In wireless sensor network (WSN), increasing the network life span remains as a crucial challenge yet to be resolved. The modeling of effectual methods is necessary for conserving the scarce energy resources in WSN. To overcome such issues, cross‐layer protocols are exploited, which concerns routing the messages with increased lifetime. This study introduces a new cross‐layer design routing model under a clustering‐based approach. More importantly, the cluster head is optimally selected by a new hybrid algorithm termed as moth flame integrated dragonfly algorithm. Moreover, the optimal selection of cluster head is carried out based on parameters such as energy consumption, delay, distance, throughput, security, and overhead. Finally, the supremacy of the presented model is proved over existing models in terms of alive node analysis and network lifetime analysis. The experimental outcomes show that the proposed algorithm for test case 3 has accomplished a higher value of 66.229, which is 29.07%, 13.33%, 26.36%, and 9.67% better than conventional ant lion optimisation approach, grouped grey wolf search optimisation, firefly replaced position update in da, and alpha wolf‐assisted whale optimization algorithm, respectively, for median case scenario.

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Recently, on the internet, the level of image and video forgery has augmented due to the augmentation in the malware, which has facilitated user (anyone) to upload, download, or share objects online comprising audio, images, or video. Recently, Convolution Neural Network (CNN) has turn into a de-facto technique for classification of multi-dimensional data and it renders standard and also highly effectual network layer arrangements. But these architectures are limited by the speed due to massive number of calculations needed for training in addition to testing the network and also, it might render less accuracy. To trounce these issues, this paper proposed to ameliorate the image and video forgery detection’s efficiency utilizing hybrid CNN. Initially, the intensive along with incremental learning phase is carried out. After that, the hybrid CNN is implemented to detect the image together with video forgery. The developed system was tested on images together with videos for different kinds of forgeries, and it was observed that the proposed work obtains more than 98% accuracy for both testing as well as validation sets.

It is necessary to correctly and precisely achieve eye localization, which is a fundamental step for the initialization for other eye localization based applications. There are various methods including special equipment based methods and image based methods to perform this task. Special equipment based methods are very accurate but not practical for day to day use. Image based approaches are user friendly, allows free head movement, avoids specialized hardware and infrared exposure but more difficult to implement. Performance is analysed for state of the art eye localization methods for real time vision interface using low grade camera that use similar objective criterion for error measurement on standard dataset for fair judgment. Finally their localization results are compared based on various error values and rank.