Bee Colony optimization techniques are inspired by the high level of mutual intelligence shown by the natural bees in the food foraging process. It is a population based natural search algorithm which provides the base to solve metaheuristic computational optimization problems. In this paper we have carried out a literature review of the applications of BCO into various areas of computational problems where they prove their worth in providing optimized solutions. We have further carried out a tabular comparison of the work performed by the various researchers by applying the the BCO as optimization algorithms for solving the Optimization Problems.

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Neural networks and IoT are some top fields of research in computer science nowadays. Inspired by this, this article works on using and creating an efficient neural networks model for colorizing images and transports them to remote systems through IoT deployment tools. This article develops two models, Alpha and Beta, for the colorization of the greyscale images. Efficient models are developed to lessen the loss rate to around 0.005. Further, it also develops an efficient model for the captioning of an image. The paper then describes the use of tools like AWS Greengrass and Docker for the deployment of different neural networks models, providing a comparative analysis among them, combining neural networks with IoT deployment tools.