The use of artificial neural networks for modelling rumen fill

2020 ◽  
Author(s):  
Rasheed Adekunle Adebayo ◽  
Mehluli Moyo ◽  
Evariste Bosco Gueguim-Kana ◽  
Ignatius Verla Nsahlai
Keyword(s):  
Neural Network ◽  
Random Forest ◽  
Learning Algorithm ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Random Forest Model ◽  
Ann Model ◽  
Forest Model ◽  
Artificial Neural ◽  
Cattle And Sheep

Artificial Neural Network (ANN) and Random Forest models for predicting rumen fill of cattle and sheep were developed. Data on rumen fill were collected from studies that reported body weights, measured rumen fill and stated diets fed to animals. Animal and feed factors that affected rumen fill were identified from each study and used to create a dataset. These factors were used as input variables for predicting the weight of rumen fill. For ANN modelling, a three-layer Levenberg-Marquardt Back Propagation Neural Network was adopted and achieved 96% accuracy in prediction of the weight of rumen fill. The precision of the ANN model’s prediction of rumen fill was higher for cattle (80%) than sheep (56%). On validation, the ANN model achieved 95% accuracy in prediction of the weight of rumen fill. A Random Forest model was trained using a binary tree-based machine-learning algorithm and achieved 87% accuracy in prediction of rumen fill. The Random Forest model achieved 16% (cattle) and 57% (sheep) accuracy in validation of the prediction of rumen fill. In conclusion, the ANN model gave better predictions of rumen fill compared to the Random Forest model and should be used in predicting rumen fill of cattle and sheep.

scholarly journals Artificial Intelligence Prediction Model for the Cost and Mortality of Renal Replacement Therapy in Aged and Super-Aged Populations in Taiwan

2019 ◽  
Vol 8 (7) ◽  
pp. 995 ◽  
Author(s):  
Shih-Yi Lin ◽  
Meng-Hsuen Hsieh ◽  
Cheng-Li Lin ◽  
Meng-Ju Hsieh ◽  
Wu-Huei Hsu ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Artificial Neural Network ◽  
Random Forest ◽  
Prediction Model ◽  
Random Forest Model ◽  
Forest Model ◽  
Artificial Neural ◽  
One Year ◽  
The Cost

Background: Prognosis of the aged population requiring maintenance dialysis has been reportedly poor. We aimed to develop prediction models for one-year cost and one-year mortality in aged individuals requiring dialysis to assist decision-making for deciding whether aged people should receive dialysis or not. Methods: We used data from the National Health Insurance Research Database (NHIRD). We identified patients first enrolled in the NHIRD from 2000–2011 for end-stage renal disease (ESRD) who underwent regular dialysis. A total of 48,153 Patients with ESRD aged ≥65 years with complete age and sex information were included in the ESRD cohort. The total medical cost per patient (measured in US dollars) within one year after ESRD diagnosis was our study’s main outcome variable. We were also concerned with mortality as another outcome. In this study, we compared the performance of the random forest prediction model and of the artificial neural network prediction model for predicting patient cost and mortality. Results: In the cost regression model, the random forest model outperforms the artificial neural network according to the mean squared error and mean absolute error. In the mortality classification model, the receiver operating characteristic (ROC) curves of both models were significantly better than the null hypothesis area of 0.5, and random forest model outperformed the artificial neural network. Random forest model outperforms the artificial neural network models achieved similar performance in the test set across all data. Conclusions: Applying artificial intelligence modeling could help to provide reliable information about one-year outcomes following dialysis in the aged and super-aged populations; those with cancer, alcohol-related disease, stroke, chronic obstructive pulmonary disease (COPD), previous hip fracture, osteoporosis, dementia, and previous respiratory failure had higher medical costs and a high mortality rate.

scholarly journals Optimalisasi Model Artificial Neural Network Menggunakan Certainty Factor (C-ANN) Untuk Pemetaan Kerawanan Tanah Longsor Skala Semi-Detil di DAS Bendo, Kabupaten Banyuwangi

2021 ◽  
Vol 35 (1) ◽  
pp. 1
Author(s):  
Syamsul Bachri ◽  
Kresno Sastro Bangun Utomo ◽  
Sumarmi Sumarmi ◽  
Mohammad Naufal Fathoni ◽  
Yulius Eka Aldianto
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Landslide Hazard ◽  
Susceptibility Mapping ◽  
Ann Model ◽  
Certainty Factor ◽  
Artificial Neural ◽  
Factor C

