Wellspring University College of Science and Computing Journal

Publications

Publications

PHYTOCHEMICAL SCREENING AND ANTIBACTERIAL EFFICACY OF AERIAL PARTS OF Phyllanthus niruri EXTRACTS AGAINST Escherichia coli ISOLATED FROM STOOL SAMPLES
  • Published by Wellspring University Journal of Science and Computing
  • Accepts manuscripts in English
Authors
1Egharevba, P. A., 2Idemudia, I. B., 1Amengialue, O. O., 2Saidu, J. Z. and 2Ekhaise, F. O.

ABSTRACT

The increasing antibiotic resistance of bacterial pathogens necessitates the search for alternative antibacterial agents. Plants have a great deposit of antibacterial compounds that have huge therapeutic potential to treat/mitigate several diseases, without any detrimental outcome. Phyllanthus niruri has been widely used across continents as folk medicine for different ailments.  This work sought to analyze the phytochemical profile and antibacterial effects of ethanol and aqueous extracts prepared from Phyllanthus niruri aerial parts against eleven (11) Escherichia coli strains obtained from stool samples. …
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ENHANCING EXAMINATION INTEGRITY THROUGH FINGERPRINT RECOGNITION SYSTEM
  • Published by Wellspring University Journal of Science and Computing
  • Accepts manuscripts in English
Authors
1Joy Odimayomi University of Benin 2Victor Osasu Eguavoen Wellspring University 3Belinda Nkem Unuigbokhai Wellspring University 4Wellspring University Journal Of Science and Computing and 5Ekhaise, F. O.

ABSTRACT

This study investigates a fingerprint recognition system utilizing Pseudo Zernike Moments (PZMs) to enhance examination integrity by mitigating impersonation risks. The performance of the PZMs method was rigorously compared to traditional Minutiae-based and Pattern-based techniques, focusing on key metrics such as recognition accuracy and processing time. The PZMs method achieved a recognition accuracy of 91.81 %, significantly higher than 88 % for Minutiae-based and 85 % for Pattern-based methods (p < 0.05), with processing times of 1011 ms, compared to 1200 ms for Minutiae-based and 900 ms for Pattern-based methods. The overall recognition rate was 98.67%, with a minimal False Rejection Rate (FRR) of 0% and a Failure to Enroll Rate (FER) of 1.33%. …
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