http://ir.mu.ac.ke:8080/jspui/handle/123456789/5 2026-07-16T20:08:14Z 2026-07-16T20:08:14Z Soy, Mary Jelangat http://ir.mu.ac.ke:8080/jspui/handle/123456789/10286 2026-07-02T12:02:55Z 2025-01-01T00:00:00Z

Economic factors determining poverty levels among women in Langas, uasin gishu county, Kenya Soy, Mary Jelangat Women’s role within society has a remarkable impact on poverty alleviation, though they are susceptible to gender-based inequalities, uncompensated caregiving, and domestic duties. This study examined the socio-economic factors influencing poverty among women in Langas, Uasin Gishu County. The specific objectives were: (i) to examine the socio-demographic factors affecting women in Langas, (ii) to determine the income levels and patterns that impact household poverty, (iii) to assess household expenditure levels and patterns that influence poverty, and (iv) to analyze women’s economic status in relation to household poverty. Guided by Sen’s Capability Approach, the study adopted a descriptive research design with a sample of 380 women selected through stratified and simple random sampling. Data were collected using structured questionnaires and analyzed using descriptive statistics, chi-square tests, and binary logistic regression. The regression results revealed that several socio demographic and economic factors significantly predict household poverty. Employment status was a significant determinant (β = -0.061, p = 0.048), suggesting that women engaged in stable employment face reduced poverty risk. Savings emerged as a protective factor (β = -0.385, p = 0.020), while reliance on credit increased vulnerability to poverty (β = 0.556, p = 0.013). Household expenditure adequacy strongly predicted poverty likelihood (β = 0.714, p = 0.001), as did disproportionate spending on food and beverages (β = 0.856, p = 0.045). Women’s economic empowerment reduced poverty risk (β = -0.537, p = 0.016), while the ability to earn income significantly lowered poverty incidence (β = 0.516, p = 0.031). Access to savings/loans for emergencies (β = -0.473, p = 0.043) and current economic knowledge/skills (β = -0.535, p = 0.027) were also protective. These findings underscore that wage stability, prudent income patterns, adequate household expenditure, and women’s empowerment collectively shape poverty outcomes. The study concludes that policy interventions should focus on enhancing women’s access to stable employment, savings opportunities, and financial literacy while reducing dependence on informal credit systems. Strengthening women’s empowerment programs can significantly reduce poverty vulnerability and improve household welfare.

2025-01-01T00:00:00Z A. Enane, Leslie Leonard, Adam Diero, Lameck Marcy, Olivier Marcel Yotebieng, Marcel http://ir.mu.ac.ke:8080/jspui/handle/123456789/10262 2026-06-25T07:17:33Z 2026-03-17T00:00:00Z

Confronting the TB-HIV syndemic in adolescents and young adults: a call to action in a time of Ccrisis A. Enane, Leslie; Leonard, Adam; Diero, Lameck; Marcy, Olivier; Marcel Yotebieng, Marcel

2026-03-17T00:00:00Z Kuttuny, Serah N. http://ir.mu.ac.ke:8080/jspui/handle/123456789/10249 2026-06-24T07:00:20Z 2025-01-01T00:00:00Z

