Topological Evaluation of Certain Computer Networks by Contraharmonic-Quadratic Indices

In various fields,different networks are used,most of the time not of a single kind;but rather a mix of at least two networks.These kinds of networks are called bridge networks which are utilized in interconnection networks of PC,portable networks,spine of internet,networks engaged with advanced mechanics,power generation interconnection,bio-informatics and substance intensify structures.Any number that can be entirely calculated by a graph is called graph invariants.Countless mathematical graph invariants have been portrayed and utilized for connection investigation during the latest twenty years.Nevertheless,no trustworthy evaluation has been embraced to pick,how much these invariants are associated with a network graph or subatomic graph.In this paper,it will discuss three unmistakable varieties of bridge networks with an incredible capacity of assumption in the field of computer science,chemistry,physics,drug industry,informatics and arithmetic in setting with physical and manufactured developments and networks,since Contraharmonic-quadratic invariants(CQIs)are recently presented and have different figure qualities for different varieties of bridge graphs or networks.The study settled the geography of bridge graphs/networks of three novel sorts with two kinds of CQI and Quadratic-Contraharmonic Indices(QCIs).The deduced results can be used for the modeling of the above-mentioned networks.

Automated Grading of Breast Cancer Histopathology Images Using Multilayered Autoencoder

Breast cancer(BC)is the most widely recognized cancer in women worldwide.By 2018,627,000 women had died of breast cancer(World Health Organization Report 2018).To diagnose BC,the evaluation of tumours is achieved by analysis of histological specimens.At present,the Nottingham Bloom Richardson framework is the least expensive approach used to grade BC aggressiveness.Pathologists contemplate three elements,1.mitotic count,2.gland formation,and 3.nuclear atypia,which is a laborious process that witness’s variations in expert’s opinions.Recently,some algorithms have been proposed for the detection of mitotic cells,but nuclear atypia in breast cancer histopathology has not received much consideration.Nuclear atypia analysis is performed not only to grade BC but also to provide critical information in the discrimination of normal breast,non-invasive breast(usual ductal hyperplasia,atypical ductal hyperplasia)and pre-invasive breast(ductal carcinoma in situ)and invasive breast lesions.We proposed a deep-stacked multi-layer autoencoder ensemble with a softmax layer for the feature extraction and classification process.The classification results show the value of the multilayer autoencoder model in the evaluation of nuclear polymorphisms.The proposed method has indicated promising results,making them more fit in breast cancer grading.