Abstract The field of electroencephalography (EEG) has made significant contributions to our understanding of the brain, our understanding of neurological diseases, and our ability to treat such diseases. Epileptic seizures, strokes, and even death can all be detected with the use of the electroencephalogram, a diagnostic technique used to record electrical activity in the brain. This research suggests using binary classification for automated epilepsy diagnosis. Patients' EEG signals are pre‐processed after being recorded. On the basis of the results of the feature extraction technique, the best traits are picked for further examination by means of a structured genetic algorithm. The EEG data are analysed and categorized as either seizure‐free or epileptic seizure‐related based on the assumption of feature optimization utilizing the support vector classifier. As a result, categorizing EEG signals is an ideal application for the suggested technique. For this purpose of accelerating the implementation of distributed computing, a CEHOC (Chaotic Elephant Herding Optimization based Classification) is used to classify the vast scope of various datasets. The results show that the CEHOC algorithm is more effective than previous versions. Precision, recall, F score, sensitivity, specificity, and accuracy are some of the metrics used to assess the effectiveness of the work provided here. The suggested work has a 99.3019% accuracy rate, a 98.2018% sensitivity rate, and a 99.1125% specificity rate. There was an F score of 99.3204%, a precision of 99.1019%, and a recall of 98.3015%. These numbers indicate that the planned action was successful.

Abstract The electroencephalography (EEG) signal is corrupted with some non-cerebral activities due to patient movement during signal measurement. These non-cerebral activities are termed as artifacts, which may diminish the superiority of acquired EEG signal statistics. The state of the art artifact elimination approaches applied canonical correlation analysis (CCA) for confiscating EEG motion artifacts accompanied by ensemble empirical mode decomposition (EEMD). An improved cascaded approach based on Gaussian elimination CCA (GECCA) and EEMD is applied to suppress EEG artifacts effectively. However, in a highly noisy environment, a novel addition of median filter before the GECCA algorithm is suggested for improving the accuracy of onslaught the EEG signal. The median filter is opted due to its edge preserving nature and speed. This proposed approach is appraised using efficacy grounds for instance Del signal to noise ratio, Lambda (λ), root mean square error and receiver operating characteristic (ROC) parameters and verified contrary to presently obtainable EEG artifacts exclusion methods. The primary concern is to improve the efficacy and precision of the proposed artifact elimination technique. The elapsed time is also calculated to evaluate the computation efficiency. Results show that the proposed algorithm is appropriate to be used as an addition to existing algorithms in use.

Security becomes a primary concern during sharing of information over the web. To overcome this problem, many security ontologies have been developed so far. The available security ontologies help to track data provenance and contain different aspects of data security like confidentiality, integrity, data availability, and access control. This paper provides a security ontology for establishing data provenance in the semantic web. The proposed ontology contains a comprehensive knowledge base of data security by consolidating all the available security ontologies and derives data provenance with annotations at the extensional level and thus lower maintenance cost. By this paper, analysts and researchers find a road map, an overview of what exists in terms of security ontologies.