Uteuzi wa lugha na mikakati ya kimawasiliano katika mandhari ya lugha mjini Eldoret Kuttuny, Serah N. Utafiti huu ulichunguza matumizi ya lugha na mikakati ya mawasiliano katika mandhari ya lugha mjini Eldoret. Madhumuni ya utafiti huu yalikuwa kubainisha maudhui mbali mbali na mzagao wao katika mabango,kutathmini kwendana kwa uteuzi wa lugha kwa matarajio ya hadhira lengwa na kuchunguza ufaafu wa vielelezo vya mabango kama mikakati ya kuwasilisha ujumbe kwa hadhira lengwa. Utafiti huu ulijikita katika madai ya nadharia ya lugha-tarajiwa na isiyotarajiwa, baadhi ya madai ya nadharia ya lugha-solo na lugha-bebwa na nadharia ya semiotiki. Data za utafiti huu zilikusanywa katika barabara mbili kuu zilizoteuliwa kimakusudi mjini Eldoret; barabara ya Oginga Odinga na barabara ya Uganda. Makundi manne ya vijana na wazee yalihojiwa na kurekodiwa katikati mwa mji wa Eldoret. Usampulishaji kusudio ulitumika. Matokeo ya utafiti huu yalidhihirisha maudhui mbali mbali na mzagao wao; benki (9.79%), elimu (1.39%), vinywaji (17.48%), bima (1.39%), uganga (0.69%), ajira (0.69%), vyakula (6.99%), Kamari (0.69%), habari na mawalisiano (18.88%), fenicha (0.69%), kilimo (9.09%), uchukuzi (9.09%), michezo (0.69%), bidhaa jumla (14.68%), matibabu (2.79%) na kongamano (2.79%). Pia, tuligundua ruwaza nne za uteuzi wa lugha; Kiingereza (49.65%), ubadilishaji msimbo (32.86%), Kiswahili (14.68%) na Kinandi (2.69%). Hatimaye, utafiti huu ulibainisha kuwa, uteuzi wa ujozi lugha katika mabango hayakuendana na matarajio ya hadhira. Hata hivyo, utafiti huu uling`amua kuwa matumizi ya viziada lugha kama vile picha, rangi na fonti mbali mbali hushadidisha mawasiliano katika matangazo ya mabango. Tasnifu hii inapendekeza kuwa, kwa kuwa mandhari ya lugha mjini Eldoret yameonyesha ubadilishaji msimbo na lugha zisizo thabiti za Sheng na Engsh huku lugha ya asili ya eneo la mji wa Eldoret, Kinandi, ikitumika kwa uchache sana. Ukweli ni kwamba haiwezekani kujumlisha hali ya mandhari ya lugha nchini Kenya kwa kujikita katika mji mmoja tu. Kwa hivyo, tunapendekeza utafiti zaidi uendeshwe katika miji mingine.

2025-01-01T00:00:00Z Cheruiyot, Victor Kipngetich http://ir.mu.ac.ke:8080/jspui/handle/123456789/10245 2026-06-23T08:24:38Z 2026-01-01T00:00:00Z

A hybrid intrusion detection model for application Layer DDOS Attacks based on K-Means and Cart Algorithms Cheruiyot, Victor Kipngetich The increase in interconnectivity and advancement in network technologies have influenced a parallel rise in Distributed Denial of Service (DDoS) attacks, and the perpetrators have become sophisticated such that previously dependable tools and techniques have become ineffective. The purpose of the study was to design an intrusion detection model based on K-Means and CART algorithms, and train and test it using the CICDDoS2019 dataset, which represents application-layer DDOS attacks. The objectives of the study were to: Determine the existing application-layer intrusion detection techniques and models; Explore the weaknesses of existing intrusion detection models; Classify the dataset using individual K-Means and CART algorithms; Develop a hybrid intrusion detection model for application-layer DDoS attacks by combining K Means and CART algorithms; and evaluate the performance of the hybrid model. The study was designed as a quantitative experimental simulation. It adopted the empirical positivist paradigm. A machine learning theory and network security theory formed the theoretical framework. The Scikit-Learn libraries were employed using Python programming to perform the analysis. The study utilised secondary data obtained from the CICDDoS2019 dataset, containing 49.59 million records of 12 unlabelled DDoS attack types including NTP, DNS, LDAP, MSSQL, NetBIOS, SNMP, SSDP, UDP, UDP-Lag, WebDDoS, SYN, and TFTP. This research used simple random sampling to select 30000 records from each attack type, yielding a dataframe of 110,000 rows and 88 columns. The Unsupervised component of the experiment requires no training and testing sets. For the supervised component using the CART algorithm, the dataset was split into 67% for training and 33% for testing. Individually, the K-Means algorithm achieved homogeneity, completeness, and V-measure scores of 50.76%, 51.95%, and 51.35% respectively. On the other hand, CART was measured on accuracy, precision, recall/sensitivity, and F1-Score and it achieved scores of 74% on all counts. The hybrid model was fundamentally a CART algorithm improved by K-means clustered features and therefore was scored on the CART algorithm metrics basis. It scored 78% on accuracy, 79% on precision, 78% on recall, and 78.5% on F1-score. The dataset proved to have high dimensionality and complexity with multiple overlapping clusters. K-Means had an average performance proving its unsuitability for this type of dataset. CART algorithm had a relatively high success in identifying application layer DDoS attacks. The hybrid model achieved a better performance score compared to its constituent models as shown by the difference between the chosen metrics and their averages. This study concludes that our hybrid intrusion detection model can outperform existing K Mean and CART algorithms in terms of accuracy, precision, recall and F1 score. The study recommends that future studies should investigate a similar model using density based clustering algorithms like DBSCAN in place of K-Means in a similar setup.

2026-01-01T00:00:00Z