Publications

Rigorous efforts should be channeled to the current low adoption of climate-smart agricultural practices (CSAPs) in sub-Saharan African countries to improve food production. What determines the adoption level and intensity of CSAPs among smallholder farmers in Kenya? While considering their joint adoption, smallholder farmers' CSAPs adoption determinants were assessed based on a sample size of 300 smallholder farmers in Western Kenya. The CSAPs considered were animal manure, soil water conservation, agroforestry, crop diversification, and crop-livestock integration. A multivariate and ordered probit models were used to assess the determinants of joint adoption of CSAPs in Western Kenya. Both complements and substitutes between CSAPs were established. The multivariate probit analysis revealed that household head's gender, education, age, family size, contact with extension agents, access to weather information, arable land, livestock owned, perceived climate change, infertile soil, and persistent soil erosion influenced CSAPs adoption. The ordered probit model revealed that gender, arable land, livestock owned, soil fertility, and constant soil erosion were crucial determinants of CSAPs adoption. The findings implied that policymakers and relevant stakeholders should consider farmer, institutional, and biophysical factors in upscaling or promoting the adoption of CSAPs.

An increase in growth and use of the internet has also resulted in attacks evolving and more novel attacks having a devastating effect are witnessed. The Intrusion Detection System (IDS) is yet to achieve maximum success due to false positives and low detection. The purpose of the study was to determine the modelling of an intrusion detection system using the Asynchronous Advantage Actor-Critic (A3C) Algorithm. In this paper we look at the following: (i) To evaluate the current machine learning techniques being used in IDS, (ii) To determine the effectiveness of using the Asynchronous Advantage Actor-Critic algorithm in anomaly detection, (iii) To select the appropriate training data set and prepare for use on A3C. A conceptual study was done in looking at these objectives. The UNSW_TRAIN and UNSW_TEST were samples selected by purposive sampling from the whole population of UNSW-NB15 dataset. Analysis of the dataset was done using Python. Key findings were that anomaly detection approach is the best approach due to its ability to detect novel attacks. Also, there is need to continue research on intrusion detection and improve solutions to the problem of false positives and fully optimize on accuracy. The UNSW-NB15 dataset is comprehensive and so all the attack types should be used so as to accurately depict the intrusions and should selected attack types be used, feature selection should be done accurately so as to reflect modern attack types.