Hishammuddin Asmuni

Fuzzy multiple heuristic orderings for examination timetabling

In this paper, we address the issue of ordering exams by simultaneously considering two separate heuristics using fuzzy methods. Combinations of two of the following three heuristic orderings are employed: largest degree, saturation degree and largest enrolment. The fuzzy weight of an exam is used to represent how difficult it is to schedule. The decreasingly ordered exams are sequentially chosen to be assigned to the last slot with least penalty cost value while the feasibility of the timetable is maintained throughout the process. Unscheduling and rescheduling exams is performed until all exams are scheduled. The proposed algorithm has been tested on 12 benchmark examination timetabling data sets and the results show that this approach can produce good quality solutions. Moreover, there is significant potential to extend the approach by including a larger range of heuristics.

Multimodal biometrics: Weighted score level fusion based on non-ideal iris and face images

The iris and face are among the most promising biometric traits that can accurately identify a person because their unique textures can be swiftly extracted during the recognition process. However, unimodal biometrics have limited usage since no single biometric is sufficiently robust and accurate in real-world applications. Iris and face biometric authentication often deals with non-ideal scenarios such as off-angles, reflections, expression changes, variations in posing, or blurred images. These limitations imposed by unimodal biometrics can be overcome by incorporating multimodal biometrics. Therefore, this paper presents a method that combines face and iris biometric traits with the weighted score level fusion technique to flexibly fuse the matching scores from these two modalities based on their weight availability. The dataset use for the experiment is self established dataset named Universiti Teknologi Malaysia Iris and Face Multimodal Datasets (UTMIFM), UBIRIS version 2.0 (UBIRIS v.2) and ORL face databases. The proposed framework achieve high accuracy, and had a high decidability index which significantly separate the distance between intra and inter distance.

A novel fuzzy approach to evaluate the quality of examination timetabling

In this paper we introduce a new fuzzy evaluation function for examination timetabling.We describe how we employed fuzzy reasoning to evaluate the quality of a constructed timetable by considering two criteria: the average penalty per student and the highest penalty imposed on any of the students. A fuzzy system was created based on a series of easy to understand rules to combine the two criteria. A significant problem encountered was how to determine the lower and upper bounds of the decision criteria for any given problem instance, in order to allow the fuzzy system to be fixed and, hence, applicable to new problems without alteration. In this work, two different methods for determining boundary settings are proposed. Experimental results are presented and the implications analysed. These results demonstrate that fuzzy reasoning can be successfully applied to evaluate the quality of timetable solutions in which multiple decision criteria are involved.

Feature extraction for different distances of visible reflection iris using multiscale sparse representation of local Radon transform

Iris recognition is a promising method by which to accurately identify a person. During the iris recognition stage, the features of the iris are extracted, including the unique, individual texture of the iris. The ability to extract the texture of the iris in non-cooperative environments from eye images captured at different distances, containing reflections, and under visible wavelength illumination will lead to increased iris recognition performance. A method that combined multiscale sparse representation of local Radon transform was proposed to down sample a normalized iris into different lengths of scales and different orientations of angles to form an iris feature vector. This research was tested using 1000 eye images from the UBIRIS.v2 database. The results showed that the proposed method performed better than existing methods when dealing with iris images captured at different distances.

A low lighting or contrast ratio visible iris recognition using iso-contrast limited adaptive histogram equalization

Condition of eye images with a low lighting or low contrast ratio between the iris and pupil is one of the challenges for iris recognition in a non-cooperative environment and under visible wavelength illumination. Incorrect iris localization can affect the performance of the iris recognition system. Iso-contrast limited adaptive histogram equalization is proposed to overcome this challenge and increase the performance of iris localization. The eye image is partitioned into the contextual sub-region; then, the proposed method transfers the pixel intensity by referring to a local intensity histogram and a newly suggested cumulative distribution function. This research was tested on 1000 eye images from the UBIRIS.v2 dataset. The results showed that the proposed method performed better than existing methods when dealing with a low lighting or low contrast ratio between the iris and pupil in the eye image.

A new hybrid imperialist swarm-based optimization algorithm for university timetabling problems

Generating timetables for an institution is a challenging and time consuming task due to different demands on the overall structure of the timetable. In this paper, a new hybrid method which is a combination of a great deluge and artificial bee colony algorithm (INMGD-ABC) is proposed to address the university timetabling problem. Artificial bee colony algorithm (ABC) is a population based method that has been introduced in recent years and has proven successful in solving various optimization problems effectively. However, as with many search based approaches, there exist weaknesses in the exploration and exploitation abilities which tend to induce slow convergence of the overall search process. Therefore, hybridization is proposed to compensate for the identified weaknesses of the ABC. Also, inspired from imperialist competitive algorithms, an assimilation policy is implemented in order to improve the global exploration ability of the ABC algorithm. In addition, Nelder-Mead simplex search method is incorporated within the great deluge algorithm (NMGD) with the aim of enhancing the exploitation ability of the hybrid method in fine-tuning the problem search region. The proposed method is tested on two differing benchmark datasets i.e. examination and course timetabling datasets. A statistical analysis t-test has been conducted and shows the performance of the proposed approach as significantly better than basic ABC algorithm. Finally, the experimental results are compared against state-of-the art methods in the literature, with results obtained that are competitive and in certain cases achieving some of the current best results to those in the literature.

