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The accuracy of classifying motor imagery (MI) activities is a significant challenge when using brain–computer interfaces (BCIs). BCIs allow people with motor impairments to control external devices directly with their brains using electroencephalogram (EEG) patterns that translate brain activity into control signals. Many researchers have been working to develop MI-based BCI recognition systems using various time-frequency feature extraction and classification approaches. However, the existing systems still face challenges in achieving satisfactory performance due to large amount of non-discriminative and ineffective features. To get around these problems, we suggested a multiband decomposition-based feature extraction and classification method that works well, along with a strong feature selection method for MI tasks. Our method starts by splitting the preprocessed EEG signal into four sub-bands. In each sub-band, we then used a common spatial pattern (CSP) technique to pull out narrowband-oriented useful features, which gives us a high-dimensional feature vector. Subsequently, we utilized an effective feature selection method, Relief-F, which reduces the dimensionality of the final features. Finally, incorporating advanced classification techniques, we classified the final reduced feature vector. To evaluate the proposed model, we used the three different EEG-based MI benchmark datasets, and our proposed model achieved better performance accuracy than existing systems. Our model's strong points include its ability to effectively reduce feature dimensionality and improve classification accuracy through advanced feature extraction and selection methods. [ABSTRACT FROM AUTHOR]

The current research was undertaken to compare the proximate composition and antioxidant activity of different parts of green and ripe passion fruit. The proximate composition, bioactive compounds and antioxidant activity of different parts of passion fruit were analyzed by standard methods. According to the results, the ash content of the peel and seed of ripe passion fruit was higher than that of the green one, but only significant (P<0.05) for the seed. Besides, the crude fiber, carbohydrates, and protein content of the peel, pulp, and seed of ripe passion fruit were relatively higher (P<0.05) than those of the green one. At P<0.05, the fat content of green peel and pulp was higher than that of ripe, but the seed content of ripe was higher. The peel and pulp of green passion fruit contain the highest amounts of total polyphenol content (TPC) and total flavonoid content (TFC) than ripe passion fruit (P<0.05). The highest amount of TPC is contained in the seed of green passion fruit than in ripe fruit (P>0.05), but the seed of ripe fruit contains TFC almost twice as much as green fruit (P<0.05). It was also found that the peel and pulp of ripe passion fruit contain a higher percentage of total antioxidant activity (TAA) and vitamin C (P<0.05) than the pulp of a green one. The seed of the green one contains a slightly higher percentage of TAA than the seed of the ripe fruit (P<0.05), but vitamin C was opposite (P<0.05). It is concluded that the pulp, peel, and seed of green and ripe passion fruit have great potential as a health-promoting source that is normally underutilized. [ABSTRACT FROM AUTHOR]

Machine translation (MT) is the process of translating text from one language to another using bilingual data sets and grammatical rules. Recent works in the field of MT have popularized sequence-to-sequence models leveraging neural attention and deep learning. The success of neural attention models is yet to be construed into a robust framework for automated English-to-Bangla translation due to a lack of a comprehensive dataset that encompasses the diverse vocabulary of the Bangla language. In this study, we have proposed an English-to-Bangla MT system using an encoder-decoder attention model using the CCMatrix corpus. Our method shows that this model can outperform traditional SMT and RBMT models with a Bilingual Evaluation Understudy (BLEU) score of 15.68 despite being constrained by the limited vocabulary of the corpus. We hypothesize that this model can be used successfully for state-of-the-art machine translation with a more diverse and accurate dataset. This work can be extended further to incorporate several newer datasets using transfer learning techniques. [ABSTRACT FROM AUTHOR]