Predicting Electric Vehicle Charging Station Availability Using Ensemble Machine Learning

Electric vehicles may reduce greenhouse gas emissions from individual mobility. Due to the long charging times, accurate planning is necessary, for which the availability of charging infrastructure must be known. In this paper, we show how the occupation status of charging infrastructure can be predicted for the next day using machine learning models— Gradient Boosting Classifier and Random Forest Classifier. Since both are ensemble models, binary training data (occupied vs. available) can be used to provide a certainty measure for predictions. The prediction may be used to adapt prices in a high-load scenario, predict grid stress, or forecast available power for smart or bidirectional charging. The models were chosen based on an evaluation of 13 different, typically used machine learning models. We show that it is necessary to know past charging station usage in order to predict future usage. Other features such as traffic density or weather have a limited effect. We show that a Gradient Boosting Classifier achieves 94.8% accuracy and a Matthews correlation coefficient of 0.838, making ensemble models a suitable tool. We further demonstrate how a model trained on binary data can perform non-binary predictions to give predictions in the categories “low likelihood” to “high likelihood”.

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Machine learning models to identify low adherence to influenza vaccination among Korean adults with cardiovascular disease

BMC Cardiovascular Disorders ◽

10.1186/s12872-021-01925-7 ◽

2021 ◽

Vol 21 (1) ◽

Author(s):

Moojung Kim ◽

Young Jae Kim ◽

Sung Jin Park ◽

Kwang Gi Kim ◽

Pyung Chun Oh ◽

...

Keyword(s):

Machine Learning ◽

Cardiovascular Disease ◽

Influenza Vaccination ◽

Machine Learning Techniques ◽

Gradient Boosting ◽

Support Vector ◽

Age Group ◽

Learning Models ◽

Extreme Gradient Boosting ◽

Machine Learning Models

Abstract Background Annual influenza vaccination is an important public health measure to prevent influenza infections and is strongly recommended for cardiovascular disease (CVD) patients, especially in the current coronavirus disease 2019 (COVID-19) pandemic. The aim of this study is to develop a machine learning model to identify Korean adult CVD patients with low adherence to influenza vaccination Methods Adults with CVD (n = 815) from a nationally representative dataset of the Fifth Korea National Health and Nutrition Examination Survey (KNHANES V) were analyzed. Among these adults, 500 (61.4%) had answered "yes" to whether they had received seasonal influenza vaccinations in the past 12 months. The classification process was performed using the logistic regression (LR), random forest (RF), support vector machine (SVM), and extreme gradient boosting (XGB) machine learning techniques. Because the Ministry of Health and Welfare in Korea offers free influenza immunization for the elderly, separate models were developed for the < 65 and ≥ 65 age groups. Results The accuracy of machine learning models using 16 variables as predictors of low influenza vaccination adherence was compared; for the ≥ 65 age group, XGB (84.7%) and RF (84.7%) have the best accuracies, followed by LR (82.7%) and SVM (77.6%). For the < 65 age group, SVM has the best accuracy (68.4%), followed by RF (64.9%), LR (63.2%), and XGB (61.4%). Conclusions The machine leaning models show comparable performance in classifying adult CVD patients with low adherence to influenza vaccination.

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A Physics-Infused Deep Learning Model for the Prediction of Refractive Indices and Its Use for the Large-Scale Screening of Organic Compound Space

10.26434/chemrxiv.8796950 ◽

2019 ◽

Author(s):

Mojtaba Haghighatlari ◽

Gaurav Vishwakarma ◽

Mohammad Atif Faiz Afzal ◽

Johannes Hachmann

Keyword(s):

