How Well Can Machine Learning Models Perform without Hydrologists? Application of Rational Feature Selection to Improve Hydrological Forecasting

In this paper, we propose a prediction algorithm, the combination of Long Short-Term Memory (LSTM) and attention model, based on machine learning models to predict the vision coordinates when watching 360-degree videos in a Virtual Reality (VR) or Augmented Reality (AR) system. Predicting the vision coordinates while video streaming is important when the network condition is degraded. However, the traditional prediction models such as Moving Average (MA) and Autoregression Moving Average (ARMA) are linear so they cannot consider the nonlinear relationship. Therefore, machine learning models based on deep learning are recently used for nonlinear predictions. We use the Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) neural network methods, originated in Recurrent Neural Networks (RNN), and predict the head position in the 360-degree videos. Therefore, we adopt the attention model to LSTM to make more accurate results. We also compare the performance of the proposed model with the other machine learning models such as Multi-Layer Perceptron (MLP) and RNN using the root mean squared error (RMSE) of predicted and real coordinates. We demonstrate that our model can predict the vision coordinates more accurately than the other models in various videos.

Download Full-text

Random forest and long short-term memory based machine learning models for classification of ion mobility spectrometry spectra

Chemical, Biological, Radiological, Nuclear, and Explosives (CBRNE) Sensing XXII ◽

10.1117/12.2585829 ◽

2021 ◽

Author(s):

Patrick C. Riley ◽

Samir V. Deshpande ◽

Brian S. Ince ◽

Brian C. Hauck ◽

Kyle P. O'Donnell ◽

...

Keyword(s):

Machine Learning ◽

Random Forest ◽

Ion Mobility ◽

Short Term Memory ◽

Learning Models ◽

Short Term ◽

Term Memory ◽

Long Short Term Memory ◽

Machine Learning Models

Download Full-text

Unscented Kalman Filter-Aided Long Short-Term Memory Approach for Wind Nowcasting

Aerospace ◽

10.3390/aerospace8090236 ◽

2021 ◽

Vol 8 (9) ◽

pp. 236

Author(s):

Junghyun Kim ◽

Kyuman Lee

Keyword(s):

Machine Learning ◽

Short Term Memory ◽

Unscented Kalman Filter ◽

Aviation Industry ◽

Learning Models ◽

Short Term ◽

Machine Learning Model ◽

Long Short Term Memory ◽

Lstm Network ◽

Machine Learning Models

Obtaining reliable wind information is critical for efficiently managing air traffic and airport operations. Wind forecasting has been considered one of the most challenging tasks in the aviation industry. Recently, with the advent of artificial intelligence, many machine learning techniques have been widely used to address a variety of complex phenomena in wind predictions. In this paper, we propose a hybrid framework that combines a machine learning model with Kalman filtering for a wind nowcasting problem in the aviation industry. More specifically, this study has three objectives as follows: (1) compare the performance of the machine learning models (i.e., Gaussian process, multi-layer perceptron, and long short-term memory (LSTM) network) to identify the most appropriate model for wind predictions, (2) combine the machine learning model selected in step (1) with an unscented Kalman filter (UKF) to improve the fidelity of the model, and (3) perform Monte Carlo simulations to quantify uncertainties arising from the modeling process. Results show that short-term time-series wind datasets are best predicted by the LSTM network compared to the other machine learning models and the UKF-aided LSTM (UKF-LSTM) approach outperforms the LSTM network only, especially when long-term wind forecasting needs to be considered.

