Mobile ad hoc network is a collection of wireless movable node. Movement of nodes results in a change in routes, requiringsome mechanism for determining new routes, Verity of routing protocol exist under MANETs for managing network but all existing routing technique spread maximal overhead so location aware base routing technique is applied that provide minimal overhead as compare to existing routing protocol, In mobile ad-hoc network energy constraint devicesare used the motive is to minimize required power need for data transmission and increase reliability of data delivery.In this paper minimizationof routing overhead is achieved which increasenetwork life time, save energy and increase packet delivery ratio. E-AODV-LAR approach is proposed whichprovide more reliable as well as minimum routing overhead, through results analysisis done on the bases of all network parameterand get efficient technique for MANETs environment.
Computer vision had reached a new level that allows robots from the limits of laboratories to explore the outside world. Even with progress in this area, robots are struggling to understand their location. The classification of the scene is an important step in understanding the scene. In many applications, a scene classifi- cation can be used such as a surveillance camera, self-driving, a household robot, and a database imaging system. Monitoring cameras are now everywhere installed. The accuracy of scene classification of indoor-outdoor techniques is weak. Using the Convolution Neural Net-work Model in VGG-16, this study attempts to im- prove accuracy. This research presents a new method for classifying images into classes using VGG-16. The algorithm’s outputs are validated using the SUN397 indoor-outdoor dataset, and outcomesdemonstrates that the suggested methodol- ogy outperforms existing technologies for indoor-outdoor scene classification. In this paper, Very Deep Convolutional Networks for Large-Scale Image Recognition” is what we implement. In ImageNet, a dataset of over 14 million images belonging to 1000 classes, the model achieves 92.7 percent top-5 test accuracy. It outperforms Alex Net by sequentially replacing large kernel-sized filters (11 and 5 in the first and second convolutional layers, respectively) with multiple 33 kernel-sized filters. We attain Training loss is 10percent and Training Accuracy is 96 percent in our projected work.
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