BAMBARA NUT (VIGNA SUBTERRAENEA (L.) VERDC) AND ITS NUTRACEUTICAL AND FUNCTIONAL PROPERTIES: A REVIEW

Nwadi, O. M. M;, Uchegbu, N. N, Okonkwo, T. M. — Tue, 07 Feb 2023 00:00:00 +0100

The aim of this paper was to review literature on the nutraceutical
and functional properties of Bambara nut in food systems/products.
Bambara nut is a very important legume which is widely consumed
in Africa as a staple food and contains high protein which makes it a
complete food. A superior alternative to sources of animal protein is
the Bambara nut, which has been called the most significant protein
among plant proteins. Bambara nut has been classified as a
functional food and its use in traditional medicine has been
emphasized, hence it has nutraceutical benefits. Some authors have
produced specialty malts from Bambara nut which is beneficial as
ingredient at both household and industrial level. The usefulness of
Bambara nut seems not to have been harnessed. The legume has
desirable functional properties which are beneficial to the food

industry. From the literature, some functional properties of
Bambara nut include foam capacity, water and oil binding capacity,
emulsifying stability. Incorporation of Bambara nut flour into
human foods have great prospects, but these prospects have to be
further investigated since Bambara nut is underutilized and underresearched.
It has also been reported that Bambara flour exhibited
superior properties (foaming and fat absorption) compared with its
protein isolate counterpart

THE PERSPECTIVE OF DATA MINING: THE STUDY OF FAKE NEWS ON SOCIAL MEDIA

Pilita A. Amahan — Tue, 17 Jan 2023 00:00:00 +0100

Today's rapidly evolving network environment is affected by the continuing problem of information authenticity. Since it has led many consumers astray, fake news detection is still a concern. This study aims to add additional research into the study of fake news on social media by gaining its perspective via the use of data mining due to the paucity of studies that identify and categorize false news pieces. The study concentrated on the extraction, categorization, and analysis of word context features, such as the seven character composition attributes word frequencies, vocabulary without white space, word length mean and variance, word vulgarity, word and phrase counter, and computation of word rarity. It includes 50 different pieces of data from Buzz Feed News' Face book social media. The bagging classifier from Weka's Meta algorithm was used to get an accuracy rating of 86%. The study suggests that future research collects additional information from other social networking sites, classifies them, and analyzes them. It also suggests evaluating the model developed during the study's model development phase and contrasting the results of the two assessments.

DYNAMIC JOURNAL OF PURE AND APPLIED SCIENCES , OZAMIZ CITY, PHILIPPINES

BAMBARA NUT (VIGNA SUBTERRAENEA (L.) VERDC) AND ITS NUTRACEUTICAL AND FUNCTIONAL PROPERTIES: A REVIEW

THE PERSPECTIVE OF DATA MINING: THE STUDY OF FAKE NEWS ON SOCIAL MEDIA