scholarly journals Artificial intelligence with deep learning algorithms to model relationships between total tree height and diameter at breast height

2020 ◽  
Vol 29 (2) ◽  
pp. e013
Author(s):  
İlker Ercanli
Keyword(s):  
Artificial Intelligence ◽  
Deep Learning ◽  
Mean Squared Error ◽  
Tree Height ◽  
Absolute Error ◽  
Diameter At Breast Height ◽  
Root Mean Squared Error ◽  
Squared Error ◽  
Hidden Layer ◽  
Total Tree

Aim of Study: As an innovative prediction technique, Artificial Intelligence technique based on a Deep Learning Algorithm (DLA) with various numbers of neurons and hidden layer alternatives were trained and evaluated to predict the relationships between total tree height (TTH) and diameter at breast height (DBH) with nonlinear least squared (NLS) regression models and nonlinear mixed effect (NLME) regression models.Area of Study: The data of this study were measured from even-aged, pure Turkish Pine (Pinus brutia Ten.) stands in the Kestel Forests located in the Bursa region of northwestern Turkey.Material and Methods: 1132 pairs of TTH-DBH measurements from 132 sample plots were used for modeling relationships between TTH, DBH, and stand attributes such as dominant height (Ho) and diameter (Do).Main Results: The combination of 100 # neurons and 8 # hidden layer in DLA resulted in the best predictive total height prediction values with Average Absolute Error (0.4188), max. Average Absolute Error (3.7598), Root Mean Squared Error (0.6942), Root Mean Squared error % (5.2164), Akaike Information Criteria (-345.4465), Bayesian Information Criterion (-330.836), the average Bias (0.0288) and the average Bias % (0.2166), and fitting abilities with r (0.9842) and Fit Index (0.9684). Also, the results of equivalence tests showed that the DLA technique successfully predicted the TTH in the validation dataset.Research highlights: These superior fitting scores coupled with the validation results in TTH predictions suggested that deep learning network models should be considered an alternative to the traditional nonlinear regression techniques and should be given importance as an innovative prediction technique.Keywords: Prediction; artificial intelligence; deep learning algorithms; number of neurons; hidden layer alternatives.Abbreviations: TTH (total tree height), DBH (diameter at breast height), OLS (ordinary least squares), NLME (nonlinear mixed effect), AIT (Artificial Intelligence Techniques), ANN (Artificial Neural Network), DLA (Deep Learning Algorithm), GPU (Graphical Processing Units), NLS (nonlinear least squared), RMSE (root mean squared error), AIC (Akaike information criteria), BIC (Bayesian information criterion), FI (fit index), AAE (average absolute error), BLUP (best linear unbiased predictor), TOST (two one-sided test method). 

scholarly journals Linear Regression Algorithm in Machine Learning through MATLAB

2021 ◽  
Vol 9 (12) ◽  
pp. 989-995
Author(s):  
Kalva Sindhu Priya
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Deep Learning ◽  
Linear Regression ◽  
Mean Squared Error ◽  
Critical Part ◽  
Squared Error ◽  
Master Level ◽  
Predicted Model ◽  
The Given

Abstract: In the present scenario, it is quite aware that almost every field is moving into machine based automation right from fundamentals to master level systems. Among them, Machine Learning (ML) is one of the important tool which is most similar to Artificial Intelligence (AI) by allowing some well known data or past experience in order to improve automatically or estimate the behavior or status of the given data through various algorithms. Modeling a system or data through Machine Learning is important and advantageous as it helps in the development of later and newer versions. Today most of the information technology giants such as Facebook, Uber, Google maps made Machine learning as a critical part of their ongoing operations for the better view of users. In this paper, various available algorithms in ML is given briefly and out of all the existing different algorithms, Linear Regression algorithm is used to predict a new set of values by taking older data as reference. However, a detailed predicted model is discussed clearly by building a code with the help of Machine Learning and Deep Learning tool in MATLAB/ SIMULINK. Keywords: Machine Learning (ML), Linear Regression algorithm, Curve fitting, Root Mean Squared Error

scholarly journals Data Mining for Healthcare Data: A Comparison of Neural Networks Algorithms

2017 ◽  
Vol 3 (1) ◽  
pp. 10
Author(s):  
Debby E. Sondakh
Keyword(s):  
Neural Networks ◽  
Multilayer Perceptron ◽  
Mean Squared Error ◽  
Absolute Error ◽  
Error Rates ◽  
Large Dataset ◽  
Training Time ◽  
Healthcare Data ◽  
Squared Error ◽  
Hidden Layer

