Damaged/missing proximity sensor induces screen mistouch when answering calls: Prediction of smartphone answering status by posture data

When the proximity sensor of a smartphone is impaired, it would easily lead to screen mistouch during conversation, which will significantly affect the user experience. However, there are relatively few studies that have been focused on the quality of user experience following sensor impairment. The purpose of this study was to compare and evaluate different machine learning models in forecasting the user’s posture during a phone call, thereby providing a compensation approach for detecting proximity to the human ear during a phone call following sensor damage. The built-in accelerometer sensors of smartphones were employed to collect posture data while users were employing their smartphones. Three main postures (holding, moving and answering) were identified; the posture data were obtained through training and prediction using five machine learning models. The results showed that the model that utilized triaxial data had better prediction accuracy than the model that used single-axis data. Furthermore, models with time-domain features had a higher accuracy rate. Among the five models, neural networks had the best prediction accuracy (0.982). The proposed approach could be of immense benefit to the users following proximity sensor damage, and would be advantageous in the design of the smartphone, particularly in the early stages of the design process.

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Application of Bioactivity Profile Based Fingerprints for Building Machine Learning Models

10.26434/chemrxiv.6969584 ◽

2018 ◽

Cited By ~ 1

Author(s):

Noé Sturm ◽

Jiangming Sun ◽

Yves Vandriessche ◽

Andreas Mayr ◽

Günter Klambauer ◽

...

Keyword(s):

Machine Learning ◽

Deep Learning ◽

High Throughput ◽

Scaffold Hopping ◽

Learning Models ◽

Industrial Data ◽

Structural Descriptors ◽

Bioactivity Profile ◽

Machine Learning Models

<div>This article describes an application of high-throughput fingerprints (HTSFP) built upon industrial data accumulated over the years. </div><div>The fingerprint was used to build machine learning models (multi-task deep learning + SVM) for compound activity predictions towards a panel of 131 targets. </div><div>Quality of the predictions and the scaffold hopping potential of the HTSFP were systematically compared to traditional structural descriptors ECFP. </div><div><br></div>

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Data science in economics: comprehensive review of advanced machine learning and deep learning methods

10.21203/rs.3.rs-91905/v1 ◽

2020 ◽

Author(s):

Saeed Nosratabadi ◽

Amir Mosavi ◽

Puhong Duan ◽

Pedram Ghamisi ◽

Filip Ferdinand ◽

...

Keyword(s):

Machine Learning ◽

Deep Learning ◽

Prediction Accuracy ◽

Data Science ◽

State Of The Art ◽

Hybrid Models ◽

The Other ◽

Learning Models ◽

Comprehensive Review

Abstract This paper provides the state of the art of data science in economics. Through a novel taxonomy of applications and methods advances in data science are investigated. The data science advances are investigated in three individual classes of deep learning models, ensemble models, and hybrid models. Application domains include stock market, marketing, E-commerce, corporate banking, and cryptocurrency. Prisma method, a systematic literature review methodology is used to ensure the quality of the survey. The findings revealed that the trends are on advancement of hybrid models as more than 51% of the reviewed articles applied hybrid model. On the other hand, it is found that based on the RMSE accuracy metric, hybrid models had higher prediction accuracy than other algorithms. While it is expected the trends go toward the advancements of deep learning models.

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Combine Clustering and Machine Learning for Enhancing the Efficiency of Energy Baseline of Chiller System

Energies ◽

10.3390/en13174368 ◽

2020 ◽

Vol 13 (17) ◽

pp. 4368 ◽

Cited By ~ 1

Author(s):

Chun-Wei Chen ◽

Chun-Chang Li ◽

Chen-Yu Lin

Keyword(s):

Machine Learning ◽

Prediction Accuracy ◽

Prediction Models ◽

Machine Learning Algorithms ◽

Learning Models ◽

Important Method ◽

Gap Statistic ◽

Machine Learning Model ◽

Key Variables

Energy baseline is an important method for measuring the energy-saving benefits of chiller system, and the benefits can be calculated by comparing prediction models and actual results. Currently, machine learning is often adopted as a prediction model for energy baselines. Common models include regression, ensemble learning, and deep learning models. In this study, we first reviewed several machine learning algorithms, which were used to establish prediction models. Then, the concept of clustering to preprocess chiller data was adopted. Data mining, K-means clustering, and gap statistic were used to successfully identify the critical variables to cluster chiller modes. Applying these key variables effectively enhanced the quality of the chiller data, and combining the clustering results and the machine learning model effectively improved the prediction accuracy of the model and the reliability of the energy baselines.

