[ITAL]K[/ITAL]-Band Imaging of 52 B3-VLA Quasars: Nucleus and Host Properties

Abstract Many studies have characterized microbiomes of western individuals. However, studies involving non-westerners are scarce. This study characterizes the skin microbiomes of Chinese individuals. Skin-associated genera, including Propionibacterium, Corynebacterium, Staphylococcus and Enhydrobacter were prevalent. Extensive inter-individual microbiome variations were detected, with core genera present in all individuals constituting a minority of genera detected. Species-level analyses presented dominance of potential opportunistic pathogens in respective genera. Host properties including age, gender and household were associated with variations in community structure. For all sampled sites, skin microbiomes within an individual is more similar than that of different co-habiting individuals, which is in turn more similar than individuals living in different households. Network analyses highlighted general and skin-site specific relationships between genera. Comparison of microbiomes from different population groups revealed race-based clustering explained by community membership (Global R = 0.968) and structure (Global R = 0.589), contributing to enlargement of the skin pan-microbiome. This study provides the foundation for subsequent in-depth characterization and microbial interactive analyses on the skin and other parts of the human body in different racial groups and an appreciation that the human skin pan-microbiome can be much larger than that of a single population.

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Dr. Phish: Phishing Website Detector

E3S Web of Conferences ◽

10.1051/e3sconf/202129701032 ◽

2021 ◽

Vol 297 ◽

pp. 01032

Author(s):

Harish Kumar ◽

Anshal Prasad ◽

Ninad Rane ◽

Nilay Tamane ◽

Anjali Yeole

Keyword(s):

Machine Learning ◽

Data Mining ◽

Machine Learning Algorithms ◽

Machine Learning Techniques ◽

Cyber Crime ◽

Data Mining Algorithms ◽

Learning Techniques ◽

Mining Algorithms ◽

Host Properties ◽

New Strategies

Phishing is a common attack on credulous people by making them disclose their unique information. It is a type of cyber-crime where false sites allure exploited people to give delicate data. This paper deals with methods for detecting phishing websites by analyzing various features of URLs by Machine learning techniques. This experimentation discusses the methods used for detection of phishing websites based on lexical features, host properties and page importance properties. We consider various data mining algorithms for evaluation of the features in order to get a better understanding of the structure of URLs that spread phishing. To protect end users from visiting these sites, we can try to identify the phishing URLs by analyzing their lexical and host-based features.A particular challenge in this domain is that criminals are constantly making new strategies to counter our defense measures. To succeed in this contest, we need Machine Learning algorithms that continually adapt to new examples and features of phishing URLs.

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