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In the remote system, wireless sensors networks are used to forward messages of specific needs by minimizing energy consumption. This process needs to maintain the hubs with various activities of the network. The network components are suitable for conventional packet transmission, but not for emergency information transmission as it consistently requires high-quality links. In emergency information transmission, more cooperation is required by nodes, but at the same time, we must minimize the energy required in emergency transmission to form topology construction, partitioning, relaying nodes clustering, and then cluster the total number of nodes. In this paper, proposed an energy-aware emergency transmission scheme which decreases the hub's energy utilization maintained between 8% and 11% in reliable data transmission, increase transmission accuracy by 25%, and packet transmission delay decreases by 600 to 700 milliseconds while increasing the number of clusters in topology. KEYWORDSclustering nodes, energy efficiency, reliable transmission, remote sensor system, wireless sensor networks INTRODUCTIONWSNs has gained wonderful attention together in academia and industry because of it's massive application potential. One of the critical areas of research is to develop energy-saving techniques, which can extend the lifetime of a WSN as much as possible. 1 Many applications such as emergency alarm, fire alarm detection, and intrusion detection require immediate, and guaranteed deliverability of the information. 2 Therefore, sensor networks provide secure and reliable data transportation in an IoT environment. 3 In a resource constraint environment such as WSNs, it is importance to propagate the information as efficiently as possible among the neighboring nodes. Consequently, minimizing the energy-consumption 4 and improving the deliverability of the information to the sink node is very critical. 5 The energy-consumption plays a crucial role, and therefore, it effects other factors such as end-to-end delay and data reliability in the proper functioning of the WSNs. (6)(7)(8) The general WSNs structure is presented in Figure 1A. Data communication analysis mostly considered as WSNs nodes which are not deployed. In this analysis surroundings nodes have connected each other with multi-hops using less energy, and it can be select based on the wireless network topology environment of node sink process are shown in Figure 1B. Cluster head selection is a very efficient method for data transmission in WSNs. In recent studies on WSN are focused on different routing algorithm for these can urgent and not an urgent information. As sensors have limited resource, energy consumption should be conserved as much as possible. In this paper, above both conditions considered while transmitting data packets for emergency transmission conditions.The remaining paper is organized as follow. Section II discussed the related work of the reliable WSNs. In section III, we present the proposed work of network topology construction and its node ...

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Cancer is a complicated worldwide health issue with an increasing death rate in recent years. With the swift blooming of the high throughput technology and several machine learning methods that have unfolded in recent years, progress in cancer disease diagnosis has been made based on subset features, providing awareness of the efficient and precise disease diagnosis. Hence, progressive machine learning techniques that can, fortunately, differentiate lung cancer patients from healthy persons are of great concern. This paper proposes a novel Wilcoxon Signed-Rank Gain Preprocessing combined with Generative Deep Learning called Wilcoxon Signed Generative Deep Learning (WS-GDL) method for lung cancer disease diagnosis. Firstly, test significance analysis and information gain eliminate redundant and irrelevant attributes and extract many informative and significant attributes. Then, using a generator function, the Generative Deep Learning method is used to learn the deep features. Finally, a minimax game (i.e., minimizing error with maximum accuracy) is proposed to diagnose the disease. Numerical experiments on the Thoracic Surgery Data Set are used to test the WS-GDL method's disease diagnosis performance. The WS-GDL approach may create relevant and significant attributes and adaptively diagnose the disease by selecting optimal learning model parameters. Quantitative experimental results show that the WS-GDL method achieves better diagnosis performance and higher computing efficiency in computational time, computational complexity, and false-positive rate compared to state-of-the-art approaches.

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