An improved multiscale retinex algorithm for motion-blurred iris images to minimize the intra-individual variations

Motion-blurred iris image is caused by less user cooperation, poor quality cameras and environmental conditions when capturing image, thus contributing to a variety of iris patterns, which are due to the shadows and noises occurring in the image. The biggest challenge dealing with motion-blurred iris image is to analyze the exact pattern of the iris image. The combination of homomorphic filtering and multiscale retinex algorithms can cope with the illumination changes and shadow removal in order to produce enhanced iris pattern. Homomorphic filtering is applied to remove shadows on motion-blurred image. The processed image that is free of shadows is then applied with multiscale retinex algorithm to improve the contrast of the image. The enhanced iris pattern that is free of shadows is then evaluated using intensity histogram to validate the proposed method. The accuracy of the proposed method is 99.2% with minimum false rejection and false acceptance rate.

A Hybrid Swarm-Based Approach to University Timetabling

This paper is concerned with the application of an automated hybrid approach in addressing the university timetabling problem. The approach described is based on the nature-inspired artificial bee colony (ABC) algorithm. An ABC algorithm is a biologically-inspired optimization approach, which has been widely implemented in solving a range of optimization problems in recent years such as job shop scheduling and machine timetabling problems. Although the approach has proven to be robust across a range of problems, it is acknowledged within the literature that there currently exist a number of inefficiencies regarding the exploration and exploitation abilities. These inefficiencies can often lead to a slow convergence speed within the search process. Hence, this paper introduces a variant of the algorithm which utilizes a global best model inspired from particle swarm optimization to enhance the global exploration ability while hybridizing with the great deluge (GD) algorithm in order to improve the local exploitation ability. Using this approach, an effective balance between exploration and exploitation is attained. In addition, a traditional local search approach is incorporated within the GD algorithm with the aim of further enhancing the performance of the overall hybrid method. To evaluate the performance of the proposed approach, two diverse university timetabling datasets are investigated, i.e., Carters examination timetabling and Socha course timetabling datasets. It should be noted that both problems have differing complexity and different solution landscapes. Experimental results demonstrate that the proposed method is capable of producing high quality solutions across both these benchmark problems, showing a good degree of generality in the approach. Moreover, the proposed method produces best results on some instances as compared with other approaches presented in the literature.

Utilizing shared interacting domain patterns and Gene Ontology information to improve protein-protein interaction prediction

Protein-protein interactions (PPIs) play a significant role in many crucial cellular operations such as metabolism, signaling and regulations. The computational methods for predicting PPIs have shown tremendous growth in recent years, but problem such as huge false positive rates has contributed to the lack of solid PPI information. We aimed at enhancing the overlap between computational predictions and experimental results in an effort to partially remove PPIs falsely predicted. The use of protein function predictor named PFP() that are based on shared interacting domain patterns is introduced in this study with the purpose of aiding the Gene Ontology Annotations (GOA). We used GOA and PFP() as agents in a filtering process to reduce false positive pairs in the computationally predicted PPI datasets. The functions predicted by PFP() were extracted from cross-species PPI data in order to assign novel functional annotations for the uncharacterized proteins and also as additional functions for those that are already characterized by the GO (Gene Ontology). The implementation of PFP() managed to increase the chances of finding matching function annotation for the first rule in the filtration process as much as 20%. To assess the capability of the proposed framework in filtering false PPIs, we applied it on the available S. cerevisiae PPIs and measured the performance in two aspects, the improvement made indicated as Signal-to-Noise Ratio (SNR) and the strength of improvement, respectively. The proposed filtering framework significantly achieved better performance than without it in both metrics.

Fusing the line intensity profile and support vector machine for removing reflections in frontal RGB color eye images

RGB eye image is prune to study reflections with large and different intensities.The fusion of line intensity profile and support vector machine is proposed.Line intensity profile is used for reflection identification.Support vector machine is used to classify between reflections and non-reflections.The proposed combination is able to decrease equal error rate of iris recognition. Iris recognition is a promising method for accurate identification of a person where the capability of iris segmentation determines its overall performance. Correct iris area has to be determined so that an individuals unique features can be extracted and compared during feature extraction and template matching processes. However, current methods fall short in correctly identifying and classifying reflections in an eye image. This has often led to errors in iris boundary localization and consequently increases the equal error rate in iris recognition. This study thus intends to propose a method that combines a line intensity profile and a support vector machine where the former identifies reflections in eye images, and the latter classifies reflections and non-reflections. The combined method was tested using 1000 eye images from the UBIRISv2 database. Results showed that the combined method provided almost 99.9% classification accuracy. Generally, it has less than 10.5% equal error rate and high decidability index in iris recognition.