Download Full-text

Three-level Sleep Stage Classification Based on Wrist-worn Accelerometry Data Alone

10.1101/2021.08.10.455812 ◽

2021 ◽

Author(s):

Jian Hu ◽

Haochang Shou

Keyword(s):

Machine Learning ◽

Sequential Analysis ◽

Short Term Memory ◽

Sleep Stage ◽

Model Performance ◽

Training Data ◽

Sleep Stages ◽

Learning Models ◽

Local Variability ◽

Machine Learning Models

Objective: The use of wearable sensor devices on daily basis to track real-time movements during wake and sleep has provided opportunities for automatic sleep quantification using such data. Existing algorithms for classifying sleep stages often require large training data and multiple input signals including heart rate and respiratory data. We aimed to examine the capability of classifying sleep stages using sensible features directly from accelerometers only with the aid of advanced recurrent neural networks. Materials and Methods: We analyzed a publicly available dataset with accelerometry data in 5s epoch length and polysomnography assessments. We developed long short-term memory (LSTM) models that take the 3-axis accelerations, angles, and temperatures from concurrent and historic observation windows to predict wake, REM and non-REM sleep. Leave-one-subject-out experiments were conducted to compare and evaluate the model performance with conventional nonsequential machine learning models using metrics such as multiclass training and testing accuracy, weighted precision, F1 score and area-under-the-curve (AUC). Results: Our sequential analysis framework outperforms traditional non-sequential models in all aspects of model evaluation metrics. We achieved an average of 65% and a maximum of 81% validation accuracy for classifying three sleep labels even with a relatively small training sample of clinical visitors. The presence of two additional derived variables, local variability and range, have shown to strongly improve the model performance. Discussion : Results indicate that it is crucial to account for deep temporal dependency and assess local variability of the features. The post-hoc analysis of individual model performances on subjects' demographic characteristics also suggest the need of including pathological samples in the training data in order to develop robust machine learning models that are capable of capturing normal and anomaly sleep patterns in the population.

Download Full-text

Sales forecasting using multivariate long short term memory network models

10.7287/peerj.preprints.27712v1 ◽

2019 ◽

Cited By ~ 1

Author(s):

Suleka Helmini ◽

Nadheesh Jihan ◽

Malith Jayasinghe ◽

Srinath Perera

Keyword(s):

Machine Learning ◽

Random Forest ◽

Short Term Memory ◽

Learning Models ◽

Short Term ◽

Term Memory ◽

Forecasting Models ◽

Non Linear ◽

Long Short Term Memory ◽

Machine Learning Models

In the retail domain, estimating the sales before actual sales become known plays a key role in maintaining a successful business. This is due to the fact that most crucial decisions are bound to be based on these forecasts. Statistical sales forecasting models like ARIMA (Auto-Regressive Integrated Moving Average), can be identified as one of the most traditional and commonly used forecasting methodologies. Even though these models are capable of producing satisfactory forecasts for linear time series data they are not suitable for analyzing non-linear data. Therefore, machine learning models (such as Random Forest Regression, XGBoost) have been employed frequently as they were able to achieve better results using non-linear data. The recent research shows that deep learning models (e.g. recurrent neural networks) can provide higher accuracy in predictions compared to machine learning models due to their ability to persist information and identify temporal relationships. In this paper, we adopt a special variant of Long Short Term Memory (LSTM) network called LSTM model with peephole connections for sales prediction. We first build our model using historical features for sales forecasting. We compare the results of this initial LSTM model with multiple machine learning models, namely, the Extreme Gradient Boosting model (XGB) and Random Forest Regressor model(RFR). We further improve the prediction accuracy of the initial model by incorporating features that describe the future that is known to us in the current moment, an approach that has not been explored in previous state-of-the-art LSTM based forecasting models. The initial LSTM model we develop outperforms the machine learning models achieving 12% - 14% improvement whereas the improved LSTM model achieves 11\% - 13\% improvement compared to the improved machine learning models. Furthermore, we also show that our improved LSTM model can obtain a 20% - 21% improvement compared to the initial LSTM model, achieving significant improvement.

