Machine Learning-based Estimation Research Articles

Do Adverse Childhood Experiences Modify the Association Between Disaster-Related Trauma and Cognitive Disability?

Identifying subpopulations that are particularly vulnerable to long-term adverse health consequences of disaster-related trauma is needed. We examined whether adverse childhood experiences (ACEs) potentiate the association between disaster-related trauma and subsequent cognitive disability among older adult disaster survivors. Data were from a prospective cohort study of older adults who survived the 2011 Great East Japan Earthquake. The baseline survey pre-dated the disaster by 7 months. We included participants who completed follow-up surveys (2013 and 2016) and did not have a cognitive disability before the disaster (n= 602). Disaster-related traumas (i.e., home loss, loss of friends or pets) and ACEs were retrospectively assessed in 2013. Cognitive disability levels in 2016 were objectively assessed. After adjusting for pre-disaster characteristics using a machine learning-based estimation approach, home loss (0.19, 95% confidence interval (CI): 0.09, 0.28) was, on average, associated with greater cognitive disability. Among individuals with ACEs, home loss was associated with even higher cognitive disability levels (0.64, 95% CI: 0.24, 1.03). Losses of friends (0.18, 95% CI: 0.05, 0.32) and pets (0.13, 95% CI: 0.02, 0.25) were associated with higher cognitive disability levels only among those with ACEs. Our findings suggest that individuals with a history of ACEs may be particularly vulnerable to adverse health consequences related to disasters.

Machine learning-based estimation of nano-lubricants viscosity in different operating conditions

The importance of improving and optimizing the energy transfer systems has been highlighted recently due to ever-increasing demand for energy in the world, and therefore the lubrication of systems has received significant attentions. The improvement of thermophysical properties by nano-particles can be considered as an efficient approach to this end. A comprehensive study dealing with constructing myriad machine learning-based models for predicting the viscosity of nano-lubricants is lacking in the literature. This research, therefore, aims at development of eight different artificial intelligence models including, Adaptive Boosting, Random Forest (RF), Ensemble Learning, Decision Tree (DT), Support Vector Machine (SVM), K-Nearest Neighbors (KNN), Convolutional Neural Network (CNN), and Multi-Layer Perceptron Artificial Neural Network (MLP-ANN) to estimate viscosity of different nano-lubricants as a function of amount of MWCNT, nanoparticle, type of lubricant, temperature, shear rate, and solid volume fraction. These models are prepared and tested based on a databank including 1086 viscosity points. The results show that the MLP and CNN models are the most accurate methods with R-squared of 0.950131 and 0.95035, respectively, while the estimation of computational time expresses that MLP and CNN models require the most computational time for training. Moreover, sensitivity analysis reveals that temperature has the most effect on viscosity of nano-lubricants.

Machine Learning-Based Estimation of COP and Multi-Objective Optimization of Operation Strategy for Heat Source Considering Electricity Cost and On-Site Consumption of Renewable Energy

Air conditioning is a significant consumer of electricity in buildings, accounting for around 40% of the total consumption. While previous studies have focused on planning methods to minimize electricity costs, recent years have seen an increasing need for energy management methods that consider environmental performance, such as CO2 emissions, alongside economic efficiency. This study proposes a mechanism to support stakeholders’ decision-making by calculating Pareto solutions based on the multi-objective optimization of economic and environmental characteristics for entities that own renewable energy generation facilities. Unlike many existing studies that assume a specific equation for COP (Coefficient of Performance) estimation, this study adopts a nonparametric COP estimation method using machine learning, resulting in a more realistic and flexible modeling of the system. The study also presents a model for selecting an operation strategy that balances environmental and economic goals, incorporating a thermal storage facility to improve the renewable energy rate. Specifically, we proposed and compared methods for calculating solutions using only the GA (Genetic Algorithm) and a two-step optimization method combining a GA and gradient-based optimization method, confirming the superiority of the two-step optimization method. The case study unveiled unique operational profiles corresponding to cost-saving, renewable-energy, and balanced orientation points, suggesting the existence of specific strategies tailored to each orientation. The findings of this study can help stakeholders make more informed decisions regarding energy management in air conditioning systems, with benefits for both the environment and the bottom line.

