The social shape of sperm: Using an integrative machine-learning approach to examine sperm ultrastructure and collective motility

Machine Learning used for many real-time issues in many organizations and the purpose of social media analytics machine learning models are used most prominently and to identify the genuine accounts and the information in the social media we are here with a new pattern of identification. In this pattern of the model, we are proposing some words which are hidden to identify the accounts with fake data and the some of the steps we are proposing will help to identify the fake and unwanted accounts in Facebook in an efficient manner. Clustering in machine learning will be used, and before that, we are proposing a suitable architecture and the process flow which can identify the fake and suspicious accounts in the social media. This article will be on machine learning implementations and will be working on OSN (online social networks). Our work will be more on Facebook which is maintaining more amount of accounts and identifying which are overruling the rules on privacy and protection of the user content. Machine learning supervised models will be used for text classification, and CNN of unsupervised learning performs the image classification, and the explanation will be given in the implementation phase. There are large numbers of algorithms we can consider for machine learning implementations in the social networking and here we considered mainly on CNN because of having the feasibility of implementation in different rules and we can eliminate the features we don’t need. Feature extraction is quite simple using CNN.

Download Full-text

Detecting malicious users in the social networks using machine learning approach

International Journal of Social Computing and Cyber-Physical Systems ◽

10.1504/ijsccps.2021.10041246 ◽

2021 ◽

Vol 2 (3) ◽

pp. 229

Author(s):

U. Tanuja ◽

B. Ramesh ◽

H.L. Gururaj ◽

V. Janhavi

Keyword(s):

Machine Learning ◽

Social Networks ◽

Learning Approach ◽

The Social ◽

Machine Learning Approach

Download Full-text

Associated factors of white matter hyperintensity volume: a machine-learning approach

Scientific Reports ◽

10.1038/s41598-021-81883-4 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Sergio Grosu ◽

Susanne Rospleszcz ◽

Felix Hartmann ◽

Mohamad Habes ◽

Fabian Bamberg ◽

...

Keyword(s):

Machine Learning ◽

White Matter ◽

Social Environment ◽

White Matter Hyperintensity ◽

Cerebral White Matter ◽

Learning Approach ◽

Male Age ◽

Independent Population ◽

The Social ◽

Machine Learning Approach

AbstractTo identify the most important parameters associated with cerebral white matter hyperintensities (WMH), in consideration of potential collinearity, we used a data-driven machine-learning approach. We analysed two independent cohorts (KORA and SHIP). WMH volumes were derived from cMRI-images (FLAIR). 90 (KORA) and 34 (SHIP) potential determinants of WMH including measures of diabetes, blood-pressure, medication-intake, sociodemographics, life-style factors, somatic/depressive-symptoms and sleep were collected. Elastic net regression was used to identify relevant predictor covariates associated with WMH volume. The ten most frequently selected variables in KORA were subsequently examined for robustness in SHIP. The final KORA sample consisted of 370 participants (58% male; age 55.7 ± 9.1 years), the SHIP sample comprised 854 participants (38% male; age 53.9 ± 9.3 years). The most often selected and highly replicable parameters associated with WMH volume were in descending order age, hypertension, components of the social environment (i.e. widowed, living alone) and prediabetes. A systematic machine-learning based analysis of two independent, population-based cohorts showed, that besides age and hypertension, prediabetes and components of the social environment might play important roles in the development of WMH. Our results enable personal risk assessment for the development of WMH and inform prevention strategies tailored to the individual patient.

Download Full-text