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Estimation of unknown speaker's height from speech

INTERNATIONAL JOURNAL OF SPEECH TECHNOLOGY 2009年第4期12卷 149-160页

作者： Mporas, Iosif Ganchev, Todor Univ Patras Dept Elect & Comp Engn Wire Commun Lab Rion 26500 Greece

In the present study, we propose a regression-based scheme for the direct estimation of the height of unknown speakers from their speech. In this scheme every speech input is decomposed via the openSMILE audio parameterization to a single feature vector that is fed to a regression model, which provides a direct estimation of the persons' height. The focus in this study is on the evaluation of the appropriateness of several linear and non-linear regression algorithms on the task of automatic height estimation from speech. The performance of the proposed scheme is evaluated on the TIMIT database, and the experimental results show an accuracy of 0.053 meters, in terms of mean absolute error, for the best performing Bagging regression algorithm. This accuracy corresponds to an averaged relative error of approximately 3%. We deem that the direct estimation of the height of unknown people from speech provides an important additional feature for improving the performance of various surveillance, profiling and access authorization applications.

关键词： Human height estimation from speech Speech processing regression algorithms

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Comprehensive evaluation of empirical algorithms for estimating land surface evapotranspiration

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AGRICULTURAL AND FOREST METEOROLOGY 2018年 256卷 334-345页

作者： Carter, Corinne Liang, Shunlin Univ Maryland Dept Geog College Pk MD 20742 USA

Many empirical algorithms for obtaining evapotranspiration (ET) from vegetation indices (VIs) have been developed, but there has been little work comparing these algorithms to each other or deriving coefficients for them using large data sets for training and validation. Twelve different vegetation index-based regression algorithms for retrieval of ET on a daily basis are reviewed and evaluated here. New coefficients have been derived for four of these algorithms using data from 181 Ameriflux and Fluxnet2015 sites and 1 km MODIS subsets centered at each site location. Algorithm validation with previously published and new coefficients was performed using one year of data from each Ameriflux and Fluxnet2015 site. There was a wide range of performance of these algorithms, with the median R-2 by site in the 0.6 to 0.7 range, median root mean square error (RMSE) about 25 W/m(2) and median bias within 10 W/m(2). When algorithm coefficients were re-derived, the RMSE and bias of the worst-performing algorithms were largely reduced, but R-2 was little changed. Agricultural and wetland sites had a low bias across most of the algorithms, and wetland sites had a higher RMSE. When several of the algorithms were re-tuned to obtain coefficients specific to each surface type, the biases of the agricultural and wetland sites were reduced to those more typical of other site types, and RMSE for agricultural and wetland sites was also reduced. The effects of linear interpolation of VIs to obtain daily LE and interpolation over periods of rapid VI change at agricultural sites were examined. No significant algorithm performance degradation was found in either case. It is recommended to use more detailed algorithms when possible, with inclusion of net radiation as a parameter along with VI at a minimum.

关键词： Evapotranspiration Remote sensing Vegetation index regression algorithms MODIS Fluxnet

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A Comparison of machine learning regression models for critical bus voltage and load mapping with regards to max reactive power in PV buses

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ELECTRIC POWER SYSTEMS RESEARCH 2021年 191卷

作者： Fachini, F. Fuly, B. I. L. Fed Unviers Itajuba Inst Elect & Energy Syst Itajuba Brazil

The aim of this paper is to compare voltage and system loading mapping capabilities of a variety of regression algorithms, such as Adaptive Network based Fuzzy Inference System (ANFIS), Artificial Neural Networks (ANN), K-Nearest Neighbors (KNN), Support Vector regression (SVR), and Decision Tree (DT). A voltage sensitivity matrix is generated from the power flow Jacobian matrix for a loading scenario near the unstable point. Principal Component Analysis (PCA) is used to separate the system, close to the critical point, in order to group the buses into coherent voltage controlling areas. For different reactive power injection scenarios, we have different bus voltages that can be mapped by the aforementioned regression algorithms. The algorithms are trained with limited amounts of data, in order to establish a fair comparison between them. The present work shows that ANFIS and KNN have a better performance in critical voltage and load prediction when compared to the rest. The academic IEEE 14 and 118 bus systems are employed with all its limits considered, so the results may be reproduced.