Deep learning framework for state of health estimation of NMC and LFP Li-ion batteries for vehicular applications

This paper proposes a data-driven estimator of capacity and energy variations of lithium-ion batteries for health monitoring purposes. The proposal uses voltage and temperature measurements as inputs to multi-layer perceptron auto-encoders to extract features and reduce their dimensionality. Then, a long-short-term memory neural network estimates capacity and energy from these features. This machine learning-based estimation framework is trained and validated with experimental ageing data for two different batteries' chemistries: lithium‑nickel‑manganese‑cobalt-oxide/graphite (NMC) and lithium‑iron-phosphate/graphite (LFP). The cells are cycled at 35 °C with a current profile adapted from the Worldwide Harmonized Light Vehicles Test Cycle. The training process is optimized to achieve high estimation accuracy and robustness to cell dispersion. The validation results show that the mean absolute percentage error is lower than 1.6 % for capacity estimation and 3.6 % for energy estimation for both battery chemistries.

ZPasteurAIzer: An AI-Enabled Solution for Product Quality Monitoring in Tunnel Pasteurization Machines

In the food and beverage industry, many foods, beers, and soft drinks need to be pasteurized in order to minimize the effect of micro-organisms on the physical stability, quality, and flavour of the product. Although modern tunnel pasteurizers provide integrated solutions for precise process monitoring and control, a great number of packaging plants continue to operate with legacy pasteurizers that require irregular manual measurements to be performed by shop floor operators in order to monitor the process. In this context, the present paper presents zPasteurAIzer, an end-to-end system that provides real-time quality monitoring for legacy tunnel pasteurization machines and constitutes a low-cost alternative to replacement or the upgrading of installed equipment by leveraging IoT technologies and AI-enabled virtual sensing techniques. We share details on the design and implementation of the system, which is based on a microservice-oriented architecture and includes functionalities such as configuration of the pasteurizer machine, data acquisition, and preprocessing methodology as well as machine learning-based estimation and live dashboard monitoring of the process parameters. Experimental work has been conducted in a real-world use case at a large brewing manufacturing plant in Greece, and the results indicate the value and potential of the proposed system.

Validation of Estimated Small Dense Low-Density Lipoprotein Cholesterol Concentration in a Japanese General Population.

A high level of directly measured small dense low-density lipoprotein cholesterol (sdLDL-C) is a strong risk factor for atherosclerotic cardiovascular disease. A method for estimating sdLDL-C by using Sampson's equation that includes levels of total cholesterol, high-density lipoprotein cholesterol (HDL-C), non-HDL-C and triglycerides (TG) has recently been proposed. We investigated the validation and exploration of estimated sdLDL-C level. The associations between measured and estimated sdLDL-C levels were investigated in 605 Japanese subjects (men/women: 280/325; mean age: 65±15 years) who received annual health check-ups in the Tanno-Sobetsu Study, a population-based cohort. Estimated sdLDL-C level was highly correlated with measured sdLDL-C level in all subjects (R2=0.701), nondiabetic subjects without any medication (n=254, R2=0.686) and subjects with diabetes mellitus (n=128, R2=0.721). Multivariable regression analysis showed that levels of non-HDL-C, TG and γ-glutamyl transpeptidase (γGTP) were independent predictors of measured sdLDL-C level. In a stratification of the LDL window, all of the subjects with a combination of high non-HDL-C (≥ 170 mg/dL) and high TG (≥ 150 mg/dL) had high levels of measured and estimated sdLDL-C (≥ 35 mg/dL). Furthermore, machine learning-based estimation of sdLDL-C level by artificial intelligence software, Prediction One, was substantially improved by using components of Sampson's equation (R2=0.803) and by using those components with the addition of γGTP and deletion of TC (R2=0.929). sdLDL-C level estimated by Sampson's equation can be used instead of measured sdLDL-C level in general practice. By building multiple machine learning models of artificial intelligence, a more accurate and practical estimation of sdLDL-C level might be possible.

