Hybridising heuristics within an estimation distribution algorithm for examination timetabling

This paper presents a hybrid hyper-heuristic approach based on estimation distribution algorithms. The main motivation is to raise the level of generality for search methodologies. The objective of the hyper-heuristic is to produce solutions of acceptable quality for a number of optimisation problems. In this work, we demonstrate the generality through experimental results for different variants of exam timetabling problems. The hyper-heuristic represents an automated constructive method that searches for heuristic choices from a given set of low-level heuristics based only on non-domain-specific knowledge. The high-level search methodology is based on a simple estimation distribution algorithm. It is capable of guiding the search to select appropriate heuristics in different problem solving situations. The probability distribution of low-level heuristics at different stages of solution construction can be used to measure their effectiveness and possibly help to facilitate more intelligent hyper-heuristic search methods

Dirasah Islamiyah 1 Pengantar Studi Al-Qur'an, Al-Hadits Fiqh Dan Pranata Sosial

ilus, xi, 130 hlm, 20.5 c

A unified approach for unconstrained off-angle iris recognition

Improving the performance of non-idealistic iris recognition has recently become one of the main focus in iris biometric research. In real-world iris image acquisitions, it is common and unavoidable to capture off-angle iris images. Such off-angle iris images are categorized as non-idealistic because they substantially degrade the performance of iris recognition. In this paper, we present a unified framework designed to improve off-angle iris recognition performance. We propose combination of least square ellipse fitting (LSEF) technique and the geometric calibration (GC) technique for the iris segmentation. For off-angle images, the improper location of iris and pupil interferes with the ability to effectively segment the inner boundary and outer boundary of the iris image. With the proposed techniques, inner and outer boundaries are fitted iteratively. For feature extraction, we propose a NeuWave Network (inspired by the Haar wavelet decomposition and neural network). The iris features are represented using the wavelet coefficients. Each different angle of the iris have its own significant coefficient and these coefficient, with a set of weights, then forms the iris template. The approach is evaluated based on recognition accuracy measured by the false rejection, false acceptance rate, and decidability index. We evaluate the algorithms with WVU-IBIDC datasets

Clustering Alkire foster-oriented quantification in measuring multidimensional poverty indicators by using intelligent adaptive neural fuzzy inference systems

Malaysia is a developing country which relies on the monetary approach when it comes to poverty measurement. The current monetary approach is simpler to measure; however, it is insensitive towards changes of the poor in multiple dimensions especially in urban area. Based on household survey data on urban province in Malaysia, this study proposes on a multidimensional poverty measurement framework, which predicts on the prominent deprived indicators based on multidimensional urban poor measurement, replacing the conventional money-metric measure. This study highlights on integration between Alkire-Foster approaches in quantification of multidimensional urban poor with Adaptive Neural Fuzzy Inference Systems (ANFIS). By addressing the deprived indicator in urban area, the combination of Alkire Foster and ANFIS approach could efficiently resolve on the issue of misfit urban poor in the country. In this study, Alkire Foster approach is proven to have promising results in improving the determination of the urban poor in Malaysia. In future, this study aims in addressing the particular combination of indicator that causes the urban poverty in Malaysia

Sistem Pemerintahan, Peradilan dan Adat Dalam Islam

XX, 538 hal., bibl.; 24c

Incorporating multiple biology based knowledge to amplify the prophecy of enzyme sub-functional classes

Based on current in silico methods, enzyme sub-functional classes is distinguished from sequence level information, local order or sequence length and order knowledge. To date, no work has been done to predict the enzyme subclasses efficiently corresponding to the ENZYME database. In order to precisely predict the sub-functional classes of enzyme, we propose a derivative feature vector labelled as APH which unifies amino acid composition, dipeptide composition, hydrophobicity and hydrophilicity. Support Vector Machine is used for prediction and the performance is evaluated using accuracy obtained over 99% and Matthew's Correlation Coefficient (MCC) over 0.99 with the aid of biological validation from in vivo studies