Three Dimensional Route Planning for Multiple Unmanned Aerial Vehicles using Salp Swarm Algorithm Priyansh Saxena, Raahat Gupta, Akshat Maheshwari, Ram Kishan Dewangan, Gaurav Kaushal, Ritu Tiwari ABV - Indian Institute of Information T echnology and Management Gwalior, MP, India * These authors contributed equally to this research Abstract: Route planning for multiple Unmanned Aerial Vehicles (UAVs) is a series of translation and rotational steps from a given start location to the destination goal location. The goal of the route planning problem is to determine the most optimal route avoiding any collisions with the obstacles present in the environment. Route planning is an NPhard optimization problem. In this paper, a newly proposed Salp Swarm Algorithm (SSA) is used, and its performance is compared with deterministic and other Nature-Inspired Algorithms (NIAs). The results illustrate that SSA outperforms all the other meta-heuristic algorithms in route planning for multiple UAVs in a 3D environment. The proposed approach improves the average cost and overall time by 1. 25% and 6 .035% Route planning is involved in many real-life applications like robot navigation, self-driving car, autonomous UAV for search and rescue operations in dangerous ground-zero situations, civilian surveillance, military combat and even commercial services like package delivery by drones.

Machine translation (MT) is an area of study in Natural Language processing which deals with the automatic translation of human language, from one language to another by the computer. Having a rich research history spanning nearly three decades, Machine translation is one of the most sought after area of research in the linguistics and computational community. In this paper, we investigate the models based on deep learning that have achieved substantial progress in recent years and becoming the prominent method in MT. We shall discuss the two main deep-learning based Machine Translation methods, one at component or domain level which leverages deep learning models to enhance the efficacy of Statistical Machine Translation (SMT) and end-to-end deep learning models in MT which uses neural networks to find correspondence between the source and target languages using the encoder-decoder architecture. We conclude this paper by providing a time line of the major research problems solved by the researchers and also provide a comprehensive overview of present areas of research in Neural Machine Translation.