scholarly journals Bed material load estimation in channels using machine learning and meta-heuristic methods

2017 ◽  
Vol 20 (1) ◽  
pp. 100-116 ◽  
Author(s):  
Shahram Sahraei ◽  
Mohammad Reza Alizadeh ◽  
Nasser Talebbeydokhti ◽  
Maryam Dehghani
Keyword(s):  
Machine Learning ◽  
Pso Algorithm ◽  
Least Square ◽  
Support Vector ◽  
Proposed Model ◽  
Load Transport ◽  
Efficient Alternative ◽  
Material Load ◽  
Decision Tree Method ◽  
Bed Material

Abstract This study is trying to develop an alternative approach to the issues of sediment transport simulation. A machine learning method, named least square support vector regression (LSSVR) and a meta-heuristic approach, called particle swarm optimization (PSO) algorithm are used to estimate bed material load transport rate. PSO algorithm is utilized to calibrate the parameters involved in the model to facilitate a desirable simulation by LSSVR. Implementing on a set of laboratory and field data, the model is capable of performing more satisfactorily in comparison to candidate traditional methods. Similarly, the proposed method has a better performance than a specific version of decision tree method. To enhance the model, the variables are scaled in logarithmic form, leading to an improvement in the results. Thus, the proposed model can be an efficient alternative to conventional approaches for the simulation of bed material load transport rates providing comparable accuracy.

scholarly journals Monitoring the Foliar Nutrients Status of Mango Using Spectroscopy-Based Spectral Indices and PLSR-Combined Machine Learning Models

2021 ◽  
Vol 13 (4) ◽  
pp. 641
Author(s):  
Gopal Ramdas Mahajan ◽  
Bappa Das ◽  
Dayesh Murgaokar ◽  
Ittai Herrmann ◽  
Katja Berger ◽  
...  
Keyword(s):  
Machine Learning ◽  
Remote Sensing ◽  
Partial Least Square ◽  
Least Square ◽  
Partial Least Square Regression ◽  
Support Vector ◽  
Spectral Indices ◽  
Learning Models ◽  
Leaf Nutrients ◽  
Machine Learning Models

Conventional methods of plant nutrient estimation for nutrient management need a huge number of leaf or tissue samples and extensive chemical analysis, which is time-consuming and expensive. Remote sensing is a viable tool to estimate the plant’s nutritional status to determine the appropriate amounts of fertilizer inputs. The aim of the study was to use remote sensing to characterize the foliar nutrient status of mango through the development of spectral indices, multivariate analysis, chemometrics, and machine learning modeling of the spectral data. A spectral database within the 350–1050 nm wavelength range of the leaf samples and leaf nutrients were analyzed for the development of spectral indices and multivariate model development. The normalized difference and ratio spectral indices and multivariate models–partial least square regression (PLSR), principal component regression, and support vector regression (SVR) were ineffective in predicting any of the leaf nutrients. An approach of using PLSR-combined machine learning models was found to be the best to predict most of the nutrients. Based on the independent validation performance and summed ranks, the best performing models were cubist (R2 ≥ 0.91, the ratio of performance to deviation (RPD) ≥ 3.3, and the ratio of performance to interquartile distance (RPIQ) ≥ 3.71) for nitrogen, phosphorus, potassium, and zinc, SVR (R2 ≥ 0.88, RPD ≥ 2.73, RPIQ ≥ 3.31) for calcium, iron, copper, boron, and elastic net (R2 ≥ 0.95, RPD ≥ 4.47, RPIQ ≥ 6.11) for magnesium and sulfur. The results of the study revealed the potential of using hyperspectral remote sensing data for non-destructive estimation of mango leaf macro- and micro-nutrients. The developed approach is suggested to be employed within operational retrieval workflows for precision management of mango orchard nutrients.