Classification has been considered as an important tool utilized for the extraction of useful information from healthcare dataset. It may be applied for recognition of disease over symptoms. This paper aims to compare and evaluate different approaches of neural networks classification algorithms for healthcare datasets. The algorithms considered here are Multilayer Perceptron, Radial Basis Function, and Voted Perceptron which are tested based on resulted classifiers accuracy, precision, mean absolute error and root mean squared error rates, and classifier training time. All the algorithms are applied for five multivariate healthcare datasets, Echocardiogram, SPECT Heart, Chronic Kidney Disease, Mammographic Mass, and EEG Eye State datasets. Among the three algorithms, this study concludes the best algorithm for the chosen datasets is Multilayer Perceptron. It achieves the highest for all performance parameters tested. It can produce high accuracy classifier model with low error rate, but suffer in training time especially of large dataset. Voted Perceptron performance is the lowest in all parameters tested. For further research, an investigation may be conducted to analyze whether the number of hidden layer in Multilayer Perceptron’s architecture has a significant impact on the training time.

PREDIKSI HARGA CRYPTOCURRENCY DENGAN METODE K-NEAREST NEIGHBOURS

2018 ◽  
Vol 14 (2) ◽  
pp. 137
Author(s):  
Haerul Fatah ◽  
Agus Subekti
Keyword(s):  
Mean Squared Error ◽  
Mean Absolute Error ◽  
Search Algorithm ◽  
Absolute Error ◽  
Nearest Neighbour ◽  
Root Mean Squared Error ◽  
Squared Error ◽  
Nearest Neighbours

Uang elektronik menjadi pilihan yang mulai ramai digunakan oleh banyak orang, terutama para pengusaha, pebisnis dan investor, karena menganggap bahwa uang elektronik akan menggantikan uang fisik dimasa depan. Cryptocurrency muncul sebagai jawaban atas kendala uang eletronik yang sangat bergantung kepada pihak ketiga. Salah satu jenis Cryptocurrency yaitu Bitcoin. Analogi keuangan Bitcoin sama dengan analogi pasar saham, yakni fluktuasi harga tidak tentu setiap detik. Tujuan dari penelitian yang dilakukan yaitu melakukan prediksi harga Cryptocurrency dengan menggunakan metode KNN (K-Nearest Neighbours). Hasil dari penelitian ini diketahui bahwa model KNN yang paling baik dalam memprediksi harga Cryptocurrency adalah KNN dengan parameter nilai K=3 dan Nearest Neighbour Search Algorithm : Linear NN Search. Dengan nilai Mean Absolute Error (MAE) sebesar 0.0018 dan Root Mean Squared Error (RMSE) sebesar 0.0089.

scholarly journals Equations Relating Compacted and Uncompacted Live Crown Ratio for Common Tree Species in the South

2010 ◽  
Vol 34 (3) ◽  
pp. 118-123 ◽  
Author(s):  
KaDonna C. Randolph
Keyword(s):  
Mean Squared Error ◽  
Model Performance ◽  
Absolute Error ◽  
Logistic Function ◽  
Root Mean Squared Error ◽  
Fit Statistics ◽  
Squared Error ◽  
Hardwood Species ◽  
Using Data ◽  
Species Specific

Abstract Species-specific equations to predict uncompacted crown ratio (UNCR) from compacted live crown ratio (CCR), tree length, and stem diameter were developed for 24 species and 12 genera in the southern United States. Using data from the US Forest Service Forest Inventory and Analysis program, nonlinear regression was used to model UNCR with a logistic function. Model performance was evaluated with standard fit statistics (root mean squared error, mean absolute error, mean error, and model efficiency) and by comparing the results of using the observed and predicted UNCR values in secondary applications. Root mean squared error for the regression models ranged from 0.062 to 0.176 UNCR and averaged 0.114 UNCR across all models. Height to live crown base calculations and crown width estimations based on the observed and predicted UNCR values were in close agreement. Overall, the models performed well for the Pinus and Taxodium genera and several individual hardwood species; however, model performance was generally poor for the Acer, Quercus, and Carya genera.

scholarly journals Performance Prediction for Higher Education Students Using Deep Learning

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Shuping Li ◽  
Taotang Liu
Keyword(s):  
Neural Network ◽  
Higher Education ◽  
Deep Learning ◽  
Mean Squared Error ◽  
Absolute Error ◽  
Warning Signs ◽  
Education Students ◽  
Squared Error ◽  
Data Prediction ◽  
Training Mode

