High mobility in intelligent transportation systems (ITS), especially vehicle to vehicle (V2V) communication, allows increasing coverage and quick assistance to the users and neighboring networks, but also degrades the performance of the entire system due to fluctuation in the wireless channel. How to obtain better quality of service (QoS) in terms of performance metrics during multimedia transmission in V2V over future generation networks (i.e., edge computing platforms) is very challenging due to the high mobility of vehicles and heterogeneity of future internet of things (IoT)-based edge computing networks. In this context, the paper contributes in three distinct ways: (i) to develop a QoS-aware, green, sustainable, reliable and available (QGSRA) algorithm to support multimedia transmission in V2V over future IoT driven edge computing networks; (ii) to implement a novel QoS optimization strategy in V2V during multimedia transmission over IoT-based edge computing platforms; (iii) to propose QoS metrics such as greenness (i.e., energy efficiency), sustainability (i.e., less battery charge consumption), reliability (i.e., less packet loss ratio), and availability (i.e., more coverage) to analyze the performance of V2V networks. Finally, the proposed QGSRA algorithm has been validated through extensive real-time data sets of vehicles to demonstrate how it outperforms conventional techniques making it a potential candidate for multimedia transmission in V2V over self-adaptive edge computing platforms.
Diabetes is a large healthcare burden worldwide. There is substantial evidence that lifestyle modifications and drug intervention can prevent diabetes, therefore, an early identification of high risk individuals is important to design targeted prevention strategies. In this paper, we present an automatic tool that uses machine learning techniques to predict the development of type 2 diabetes mellitus (T2DM). Data generated from an oral glucose tolerance test (OGTT) was used to develop a predictive model based on the support vector machine (SVM). We trained and validated the models using the OGTT and demographic data of 1,492 healthy individuals collected during the San Antonio Heart Study. This study collected plasma glucose and insulin concentrations before glucose intake and at three time-points thereafter (30, 60 and 120 min). Furthermore, personal information such as age, ethnicity and body-mass index was also a part of the data-set. Using 11 OGTT measurements, we have deduced 61 features, which are then assigned a rank and the top ten features are shortlisted using minimum redundancy maximum relevance feature selection algorithm. All possible combinations of the 10 best ranked features were used to generate SVM based prediction models. This research shows that an individual’s plasma glucose levels, and the information derived therefrom have the strongest predictive performance for the future development of T2DM. Significantly, insulin and demographic features do not provide additional performance improvement for diabetes prediction. The results of this work identify the parsimonious clinical data needed to be collected for an efficient prediction of T2DM. Our approach shows an average accuracy of 96.80% and a sensitivity of 80.09% obtained on a holdout set.
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