scholarly journals Coordinate Transformation between Global and Local Datums Based on Artificial Neural Network with K-Fold Cross-Validation: A Case Study, Ghana

2019 ◽  
Vol 23 (1) ◽  
pp. 67-77 ◽  
Author(s):  
Yao Yevenyo Ziggah ◽  
Hu Youjian ◽  
Alfonso Rodrigo Tierra ◽  
Prosper Basommi Laari
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Coordinate Transformation ◽  
Cross Validation ◽  
Data Partitioning ◽  
Transformation Model ◽  
Data Partition ◽  
Data Set ◽  
Artificial Neural ◽  
Fold Cross Validation

The popularity of Artificial Neural Network (ANN) methodology has been growing in a wide variety of areas in geodesy and geospatial sciences. Its ability to perform coordinate transformation between different datums has been well documented in literature. In the application of the ANN methods for the coordinate transformation, only the train-test (hold-out cross-validation) approach has usually been used to evaluate their performance. Here, the data set is divided into two disjoint subsets thus, training (model building) and testing (model validation) respectively. However, one major drawback in the hold-out cross-validation procedure is inappropriate data partitioning. Improper split of the data could lead to a high variance and bias in the results generated. Besides, in a sparse dataset situation, the hold-out cross-validation is not suitable. For these reasons, the K-fold cross-validation approach has been recommended. Consequently, this study, for the first time, explored the potential of using K-fold cross-validation method in the performance assessment of radial basis function neural network and Bursa-Wolf model under data-insufficient situation in Ghana geodetic reference network. The statistical analysis of the results revealed that incorrect data partition could lead to a false reportage on the predictive performance of the transformation model. The findings revealed that the RBFNN and Bursa-Wolf model produced a transformation accuracy of 0.229 m and 0.469 m, respectively. It was also realised that a maximum horizontal error of 0.881 m and 2.131 m was given by the RBFNN and Bursa-Wolf. The obtained results per the cadastral surveying and plan production requirement set by the Ghana Survey and Mapping Division are applicable. This study will contribute to the usage of K-fold cross-validation approach in developing countries having the same sparse dataset situation like Ghana as well as in the geodetic sciences where ANN users seldom apply the statistical resampling technique.

scholarly journals Modeling Baseline Energy Using Artificial Neural Network – A Small Dataset Approach

2018 ◽  
Vol 12 (2) ◽  
pp. 662
Author(s):  
Wan Nazirah Wan Md Adnan ◽  
Nofri Yenita Dahlan ◽  
Ismail Musirin
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Cross Validation ◽  
Model Development ◽  
Energy Model ◽  
Ann Model ◽  
Data Set ◽  
Validation Technique ◽  
Artificial Neural ◽  
Small Dataset

In this work, baseline energy model development using Artificial Neural Network (ANN) with resampling techniques; Cross Validation (CV) and Bootstrap (BS) are presented. Resampling techniques are used to examine the ability of the ANN model to deal with a small dataset. Working days, class days and Cooling Degree Days (CDD) are used as ANN input meanwhile the ANN output is monthly electricity consumption. The coefficient of correlation (R) is used as performance function to evaluate the model accuracy. For this analysis, R is calculated for the entire data set (R_all) and separately for training set (R_train), validation set (R_valid) dan testing set (R_test). The closer R to 1, the higher similarities between targeted and predicted output. The total of two different models with several number of neurons are developed and compared. It can be concluded that all models are capable to train the network. Artificial Neural Network with Bootstrap Cross Validation technique (ANN-BSCV) outperforms Artificial Neural Network with Cross Validation technique (ANN-CV).  The 3-6-1 ANN-BSCV, with R_train = 0.95668, R_valid = 0.97553, R_test = 0.85726 and R_all = 0.94079 is selected as the baseline energy model to predict energy consumption for Option C IPMVP.

scholarly journals Prediksi Hipertensi menggunakan Decision Tree, Naïve Bayes dan Artificial Neural Network pada software KNIME

