Hybrid ensemble learning framework for epileptic seizure detection using electroencephalograph signals

Rachappa, Chetana; Kapanaiah, Mahantesh; Nagaraju, Vidhyashree

doi:10.11591/ijeecs.v28.i3.pp1502-1509

Cited by 3 publications

(3 citation statements)

References 20 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Furthermore, the XGBoost algorithm was selected for pregnancy risk monitoring with 96% accuracy [44] whereas an improvement of the proposed approach combining CNN and XGBoost methodology was proposed for renal stone diagnosis [45], breast cancer detection [46] and image classification [47] with accuracies of 99.5 %. Finally, feature selection combined with ensemble learning was proposed for epileptic seizure detection and classification from electroencephalogram signals with an effectiveness of 96% [48], [49]. Similarly, feature selection and ensemble learning were additionally proposed in the economic sector.…”

Section: Resultsmentioning

confidence: 99%

Predictive maintenance framework for assessing health state of centrifugal pumps

Mallioris,

Diamantis,

Bialas

et al. 2024

IJ-AI

View full text Add to dashboard Cite

Combined with advances in sensing technologies and big data analytics, critical information can be extracted from continuous production processes for predicting the health state of equipment and safeguarding upcoming failures. This research presents a methodology for applying predictive maintenance (PdM) solutions and showcases a PdM application for health state prediction and condition monitoring, increasing the safety and productivity of centrifugal pumps for a sustainable and resilient PdM ecosystem. Measurements depicting the healthy and maintenance-prone stages of two centrifugal pumps were collected on the university campus. The dataset consists of 5,118 records and includes both running and standstill values. Additionally, Spearman statistical analysis was conducted to measure the correlation of collected measurements with the predicted output of machine conditions and select the most appropriate features for model optimization. Several machine learning (ML) algorithms, namely random forest (RF), Naïve Bayes, support vector machines (SVM), and <a name="_Hlk137498016"></a>extreme gradient boosting (XGBoost) were analyzed and evaluated during the data mining process. The results indicated the effectiveness and efficiency of XGBoost for the health state prediction of centrifugal pumps. The contribution of this research is to propose an effective framework collectong multistage health data for PdM applications and showcase its effectiveness in a real-world use case.

show abstract

Section: Resultsmentioning

confidence: 99%

Predictive maintenance framework for assessing health state of centrifugal pumps

Mallioris,

Diamantis,

Bialas

et al. 2024

IJ-AI

View full text Add to dashboard Cite

show abstract

“…It becomes tedious to construct a generic technique to obtain high sensitivity [20]. In [21] a hybrid ensemble learning framework that systematically combines preprocessing methods with ensemble machine learning algorithms specifically, principal component analysis (PCA) and t-distributed stochastic neighbor embedding (t-SNE) combined along k-means clustering followed by ensemble learning such as extreme gradient boosting algorithms (XGBoost) and random forest is considered. However, in [22], using 13 layers, deep CNN architecture is considered.…”

Section: Literature Surveymentioning

confidence: 99%

Application of Conv-1D and Bi-LSTM to Classify and Detect Epilepsy in EEG Data

R¹,

Rao²,

Mahantesh³

2023

IJACSA

View full text Add to dashboard Cite

EEG is used to study the electrical changes in the brain and can derive a conclusion as epileptic or not, using an automated method for accurate detection of seizures. Deep learning, a technique ahead of machine learning tools, can selfdiscover related data for the detection and classification of EEG analysis. Our work focuses on deep neural network architecture to visualize the temporal dependencies in EEG signals. Algorithms and models based on Deep Learning techniques like Conv1D, Conv1D + LSTM, and Conv1D + Bi-LSTM for binary and multiclass classification. Convolution Neural Networks can spontaneously extract and learn features independently in the multichannel time-series EEG signals. Long Short-Term Memory (LSTM) network, with its selective memory retaining capability, Fully Connected (FC) layer, and softmax activation, discover hidden sparse features from EEG signals and predicts labels as output. Two independent LSTM networks combine to form Bi-LSTM in opposite directions and appreciate added visibility to upcoming information to provide efficient work contrary to previous methods. Long-term EEG recordings on the Bonn EEG database, Hauz Khas epileptic database, and Epileptic EEG signals from Spandana Hospital, Bangalore, assess performance. Metrics like precision, recall, f1-score, and support exhibit an improvement over traditional ML algorithms evaluated in the literature.

show abstract

“…To identify and categorize different plant diseases using the current model, human intervention is necessary due to several factors, including a lengthy training period, high storage costs, and high computational costs. [3]. Experts examine plants continuously over a long period of time in order to manually observe any infections that may be present.…”

Section: Introductionmentioning

confidence: 99%

Implementation of Deep CNN Model for the Detection of Plant Leaf Disease

Deepa,

Vijayanand,

Danesh

et al. 2023

IJRITCC

View full text Add to dashboard Cite

The potato is the most important tuber crop in the world, and it is grown in about 125 different nations. Potato is the crop that is most commonly consumed by a billion people worldwide, virtually every day, behind rice and wheat. However, a number of bacterial and fungal diseases are causing the potato crop's quality and yield to decline. Potato Leaf diseases must be promptly identified and prevented to increase production. Various researchers look for solutions to protect plants instead of traditional processes which take more time. Recent technological developments have thrown up many alternates to traditional methods which are labour intensive. The application of AlexNet model Deep Convolutional Neural Network(CNN) to recognise diseases in potato plants avoids the disadvantages of selecting disease spot features artificially and makes more objective the plant disease feature extraction. It improves research efficiency and speeds up technology transformation. Accuracies ranging from 85% - to 95% were obtained using AlexNet model Deep.

show abstract

Hybrid ensemble learning framework for epileptic seizure detection using electroencephalograph signals

Cited by 3 publications

References 20 publications

Predictive maintenance framework for assessing health state of centrifugal pumps

Predictive maintenance framework for assessing health state of centrifugal pumps

Application of Conv-1D and Bi-LSTM to Classify and Detect Epilepsy in EEG Data

Implementation of Deep CNN Model for the Detection of Plant Leaf Disease

Contact Info

Product

Resources

About