Browsing by Author "Chimwayi, Kerina Blessmore"

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    An Evaluation of the Effectiveness of E-banking Security Strategies in Zimbabwe: A Case Study of Zimbabwean Commercial Banks
    (Journal of Internet Banking and Commerce (JIBC), 2012-12) Zimucha, Tafadzwa; Zanamwe, Ngonidzashe; Chimwayi, Kerina Blessmore; Chakwizira, Elijah; Mapungwana, Petronella; Maduku, Tinovimbanashe
    he introduction of e-banking has often been believed to be inhibited by security threats among other issues. Few studies have evaluated the effectiveness of e-banking security strategies in spite of its apparent impact on adoption of e-banking. This research evaluated the effectiveness of e-banking security strategies in Zimbabwe. The adoption of e-banking by commercial banks increases security risks, potentially exposing their isolated systems to open and risky environments. The increasing popularity of e-banking has attracted the attention of both legitimate and illegitimate banking practices, thereby, exposing customers to criminal activities, fraud, thefts and various other threats of similar nature. Thus, it is imperative to deliver a secure e-banking system to avoid the risk of negative effects which include clients accounts being tampered with, phishing or identity theft. In order to achieve our broad aim, a representative sample of 31 participants from 15 commercial banks completed questionnaires which were analysed using SPSS. From the analysis, it was found out that the majority of banks are using between four and five e-banking security strategies. The strategies included passwords, firewalls, encryption, virtual keyboards, pins and access codes and secure socket layers. It was therefore concluded that Zimbabwean banks are effective in offering secure e-banking services.
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    Risk Level Prediction of Chronic Kidney Disease Using Neuro- Fuzzy and Hierarchical Clustering Algorithm (s)
    (International Journal of Multimedia and Ubiquitous Engineering, 2017-01-02) Chimwayi, Kerina Blessmore; Haris, Noorie; Caytiles, Ronnie D; Iyengar, Ch. S. N
    Chronic Kidney Disease (CKD) is usually characterized by a gradual loss of the functioning which the kidney does over time due to various factors. Early prediction and treatment save the kidney and halts the progress of CKD. CKD disease is being viewed as global public health issue for the past decade. The greatest threat for this deadly disease is developing countries where getting therapy is very expensive. The importance of predicting individuals who are at risk of CKD as well as applying clustering techniques cannot be underestimated since these can modify the progression of the disease. Identifying the silent killer disease early offers best opportunities for implementing possible strategies for lessening the probability of kidney loss. Neuro-fuzzy algorithm is applied to determine the risk of CKD in patients. Predictions done using neuro-fuzzy gave an accuracy of 97 percent. Using selected features, prediction for CKD disease is done so as to identify the risk. The results of the prediction are clustered to identify the percentage of patients with a high risk of having kidney disease who have a higher probability of being diabetic. Using hierarchical clustering three clusters formed show that there is a strong relationship between chronic kidney and diabetes