Kerawanan longsor di DAS Bendo termasuk dalam kerawanan kelas sedang hingga tinggi. Sampai dengan saat ini, pemetaan rawan longsor di DAS Bendo baru dilakukan pada  skala pemetaan 1:250.000. Penelitian ini bertujuan untuk melakukan pemodelan pemetaan kerawanan longsor di DAS Bendo pada skala semi-detil. Metode yang digunakan dalam penelitian ini adalah optimalisasi model artificial neural network menggunakan certainty factor (C-ANN). Peta kerawanan dibangun berdasarkan faktor pengontrol tanah longsor yang berkorelasi positif terhadap kejadian longsor menggunakan Certainty Factor. Sedangkan pemodelan prediksi kerawanan menggunakan model ANN, khususnya arsitektur BPNN (back-propagation neural network). Hasil pemodelan menunjukkan bahwa model C-ANN (7 variabel independen) memiliki nilai AUC (0,916) lebih tinggi daripada model ANN (0,778). Faktor redundansi data, multikolinieritas data, dan proporsi kejadian longsor terhadap cakupan wilayah penelitian mengakibatkan ketidakpastian dalam data variabel independen. Melalui penelitian ini ditemukan hasil bahwa kondisi kerawanan longsor di DAS Bendo masuk kategori tinggi, khususnya pada lereng atas Gunung Ijen, Rante, dan Merapi. Landslide disaster in DAS Bendo is categorized as moderate to highly susceptible. Until today, landslide hazard mapping in DAS Bendo has been carried out with a scale 1:250.000. This study aimed to model landslide susceptibility mapping on a semi-detailed scale. The method used in this research was the integration of the Certainty Factor with Artificial Neural Network models (C-ANN).The development of susceptibility mapping based on factors that positively correlate to landslide events using Certainty Factor. While the susceptibility prediction model using the ANN model, specifically the BPNN (back-propagation neural network) architecture. Modelling results show that the C-ANN model (7 independent variables) has an AUC value (0.916) higher than the ANN model (0.778). Data redundancy factors, multicollinearity of data, and the proportion of landslide events to the study area's coverage resulted in uncertainty in the independent variable data. This research found that the Landslide hazard in the Bendo Watershed is in the high category, especially on the upper slopes of Mount Ijen, Rante, and Merapi.

scholarly journals Using Artificial Neural Networks in Predicting the Level of Stress among Military Conscripts

Mathematics ◽  
2021 ◽  
Vol 9 (6) ◽  
pp. 626
Author(s):  
Svajone Bekesiene ◽  
Rasa Smaliukiene ◽  
Ramute Vaicaitiene
Keyword(s):  
Neural Network ◽  
Transfer Functions ◽  
Back Propagation ◽  
Armed Forces ◽  
Activation Function ◽  
Optimal Number ◽  
Ann Model ◽  
Back Propagation Algorithm ◽  
Classification Rate ◽  
Artificial Neural

The present study aims to elucidate the main variables that increase the level of stress at the beginning of military conscription service using an artificial neural network (ANN)-based prediction model. Random sample data were obtained from one battalion of the Lithuanian Armed Forces, and a survey was conducted to generate data for the training and testing of the ANN models. Using nonlinearity in stress research, numerous ANN structures were constructed and verified to limit the optimal number of neurons, hidden layers, and transfer functions. The highest accuracy was obtained by the multilayer perceptron neural network (MLPNN) with a 6-2-2 partition. A standardized rescaling method was used for covariates. For the activation function, the hyperbolic tangent was used with 20 units in one hidden layer as well as the back-propagation algorithm. The best ANN model was determined as the model that showed the smallest cross-entropy error, the correct classification rate, and the area under the ROC curve. These findings show, with high precision, that cohesion in a team and adaptation to military routines are two critical elements that have the greatest impact on the stress level of conscripts.

scholarly journals Stock Market Prediction Using Artificial Neural Networks

2012 ◽  
Vol 6-7 ◽  
pp. 1055-1060 ◽  
Author(s):  
Yang Bing ◽  
Jian Kun Hao ◽  
Si Chang Zhang
Keyword(s):  
Neural Network ◽  
Prediction Models ◽  
Learning Algorithm ◽  
Stock Exchange ◽  
Back Propagation ◽  
Composite Index ◽  
Network Models ◽  
Back Propagation Neural Network ◽  
Neural Network Models ◽  
The Neural Network

In this study we apply back propagation Neural Network models to predict the daily Shanghai Stock Exchange Composite Index. The learning algorithm and gradient search technique are constructed in the models. We evaluate the prediction models and conclude that the Shanghai Stock Exchange Composite Index is predictable in the short term. Empirical study shows that the Neural Network models is successfully applied to predict the daily highest, lowest, and closing value of the Shanghai Stock Exchange Composite Index, but it can not predict the return rate of the Shanghai Stock Exchange Composite Index in short terms.

