Predictive Model for Raised Floor Plenum Data Center using Artificial Neural Networks

2021 ◽  
pp. 102397
Author(s):  
Anashusen Saiyad ◽  
Asif Patel ◽  
Yogesh Fulpagare ◽  
Atul Bhargav
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Predictive Model ◽  
Data Center ◽  
Artificial Neural

Developing a Predictive Model for Plasmodium knowlesi–Susceptible Areas in Malaysia Using Geospatial Data and Artificial Neural Networks

2021 ◽  
pp. 101053952110486
Author(s):  
Rozita Hod ◽  
Siti Aisah Mokhtar ◽  
Farrah Melissa Muharam ◽  
Ummi Kalthom Shamsudin ◽  
Jamal Hisham Hashim
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Environmental Factors ◽  
Predictive Model ◽  
Land Surface ◽  
Plasmodium Knowlesi ◽  
Geospatial Data ◽  
Susceptibility Index ◽  
Malaria Susceptibility ◽  
Artificial Neural

Plasmodium knowlesi is an emerging species for malaria in Malaysia, particularly in East Malaysia. This infection contributes to almost half of all malaria cases and deaths in Malaysia and poses a challenge in eradicating malaria. The aim of this study was to develop a predictive model for P. knowlesi susceptibility areas in Sabah, Malaysia, using geospatial data and artificial neural networks (ANNs). Weekly malaria cases from 2013 to 2014 were used to identify the malaria hotspot areas. The association of malaria cases with environmental factors (elevation, water bodies, and population density, and satellite images providing rainfall, land surface temperature, and normalized difference vegetation indices) were statistically determined. The significant environmental factors were used as input for the ANN analysis to predict malaria cases. Finally, the malaria susceptibility index and zones were mapped out. The results suggested integrating geospatial data and ANNs to predict malaria cases, with overall correlation coefficient of 0.70 and overall accuracy of 91.04%. From the malaria susceptibility index and zoning analyses, it was found that areas located along the Crocker Range of Sabah and the East part of Sabah were highly susceptible to P. knowlesi infections. Following this analysis, targetted entomological mapping and malaria control programs can be initiated.

scholarly journals What If Violent Behavior Was a Coping Strategy? Approaching a Model Based on Artificial Neural Networks

2020 ◽  
Vol 12 (18) ◽  
pp. 7396
Author(s):  
Juan Pedro Martínez Ramón ◽  
Francisco Manuel Morales Rodríguez
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Coping Strategies ◽  
School Violence ◽  
Predictive Model ◽  
Perceived Stress ◽  
Coping Strategy ◽  
Model Based ◽  
Violent Behaviors ◽  
Artificial Neural

The aggressor sets in motion dysfunctional and violent behaviors with others in the dynamic of bullying. These behaviors can be understood as misfit coping strategies in response to environmental demands perceived as stressful, putting at risk the quality of education. The aim of this study was to develop a predictive model based on artificial neural networks (ANN) to forecast a violent coping strategy based on perceived stress, resilience, other coping strategies and various socio-demographic variables. For this purpose, the Stress Coping Questionnaire (SCQ), the Perceived Stress Scale (PSS) and the Brief Resilient Coping Scale (BRCS) were administered to 283 participants from the educational field (71.5% women). The design was cross-sectional. An inferential analysis (multilayer perception ANN) was performed with SPSS version 24. The results showed a predictive model that took into consideration the subject’s stress levels, personal assessment and strategies such as negative self-targeting or avoidance to predict open emotional expression (a coping strategy defined by violent behaviors) in approximately four out of five cases. The conclusions emphasis the need for considering problem solving, stress management and coping skills to prevent school violence and improve the social environment through sustainable psychological measures.

A predictive model for slip resistance using artificial neural networks

1995 ◽  
Vol 27 (3) ◽  
pp. 374-381 ◽  
Author(s):  
JANET M. TWOMEY ◽  
ALICE E. SMITH ◽  
MARK S. REDFERN
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Predictive Model ◽  
Slip Resistance ◽  
Artificial Neural
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