scholarly journals A Smartphone-Based Cursor Position System in Cross-Device Interaction Using Machine Learning Techniques

Sensors ◽  
2021 ◽  
Vol 21 (5) ◽  
pp. 1665
Author(s):  
Juechen Yang ◽  
Jun Kong ◽  
Chunying Zhao
Keyword(s):  
Machine Learning ◽  
Mobile Devices ◽  
Mobile Device ◽  
Position System ◽  
Gradient Boosting ◽  
Learning Models ◽  
Large Display ◽  
Cursor Position ◽  
Device Interaction ◽  
Machine Learning Models

The use of mobile devices, especially smartphones, has become popular in recent years. There is an increasing need for cross-device interaction techniques that seamlessly integrate mobile devices and large display devices together. This paper develops a novel cross-device cursor position system that maps a mobile device’s movement on a flat surface to a cursor’s movement on a large display. The system allows a user to directly manipulate objects on a large display device through a mobile device and supports seamless cross-device data sharing without physical distance restrictions. To achieve this, we utilize sound localization to initialize the mobile device position as the starting location of a cursor on the large screen. Then, the mobile device’s movement is detected through an accelerometer and is accordingly translated to the cursor’s movement on the large display using machine learning models. In total, 63 features and 10 classifiers were employed to construct the machine learning models for movement detection. The evaluation results have demonstrated that three classifiers, in particular, gradient boosting, linear discriminant analysis (LDA), and naïve Bayes, are suitable for detecting the movement of a mobile device.

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

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.

scholarly journals Predicting Electric Vehicle Charging Station Availability Using Ensemble Machine Learning

Energies ◽  
2021 ◽  
Vol 14 (23) ◽  
pp. 7834
Author(s):  
Christopher Hecht ◽  
Jan Figgener ◽  
Dirk Uwe Sauer
Keyword(s):  
Machine Learning ◽  
Binary Data ◽  
Training Data ◽  
Gradient Boosting ◽  
Traffic Density ◽  
Learning Models ◽  
Charging Infrastructure ◽  
Ensemble Models ◽  
Charging Station ◽  
Machine Learning Models

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”.

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

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.

scholarly journals Benchmarking of Machine Learning Models to Assist the Prognosis of Tuberculosis

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.

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

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.

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

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.

scholarly journals Machine Learning Approach to Reduce Alert Fatigue Using a Disease Medication–Related Clinical Decision Support System: Model Development and Validation (Preprint)

2020 ◽  
Author(s):  
Tahmina Nasrin Poly ◽  
Md.Mohaimenul Islam ◽  
Muhammad Solihuddin Muhtar ◽  
Hsuan-Chia Yang ◽  
Phung Anh (Alex) Nguyen ◽  
...  
Keyword(s):  
Machine Learning ◽  
Decision Support ◽  
Clinical Decision Support ◽  
Prediction Models ◽  
Clinical Decision ◽  
Gradient Boosting ◽  
Support Vector ◽  
Learning Models ◽  
Alert Fatigue ◽  
Machine Learning Models

BACKGROUND Computerized physician order entry (CPOE) systems are incorporated into clinical decision support systems (CDSSs) to reduce medication errors and improve patient safety. Automatic alerts generated from CDSSs can directly assist physicians in making useful clinical decisions and can help shape prescribing behavior. Multiple studies reported that approximately 90%-96% of alerts are overridden by physicians, which raises questions about the effectiveness of CDSSs. There is intense interest in developing sophisticated methods to combat alert fatigue, but there is no consensus on the optimal approaches so far. OBJECTIVE Our objective was to develop machine learning prediction models to predict physicians’ responses in order to reduce alert fatigue from disease medication–related CDSSs. METHODS We collected data from a disease medication–related CDSS from a university teaching hospital in Taiwan. We considered prescriptions that triggered alerts in the CDSS between August 2018 and May 2019. Machine learning models, such as artificial neural network (ANN), random forest (RF), naïve Bayes (NB), gradient boosting (GB), and support vector machine (SVM), were used to develop prediction models. The data were randomly split into training (80%) and testing (20%) datasets. RESULTS A total of 6453 prescriptions were used in our model. The ANN machine learning prediction model demonstrated excellent discrimination (area under the receiver operating characteristic curve [AUROC] 0.94; accuracy 0.85), whereas the RF, NB, GB, and SVM models had AUROCs of 0.93, 0.91, 0.91, and 0.80, respectively. The sensitivity and specificity of the ANN model were 0.87 and 0.83, respectively. CONCLUSIONS In this study, ANN showed substantially better performance in predicting individual physician responses to an alert from a disease medication–related CDSS, as compared to the other models. To our knowledge, this is the first study to use machine learning models to predict physician responses to alerts; furthermore, it can help to develop sophisticated CDSSs in real-world clinical settings.

scholarly journals A Comparative Analysis of Machine Learning Algorithms in Design Process of Adaptive Traffic Signal Control System

2022 ◽  
Vol 2161 (1) ◽  
pp. 012054
Author(s):  
R M Savithramma ◽  
R Sumathi ◽  
H S Sudhira
Keyword(s):  
Machine Learning ◽  
Comparative Analysis ◽  
Regression Tree ◽  
Gradient Boosting ◽  
Signal Control ◽  
Traffic Signal Control ◽  
Suitable Model ◽  
Traffic Classification ◽  
Learning Models ◽  
Machine Learning Models

