FOCIM http://41.89.49.13:8080/xmlui/handle/123456789/1143 2026-07-22T08:04:58Z 2026-07-22T08:04:58Z Muthoni, Emma http://41.89.49.13:8080/xmlui/handle/123456789/1452 2019-07-25T04:26:31Z 2018-01-01T00:00:00Z

IoT For African Smart Cities: A Model For A Smart Solid Waste Management System In Nairobi Muthoni, Emma

2018-01-01T00:00:00Z Mwangi, Peter M http://41.89.49.13:8080/xmlui/handle/123456789/1442 2019-04-09T12:34:59Z 2018-01-01T00:00:00Z

An Approach For Determining Energy Efficient Security Protocol For Wireless Sensor Networks. Mwangi, Peter M Sensor networks are one of the dominant technology trends in the coming decades and the use of wireless Sensor Networks (WSNs) is bringing huge changes in data gathering, processing and propagation of different environments and applications. These sensor networks are composed of hundreds, and potentially thousands of tiny sensor nodes, functioning autonomously, and in many cases, with limited resources (i.e. computation, storage, and battery) this shorten the life span of sensor nodes. Cost constraints and the need for ubiquitous, invisible distributions will result in small sized, resource-constrained sensor nodes. With this constraint, it is very hard to implement security protocols. Cost constraints and the need for ubiquitous, invisible distributions will result in small sized, resource-constrained sensor nodes. With this constraint, it is very hard to implement security protocols. The research was aimed at evaluating wireless senor network security protocol in terms of their energy efficient and determine on that has better energy consumption. The research simulates using NS-2 because it is an open source, discrete-event network simulator that provides support for a simulation of main protocols, routing, multicast protocols for wired and wireless networks.

2018-01-01T00:00:00Z Karonei, Ernest K http://41.89.49.13:8080/xmlui/handle/123456789/1400 2019-02-06T09:38:27Z 2018-01-01T00:00:00Z

Adoption Model For Smart Learning Devices As E-learning Tools In Tvet Institution: A Case Study Of Rvtti Karonei, Ernest K Smart learning is the growth of e-learning from the 19th century to 21st-century generations, which enhances the missing component of an e-learning solution. Smart learning most suits for those smartboards, laptops, and smartphones; PDA's and tablets users in TVET education institutes (PILZ, Matthias, 2012). So, exploiting smart devices in educational institutions is mainly measured as enriched tools to facilitate learning. Innovations in smart learning can lead to a changing paradigm in TVET education which smart technologies are believed to have the potential to be used in teaching and learning in technical institutions. This study discusses and exploits various ways which smart devices can be used as a facilitating tool for e-learning in TVET institutions in Kenya. The study describes the multiple techniques and perceptions of using smart devices to aid in designing and developing e- learning content, approaches to teaching and activity development for learners. Also, this study explore the challenges being faced at the moment and the issues of change management to e-learning in the TVET institutions. The study uses the survey of experience to identify various factors influencing the use and adoption of smart devices as e-learning tools. Survey of experience, through descriptive statistics is used to develop the adoption model. The evaluation of the model was done using the k-fold validation technique. Based on these operations, the study concluded that the intention to use smart devices, and subsequent use is influenced by the Performance Expectancy, Perceived Mobility, Inhibiting Conditions and Facilitating Conditions. Leveraging the results this study enhances and harmonizes the quality of technical and vocational curriculum in line with current technological needs, while streamlining the current existing technologies in the delivery of learning in TVET institutions.

2018-01-01T00:00:00Z Areba, James B http://41.89.49.13:8080/xmlui/handle/123456789/1392 2019-01-23T08:47:17Z 2018-01-01T00:00:00Z

Credit Risk Assessment For Customers Of Kenyan Commercial Banks: A Case Of Co-operative Bank Areba, James B Over the last two decades, credit assessments made by commercial banks have been evolving. Instead of the traditional assessment of the banks’ credit experts which is subjective, increased credit risk means that comprehensive mathematical and statistical models must now be used. However, credit risk scoring applied for most commercial banks is not very effective, since a lot of defaulting exists which leaves the banks disadvantaged. The main purpose of this study is to conduct comprehensive modeling for effectively assessing credit risk. Primary data was collected through interviews with personnel related to credit control to establish the existing credit scoring methods and their viability. The target population included 50 staff members of Co-operative banks in Nairobi who comprised of credit officers, staff in risk management and staff ICT departments. The primary data was supplemented by secondary data gathered from bank records, company statistics, financial periodicals, books, journal articles and reports. The data was analyzed using descriptive and inferential statistics. The descriptive statistics include frequency distribution tables and measures of central tendency and measures of variability. Different inferential methods are tried and tested, leading to a conclusion that principal components analysis and logistic regression provide a suitable set of methods. Principal components analysis is used to identify significant variables among the many variables that can be used to assess credit risk. With fewer and effective measures of model performance, model development becomes a much more efficient process, the same goes for variable selection. Since the data used is only a small sample of the population, a resampling method is applied that is used to get stable estimation of credit scoring using a dataset of reasonable size. The developed model for credit risk scoring will inform management for decision making and provide predictive information on the potential for delinquency or default that may be used in the loan approval process and risk pricing. The study is expected to be of value to the various stakeholders who will include the management of commercial banks in Kenya and other financial institutions; to the CBK as the regulator, to the borrowers and to scholars and researchers.

2018-01-01T00:00:00Z