ABSTRACT
The extreme rise of the Internet of Things and the increasing access of people to web applications have led to the expanding use of diverse e-commerce solutions, which was even more obvious during the COVID-19 pandemic. Large amounts of heterogeneous data from multiple sources reside in e-commerce environments and are often characterized by data source inaccuracy and unreliability. In this regard, various fusion techniques can play a crucial role in addressing such challenges and are extensively used in numerous e-commerce applications. This paper's goal is to conduct an academic literature review of prominent fusion-based solutions that can assist in tackling the everyday challenges the e-commerce environments face as well as in their needs to make more accurate and better business decisions. For categorizing the solutions, a novel 4-fold categorization approach is introduced including product-related, economy-related, business-related, and consumer-related solutions, followed by relevant subcategorizations, based on the wide variety of challenges faced by e-commerce. Results from the 65 fusion-related solutions included in the paper show a great variety of different fusion applications, focusing on the fusion of already existing models and algorithms as well as the existence of a large number of different machine learning techniques focusing on the same e-commerce-related challenge.
Subject(s)
COVID-19 , Pandemics , Algorithms , Commerce , HumansABSTRACT
The extreme rise of the Internet of Things and the increasing access of people to web applications have led to the expanding use of diverse e-commerce solutions, which was even more obvious during the COVID-19 pandemic. Large amounts of heterogeneous data from multiple sources reside in e-commerce environments and are often characterized by data source inaccuracy and unreliability. In this regard, various fusion techniques can play a crucial role in addressing such challenges and are extensively used in numerous e-commerce applications. This paper's goal is to conduct an academic literature review of prominent fusion-based solutions that can assist in tackling the everyday challenges the e-commerce environments face as well as in their needs to make more accurate and better business decisions. For categorizing the solutions, a novel 4-fold categorization approach is introduced including product-related, economy-related, business-related, and consumer-related solutions, followed by relevant subcategorizations, based on the wide variety of challenges faced by e-commerce. Results from the 65 fusion-related solutions included in the paper show a great variety of different fusion applications, focusing on the fusion of already existing models and algorithms as well as the existence of a large number of different machine learning techniques focusing on the same e-commerce-related challenge.
ABSTRACT
The ever-increasing demand for transportation of people and goods as well as the massive accumulation of population in urban centers have increased the need for appropriate infrastructure and system development in order to efficiently manage the constantly increasing and diverse traffic flows. Moreover, given the rapid growth and the evolution of Information and Communication Technologies (ICT), the development of intelligent traffic management systems that go beyond traditional approaches is now more feasible than ever. Nowadays, highways often have sensors installed across their range that collect data such as speed, density, direction and so on. In addition, the rapid evolution of vehicles with installed computer systems and sensors on board, provides a very large amount of data, ranging from very simple features such as speed, acceleration, etc. to very complex data like the driver’s situation and driving behavior. However, these data alone and without any further processing, cannot solve the congestion problem. Therefore, the development of complex computational methods and algorithms underpins the chance to process these data in a fast and reliable way. The purpose of this paper is to present a traffic control ramp metering (RM) method based on machine learning and to study its impact on a selected highway segment.