dc.contributor.author | HIMEL, GALIB MUHAMMAD SHAHRIAR | |
dc.date.accessioned | 2019-02-20T08:45:57Z | |
dc.date.available | 2019-02-20T08:45:57Z | |
dc.date.issued | 2019-02-16 | |
dc.identifier.uri | http://dspace.uiu.ac.bd/handle/52243/823 | |
dc.description.abstract | The purpose of the research is to design a new drone architecture which will be capable
of moving autonomously through most of the environments by interacting with the server
itself. The main feature is that it will be able to move through air, land and water.
Nowadays various drones are available. But a drone which can move through any
environment is never seen before. The main motive is to use the drone in rescue mission
and get accuracy in rescue missions in the cases of environmental & natural calamities
and make it easier than previous times. Besides this drone will use the environmental data
and bio information as a method of communication while interacting with the
environment. Environmental data learning can be used to communicate with the nature
and gather bio-information and environmental data from the environment and living
beings. We hope this drone will be also able to move under water. For communicating
with the environment and learning about the environment this drone will use two kinds of
database: temporary database and online database. This research paper proposes a new
design and modest algorithm which will help to move the drone autonomously avoiding
most of the obstacles in its way. By gathering bio-information and environmental data the
proposed ALW (Air, Land, and Water) drone will be able to communicate with the
environment and adapt the changes in the environment and communicate with the server
to act properly to be successful in its rescue mission. In this paper we used R Studio & R
programming language to compare the accuracy results among several Machine Learning
algorithms for several datasets. Based on the simulation results we can say that the drone
will be able to predict the disaster accurately and recognize animal correctly. | en_US |
dc.language.iso | en_US | en_US |
dc.publisher | United International University | en_US |
dc.subject | drone | en_US |
dc.subject | Animal Recognition | en_US |
dc.subject | Deep Learning | en_US |
dc.subject | Image Recognition | en_US |
dc.title | A Design of an Autonomous Drone for Animal Recognition | en_US |
dc.type | Thesis | en_US |