Evaluation of Two Modeling Methods for Generating Heavy-Truck Trips at an Intermodal Facility by Using Vessel Freight Data

2005 ◽  
Vol 1906 (1) ◽  
pp. 113-120 ◽  
Author(s):  
Pradeep Sarvareddy ◽  
Haitham Al-Deek ◽  
Jack Klodzinski ◽  
Georgios Anagnostopoulos
Keyword(s):  
Neural Network ◽  
Storage Time ◽  
Learning Algorithm ◽  
Generation Model ◽  
Trip Generation ◽  
Modeling Techniques ◽  
Heavy Truck ◽  
Input Variables ◽  
Lag Variables ◽  
Bpnn Model

A methodology for building a truck trip generation model by use of artificial neural networks from vessel freight data has been developed and successfully applied to five Florida seaports. The backpropagation neural network (BPNN) algorithm was used in the design. Although the methodology was sound, a new model had to be developed for each of these intermodal facilities. Lead and lag variables were necessary input variables for most models to account for commodities stored on port property before export or pickup after import. Other modeling techniques were researched, and a fully recurrent neural network (FRNN) trained by the real-time recurrent learning algorithm was selected to develop a model for Port Canaveral and compare with a BPNN model. FRNN is dynamic in nature and was found to relate to the storage time of the commodities to truck trip generation. A developed Port Canaveral BPNN model was successfully validated at the 95% confidence level with collected field data. It was applied to conduct a short-term forecast of the port's truck traffic for 5 years. The average annual growth of trucks based on the estimated freight activity under the BPNN model was 5.07%. The Port Canaveral FRNN model adequately estimated the current conditions but failed to forecast truck growth. The FRNN model required more data for forecasting than backpropagation. However, when more consecutive data are available for training, FRNN may produce more accurate results.

Soil Moisture Inversion Using AMSR-E Remote Sensing Data: An Artificial Neural Network Approach

2014 ◽  
Vol 501-504 ◽  
pp. 2073-2076 ◽  
Author(s):  
Xing Mei Xie ◽  
Jing Wen Xu ◽  
Jun Fang Zhao ◽  
Shuang Liu ◽  
Peng Wang
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Soil Moisture ◽  
Learning Algorithm ◽  
Estimation Accuracy ◽  
Neural Network Approach ◽  
Near Surface ◽  
Artificial Neural ◽  
Moisture Estimation ◽  
Bpnn Model

In this work artificial neural network with a back-propagation learning algorithm (BPNN) is employed to solve soil moisture retrieval for Sichuan Middle Hilly Area in China. Eighteen kinds of BPNN models have been developed using AMSR-E observations to retrieve soil moisture. The results show that the 18.7GHz band has some positive effect on improving soil moisture estimation accuracy while the 36.5GHz may interfere with deriving soil moisture, and vertical brightness temperature has a closer relationship with observed near-surface soil moisture than horizontal TB. The BPNN model driven by vertical and horizontal TB dataset at 6.9GHz and 10.7GHz frequency has the best performance of all the BPNN models withr value of 0.4968 and RMSE 10.2976%. Generally, the BPNN model is more suitable for soil moisture estimation than NASA product for the study area and can provide significant soil moisture information due to its ability of capturing non-linear and complex relationship.

Monitoring-System Development of Gillnet Using Artificial Neural Network

2020 ◽  
Author(s):  
Chungkuk Jin ◽  
HanSung Kim ◽  
JeongYong Park ◽  
MooHyun Kim ◽  
Kiseon Kim
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Big Data ◽  
Learning Algorithm ◽  
System Development ◽  
Ann Model ◽  
Ann Models ◽  
Artificial Neural ◽  
Input Variables ◽  
Artificial Neural Network Ann

Abstract This paper presents a method for detecting damage to a gillnet based on sensor fusion and the Artificial Neural Network (ANN) model. Time-domain numerical simulations of a slender gillnet were performed under various wave conditions and failure and non-failure scenarios to collect big data used in the ANN model. In training, based on the results of global performance analyses, sea states, accelerations of the net assembly, and displacements of the location buoy were selected as the input variables. The backpropagation learning algorithm was employed in training to maximize damage-detection performance. The output of the ANN model was the identification of the particular location of the damaged net. In testing, big data, which were not used in training, were utilized. Well-trained ANN models detected damage to the net even at sea states that were not included in training with high accuracy.

scholarly journals Prediction of Grain Yield in Henan Province Based on Grey BP Neural Network Model