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Online learning behavior analysis based on machine learning

Asian Association of Open Universities Journal ◽

10.1108/aaouj-08-2019-0029 ◽

2019 ◽

Vol 14 (2) ◽

pp. 97-106

Author(s):

Ning Yan ◽

Oliver Tat-Sheung Au

Keyword(s):

Machine Learning ◽

Online Learning ◽

Correlation Analysis ◽

Prediction Accuracy ◽

Classification Models ◽

Limited Data ◽

Learning Models ◽

Learning Behavior ◽

Content Type ◽

Machine Learning Models

Purpose The purpose of this paper is to make a correlation analysis between students’ online learning behavior features and course grade, and to attempt to build some effective prediction model based on limited data. Design/methodology/approach The prediction label in this paper is the course grade of students, and the eigenvalues available are student age, student gender, connection time, hits count and days of access. The machine learning model used in this paper is the classical three-layer feedforward neural networks, and the scaled conjugate gradient algorithm is adopted. Pearson correlation analysis method is used to find the relationships between course grade and the student eigenvalues. Findings Days of access has the highest correlation with course grade, followed by hits count, and connection time is less relevant to students’ course grade. Student age and gender have the lowest correlation with course grade. Binary classification models have much higher prediction accuracy than multi-class classification models. Data normalization and data discretization can effectively improve the prediction accuracy of machine learning models, such as ANN model in this paper. Originality/value This paper may help teachers to find some clue to identify students with learning difficulties in advance and give timely help through the online learning behavior data. It shows that acceptable prediction models based on machine learning can be built using a small and limited data set. However, introducing external data into machine learning models to improve its prediction accuracy is still a valuable and hard issue.

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Cyber-Physical LPG Debutanizer Distillation Columns: Machine Learning-Based Soft Sensors for Product Quality Monitoring

10.20944/preprints202110.0364.v1 ◽

2021 ◽

Author(s):

Jože M. Rožanec ◽

Elena Trajkova ◽

Jinzhi Lu ◽

Nikolaos Sarantinoudis ◽

Georgios Arampatzis ◽

...

Keyword(s):

Machine Learning ◽

Product Quality ◽

Learning Models ◽

State Monitoring ◽

Soft Sensors ◽

Distillation Columns ◽

Operational Conditions ◽

Equipment State ◽

Machine Learning Models

Refineries execute a series of interlinked processes, where the product of one unit serves as the input to another process. Potential failures within these processes affect the quality of the end products, operational efficiency, and revenue of the entire refinery. In this context, implementation of a real-time cognitive module, referring to predictive machine learning models, enables to provide equipment state monitoring services and to generate decision-making for equipment operations. In this paper, we propose two machine learning models: 1) to forecast the amount of pentane (C5) content in the final product mixture; 2) to identify if C5 content exceeds the specification thresholds for the final product quality. We validate our approach by using a use case from a real-world refinery. In addition, we develop a visualization to assess which features are considered most important during feature selection, and later by the machine learning models. Finally, we provide insights on the sensor values in the dataset, which help to identify the operational conditions for using such machine learning models.

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Cyber-Physical LPG Debutanizer Distillation Columns: Machine-Learning-Based Soft Sensors for Product Quality Monitoring

Applied Sciences ◽

10.3390/app112411790 ◽

2021 ◽

Vol 11 (24) ◽

pp. 11790

Author(s):

Jože Martin Rožanec ◽

Elena Trajkova ◽

Jinzhi Lu ◽

Nikolaos Sarantinoudis ◽

George Arampatzis ◽

...

Keyword(s):

Machine Learning ◽

Product Quality ◽

Learning Models ◽

State Monitoring ◽

Soft Sensors ◽

Distillation Columns ◽

Operational Conditions ◽

Equipment State ◽

Machine Learning Models

Refineries execute a series of interlinked processes, where the product of one unit serves as the input to another process. Potential failures within these processes affect the quality of the end products, operational efficiency, and revenue of the entire refinery. In this context, implementation of a real-time cognitive module, referring to predictive machine learning models, enables the provision of equipment state monitoring services and the generation of decision-making for equipment operations. In this paper, we propose two machine learning models: (1) to forecast the amount of pentane (C5) content in the final product mixture; (2) to identify if C5 content exceeds the specification thresholds for the final product quality. We validate our approach using a use case from a real-world refinery. In addition, we develop a visualization to assess which features are considered most important during feature selection, and later by the machine learning models. Finally, we provide insights on the sensor values in the dataset, which help to identify the operational conditions for using such machine learning models.

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Data Science in Economics: Comprehensive Review of Advanced Machine Learning and Deep Learning Methods

10.20944/preprints202010.0263.v1 ◽

2020 ◽

Author(s):

Saeed Nosratabadi ◽

Amir Mosavi ◽

Puhong Duan ◽

Pedram Ghamisi ◽

Filip Ferdinand ◽

...

Keyword(s):

Machine Learning ◽

Deep Learning ◽

Prediction Accuracy ◽

Data Science ◽

State Of The Art ◽

Hybrid Models ◽

The Other ◽

Learning Models ◽

Comprehensive Review

This paper provides the state of the art of data science in economics. Through a novel taxonomy of applications and methods advances in data science are investigated. The data science advances are investigated in three individual classes of deep learning models, ensemble models, and hybrid models. Application domains include stock market, marketing, E-commerce, corporate banking, and cryptocurrency. Prisma method, a systematic literature review methodology is used to ensure the quality of the survey. The findings revealed that the trends are on advancement of hybrid models as more than 51% of the reviewed articles applied hybrid model. On the other hand, it is found that based on the RMSE accuracy metric, hybrid models had higher prediction accuracy than other algorithms. While it is expected the trends go toward the advancements of deep learning models.

