A survey on context awareness in big data analytics for business applications
- Authors: Dinh, Loan , Karmakar, Gour , Kamruzzaman, Joarder
- Date: 2020
- Type: Text , Journal article
- Relation: Knowledge and Information Systems Vol. 62, no. 9 (2020), p. 3387-3415
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- Description: The concept of context awareness has been in existence since the 1990s. Though initially applied exclusively in computer science, over time it has increasingly been adopted by many different application domains such as business, health and military. Contexts change continuously because of objective reasons, such as economic situation, political matter and social issues. The adoption of big data analytics by businesses is facilitating such change at an even faster rate in much complicated ways. The potential benefits of embedding contextual information into an application are already evidenced by the improved outcomes of the existing context-aware methods in those applications. Since big data is growing very rapidly, context awareness in big data analytics has become more important and timely because of its proven efficiency in big data understanding and preparation, contributing to extracting the more and accurate value of big data. Many surveys have been published on context-based methods such as context modelling and reasoning, workflow adaptations, computational intelligence techniques and mobile ubiquitous systems. However, to our knowledge, no survey of context-aware methods on big data analytics for business applications supported by enterprise level software has been published to date. To bridge this research gap, in this paper first, we present a definition of context, its modelling and evaluation techniques, and highlight the importance of contextual information for big data analytics. Second, the works in three key business application areas that are context-aware and/or exploit big data analytics have been thoroughly reviewed. Finally, the paper concludes by highlighting a number of contemporary research challenges, including issues concerning modelling, managing and applying business contexts to big data analytics. © 2020, Springer-Verlag London Ltd., part of Springer Nature.
Assessing trust level of a driverless car using deep learning
- Authors: Karmakar, Gour , Chowdhury, Abdullahi , Das, Rajkumar , Kamruzzaman, Joarder , Islam, Syed
- Date: 2021
- Type: Text , Journal article
- Relation: IEEE Transactions on Intelligent Transportation Systems Vol. 22, no. 7 (2021), p. 4457-4466
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- Description: The increasing adoption of driverless cars already providing a shift to move away from traditional transportation systems to automated ones in many industrial and commercial applications. Recent research has justified that driverless vehicles will considerably reduce traffic congestions, accidents, carbon emissions, and enhance the accessibility of driving to wider cross-section of people and lifestyle choices. However, at present, people's main concerns are about its privacy and security. Since traditional protocol layers based security mechanisms are not so effective for a distributed system, trust value-based security mechanisms, a type of pervasive security, are appearing as popular and promising techniques. A few statistical non-learning based models for measuring the trust level of a driverless are available in the current literature. These are not so effective because of not being able to capture the extremely distributed, dynamic, and complex nature of the traffic systems. To bridge this research gap, in this paper, for the first time, we propose two deep learning-based models that measure the trustworthiness of a driverless car and its major On-Board Unit (OBU) components. The second model also determines its OBU components that were breached during the driving operation. Results produced using real and simulated traffic data demonstrate that our proposed DNN based deep learning models outperform other machine learning models in assessing the trustworthiness of individual car as well as its OBU components. The average precision of detection accuracies for the car, LiDAR, camera, and radar are 0.99, 0.96, 0.81, and 0.83, respectively, which indicates the potential real-life application of our models in assessing the trust level of a driverless car. © 2000-2011 IEEE.
Detection and separation of generic-shaped objects by fuzzy clustering
- Authors: Ali, Mohammad , Karmakar, Gour , Dooley, Laurence
- Date: 2010
- Type: Text , Journal article
- Relation: International Journal of Intelligent Computing and Cybernetics Vol. 3, no. 3 (2010 2010), p. 365-390
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- Description: Image segmentation involves the separation of mutually exclusive regions/objects of interest (Gonzalez and Woods, 2002), and is integral to the image processing, coding and interpretation domains, with examples of some of the eclectic range of applications including: image analysis, robot vision, automatic car assembly, security surveillance systems, object recognition and medical imaging (Gonzalez and Woods, 2002; Hoppner et al., 1999; Pham and Prince, 1999; Gath and Geva, 1989; Pal and Pal, 1993). As there are potentially a very large number of perceptual objects in an image, with subtle variations between them, this makes generalised object-based segmentation an especially challenging task.