Artificial intelligence to link environmental endocrine disruptors (EEDs) with bone diseases

2021 ◽  
Author(s):  
Khaled A. Al-Utaibi ◽  
M. Idrees ◽  
Ayesha Sohail ◽  
Fatima Arif ◽  
Alessandro Nutini ◽  
...  
Keyword(s):  
Neural Network ◽  
Bone Mass ◽  
Network Model ◽  
Neural Network Model ◽  
The United States ◽  
Environmental Stresses ◽  
Bone Diseases ◽  
Mineral Density ◽  
Weather Situation ◽  
The Impact

Our endocrine system is not only complex, but is also enormously sensitive to the imbalances caused by the environmental stressors, extreme weather situation, and other geographical factors. The endocrine disruptions are associated with the bone diseases. Osteoporosis is a bone disorder that occurs when bone mineral density and bone mass decrease. It affects women and men of all races and ethnic groups, causing bone weakness and the risk of fractures. Environmental stresses are referred to physical, chemical, and biological factors that can impact species productivity. This research aims to examine the impact of environmental stresses on bone diseases like osteoporosis and low bone mass (LBM) in the United States (US). For this purpose, we use an artificial neural network model to evaluate the correlation between the data. A multilayer neural network model is constructed using the Levenberg–Marquardt training algorithm, and its performance is evaluated by mean absolute error and coefficient of correlation. The data of osteoporosis and LBM cases in the US are divided into three groups, including gender group, age group, and race/ethnicity group. Each group shows a positive correlation with environmental stresses and thus the endocrinology.

scholarly journals Statistical Modeling and Prediction for Tourism Economy Using Dendritic Neural Network

2017 ◽  
Vol 2017 ◽  
pp. 1-9 ◽  
Author(s):  
Ying Yu ◽  
Yirui Wang ◽  
Shangce Gao ◽  
Zheng Tang
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Rapid Development ◽  
Seasonal Trend ◽  
Moving Averages ◽  
Forecasting Methods ◽  
Tourism Economy ◽  
The Impact ◽  
D Model

With the impact of global internationalization, tourism economy has also been a rapid development. The increasing interest aroused by more advanced forecasting methods leads us to innovate forecasting methods. In this paper, the seasonal trend autoregressive integrated moving averages with dendritic neural network model (SA-D model) is proposed to perform the tourism demand forecasting. First, we use the seasonal trend autoregressive integrated moving averages model (SARIMA model) to exclude the long-term linear trend and then train the residual data by the dendritic neural network model and make a short-term prediction. As the result showed in this paper, the SA-D model can achieve considerably better predictive performances. In order to demonstrate the effectiveness of the SA-D model, we also use the data that other authors used in the other models and compare the results. It also proved that the SA-D model achieved good predictive performances in terms of the normalized mean square error, absolute percentage of error, and correlation coefficient.

Market orientation and performance: modelling a neural network

2009 ◽  
Vol 43 (3/4) ◽  
pp. 421-437 ◽  
Author(s):  
Manuela Silva ◽  
Luiz Moutinho ◽  
Arnaldo Coelho ◽  
Alzira Marques
Keyword(s):  
Neural Network ◽  
Market Orientation ◽  
Network Model ◽  
Neural Network Model ◽  
Performance Modelling ◽  
Network Modelling ◽  
Content Type ◽  
Market Intelligence ◽  
And Performance ◽  
The Impact

PurposeThis paper aims to investigate the impact of market orientation (MO) on performance using a neural network model in order to find new linkages and new explanations for this relationship.Design/methodology/approachThis investigation is based on a survey data collection from a sample of 192 Portuguese companies. A neural network model has been developed to identify the effects of each dimension of MO on each dimension of performance.FindingsRelationship among MO and performance was corroborated but MO's impact is poor and based on its first dimension, market intelligence generation.Research limitations/implicationsFurther research in this field should be conducted using other tools offered by neural network modelling.Practical implicationsManagers should give more attention to cross‐functional co‐ordination in order to improve market intelligence dissemination and responsiveness and, thus, global performance.Originality/valueThe paper presents the development of a neural network model to analyse this relationship.

