Ship Identification on Satellite Image Using Convolutional Neural Network and Random Forest

Ship identification on satellite imagery can be used for fisheries management, monitoring of smuggling activities, ship traffic services, and naval warfare. However, high-resolution satellite imagery also makes the segmentation of the ship difficult in the background, so that to handle it requires reliable features so that it can be identified adequately between large vessels, small vessels and not ships. The Convolutional Neural Network (CNN) method, which has the advantage of being able to extract features automatically and produce reliable features that facilitate ship identification. This study combines CNN ZFNet architecture with the Random Forest method. The training was conducted with the aim of knowing the accuracy of the ZFNet layers to produce the best features, which are characterized by high accuracy, combined with the Random Forest method. Testing the combination of this method is done with two parameters, namely batch size and a number of trees. The test results identify large vessels with an accuracy of 87.5% and small vessels with an accuracy of not up to 50%

Network Slicing Using FlowVisor for Enforcement of Bandwidth Isolation in SDN Virtual Networks

Software-defined networking (SDN) is becoming increasingly popular because of features such as programming control, embedded monitoring, fine-grained control, flexibility, support for many tenants, and scalability. Problems with the prior design, known as the conventional network, include the need to configure each network device individually, decentralized control, and a persistent issue with tenant enforcement for multitenant support. Tenants are unable to administer their networks without disturbing their neighbours. In this research, network slicing on SDN will ensure tenant isolation using FlowVisor and an SDN controller. Flowspace, which is part of FlowVisor capable of implementing network isolation, is for isolation in this research. Multitenancy is supported in SDN via the network slicing technique. Two types of renters were employed, and two testing procedures connectivity and functionality were run to meet the research objectives. This research produced several findings, including that all hosts were correctly linked, and the connection was achieved without turning on FlowVisor. The host function can only send and receive data from hosts with the same tenant. The research results show that FlowVisor can be applied for isolation enforcement. As a result of each tenant utilising their slice of the network without being interrupted by other slices, this research finds that utilising FlowVisor to construct Flowspace can segment the network to allow multitenancy. Expanding the number of slices for more study and testing in a real-world setting is possible