Offloading the computational complexity of transfer learning with generic features.

Deep learning approaches are generally complex, requiring extensive computational resources and having high time complexity. Transfer learning is a state-of-the-art approach to reducing the requirements of high computational resources by using pre-trained models without compromising accuracy and performance. In conventional studies, pre-trained models are trained on datasets from different but similar domains with many domain-specific features. The computational requirements of transfer learning are directly dependent on the number of features that include the domain-specific and the generic features. This article investigates the prospects of reducing the computational requirements of the transfer learning models by discarding domain-specific features from a pre-trained model. The approach is applied to breast cancer detection using the dataset curated breast imaging subset of the digital database for screening mammography and various performance metrics such as precision, accuracy, recall, F1-score, and computational requirements. It is seen that discarding the domain-specific features to a specific limit provides significant performance improvements as well as minimizes the computational requirements in terms of training time (reduced by approx. 12%), processor utilization (reduced approx. 25%), and memory usage (reduced approx. 22%). The proposed transfer learning strategy increases accuracy (approx. 7%) and offloads computational complexity expeditiously.

Seasonal Variation, Fractional Isolation and Nanoencapsulation of Antioxidant Compounds of Indian Blackberry (Syzygium cumini).

Indian blackberry (Syzygium cumini L.) is an evergreen tree in the Myrtaceae family. It is used in traditional medicine due to its significant bioactivities and presence of polyphenols with antioxidant activities. The present study describes the effect of seasonal variations on Indian blackberry leaf essential oil yield and chemical composition, production of fractions from essential oil using high vacuum fractional distillation and slow cooling to low temperature (-50 °C) under vacuum, and bioactivities of the essential oil, fractions, and nanoparticles. The results show that Indian blackberry essential oil yield was higher in spring season as compared to winter season. Indian blackberry essential oil fractionation processes were effective in separating and concentrating compounds with desired bioactivities. The bioactivities shown by magnesium nanoparticles were comparatively higher than barium nanoparticles.