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Bingjun Li ◽  
Yifan Zhang ◽  
Shuhua Zhang ◽  
Wenyan Li
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Henan Province ◽  
Grain Production ◽  
Relational Analysis ◽  
Key Variables ◽  
Input Variables ◽  
Bp Neural Network Model ◽  
Grey Relational ◽  
Bpnn Model

BP neural network (BPNN) is widely used due to its good generalization and robustness, but the model has the defect that it cannot automatically optimize the input variables. In response to this problem, this study uses the grey relational analysis method to rank the importance of input variables, obtains the key variables and the best BPNN model structure through multiple training and learning for the BPNN models, and proposes a variable optimization selection algorithm combining grey relational analysis and BP neural network. The predicted values from the metabolic GM (1, 1) model for key variables was used as input to the best BPNN model for prediction modeling, and a grey BP neural network model prediction model (GR-BPNN) was proposed. The long short-term memory neural network (LSTM), convolutional neural network (CNN), traditional BP neural network (BP), GM (1, N) model, and stepwise regression (SR) are also implemented as benchmark models to prove the superiority and applicability of the new model. Finally, the GR-BPNN forecasting model was applied to the grain yield forecast of the whole province and subregions for Henan Province. The forecasting results found that the growth rate of grain production in Henan Province slowed down and the center of gravity for grain production shifted northwards.

scholarly journals Development and Validation of a Machine Learning Algorithm for Prediction of Platelet Transfusion Efficiency in Patients with Hematological Diseases

Blood ◽  
2019 ◽  
Vol 134 (Supplement_1) ◽  
pp. 2454-2454
Author(s):  
Meng Wang ◽  
Jian Cheng ◽  
Xiuwen Li ◽  
Baoan Chen
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Platelet Count ◽  
Network Model ◽  
Neural Network Model ◽  
Platelet Transfusion ◽  
Storage Time ◽  
Learning Algorithm ◽  
Machine Learning Algorithm ◽  
Hematological Diseases

Background: Prophylactic transfusion of platelets provides a good protection for the implementation of invasive procedures and the prevention of bleeding events during chemotherapy and hematopoietic stem cell transplantation in patients who are suffering from hematological diseases. The issues that what is the optimal dose of platelet transfusion and how to monitor platelet transfusion efficiency are important due to the short storage time of platelet products, rising clinical demands, and decreasing donors. It is worthy that there are amount of studies have been conducted in the past to explore factors that may affect the clinical efficacy of platelet transfusion and characteristics of patients under these different transfusion effects. While, previous studies failed to exactly address the problems of clinical needs for platelet transfusion. Methods: The aim of our study is to develop a model to evaluate the efficacy of platelet transfusion and the statistic method of machine learning algorithm (ML) was involved. The differences between this algorithm and traditional methods are that the former one can continuously be learning from the data and form a self-training model, therefore ML is more accurate than traditional artificial models and generally independent of the model and parameters themselves. We further take the multi-layer fully connected layer neural network model (MLNN) into our consideration because it simulates the multi-layer interconnection of human nervous systems, which is suitable for processing inaccurate and fuzzy information. In our study, the establishment of a neural network model was used to make a multiple-dimension analysis between factors affecting platelet transfusion efficacy and platelet count added value correction index (aCCI), and explore the correlation among these influencing factors as well. Results: The study utilized the data relative to 1840 platelet transfusions performed in 460 patients with hematological diseases. The participants ranged in age from 16 to 92, and the median age was 59.5. There were 199 females and 261 men. The whole data was divided to 2 parts, 2/3 of them were analyzed as a training set and the others were used for validating. We selected 30 factors (including patient-related factors and transfusion product-related characteristics) that may affect the efficacy of platelet transfusion except the storage time of platelet (all data < 2 days), and established a model for predicting platelet transfusion efficacy based on the volume of platelet transfusion. After the model was established, it was tested for goodness of fit, and the results showed that the LOSS value tended to be stable. Conclusions: The establishment of this model may not only be used for predicting the platelet count after platelet transfusions in patients and the amount of platelets that need to be transfused, but also provide supports for the solution of related problems. Disclosures No relevant conflicts of interest to declare.