Adaptive network architecture and firefly algorithm for biogas heating model aided by photovoltaic thermal greenhouse system

2018 ◽  
Vol 29 (7) ◽  
pp. 1073-1097 ◽  
Author(s):  
Gurinderpal Singh ◽  
VK Jain ◽  
Amanpreet Singh
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Network Architecture ◽  
Error Function ◽  
Learning Performance ◽  
Heating Efficiency ◽  
Heating Model ◽  
The Impact ◽  
Self Adaptive

The photovoltaic thermal greenhouse system highly supports the production of biogas. The system’s prime advantage is biogas heating and crop drying through varied directions of air flow. Further, it diminishes the upward loss of the system. This paper aims to model a practical greenhouse system for obtaining the precise estimation of the heating efficiency, given by the solar radiance. The simulation model adopts the self-adaptive firefly neural network model that applies on known experimental data. Therefore, the error function between the model outcome and the experimental outcome is substantially minimized. The performance analysis involves an effective comparative study on the root mean square error between the adopted self-adaptive firefly neural network model and the conventional models such as Levenberg–Marquardt neural network and firefly neural network. Later, the impact of self-adaptiveness, FF update and learning performance on attaining the knowledge regarding the characteristics of SAFF algorithm is analysed to yield better performance.

scholarly journals Effect of frost on plants, leaves, and forecast of frost events using convolutional neural networks

2021 ◽  
Vol 17 (10) ◽  
pp. 155014772110537
Author(s):  
Sobia Wassan ◽  
Chen Xi ◽  
NZ Jhanjhi ◽  
Laiqa Binte-Imran
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
The United States ◽  
Plant Structure ◽  
Weather Extremes ◽  
Model Framework ◽  
Data Set ◽  
Frost Event

Climate change brings many changes in a physical environment like plants and leaves. The flowers and plants get affected by natural climate and local weather extremes. However, the projected increase in the frost event causes sensitivity in plant reproduction and plant structure vegetation. The timing of growing and reproduction might be an essential tactic by which plant life can avoid frost. Flowers are more sensitive to hoarfrost than leaves but more sensitive to frost in most cases. In most cases, frost affects the size of the plant, its growth, and the production of seeds. In this article, we examined that how frost affects plants and flowers? How it affects the roots and prevents the growth of plants, vegetables, and fruits? Furthermore, we predicted how the frost will grow and how we should take early precautions to protect our crops? We presented the convolutional neural network model framework and used the conv1d algorithm to evaluate one-dimensional data for frost event prediction. Then, as part of our model contribution, we preprocessed the data set. The results were comparable to four weather stations in the United States. The results showed that our convolutional neural network model configuration is reliable.

An Artificial Neural Network Model to Reduce False Alarms in Satellite Precipitation Products Using MODIS and CloudSat Observations

2013 ◽  
Vol 14 (6) ◽  
pp. 1872-1883 ◽  
Author(s):  
Nasrin Nasrollahi ◽  
Kuolin Hsu ◽  
Soroosh Sorooshian
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Artificial Neural Network Model ◽  
The United States ◽  
Training Dataset ◽  
Detection Accuracy ◽  
Satellite Precipitation ◽  
Artificial Neural

Abstract The Moderate Resolution Imaging Spectroradiometer (MODIS) instrument aboard the NASA Earth Observing System (EOS) Aqua and Terra platform with 36 spectral bands provides valuable information about cloud microphysical characteristics and therefore precipitation retrievals. Additionally, CloudSat, selected as a NASA Earth Sciences Systems Pathfinder satellite mission, is equipped with a 94-GHz radar that can detect the occurrence of surface rainfall. The CloudSat radar flies in formation with Aqua with only an average of 60 s delay. The availability of surface rain presence based on CloudSat together with the multispectral capabilities of MODIS makes it possible to create a training dataset to distinguish false rain areas based on their radiances in satellite precipitation products [e.g., Precipitation Estimation from Remotely Sensed Information Using Artificial Neural Networks (PERSIANN)]. The brightness temperatures of six MODIS water vapor and infrared channels are used in this study along with surface rain information from CloudSat to train an artificial neural network model for no-rain recognition. The results suggest a significant improvement in detecting nonprecipitating regions and reducing false identification of precipitation. Also, the results of the case studies of precipitation events during the summer and winter of 2007 over the United States show an accuracy of 77% no-rain identification and 93% detection accuracy, respectively.

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