Machine Learning-Based Estimation of the Compressive Strength of Self-Compacting Concrete: A Multi-Dataset Study

This paper aims at performing a comparative study to investigate the predictive capability of machine learning (ML) models used for estimating the compressive strength of self-compacting concrete (SCC). Seven prominent ML models, including deep neural network regression (DNNR), extreme gradient boosting machine (XGBoost), gradient boosting machine (GBM), adaptive boosting machine (AdaBoost), support vector regression (SVR), Levenberg–Marquardt artificial neural network (LM-ANN), and genetic programming (GP), are employed. Four experimental datasets, compiled in previous studies, are used to construct the ML-based methods. The models’ generalization capabilities are reliably evaluated by 20 independent runs. Experimental results point out the superiority of the DNNR, which has excelled other models in three out of four datasets. The XGBoost is the second-best model, which has gained the first rank in one dataset. The outcomes point out the great potential of the utilized ML approaches in modeling the compressive strength of SCC. In more details, the coefficient of determination (R2) surpasses 0.8 and the mean absolute percentage error (MAPE) is always below 15% for all datasets. The best results of R2 and MAPE are 0.93 and 7.2%, respectively.

Machine learning-based estimation of energy dissipation capacity of RC shear walls

In current practice, the seismic design of reinforced concrete shear walls primarily relies on the wall shear strength as well as proper reinforcement detailing to ensure sufficient ductility. However, experimental results have shown that the seismic (input) demands are met by the energy dissipation capacity in the structural members; therefore, the influence of hysteretic behavior on seismic behavior is considerable. An energy-based approach that reflects the effect of repeated loads in seismic performance – which is typically neglected in the seismic codes – would serve as a supplemental index in the design process. With this motivation, a predictive model is proposed to estimate the energy dissipation capacity of reinforced concrete shear walls. A comprehensive database consisting of 312 shear walls tested under cyclic loading and a widely used and powerful machine learning method, namely Gaussian Process Regression (GPR), is used to investigate the effects of wall design parameters (e.g. wall geometry, reinforcement details) on energy dissipation capacity and to develop the predictive model as a function of such parameters. Eighteen design parameters are shown to influence the energy dissipation, the most important of which are identified by applying sequential backward elimination and feature selection methods. The ability of the proposed model to make robust and accurate predictions is validated based on unused data with a prediction accuracy (the ratio of predicted/actual values) of around 1.00 and a coefficient of determination (R2) of 0.93. The outcomes of this study are believed to contribute to the wall design process by (i) defining the most influential wall properties on the seismic energy dissipation capacity of shear walls and (ii) providing predictive models that can enable comparisons of different wall design configurations to achieve higher energy dissipation capacity.

Machine learning-based estimation of riverine nutrient concentrations and associated uncertainties caused by sampling frequencies

Accurate and sufficient water quality data is essential for watershed management and sustainability. Machine learning models have shown great potentials for estimating water quality with the development of online sensors. However, accurate estimation is challenging because of uncertainties related to models used and data input. In this study, random forest (RF), support vector machine (SVM), and back-propagation neural network (BPNN) models are developed with three sampling frequency datasets (i.e., 4-hourly, daily, and weekly) and five conventional indicators (i.e., water temperature (WT), hydrogen ion concentration (pH), electrical conductivity (EC), dissolved oxygen (DO), and turbidity (TUR)) as surrogates to individually estimate riverine total phosphorus (TP), total nitrogen (TN), and ammonia nitrogen (NH4+-N) in a small-scale coastal watershed. The results show that the RF model outperforms the SVM and BPNN machine learning models in terms of estimative performance, which explains much of the variation in TP (79 ± 1.3%), TN (84 ± 0.9%), and NH4+-N (75 ± 1.3%), when using the 4-hourly sampling frequency dataset. The higher sampling frequency would help the RF obtain a significantly better performance for the three nutrient estimation measures (4-hourly > daily > weekly) for R2 and NSE values. WT, EC, and TUR were the three key input indicators for nutrient estimations in RF. Our study highlights the importance of high-frequency data as input to machine learning model development. The RF model is shown to be viable for riverine nutrient estimation in small-scale watersheds of important local water security.