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An Evaluation of Wearable Inertial Sensor Configuration and Supervised Machine Learning Models for Automatic Punch Classification in Boxing

IoT ◽

10.3390/iot1020021 ◽

2020 ◽

Vol 1 (2) ◽

pp. 360-381

Author(s):

Matthew T. O. Worsey ◽

Hugo G. Espinosa ◽

Jonathan B. Shepherd ◽

David V. Thiel

Keyword(s):

Machine Learning ◽

Prediction Accuracy ◽

Inertial Sensors ◽

Inertial Sensor ◽

Supervised Machine Learning ◽

Learning Models ◽

Significant Statistical Difference ◽

Sensor Configuration ◽

Wearable Inertial Sensors ◽

Machine Learning Models

Machine learning is a powerful tool for data classification and has been used to classify movement data recorded by wearable inertial sensors in general living and sports. Inertial sensors can provide valuable biofeedback in combat sports such as boxing; however, the use of such technology has not had a global uptake. If simple inertial sensor configurations can be used to automatically classify strike type, then cumbersome tasks such as video labelling can be bypassed and the foundation for automated workload monitoring of combat sport athletes is set. This investigation evaluates the classification performance of six different supervised machine learning models (tuned and untuned) when using two simple inertial sensor configurations (configuration 1—inertial sensor worn on both wrists; configuration 2—inertial sensor worn on both wrists and third thoracic vertebrae [T3]). When trained on one athlete, strike prediction accuracy was good using both configurations (sensor configuration 1 mean overall accuracy: 0.90 ± 0.12; sensor configuration 2 mean overall accuracy: 0.87 ± 0.09). There was no significant statistical difference in prediction accuracy between both configurations and tuned and untuned models (p > 0.05). Moreover, there was no significant statistical difference in computational training time for tuned and untuned models (p > 0.05). For sensor configuration 1, a support vector machine (SVM) model with a Gaussian rbf kernel performed the best (accuracy = 0.96), for sensor configuration 2, a multi-layered perceptron neural network (MLP-NN) model performed the best (accuracy = 0.98). Wearable inertial sensors can be used to accurately classify strike-type in boxing pad work, this means that cumbersome tasks such as video and notational analysis can be bypassed. Additionally, automated workload and performance monitoring of athletes throughout training camp is possible. Future investigations will evaluate the performance of this algorithm on a greater sample size and test the influence of impact window-size on prediction accuracy. Additionally, supervised machine learning models should be trained on data collected during sparring to see if high accuracy holds in a competition setting. This can help move closer towards automatic scoring in boxing.

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A Dynamic Convolutional Neural Network Based Shared-Bike Demand Forecasting Model

ACM Transactions on Intelligent Systems and Technology ◽

10.1145/3447988 ◽

2021 ◽

Vol 12 (6) ◽

pp. 1-24

Author(s):

Shaojie Qiao ◽

Nan Han ◽

Jianbin Huang ◽

Kun Yue ◽

Rui Mao ◽

...

Keyword(s):

Neural Network ◽

Machine Learning ◽

Convolutional Neural Network ◽

Prediction Accuracy ◽

Demand Forecasting ◽

Forecasting Model ◽

Learning Models ◽

Bike Sharing ◽

Demand Forecasting Model ◽

Machine Learning Models

Bike-sharing systems are becoming popular and generate a large volume of trajectory data. In a bike-sharing system, users can borrow and return bikes at different stations. In particular, a bike-sharing system will be affected by weather, the time period, and other dynamic factors, which challenges the scheduling of shared bikes. In this article, a new shared-bike demand forecasting model based on dynamic convolutional neural networks, called SDF , is proposed to predict the demand of shared bikes. SDF chooses the most relevant weather features from real weather data by using the Pearson correlation coefficient and transforms them into a two-dimensional dynamic feature matrix, taking into account the states of stations from historical data. The feature information in the matrix is extracted, learned, and trained with a newly proposed dynamic convolutional neural network to predict the demand of shared bikes in a dynamical and intelligent fashion. The phase of parameter update is optimized from three aspects: the loss function, optimization algorithm, and learning rate. Then, an accurate shared-bike demand forecasting model is designed based on the basic idea of minimizing the loss value. By comparing with classical machine learning models, the weight sharing strategy employed by SDF reduces the complexity of the network. It allows a high prediction accuracy to be achieved within a relatively short period of time. Extensive experiments are conducted on real-world bike-sharing datasets to evaluate SDF. The results show that SDF significantly outperforms classical machine learning models in prediction accuracy and efficiency.

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