فهرست مطالب هادی پورمحمدی

Mosquitoes are the main cause of the spread of dangerous diseases such as malaria, yellow fever, dengue fever, and Zika. The most effective way to control these diseases is to correctly identify the types of mosquito species. In the traditional method of identifying mosquitoes, identification is based on morphological diagnoses by specialized human beings with special skills. The most important classification challenge is to reduce the number of experts and the great diversity of different species of mosquitoes. In order to overcome this challenge, developing an automated method based on deep learning architectures to identify and classify mosquitoes will be a valuable resource for non-specialists.This study proposes a convolutional network model that integrates the ResNet101 architecture and the Mask_RCNN technique to segment and classifies mosquito images. 2354 mosquito images of three species of Anopheles, Aedes, and Culex are compared with each other. In the proposed model, instead of entering the network as a complete image of a mosquito, first, the images are segmented, and then different parts of the abdomen, legs, wings, and head are given to the network as input. The corresponding binary mask of the described parts of the mosquito body is produced by the convolution network to extract the feature for each separate part and then calculate the loss value between the classified values and the image label. The evaluation results showed that the extraction of mosquito anatomy images affects the faster classification of images and the network performed better with 97.84% accuracy than normal.

Phylogenetics is a field in bioinformatics that categorizes and structures the evolutionary hisotiry of currently living species. Phylogenetics uses network tools to represent evolutionary histories in a comprehensive model. The simpleset possible network model is a tree model that is called a phylogenetic tree. Phylogenetic trees are divided into two categories, which are rooted and un-rooted. In this research, rooted phylogenetic trees are of interest. There are different types of input for constructing rooted phylogenetic trees. Rooted triplets are one of the important inputs for constructing rooted phylogenetic trees, which are used in this study. A rooted triplet is a rooted binary tree with three leaves. The rooted triplet is the most simplest model, which contains valuable information. The problem of building a rooted phylogenetic tree that includes the maximum number of a set of rooted triplets is an optimizataion problem, known as MRTC problem. The important challenge in phylogenetics is that MRTC is NP-hard. Therefore, the focus of this research is to introduce a novel algorithm for MRTC problem. The aim of the new algorithm is to improve the consistency of input rooted triplets with the final rooted phylogenetic tree. In order to indicate the performance of the new algorithm, it is compared with on of the best methods on biological data which is called TRH. The Experimental results on triplets that are obtained from biological data show that our new algorithm outperforms TRH base on time complexity and consistency.