In today's world, most of the people are using social networks for day-to-day activities. The most frequently used social sites are Facebook, Twitter, Google+, etc. These popular social networks are used by some of the users for abnormal or illegal activities. It is very important and necessary to identify and avoid such illegal activities without harming anyone in the society. In recent decades, social networks are becoming a popular research area for most researchers. Many authors are doing research on social network datasets and proposing various anomaly detection mechanisms to identify anomalous activities in both static and dynamic growing social networks. Various anomaly detection techniques are proposed by the authors to investigate malicious activities in social networks. In general, the process of identifying anomaly activities of the users in the given dataset is called anomaly detection. The anomaly detection in social networks is the process of investigating whether the users of the given social networks are involved in illegal activities or not. In this work, we proposed a most elegant approach to identify the anomalous or outlier users in the given social network. The proposed approach is considering the users participated in multiple communities of social networks. The designed algorithms are implemented and tested in a big data environment three node cluster using open source Hadoop ecosystem tools. Algorithm1 is used to investigate the nodes/users who participated in multiple communities of the given social network’s dataset. Algorithm2 takes the set of users participated in multiple communities and apply graph metrics such as degree and community score to predict the users involved in the anomalous activity.
In the nuclear medicine, beta nuclides are released during the treatment. This beta interacts with bone and muscle and produces external Bremsstrahlung (EB) radiation. Present work formulated a new method to evaluate the EB spectrum and hence the Bremsstrahlung dose of therapeutic beta nuclides (Lu-177, Sr-90, Sm-153, I-153, Cs-137, Au-201, Dy-165, Mo-99, Sr-89, Fe-59, P-32, Ho-166, Sr-92, Re-188, Y-90, Pr-147, Co-60, K-42) in bone and muscle. The Bremsstrahlung yields of these beta nuclides are also estimated. Bremsstrahlung production is higher in bone than that of muscle. Presented data provides a quick and convenient reference for radiation protection and it can be quickly employed to give a first pass dose estimate prior to a more detailed experimental study.
The photon interaction parameters such as mass attenuation coefficient (μ/ρ), effective atomic number (Z eff) and effective electron density (N el) must be identical for the phantom material and their tissue. In the present study, the μ/ρ, Z eff, and N el for muscle, breast, lung tissue have been computed, and their substitutes such as Griffith muscle, Griffith breast, Griffith lung, Alderson muscle A, Alderson muscle, and Alderson lung. Also compared were μ/ρ, Z eff, and N el for muscle, breast, lung tissue, and their substitutes. It can be shown that Alderson muscle B is better substitute for muscle than Griffith muscle and Alderson muscle A. Similarly, the photon interaction parameters of tissue substitutes of lung and breast with their original tissue were also compared.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
customersupport@researchsolutions.com
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.