Identification of
            
              Lycium barbarum
            
            varieties based on hyperspectral imaging technique and competitive adaptive reweighted sampling
            ‐
            whale optimization algorithm
            ‐
            support vector machine

The dataset distribution of actual logging is asymmetric, as most logging data are unlabeled. With the traditional classification model, it is hard to predict the oil and gas reservoir accurately. Therefore, a novel approach to the oil layer recognition model using the improved whale swarm algorithm (WOA) and semi-supervised support vector machine (S3VM) is proposed in this paper. At first, in order to overcome the shortcomings of the Whale Optimization Algorithm applied in the parameter-optimization of the S3VM model, such as falling into a local optimization and low convergence precision, an improved WOA was proposed according to the adaptive cloud strategy and the catfish effect. Then, the improved WOA was used to optimize the kernel parameters of S3VM for oil layer recognition. In this paper, the improved WOA is used to test 15 benchmark functions of CEC2005 compared with five other algorithms. The IWOA–S3VM model is used to classify the five kinds of UCI datasets compared with the other two algorithms. Finally, the IWOA–S3VM model is used for oil layer recognition. The result shows that (1) the improved WOA has better convergence speed and optimization ability than the other five algorithms, and (2) the IWOA–S3VM model has better recognition precision when the dataset contains a labeled and unlabeled dataset in oil layer recognition.

Download Full-text

Whale optimization algorithm fused with SVM to detect stress in EEG signals

Intelligent Decision Technologies ◽

10.3233/idt-200047 ◽

2021 ◽

Vol 15 (1) ◽

pp. 87-97

Author(s):

Richa Gupta ◽

M. Afshar Alam ◽

Parul Agarwal

Keyword(s):

Optimization Algorithm ◽

Whale Optimization Algorithm ◽

Humpback Whales ◽

Support Vector ◽

Eeg Signals ◽

Hunting Behavior ◽

Positron Emission ◽

Whale Optimization ◽

Signal Activity ◽

Work Done

Identifying stress and its level has always been a challenging area for researchers. A lot of work is going on around the world on the same. An attempt has been made by the authors in this paper as they present a methodology for detecting stress in EEG signals. Electroencephalogram (EEG) is commonly used to acquire brain signal activity. Though there exist other techniques to extract the same like Functional magnetic resonance imaging (fMRI), positron emission tomography (PET) we have used EEG as it is economical. We have used an open-source dataset for EEG data. Various images are used as the target stressor for collecting EEG signals. After feature selection and extraction, a support vector machine (SVM) with a whale optimization algorithm (WOA) in its kernel function for classification is used. WOA is a bio-inspired meta-heuristic algorithm, based on the hunting behavior of humpback whales. Using this method, we had obtained 91% accuracy for detecting the stress. The paper also compared the previous work done in detecting stress with the work proposed in this paper.

Download Full-text