Euclidean distance stratified random sampling based clustering model for big data mining

Pandey, Kamlesh Kumar; Shukla, Diwakar

doi:10.1002/cmm4.1206

Cited by 2 publications

(2 citation statements)

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“…As not, all the channels contributed to the recognition of emotion, the existing channel selection methods have been analyzed [16,17], and the ES method is being chosen for the channel selection in the proposed work. Te efective technique used to recognize the original brain signals from various artifacts is the DWT, and it is one of the most widely used methods to decompose the original EEG signal into frequency bands that are functionally distinct such as delta (0.5-4 Hz), theta (4-8 Hz), alpha (8)(9)(10)(11)(12), beta (12)(13)(14)(15)(16)(17)(18)(19)(20)(21)(22)(23)(24)(25)(26)(27)(28)(29)(30), and gamma . Te DWT provides more efciency than other conventional methods in the separation of waves [18].…”

Section: Introductionmentioning

confidence: 99%

“…The traditional data mining algorithms suffer from computational deficiency, and sampling is an effective data reduction technique to reduce the computational cost and speed with high efficiency. Among the various random sampling methods, the stratified sampling technique suits the need of this work of dividing the available dataset into various strata and picking a random item from each group as items in a stratum will have common characteristics [ 26 ]. This sampling method is widely used in human research.…”

Section: Introductionmentioning

confidence: 99%

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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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