Techno Com ◽  
2020 ◽  
Vol 19 (4) ◽  
pp. 353-363
Author(s):  
Mayanda Mega Santoni ◽  
Nurul Chamidah ◽  
Nurhafifah Matondang
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Artificial Neural Network ◽  
Decision Tree ◽  
Cross Validation ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Network Data ◽  
Artificial Neural ◽  
Fold Cross Validation

Hipertensi merupakan salah satu penyakit tidak menular yang dapat menyebabkan kematian karena meningkatkan resiko munculnya berbagai penyakit seperti gagal ginjal, gagal jantung, bahkan stroke. Resiko hipertensi disebabkan oleh beberapa faktor penyebab seperti usia, keturunan, pola makan dan olahraga, dan merokok. Teknologi artificial intelligence yakni machine learning dimanfaatkan di bidang kesehatan khususnya prediksi penyakit hipertensi. Pada penelitian ini diimplementasi tiga algoritma machine learning yakni decision tree, naïve bayes dan artificial neural networks. Data yang digunakan pada penelitian ini sebanyak 274 data yang diperoleh dari hasil kuesioner dengan 26 pertanyaan, dimana 25 pertanyaan adalah variabel faktor resiko dan satu pertanyaan merupakan kelas yang menyatakan responden memiliki riwayat hipertensi atau tidak. Data diolah menggunakan platform analisis data yakni KNIME. Sebelum data diolah untuk membangun model klasifikasi menggunakan decision tree, naïve bayes dan artificial neural network, data dipraproses terlebih dahulu dengan melakukan imputasi missing value, oversampling dan normalisasi data. Selanjutnya pembagian data menggunakan 5-fold cross validation. Model klasifikasi yang diperoleh dievaluasi menggunakan nilai akurasi, recall dan precision. Hasil evaluasi dari eksperimen yang dilakukan diperoleh bahwa algoritma artificial neural network memiliki tingkat performa lebih baik dibandingkan decision tree dan naïve bayes dengan nilai akurasi sebesar 94.7%, recall sebesar 91.5% dan precision sebesar 97.7%.

scholarly journals Deteksi Motif Batik Menggunakan Ekstraksi Tekstur dan Jaringan Syaraf Tiruan

2021 ◽  
Vol 10 (2) ◽  
pp. 69
Author(s):  
Raynaldi Fatih Amanullah ◽  
Ade Pujianto ◽  
Bayu Trisna Pratama ◽  
Kusrini Kusrini
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Local Binary Pattern ◽  
Cross Validation ◽  
Level 3 ◽  
Artificial Neural ◽  
Fold Cross Validation ◽  
F Measure

<p>Penelitian ini mengajukan sebuah metode klasifikasi batik menggunakan ekstraksi fitur tekstur serta klasifikasi menggunakan jaringan syaraf tiruan (artificial neural network). Metode ekstraksi fitur tekstur yang digunakan adalah DWT, GLCM dan Local Binary Pattern (LBP). Ekstraksi fitur dengan GLCM dan DWT menghasilkan 24 fitur yang terdiri dari 5 fitur GLCM dan koefisien energi dalam 4 sub-band channel hasil dekomposisi. Sementara ekstraksi fitur dengan LBP dilakukan dengan membangun histogram dari matriks hasil LBP. Fitur-fitur tersebut kemudian menjadi masukan bagi jaringan syaraf tiruan untuk kemudian diklasifikasi dalam 5 jenis batik: buketan, ceplok, kawung, parang dan truntum. Dataset yang digunakan adalah 50 gambar batik (10 untuk setiap motif) yang diambil secara acak di internet. Pengujian dilakukan dengan membandingkan dua kelompok fitur: DWT-GLCM dan DWT-GLCM-LBP untuk mengetahui metode ekstraksi fitur yang lebih baik dalam deteksi motif batik. Metode pengujian yang digunakan adalah K-Fold Cross Validation dengan hasil berupa confusion matriks untuk kemudian dihitung nilai akurasi dan F-Measure-nya. Hasil pengujian menunjukkan bahwa penggunaan ekstraksi fitur tekstur DWT-GLCM-LBP mampu mencapai akurasi 74% dengan jenis dekomposisi Daubechies 4 level 3.</p>

scholarly journals Prediction of University Student Performance Based on Tracer Study Dataset Using Artificial Neural Network