Application of Neural Networks in Perishable Inventories Management

2015 ◽  
Vol 752-753 ◽  
pp. 1424-1429
Author(s):  
Wichittra Assawawongmethee ◽  
Wimalin Laosiritaworn
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Inventory Management ◽  
Storage Time ◽  
Perishable Goods ◽  
The Neural Network ◽  
Group A ◽  
Input Variables ◽  
Abc Analysis

Classifying inventories into different groups based on the importance of each category of material is necessary for inventory management when there are a large number of inventories to be managed. In order to plan and determine effective policies in the management of each material, it is essential that the inventories be properly classified. One of the most popular methods used in classifying inventories is the ABC analysis, which is the classification of inventories based on their actual values. In the food-processing industry, for example, where inventories are often of perishable goods, the quality of inventories will decrease with storage time. Storage time is therefore considered a major factor when managing this inventory. In this research, the criterion of storage time was considered alongside others, including prices of materials per unit, amount of use, worth of use, and duration. However, since the classification of the inventories in this study was based on various complicated criteria, neural networks were applied. By using previous classifications as the input variables, we were able to apply a neural network to produce output variables and classify each inventory category into group A, B, or C. Neural networks were used to manage 105 inventories of the processing and product developing plant of the Royal Project Foundation. The findings showed that the neural network could effectively classify those inventories into groups A, B, and C, and that the accuracy of this classification was 84.35%.

Share Market Data Prediction Strategies using Deep Learning Algorithm

2019 ◽  
Vol 13 ◽  
Author(s):  
A John. ◽  
D. Praveen Dominic ◽  
M. Adimoolam ◽  
N. M. Balamurugan
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Stock Market ◽  
Predictive Analytics ◽  
Learning Algorithm ◽  
Market Price ◽  
Stochastic Gradient ◽  
Stochastic Gradient Descent ◽  
Mining Machine ◽  
Gradient Descent Algorithm

Background:: Predictive analytics has a multiplicity of statistical schemes from predictive modelling, data mining, machine learning. It scrutinizes present and chronological data to make predictions about expectations or if not unexplained measures. Most predictive models are used for business analytics to overcome loses and profit gaining. Predictive analytics is used to exploit the pattern in old and historical data. Objective: People used to follow some strategies for predicting stock value to invest in the more profit-gaining stocks and those strategies to search the stock market prices which are incorporated in some intelligent methods and tools. Such strategies will increase the investor’s profits and also minimize their risks. So prediction plays a vital role in stock market gaining and is also a very intricate and challenging process. Method: The proposed optimized strategies are the Deep Neural Network with Stochastic Gradient for stock prediction. The Neural Network is trained using Back-propagation neural networks algorithm and stochastic gradient descent algorithm as optimal strategies. Results: The experiment is conducted for stock market price prediction using python language with the visual package. In this experiment RELIANCE.NS, TATAMOTORS.NS, and TATAGLOBAL.NS dataset are taken as input dataset and it is downloaded from National Stock Exchange site. The artificial neural network component including Deep Learning model is most effective for more than 100,000 data points to train this model. This proposed model is developed on daily prices of stock market price to understand how to build model with better performance than existing national exchange method.

scholarly journals Predictive modelling of soils’ hydraulic conductivity using artificial neural network and multiple linear regression

2021 ◽  
Vol 3 (2) ◽  
Author(s):  
Charles Gbenga Williams ◽  
Oluwapelumi O. Ojuri
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Hydraulic Conductivity ◽  
Linear Regression ◽  
Multiple Linear Regression ◽  
Coarse Grained ◽  
Soil Types ◽  
Wide Range ◽  
Artificial Neural ◽  
Input Variables

AbstractAs a result of heterogeneity nature of soils and variation in its hydraulic conductivity over several orders of magnitude for various soil types from fine-grained to coarse-grained soils, predictive methods to estimate hydraulic conductivity of soils from properties considered more easily obtainable have now been given an appropriate consideration. This study evaluates the performance of artificial neural network (ANN) being one of the popular computational intelligence techniques in predicting hydraulic conductivity of wide range of soil types and compared with the traditional multiple linear regression (MLR). ANN and MLR models were developed using six input variables. Results revealed that only three input variables were statistically significant in MLR model development. Performance evaluations of the developed models using determination coefficient and mean square error show that the prediction capability of ANN is far better than MLR. In addition, comparative study with available existing models shows that the developed ANN and MLR in this study performed relatively better.

scholarly journals Snow Depth Fusion Based on Machine Learning Methods for the Northern Hemisphere

2021 ◽  
Vol 13 (7) ◽  
pp. 1250
Author(s):  
Yanxing Hu ◽  
Tao Che ◽  
Liyun Dai ◽  
Lin Xiao
Keyword(s):  
Machine Learning ◽  
Northern Hemisphere ◽  
Snow Depth ◽  
Learning Algorithm ◽  
Random Forest Regression ◽  
Machine Learning Methods ◽  
Long Time ◽  
In Situ Observations ◽  
Input Variables

