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How Artificial Intelligence is Influencing the Drone Industry For Improved Performance - BartDay

#artificialintelligence

The global Artificial Intelligence (AI) -based Drone Software market size is expected to continue its rapid growth through the next five years, according to several reports. A Research And Markets report said that: "Digital industries are now implementing AI in their devices to improve in their fields across the globe. Application of AI in drone is one such advancement which has brought a revolutionary change in the operations of the industries. AI enables storing and managing the data in bulk which enables the drones to give better performance. The application of AI can enable the drones to function as per the user's command and with longer distance coverage. In addition, AI integrated drone enables the industries to keep a bird-eye view of the land for vigilance & mapping purpose. The increased income levels have brought up new demands that have resulted in increasing supply of goods. Manufacturers are bringing in new features by implementing AI in their devices such as mobiles so ...


How Artificial Intelligence is Influencing the Drone Industry For Improved Performance

#artificialintelligence

PALM BEACH, Florida, July 16, 2020 /PRNewswire/ — The global Artificial Intelligence (AI) -based Drone Software market size is expected to continue …


Improved Performance of Unsupervised Method by Renovated K-Means

Ashok, P., Nawaz, G. M Kadhar, Elayaraja, E., Vadivel, V.

arXiv.org Machine Learning

Clustering is a separation of data into groups of similar objects. Every group called cluster consists of objects that are similar to one another and dissimilar to objects of other groups. In this paper, the K-Means algorithm is implemented by three distance functions and to identify the optimal distance function for clustering methods. The proposed K-Means algorithm is compared with K-Means, Static Weighted K-Means (SWK-Means) and Dynamic Weighted K-Means (DWK-Means) algorithm by using Davis Bouldin index, Execution Time and Iteration count methods. Experimental results show that the proposed K-Means algorithm performed better on Iris and Wine dataset when compared with other three clustering methods.