Predicting students’ performance is very important in matters related to higher education as well as with regard to deep learning and its relationship to educational data. Prediction of students’ performance provides support in selecting courses and designing appropriate future study plans for students. In addition to predicting the performance of students, it helps teachers and managers to monitor students in order to provide support to them and to integrate the training programs to obtain the best results. One of the benefits of student’s prediction is that it reduces the official warning signs as well as expelling students because of their inefficiency. Prediction provides support to the students themselves through their choice of courses and study plans appropriate to their abilities. The proposed method used deep neural network in prediction by extracting informative data as a feature with corresponding weights. Multiple updated hidden layers are used to design neural network automatically; number of nodes and hidden layers controlled by feed forwarding and backpropagation data are produced by previous cases. The training mode is used to train the system with labeled data from dataset and the testing mode is used for evaluating the system. Mean absolute error (MAE) and root mean squared error (RMSE) with accuracy used for evolution of the proposed method. The proposed system has proven its worth in terms of efficiency through the achieved results in MAE (0.593) and RMSE (0.785) to get the best prediction.

scholarly journals Rainfall Analysis and Forecasting Using Deep Learning Technique

2021 ◽  
Vol 2 (2) ◽  
pp. 1-11
Author(s):  
Pragati Kanchan ◽  
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Mean Squared Error ◽  
Predictive Accuracy ◽  
Mean Absolute Percentage Error ◽  
Percentage Error ◽  
Root Mean Squared Error ◽  
Rainfall Prediction ◽  
Absolute Percentage Error ◽  
Squared Error

Rainfall forecasting is very challenging due to its uncertain nature and dynamic climate change. It's always been a challenging task for meteorologists. In various papers for rainfall prediction, different Data Mining and Machine Learning (ML) techniques have been used. These techniques show better predictive accuracy. A deep learning approach has been used in this study to analyze the rainfall data of the Karnataka Subdivision. Three deep learning methods have been used for prediction such as Artificial Neural Network (ANN) - Feed Forward Neural Network, Simple Recurrent Neural Network (RNN), and the Long Short-Term Memory (LSTM) optimized RNN Technique. In this paper, a comparative study of these three techniques for monthly rainfall prediction has been given and the prediction performance of these three techniques has been evaluated using the Mean Absolute Percentage Error (MAPE%) and a Root Mean Squared Error (RMSE%). The results show that the LSTM Model shows better performance as compared to ANN and RNN for Prediction. The LSTM model shows better performance with mini-mum Mean Absolute Percentage Error (MAPE%) and Root Mean Squared Error (RMSE%).

scholarly journals Prediksi Inflasi Indonesia Memakai Model ARIMA dan Artificial Neural Network

2019 ◽  
Vol 5 (1) ◽  
Author(s):  
Wahyudin S
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Analysis Of Variance ◽  
Mean Squared Error ◽  
Statistics Analysis ◽  
Root Mean Squared Error ◽  
Squared Error ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Hidden Layer

Inflasi merupakan indikator makro ekonomi yang sangat penting. Berbagai macam metoda prediksi inflasi Indonesia telah dipublikasikan. Namun pencarian metoda prediksi inflasi yang lebih akurat masih menjadi topik menarik. Pada penulisan ini diusulkan sebuah metoda baru untuk prediksi inflasi memakai model ARIMA dan Artificial Neural Network (ANN). Data inflasi yang digunakan adalah data inflasi bulanan year-on-year dari tahun 2010 sampai dengan tahun 2018 yang diterbitkan oleh Badan Pusat Statistik (BPS). Pertama dibuat 2 model ARIMA yaitu model ARIMA tanpa siklus tahunan dan dengan siklus tahunan. Prosedur standar dan diagostics test telah dilakukan antara lain: summary of statistics, analysis of variance (ANOVA), significance of coefficients test, residuals normality, heterocesdacity, dan stability. Dari hasil perbandingan kinerja memakai Root Mean Squared Error (RMSE) diperoleh bahwa model ARIMA dengan siklus tahunan lebih baik. Model tersebut berupa model ARIMA (2,1,0) (2,0,0) [12]. Kemudian, untuk meningkatkan kinerja prediksi inflasi, ANN telah dibuat berbasis model ARIMA tersebut. Model ANN memakai satu hidden layer dan dua neuron. Hasil pengujian menunjukkan bahwa model ANN menghasilkan RMSE yang lebih kecil daripada model ARIMA (2,1,0) (2,0,0) [12]. Hal ini kemungkinan disebabkan oleh kemampuan mengolah hubungan nonlinear antara variabel target dan variabel penjelas.

