Advancing Stress Detection Methodology with Deep Learning Techniques Targeting UX Evaluation in AAL Scenarios: Applying Embeddings for Categorical Variables

Physiological measurements have been widely used by researchers and practitioners in order to address the stress detection challenge. So far, various datasets for stress detection have been recorded and are available to the research community for testing and benchmarking. The majority of the stress-related available datasets have been recorded while users were exposed to intense stressors, such as songs, movie clips, major hardware/software failures, image datasets, and gaming scenarios. However, it remains an open research question if such datasets can be used for creating models that will effectively detect stress in different contexts. This paper investigates the performance of the publicly available physiological dataset named WESAD (wearable stress and affect detection) in the context of user experience (UX) evaluation. More specifically, electrodermal activity (EDA) and skin temperature (ST) signals from WESAD were used in order to train three traditional machine learning classifiers and a simple feed forward deep learning artificial neural network combining continues variables and entity embeddings. Regarding the binary classification problem (stress vs. no stress), high accuracy (up to 97.4%), for both training approaches (deep-learning, machine learning), was achieved. Regarding the stress detection effectiveness of the created models in another context, such as user experience (UX) evaluation, the results were quite impressive. More specifically, the deep-learning model achieved a rather high agreement when a user-annotated dataset was used for validation.

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Deep Learning for Laryngopharyngeal Reflux Diagnosis

Applied Sciences ◽

10.3390/app11114753 ◽

2021 ◽

Vol 11 (11) ◽

pp. 4753

Author(s):

Gen Ye ◽

Chen Du ◽

Tong Lin ◽

Yan Yan ◽

Jack Jiang

Keyword(s):

Deep Learning ◽

Speech Processing ◽

Data Augmentation ◽

Laryngopharyngeal Reflux ◽

Ph Monitoring ◽

Binary Classification ◽

Classification Problem ◽

Learning Approaches ◽

Learning Techniques ◽

Auc Value

(1) Background: Deep learning has become ubiquitous due to its impressive performance in various domains, such as varied as computer vision, natural language and speech processing, and game-playing. In this work, we investigated the performance of recent deep learning approaches on the laryngopharyngeal reflux (LPR) diagnosis task. (2) Methods: Our dataset is composed of 114 subjects with 37 pH-positive cases and 77 control cases. In contrast to prior work based on either reflux finding score (RFS) or pH monitoring, we directly take laryngoscope images as inputs to neural networks, as laryngoscopy is the most common and simple diagnostic method. The diagnosis task is formulated as a binary classification problem. We first tested a powerful backbone network that incorporates residual modules, attention mechanism and data augmentation. Furthermore, recent methods in transfer learning and few-shot learning were investigated. (3) Results: On our dataset, the performance is the best test classification accuracy is 73.4%, while the best AUC value is 76.2%. (4) Conclusions: This study demonstrates that deep learning techniques can be applied to classify LPR images automatically. Although the number of pH-positive images used for training is limited, deep network can still be capable of learning discriminant features with the advantage of technique.

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Predicting Takeover Success Using Machine Learning Techniques

Journal of Business & Economics Research (JBER) ◽

10.19030/jber.v10i10.7264 ◽

2012 ◽

Vol 10 (10) ◽

pp. 547

Author(s):

Mei Zhang ◽

Gregory Johnson ◽

Jia Wang

Keyword(s):

Machine Learning ◽

Learning Community ◽

Binary Classification ◽

Classification Problem ◽

Machine Learning Techniques ◽

Success Prediction ◽

Support Vector ◽

Font Size ◽

Network Support ◽

Learning Techniques

A takeover success prediction model aims at predicting the probability that a takeover attempt will succeed by using publicly available information at the time of the announcement. We perform a thorough study using machine learning techniques to predict takeover success. Specifically, we model takeover success prediction as a binary classification problem, which has been widely studied in the machine learning community. Motivated by the recent advance in machine learning, we empirically evaluate and analyze many state-of-the-art classifiers, including logistic regression, artificial neural network, support vector machines with different kernels, decision trees, random forest, and Adaboost. The experiments validate the effectiveness of applying machine learning in takeover success prediction, and we found that the support vector machine with linear kernel and the Adaboost with stump weak classifiers perform the best for the task. The result is consistent with the general observations of these two approaches.