Estimation of Respiratory Rate from Breathing Audio.

The COVID-19 pandemic has fueled exponential growth in the adoption of remote delivery of primary, specialty, and urgent health care services. One major challenge is the lack of access to physical exam including accurate and inexpensive measurement of remote vital signs. Here we present a novel method for machine learning-based estimation of patient respiratory rate from audio. There exist non-learning methods but their accuracy is limited and work using machine learning known to us is either not directly useful or uses non-public datasets. We are aware of only one publicly available dataset which is small and which we use to evaluate our algorithm. However, to avoid the overfitting problem, we expand its effective size by proposing a new data augmentation method. Our algorithm uses the spectrogram representation and requires labels for breathing cycles, which are used to train a recurrent neural network for recognizing the cycles. Our augmentation method exploits the independence property of the most periodic frequency components of the spectrogram and permutes their order to create multiple signal representations. Our experiments show that our method almost halves the errors obtained by the existing (non-learning) methods. Clinical Relevance- We achieve a Mean Absolute Error (MAE) of 1.0 for the respiratory rate while relying only on an audio signal of a patient breathing. This signal can be collected from a smartphone such that physicians can automatically and reliably determine respiratory rate in a remote setting.

Machine learning-based estimation of soil’s true air-entry value from GSD curves

The application of machine learning (ML) methods has proven to be promising in dealing with a wide range of geotechnical engineering problems in recent years. ML methods have already been used for the prediction of soil water retention curves (SWRC) and estimation of air-entry values (AEV). However, the reported works in the literature are generally based on limited data and conventional, less accurate approaches for AEV estimation. In this paper, a large database, known as UNsaturated SOil hydraulic DAtabase (UNSODA), is studied and the conventional and true AEVs of 790 soil samples are estimated based on determination methods reported in the literature. A ML approach is then employed for the development of a predictive model for the estimation of true AEV from water content-based SWRCs of a wide range of soil types taking into account the impact of bulk density and grain size distribution parameters. The obtained results reveal an enhanced accuracy in AEV determination, featuring R2 values of 0.964, 0.901 and 0.851 for training, validation, and testing data, respectively, which confirm the high-level performance of the developed ML model. Based on the results of a sensitivity analysis, the particle sizes of 50 and 250 µm are found to have the highest impact on the AEV estimation.

Evaluating the impact of feature selection consistency in software prediction

Many empirical software engineering studies have employed feature selection algorithms to exclude the irrelevant and redundant features from the datasets with the aim to improve prediction accuracy achieved with machine learning-based estimation models as well as their generalizability. However, little has been done to investigate how consistently these feature selection algorithms produce features/metrics across different training samples, which is an important point for the interpretation of the trained models. The interpretation of the models largely depends on the features of the analyzed datasets, so it is recommended to evaluate the potential of various feature selection algorithms in terms of how consistently they extract features from the employed datasets. In this study, we consider eight different feature selection algorithms to evaluate how consistently they select features across different folds of k-fold cross-validation as well as when small changes are made in the training data. To provide a stable and generalized conclusion, we investigate data from two different domains, i.e., six datasets from the domain of Software Development Effort Estimation (SDEE) and six datasets from the Software Fault Prediction (SFP) domain. Our results reveal that a feature selection algorithm could produce 20-100% inconsistent features with an SDEE dataset and 18.8-95.3% inconsistent features in the case of an SFP dataset. The analysis also reveals that it is not necessarily true that the most consistent feature selection algorithm results to be the most accurate one (i.e., leads to better prediction accuracy) in the case of SDEE datasets, while with SFP datasets, the analysis highlights that the most consistent feature selection algorithm also results to be the most accurate in predicting faults. • Not all feature selection algorithms are reported as consistent. • Best-First Search (backward) is the most consistent feature selection algorithm. • Harmony Search is the least consistent feature selection algorithm with SDEE.