BioSignal modelling for prediction of cardiac diseases using intra group selection method

2021 ◽  
Vol 15 (1) ◽  
pp. 151-160
Author(s):  
Hemant P. Kasturiwale ◽  
Sujata N. Kale
Keyword(s):  
Machine Learning ◽  
Nervous System ◽  
Random Forest ◽  
Normal Sinus Rhythm ◽  
Heart Defects ◽  
Support Vector ◽  
Autonomous Nervous System ◽  
Cardiac Diseases ◽  
Vast Number ◽  
Proposed Model

The Autonomous Nervous System (ANS) controls the nervous system and Heart Rate Variability (HRV) can be used as a diagnostic tool to diagnose heart defects. HRV can be classified into linear and nonlinear HRV indices which are used mostly to measure the efficiency of the model. For prediction of cardiac diseases, the selection and extraction features of machine learning model are effective. The available model used till date is based on HRV indices to predict the cardiac diseases accurately. The model could hardly throw light on specifics of indices, selection process and stability of the model. The proposed model is developed considering all facet electrocardiogram amplitude (ECG), frequency components, sampling frequency, extraction methods and acquisition techniques. The machine learning based model and its performance shall be tested using the standard BioSignal method, both on the data available and on the data obtained by the author. This is unique model developed by considering the vast number of mixtures sets and more than four complex cardiac classes. The statistical analysis is performed on a variety of databases such as MIT/BIH Normal Sinus Rhythm (NSR), MIT/BIH Arrhythmia (AR) and MIT/BIH Atrial Fibrillation (AF) and Peripheral Pule Analyser using feature compatibility techniques. The classifiers are trained for prediction with approximately 40000 sets of parameters. The proposed model reaches an average accuracy of 97.87 percent and is sensitive and précised. The best features are chosen from the different HRV features that will be used for classification. The present model was checked under all possible subject scenarios, such as the raw database and the non-ECG signal. In this sense, robustness is defined not only by the specificity parameter, but also by other measuring output parameters. Support Vector Machine (SVM), K-nearest Neighbour (KNN), Ensemble Adaboost (EAB) with Random Forest (RF) are tested in a 5% higher precision band and a lower band configuration. The Random Forest has produced better results, and its robustness has been established.

Civil Aeroengine Fault Diagnosis Based on Fuzzy Least Square Support Vector Machine

2011 ◽  
Vol 130-134 ◽  
pp. 2047-2050 ◽  
Author(s):  
Hong Chun Qu ◽  
Xie Bin Ding
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Support Vector Machine ◽  
Fault Diagnosis ◽  
Coefficient Matrix ◽  
Least Square ◽  
Support Vector ◽  
Influence Coefficient ◽  
Structural Risk ◽  
Better Than

SVM(Support Vector Machine) is a new artificial intelligence methodolgy, basing on structural risk mininization principle, which has better generalization than the traditional machine learning and SVM shows powerfulability in learning with limited samples. To solve the problem of lack of engine fault samples, FLS-SVM theory, an improved SVM, which is a method is applied. 10 common engine faults are trained and recognized in the paper.The simulated datas are generated from PW4000-94 engine influence coefficient matrix at cruise, and the results show that the diagnostic accuracy of FLS-SVM is better than LS-SVM.

A NOVEL FIVE-CATEGORY LOAN-RISK EVALUATION MODEL USING MULTICLASS LS-SVM BY PSO

2012 ◽  
Vol 11 (04) ◽  
pp. 857-874 ◽  
Author(s):  
JIE CAO ◽  
HONGKE LU ◽  
WEIWEI WANG ◽  
JIAN WANG
Keyword(s):  
Financial Regulation ◽  
Evaluation Model ◽  
Least Square ◽  
Support Vector ◽  
Accuracy Rate ◽  
Rural Credit ◽  
Proposed Model ◽  
Loan Risk ◽  
Regulation Approach ◽  
Risk Evaluation Model

