scholarly journals Neural network training in SCILAB for classifying mango (Mangifera indica) according to maturity level using the RGB color model

2021 ◽  
Vol 1 (3) ◽  
pp. 186
Author(s):  
Eduardo Castillo-Castaneda
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Raw Materials ◽  
Mangifera Indica ◽  
Color Model ◽  
Maturity Level ◽  
Network Training ◽  
The Neural Network ◽  
Blue Background

<p style='text-indent:20px;'>Industries that use fruits as raw materials must, at some point in the process, classify them to discard the unsuitable ones and thus ensure the quality of the final product. To produce mango nectar, it is necessary to ensure that the mango is mature enough to start the extraction of the nectar; however, sorting thousands of mangoes may require many people, who can easily lose attention and reduce the accuracy of the result. Such kind of decision can be supported by current Artificial Intelligence techniques. The theoretical details of the processing are presented, as well as the programming code of the neural network using SCILAB as a computer language; the code includes the color extraction from mango images. SCILAB programming is simple, efficient and does not require computers with large processing capacity. The classification was validated with 30 images (TIF format) of Manila variety mango; the mangoes were placed on a blue background to easily separate the background from the object of interest. Four and six mangoes were used to train the neural network. This application of neural networks is part of an undergraduate course on artificial intelligence, which shows the potential of these techniques for solving real and concrete problems.</p>

scholarly journals System of providing sustainability of tower cranes from overturn in extreme wind loads

2018 ◽  
Vol 224 ◽  
pp. 02086
Author(s):  
Pavel Sorokin ◽  
Alexey Mishin ◽  
Vitaliy Antsev ◽  
Alexey Red’kin
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Network Models ◽  
Wind Loads ◽  
Neural Network Models ◽  
Quality Of Work ◽  
Development Stages ◽  
The Neural Network ◽  
Extreme Wind

The article is devoted to the issues of ensuring stability of tower cranes from overturn. The development stages of devices for ensuring tower cranes safety are examined and their shortcomings are revealed. The system consisting of subsystems and drives is proposed and their interaction is presented. The article deals with a subsystem based on artificial intelligence methods. The neural network models of forecasting wind parameters are developed. The quality of work of neural network models is estimated. The ways of further topic development are suggested.

scholarly journals Estimation of Overtopping Discharges with Deep Neural Network(DNN) Method

2021 ◽  
Vol 8 (4) ◽  
pp. 229-236
Author(s):  
Changkyum Kim ◽  
Insik Chun ◽  
Byungcheol Oh
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Cost Function ◽  
Deep Neural Network ◽  
Multiple Linear Regression Model ◽  
Predictive Performance ◽  
Data Sets ◽  
Network Training ◽  
The Neural Network ◽  
The Cost

An Artificial Intelligence(AI) study was conducted to calculate overtopping discharges for various coastal structures. The Deep Neural Network(DNN), one of the artificial intelligence methods, was employed in the study. The neural network was trained, validated and tested using the EurOtop database containing the experimental data collected from all over the world. To improve the accuracy of the deep neural network results, all data were non-dimensionalized and max-min normalized as a preprocessing process. L2 regularization was also introduced in the cost function to secure the convergence of iterative learning, and the cost function was optimized using RMSProp and Adam techniques. In order to compare the performance of DNN, additional calculations based on the multiple linear regression model and EurOtop’s overtopping formulas were done as well, using the data sets which were not included in the network training. The results showed that the predictive performance of the AI technique was relatively superior to the two other methods.

High-resolution soil moisture retrieval using a Neural Network approach from Sentinel-1 SAR data

2020 ◽  
Author(s):  
Qi Gao ◽  
Maria Jose Escorihuela ◽  
Nemesio Rodriguez-Fernandez ◽  
Olivier Merlin ◽  
Mehrez Zribi
Keyword(s):  
Neural Network ◽  
Soil Moisture ◽  
High Resolution ◽  
Neural Network Training ◽  
Neural Network Approach ◽  
Auxiliary Data ◽  
Network Training ◽  
The Neural Network ◽  
Sar Data