Abstract In recent decades machine learning technology has proved its efficiency in most sectors by making human life easier. With this popularity and efficiency, it is applied to design traffic signal control systems to mitigate traffic congestion and distribute waiting delays. Hence, many researchers around the world are working to address this issue. As a part of the solution, this article presents a comparative analysis of various machine learning models to come up with a suitable model for an isolated intersection. In this context, eight machine learning models including Linear Regression, Ridge, Lasso, Support Vector Regression, k-Nearest Neighbour, Decision Tree, Random Forest, and Gradient Boosting Regression Tree are selected. Shivakumara Swamiji Circle (SSC), one of the intersections in Tumakuru, Karnataka, India is selected as a case study area. Essential data is collected from SSC through videography. The selected models are developed to predict green time based on traffic classification and volume in Passenger Car Units (PCU) for each phase on the PyCharm platform. The models are evaluated based on various performance metrics. Results revealed that all the selected models predict green splits with 91% accuracy using traffic classification as input, whereas, models showed 85% accuracy with PCU as input. And also, Gradient Boosting Regression Tree is the best suitable model for the selected intersection, whereas, Decision Tree is not referred model for this application.

scholarly journals Characterising the nationwide burden and predictors of unkept outpatient appointments in the National Health Service in England: A cohort study using a machine learning approach

PLoS Medicine ◽  
2021 ◽  
Vol 18 (10) ◽  
pp. e1003783
Author(s):  
Sion Philpott-Morgan ◽  
Dixa B. Thakrar ◽  
Joshua Symons ◽  
Daniel Ray ◽  
Hutan Ashrafian ◽  
...  
Keyword(s):  
Machine Learning ◽  
National Health Service ◽  
Health Service ◽  
National Health ◽  
Outpatient Clinic ◽  
Gradient Boosting ◽  
Learning Models ◽  
Targeted Interventions ◽  
Outpatient Appointments ◽  
Machine Learning Models

Background Unkept outpatient hospital appointments cost the National Health Service £1 billion each year. Given the associated costs and morbidity of unkept appointments, this is an issue requiring urgent attention. We aimed to determine rates of unkept outpatient clinic appointments across hospital trusts in the England. In addition, we aimed to examine the predictors of unkept outpatient clinic appointments across specialties at Imperial College Healthcare NHS Trust (ICHT). Our final aim was to train machine learning models to determine the effectiveness of a potential intervention in reducing unkept appointments. Methods and findings UK Hospital Episode Statistics outpatient data from 2016 to 2018 were used for this study. Machine learning models were trained to determine predictors of unkept appointments and their relative importance. These models were gradient boosting machines. In 2017–2018 there were approximately 85 million outpatient appointments, with an unkept appointment rate of 5.7%. Within ICHT, there were almost 1 million appointments, with an unkept appointment rate of 11.2%. Hepatology had the highest rate of unkept appointments (17%), and medical oncology had the lowest (6%). The most important predictors of unkept appointments included the recency (25%) and frequency (13%) of previous unkept appointments and age at appointment (10%). A sensitivity of 0.287 was calculated overall for specialties with at least 10,000 appointments in 2016–2017 (after data cleaning). This suggests that 28.7% of patients who do miss their appointment would be successfully targeted if the top 10% least likely to attend received an intervention. As a result, an intervention targeting the top 10% of likely non-attenders, in the full population of patients, would be able to capture 28.7% of unkept appointments if successful. Study limitations include that some unkept appointments may have been missed from the analysis because recording of unkept appointments is not mandatory in England. Furthermore, results here are based on a single trust in England, hence may not be generalisable to other locations. Conclusions Unkept appointments remain an ongoing concern for healthcare systems internationally. Using machine learning, we can identify those most likely to miss their appointment and implement more targeted interventions to reduce unkept appointment rates.

scholarly journals Machine learning methods to predict mechanical ventilation and mortality in patients with COVID-19

PLoS ONE ◽  
2021 ◽  
Vol 16 (4) ◽  
pp. e0249285
Author(s):  
Limin Yu ◽  
Alexandra Halalau ◽  
Bhavinkumar Dalal ◽  
Amr E. Abbas ◽  
Felicia Ivascu ◽  
...  
Keyword(s):  
Machine Learning ◽  
Mechanical Ventilation ◽  
Hospital Mortality ◽  
Emergency Room ◽  
Vital Signs ◽  
High Accuracy ◽  
Gradient Boosting ◽  
Learning Models ◽  
Increased Risk ◽  
Machine Learning Models

Background The Coronavirus disease 2019 (COVID-19) pandemic has affected millions of people across the globe. It is associated with a high mortality rate and has created a global crisis by straining medical resources worldwide. Objectives To develop and validate machine-learning models for prediction of mechanical ventilation (MV) for patients presenting to emergency room and for prediction of in-hospital mortality once a patient is admitted. Methods Two cohorts were used for the two different aims. 1980 COVID-19 patients were enrolled for the aim of prediction ofMV. 1036 patients’ data, including demographics, past smoking and drinking history, past medical history and vital signs at emergency room (ER), laboratory values, and treatments were collected for training and 674 patients were enrolled for validation using XGBoost algorithm. For the second aim to predict in-hospital mortality, 3491 hospitalized patients via ER were enrolled. CatBoost, a new gradient-boosting algorithm was applied for training and validation of the cohort. Results Older age, higher temperature, increased respiratory rate (RR) and a lower oxygen saturation (SpO2) from the first set of vital signs were associated with an increased risk of MV amongst the 1980 patients in the ER. The model had a high accuracy of 86.2% and a negative predictive value (NPV) of 87.8%. While, patients who required MV, had a higher RR, Body mass index (BMI) and longer length of stay in the hospital were the major features associated with in-hospital mortality. The second model had a high accuracy of 80% with NPV of 81.6%. Conclusion Machine learning models using XGBoost and catBoost algorithms can predict need for mechanical ventilation and mortality with a very high accuracy in COVID-19 patients.

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