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.
Quantum computation has the ability to revolutionize the treatment of patients. Quantum computing can help to detect diseases by identifying and forecasting malfunctions. But there's a threat associated here (i.e., healthcare data among the most popular cybercriminal targets, IoT devices notoriously lacking in effective safeguards, and quantum computers on the brink of an encryption/decryption breakthrough). Health agencies need a security prognosis and treatment plan as soon as possible. Healthcare companies recently worry more about the quantum security threats. The biggest threat of healthcare data breaches has come in the form of identity theft. There should be a strong mechanism to combat the security gaps in existing healthcare industry. If the healthcare data are available on the network, an attacker may try to modify, intercept, or even view this data stream. With the use of quantum security, the quantum state of these photons changes alert the security pros that someone is trying to breach the link.
One of the most challenging tasks in today’s era is to deliver personalized information to the user or group of users according to their preferences. The decision making process is used as a tool by the recommender systems to suggests various products and items. The goal of recommender system is to deliver germane information to the user based on their likings. In this research work, we study and compare the effect of similarity measures used in k-means clustering in movie recommender systems. Our proposed method used the sampling, PCA and k-means clustering to recommend movie from the MovieLens dataset. In the whole process, some similarity measures are used in k-means clustering such as Euclidean, Minkowski, Mahalanobis, Cosine similarity and Pearson correlation. The aim of our work is to study the effect of similar measures in movie recommender systems in terms of standard deviation (SD), mean absolute error (MAE), root mean square error (RMSE), t-value, Dunn Matrix, average similarity and computational time using publicly available MovieLens dataset. The results achieved from the experiments indicates that Cosine similarity is best technique in movie recommender system in terms of accuracy, efficiency and processing speed and also able to get MAE of 0.65, which is best between all similarity measures.
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