&lt;p&gt;High-resolution soil moisture product is important for agriculture-related managements including irrigation. We have investigated the Change Detection (CD) method using Sentinel-1 data for 100 m resolution soil moisture retrieval and got a Root Mean Square Error (RMSE) about 0.6 m&lt;sup&gt;3&lt;/sup&gt;/m&lt;sup&gt;3&lt;/sup&gt;. However, the result of this approach is not accurate enough for high-density crops like corn. Another approach needs to be studied to get better accuracy over all types of crops. The artificial neural network (NN) technique, which involves nonlinear parameterized mapping from an input vector to an output vector, is an appropriate tool for retrieving geophysical parameters from remote sensing data. Many studies have explored the NN approach for processing remotely sensed data, including retrieving soil moisture, however, only a few studies [Notarnicola et al., 2010; Paloscia et al., 2013, etc.] had investigated NN for soil moisture estimation over vegetation-covered areas, especially in a large scale.&lt;/p&gt;&lt;p&gt;The objective of this study is to develop an approach based on neural networks to estimate soil moisture at high resolution over vegetation-covered areas from Sentinel-1 C-band SAR data. The quality of the output results depends directly on the quality of the input data used to train the NN and the reference data for the training, therefore, we performed our study over Catalonia, where we have many auxiliary data. The study is performed using both VV and VH polarization over the whole Catalonia. Apart from Sentinel-1 SAR data, auxiliary data including Sentinel-2 NDVI, SMAP soil moisture, CCI (ESA Climate Change Initiative) land cover, SIGPAC (Sistema de Informaci&amp;#243;n Geogr&amp;#225;fica de Parcelas Agr&amp;#237;colas) land cover, irrigation index and crop type information from SIGPAC, and DEM (Digital elevation model) are also used for approach development. DISPATCH (Disaggregation based on Physical and Theoretical scale Change) soil moisture product at 1 km resolution is considered as the target in the Neural Network training, adding great value to our study. To prepare the Neural Network training, all data sets are co-registered at 1 km resolution within the same size and resampled for the same dates within one year (2017). Two indexes describing the normalized backscatter difference and soil moisture are introduced as equation (1) and (2):&lt;/p&gt;&lt;table&gt;&lt;tbody&gt;&lt;tr&gt;&lt;td&gt;Index&lt;sub&gt;1 &lt;/sub&gt;= (&amp;#963;&lt;sup&gt;0&lt;/sup&gt;&lt;sub&gt;i &lt;/sub&gt;- &amp;#963;&lt;sup&gt;0&lt;/sup&gt;&lt;sub&gt;min&lt;/sub&gt;) / (&amp;#963;&lt;sup&gt;0&lt;/sup&gt;&lt;sub&gt;max &lt;/sub&gt;- &amp;#963;&lt;sup&gt;0&lt;/sup&gt;&lt;sub&gt;min&lt;/sub&gt;)&lt;/td&gt; &lt;td&gt;(1)&lt;/td&gt; &lt;/tr&gt;&lt;tr&gt;&lt;td&gt;Index&lt;sub&gt;2 &lt;/sub&gt;= SM&lt;sub&gt;min&lt;/sub&gt; + (SM&lt;sub&gt;max &lt;/sub&gt;- SM&lt;sub&gt;min&lt;/sub&gt;) * Index&lt;sub&gt;1&lt;/sub&gt;&lt;/td&gt; &lt;td&gt;(2)&lt;/td&gt; &lt;/tr&gt;&lt;/tbody&gt;&lt;/table&gt;&lt;p&gt;&amp;#160;&lt;/p&gt;&lt;p&gt;Different parameters were tested to train the Neural Network approach, the preliminary results show a correlation value compared with DISPATCH product about 0.71 over croplands, 0.73 over irrigated fields, and 0.65 over forests, considering Index1, Index2 and SMAP soil moisture. Works are still on-going to try to improve the results by better analyzing the SAR data performance over different fields and conditions. The final goal of the study is to produce 100 m resolution soil moisture product. After 1 km resolution study, we will apply the approach at 100m resolution, and the in-situ soil moisture will be used for validation.&lt;/p&gt;&lt;p&gt;This work is inscribed within the Water4Ever project, which is funded by the European Commission under the framework of the ERA-NET COFUND WATERWORKS 2015 Programme.&amp;#160;&lt;/p&gt;

scholarly journals Early Stopping Criteria for Levenberg-Marquardt Based Neural Network Training Optimization

2018 ◽  
Vol 7 (4.36) ◽  
pp. 1194
Author(s):  
Azizah Suliman ◽  
Batyrkhan Omarov
Keyword(s):  
Neural Network ◽  
Classification Problem ◽  
Correct Choice ◽  
Network Training ◽  
The Neural Network ◽  
Marquardt Algorithm ◽  
Levenberg Marquardt ◽  
Stop Condition ◽  
Hidden Neurons

In this research we train a direct distributed neural network using Levenberg-Marquardt algorithm. In order to prevent overtraining, we proposed correctly recognized image percentage based on early stop condition and conduct the experiments with different stop thresholds for image classification problem. Experiment results show that the best early stop condition is 93% and other increase in stop threshold can lead to decrease in the quality of the neural network. The correct choice of early stop condition can prevent overtraining which led to the training of a neural network with considerable number of hidden neurons.  

scholarly journals Comparison of predicted psychological distress among workers between artificial intelligence and psychiatrists: a cross-sectional study in Tsukuba Science City, Japan

BMJ Open ◽  
2021 ◽  
Vol 11 (6) ◽  
pp. e046265
Author(s):  
Shotaro Doki ◽  
Shinichiro Sasahara ◽  
Daisuke Hori ◽  
Yuichi Oi ◽  
Tsukasa Takahashi ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Psychological Distress ◽  
Cross Sectional Study ◽  
Sectional Study ◽  
Cross Sectional ◽  
Explanatory Variables ◽  
The Neural Network ◽  
Significant Difference ◽  
Total Data

