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Cognitive AmBC-NOMA IoV-MTS networks with IQI : reliability and security analysis

- Li, Xingwang, Zheng, Yike, Alshehri, Mohammad, Hai, Linpeng, Balasubramanian, Venki, Zeng, Ming, Nie, Gaofeng

  • Authors: Li, Xingwang , Zheng, Yike , Alshehri, Mohammad , Hai, Linpeng , Balasubramanian, Venki , Zeng, Ming , Nie, Gaofeng
  • Date: 2023
  • Type: Text , Journal article
  • Relation: IEEE Transactions on Intelligent Transportation Systems Vol. 24, no. 2 (2023), p. 2596-2607
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  • Description: Internet-of-Vehicle (IoV) enabled Maritime Transportation Systems (MTS) communication is anticipated to support ultra-reliable and low latency, diverse quality-of-service (QoS) and large-scale connectivities. To meet such stringent demands, a cognitive ambient backscatter non-orthogonal multiple access (C-AmBC-NOMA) IoV-MTS network is proposed. We explore the reliable and secure performance of the proposed C-AmBC-NOMA IoV-MTS network with in-phase and quadrature phase imbalance (IQI) at radio-frequency (RF) front-ends and the existence of an eavesdropper. In particular, the analytical expressions on the outage probability (OP) and intercept probability (IP) are obtained after a series of calculations. For a deeper understanding, we discuss the asymptotic behavior of OPs in the high signal-to-noise ratio (SNR) region, the diversity orders of OPs, and IPs in the high main-to-eavesdropper ratio (MER) regime. The results of Monte-Carlo simulation and a series of corresponding theoretical analysis show that: i) As the SNR approaches infinity, the OPs tend to be fixed non-negative values, indicating that the diversity orders of the OPs have error floors; ii) When the MER approaches infinity, the IPs of legitimate users decrease continuously, while the IP of backscatter device (BD) increases; iii) Compared with the system performance under ideal condition, the system performance is less reliable under IQI condition, but the security performance is enhanced; iv) By carefully selecting the system parameters, a trade-off can be achieved between reliability and security. © 2000-2011 IEEE.

An intelligent heart disease prediction system based on swarm-artificial neural network

- Nandy, Sudarshan, Adhikari, Mainak, Balasubramanian, Venki, Menon, Varun, Li, Xingwang, Zakarya, Muhammad

  • Authors: Nandy, Sudarshan , Adhikari, Mainak , Balasubramanian, Venki , Menon, Varun , Li, Xingwang , Zakarya, Muhammad
  • Date: 2023
  • Type: Text , Journal article
  • Relation: Neural Computing and Applications Vol. 35, no. 20 (2023), p. 14723-14737
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  • Description: The accurate prediction of cardiovascular disease is an essential and challenging task to treat a patient efficiently before occurring a heart attack. In recent times, various intelligent healthcare frameworks have been designed with different machine learning and swarm optimization techniques for cardiovascular disease prediction. However, most of the existing strategies failed to achieve higher accuracy for cardiovascular disease prediction due to the lack of data-recognized techniques and proper prediction methodology. Motivated by the existing challenges, in this paper, we propose an intelligent healthcare framework for predicting cardiovascular heart disease based on Swarm-Artificial Neural Network (Swarm-ANN) strategy. Initially, the proposed Swarm-ANN strategy randomly generates predefined numbers of Neural Networks (NNs) for training and evaluating the framework based on their solution consistency. Additionally, the NN populations are trained by two stages of weight changes and their weight is adjusted by a newly designed heuristic formulation. Finally, the weight of the neurons is modified by sharing the global best weight with other neurons and predicts the accuracy of cardiovascular disease. The proposed Swarm-ANN strategy achieves 95.78% accuracy while predicting the cardiovascular disease of the patients from a benchmark dataset. The simulation results exhibit that the proposed Swarm-ANN strategy outperforms the standard learning techniques in terms of various performance matrices. © 2021, The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature.
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