Big Data Analytics Applying the Fusion Approach of Multicriteria Decision Making with Deep Learning Algorithms
This paper addresses the problem of making decisions in big data analytics for various industries by proposing a fusion approach, but it is an incremental contribution.
This paper explores the application of multicriteria decision-making fused with deep learning algorithms to address challenges in big data analytics, aiming to extract useful knowledge from the increasing volume, velocity, and variety of data. It proposes new studies that combine data-driven techniques to provide insights for various fields.
Data is evolving with the rapid progress of population and communication for various types of devices such as networks, cloud computing, Internet of Things (IoT), actuators, and sensors. The increment of data and communication content goes with the equivalence of velocity, speed, size, and value to provide the useful and meaningful knowledge that helps to solve the future challenging tasks and latest issues. Besides, multicriteria based decision making is one of the key issues to solve for various issues related to the alternative effects in big data analysis. It tends to find a solution based on the latest machine learning techniques that include algorithms like decision making and deep learning mechanism based on multicriteria in providing insights to big data. On the other hand, the derivations are made for it to go with the approximations to increase the duality of runtime and improve the entire system's potentiality and efficacy. In essence, several fields, including business, agriculture, information technology, and computer science, use deep learning and multicriteria-based decision-making problems. This paper aims to provide various applications that involve the concepts of deep learning techniques and exploiting the multicriteria approaches for issues that are facing in big data analytics by proposing new studies with the fusion approaches of data-driven techniques.