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Bharti, Priyanka
- Resilience and Depression Among the Adults of Eastern Uttar Pradesh
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Authors
Affiliations
1 Department of Psychology, D.D.U.Gorakhpur University, Gorakhpur, IN
2 Department of Psychology, D.D.U.Gorakhpur University, Gorakhpur, Uttar Pradesh, IN
1 Department of Psychology, D.D.U.Gorakhpur University, Gorakhpur, IN
2 Department of Psychology, D.D.U.Gorakhpur University, Gorakhpur, Uttar Pradesh, IN
Source
IAHRW International Journal of Social Sciences Review, Vol 3, No 3 (2015), Pagination: 421-423Abstract
The problem of depression is very common and serious among people. The depression can affect a person's thought, feeling, behavior and sense of wellbeing whereas Resilience is the process of adapting well in the face of adversity, trauma, tragedy, threats and significant source of stress. The purpose of this study was to explore the nature of depression among the low and high resilient people. A sample of 180 respondents (90 low resilient& 90 high resilient) of two genders Male & Female and three age groups (18-22, 23-28 & 29-35) were taken. Thus the design was 2×2×3.Brief Resilience scale and Beck depression inventory was used. The analyses revealed that the two genders differ significantly on the level of depression. The interaction effect of resilience and gender was found to be significant further the interaction of resilience, gender and age was also found to be significant.Keywords
Resilience, Depression, Adults.- Specifying CPU Requirements for HPC Applications via ML Techniques
Abstract Views :218 |
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Authors
Affiliations
1 School of C&IT, REVA University, Bengaluru, IN
2 School of CSA, REVA University, Bengaluru, IN
1 School of C&IT, REVA University, Bengaluru, IN
2 School of CSA, REVA University, Bengaluru, IN
Source
International Journal of Advanced Networking and Applications, Vol 10, No SP 5 (2019), Pagination: 1-3Abstract
Resource distribution in data centers is difficult for service providers because of the structures of usage and condition setup decisions. Customers encounter issues to anticipate the amount of CPU and memory required for job execution, and henceforth are not ready to assess when work yield shall be accessible to plan for next analyses. Systems that utilize cluster scheduler structures to gauge job execution time exists in the literature. Notwithstanding, we have seen that such methods are not appropriate for anticipating CPU utilization. In this paper, we assist customers to figure out their applications CPU usage utilizing machine learning (ML) techniques. We analyze how scheduler can be utilized to predict CPU utilization through ML techniques, and its evaluation on two frameworks containing an enormous number of user jobs.Keywords
HPC, CPU Prediction, Machine Learning.References
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