Leigh Johnson, James Hall, Raymond F. Palmer, Sid E. O?Bryant and on behalf of the Health and Aging Brain Study (HABS?HD) Study Team
The Health and Aging Brain Study?Health Disparities (HABS?HD) project seeks to understand the biological, social, and environmental factors that impact brain aging among diverse communities. A common issue for HABS?HD is missing data. It is impossible to...
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Li Cai, Cong Sha, Jing He and Shaowen Yao
Traffic flows (e.g., the traffic of vehicles, passengers, and bikes) aim to reveal traffic flow phenomena generated by traffic participants in traffic activities. Various studies of traffic flows rely heavily on high-quality traffic data. The taxi GPS tr...
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Cong Li, Xupeng Ren and Guohui Zhao
Ground meteorological observation data (GMOD) are the core of research on earth-related disciplines and an important reference for societal production and life. Unfortunately, due to operational issues or equipment failures, missing values may occur in G...
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Andrés F. Ochoa-Muñoz and Javier E. Contreras-Reyes
Missing or unavailable data (NA) in multivariate data analysis is often treated with imputation methods and, in some cases, records containing NA are eliminated, leading to the loss of information. This paper addresses the problem of NA in multiple facto...
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Gaurav Narkhede, Anil Hiwale, Bharat Tidke and Chetan Khadse
Day by day pollution in cities is increasing due to urbanization. One of the biggest challenges posed by the rapid migration of inhabitants into cities is increased air pollution. Sustainable Development Goal 11 indicates that 99 percent of the world?s u...
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Saul G. Ramirez, Gustavious Paul Williams, Norman L. Jones, Daniel P. Ames and Jani Radebaugh
Obtaining and managing groundwater data is difficult as it is common for time series datasets representing groundwater levels at wells to have large gaps of missing data. To address this issue, many methods have been developed to infill or impute the mis...
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Yufan Qian, Limei Tian, Baichen Zhai, Shufan Zhang and Rui Wu
Missing observations in time series will distort the data characteristics, change the dataset expectations, high-order distances, and other statistics, and increase the difficulty of data analysis. Therefore, data imputation needs to be performed first. ...
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Fahima Noor, Sanaulla Haq, Mohammed Rakib, Tarik Ahmed, Zeeshan Jamal, Zakaria Shams Siam, Rubyat Tasnuva Hasan, Mohammed Sarfaraz Gani Adnan, Ashraf Dewan and Rashedur M. Rahman
Bangladesh is in the floodplains of the Ganges, Brahmaputra, and Meghna River delta, crisscrossed by an intricate web of rivers. Although the country is highly prone to flooding, the use of state-of-the-art deep learning models in predicting river water ...
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Chao Zhou and Tao Zhang
In real applications, massive data with graph structures are often incomplete due to various restrictions. Therefore, graph data imputation algorithms have been widely used in the fields of social networks, sensor networks, and MRI to solve the graph dat...
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