Article
Meteorology & Atmospheric Sciences
Qing Yang, Xinyi Shen, Feifei Yang, Emmanouil N. Anagnostou, Kang He, Chongxun Mo, Hojjat Seyyedi, Albert J. Kettner, Qingyuan Zhang
Summary: This study developed a flood property insurance claims model using random forest, which can predict the number of property insurance claims resulting from flood events. By integrating various data sources including building locations, topography, precipitation, and other variables, the model was evaluated and showed acceptable performance. The model can be applied in assessing flood impact and enhancing flood resilience.
BULLETIN OF THE AMERICAN METEOROLOGICAL SOCIETY
(2022)
Article
Computer Science, Information Systems
Chaoqun Yue, Shweta Ware, Reynaldo Morillo, Jin Lu, Chao Shang, Jinbo Bi, Jayesh Kamath, Alexander Russell, Athanasios Bamis, Bing Wang
Summary: Recent studies have shown that fusing GPS and WiFi data can predict depression more accurately. More complete data leads to stronger correlations and improves the accuracy of depression prediction.
IEEE TRANSACTIONS ON BIG DATA
(2021)
Article
Computer Science, Theory & Methods
Praveen Kumar Donta, Boris Sedlak, Victor Casamayor Pujol, Schahram Dustdar
Summary: Distributed computing continuum systems (DCCS) leverage a large number of computing devices to process data generated by edge devices. These devices not only perform computations but also produce diverse data, which presents challenges due to the heterogeneous nature of DCCS. This paper discusses these challenges in relation to big data and proposes a general governance and sustainable architecture for DCCS.
JOURNAL OF BIG DATA
(2023)
Article
Computer Science, Artificial Intelligence
Damien Dablain, Bartosz Krawczyk, Nitesh Chawla
Summary: In this study, we propose a novel oversampling algorithm called DeepSMOTE for deep learning models, which generates high-quality artificial images to increase the number of samples for minority classes and balance the training set.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2023)
Article
Construction & Building Technology
Hui Bi, Zhirui Ye
Summary: The study investigated the travel behavior of ridesourcing users in Chengdu using the LDA model, finding that the mode of life for people in Chengdu is represented by the time frame of Nine to Ten. Ridesourcing is not yet widely used as a commuter tool, and many people work overtime in the evenings, particularly on Saturdays.
SUSTAINABLE CITIES AND SOCIETY
(2021)
Review
Chemistry, Multidisciplinary
Sung Ryul Shim, Joon-Ho Lee, Jae Heon Kim
Summary: In terms of medical health, the era of data science has brought significant changes. Big data in healthcare, including medical data, genome data, and lifelog data, plays a crucial role. Among them, public medical data is particularly important for research and policy making due to its large patient base and representativeness. However, utilizing public health big data and designing studies presents challenges, and systematic review (SR) methodology can offer valuable assistance. This review highlights the importance of big data research for public interest, introduces important public medical big data in Korea, and demonstrates how SR can be applied in research using such data.
APPLIED SCIENCES-BASEL
(2023)
Article
Computer Science, Theory & Methods
Haiyan Wang, Jiaming Feng, Ke Li, Lisi Chen
Summary: This paper investigates and summarizes the existing literature on data, technologies, and systems related to self-driving vehicles in the context of the continuous development of Autonomous Vehicle System (AVS). The survey covers representative studies on collision avoidance, automatic lane-changing maneuver, object detection (including pedestrian detection and obstacle detection), and vehicle trajectory prediction. The findings of this survey provide new insights that can guide researchers and software engineers in the fields of self-driving data management systems and autonomous vehicle systems.
FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE
(2022)
Review
Public, Environmental & Occupational Health
Leora Horwitz, Holly A. Krelle
Summary: Rapid randomized controlled trials are uncommon in health care quality improvement and systems interventions. By integrating clinical trial methodology with QI work, researchers are able to combine the robustness of RCTs with the practical knowledge of QI. These rapid trials allow for quick testing of multiple intervention variations, similar to A/B testing in the tech sector, and provide a standard of evidence often lacking in QI.
ANNUAL REVIEW OF PUBLIC HEALTH
(2023)
Article
Critical Care Medicine
Jose Suarez
Summary: Big data and artificial intelligence are increasingly utilized in neurocritical care, aiding in data analysis, prediction, and decision-making in medical practice. Collaboration between multiple centers is crucial for the advancement and validation of AI technologies in healthcare to ensure fair and effective use in clinical settings.
NEUROCRITICAL CARE
(2022)
Article
Computer Science, Theory & Methods
Behroz Mirza, Tahir Q. Syed, Behraj Khan, Yameen Malik
Summary: The article demonstrates the expansion of solution space for Big Data challenges with deep learning moving beyond traditional applications, presenting a framework to address specific Big Data challenges.
ACM COMPUTING SURVEYS
(2021)
Review
Agriculture, Dairy & Animal Science
Giovanni Franzo, Matteo Legnardi, Giulia Faustini, Claudia Maria Tucciarone, Mattia Cecchinato
Summary: In the future, the demand for poultry meat and eggs is predicted to increase with population growth. This expansion brings both opportunities and challenges such as pollution, competition for resources, animal welfare concerns, and infectious diseases. Optimization and increased efficiency are needed in poultry production, and the use of big data offers the opportunity to develop tools to maximize farm profitability and reduce impacts. Sensor technologies and advanced statistical techniques are discussed, as well as the progress in pathogen genome sequencing and analysis.
Review
Materials Science, Multidisciplinary
Quan Shi, Jue Tang, Mansheng Chu
Summary: This paper focuses on the application of artificial intelligence technology in blast furnace (BF) ironmaking. It summarizes and analyzes the current intelligent BF ironmaking technology from five aspects, including BF data management, time delay and correlation analysis, BF key variable prediction, BF status evaluation, and multi-objective intelligent optimization of BF operations. Solutions, suggestions, and future outlooks are provided. The work aims to increase the process operator's awareness and understanding of intelligent BF technology and promote the practical application of intelligent technology in BF ironmaking by combining big data technology with the process.
INTERNATIONAL JOURNAL OF MINERALS METALLURGY AND MATERIALS
(2023)
Article
Computer Science, Information Systems
Yi Wei, Mei Xue, Xin Liu, Pengxiang Xu
Summary: This paper proposes a new method for accurately selecting training data by fitting a mixture model and dynamically dividing the training set. The method achieves good performance in experiments.
FRONTIERS OF COMPUTER SCIENCE
(2022)
Article
Computer Science, Information Systems
Khalid Haseeb, Irshad Ahmad, Israr Iqbal Awan, Jaime Lloret, Ignacio Bosch
Summary: Health applications using IoMT are becoming popular in smart cities, but face challenges like resource consumption prediction and data security. By integrating machine learning and SDN architecture, system efficiency and network performance can be significantly improved.
Review
Clinical Neurology
Yuzhe Liu, Yuan Luo, Andrew M. Naidech
Summary: Significant advances in medical data accumulation, computational techniques, and management have been made in the last decade. Big data and computational methods can address gaps in patient selection, complications prediction, and outcome understanding. Automated neuroimaging analysis can help triage patients, and data-intensive techniques enable accurate risk calculations for timely prediction of adverse events.