Machine Learning-Based Estimation of Output Current Ripple in PFC-IBC Used in Battery Charger of Electrical Vehicles: A Comparison of LR, RF and ANN Techniques

In this study, an artificial neural network (ANN) model is developed for the purpose of estimating the output current ripple of a power factor correction (PFC) AC/DC interleaved boost converter (IBC) used in battery charger of electrical vehicles (EVs) based on the inductance current ripple, switching frequency and load changes. Besides, the improved ANN model is compared with some different machine learning (ML) techniques like linear regression (LR), random forest (RF). The PFC-IBC is simulated with the PSIM simulation program to estimate the output current ripple. As a result, 336 output current ripple values are obtained based on inductance current ripple, different switching frequency and load changes. Then, the value of output current ripple is estimated by training the input parameters with LR, RF and ANN machine learning techniques (MLTs) for controlling the current harmonics drawn from the grid and for reliable charging of batteries. It is seen that the estimation value obtained with MLTs is quite compatible with the actual value obtained with the simulation. In addition, in the study carried out with the simulation, it takes a period of several days to obtain the estimation results; whereas, the operation of estimation with MLTs can be completed in a short period such as a few minutes. This clearly reveals the advantage of the MLTs. Therefore, this value is estimated through the MLTs with a high accuracy before the design of the charging device in order to maintain at a secure level the output current ripple posing considerable importance in electrical vehicle battery charge. Also, in this estimation process, LR, RF and developed ANN techniques are examined and compared separately in the WEKA program and it is observed that the developed ANN model proposes better results than other techniques.

Machine Learning-Based Estimation of Ground Reaction Forces and Knee Joint Kinetics from Inertial Sensors While Performing a Vertical Drop Jump.

Nowadays, the use of wearable inertial-based systems together with machine learning methods opens new pathways to assess athletes’ performance. In this paper, we developed a neural network-based approach for the estimation of the Ground Reaction Forces (GRFs) and the three-dimensional knee joint moments during the first landing phase of the Vertical Drop Jump. Data were simultaneously recorded from three commercial inertial units and an optoelectronic system during the execution of 112 jumps performed by 11 healthy participants. Data were processed and sorted to obtain a time-matched dataset, and a non-linear autoregressive with external input neural network was implemented in Matlab. The network was trained through a train-test split technique, and performance was evaluated in terms of Root Mean Square Error (RMSE). The network was able to estimate the time course of GRFs and joint moments with a mean RMSE of 0.02 N/kg and 0.04 N·m/kg, respectively. Despite the comparatively restricted data set and slight boundary errors, the results supported the use of the developed method to estimate joint kinetics, opening a new perspective for the development of an in-field analysis method.

Machine learning-based estimation of ground-level NO2 concentrations over China

Most current scientific research on NO2 remote sensing focuses on tropospheric NO2 column concentrations rather than ground-level NO2 concentrations; however, ground-level NO2 concentrations are more related to anthropogenic emissions and human health. This study proposes a machine learning estimation method for retrieving the ground-level NO2 concentrations throughout China based on the tropospheric NO2 column concentrations from the TROPOspheric Monitoring Instrument (TROPOMI) and multisource geographic data from 2018 to 2020. This method adopts the XGBoost machine learning model characterized by a strong fitting ability and complex model structure, which can explain the complex nonlinear and high-order relationships between ground-measured NO2 and its influencing factors. The R2 values between the retrievals and the validation and test datasets are 0.67 and 0.73, respectively, which suggests that the proposed method can reliably retrieve the ground-level NO2 concentrations across China. The distribution characteristics, seasonal variations and interannual differences in ground-level NO2 concentrations are further analyzed based on the retrieval results, demonstrating that the ground-level NO2 concentrations exhibit significant geographical and seasonal variations, with high concentrations in winter and low concentrations in summer, and the highly polluted regions are concentrated mainly in Beijing-Tianjin-Hebei (BTH), the Yangtze River Delta (YRD), the Pearl River Delta (PRD), Cheng-Yu District (CY) and other urban agglomerations. Finally, the interannual variation in the ground-level NO2 concentrations indicates that pollution decreased continuously from 2018 to 2020.