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Predicting Anesthetic Infusion Events Using Machine Learning

10.21203/rs.3.rs-783161/v1 ◽

2021 ◽

Author(s):

Naoki Miyaguchi ◽

Koh Takeuchi ◽

Hisashi Kashima ◽

Mizuki Morita ◽

Hiroshi Morimatsu

Keyword(s):

Machine Learning ◽

Flow Rate ◽

Short Term Memory ◽

Binary Classification ◽

Classification Problem ◽

Clinical Findings ◽

Support Vector ◽

Learning Models ◽

Continuous Administration ◽

Machine Learning Models

Abstract Recently, research has been conducted to automatically control anesthesia using machine learning, with the aim of alleviating the shortage of anesthesiologists. In this study, we address the problem of predicting decisions made by anesthesiologists during surgery using machine learning; specifically, we formulate a decision making problem by increasing the flow rate at each time point in the continuous administration of analgesic remifentanil as a supervised binary classification problem. The experiments were conducted to evaluate the prediction performance using six machine learning models: logistic regression, support vector machine, random forest, LightGBM, artificial neural network, and long short-term memory (LSTM), using 210 case data collected during actual surgeries. The results demonstrated that when predicting the future increase in flow rate of remifentanil after 1 min, the model using LSTM was able to predict with scores of 0.659 for sensitivity, 0.732 for specificity, and 0.753 for ROC-AUC; this demonstrates the potential to predict the decisions made by anesthesiologists using machine learning. Furthermore, we examined the importance and contribution of the features of each model using shapley additive explanations—a method for interpreting predictions made by machine learning models. The trends indicated by the results were partially consistent with known clinical findings.

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Stress Detection using Machine Learning and Deep Learning

Journal of Physics Conference Series ◽

10.1088/1742-6596/1997/1/012019 ◽

2021 ◽

Vol 1997 (1) ◽

pp. 012019

Author(s):

Z. Zainudin ◽

S. Hasan ◽

S.M. Shamsuddin ◽

S. Argawal

Keyword(s):

Machine Learning ◽

Deep Learning ◽

Stress Detection

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Predicting mortality among patients with liver cirrhosis in electronic health records with machine learning

PLoS ONE ◽

10.1371/journal.pone.0256428 ◽

2021 ◽

Vol 16 (8) ◽

pp. e0256428

Author(s):

Aixia Guo ◽

Nikhilesh R. Mazumder ◽

Daniela P. Ladner ◽

Randi E. Foraker

Keyword(s):

Machine Learning ◽

Liver Cirrhosis ◽

Deep Learning ◽

Risk Prediction ◽

The United States ◽

Meld Score ◽

Receiver Operating Curve ◽

Categorical Variables ◽

Electronic Health Record Data ◽

Electronic Health

Objective Liver cirrhosis is a leading cause of death and effects millions of people in the United States. Early mortality prediction among patients with cirrhosis might give healthcare providers more opportunity to effectively treat the condition. We hypothesized that laboratory test results and other related diagnoses would be associated with mortality in this population. Our another assumption was that a deep learning model could outperform the current Model for End Stage Liver disease (MELD) score in predicting mortality. Materials and methods We utilized electronic health record data from 34,575 patients with a diagnosis of cirrhosis from a large medical center to study associations with mortality. Three time-windows of mortality (365 days, 180 days and 90 days) and two cases with different number of variables (all 41 available variables and 4 variables in MELD-NA) were studied. Missing values were imputed using multiple imputation for continuous variables and mode for categorical variables. Deep learning and machine learning algorithms, i.e., deep neural networks (DNN), random forest (RF) and logistic regression (LR) were employed to study the associations between baseline features such as laboratory measurements and diagnoses for each time window by 5-fold cross validation method. Metrics such as area under the receiver operating curve (AUC), overall accuracy, sensitivity, and specificity were used to evaluate models. Results Performance of models comprising all variables outperformed those with 4 MELD-NA variables for all prediction cases and the DNN model outperformed the LR and RF models. For example, the DNN model achieved an AUC of 0.88, 0.86, and 0.85 for 90, 180, and 365-day mortality respectively as compared to the MELD score, which resulted in corresponding AUCs of 0.81, 0.79, and 0.76 for the same instances. The DNN and LR models had a significantly better f1 score compared to MELD at all time points examined. Conclusion Other variables such as alkaline phosphatase, alanine aminotransferase, and hemoglobin were also top informative features besides the 4 MELD-Na variables. Machine learning and deep learning models outperformed the current standard of risk prediction among patients with cirrhosis. Advanced informatics techniques showed promise for risk prediction in patients with cirrhosis.