2021 ◽  
Vol 5 (2) ◽  
pp. 72-82
Author(s):  
Zahrina Aulia Adriani ◽  
Irma Palupi
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Standard Deviation ◽  
Student Performance ◽  
Cross Validation ◽  
Training Data ◽  
Tracer Study ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Fold Cross Validation

In order to increase student performance, several universities use machine learning to analyze and evaluate their data so that it enables to improve the quality of education in the university. To get a new insight from the tracer study dataset as the relevance between university performance and student capability with business and industries work, the author will develop a model to predict student performance based on the tracer study dataset using Artificial Neural Network (ANN). For obtaining attributes that correspond to labels, Phi Coefficient Correlation will be used to select the attributes with high correlation as Feature Selection. The author is also performing the oversampling method using Synthetic Minority Oversampling Technique (SMOTE) because this dataset is imbalanced and evaluates the model using K-Fold Cross-Validation. According to K-Fold Cross Validation, the result shows that K = 3 has a low standard deviation of evaluation score and it's the best candidate of K to split the dataset. The average standard deviation is 0.038 for all score evaluations (Accuracy, Precision, Recall, and F-1 Score). After applied SMOTE to treating the imbalanced dataset with the data splitting 65 training data and 35 testing data, the accuracy value increases by 10% from 0.77 to 0.87.

scholarly journals Deep artificial neural network based on environmental sound data for the generation of a children activity classification model

2020 ◽  
Vol 6 ◽  
pp. e308
Author(s):  
Antonio García-Domínguez ◽  
Carlos E. Galvan-Tejada ◽  
Laura A. Zanella-Calzada ◽  
Hamurabi Gamboa ◽  
Jorge I. Galván-Tejada ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Cross Validation ◽  
Classification Model ◽  
Activity Classification ◽  
Environmental Sound ◽  
Artificial Neural ◽  
The Creation ◽  
Fold Cross Validation ◽  
Sound Data

Children activity recognition (CAR) is a subject for which numerous works have been developed in recent years, most of them focused on monitoring and safety. Commonly, these works use as data source different types of sensors that can interfere with the natural behavior of children, since these sensors are embedded in their clothes. This article proposes the use of environmental sound data for the creation of a children activity classification model, through the development of a deep artificial neural network (ANN). Initially, the ANN architecture is proposed, specifying its parameters and defining the necessary values for the creation of the classification model. The ANN is trained and tested in two ways: using a 70–30 approach (70% of the data for training and 30% for testing) and with a k-fold cross-validation approach. According to the results obtained in the two validation processes (70–30 splitting and k-fold cross validation), the ANN with the proposed architecture achieves an accuracy of 94.51% and 94.19%, respectively, which allows to conclude that the developed model using the ANN and its proposed architecture achieves significant accuracy in the children activity classification by analyzing environmental sound.

scholarly journals NOVEL APPROACH TO IMPROVE GEOCENTRIC TRANSLATION MODEL PERFORMANCE USING ARTIFICIAL NEURAL NETWORK TECHNOLOGY

2017 ◽  
Vol 23 (1) ◽  
pp. 213-233 ◽  
Author(s):  
Yao Yevenyo Ziggah ◽  
Hu Youjian ◽  
Prosper Basommi Laari ◽  
Zhenyang Hui
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Coordinate Transformation ◽  
Position Error ◽  
Transformation Model ◽  
Network Technology ◽  
Horizontal Position ◽  
Translation Model ◽  
Artificial Neural ◽  
Neural Network Technology

