TreeNet analysis of human stress behavior using socio-mobile data

Padmaja, B.; Prasad, V. Ramachandra; Sunitha, K. V. N.

doi:10.1186/s40537-016-0054-3

Cited by 8 publications

(4 citation statements)

References 26 publications

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“…In this respect, it can be said that the TreeNet method produces more unbiased (Robust) results and performs better than the Random Forest method. Indeed, Padmaja et al (2016) reported in their studies that the TreeNet method was more successful than the Random forest method. In the same vein, in the study conducted by Subasi et al (2022), it was reported that Stochastic Gradient Boosting method (another literature use of the TreeNet method) performed better compared to the Random Forest, Support Vector Machines, Knearest neighbours algorithm and artificial neural networks for RMSE, MSE, MAE and RAE performance criteria.…”

Section: Discussionmentioning

confidence: 99%

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A comparative study of ensemble methods in the field of education: Bagging and Boosting algorithms

ŞEVGİN

2023

International Journal of Assessment Tools in Education

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This study aims to conduct a comparative study of Bagging and Boosting algorithms among ensemble methods and to compare the classification performance of TreeNet and Random Forest methods using these algorithms on the data extracted from ABİDE application in education. The main factor in choosing them for analyses is that they are Ensemble methods combining decision trees via Bagging and Boosting algorithms and creating a single outcome by combining the outputs obtained from each of them. The data set consists of mathematics scores of ABİDE (Academic Skills Monitoring and Evaluation) 2016 implementation and various demographic variables regarding students. The study group involves 5000 students randomly recruited. On the deletion of loss data and assignment procedures, this number decreased to 4568. The analyses showed that the TreeNet method performed more successfully in terms of classification accuracy, sensitivity, F1-score and AUC value based on sample size, and the Random Forest method on specificity and accuracy. It can be alleged that the TreeNet method is more successful in all numerical estimation error rates for each sample size by producing lower values compared to the Random Forest method. When comparing both analysis methods based on ABİDE data, considering all the conditions, including sample size, cross validity and performance criteria following the analyses, TreeNet can be said to exhibit higher classification performance than Random Forest. Unlike a single classifier or predictive method, the classification or prediction of multiple methods by using Boosting and Bagging algorithms is considered important for the results obtained in education.

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Section: Discussionmentioning

confidence: 99%

“…The TreeNet method is based on stochastic gradient boosting algorithm to determine the weights used in the training and classification phases of the incremental iteration (Padmaja et al, 2016). Stochastic gradient boosting, developed by Friedman (2002), is used to address a regression task by optimizing the mean squared error.…”

Section: Treenetmentioning

confidence: 99%

A comparative study of ensemble methods in the field of education: Bagging and Boosting algorithms

ŞEVGİN

2023

International Journal of Assessment Tools in Education

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“…without users elucidation effort. Padmaja et al (2016a;Sunitha et al, 2018;Padmaja et al, 2016b;2016c;2018c) given approaches, for activity recognition in various environments like WSNs, IoT etc. and analyzes the challenges involved in application to practical scenarios.…”

Section: Literature Reviewmentioning

confidence: 99%

Distributed and Parallel Decision Forest for Human Activities Prediction: Experimental Analysis on HAR-Smartphones Dataset

Padmaja¹,

Vaddella²,

Sunitha³

2019

Journal of Computer Science

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Sensor-based human motion detection requires the subtle amount of knowledge about various human activities from fitted sensor observations and readings. The prevalent pattern recognition methodologies have made immense progress over recent years. Nonetheless, these kind of methods usually rely on the particular heuristic variable extraction, which could inhibit generalization realization. This paper presents a distributed and parallel decision forest approach for modeling the Human Activity Recognition Using Smartphones Data. We made an attempt to achieve an optimal generalization performance with possible reduction in overfitting. Later, we compared the performance of proposed procedure with some existing approaches. It is observed that our adopted procedure outperforms with comparatively better statistical performance measures. It also gained 4.7x speed up in computation.

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“…There are various methods in existence to analyze stress which includes analysis of stress in voice [ 5 ], detection of stress using image processing which is a system that detects stress by analyzing facial expression [ 6 ], and analysis of human stress by investigating mobile phones that is a design of collecting of information or data from smartphones, surveys, and call logs [ 7 ]. The traditional methods using the EEG signals have numerous drawbacks which include external factors such as sweating, room temperature, and invasive procedure.…”

Section: Introductionmentioning

confidence: 99%

An Efficient AP-ANN-Based Multimethod Fusion Model to Detect Stress through EEG Signal Analysis

K¹,

Jayanthy²

2022

Computational Intelligence and Neuroscience

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Stress is a universal emotion that every human experiences daily. Psychologists say stress may lead to heart attack, depression, hypertension, strokes, or even sudden death. Many technical explorations like stress detection through facial expression, speech, text, physical behaviors, etc., were explored, but no consensus has been reached on the best method. The advancement in biomedical engineering yielded a rapid development of electroencephalogram (EEG) signal analysis that has inspired the idea of a multimethod fusion approach for the first time which employs multiple techniques such as discrete wavelet transform (DWT) for de-noising, adaptive synthetic sampling (ADASYN) for class balancing, and affinity propagation (AP) as a stratified sampling model along with the artificial neural network (ANN) as the classifier model for human emotion classification. From the EEG recordings of the DEAP dataset, the artifacts are removed, the signal is decomposed using a DWT, and features are extracted and fused to form the feature vector. As the dataset is high-dimensional, feature selection is done and ADASYN is used to address the imbalance of classes resulting in large-scale data. The innovative idea of the proposed system is to perform sampling using affinity propagation as a stratified sampling-based clustering algorithm as it determines the number of representative samples automatically which makes it superior to the K-Means, K-Medoid, that requires the K-value. Those samples are used as inputs to various classification models, the comparison of the AP-ANN, AP-SVM, and AP-RF is done, and their most important five performance metrics such as accuracy, precision, recall, F1-score, and specificity were compared. From our experiment, the AP-ANN model provides better accuracy of 86.8% and greater precision of 85.7%, a higher F1 score of 84.9%, a recall rate of 84.1%, and a specificity value of 89.2% which altogether provides better results than the other existing algorithms.

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TreeNet analysis of human stress behavior using socio-mobile data

Cited by 8 publications

References 26 publications

A comparative study of ensemble methods in the field of education: Bagging and Boosting algorithms

A comparative study of ensemble methods in the field of education: Bagging and Boosting algorithms

Distributed and Parallel Decision Forest for Human Activities Prediction: Experimental Analysis on HAR-Smartphones Dataset

An Efficient AP-ANN-Based Multimethod Fusion Model to Detect Stress through EEG Signal Analysis

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