Blockchain-based online education content ranking.

Nowadays, internet technology plays a vital role in all the fields of our daily lives ranging from the world economy, professional careers, higher education, and almost all the spheres that are deeply impacted. In the current situation, due to COVID19, the dependence on the Internet for almost everything, including learning, getting daily needs, etc., is heavily dependent on the Internet.

A case report of extensive cerebral venous sinus thrombosis: a sequela of COVID-19?

Coronavirus disease 2019 (COVID-19) is an infectious respiratory disease that is often the trigger for thrombotic complications. Cerebral venous sinus thrombosis (CVST) represents a small percentage of strokes, frequently proving to be a diagnostic challenge. We report a 31-year-old lady presenting with a persistent headache, 18 weeks after a mild COVID-19 illness.

Hybrid control approaches for hands-free high level human-computer interface-a review.

For more than a decade, more number of human-machine interfaces had been developed by various combination of user inputs such as speech, hand and head gestures, eye gaze and body movements, etc. And many research issues have been addressed, including facial expression recognition, human emotion analysis, speech recognition/synthesis, human-computer interaction, virtual reality and augmented reality interaction, etc. As a result, the development of a hybrid approach becomes a central issue for hands-free high-level human computer, to help elderly and disabled people.

A review on the pattern detection methods for epilepsy seizure detection from EEG signals.

Over several years, research had been conducted for the detection of epileptic seizures to support an automatic diagnosis system to comfort the clinicians' encumbrance. In this regard, a number of research papers have been published for the identification of epileptic seizures. A thorough review of all these papers is required.

Evaluation of time domain features using best feature subsets based on mutual information for detecting epilepsy.

In this pattern recognition study of detecting epilepsy, the first time the authors have attempted to use time domain (TD) features such as waveform length (WL), number of zero-crossings (ZC) and number of slope sign changes (SSC) which are extracted from the discrete wavelet transform (DWT) for the detecting the epilepsy for University of Bonn datasets and real-time clinical data. The performance of these TD features is studied along with mean absolute value (MAV) which has been attempted by other researchers. The performance of the TD features derived from DWT is studied using naive Bayes (NB) and support vector machines (SVM) for five different datasets from University of Bonn with 14 different combinations datasets and 24 patients datasets from Christian Medical College and Hospital (CMCH), India database.