http://41.74.91.244:8080/handle/123456789/38 2026-04-04T14:13:11Z 2026-04-04T14:13:11Z Boateng L. http://41.74.91.244:8080/handle/123456789/569 2023-05-09T12:29:45Z 2013-01-01T00:00:00Z

Some physiochemical and heavy metal concentration in surface water streams of Tutuka in the Kenyasi mining catchment area Boateng L. This research was conducted in the Akantansu stream of Tutuka in Kenyasi in the Brong Ahafo Region of Ghana in the months of October and November 2010 and January 2011. The major objectives of the study were to measure levels of pH, BOD (biochemical oxygen demand), lead, chromium, and arsenic in the Akantansu stream of Tutuka and to find ways that the community could ensure safe water use. To achieve the objectives of the study, sampling was done over a period of three months and data was collected and analyzed into graphs and ANOVA tables. The research revealed that the levels of arsenic and BOD were high as compared to the standards of WHO and EPA. If the people of Tutuka continue to use the stream, they may experience negative health effects (e.g., nausea, vomiting, diarrhea, etc.). The level of pH, chromium and lead was acceptable as compared to the standard of WHO and EPA. Copyright � 2013 by ASME. Boateng, L., University of Education Winneba Ghana, P. O. Box 40, Mampong, Ghana

2013-01-01T00:00:00Z Stoffelsma L. Spooren W. Mwinlaaru I.N. Antwi V. http://41.74.91.244:8080/handle/123456789/378 2023-06-26T09:53:03Z 2020-01-01T00:00:00Z

The morphology-vocabulary- reading mechanism and its effect on students� academic achievement in an English L2 context Stoffelsma L.; Spooren W.; Mwinlaaru I.N.; Antwi V. The high lexical density and complex morphology of written standard English in academic and administrative contexts have raised concerns about their effect on reading proficiency across educational levels. This study provides empirical evidence of a serial multiple mediator model supporting the relationship between English L2 students� morphological awareness, vocabulary knowledge, reading proficiency and academic achievement. Comparisons were made at two levels: general English and academic English. Data were acquired from 454 second- and third-year English L2 university students in Ghana, West Africa. Using two different mediation analyses, the study produced significant evidence for a two-mediator model at both levels. Morphological awareness is modelled as affecting academic achievement through four different pathways: indirectly through vocabulary, indirectly through reading comprehension, indirectly through vocabulary and reading comprehension sequentially, and directly. This shows that knowledge of morphology both directly and indirectly influences academic achievement at tertiary level in English L2 contexts. � 2020 Elsevier Ltd Stoffelsma, L., Department of Linguistics & Modern Languages, University of South Africa (UNISA), Pretoria, South Africa, Centre for Language Studies, Faculty of Arts, Radboud University Nijmegen, P.O. Box 9103, NL-6500 HD, Nijmegen, Netherlands; Spooren, W., Full Professor Discourse Studies of Dutch, Centre for Language Studies, Faculty of Arts, Radboud University Nijmegen, Room E6.29 / E13.04, P.O. Box 9103, NL-6500 HD, Nijmegen, Netherlands; Mwinlaaru, I.N., Department of English, University of Cape Coast, Cape Coast, Ghana; Antwi, V., Department of Physics, University of Education Winneba, Winneba, Ghana

2020-01-01T00:00:00Z Gbagbo F.Y. Nkrumah J. http://41.74.91.244:8080/handle/123456789/360 2023-06-27T10:31:54Z 2021-01-01T00:00:00Z

Self-medication among pregnant women in two municipalities in the Central Region of Ghana Gbagbo F.Y.; Nkrumah J. Self-medication in pregnancy is a concern in Ghana. We assessed the practice among 136 pregnant women in Effutu and Agona West Municipalities using facility-based, cross-sectional design and mixed method approach of data collection. Our findings show that pregnant women of varying backgrounds self-medicate for sociocultural and economic reasons, with 69% prevalence, motivated by cheaper treatment cost (17%), minor ailments (29%) and positive outcomes (33%). Commonly used medications include antibiotics (23%), pain killers (20%) and herbal preparations (19%). We recommend further studies on pharmacological compositions of the medications used and effects on pregnancy outcomes to inform policy and programs decisions. � 2020 Taylor & Francis Group, LLC. Gbagbo, F.Y., Department of Health Administration & Education, Faculty of Science Education, University of Education, Winneba, Winneba, Ghana; Nkrumah, J., Department of Health Administration & Education, Faculty of Science Education, University of Education, Winneba, Winneba, Ghana

2021-01-01T00:00:00Z Ampomah E.K. Nyame G. Qin Z. Addo P.C. Gyamfi E.O. Gyan M. http://41.74.91.244:8080/handle/123456789/313 2023-07-06T15:00:47Z 2021-01-01T00:00:00Z

Stock market prediction with gaussian naive bayes machine learning algorithm Ampomah E.K.; Nyame G.; Qin Z.; Addo P.C.; Gyamfi E.O.; Gyan M. The stock market is one of the key sectors of a country's economy. It provides investors with an opportunity to invest and gain returns on their investment. Predicting the stock market is a very challenging task and has attracted serious interest from researchers from many fields such as statistics, artificial intelligence, economics, and finance. An accurate prediction of the stock market reduces investment risk in the market. Different approaches have been used to predict the stock market. The performances of Machine learning (ML) models are typically superior to those of statistical and econometric models. The ability of Gaussian Naive Bayes ML algorithm to predict stock price movement has not been addressed properly in the existing literature, hence this attempt to fill that gap in the literature by evaluating the performance of GNB algorithm when combined with different feature scaling and feature extraction techniques in stock price movement prediction. The performance of the GNB models set up were ranked using the Kendall's test of concordance for the various evaluation metrics used. The results indicated that, the predictive model based on integration of GNB algorithm and Linear Discriminant Analysis (GNB_LDA) outperformed all the other models of GNB considered in three of the four evaluation metrics (i.e., accuracy, F1-score, and AUC). Similarly, the predictive model based on GNB algorithm, Min-Max scaling, and PCA produced the best rank using the specificity results. In addition, GNB produced better performance with Min-Max scaling technique than it does with standardization scaling techniques. 2021 Slovene Society Informatika. All rights reserved. Ampomah, E.K., School of Information and Software Engineering, University of Electronic Science and Technology of China, China; Nyame, G., Department of Information Technology Education, Akenten Appiah-Menka University of Skills Training and Entrepreneurial Development, Kumasi-Ghana, Ghana; Qin, Z., School of Information and Software Engineering, University of Electronic Science and Technology of China, China; Addo, P.C., School of Management and Economics, University of Electronic Science and Technology of China, China; Gyamfi, E.O., School of Information and Software Engineering, University of Electronic Science and Technology of China, China; Gyan, M., Department of Physics Education, University of Education, Winneba-Ghana, Ghana

2021-01-01T00:00:00Z