In this study, a machine learning algorithm was introduced to fuse gridded snow depth datasets. The input variables of the machine learning method included geolocation (latitude and longitude), topographic data (elevation), gridded snow depth datasets and in situ observations. A total of 29,565 in situ observations were used to train and optimize the machine learning algorithm. A total of five gridded snow depth datasets—Advanced Microwave Scanning Radiometer for the Earth Observing System (AMSR-E) snow depth, Global Snow Monitoring for Climate Research (GlobSnow) snow depth, Long time series of daily snow depth over the Northern Hemisphere (NHSD) snow depth, ERA-Interim snow depth and Modern-Era Retrospective Analysis for Research and Applications, version 2 (MERRA-2) snow depth—were used as input variables. The first three snow depth datasets are retrieved from passive microwave brightness temperature or assimilation with in situ observations, while the last two are snow depth datasets obtained from meteorological reanalysis data with a land surface model and data assimilation system. Then, three machine learning methods, i.e., Artificial Neural Networks (ANN), Support Vector Regression (SVR), and Random Forest Regression (RFR), were used to produce a fused snow depth dataset from 2002 to 2004. The RFR model performed best and was thus used to produce a new snow depth product from the fusion of the five snow depth datasets and auxiliary data over the Northern Hemisphere from 2002 to 2011. The fused snow-depth product was verified at five well-known snow observation sites. The R2 of Sodankylä, Old Aspen, and Reynolds Mountains East were 0.88, 0.69, and 0.63, respectively. At the Swamp Angel Study Plot and Weissfluhjoch observation sites, which have an average snow depth exceeding 200 cm, the fused snow depth did not perform well. The spatial patterns of the average snow depth were analyzed seasonally, and the average snow depths of autumn, winter, and spring were 5.7, 25.8, and 21.5 cm, respectively. In the future, random forest regression will be used to produce a long time series of a fused snow depth dataset over the Northern Hemisphere or other specific regions.

scholarly journals Deep Learning Algorithm Trained with COVID-19 Pneumonia Also Identifies Immune Checkpoint Inhibitor Therapy-Related Pneumonitis

Cancers ◽  
2021 ◽  
Vol 13 (4) ◽  
pp. 652 ◽  
Author(s):  
Carlo Augusto Mallio ◽  
Andrea Napolitano ◽  
Gennaro Castiello ◽  
Francesco Maria Giordano ◽  
Pasquale D'Alessio ◽  
...  
Keyword(s):  
Neural Network ◽  
Computed Tomography ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Immune Checkpoint ◽  
Immune Checkpoint Inhibitor ◽  
Learning Algorithm ◽  
Checkpoint Inhibitor ◽  
Deep Convolutional Neural Network ◽  
Deep Learning Algorithm

Background: Coronavirus disease 2019 (COVID-19) pneumonia and immune checkpoint inhibitor (ICI) therapy-related pneumonitis share common features. The aim of this study was to determine on chest computed tomography (CT) images whether a deep convolutional neural network algorithm is able to solve the challenge of differential diagnosis between COVID-19 pneumonia and ICI therapy-related pneumonitis. Methods: We enrolled three groups: a pneumonia-free group (n = 30), a COVID-19 group (n = 34), and a group of patients with ICI therapy-related pneumonitis (n = 21). Computed tomography images were analyzed with an artificial intelligence (AI) algorithm based on a deep convolutional neural network structure. Statistical analysis included the Mann–Whitney U test (significance threshold at p < 0.05) and the receiver operating characteristic curve (ROC curve). Results: The algorithm showed low specificity in distinguishing COVID-19 from ICI therapy-related pneumonitis (sensitivity 97.1%, specificity 14.3%, area under the curve (AUC) = 0.62). ICI therapy-related pneumonitis was identified by the AI when compared to pneumonia-free controls (sensitivity = 85.7%, specificity 100%, AUC = 0.97). Conclusions: The deep learning algorithm is not able to distinguish between COVID-19 pneumonia and ICI therapy-related pneumonitis. Awareness must be increased among clinicians about imaging similarities between COVID-19 and ICI therapy-related pneumonitis. ICI therapy-related pneumonitis can be applied as a challenge population for cross-validation to test the robustness of AI models used to analyze interstitial pneumonias of variable etiology.

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