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The recent trend, wireless communications has became one of the striving areas for the developments of cellular technologies. This is because, many people around the world would like to connect to the other, seamlessly, anytime anywhere. Therefore, the selection of the best network connection becomes crucial with the best quality of service. For that reason the fourth generation heterogeneous wireless networks are been introduced where in the mobile terminals in this network will roll freely across different wireless systems and they continuously will undergo vertical and horizontal handovers. This paper introduces a novel fuzzy multiobjective optimization on the basis of ratio analysis (FMOORA) as an effective methodology in the selection of optimal network that provides a best balance between the performance and energy consumption. The resulted decision was validated for acceptance by the well agreed method gray relational analysis (GRA) in the multi-attribute decision making problems.

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The deep learning concept for performing object detection plans to minimize the labeling cost by identifying the samples which increase the detection into the unlabeled pool. According to the object detection than the classification process as the designing and selection of procedure is very essential. The related works have been implemented the aggregation data process of several outputs and batch box process for improving the performance evaluation. The mean Average Precision 𝒎𝑨𝑷 is the main performance metric for identifying the accuracy of the object detection. The class imbalance problem has been solved using the background class in every group of sample images. The loss related weight algorithm for training group is proposed in this paper utilizing the batch boxes, aggregating data and also the 𝒎𝑨𝑷 enhancements are addressed to solve the class imbalance problem. Additionally, a sampling process is used for identifying the uncertainty and enhancing the object detection process. The performance results illustrate that the proposed framework generates good performance than the relevant technique and it will be used for realtime applications in an efficient manner.

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This research work confronts a sender-based responsive and novel protocol named “Intelligent Energy-Aware Multiple restraints Secured Optimal Routing (IEAMSOR)” protocol for WSNs. In order to deal with the various emerges like packet routing, node mobility, and energy optimization as well as energy balancing in WSNs. The proposed protocol accounts for the basic QoS restraints such as Delay, HopCount and Energy Level for each link of ‘n’ number of routes and predicts the best optimal path among these in-between sender and receiver nodes throughout the route discovery process. It also assures the energy level of each node existing on the route during the route reply process. It incorporates the modified mobility prediction approach in order to estimate the stableness of link failure time for every link of each path during the route reply process. The main objective of this work to achieve the energy balancing among the nodes is achieved through fuzzy rules based node’s trust classification is introduced and based on this energy weight of each node is adjusted according to their trustworthiness. It accomplishes the path sustainment process when the link among the two nodes goes down. Moreover, the proposed model has been given careful attention for selecting additional substitute routes throughout link failure. The experimental results have seemed that the IEAMSOR protocol performs better than the existing traditional protocols.