ObjectivesPsychological distress is a worldwide problem and a serious problem that needs to be addressed in the field of occupational health. This study aimed to use artificial intelligence (AI) to predict psychological distress among workers using sociodemographic, lifestyle and sleep factors, not subjective information such as mood and emotion, and to examine the performance of the AI models through a comparison with psychiatrists.DesignCross-sectional study.SettingWe conducted a survey on psychological distress and living conditions among workers. An AI model for predicting psychological distress was created and then the results were compared in terms of accuracy with predictions made by psychiatrists.ParticipantsAn AI model of the neural network and six psychiatrists.Primary outcomeThe accuracies of the AI model and psychiatrists for predicting psychological distress.MethodsIn total, data from 7251 workers were analysed to predict moderate and severe psychological distress. An AI model of the neural network was created and accuracy, sensitivity and specificity were calculated. Six psychiatrists used the same data as the AI model to predict psychological distress and conduct a comparison with the AI model.ResultsThe accuracies of the AI model and psychiatrists for predicting moderate psychological distress were 65.2% and 64.4%, respectively, showing no significant difference. The accuracies of the AI model and psychiatrists for predicting severe psychological distress were 89.9% and 85.5%, respectively, indicating that the AI model had significantly higher accuracy.ConclusionsA machine learning model was successfully developed to screen workers with depressed mood. The explanatory variables used for the predictions did not directly ask about mood. Therefore, this newly developed model appears to be able to predict psychological distress among workers easily, regardless of their subjective views.

Construction of first-class university course based on artificial intelligence and neural network algorithm

2020 ◽  
pp. 1-12
Author(s):  
Yingli Duan
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Vocational Training ◽  
Network Algorithms ◽  
Teaching Efficiency ◽  
Neural Network Algorithm ◽  
Solid Foundation ◽  
University Level ◽  
University Course

Curriculum is the basis of vocational training, its development level and teaching efficiency determine the realization of vocational training objectives, as well as the quality and level of major vocational academic training. Therefore, the development of curriculum is an important issue. And affect the school’s teaching capacity building. The analysis of the latest developments in the main courses shows that there are some deviations or irrationalities in the curriculum in some colleges and universities, and the general problems of understanding the latest courses, such as lack of solid foundation in curriculum setting, unclear direction of objectives, unclear reform ideas, inadequate and systematic construction measures, lack of attention to the quality of education. This paper explains the rules for the establishment of first-level courses, clarifies the ideas and priorities of architecture, and explores strategies for building university-level courses using knowledge of artificial intelligence and neural network algorithms in order to gain experience from them.

Self-validating deep learning of continental hydrology through satellite gravimetry and altimetry

2021 ◽  
Author(s):  
Christopher Irrgang ◽  
Jan Saynisch-Wagner ◽  
Robert Dill ◽  
Eva Boergens ◽  
Maik Thomas
Keyword(s):  
Neural Network ◽  
Satellite Altimetry ◽  
Water Cycle ◽  
Global Water Cycle ◽  
Network Training ◽  
The Neural Network ◽  
Continental Hydrology ◽  
Terrestrial Water ◽  
Combine Machine ◽  
Groundwater Basins

&lt;p&gt;Space-borne observations of terrestrial water storage (TWS) are an essential ingredient for understanding the Earth's global water cycle, its susceptibility to climate change, and for risk assessments of ecosystems, agriculture, and water management. However, the complex distribution of water masses in rivers, lakes, or groundwater basins remains elusive in coarse-resolution gravimetry observations. We combine machine learning, numerical modeling, and satellite altimetry to build and train a downscaling neural network that recovers simulated TWS from synthetic space-borne gravity observations. The neural network is designed to adapt and validate its training progress by considering independent satellite altimetry records. We show that the neural network can accurately derive TWS anomalies in 2019 after being trained over the years 2003 to 2018. Specifically for validated regions in the Amazonas, we highlight that the neural network can outperform the numerical hydrology model used in the network training.&lt;/p&gt;&lt;p&gt;https://agupubs.onlinelibrary.wiley.com/doi/10.1029/2020GL089258&lt;/p&gt;

scholarly journals Artificial Intelligence Techniques for Phishing Detection

2019 ◽  
Vol 8 (11) ◽  
pp. 2330-2335
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Neural Networks ◽  
Neural Systems ◽  
Element Determination ◽  
Artificial Intelligence Techniques ◽  
The Neural Network ◽  
Proper Number ◽  
Phishing Detection

The objective of this undertaking is to apply neural systems to phishing email recognition and assess the adequacy of this methodology. We structure the list of capabilities, process the phishing dataset, and execute the Neural Network frameworks. we analyze its exhibition against that of other real Artificial Intelligence Techniques – DT , K-nearest , NB and SVM machine.. The equivalent dataset and list of capabilities are utilized in the correlation. From the factual examination, we infer that Neural Networks with a proper number of concealed units can accomplish acceptable precision notwithstanding when the preparation models are rare. Additionally, our element determination is compelling in catching the qualities of phishing messages, as most AI calculations can yield sensible outcomes with it.

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