Abstract
Mobile phones have become one of the most common and essential tools of humans life. What initially started as a basic hand model with telephone like features has developed into a mini computer that has all our personal and professional life stored into it. But is this information protected and safe? Hacking and using our information has been a common problem for a long time. In case of websites, we have several tools and applications to protect our computer from hacking and blocking the advertisements that can track our computer. But there are no tools like this available for mobile applications. We install several applications and provide access to all our information stored in our phone. It is the duty of the operating system makers like Google (Play Store) and Apple (App store) to create proper guidelines for the applications to protect our privacy, and enforce them. But with the increasing number of applications and its usage, not everything is policed properly. For example, even though applications installed in apple iPhone requires the permission of users to access and transfer the data, the apple does not intervene and check these applications if they are accessing only the permitted data or not. This research paper concentrates on developing an algorithm to prevent and detect uncertainty in these applications. The algorithm constructed is based on deep learning model, which is a consolidated tool used to predict the uncertainty in mobile computing. The challenge of calculating this uncertainty estimation is done by learning both target output and its corresponding variance. Then a similar estimation is performed in the newly constructed result model which contains one network for target output and another for variance. This method prevents error occurrence at a higher percentage than other algorithm available.
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Rajawat, A.S., Upadhyay, P., Upadhyay, A. (2021). Novel Deep Learning Model for Uncertainty Prediction in Mobile Computing. In: Arai, K., Kapoor, S., Bhatia, R. (eds) Intelligent Systems and Applications. IntelliSys 2020. Advances in Intelligent Systems and Computing, vol 1250. Springer, Cham. https://doi.org/10.1007/978-3-030-55180-3_49
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