Wading corvus optimization based text generation using deep CNN and BiLSTM classifiers. (September 2022)

• Record Type: Journal Article
• Authors: Rathod, Vasundhara S.; Tiwari, Ashish; Kakde, Omprakash G.
• Is Part Of: Biomedical signal processing and control. Volume 78(2022)
• Publication Date: 2022-09
• Subjects: Deep CNN -- BiLSTM -- Feature extraction -- Wading Corvus optimization -- EEG signal; Signal processing -- Periodicals; Biomedical engineering -- Periodicals; Signal Processing, Computer-Assisted -- Periodicals; Image Processing, Computer-Assisted -- Periodicals; Biomedical Engineering -- Periodicals; 610.28
• DOI: 10.1016/j.bspc.2022.103969
• Languages: English
• ISSNs: 1746-8094

Abstract

Highlights: The highlights of the article are given below for your kind perusal. Kindly, consider and forward my article for further processes. This research mainly concentrates on the prediction of texts from the EEG signal from the brain and suggestion of the next letters through BiLSTM classifier. The data is firstcollected from the repository and the preprocessing is performed using band pass filter inorder to overcome the interference of the signals. The bands are separated according to their frequencies and then, the essential features, such as statistical features, common spatial pattern, and quadruple symmetric pattern are extracted using the feature extraction techniques. Finally, the text is predicted using the Wading Corvus optimization-based deep CNN classifier, where the deep CNN classifier is optimally tuned using the wading corvus optimization. The suggestion of other letters based on the previous search is predicted using the BiLSTM classifier. Abstract: Many individuals suffer fromlocked-in syndrome or motor neuron disorders, leading to a loss of capability to control their muscles except eye movement. Many researchers have contributed to perform communication through blinking of eyes using classification. This research mainly concentrates on the prediction of texts from the EEG signal from the brain and suggestion of the next letters through BiLSTM classifier. The data is firstcollected from the repository and preprocessed using band pass filter inorder to overcome the interference of the signals. The bands are separated according to their frequencies and then statistical features, common spatial pattern, and quadruple symmetric pattern are extracted using feature extraction techniques. Finally, the text is predicted using the Wading Corvus optimization-based deep CNNclassifier, where the deep CNN classifier is optimally tuned using the wading Corvusoptimization. The suggestion of other letters based on the previous search ispredicted using the BiLSTM classifier. The efficiency of the Wading Corvus optimization-based deep CNN classifier is proved by measuring the parameters, such as accuracy, precision and recall, which are reported to be93.893%, 93.358%, and 95.571%, respectively. The supremacy of the BiLSTM classifier is proved by measuring the training loss, and the classifier attains a loss of 0.262, which is low compared with the existing state-of-art methods.