Performance Analysis on Machine Learning-Based Channel Estimation

Recently, machine learning-based channel estimation has attracted much attention. The performance of machine learning-based estimation has been validated by simulation experiments. However, little attention has been paid to the theoretical performance analysis. In this paper, we investigate the mean square error (MSE) performance of machine learning-based estimation. Hypothesis testing is employed to analyze its MSE upper bound. Furthermore, we build a statistical model for hypothesis testing, which holds when the linear learning module with a low input dimension is used in machine learning-based channel estimation, and derive a clear analytical relation between the size of the training data and performance. Then, we simulate the machine learning-based channel estimation in orthogonal frequency division multiplexing (OFDM) systems to verify our analysis results. Finally, the design considerations for the situation where only limited training data is available are discussed. In this situation, our analysis results can be applied to assess the performance and support the design of machine learning-based channel estimation.

Machine learning-based estimation of potato chlorophyll content at different growth stage using UAV hyperspectral data

Machine learning-based estimation of potato chlorophyll content at different growth stage using UAV hyperspectral data

Machine learning-based estimation of cognitive performance using regional brain MRI markers: the Northern Manhattan Study

High dimensional neuroimaging datasets and machine learning have been used to estimate and predict domain-specific cognition, but comparisons with simpler models composed of easy-to-measure variables are limited. Regularization methods in particular may help identify regions-of-interest related to domain-specific cognition. Using data from the Northern Manhattan Study, a cohort study of mostly Hispanic older adults, we compared three models estimating domain-specific cognitive performance: sociodemographics and APOE ε4 allele status (basic model), the basic model and MRI markers, and a model with only MRI markers. We used several machine learning methods to fit our regression models: elastic net, support vector regression, random forest, and principal components regression. Model performance was assessed with the RMSE, MAE, and R2 statistics using 5-fold cross-validation. To assess whether prediction models with imaging biomarkers were more predictive than prediction models built with randomly generated biomarkers, we refit the elastic net models using 1000 datasets with random biomarkers and compared the distribution of the RMSE and R2 in models using these random biomarkers to the RMSE and R2 from observed models. Basic models explained ~ 31-38% of the variance in domain-specific cognition. Addition of MRI markers did not improve estimation. However, elastic net models with only MRI markers performed significantly better than random MRI markers (one-sided P < .05) and yielded regions-of-interest consistent with previous literature and others not previously explored. Therefore, structural brain MRI markers may be more useful for etiological than predictive modeling.

Two-Dimensional Image-Based Screening Tool for Infants with Positional Cranial Deformities: A Machine Learning Approach.

Positional cranial deformities are relatively common conditions, characterized by asymmetry and changes in skull shape. Although three-dimensional (3D) scanning is the gold standard for diagnosing such deformities, it requires expensive laser scanners and skilled maneuvering. We therefore developed an inexpensive, fast, and convenient screening method to classify cranial deformities in infants, based on single two-dimensional vertex cranial images. In total, 174 measurements from 80 subjects were recorded. Our screening software performs image processing and machine learning-based estimation related to the deformity indices of the cranial ratio (CR) and cranial vault asymmetry index (CVAI) to determine the severity levels of brachycephaly and plagiocephaly. For performance evaluations, the estimated CR and CVAI values were compared to the reference data obtained using a 3D cranial scanner. The CR and CVAI correlation coefficients obtained via support vector regression were 0.85 and 0.89, respectively. When the trained model was evaluated using the unseen test data for the three CR and three CVAI classes, an 86.7% classification accuracy of the proposed method was obtained for both brachycephaly and plagiocephaly. The results showed that our method for screening cranial deformities in infants could aid clinical evaluations and parental monitoring of the progression of deformities at home.

Deep learning for radio propagation: Using image-driven regression to estimate path loss in urban areas

Abstract Radio propagation modeling and path loss prediction have been the subject of many machine learning-based estimation attempts. Our current work uses deep learning for the task in question, trying to exploit the potential of applying convolutional neural networks in order to perform predictions based on images. A comparison between data-driven and image-driven estimations has been carried out in order to assess the proposed method. The results show that an appropriately chosen image can, per se, be treated as an alternative to a vector of tabular data and produce reliable predictions. The effect of the image’s size has also been examined.