Nseobong P. Uto

TL;DR: In this article, a transfer learning approach was used to classify patients' response to failed treatments due to adverse drug reactions, where a soft computing model was pre-trained to cluster CD4+ counts and viral loads of treatment change episodes (TCEs) processed from two disparate sources: the Stanford HIV drug resistant database ( https://hivdb.stanford.edu ).

TL;DR: In this chapter, a Spoken-Computer Aided Language Learning (CALL) system is developed, which consists of online and offline sections with four interacting components, aimed at aiding users in the construction and pronunciation of Ibibio words.

TL;DR: The balanced semi-Latin rectangles as mentioned in this paper are a subclass of rectangles with a property of balance where no two distinct pairs of symbols (treatments) differ in their concurrences, that is, each pair of distinct treatments concurs a constant number of times in the design.

TL;DR: This paper investigated the effect of speaker variability on emotions and languages, and proposed a classification system to achieve these, speech features such as the fundamental frequency (F0) and intensity of two languages (Ibibio, New Benue Congo and Yoruba, Niger Congo) were exploited.

TL;DR: This research proposed a collaborative framework of hybridized (Machine Learning and Natural Language Processing) techniques to discover hidden genome patterns and feature predictors, for HIV-1 genome sequences mining, which would assist in the development of decision support systems for easy contact tracing, infectious disease surveillance, and studying the progressive evolution of the reference HIV- 1 genome.