关键词： regression algorithms ANFIS KNN PCA Voltage controlling areas

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Forward stage-wise ensemble regression algorithm to improve base regressors prediction ability: an empirical study

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EXPERT SYSTEMS 2014年第1期31卷 1-8页

作者： Ozcift, Akin Gaziantep Univ Comp Programming Div Gaziantep Vocat Sch Higher Educ Gaziantep Turkey

Analysis of scientific data requires accurate regressor algorithms to decrease prediction errors. Lots of machine learning algorithms, that is, neural networks, rule-based algorithms, regression trees and some kinds of lazy learners, are used to realize this need. In recent years, different ensemble regression strategies were improved to obtain enhanced predictors with lower forecasting errors. Ensemble algorithms combine good models that make errors in different parts of analyzed data. There are mainly two approaches in ensemble regression algorithm generation; boosting and bagging. The aim of this article is to evaluate a boosting-based ensemble approach, forward stage-wise additive modelling (FSAM), to improve some widely used base regressors’ prediction ability. We used 10 regression algorithms in four different types to make predictions on 10 diverse data from different scientific areas and we compared the experimental results in terms of correlation coefficient, mean absolute error, and root mean squared error metrics. Furthermore, we made use of scatter plots to demonstrate the effect of ensemble modelling on the prediction accuracies of evaluated algorithms. We empirically obtained that in general FSAM enhances the accuracies of base regressors or it at least maintains the base regressor performance.

关键词： forward stage-wise additive modelling boosting ensemble regression regression algorithms

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Comparative Analysis Using Multiple regression Models for Forecasting Photovoltaic Power Generation

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ENERGIES 2024年第7期17卷

作者： Abdullah, Burhan U. Din Khanday, Shahbaz Ahmad Islam, Nair Ul Lata, Suman Fatima, Hoor Nengroo, Sarvar Hussain Sharda Univ Sch Engn & Technol Knowledge Pk 3 Greater Noida 201306 Uttar Pradesh India Korea Adv Inst Sci & Technol Cho Chun Shik Grad Sch Mobil Daejeon 34051 South Korea Tech Univ Denmark DTU Dept Engn & Technol DK-2800 Ballerup Denmark

Effective machine learning regression models are useful toolsets for managing and planning energy in PV grid-connected systems. Machine learning regression models, however, have been crucial in the analysis, forecasting, and prediction of numerous parameters that support the efficient management of the production and distribution of green energy. This article proposes multiple regression models for power prediction using the Sharda University PV dataset (2022 Edition). The proposed regression model is inspired by a unique data pre-processing technique for forecasting PV power generation. Performance metrics, namely mean absolute error (MAE), mean squared error (MSE), root mean squared error (RMSE), R2-score, and predicted vs. actual value plots, have been used to compare the performance of the different regression. Simulation results show that the multilayer perceptron regressor outperforms the other algorithms, with an RMSE of 17.870 and an R2 score of 0.9377. Feature importance analysis has been performed to determine the most significant features that influence PV power generation.

关键词： photovoltaic regression algorithms mean absolute error mean squared error root mean squared error grid forecasting

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Movement-Based Control for Upper-Limb Prosthetics: Is the regression Technique the Key to a Robust and Accurate Control?

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FRONTIERS IN NEUROROBOTICS 2018年 12卷 41页

作者： Legrand, Mathilde Merad, Manelle de Montalivet, Etienne Roby-Brami, Agnes Jarrasse, Nathanael Sorbonne Univ CNRS INSERM ISIRInst Syst Intelligents & Robot Paris France