Download Full-text

Sales forecasting using multivariate long short term memory network models

10.7287/peerj.preprints.27712 ◽

2019 ◽

Author(s):

Suleka Helmini ◽

Nadheesh Jihan ◽

Malith Jayasinghe ◽

Srinath Perera

Keyword(s):

Machine Learning ◽

Random Forest ◽

Short Term Memory ◽

Learning Models ◽

Short Term ◽

Term Memory ◽

Forecasting Models ◽

Non Linear ◽

Long Short Term Memory ◽

Machine Learning Models

In the retail domain, estimating the sales before actual sales become known plays a key role in maintaining a successful business. This is due to the fact that most crucial decisions are bound to be based on these forecasts. Statistical sales forecasting models like ARIMA (Auto-Regressive Integrated Moving Average), can be identified as one of the most traditional and commonly used forecasting methodologies. Even though these models are capable of producing satisfactory forecasts for linear time series data they are not suitable for analyzing non-linear data. Therefore, machine learning models (such as Random Forest Regression, XGBoost) have been employed frequently as they were able to achieve better results using non-linear data. The recent research shows that deep learning models (e.g. recurrent neural networks) can provide higher accuracy in predictions compared to machine learning models due to their ability to persist information and identify temporal relationships. In this paper, we adopt a special variant of Long Short Term Memory (LSTM) network called LSTM model with peephole connections for sales prediction. We first build our model using historical features for sales forecasting. We compare the results of this initial LSTM model with multiple machine learning models, namely, the Extreme Gradient Boosting model (XGB) and Random Forest Regressor model(RFR). We further improve the prediction accuracy of the initial model by incorporating features that describe the future that is known to us in the current moment, an approach that has not been explored in previous state-of-the-art LSTM based forecasting models. The initial LSTM model we develop outperforms the machine learning models achieving 12% - 14% improvement whereas the improved LSTM model achieves 11\% - 13\% improvement compared to the improved machine learning models. Furthermore, we also show that our improved LSTM model can obtain a 20% - 21% improvement compared to the initial LSTM model, achieving significant improvement.

Download Full-text

Analysis and Prediction of Unplanned Intensive Care Unit Readmission using Recurrent Neural Networks with Long Short-Term Memory

10.1101/385518 ◽

2018 ◽

Cited By ~ 1

Author(s):

Yu-Wei Lin ◽

Yuqian Zhou ◽

Faraz Faghri ◽

Michael J. Shaw ◽

Roy H. Campbell

Keyword(s):

Machine Learning ◽

Neural Networks ◽

Recurrent Neural Networks ◽

Short Term Memory ◽

Unplanned Readmission ◽

Learning Models ◽

Short Term ◽

Term Memory ◽

Long Short Term Memory ◽

Machine Learning Models

AbstractBackgroundUnplanned readmission of a hospitalized patient is an extremely undesirable outcome as the patient may have been exposed to additional risks. The rates of unplanned readmission are, therefore, regarded as an important performance indicator for the medical quality of a hospital and healthcare system. Identifying high-risk patients likely to suffer from readmission before release benefits both the patients and the medical providers. The emergence of machine learning to detect hidden patterns in complex, multi-dimensional datasets provides unparalleled opportunities to develop efficient discharge decision-making support system for physicians.Methods and FindingsWe used supervised machine learning approaches for ICU readmission prediction. We used machine learning methods on comprehensive, longitudinal clinical data from the MIMIC-III to predict the ICU readmission of patients within 30 days of their discharge. We have utilized recent machine learning techniques such as Recurrent Neural Networks (RNN) with Long Short-Term Memory (LSTM), by this we have been able incorporate the multivariate features of EHRs and capture sudden fluctuations in chart event features (e.g. glucose and heart rate) that are significant in time series with temporal dependencies, which cannot be properly captured by traditional static models, but can be captured by our proposed deep neural network based model. We incorporate multiple types of features including chart events, demographic, and ICD9 embeddings. Our machine learning models identifies ICU readmissions at a higher sensitivity rate (0.742) and an improved Area Under the Curve (0.791) compared with traditional methods. We also illustrate the importance of each portion of the features and different combinations of the models to verify the effectiveness of the proposed model.ConclusionOur manuscript highlights the ability of machine learning models to improve our ICU decision making accuracy, and is a real-world example of precision medicine in hospitals. These data-driven results enable clinicians to make assisted decisions within their patient cohorts. This knowledge could have immediate implications for hospitals by improving the detection of possible readmission. We anticipate that machine learning models will improve patient counseling, hospital administration, allocation of healthcare resources and ultimately individualized clinical care.