scholarly journals Prediction Of Use Of Electric Kwh Using Support Vector Regression (SVR) Method (Case Study: Pt Pln (Persero) Rayon Seririt)

Repositor ◽  
2020 ◽  
Vol 2 (4) ◽  
pp. 525
Author(s):  
Rima Mediana ◽  
Setio Basuki ◽  
Nur Hayatin
Keyword(s):  
Radial Basis Function ◽  
Support Vector Regression ◽  
Basis Function ◽  
Mean Squared Error ◽  
Mean Absolute Error ◽  
Absolute Error ◽  
Support Vector ◽  
Root Mean Squared Error ◽  
Squared Error ◽  
Radial Basis

AbstrakPeranan listrik sangat penting bagi kehidupan masyarakat, begitu pentingnya peranan listrik tentu saja berdampak pada kebutuhan listrik yang begitu besar, maka PT. PLN (Persero) Rayon Seririt sebagai penyedia tenaga listrik harus bisa memprediksi besarnya peggunaan listrik rumah tangga setiap harinya. Selain itu menyebabkan semakin besar pula pemakian kwh listik, apabila pemakaian kwh listrik tidak diolah dengan baik akan menimbulkan beban energi listrik yang tidak terbendung. Dengan permasalahan yang telah diuraikan, penelitian ini menerapkan algoritma Support Vector Regression dalam Prediksi Pemakain KWH Listrik untuk mengetahui besarnya pemakaian kwh listrik yang akan datang. Berdasarkan hasil pengujian yang dilakukan hasil nilai akurasi terbaik Mean Absolute Error (MAE) sebesar 133560,1, Root Mean Squared Error (RMSE) sebesar 167664,1, dan Koefisien Korelasi sebesar 84,0 pada kernel polynomial. Sehingga algoritma Support Vector Regression dan fungsi kernel Radial Basis Function (RBF) cocok digunakan dalam memprediksi pemakaian kwh listrik.AbstractThe role of electricity is really significant for societies' live and it brings the huge impacts on the needs of electricity. This circumstance makes PT. PLN (Persero) Rayon Seririt as the provider of electricity must be able to predict the amount of household electricity usage steadily. This also causes the greater use of kwh electricity, if the use of kwh electricity is not treated properly, it will cause the burden of electrical energy is unstoppable.  Through the problems that have been elaborated, this study implements the Support Vector Regression algorithm in the prediction of kwh electricity usage to know the amount of  kwh electricity usage that will come.Based on the results of tests that have been conducted,  the result of best accuracy value Mean Absolute Error (MAE) equal to 133560,1, Root Mean Squared Error (RMSE) equal to 133560,1, and Correlation Coefficient equal to 84,0 at Radial Base Function kernel. It means, the Support Vector Regression algorithm and Radial Basis Function kernel function (RBF) are suitable to predict the use of kwh electricity.

scholarly journals Algoritma Deep Learning-LSTM untuk Memprediksi Umur Transformator

2021 ◽  
Vol 8 (3) ◽  
pp. 539
Author(s):  
Ayu Ahadi Ningrum ◽  
Iwan Syarif ◽  
Agus Indra Gunawan ◽  
Edi Satriyanto ◽  
Rosmaliati Muchtar
Keyword(s):  
Quality Of Service ◽  
Deep Learning ◽  
Linear Regression ◽  
Multilayer Perceptron ◽  
Mean Squared Error ◽  
Series Data ◽  
Gradient Boosting ◽  
Root Mean Squared Error ◽  
Squared Error