Due to the limitations of myoelectric control (such as dependence on muscular fatigue and on electrodes shift, difficulty in decoding complex patterns or in dealing with simultaneous movements), there is a renewal of interest in the movement-based control approaches for prosthetics. The latter use residual limb movements rather than muscular activity as command inputs, in order to develop more natural and intuitive control techniques. Among those, several research works rely on the interjoint coordinations that naturally exist in human upper limb movements. These relationships are modeled to control the distal joints (e.g., elbow) based on the motions of proximal ones (e.g., shoulder). The regression techniques, used to model the coordinations, are various [Artificial Neural Networks, Principal Components Analysis (PCA), etc.] and yet, analysis of their performance and impact on the prosthesis control is missing in the literature. Is there one technique really more efficient than the others to model interjoint coordinations? To answer this question, we conducted an experimental campaign to compare the performance of three common regression techniques in the control of the elbow joint on a transhumeral prosthesis. Ten non-disabled subjects performed a reaching task, while wearing an elbow prosthesis which was driven by several interjoint coordination models obtained through different regression techniques. The models of the shoulder-elbow kinematic relationship were built from the recordings of fifteen different non-disabled subjects that performed a similar reaching task with their healthy arm. Among Radial Basis Function Networks (RBFN), Locally Weighted regression (LWR), and PCA, RBFN was found to be the most robust, based on the analysis of several criteria including the quality of generated movements but also the compensatory strategies exhibited by users. Yet, RBFN does not significantly outperform LWR and PCA. The regression technique seems not to be the most

关键词： upper-limb prosthetics movement-based control shoulder-elbow coordinations regression algorithms motor strategy

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Smart Stores: A scalable foot traffic collection and prediction system 1

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17th Industrial Conference on Data Mining (ICDM)

作者： Abrishami, Soheila Kumar, Piyush Nienaber, Wickus Florida State Univ Tallahassee FL 32306 USA Bloom Intelligence Tallahassee FL 32303 USA

ISBN: (数字)9783319627014

ISBN: (纸本)9783319627014;9783319627007

An accurate foot traffic prediction system can help retail businesses, physical stores, and restaurants optimize their labor schedule and costs, and reduce food wastage. In this paper, we design a large scale data collection and prediction system for store foot traffic. Our data has been collected from wireless access points deployed at over 100 businesses across the United States for a period of more than one year. This data is centrally processed and analyzed to predict the foot traffic for the next 168 hours (a week). Our current predictor is based on Support Vector regression (SVR). There are a few other predictors that we have found that are similar in accuracy to SVR. For our collected data the average foot traffic per hour is 35 per store. Our prediction result is on average within 22% of the actual result for a 168 hour (a week) period.

关键词： Time series forecasting regression algorithms Foot traffic Wireless access point Bloom Intelligence

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Analysis of the Dimensionality Issues in House Price Forecasting Modeling 5

Analysis of the Dimensionality Issues in House Price Forecas...

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5th IEEE International Conference of Women in Data Science (WiDS-PSU)

作者： Sakri, Sapiah Binti Ali, Zaiton Princess Nourah bint Abdulrahman Univ Coll Comp & Informat Sci Dept Informat Syst Riyadh 11671 Saudi Arabia Prince Sultan Univ Dept Finance Coll Business Adm Riyadh Saudi Arabia

ISBN: (纸本)9781665408127

House price fluctuations in the real estate market occur due to the effects of other variables that are correlated to housing prices. Some prices cannot be controlled or it is not known when they will increase or decrease. These fluctuations accord with the hedonic pricing theory, which suggests that house pricing depends on the composite features. Our study explores this econometric concept using machine teaming algorithms in the effort to accurately forecast house prices. To build the forecasted models, we use the Ames housing dataset, which include 82 explanatory features and 2930 entries on housing sales in Ames, Iowa, USA. We use particle swarm optimization feature selection to address the dataset dimensionality issues for both classification-based models and regression-based models. With dimension reduction, the experimental results demonstrate that the house price prediction accuracy increased from 81.4% to 84.4% for classification-based models, and the price prediction error decreased from 13.3% to 6.9% for regression-based models. The experimental results demonstrate that there is a substantial difference between using and not using feature selection in terms of the performance in predicting housing prices. The main benefit of applying feature selection is that it facilitates the selection of the more relevant and correlated features to prevent overfitting of the model with too many irrelevant features.

