Efficient healthcare service based on Stacking Ensemble

Joo, Yunsang; Lee, Seungwon; Kim, Hyoungju; Kim, Pankoo; Hwang, Seong Oun; Choi, Chang

doi:10.1145/3440943.3444727

Cited by 2 publications

(5 citation statements)

References 7 publications

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“…We utilized poincaré plots to display and evaluate heart rate variability (HRV) normality, excluding those with noisy heart rate patterns and assessing heart health [ 55 ]. The standard deviation of the instantaneous beat-to-beat NN interval variability (minor axis of the SD1), the standard deviation of the continuous long-term RR interval variability (major-axis of SD2), and the axis ratio (SD2/SD1) based on the analysis of this plot [ 54 ]. A higher or lower heart rate variability (HRV) is determined by the ratio (SD1/SD2), with a higher proportion indicating excellent health and a lower rate indicating poor health.…”

Section: Resultsmentioning

confidence: 99%

“…And SDNN is the standard deviation of the NNI series(). Thus, a higher heart rate of HRV or a lower heart rate of HRV depends on (SD1/SD2) [ 53 ] We also derive frequency domain that indicates the power spectrum of order 12 by integration of low frequency (LF) heartbeats (0.04 to 0.15 Hz) and high-frequency (HF) (0.15 Hz to 0.4 Hz) [ 54 ]. For Motion: We combined the X, Y, and Z characteristics into a single component called Motion.…”

Section: Methodsmentioning

confidence: 99%

“…We also derive frequency domain that indicates the power spectrum of order 12 by integration of low frequency (LF) heartbeats (0.04 to 0.15 Hz) and high-frequency (HF) (0.15 Hz to 0.4 Hz) [ 54 ].…”

Section: Methodsmentioning

confidence: 99%

“…Finally, Stacking consists of training a meta-model to produce outputs based on the outputs returned by lower-layer weak learners as shown in Figure 8 [ 54 ].…”

Section: Methodsmentioning

confidence: 99%

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Evaluating Ensemble Learning Methods for Multi-Modal Emotion Recognition Using Sensor Data Fusion

Younis

Zaki

Kanjo

et al. 2022

Sensors

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Automatic recognition of human emotions is not a trivial process. There are many factors affecting emotions internally and externally. Expressing emotions could also be performed in many ways such as text, speech, body gestures or even physiologically by physiological body responses. Emotion detection enables many applications such as adaptive user interfaces, interactive games, and human robot interaction and many more. The availability of advanced technologies such as mobiles, sensors, and data analytics tools led to the ability to collect data from various sources, which enabled researchers to predict human emotions accurately. Most current research uses them in the lab experiments for data collection. In this work, we use direct and real time sensor data to construct a subject-independent (generic) multi-modal emotion prediction model. This research integrates both on-body physiological markers, surrounding sensory data, and emotion measurements to achieve the following goals: (1) Collecting a multi-modal data set including environmental, body responses, and emotions. (2) Creating subject-independent Predictive models of emotional states based on fusing environmental and physiological variables. (3) Assessing ensemble learning methods and comparing their performance for creating a generic subject-independent model for emotion recognition with high accuracy and comparing the results with previous similar research. To achieve that, we conducted a real-world study “in the wild” with physiological and mobile sensors. Collecting the data-set is coming from participants walking around Minia university campus to create accurate predictive models. Various ensemble learning models (Bagging, Boosting, and Stacking) have been used, combining the following base algorithms (K Nearest Neighbor KNN, Decision Tree DT, Random Forest RF, and Support Vector Machine SVM) as base learners and DT as a meta-classifier. The results showed that, the ensemble stacking learner technique gave the best accuracy of 98.2% compared with other variants of ensemble learning methods. On the contrary, bagging and boosting methods gave (96.4%) and (96.6%) accuracy levels respectively.

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Section: Resultsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

“…Finally, Stacking consists of training a meta-model to produce outputs based on the outputs returned by lower-layer weak learners as shown in Figure 8 [ 54 ].…”

Section: Methodsmentioning

confidence: 99%

See 2 more Smart Citations

Evaluating Ensemble Learning Methods for Multi-Modal Emotion Recognition Using Sensor Data Fusion

Younis

Zaki

Kanjo

et al. 2022

Sensors

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show abstract

“…CatBoost implements the gradient boosting algorithm that utilizes an ordered boosting approach. It is built upon the foundation of the gradient-boosting algorithm and employs oblivious decision trees as base predictors [14]. It automatically handles missing values and performs internal categorical feature encoding, simplifying data preparation.…”

Section: Catboost Classifiermentioning

confidence: 99%

An empirically based object-oriented testing using Machine learning

Sindhu,

Peruri,

Yalavarthi

2024

EAI Endorsed Trans IoT

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INTRODUCTION: The rapid growth of machine learning has the potential to revolutionize various industries and applications by automating complex tasks and enhancing efficiency. Effective software testing is crucial for ensuring software quality and minimizing resource expenses in software engineering. Machine learning techniques play a vital role in software testing by aiding in test case prioritization, predicting software defects, and analyzing test results. OBJECTIVES: The primary objective of this study is to explore the use of machine learning algorithms for software defect prediction. METHODS: Machine Learning models including Random Forest Classifier, Logistic Regression, K Nearest Neighbors, Gradient Boosting Classifiers, Catboost Classifier, and Convolutional Neural Networks have been employed for the study. The dataset includes a wide range of features relevant to software defect prediction and evaluates the performance of different prediction models. The study also focussed on developing hybrid models using stacking classifiers, which combine multiple individual models to improve accuracy. RESULTS: The experimental results show that the hybrid models combining CatBoost and Convolutional Neural Network have outperformed individual models, achieving the highest accuracy of 89.5%, highlighting the effectiveness of combining machine learning algorithms for software defect prediction. CONCLUSION: In conclusion, this study sheds light on the pivotal role of machine learning in enhancing software defect prediction.

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Efficient healthcare service based on Stacking Ensemble

Cited by 2 publications

References 7 publications

Evaluating Ensemble Learning Methods for Multi-Modal Emotion Recognition Using Sensor Data Fusion

Evaluating Ensemble Learning Methods for Multi-Modal Emotion Recognition Using Sensor Data Fusion

An empirically based object-oriented testing using Machine learning

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