Five-category loan classification (FCLC) is an international financial regulation approach. Recently, the application and implementation of FCLC in the Chinese microfinance bank has mostly relied on subjective judgment, and it is difficult to control and lower loan risk. In view of this, this paper is dedicated to researching and solving this problem by constructing the FCLC model based on improved particle-swarm optimization (PSO) and the multiclass, least-square, support-vector machine (LS-SVM). First, LS-SVM is the extension of SVM, which is proposed to achieve multiclass classification. Then, improved PSO is employed to determine the parameters of multiclass LS-SVM for improving classification accuracy. Finally, some experiments are carried out based on rural credit cooperative data to demonstrate the performance of our proposed model. The results show that the proposed model makes a distinct improvement in the accuracy rate compared with one-vs.-one (1-v-1) LS-SVM, one-vs.-rest (1-v-r) LS-SVM, 1-v-1 SVM, and 1-v-r SVM. In addition, it is an effective tool in solving the problem of loan-risk rating.

Research on Combined Prediction Model for Busy Telephone Traffic

2014 ◽  
Vol 610 ◽  
pp. 789-796
Author(s):  
Jiang Bao Li ◽  
Zhen Hong Jia ◽  
Xi Zhong Qin ◽  
Lei Sheng ◽  
Li Chen
Keyword(s):  
Prediction Model ◽  
Prediction Accuracy ◽  
Low Frequency ◽  
Prediction Method ◽  
Pso Algorithm ◽  
Frequency Component ◽  
Least Square ◽  
High Frequency Component ◽  
Support Vector ◽  
Telephone Traffic

In order to improve the prediction accuracy of busy telephone traffic, this study proposes a busy telephone traffic prediction method that combines wavelet transformation and least square support vector machine (lssvm) model which is optimized by particle swarm optimization (pso) algorithm. Firstly, decompose the pretreatment of busy telephone traffic data with mallat algorithm and get low frequency component and high frequency component. Secondly, reconfigure each component and use pso_lssvm model predict each reconfigured one. Then the busy telephone traffic can be achieved. The experimental results show that the prediction model has higher prediction accuracy and stability.

scholarly journals Impacts of multicollinearity on CAPT modalities: An heterogeneous machine learning framework for computer-assisted French phoneme pronunciation training

PLoS ONE ◽  
2021 ◽  
Vol 16 (10) ◽  
pp. e0257901
Author(s):  
Yanjing Bi ◽  
Chao Li ◽  
Yannick Benezeth ◽  
Fan Yang
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Support Vector Machines ◽  
Partial Least Square ◽  
Least Square ◽  
Support Vector ◽  
Computer Assisted ◽  
Long Distance ◽  
Relationship Analysis ◽  
Vector Machines

Phoneme pronunciations are usually considered as basic skills for learning a foreign language. Practicing the pronunciations in a computer-assisted way is helpful in a self-directed or long-distance learning environment. Recent researches indicate that machine learning is a promising method to build high-performance computer-assisted pronunciation training modalities. Many data-driven classifying models, such as support vector machines, back-propagation networks, deep neural networks and convolutional neural networks, are increasingly widely used for it. Yet, the acoustic waveforms of phoneme are essentially modulated from the base vibrations of vocal cords, and this fact somehow makes the predictors collinear, distorting the classifying models. A commonly-used solution to address this issue is to suppressing the collinearity of predictors via partial least square regressing algorithm. It allows to obtain high-quality predictor weighting results via predictor relationship analysis. However, as a linear regressor, the classifiers of this type possess very simple topology structures, constraining the universality of the regressors. For this issue, this paper presents an heterogeneous phoneme recognition framework which can further benefit the phoneme pronunciation diagnostic tasks by combining the partial least square with support vector machines. A French phoneme data set containing 4830 samples is established for the evaluation experiments. The experiments of this paper demonstrates that the new method improves the accuracy performance of the phoneme classifiers by 0.21 − 8.47% comparing to state-of-the-arts with different data training data density.

