Faculty Publications
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Item Real-time big data analytics framework with data blending approach for multiple data sources in smart city applications(West University of Timisoara, 2020) Manjunatha, S.; Annappa, A.Advancement in Information Communication Technology (ICT) and the Internet of Things (IoT) has to lead to the continuous generation of a large amount of data. Smart city projects are being implemented in various parts of the world where analysis of public data helps in providing a better quality of life. Data analytics plays a vital role in many such data-driven applications. Real-time analytics for finding valuable insights at the right time using smart city data is crucial in making appropriate decisions for city administration. It is essential to use multiple data sources as input for the analysis to achieve better and more accurate data-driven solutions. It helps in finding more accurate solutions and making appropriate decisions. Public safety is one of the major concerns in any smart city project in which real-time analytics is much useful in the early detection of valuable data patterns. It is crucial to find early predictions of crime-related incidents and generating emergency alerts for making appropriate decisions to provide security to the people and safety of the city infrastructure. This paper discusses the proposed real-time big data analytics framework with data blending approach using multiple data sources for smart city applications. Analytics using multiple data sources for a specific data-driven solution helps in finding more data patterns, which in turn increases the accuracy of analytics results. The data preprocessing phase is a challenging task in data analytics when data being ingested continuously in real-time into the analytics system. The proposed system helps in the preprocessing of real-time data with data blending of multiple data sources used in the analytics. The proposed framework is beneficial when data from multiple sources are ingested in real-time as input data and is also flexible to use any additional data source of interest. The experimental work carried out with the proposed framework using multiple data sources to find the crime-related insights in real-time helps the public safety solutions in the smart city. The experimental outcome shows that there is a significant increase in the number of identified useful data patterns as the number of data sources increases. A real-time based emergency alert system to help the public safety solution is implemented using a machine learning-based classification algorithm with the proposed framework. The experiment is carried out with different classification algorithms, and the results show that Naive Bayes classification performs better in generating emergency alerts. © 2020 SCPE.Item Data-driven approaches in concrete science: Applications, challenges and future prospects(ICE Publishing, 2025) Barbhuiya, S.; Das, B.B.; Adak, D.This review paper provides a comprehensive exploration of integrating data-driven approaches in the domain of concrete science. The paper commences with an introduction elucidating the background and context of data-driven concrete science, outlining objectives and scope, and underscoring the importance of data-driven methodologies. Subsequently, it delves into the traditional analytical approaches and the potential for data-driven methods. The paper elucidates data collection and pre-processing techniques tailored to the domain, encompassing concrete-related data types, collection methodologies, and data pre-processing strategies. Moreover, it extensively covers data-driven modelling and prediction in concrete science, presenting an overview of data-driven models, machine learning techniques deep learning approaches and integration of big data analytics. The review consolidates insights into diverse applications, including concrete strength prediction, durability analysis and concrete microstructure characterisation, employing data-driven approaches. Furthermore, it highlights challenges and opportunities in this burgeoning field, encompassing data quality and availability, interpretability and explainability of models, and ethical consideration. The paper concludes with recommendations for researchers and practitioners aiming to harness the full potential of data-driven methodologies. © 2025 Emerald Publishing Limited: All rights reserved.