关键词： Classification algorithms hedonic house pricing model house price prediction particle swarm optimization feature selection regression algorithms

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Non-invasive modeling of heart rate and blood pressure from a photoplethysmography by using machine learning techniques

Non-invasive modeling of heart rate and blood pressure from ...

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International Conference on COMmunication Systems and NETworkS (COMSNETS)

作者： Nidigattu, Govinda Rao Mattela, Govardhan Jana, Sayan Indian Inst Technol IIT Hyderabad India Redpine Signals Inc San Jose CA 95131 USA

ISBN: (纸本)9781728131870

Cardiovascular diseases(CVD) is one of the major causes of deaths in the world, which may damage the endothelium cells which may lead to atherosclerosis and cardiac arrhythmias. Blood pressure is an important parameter and indicator in cardiovascular disease, patients with CVD who have multiple risk factors such as hypertension, stress, and obesity have been increasing. Therefore, it is important in the field of cardiovascular disease prevention to predict those at risk of cardiovascular diseases in the general population. Electrocardiogram is not suitable for wearable devices and PPG is a non-invasive, inexpensive, and convenient diagnostic tool for monitoring of heart and blood pressure. Here, we present a PPG (photoplethysmography) based non-invasive detection of heart rate and blood pressure, containing 1260 segments from 140 subjects an age range of 20 - 50 years. Data acquisition was carried out using the standard operating procedures. The present study investigates the photoplethysmography signal filtering of various noise removal, extraction of PPG morphological features and its derivatives which contain a blood circulatory system information, and finally derived forty five diagnostic engineered features. A novel signal processing technique was applied to extract salient pulse wave for heart rate and blood pressure detection. The subset of optimal features was extracted using feature extraction methods in relation to the physiology of heart rate and blood pressure processes. Prediction of heart rate, systolic blood pressure and diastolic blood pressure with the root mean squared error (RMSE) of 4.3 beats per minute, 5.7 mmHg and 5.5 mmHg between Sphygmomanometer and PPG from 10-fold cross-validation method.

关键词： Cardiovascular diseases photoplethysmography heart rate systolic blood pressure diastolic blood pressure signal filtering feature engineering machine learning regression algorithms

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Crop Recommendation and Yield Estimation Using Machine Learning

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Journal of Mobile Multimedia 2022年第3期18卷 861-884页

作者： Ashwitha, A. Latha, C.A. Dept. of ISE MSRIT Bangalore India Dept. of CSE AMCEC Bangalore India Affiliated to Visvesvaraya Technological University Karnataka Belagavi India

In most developing countries like India, Agriculture is seen as one of the most widely followed habitations and contributes majorly to the country’s economy. Agriculture provides the primary source of food, income, livelihood and employment to the majority of rural populations in India. Many crops are destroyed every year due to a lack of technical knowledge and unpredictable weather patterns such as temperature, rainfall, and other atmospheric parameters, which play a massive role in deciding the crop yield and profit. Therefore, choosing the right crop to grow and enhancing crop yield is an essential aspect of improving real-life farming scenarios. One of the motives is to collect and integrate the agricultural data from specific regions that may be used to analyse the optimal crop and estimate the crop yield. This script is novel by using simple crop, soil and weather parameters like crop, the area under cultivation, nitrogen, phosphorus and potassium content of the soil, season, average rainfall and temperature of a district in Karnataka, India. The user can predict the most suitable crop and its estimated yield for a chosen year. This model uses primary classification, techniques like the random forest, k-NN, logistic regression, decision tree, XGBoost, SVM and gradient boosting classifier for determining the most suitable crop and regression algorithms like Linear regression, k-NN, DBSCAN, Random Forest and ANN algorithm to estimate the yield of the most optimal crop identified earlier. The algorithm that has the least mean error is chosen for prediction and estimation and thus gives better results than the particular machine learning algorithm domain. There is a web interface for ease of use for end-users. Therefore, this project assists the farmers in choosing the suitable crop that can be grown in a particular region during a specific season or specific period and estimate its yield and predict if the recommended crop is profitable. Hence this project helps

关键词： Agriculture Classification algorithms Crop recommendation Machine learning regression algorithms Yield estimation

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