<p>Kualitas dan ketersediaan pasokan listrik menjadi hal yang sangat penting. Kegagalan pada transformator menyebabkan pemadaman listrik yang dapat menurunkan kualitas layanan kepada pelanggan. Oleh karena itu, pengetahuan tentang umur transformator sangat penting untuk menghindari terjadinya kerusakan transformator secara mendadak yang dapat mengurangi kualitas layanan pada pelanggan. Penelitian ini bertujuan untuk mengembangkan aplikasi yang dapat memprediksi umur transformator secara akurat menggunakan metode <em>Deep Learning-LSTM. LSTM </em>adalah metode yang dapat digunakan untuk mempelajari suatu pola pada data deret waktu. Data yang digunakan dalam penelitian ini bersumber dari 25 unit transformator yang meliputi data dari sensor arus, tegangan, dan suhu. Analisis performa yang digunakan untuk mengukur kinerja LSTM adalah <em>Root Mean Squared Error</em> (RMSE) dan <em>Squared Correlation (SC</em>). Selain LSTM, penelitian ini juga menerapkan <em>algoritma Multilayer Perceptron, Linear Regression,</em> dan <em>Gradient Boosting Regressor</em> sebagai algoritma pembanding.  Hasil eksperimen menunjukkan bahwa LSTM mempunyai kinerja yang sangat bagus setelah dilakukan pencarian komposisi data, seleksi fitur menggunakan algoritma KBest dan melakukan percobaan beberapa variasi parameter. Hasil penelitian menunjukkan bahwa metode <em>Deep Learning-LSTM</em> mempunyai kinerja yang lebih baik daripada 3 algoritma lain yaitu nilai RMSE= 0,0004 dan nilai <em>Squared Correlation</em>= 0,9690.</p><p> </p><p><em><strong>Abstract</strong></em></p><p><em></em><em>The quality and availability of the electricity supply is very important. Failures in the transformer cause power outages which can reduce the quality of service to customers. Therefore, knowledge of transformer life is very important to avoid sudden transformer damage which can reduce the quality of service to customers. This study aims to develop applications that can predict transformer life accurately using the Deep Learning-LSTM method. LSTM is a method that can be used to study a pattern in time series data. The data used in this research comes from 25 transformer units which include data from current, voltage, and temperature sensors. The performance analysis used to measure LSTM performance is Root Mean Squared Error (RMSE) and Squared Correlation (SC). Apart from LSTM, this research also applies the Multilayer Perceptron algorithm, Linear Regression, and Gradient Boosting Regressor as a comparison algorithm. The experimental results show that LSTM has a very good performance after searching for the composition of the data, selecting features using the KBest algorithm and experimenting with several parameter variations. The results showed that the Deep Learning-LSTM method had better performance than the other 3 algorithms, namely the value of RMSE = 0.0004 and the value of Squared Correlation = 0.9690.</em></p>

scholarly journals Non-invasive Estimation of Haemoglobin Level Using PCA and Artificial Neural Networks

2019 ◽  
Vol 13 (1) ◽  
pp. 114-119 ◽  
Author(s):  
M. Lakshmi ◽  
P. Manimegalai
Keyword(s):  
Principal Components ◽  
Mean Squared Error ◽  
Mean Absolute Error ◽  
Absolute Error ◽  
Root Mean Squared Error ◽  
Non Invasive ◽  
Squared Error ◽  
Wide Range ◽  
R Value ◽  
Invasive Measurement

Objective: Haemoglobin(Hb) measurement is generally performed by the traditional “fingerstick” test i.e., by invasively drawing blood from the body. Although the conventional laboratory measurement is accurate, it has its own limitations such as time delay, inconvenience of the patient, exposure to biohazards and the lack of real-time monitoring in critical situations. Non-invasive Haemoglobin Measurement (SpHb) has gained enormous attention among researches and can provide an earlier diagnosis to polycythemia, anaemia, various cardiovascular diseases, etc. Currently, Photoplethysmograph signal (PPG) is used for measuring oxygen saturation, to monitor the depth of anesthesia, heart rate and respiration monitoring. But through detailed statistical analysis, PPG signal can provide further information about various blood components. Investigation / Methodology: In this paper, an approach for non-invasive measurement of Hb using PPG, Principal Component Analysis (PCA) and Neural Network is proposed. A transmissive type PPG sensor is developed which is interfaced with Crowduino for the acquisition of PPG. From the obtained PPG signal, Principal Components (PC) are extracted. SpHb is predicted followed by the extraction of features from the PC. The analysis involves the SpHb prediction using a single PC, double PC and finally all the three PC. The predicted SpHb is evaluated with Hblab in terms of R-value, Mean Absolute Error, Mean Squared Error and Root Mean Squared Error. Conclusion: An approach for non-invasive measurement of Hb using Principal Components obtained from the PPG signal is discussed. The SpHb value is compared with the Hblab values. Correlation R-value between SpHb and Hblab is 0.77 when three principal components are used. Mean Absolute Error (MAE), Mean Squared Error (MSE), Root Mean Squared Error (RMSE) between SpHb and Hblab are 0.3, 0.44 and 0.6633 respectively when SpHb is measured with three principal components. It is evident from the result analysis that SpHb shows the promising result when all the three principal components are used. However, one of the limitations of the work is that the population setting chosen for the work does not include paediatric patients, accurately ill patient, pregnant population and surgical patients. With detailed analysis on a wide range of population setting, Hb prediction using PPG is a promising approach for non-invasive measurement.

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