Smart city progress from classical homogenous technologies with limited facility to heterogeneous interconnected network with immense capabilities. Furthermore, there is a good concern in expanding the scope of application in the smart city. The primary objective of the smart city is to achieve optimization and reinforce the Quality of Service (QoS) of applications by cleverer usage of urban resources. The QoS in the network is measured using several factors like end-end delay, energy consumption, packet loss and throughput. Several pitfalls are experienced in the existing routing innovation. In this proposal, a new technology-based routing structure is proposed. Road Side Units (RSU) will allow the planners to deploy the application without unfamiliar tools for data process and gathering. Data forwarding, acquisition and diffusion are simplified by RSU. K-Nearest Neighbor is used for finding the nearest neighbor nodes and it is optimized using Whale optimization Algorithm (WOA). The evaluation outcomes prove that the intended routing plot provides much spectacle than existing protocols for real time applications.
Lung cancer is a worldwide precarious disease and it is encouraged by the abnormal growth of cells in bronchi. Spotting the cancer cells is unknown until it leads to respiration issues and the muddling of organs working. Due to problems, limited or incorrect selection of hypothesis space, and dropping into local minima, single learners often give erratic output in an existing approach. The ensemble method accomplished a dataset that is free and composed of computed tomography (CT) images. The annotation process reveals observed lung lesions and provides a degree of malignancy for each lesion. Detection of benign and malignant nodules is recognized using deep convolutional frameworks AlexNet, SqueezeNet, GoogleNet, ResNet, and Inception ResNet, achieves higher accuracy (93%) than other convolutional neural networks (CNNs). Eight machine learning methods are involved for achieving better performance. The prediction probability obtained from CNN is applied as input to support vector machines (SVM), K‐nearest neighbours (KNN), naive Bayes (NB), multi‐layer perceptron (MLP), decision trees (DT), gradient boosted regression trees (GBRT), and adaptive boosting. The composition of GoogleNet model and AdaBoost classifier reached the most coherent classification accuracy as 99%. This is one of the best ways to analyse early detection and it increases the survival rate. Therefore, the result from the proposed deep CNN and ML technique achieves better precision than sputum cytology, X‐Ray process, and earlier detection of lung cancer.
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