http://hdl.handle.net/123456789/515 2026-04-09T23:46:54Z http://hdl.handle.net/123456789/528 PA Risk-Oriented Conservative Framework for Effective Deployment of Cloud-based e-Services in Higher Education Kirori, Z. Cloud Computing is irrefutably one of the greatest computing innovations in modern times. This utility- based platform promises to open up new opportunities in a wide range of computing domains, such as research, entrepreneurship, green computing, high performance computing, and pervasive intelligence among others. The basic tenet of this on-demand paradigm is to remove the burden where organizations would have to establish elaborate Information and Communication Technology data centers and instead offload part or all the elaborate Information and Communication Technology Infrastructure to a Cloud Solution Provider or through a third party for access across the Internet by hiring software, application platform as well as the ICT infrastructure. The uptake of this technology holds the promise of driving down cost while fostering innovation and promoting agility in running elaborate Information and Communication Technology departments. The constant need to store, update and manage on-site elaborate Information and Communication Technology infrastructures in academic institutions particularly institutions of higher learning for research and training activities is undoubtedly a costly exercise. For this reason, migration towards cloud services has and continues to be a running agenda in boardroom policies for these institutions. However, the full potential of this marvelous technology is yet to be realized in higher education due to deployment challenges. This paper reviews existing Cloud Computing deployment models and suggests a risk-based conservative approach for Cloud Computing uptake 2019-01-01T00:00:00Z http://hdl.handle.net/123456789/526 Policy interpretation, project management practices and performance of construction projects Kamau, S. J.; Rambo; Mbugua, J.; Rambo C. M. he study sought to determine whether the performance of construction projects was influenced by school infrastructure policy interpretation and whether project management practices mediated that relationship. A cross-sectional survey using a correlational design was used. The target population comprised of 920 headteachers and 82 District Education Officers (DEOs) in all the 13 regions of Somaliland. Purposive sampling and proportionate stratified random sampling with replacement were used to sample 257 headteachers while simple random sampling was used to sample 20 DEOs. Data collection was by self- administered questionnaires for headteachers and semi-structured interviews for DEOs. Questionnaires pilot testing was done on 28 headteachers. Variable relationships were tested using t-tests at 5% level of significance. School infrastructure policy interpretation exerted a significant direct effect (b = -0.3215, p< 0.001, R2 = 0.4183) on the performance of construction projects. Project management practices mediated the relationship with a significant positive indirect effect of 0.4548, CI [0.3505, 0.5642]. A direct negative linear relationship existed between school infrastructure policy interpretation and the performance of construction projects. Policy interpretation exerts its influence on the performance of construction projects through project management practices. 2018-01-01T00:00:00Z http://hdl.handle.net/123456789/518 Determination of Statistically Significant Variables Affecting Inflation in The Kenyan Economy Gitonga, H. M; Karomo, J.; Mukwami, F The main purpose of this research was to establish the statistically significant factors that determine the rate of inflation in Kenya for duration of 30 years from 1987 up to and including 2017. The research considered data in the areas that included money supply, Central bank rates, currency exchange rates, salaries of employed Kenyans, basket prices of foodstuffs, the price of petroleum products, rates of corruption in the country and Political stability as a dummy variable 2018-10-01T00:00:00Z http://hdl.handle.net/123456789/324 Performance Analysis of Stochastic Gradient Descent - Based Algorithms for Time Series Sequence Modeling. Kirori, Z In many modern computer applications such as Market Analysis, Critical Care, Speech Recognition, Physical Plant Monitoring, Sleep Stage Classification, Biological Population Tracking, data is captured over the course of time, constituting a Time-Series. Time-Series data often contain temporal information dependencies that cause two otherwise identical points of time to belong to different classes or predict different behavior. This inherent characteristic increases the difficulty of processing such data. Deep Machine Learning (DML) techniques possess the inherent ability for analyzing and making predictions about such data. By its nature, DML requires extensive provision of resources key amongst which is the model computation time. Several optimization algorithms have been invented in the recent past and compare differently in terms of their resource needs. The most popular class of optimization algorithms is based on the classical stochastic gradient descent (SGD) algorithm due to its ability to converge within reasonable time bounds. This paper is part of a larger project investigating optimization procedures for deep learning tasks based on the SGD. Specifically, we report on the comparative performance capabilities of the most popular SGD based algorithms for task of Time Series prediction namely. From our analysis of the six of these algorithms, we noted that ADAMAX is most appropriate for online learning while RMSPROP is the least affected by over-fitting for long training cycles 2019-06-01T00:00:00Z