Areej Fatima; Muhammad Adnan Khan; Sagheer Abbas; Muhammad Waqas; Leena Anum; Muhammad Asif
Abstract
Internet of Things (IoT) approach is empowering smart city creativities all over the world. There is no specific tool or criteria for the evaluation of the services offered by the smart city. In this paper, a new Multilayer Fuzzy Inference System (MFIS) is proposed for the assessment of ...
Read More
Internet of Things (IoT) approach is empowering smart city creativities all over the world. There is no specific tool or criteria for the evaluation of the services offered by the smart city. In this paper, a new Multilayer Fuzzy Inference System (MFIS) is proposed for the assessment of the Planet Factors of smart city (PFSC). The PFSC system is categorized into two levels. The proposed MFIS based expert system can categories the evaluation level of planet factors of the smart city into low, satisfied, or good.
Anwar Saeed; Muhammad Yousif; Areej Fatima; Sagheer Abbas; Muhammad Adnan Khan; Leena Anum; Ali Akram
Abstract
With the innovation of cloud computing industry lots of services were provided based on different deployment criteria. Nowadays everyone tries to remain connected and demand maximum utilization of resources with minimum timeand effort. Thus, making it an important challenge in cloud computing ...
Read More
With the innovation of cloud computing industry lots of services were provided based on different deployment criteria. Nowadays everyone tries to remain connected and demand maximum utilization of resources with minimum timeand effort. Thus, making it an important challenge in cloud computing for optimum utilization of resources. To overcome this issue, many techniques have been proposed shill no comprehensive results have been achieved. Cloud Computing offers elastic and scalable resource sharing services by using resource management. In this article, a hybrid approach has been proposed with an objective to achieve the maximum resource utilization. In this proposed method, adaptive back propagation neural network and multi-level priority-based scheduling are being carried out for optimum resource utilization. This hybrid technique will improve the utilization of resources in cloud computing. This shows result in simulation-based on the form of MSE and Regression with job dataset, on behalf of the comparison of three algorithms like Scaled Conjugate Gradient (SCG), Levenberg Marquardt (LM) and Bayesian Regularization (BR). BR gives a better result with 60 hidden layers Neurons to other algorithms. BR gives 2.05 MSE and 95.8 regressions in Validation, LM gives 2.91 MSE and 94.06 regressions with this and SCG gives 3.92 MSE and 91.85 regressions.