In this paper we have proposed the implementation of optimized path. We are residing on a geographical area and there is no road. So path planning is a key factor to find out the optimized path to travel to destination. This paper describes a novel approach of autonomous navigation for outdoor vehicles which includes terrain mapping, obstacle detection and avoidance, and goal seeking in cross-country using Swarm Intelligence. This paper combines the strengths of both Particle Swarm optimization (PSO) for finding out the natural paths moreover keeping the obstacle detection from the satellite image, and Biogeography Based Optimization (BBO) algorithm for obstacle avoidance and move towards the shortest path to the goal. In this we have used the classified image. And find out the shortest path in order to find the cross country path planning phenomenon. We have assumed the source and destination in image and various paths which are called the natural paths generated by particle swarm optimization. The localization of islands positions has been done and through that the final optimized path which is called the shortest path has been find out for the destination. The HSI which is taken in islands is the shortest distance from the destination.
Clustering is one of the data analysis methods that are widely used in data mining. In this method, we partitioned the data into different subset which is known as cluster. Cluster analysis is the data reduction toll for classifying a "mountain" of information into manageable meaningful piles. This method is vast research area in the field of data mining. In this paper, a partitioning clustering method that is K-Medoids algorithm is used with Bat algorithm. We proposed a new algorithm based on the echolocation behaviour of bats to know the initial value to overcome the K-Medoids issues. In this algorithm, we can find the initial representative object easily with the help of using Bat algorithm. They provide us better cluster analysis and we can achieve efficiency. This paper introduces the combination of K-Medoids clustering algorithm and Bat Algorithm. In this paper we show the difference between K-Medoid Clustering Technique with Bat Algorithm & K-medoid itself.
The planning of optimal path is an important research domain due to vast applications of optimal path planning in the robotics, simulation and modeling, computer graphics, virtual reality estimation and animation, and bioinformatics. The optimal path planning application demands to determine the collision free shortest and optimal path. There can be numerous possibilities that to find the path with optimal length based on different types of available obstacles during the path and different types of workspace environment. This research work aims to identify the optimum path from the initial source-point to final point for the unknown workspace environment consists of static obstacles. For this experimentation, swarm intelligence based hybrid concepts are considered as the work collaboration and intelligence behavior of swarm agents provides the resourceful solution of NP hard problems. Here, the hybridization of concepts makes the solution of problem more efficient. Among swarm intelligence concepts, cuckoo search (CS) algorithm is one of the efficient algorithms due to clever behavior and brood parasitic property of cuckoo birds. In this research work, two hybrid concepts are proposed. First algorithm is the hybridized concept of cuckoo search with bat algorithm (BA) termed as CS-BAPP. Another algorithm is the hybridized concept of cuckoo search with firefly algorithm (FA) termed as CS-FAPP. Both algorithms are initially tested on the benchmarks functions and applied to the path planning problem. For path planning, a real time dataset area of Alwar region situated at Rajasthan (India) is considered. The selected region consists of urban and dense vegetation land cover features. The results for the optimal path planning on Alwar region are assessed using the evaluation metrics of minimum number of iterations, error rate, success rate, and simulation time. Moreover, the results are also compared with the individual FA, BA, and CS along with the comparison of hybrid concepts.
The principle point of this paper is to talk about the Video surveillance system plays major role in the security applications now a days. The need for surveillance systems are increasing drastically. Internet of things makes Video surveillance more effective. Internet of Things is an interrelated communication network for several systems and having the ability to transfer the information through the network without human or computer interaction. This paper reviews the network traffic due to Video surveillance and its management with Software Defined Networking. This paper also reviews the applications of Internet of things in Video surveillance system.
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