Machine Learning ◽

Deep Learning ◽

Large Scale ◽

Organic Molecules ◽

Learning Model ◽

Training Data ◽

Refractive Indices ◽

Learning Models ◽

Deep Learning Model ◽

Machine Learning Models

<div><div><div><p>We present a multitask, physics-infused deep learning model to accurately and efficiently predict refractive indices (RIs) of organic molecules, and we apply it to a library of 1.5 million compounds. We show that it outperforms earlier machine learning models by a significant margin, and that incorporating known physics into data-derived models provides valuable guardrails. Using a transfer learning approach, we augment the model to reproduce results consistent with higher-level computational chemistry training data, but with a considerably reduced number of corresponding calculations. Prediction errors of machine learning models are typically smallest for commonly observed target property values, consistent with the distribution of the training data. However, since our goal is to identify candidates with unusually large RI values, we propose a strategy to boost the performance of our model in the remoter areas of the RI distribution: We bias the model with respect to the under-represented classes of molecules that have values in the high-RI regime. By adopting a metric popular in web search engines, we evaluate our effectiveness in ranking top candidates. We confirm that the models developed in this study can reliably predict the RIs of the top 1,000 compounds, and are thus able to capture their ranking. We believe that this is the first study to develop a data-derived model that ensures the reliability of RI predictions by model augmentation in the extrapolation region on such a large scale. These results underscore the tremendous potential of machine learning in facilitating molecular (hyper)screening approaches on a massive scale and in accelerating the discovery of new compounds and materials, such as organic molecules with high-RI for applications in opto-electronics.</p></div></div></div>

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Comparison of Resampling Algorithms to Address Class Imbalance when Developing Machine Learning Models to Predict Foodborne Pathogen Presence in Agricultural Water

Frontiers in Environmental Science ◽

10.3389/fenvs.2021.701288 ◽

2021 ◽

Vol 9 ◽

Author(s):

Daniel Lowell Weller ◽

Tanzy M. T. Love ◽

Martin Wiedmann

Keyword(s):

Machine Learning ◽

Random Forest ◽

Predictive Models ◽

Training Data ◽

Agricultural Water ◽

Learning Models ◽

Safety Hazards ◽

E Coli ◽

Resampling Method ◽

Machine Learning Models

Recent studies have shown that predictive models can supplement or provide alternatives to E. coli-testing for assessing the potential presence of food safety hazards in water used for produce production. However, these studies used balanced training data and focused on enteric pathogens. As such, research is needed to determine 1) if predictive models can be used to assess Listeria contamination of agricultural water, and 2) how resampling (to deal with imbalanced data) affects performance of these models. To address these knowledge gaps, this study developed models that predict nonpathogenic Listeria spp. (excluding L. monocytogenes) and L. monocytogenes presence in agricultural water using various combinations of learner (e.g., random forest, regression), feature type, and resampling method (none, oversampling, SMOTE). Four feature types were used in model training: microbial, physicochemical, spatial, and weather. “Full models” were trained using all four feature types, while “nested models” used between one and three types. In total, 45 full (15 learners*3 resampling approaches) and 108 nested (5 learners*9 feature sets*3 resampling approaches) models were trained per outcome. Model performance was compared against baseline models where E. coli concentration was the sole predictor. Overall, the machine learning models outperformed the baseline E. coli models, with random forests outperforming models built using other learners (e.g., rule-based learners). Resampling produced more accurate models than not resampling, with SMOTE models outperforming, on average, oversampling models. Regardless of resampling method, spatial and physicochemical water quality features drove accurate predictions for the nonpathogenic Listeria spp. and L. monocytogenes models, respectively. Overall, these findings 1) illustrate the need for alternatives to existing E. coli-based monitoring programs for assessing agricultural water for the presence of potential food safety hazards, and 2) suggest that predictive models may be one such alternative. Moreover, these findings provide a conceptual framework for how such models can be developed in the future with the ultimate aim of developing models that can be integrated into on-farm risk management programs. For example, future studies should consider using random forest learners, SMOTE resampling, and spatial features to develop models to predict the presence of foodborne pathogens, such as L. monocytogenes, in agricultural water when the training data is imbalanced.

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Estimation of Chlorophyll-a Concentrations in Small Water Bodies: Comparison of Fused Gaofen-6 and Sentinel-2 Sensors

Remote Sensing ◽

10.3390/rs14010229 ◽

2022 ◽

Vol 14 (1) ◽

pp. 229

Author(s):

Jiarui Shi ◽

Qian Shen ◽

Yue Yao ◽

Junsheng Li ◽

Fu Chen ◽

...