Test of selected bed-material load transport models: Nottawasaga River, Ontario

1994 ◽  
Vol 21 (5) ◽  
pp. 770-777 ◽  
Author(s):  
T. J. Chandler ◽  
R. A. Kostaschuk
Keyword(s):  
Sediment Transport ◽  
Engineering Application ◽  
Model Performance ◽  
Material Transport ◽  
Flow Strength ◽  
Transport Models ◽  
Data Scatter ◽  
Load Transport ◽  
Material Load ◽  
Bed Material

Predictions from 13 bed-material load sediment transport models are compared with 19 measurements of bed-material transport in Nottawasaga River, Ontario, using summary plots and geometric statistics. Model selection is based on recent engineering application and suitability for the flow and sediment conditions of the river. The models of Laursen (1958) and Yang (1979) perform best, followed by those of Ackers and White (1973). The models of Van Rijn (1984), Maddock (1976), Karim and Kennedy (1983), Brownlie (1981), and Yang (1973) have considerable data scatter. The models of Engelund and Hansen (1967) and Shen and Hung (1972) are the poorest predictors. Poor model performance is primarily due to overestimation of flow strength needed for particle entrainment and an excessively steep slope in the relations between flow strength and sediment transport. Key words: bed-material load transport models, test, Nottawasaga River.

Classification of plant diseases using machine and deep learning

2021 ◽  
Vol 11 (1) ◽  
pp. 491-508
Author(s):  
Monika Lamba ◽  
Yogita Gigras ◽  
Anuradha Dhull
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Plant Disease ◽  
Nearest Neighbor ◽  
Early Recognition ◽  
Activation Function ◽  
Plant Diseases ◽  
Support Vector ◽  
Specificity And Sensitivity ◽  
Proposed Model

Abstract Detection of plant disease has a crucial role in better understanding the economy of India in terms of agricultural productivity. Early recognition and categorization of diseases in plants are very crucial as it can adversely affect the growth and development of species. Numerous machine learning methods like SVM (support vector machine), random forest, KNN (k-nearest neighbor), Naïve Bayes, decision tree, etc., have been exploited for recognition, discovery, and categorization of plant diseases; however, the advancement of machine learning by DL (deep learning) is supposed to possess tremendous potential in enhancing the accuracy. This paper proposed a model comprising of Auto-Color Correlogram as image filter and DL as classifiers with different activation functions for plant disease. This proposed model is implemented on four different datasets to solve binary and multiclass subcategories of plant diseases. Using the proposed model, results achieved are better, obtaining 99.4% accuracy and 99.9% sensitivity for binary class and 99.2% accuracy for multiclass. It is proven that the proposed model outperforms other approaches, namely LibSVM, SMO (sequential minimal optimization), and DL with activation function softmax and softsign in terms of F-measure, recall, MCC (Matthews correlation coefficient), specificity and sensitivity.

A COMPARITIVE ANALYSIS AND TIME SERIES FORECASTING OF MONTHLY STREAM FLOW DATA USING HYBRID MODEL

2015 ◽  
Vol 76 (13) ◽  
Author(s):  
Siraj Muhammed Pandhiani ◽  
Ani Shabri
Keyword(s):  
Hybrid Model ◽  
Stream Flow ◽  
Mean Absolute Error ◽  
Absolute Error ◽  
Least Square ◽  
Support Vector ◽  
Discrete Wavelet ◽  
Flow Data ◽  
Indus River ◽  
Proposed Model

In this study, new hybrid model is developed by integrating two models, the discrete wavelet transform and least square support vector machine (WLSSVM) model. The hybrid model is then used to measure for monthly stream flow forecasting for two major rivers in Pakistan. The monthly stream flow forecasting results are obtained by applying this model individually to forecast the rivers flow data of the Indus River and Neelum Rivers. The root mean square error (RMSE), mean absolute error (MAE) and the correlation (R) statistics are used for evaluating the accuracy of the WLSSVM, the proposed model. The results are compared with the results obtained through LSSVM. The outcome of such comparison shows that WLSSVM model is more accurate and efficient than LSSVM.

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