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Psychophysiological Indicators for Modeling User Experience in Interactive Digital Entertainment

Sensors ◽

10.3390/s19050989 ◽

2019 ◽

Vol 19 (5) ◽

pp. 989 ◽

Cited By ~ 3

Author(s):

Martin Čertický ◽

Michal Čertický ◽

Peter Sinčák ◽

Gergely Magyar ◽

Ján Vaščák ◽

...

Keyword(s):

Machine Learning ◽

User Experience ◽

Electrodermal Activity ◽

Machine Learning Algorithms ◽

Training Data ◽

Consumer Experience ◽

Time Data ◽

Psychophysiological Measures ◽

Digital Entertainment ◽

Group Interviews

Analyses of user experience in the electronic entertainment industry currently rely on self-reporting methods, such as surveys, ratings, focus group interviews, etc. We argue that self-reporting alone carries inherent problems—mainly the misinterpretation and temporal delay during longer experiments—and therefore, should not be used as a sole metric. To tackle this problem, we propose the possibility of modeling consumer experience using psychophysiological measures and demonstrate how such models can be trained using machine learning methods. We use a machine learning approach to model user experience using real-time data produced by the autonomic nervous system and involuntary psychophysiological responses. Multiple psychophysiological measures, such as heart rate, electrodermal activity, and respiratory activity, have been used in combination with self-reporting to prepare training sets for machine learning algorithms. The training data was collected from 31 participants during hour-long experiment sessions, where they played multiple video-games. Afterwards, we trained and compared the results of four different machine learning models, out of which the best one produced ∼96% accuracy. The results suggest that psychophysiological measures can indeed be used to assess the enjoyment of digital entertainment consumers.

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Comparison of Deep-Learning and Conventional Machine-Learning Methods for the Automatic Recognition of the Hepatocellular Carcinoma Areas from Ultrasound Images

Sensors ◽

10.3390/s20113085 ◽

2020 ◽

Vol 20 (11) ◽

pp. 3085 ◽

Cited By ~ 2

Author(s):

Raluca Brehar ◽

Delia-Alexandrina Mitrea ◽

Flaviu Vancea ◽

Tiberiu Marita ◽

Sergiu Nedevschi ◽

...

Keyword(s):

Machine Learning ◽

Hepatocellular Carcinoma ◽

Deep Learning ◽

Binary Classification ◽

Classification Performance ◽

Ultrasound Images ◽

Learning Methods ◽

Machine Learning Methods ◽

Segmentation Accuracy ◽

Conventional Machine

The emergence of deep-learning methods in different computer vision tasks has proved to offer increased detection, recognition or segmentation accuracy when large annotated image datasets are available. In the case of medical image processing and computer-aided diagnosis within ultrasound images, where the amount of available annotated data is smaller, a natural question arises: are deep-learning methods better than conventional machine-learning methods? How do the conventional machine-learning methods behave in comparison with deep-learning methods on the same dataset? Based on the study of various deep-learning architectures, a lightweight multi-resolution Convolutional Neural Network (CNN) architecture is proposed. It is suitable for differentiating, within ultrasound images, between the Hepatocellular Carcinoma (HCC), respectively the cirrhotic parenchyma (PAR) on which HCC had evolved. The proposed deep-learning model is compared with other CNN architectures that have been adapted by transfer learning for the ultrasound binary classification task, but also with conventional machine-learning (ML) solutions trained on textural features. The achieved results show that the deep-learning approach overcomes classical machine-learning solutions, by providing a higher classification performance.

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An Improved Binary Relevance Algorithm for Multi-Label Classification

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.536-537.394 ◽

2014 ◽

Vol 536-537 ◽

pp. 394-398 ◽

Cited By ~ 2

Author(s):

Tao Guo ◽

Gui Yang Li

Keyword(s):

Machine Learning ◽

Binary Classification ◽

Classification Problem ◽

Learning Task ◽

Binary Classifier ◽

Classification Problems ◽

Binary Relevance ◽

Label Information ◽

Benchmark Datasets

Multi-label classification (MLC) is a machine learning task aiming to predict multiple labels for a given instance. The widely known binary relevance (BR) learns one classifier for each label without considering the correlation among labels. In this paper, an improved binary relevance algorithm (IBRAM) is proposed. This algorithm is derived form binary relevance method. It sets two layers to decompose the multi-label classification problem into L independent binary classification problems respectively. In the first layer, binary classifier is built one for each label. In the second layer, the label information from the first layer is fully used to help to generate final predicting by consider the correlation among labels. Experiments on benchmark datasets validate the effectiveness of proposed approach against other well-established methods.

