<span>Wireless mesh network (WMN) is a new trend in wireless communication promising greater flexibility, reliability, and performance over traditional wireless local area network (WLAN). Test bed analysis and emulation plays an essential role in valuation of software defined wireless network and node mobility is the prominent feature of next generation software defined wireless network. In this study, the mobility models employed for moving mobile stations in software defined wireless network are explored. Moreover, the importance of mobility model within software defined wireless mesh network for enhancing the performance through handover-based load balancing is analyzed. The mobility models for the next generation software defined wireless network are explored. Furthermore, we have presented the mobility models in the mininet-Wi-Fi test bed, and evaluated the performance of Gauss Marko’s mobility model.</span>
Cardiac illness is one of the unpredictable infections and around the world numerous individuals experienced this sickness. On schedule and effective recognizable proof of coronary illness assumes a critical part in medical care, especially in the arena of cardiology. A productive and precise framework is proposed to finding coronary illness and the framework depends on AI procedures. Supervised learning algorithms such as Multi-Layer Perceptron (MLP), Multinomial Logistic Regression (MLR), Fuzzy Unordered Rule Induction Algorithm (FURIA) and C4.5 are then used to model CAD cases. This approach is tested on medical data that has 26 features and 335. MLR accomplishes most noteworthy expectation precision of 88.4 %. This methodology is benchmarked on Cleveland heart coronary illness information also. For this situation additionally, MLR, beats different methods. Projected hybridized model increases the exactness of arrangement calculations from 8.3 % to 11.4 % for the Cleaveland information. The proposed technique is, along these lines, a promising tool for finding CAD patients with improved forecast precision.
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