scholarly journals Prediction of Volatile Organic Compounds (VOCs) From Decomposition of Local Household Food Waste Using the Artificial Neural Network

2019 ◽  
Vol 9 (1) ◽  
pp. 5773-5779
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Volatile Organic Compounds ◽  
Organic Compounds ◽  
Food Waste ◽  
Back Propagation ◽  
Ann Model ◽  
Neuron Network ◽  
Volatile Organic ◽  
Artificial Neural

This study examines the potential of artificial neural network (ANN) to predict Total Volatile Organic Compounds (TVOCs) released via decomposition of local food wastes. To mimic the decomposition process, a bioreactor was designed to stimulate the food waste storage condition. The food waste was modeled based on the waste composition from a residential area. A feed forward multilayer back propagation (Levenberg – Marquardt training algorithm) was then developed to predict the TVOCs. The findings indicate that a two-layer artificial neuron network (ANN) with six input variables and these include (outside and inside temperature, pH, moisture content, oxygen level, relative humidity) with a total of eighty eight (88) data are used for the modeling purpose. The network with the highest regression coefficient (R) is 0.9967 and the lowest Mean Square Error (MSE) is 0.00012 (nearest to the value of zero) has been selected as the Optimum ANN model. The findings of this study suggest the most suitable ANN model that befits the research objective is ANN model with one (1) hidden layer with fifteen (15) hidden neurons. Additionally, it is critical to note that the results from the experiment and predicted model are in good agreement.

Application of Neural Network in Urban Land Use Suitability Evaluation

2011 ◽  
Vol 474-476 ◽  
pp. 681-686
Author(s):  
Xiao Rui Zhang ◽  
Gang Chen
Keyword(s):  
Neural Network ◽  
Land Use ◽  
Artificial Neural Network ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Urban Land ◽  
Urban Land Use ◽  
Suitability Evaluation ◽  
Land Use Suitability ◽  
Artificial Neural

Urban land use suitability evaluation is the basic work of urban land use planning and management. The evaluation method is a core in urban land use suitability evaluation. Traditional urban land use suitability evaluation methods are GIS-based methods which often can not get satisfactory results for the complex nonlinear urban land use system. Artificial neural network is a frontier theory of complex non-linearity scientific and artificial intelligence science. It is a new method to evaluate urban land use suitability. This paper took the land use suitability evaluation of Hefei city as an example, building a back propagation neural network with 8 neurous of input layer, 5 neurons of hide layer and 3 neurons of output layer. The analysis shows: the high suitability area is 682.27 km2in Hefei city, being about 8.73% of the total study area; the middle suitability area is 5965.76 km2, or about 76.33% of the total area and the low suitability area is 1167.35 km2, or about 14.94% of the total area. The results reflect the actual situation in Hefei city. The study shows that the back propagation neural network model can overcome the shortcomings of traditional evaluation methods. It means that artificial neural network is suitable for urban land use suitability evaluation. This reflects that artificial neural network has great academic value and application prospect in urban land use suitability evaluation. It also reflects that this study can provide a new idea and method for urban land use suitability evaluation.

THE STAG OIL FIELD FORMATION EVALUATION: A NEURAL NETWORK APPROACH

1999 ◽  
Vol 39 (1) ◽  
pp. 451 ◽  
Author(s):  
H. Crocker ◽  
C.C. Fung ◽  
K.W. Wong
Keyword(s):  
Neural Network ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Oil Field ◽  
Training Data ◽  
Ann Model ◽  
Neural Network Approach ◽  
Data Set ◽  
Formation Evaluation ◽  
Core Data

The producing M. australis Sandstone of the Stag Oil Field is a bioturbated glauconitic sandstone that is difficult to evaluate using conventional methods. Well log and core data are available for the Stag Field and for the nearby Centaur–1 well. Eight wells have log data; six also have core data.In the past few years artificial intelligence has been applied to formation evaluation. In particular, artificial neural networks (ANN) used to match log and core data have been studied. The ANN approach has been used to analyse the producing Stag Field sands. In this paper, new ways of applying the ANN are reported. Results from simple ANN approach are unsatisfactory. An integrated ANN approach comprising the unsupervised Self-Organising Map (SOM) and the Supervised Back Propagation Neural Network (BPNN) appears to give a more reasonable analysis.In this case study the mineralogical and petrophysical characteristics of a cored well are predicted from the 'training' data set of the other cored wells in the field. The prediction from the ANN model is then used for comparison with the known core data. In this manner, the accuracy of the prediction is determined and a prediction qualifier computed.This new approach to formation evaluation should provide a match between log and core data that may be used to predict the characteristics of a similar uncored interval. Although the results for the Stag Field are satisfactory, further study applying the method to other fields is required.

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