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Characterization of Symbolic Rules Embedded in Deep DIMLP Networks: A Challenge to Transparency of Deep Learning

Journal of Artificial Intelligence and Soft Computing Research ◽

10.1515/jaiscr-2017-0019 ◽

2017 ◽

Vol 7 (4) ◽

pp. 265-286 ◽

Cited By ~ 41

Author(s):

Guido Bologna ◽

Yoichi Hayashi

Keyword(s):

Neural Networks ◽

Deep Learning ◽

Predictive Accuracy ◽

Binary Classification ◽

Classification Problem ◽

Rule Extraction ◽

Breast Cancer Dataset ◽

Cancer Dataset ◽

Digit Recognition ◽

Rule Complexity

AbstractRule extraction from neural networks is a fervent research topic. In the last 20 years many authors presented a number of techniques showing how to extract symbolic rules from Multi Layer Perceptrons (MLPs). Nevertheless, very few were related to ensembles of neural networks and even less for networks trained by deep learning. On several datasets we performed rule extraction from ensembles of Discretized Interpretable Multi Layer Perceptrons (DIMLP), and DIMLPs trained by deep learning. The results obtained on the Thyroid dataset and the Wisconsin Breast Cancer dataset show that the predictive accuracy of the extracted rules compare very favorably with respect to state of the art results. Finally, in the last classification problem on digit recognition, generated rules from the MNIST dataset can be viewed as discriminatory features in particular digit areas. Qualitatively, with respect to rule complexity in terms of number of generated rules and number of antecedents per rule, deep DIMLPs and DIMLPs trained by arcing give similar results on a binary classification problem involving digits 5 and 8. On the whole MNIST problem we showed that it is possible to determine the feature detectors created by neural networks and also that the complexity of the extracted rulesets can be well balanced between accuracy and interpretability.

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ABCnet : Self-Attention based Atom, Bond Message Passing Network for Predicting Drug-Target Interaction

10.1101/2021.12.27.474154 ◽

2021 ◽

Author(s):

Segyu Lee ◽

Junil Bang ◽

Sungeun Hong ◽

Woojung Jang

Keyword(s):

Deep Learning ◽

Drug Development ◽

Binding Affinity ◽

Message Passing ◽

Drug Target ◽

Binary Classification ◽

Classification Problem ◽

Molecular Structures ◽

Target Interaction ◽

Molecular Features

Drug-target interaction (DTI) is a methodology for predicting the binding affinity between a compound and a target protein, and a key technology in the derivation of candidate substances in drug discovery. As DTI experiments have progressed for a long time, a substantial volume of chemical, biomedical, and pharmaceutical data have accumulated. This accumulation of data has occurred contemporaneously with the advent of the field of big data, and data-based machine learning methods could significantly reduce the time and cost of drug development. In particular, the deep learning method shows potential when applied to the fields of vision and speech recognition, and studies to apply deep learning to various other fields have emerged. Research applying deep learning is underway in drug development, and among various deep learning models, a graph-based model that can effectively learn molecular structures has received more attention as the SOTA in experimental results were achieved. Our study focused on molecular structure information among graph-based models in message passing neural networks. In this paper, we propose a self-attention-based bond and atom message passing neural network which predicts DTI by extracting molecular features through a graph model using an attention mechanism. Model validation experiments were performed after defining binding affinity as a regression and classification problem: binary classification to predict the presence or absence of binding to the drug-target, and regression to predict binding affinity to the drug-target. Classification was performed with BindingDB, and regression was performed with the DAVIS dataset. In the classification problem, ABCnet showed higher performance than MPNN, as it does in the existing study, and in regression, the potential of ABCnet was checked compared to that of SOTA. Experiments indicated that in binary classification, ABCnet has an average performance improvement of 1% than other MPNN on the DTI task, and in regression, ABCnet has CI and performance degradation between 0.01 and 0.02 compared to SOTA.

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