Keyword(s):

Machine Learning ◽

Chlorophyll A ◽

Water Bodies ◽

Gradient Boosting ◽

Learning Models ◽

Small Water ◽

Extreme Gradient Boosting ◽

Machine Learning Models ◽

Sentinel 2 ◽

Small Water Bodies

Chlorophyll-a concentrations in water bodies are one of the most important environmental evaluation indicators in monitoring the water environment. Small water bodies include headwater streams, springs, ditches, flushes, small lakes, and ponds, which represent important freshwater resources. However, the relatively narrow and fragmented nature of small water bodies makes it difficult to monitor chlorophyll-a via medium-resolution remote sensing. In the present study, we first fused Gaofen-6 (a new Chinese satellite) images to obtain 2 m resolution images with 8 bands, which was approved as a good data source for Chlorophyll-a monitoring in small water bodies as Sentinel-2. Further, we compared five semi-empirical and four machine learning models to estimate chlorophyll-a concentrations via simulated reflectance using fused Gaofen-6 and Sentinel-2 spectral response function. The results showed that the extreme gradient boosting tree model (one of the machine learning models) is the most accurate. The mean relative error (MRE) was 9.03%, and the root-mean-square error (RMSE) was 4.5 mg/m3 for the Sentinel-2 sensor, while for the fused Gaofen-6 image, MRE was 6.73%, and RMSE was 3.26 mg/m3. Thus, both fused Gaofen-6 and Sentinel-2 could estimate the chlorophyll-a concentrations in small water bodies. Since the fused Gaofen-6 exhibited a higher spatial resolution and Sentinel-2 exhibited a higher temporal resolution.

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Benchmarking of Machine Learning Models to Assist the Prognosis of Tuberculosis

10.20944/preprints202103.0284.v2 ◽

2021 ◽

Author(s):

Maicon Herverton Lino Ferreira da Silva Barros ◽

Geovanne Oliveira Alves ◽

Lubnnia Morais Florêncio Souza ◽

Élisson da Silva Rocha ◽

João Fausto Lorenzato de Oliveira ◽

...

Keyword(s):

Machine Learning ◽

Clinical Symptoms ◽

Treatment Decision ◽

Gradient Boosting ◽

Original Form ◽

Learning Models ◽

Data Set ◽

Risk Of Death ◽

Increased Risk ◽

Machine Learning Models

Tuberculosis (TB) is an airborne infectious disease caused by organisms in the Mycobacterium tuberculosis (Mtb) complex. In many low and middle-income countries, TB remains a major cause of morbidity and mortality. Once a patient has been diagnosed with TB, it is critical that healthcare workers make the most appropriate treatment decision given the individual conditions of the patient and the likely course of the disease based on medical experience. Depending on the prognosis, delayed or inappropriate treatment can result in unsatisfactory results including the exacerbation of clinical symptoms, poor quality of life, and increased risk of death. This work benchmarks machine learning models to aid TB prognosis using a Brazilian health database of confirmed cases and deaths related to TB in the State of Amazonas. The goal is to predict the probability of death by TB thus aiding the prognosis of TB and associated treatment decision making process. In its original form, the data set comprised 36,228 records and 130 fields but suffered from missing, incomplete, or incorrect data. Following data cleaning and preprocessing, a revised data set was generated comprising 24,015 records and 38 fields, including 22,876 reported cured TB patients and 1,139 deaths by TB. To explore how the data imbalance impacts model performance, two controlled experiments were designed using (1) imbalanced and (2) balanced data sets. The best result is achieved by the Gradient Boosting (GB) model using the balanced data set to predict TB-mortality, and the ensemble model composed by the Random Forest (RF), GB and Multi-layer Perceptron (MLP) models is the best model to predict the cure class.

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Building-damage detection method based on machine learning utilizing aerial photographs of the Kumamoto earthquake

Earthquake Spectra ◽

10.1177/8755293019901309 ◽

2020 ◽

Vol 36 (3) ◽

pp. 1166-1187 ◽

Cited By ~ 4

Author(s):

Shohei Naito ◽

Hiromitsu Tomozawa ◽

Yuji Mori ◽

Takeshi Nagata ◽

Naokazu Monma ◽

...

