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.

Predictive model for thick steel laser cutting quality using artificial neural networks

2007 ◽  
Author(s):  
M. Sundar ◽  
A. K. Nath ◽  
D. K. Bandyopadhyay ◽  
S. P. Chaudhuri ◽  
P. K. Dey ◽  
...  
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Predictive Model ◽  
Laser Cutting ◽  
Thick Steel ◽  
Artificial Neural

scholarly journals Approximation of the process of changes in deformation of land surface using artificial neural networks

2018 ◽  
Vol 55 ◽  
pp. 00009
Author(s):  
Maria Mrówczyńska ◽  
Jacek Sztubecki
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Land Surface ◽  
Reference System ◽  
Surface Deformation ◽  
Copper Mining ◽  
Vertical Displacements ◽  
Artificial Neural ◽  
The Stability ◽  
The Impact

Artificial neural networks are an interesting method for modelling phenomena, including spatial phenomena, which are difficult to describe with known mathematical models. The properties of neural networks enable their practical application for solving such problems as: approximation, interpolation, identification and classification of patterns, compression, prediction, etc. The article presents the use of multilayer feedforward artificial neural networks for describing the process of changes in land surface deformation in the area of the Legnica-Głogów Copper Mining Centre, located in the southern part of the Fore Sudetic Monocline. Results provided by geodesic monitoring, which consists of land surveying and interpreting data obtained in this way, are undoubtedly significant in terms of identifying the impact of mining on the land surface the results of measurements carried out by precise levelling in the years 19672014 were used to determine changes in land deformation in the Legnica-Głogów Copper Mining Centre. The concept of a flexible reference system was used to assess the stability of points in the measurement and control network stabilized in order to determine vertical displacements. However, the reference system itself was identified on the basis of the critical value of the increment of the square of the norm of corrections to the observations.

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.

An ROP Predictive Model in Nile Delta Area Using Artificial Neural Networks

2017 ◽  
Author(s):  
Mustafa M. Amer ◽  
Abdel Sattar Dahab ◽  
Abdel-Alim Hashem El-Sayed
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Predictive Model ◽  
Nile Delta ◽  
Delta Area ◽  
Artificial Neural

Construction of a predictive model for concentration of nickel and vanadium in vacuum residues of crude oils using artificial neural networks and LIBS

2012 ◽  
Vol 51 (7) ◽  
pp. B108 ◽  
Author(s):  
José L. Tarazona ◽  
Jáder Guerrero ◽  
Rafael Cabanzo ◽  
E. Mejía-Ospino
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Predictive Model ◽  
Crude Oils ◽  
Artificial Neural

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
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