National Defence University, Pakistan

About: National Defence University, Pakistan is a education organization based out in Islamabad, Pakistan. It is known for research contribution in the topics: Decision support system & Population. The organization has 802 authors who have published 816 publications receiving 3701 citations. The organization is also known as: National Defence University of Pakistan & National Defence University Islamabad.

Promoting gender sensitivity: Disaster risk reduction in Pakistan

Abstract: Gender sensitization is a theory that a maximum number of policy analysts discover easy to implement, but very few perform well. The same can be said about disaster risk reduction. When these two concerns are put together to mainstream gender into disaster risk reduction (DRR), concerned organizations and experts find gaps in planning and implementation of policies. This is not because the job is integrally challenging; rather, there is not adequate practical guidance and pragmatic information. This paper aims to increase awareness about gender issues in disaster risk reduction, to improve government capability to address gender problems in DRR and to encourage Pakistan’s government to incorporate gender perception into DRR legislatures, strategies and plans for sustainable development. The importance of the Beijing Agenda for global action on gender sensitive DRR and the Manila Declaration for Global Action on gender in climate change and DRR have been highlighted, with the help of information from other developing countries, to develop a road map for Pakistan. Capacity development and gender-aware knowledge products are the two main areas with the help of which concerns regarding gender sensitivity can be addressed in disaster preparedness, recovery and rehabilitation.

Weak Signal Detection in Noisy Chaotic Time Series Using ORBFNN

Abstract: This paper considers the problem of detection of weak signal detection in noisy chaotic time series using an Optimal Radial Basis Function Neural Network(ORBFNN). Based on chaotic dynamic mechanism, using ORBFNN to establish the forecast model of chaotic time series. When noise exists, to determine the structure of an optimal RBF predictor, we propose a new technique called the cross-validated subspace method to estimate the optimum number of hidden units. Which is used to identify a suitable number of hidden units by detecting the dimension of the subspace spanned by the signal eigenvectors, the cross validation method is applied to prevent the problem of overfitting. The results of theoretical analysis and simulation indicate the effectiveness of the ORBFNN predictor. The infection degree of noise is evaluated in quantity in the end. Results show that the proposed ORBFNN predictor can provide a further improvement in signal detection performance. Keywords-Chaos; predictio; weak signal detection