Keyword(s):

Neural Network ◽

Machine Learning ◽

Convolutional Neural Network ◽

Training Data ◽

Aerial Photographs ◽

Learning Models ◽

Visual Interpretation ◽

Damage Classification ◽

Kumamoto Earthquake ◽

Machine Learning Models

This article presents a method for detecting damaged buildings in the event of an earthquake using machine learning models and aerial photographs. We initially created training data for machine learning models using aerial photographs captured around the town of Mashiki immediately after the main shock of the 2016 Kumamoto earthquake. All buildings are classified into one of the four damage levels by visual interpretation. Subsequently, two damage discrimination models are developed: a bag-of-visual-words model and a model based on a convolutional neural network. Results are compared and validated in terms of accuracy, revealing that the latter model is preferable. Moreover, for the convolutional neural network model, the target areas are expanded and the recalls of damage classification at the four levels range approximately from 66% to 81%.

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Assessing Continuous Operator Workload With a Hybrid Scaffolded Neuroergonomic Modeling Approach

Human Factors The Journal of the Human Factors and Ergonomics Society ◽

10.1177/0018720816672308 ◽

2017 ◽

Vol 59 (1) ◽

pp. 134-146 ◽

Cited By ~ 8

Author(s):

Brett J. Borghetti ◽

Joseph J. Giametta ◽

Christina F. Rusnock

Keyword(s):

Machine Learning ◽

Adaptive Systems ◽

Model Performance ◽

Machine Learning Algorithms ◽

Training Data ◽

State Assessments ◽

Learning Models ◽

Dynamic Task ◽

Operator Workload ◽

Machine Learning Models

Objective: We aimed to predict operator workload from neurological data using statistical learning methods to fit neurological-to-state-assessment models. Background: Adaptive systems require real-time mental workload assessment to perform dynamic task allocations or operator augmentation as workload issues arise. Neuroergonomic measures have great potential for informing adaptive systems, and we combine these measures with models of task demand as well as information about critical events and performance to clarify the inherent ambiguity of interpretation. Method: We use machine learning algorithms on electroencephalogram (EEG) input to infer operator workload based upon Improved Performance Research Integration Tool workload model estimates. Results: Cross-participant models predict workload of other participants, statistically distinguishing between 62% of the workload changes. Machine learning models trained from Monte Carlo resampled workload profiles can be used in place of deterministic workload profiles for cross-participant modeling without incurring a significant decrease in machine learning model performance, suggesting that stochastic models can be used when limited training data are available. Conclusion: We employed a novel temporary scaffold of simulation-generated workload profile truth data during the model-fitting process. A continuous workload profile serves as the target to train our statistical machine learning models. Once trained, the workload profile scaffolding is removed and the trained model is used directly on neurophysiological data in future operator state assessments. Application: These modeling techniques demonstrate how to use neuroergonomic methods to develop operator state assessments, which can be employed in adaptive systems.

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Chained Anomaly Detection Models for Federated Learning: An Intrusion Detection Case Study

Applied Sciences ◽

10.3390/app8122663 ◽

2018 ◽

Vol 8 (12) ◽

pp. 2663 ◽

Cited By ~ 11

Author(s):

Davy Preuveneers ◽

Vera Rimmer ◽

Ilias Tsingenopoulos ◽

Jan Spooren ◽

Wouter Joosen ◽

...

Keyword(s):

