Routing Protocols Evaluation Review in Simple and Cloud Environment

In the field of information technology there are many computer jargons like cloud computing Ad-hoc, Software Define Network (SDN), network function virtualization (NFV) , and virtual machine (VM), etc. This review paper is basically a blend of brief study and review of many routing protocols used for Mobile ad hoc Networks (MANET) in the cloud as well as in simple network environment i.e. without cloud computing. This paper would also suggest the different challenges that are facing in cloud computing. The description of the different network simulators used in networking like NS2 tool, Opnet and Cisco packet tracer. The different metrics that are used in the networking are briefly explained. MANET is a group of wireless nodes that do not need centralized controlling entity as it rapidly moveschanges and forms networks to the nearest networking nodes

A Survey on Wired and Wireless Network

The wireless industry is going very fast nowadays. We can easily see the evolution from 2G to 3G and now advance to the 4G and 5G network. Before wireless networks, wired networks were commonly used in every field. But there were some disadvantages regarding mobility, quality of service and connectivity. Wired network bounded the region of the working area for the internet and it requires multiple wires to connect computer from one device to another. While on the other hand wireless network is an open source for everyone to use the internet. There is no limitation of the region and no issue regarding connectivity because data is transfer through signal which includes frequency in the form of waves. But there are also some disadvantages of wireless network regarding cost, speed,coverage, bandwidth etc. If we talk about the better network so it depends on the situation and problem

A comparative analysis of machine learning approaches for plant disease identification

Background: The problems to leaf in plants are very severe and they usually shorten the lifespan of plants. Leaf diseases are mainly caused due to three types of attacks including viral, bacterial or fungal. Diseased leaves reduce the crop production and affect the agricultural economy. Since agriculture plays a vital role in the economy, thus effective mechanism is required to detect the problem in early stages.Methods: Traditional approaches used for the identification of diseased plants are based on field visits which is time consuming and tedious. In this paper a comparative analysis of machine learning approaches has been presented for the identification of healthy and non-healthy plant leaves. For experimental purpose three different types of plant leaves have been selected namely, cabbage, citrus and sorghum. In order to classify healthy and non-healthy plant leaves color based features such as pixels, statistical features such as mean, standard deviation, min, max and descriptors such as Histogram of Oriented Gradients (HOG) have been used.Results:  382 images of cabbage, 539 images of citrus and 262 images of sorghum were used as the primary dataset. The 40% data was utilized for testing and 60% were used for training which consisted of both healthy and damaged leaves. The results showed that random forest classifier is the best machine method for classification of healthy and diseased plant leaves.Conclusion:  From the extensive experimentation it is concluded that features such as color information, statistical distribution and histogram of gradients provides sufficient clue for the classification of healthy and non-healthy plants