the technology to automate the recommendations for big data analytic models that define data characteristics and problems).

Wireless Sensor Network involves in the communication task which demands the devices to form a connected network for collecting and disseminating information through radio transmission. The main objective of the Wireless Sensor Network is to extend the network lifetime in the operational environment, to charge or to exchange the sensor node batteries is probably an impossible/unfeasible activity. The clustered network aims to select CHs that minimize transmission costs and energy. To maximize the network lifetime, optimal CH selection is important. Selections of CH are Non deterministic Polynomial (NP) hard. Recently natural swarm inspired algorithms like Particle Swarm Optimization (PSO) and Ant Colony Optimization (ACO) have found their way into this domain and proved their effectiveness. In this work the BFO is adapted for cluster head selection so that multiple objectives like reduced packet delivery ratio, improved cluster formation, improved network life time and reduced end to end delay are achieved. Also a novel Hybrid algorithm using Bacterial foraging Optimization (BFO) -Bee swarm Optimization (BSO) is attempted to analysis the number of clustered formed, end to end delay, packet drop ratio and lifetime.

In recent days, unmanned aerial vehicles (UAVs) becomes more familiar because of its versatility, automation abilities, and low cost. Dynamic scene classification gained significant interest among the UAV-based surveillance systems, e.g., high-voltage power line and forest fire monitoring, which facilitate the object detection, tracking process and drastically enhances the outcome of visual surveillance. This paper proposes a new optimal deep learning-based scene classification model captured by UAVs. The proposed model involves a residual network-based features extraction (RNBFE) which extracts features from the diverse convolution layers of a deep residual network. In addition, the several parameters in RNBFE lead to many configuration errors due to manual parameter tuning. So, self-adaptive global best harmony search (SGHS) algorithm is employed for tuning the parameters of the RNBFE. The resultant feature vectors undergo classification by the use of latent variable support vector machine (LVSVM) model. The presented optimal RNBFE (ORNBFE) model has been tested using two open access datasets namely UC Merced (UCM) Land Use Dataset and WHU-RS Dataset. The presented technique attains maximum scene classification accuracy over the other recently proposed methods.

Summary Due to the significant utilization of terrestrial communication, Low Earth Orbit (LEO) satellite network is a critical part of satellite communication networks owing to its several benefits. But the efficient and trustworthy routing for LEO satellite networks (LSNs) is a difficult process because of dynamic topology, adequate link changes, and imbalanced communication load. This study devises a new hybridization of extreme learning machine (ELM) with multitask beetle antennae search (MBAS) algorithm‐based distributed routing called the MBAS‐ELM model. The proposed model determines the routes based on traffic forecasting with respect to the level of traffic circulation on the earth. The proposed method is employed for traffic forecasting at the satellite nodes (SNs). To identify the optimal routes, mobile agents (MAs) are applied to concurrently and autonomously determine for LSNs and make a decision on routing data. The experimental outcome has showcased the effective performance of the proposed model over the compared models in terms of different measures, namely, average delay, packet loss ratio (PLR), and queuing delay. The results are validated under varying simulation time and data sensing rates. The obtained outcome pointed out the superior performance of the proposed MBAS‐ELM model compared with other methods.

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