This community holds past university examination past papers https://repository.cuk.ac.ke/handle/123456789/306 2026-04-10T17:22:32Z 2026-04-10T17:22:32Z https://repository.cuk.ac.ke/handle/123456789/1904 2026-03-30T11:41:46Z 2018-12-01T00:00:00Z

COURSE NAME: REAL ESTATE FINANCE AND INVESTMENT A past paper on real estate finance and investment.

2018-12-01T00:00:00Z Kariuki Kanja, George Mbandu Angolo, Shem Shikali, Casper https://repository.cuk.ac.ke/handle/123456789/1886 2026-01-15T12:21:20Z 2025-10-01T00:00:00Z

Uncovering Sentiment-Based Predictors of Cyber Defacement Attacks: A Case of Online Discourse on X-Platform Kariuki Kanja, George; Mbandu Angolo, Shem; Shikali, Casper This paper discussed the possibility of utilizing a sentiment analysis of online discussions on X platform (which was previously X) as a predictor of cyber defacement attacks. It bridged a serious gap in the literature on cybersecurity, where the focus has been on technical signatures and little consideration has been made on socio-technical antecedents. The hypothesis that spikes of negative public sentiment might be predictive indicators of ideologically motivated cases of defacement was tested in the study. A hybrid sentiment analysis model was used, which incorporates lexicon-based VADER model with machine learning classifiers, such as Naive Bayes and Long Short-Term Memory networks. The data consisted of 503456 posts related to cybersecurity and the data were compared to the verified cases of defacement in repositories like Zone-H using time-series analysis, Pearson correlation, and cross-correlation functions. Findings indicated that negative sentiment only comprised of 8.6% of the posts with the majority being neutral (50.9) and positive (40.5). The temporal analysis showed that there is not a substantial change in negative sentiment, but short bursts of negative sentiment are associated with cybersecurity disclosure. The cross-correlation analysis showed only weak contemporaneous correlation (r ≈ 0.12, lag = 0 days) but no predictive correlation in negative lags. The stacked ensemble model (Naïve Bayes, BiLSTM, ARIMA) was very strong in classification (Accuracy = 0.8568, F1 = 0.8055, ROC-AUC = 0.9116) but mainly it was very sensitive to concurrent or retrospective signals. The research established that aggregate sentiment does not provide predictive information, socio-technical prediction would combat inactive fine-grained and entity-specific signals combined with technical threat knowledge. A research article published in the journal of information security.

2025-10-01T00:00:00Z https://repository.cuk.ac.ke/handle/123456789/1884 2026-01-15T09:35:33Z 2022-12-14T00:00:00Z

UNIT TITLE: BUSINESS SYSTEM MODELLING A PAST PAPER ON END OF SEMESTER EXAMINATION DECEMBER -2022 EXAMINATION FOR THE DEGREE OF BACHELOR OF BUSINESS AND INFORMATION TECHNOLOGY, INFORMATION TECHNOLOGY (YR III SEM II)

2022-12-14T00:00:00Z https://repository.cuk.ac.ke/handle/123456789/1883 2026-01-15T09:30:08Z 2022-08-31T00:00:00Z

UNIT TITLE: BUSINESS SYSTEM MODELLING A PAST PAPER ON END OF SEMESTER EXAMINATION AUGUST - 2022 EXAMINATION FOR THE DEGREE BACHELOR OF INFORMATION TECHNOLOGY (YR III SEM I)

2022-08-31T00:00:00Z