Download Full-text

Quantum Optical Experiments Modeled by Long Short-Term Memory

Photonics ◽

10.3390/photonics8120535 ◽

2021 ◽

Vol 8 (12) ◽

pp. 535

Author(s):

Thomas Adler ◽

Manuel Erhard ◽

Mario Krenn ◽

Johannes Brandstetter ◽

Johannes Kofler ◽

...

Keyword(s):

Machine Learning ◽

Short Term Memory ◽

Random Search ◽

High Dimensional ◽

Learning Models ◽

Short Term ◽

Term Memory ◽

Output State ◽

Long Short Term Memory ◽

Machine Learning Models

We demonstrate how machine learning is able to model experiments in quantum physics. Quantum entanglement is a cornerstone for upcoming quantum technologies, such as quantum computation and quantum cryptography. Of particular interest are complex quantum states with more than two particles and a large number of entangled quantum levels. Given such a multiparticle high-dimensional quantum state, it is usually impossible to reconstruct an experimental setup that produces it. To search for interesting experiments, one thus has to randomly create millions of setups on a computer and calculate the respective output states. In this work, we show that machine learning models can provide significant improvement over random search. We demonstrate that a long short-term memory (LSTM) neural network can successfully learn to model quantum experiments by correctly predicting output state characteristics for given setups without the necessity of computing the states themselves. This approach not only allows for faster search, but is also an essential step towards the automated design of multiparticle high-dimensional quantum experiments using generative machine learning models.

Download Full-text

Short-Term Prediction of COVID-19 Cases Using Machine Learning Models

Applied Sciences ◽

10.3390/app11094266 ◽

2021 ◽

Vol 11 (9) ◽

pp. 4266

Author(s):

Md. Shahriare Satu ◽

Koushik Chandra Howlader ◽

Mufti Mahmud ◽

M. Shamim Kaiser ◽

Sheikh Mohammad Shariful Islam ◽

...

Keyword(s):

Machine Learning ◽

Web Application ◽

Learning Models ◽

Short Term ◽

Infected People ◽

Infected Case ◽

First Case ◽

Novel Coronavirus ◽

Short Term Prediction ◽

Machine Learning Models

The first case in Bangladesh of the novel coronavirus disease (COVID-19) was reported on 8 March 2020, with the number of confirmed cases rapidly rising to over 175,000 by July 2020. In the absence of effective treatment, an essential tool of health policy is the modeling and forecasting of the progress of the pandemic. We, therefore, developed a cloud-based machine learning short-term forecasting model for Bangladesh, in which several regression-based machine learning models were applied to infected case data to estimate the number of COVID-19-infected people over the following seven days. This approach can accurately forecast the number of infected cases daily by training the prior 25 days sample data recorded on our web application. The outcomes of these efforts could aid the development and assessment of prevention strategies and identify factors that most affect the spread of COVID-19 infection in Bangladesh.

Download Full-text

Ambient Monitoring of Gait and Machine Learning Models for Dynamic and Short-Term Falls Risk Assessment in People With Dementia

10.36227/techrxiv.16943395 ◽

2021 ◽

Author(s):

Navid Korhani ◽

Babak Taati ◽

Andrea Iaboni ◽

Andrea Sabo ◽

Sina Mehdizadeh ◽

...

Keyword(s):

Machine Learning ◽

Risk Assessment ◽

Fall Risk ◽

Learning Models ◽

Short Term ◽

Ambient Monitoring ◽

Clinical Assessments ◽

People With Dementia ◽

Time Of Admission ◽

Machine Learning Models

Data consists of baseline clinical assessments of gait, mobility, and fall risk at the time of admission of 54 adults with dementia. Furthermore, it includes the participants' daily medication intake in three medication categories, and frequent assessments of gait performed via a computer vision-based ambient monitoring system.

Download Full-text