Neural Network ◽

Machine Learning ◽

Intrusion Detection ◽

Anomaly Detection ◽

Training Data ◽

Learning Models ◽

Traditional System ◽

Blockchain Technology ◽

Malicious Behavior ◽

Machine Learning Models

The adoption of machine learning and deep learning is on the rise in the cybersecurity domain where these AI methods help strengthen traditional system monitoring and threat detection solutions. However, adversaries too are becoming more effective in concealing malicious behavior amongst large amounts of benign behavior data. To address the increasing time-to-detection of these stealthy attacks, interconnected and federated learning systems can improve the detection of malicious behavior by joining forces and pooling together monitoring data. The major challenge that we address in this work is that in a federated learning setup, an adversary has many more opportunities to poison one of the local machine learning models with malicious training samples, thereby influencing the outcome of the federated learning and evading detection. We present a solution where contributing parties in federated learning can be held accountable and have their model updates audited. We describe a permissioned blockchain-based federated learning method where incremental updates to an anomaly detection machine learning model are chained together on the distributed ledger. By integrating federated learning with blockchain technology, our solution supports the auditing of machine learning models without the necessity to centralize the training data. Experiments with a realistic intrusion detection use case and an autoencoder for anomaly detection illustrate that the increased complexity caused by blockchain technology has a limited performance impact on the federated learning, varying between 5 and 15%, while providing full transparency over the distributed training process of the neural network. Furthermore, our blockchain-based federated learning solution can be generalized and applied to more sophisticated neural network architectures and other use cases.

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Active learning for the power factor prediction in diamond-like thermoelectric materials

npj Computational Materials ◽

10.1038/s41524-020-00439-8 ◽

2020 ◽

Vol 6 (1) ◽

Author(s):

Ye Sheng ◽

Yasong Wu ◽

Jiong Yang ◽

Wencong Lu ◽

Pierre Villars ◽

...

Keyword(s):

Machine Learning ◽

Active Learning ◽

Transport Properties ◽

Gradient Boosting ◽

Learning Models ◽

Material Development ◽

Materials Genome ◽

P Type ◽

Type Power ◽

Machine Learning Models

Abstract The Materials Genome Initiative requires the crossing of material calculations, machine learning, and experiments to accelerate the material development process. In recent years, data-based methods have been applied to the thermoelectric field, mostly on the transport properties. In this work, we combined data-driven machine learning and first-principles automated calculations into an active learning loop, in order to predict the p-type power factors (PFs) of diamond-like pnictides and chalcogenides. Our active learning loop contains two procedures (1) based on a high-throughput theoretical database, machine learning methods are employed to select potential candidates and (2) computational verification is applied to these candidates about their transport properties. The verification data will be added into the database to improve the extrapolation abilities of the machine learning models. Different strategies of selecting candidates have been tested, finally the Gradient Boosting Regression model of Query by Committee strategy has the highest extrapolation accuracy (the Pearson R = 0.95 on untrained systems). Based on the prediction from the machine learning models, binary pnictides, vacancy, and small atom-containing chalcogenides are predicted to have large PFs. The bonding analysis reveals that the alterations of anionic bonding networks due to small atoms are beneficial to the PFs in these compounds.

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Time-domain Feature and Ensemble Model based Classification of EMG Signals for Hand Gesture Recognition

10.21203/rs.3.rs-605286/v1 ◽

2021 ◽

Author(s):

Debarati Bhattacharjee ◽

Munesh Singh

Keyword(s):

Machine Learning ◽

Time Domain ◽

Electrical Current ◽

Clinical Diagnostics ◽

Gradient Boosting ◽

Learning Models ◽

Absolute Value ◽

Feature Pair ◽

Emg Signal ◽

Machine Learning Models

Abstract The electromyography (EMG) signal is the electrical current generated in muscles due to the inter-change of ions during their contractions. It has many applications in clinical diagnostics and the biomedical field. This paper has experimented with various ensemble algorithms and time-domain features to classify eight types of hand gestures. To train and test the machine learning models, we have extracted eight types of time-domain features from the raw EMG signals, such as integrated EMG (IEMG), variance, mean absolute value (MAV), modified mean absolute value type 1, waveform length, root mean square, average amplitude change, and difference absolute standard deviation value. The ensemble machine learning models are based on stacking, bagging, and gradient boosting. We have used four different-sized training sets to evaluate the performance of these classifiers. From the performance evaluation, we have identified the XG-Boost (gblinear) classifier with the IEMG feature as the best classifier-feature pair. The proposed classifier-feature pair has given better performance with a classification accuracy of 98.33% and a processing time of 5.67 μs for one vector than the existing extended associative memory-MAV classifier-feature pair.

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