Abstract: Geocentric translation model (GTM) in recent times has not gained much popularity in coordinate transformation research due to its attainable accuracy. Accurate transformation of coordinate is a major goal and essential procedure for the solution of a number of important geodetic problems. Therefore, motivated by the successful application of Artificial Intelligence techniques in geodesy, this study developed, tested and compared a novel technique capable of improving the accuracy of GTM. First, GTM based on official parameters (OP) and new parameters determined using the arithmetic mean (AM) were applied to transform coordinate from global WGS84 datum to local Accra datum. On the basis of the results, the new parameters (AM) attained a maximum horizontal position error of 1.99 m compared to the 2.75 m attained by OP. In line with this, artificial neural network technology of backpropagation neural network (BPNN), radial basis function neural network (RBFNN) and generalized regression neural network (GRNN) were then used to compensate for the GTM generated errors based on AM parameters to obtain a new coordinate transformation model. The new implemented models offered significant improvement in the horizontal position error from 1.99 m to 0.93 m.

Random Forest (RF) and Artificial Neural Network (ANN) Algorithms for LULC Mapping

2020 ◽  
Vol 38 (4A) ◽  
pp. 510-514
Author(s):  
Tay H. Shihab ◽  
Amjed N. Al-Hameedawi ◽  
Ammar M. Hamza
Keyword(s):  
Neural Network ◽  
Remote Sensing ◽  
Artificial Neural Network ◽  
Random Forest ◽  
Satellite Image ◽  
Landsat 8 ◽  
Optical Remote Sensing ◽  
Data Set ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

In this paper to make use of complementary potential in the mapping of LULC spatial data is acquired from LandSat 8 OLI sensor images are taken in 2019.  They have been rectified, enhanced and then classified according to Random forest (RF) and artificial neural network (ANN) methods. Optical remote sensing images have been used to get information on the status of LULC classification, and extraction details. The classification of both satellite image types is used to extract features and to analyse LULC of the study area. The results of the classification showed that the artificial neural network method outperforms the random forest method. The required image processing has been made for Optical Remote Sensing Data to be used in LULC mapping, include the geometric correction, Image Enhancements, The overall accuracy when using the ANN methods 0.91 and the kappa accuracy was found 0.89 for the training data set. While the overall accuracy and the kappa accuracy of the test dataset were found 0.89 and 0.87 respectively.

Watermelon Sorting Process by Frequency Identification and Artificial Neural Network

2021 ◽  
Author(s):  
Komsan Wongkalasin ◽  
Teerapon Upachaban ◽  
Wacharawish Daosawang ◽  
Nattadon Pannucharoenwong ◽  
Phadungsak Ratanadecho
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Selection Process ◽  
Network Processor ◽  
Data Set ◽  
The Neural Network ◽  
Frequency Identification ◽  
Sorting Process ◽  
Artificial Neural ◽  
Reasonable Prediction

This research aims to enhance the watermelon’s quality selection process, which was traditionally conducted by knocking the watermelon fruit and sort out by the sound’s character. The proposed method in this research is generating the sound spectrum through the watermelon and then analyzes the response signal’s frequency and the amplitude by Fast Fourier Transform (FFT). Then the obtained data were used to train and verify the neural network processor. The result shows that, the frequencies of 129 and 172 Hz were suit to be used in the comparison. Thirty watermelons, which were randomly selected from the orchard, were used to create a data set, and then were cut to manually check and match to the fruits’ quality. The 129 Hz frequency gave the response ranging from 13.57 and above in 3 groups of watermelons quality, including, not fully ripened, fully ripened, and close to rotten watermelons. When the 172 Hz gave the response between 11.11–12.72 in not fully ripened watermelons and those of 13.00 or more in the group of close to rotten and hollow watermelons. The response was then used as a training condition for the artificial neural network processor of the sorting machine prototype. The verification results provided a reasonable prediction of the ripeness level of watermelon and can be used as a pilot prototype to improve the efficiency of the tools to obtain a modern-watermelon quality selection tool, which could enhance the competitiveness of the local farmers on the product quality control.

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