Fused CNN-LSTM deep learning emotion recognition model using electroencephalography signals.

The traditional machine learning-based emotion recognition models have shown effective performance for classifying Electroencephalography (EEG) based emotions. The different machine learning algorithms outperform the various EEG based emotion models for valence and arousal. But the downside is to devote numerous efforts to designing features from the given noisy signals which are also a very time-consuming process.

Learning-based classification of valence emotion from electroencephalography.

The neuroimaging research field has been revolutionized with the development of human cognitive functions without the use of brain pathways. To assist such systems, electroencephalography (EEG) based measures play an important role. In this study, the publicly available database of emotion analysis using physiological signals, has been used to identify the human emotions such as valence (positive/negative) from the given recorded EEG signals.

Pharmacokinetic modelling of microencapsulated metronidazole.

The aim of present study is to develop a pharmacokinetic model for microencapsulated metronidazole to predict drug absorption pattern in healthy human and validate this model internally. Metronidazole was microencapsulated into ethylcellulose shells followed by the conversion of these microcapsules into tablets. Dissolution study of tablets was conducted in 450 mL double distilled water, 0.