Fuzzy methodologies for automated University timetabling solution construction and evaluation

This thesis presents an investigation into the use of fuzzy methodologies for University timetabling problems. The first area of investigation is the use of fuzzy techniques to combine multiple heuristic orderings within the construction of timetables. Different combinations of multiple heuristic ordering were examined, considering five graph-based heuristic orderings - Largest Degree, Saturation Degree, Largest Enrolment, Largest Coloured Degree and Weighted Largest Degree. The initial development utilised only two heuristic orderings simultaneously and subsequent development went on to incorporate three heuristic orderings simultaneously. A central hypothesis of this thesis is that this approach provides a more realistic scheme for measuring the difficulty of assigning events to time slots than the use of a single heuristic alone. Experimental results demonstrated that the fuzzy multiple heuristic orderings (with parameter tuning) outperformed all of the single heuristic orderings and non-fuzzy linear weighting factors. Comprehensive analysis has provided some key insights regarding the implementation of multiple heuristic orderings. Producing examination timetables automatically has been the subject of much research. It is generally the case that a number of alternative solutions that satisfy all the hard criteria are possible. Indeed, there are usually a very large number of such feasible solutions. Some method is required to permit the overall quality of different solutions to be quantified, in order to allow them to be compared, so that the best may be selected. In response to that demand, the second area of investigation of this thesis is concerned with a new evaluation function for examination timetabling problems. A novel approach, in which fuzzy methods are used to evaluate the end solution quality, separate from the objective functions used in solution generation, represents a significant addition to the literature. The proposed fuzzy evaluation function provides a mechanism to allow an overall decision in evaluating the quality of a timetable solution to be made based on common sense rules that encapsulate the notion that the timetable solution quality increases as both the average penalty and the highest penalty decrease. New algorithms to calculate what is loosely termed the lower limits and upper limits of the proximity cost function for any problem instance are also presented. These limits may be used to provide a good indication of how good any timetable solution is. Furthermore, there may be an association between the proposed lower limit and the formal lower bound. This is the first time that lower limits (other than zero) have been established for proximity cost evaluation of timetable solutions

Automatic generation of test cases from activity diagrams for UML based testing (UBT)

Activity diagrams are one of UML behavioural models suitable for system testing because it has the capacity to effectively describe the behaviours of systems under development. In this paper, a technique is proposed that generates test cases from activity diagrams by constructing an activity flow tree (AFT) which stores all the information extracted from the model file of the diagram through the help of a parser. Then, we applied an algorithm to generate test cases from the constructed tree. Test cases were generated based on the elements of activity diagrams such as activity sequences, associated descriptions and conditions. The proposed technique generated accurate test cases that completely tallied with the modeled requirements in the diagram. We utilized all-paths, basic pair paths, conditions, branches and transition criteria for generating test cases using ATM withdrawal operation software as a case study

A modified migrating bird optimization for university course timetabling problem

University course timetabling problem is a dilemma which educational institutions are facing due to various demands to be achieved in limited resources. Migrating bird optimization (MBO) algorithm is a new meta-heuristic algorithm which is inspired by flying formation of migrating birds. It has been applied successfully in tackling quadratic assignment problem and credit cards fraud detection problem. However, it was reported that MBO will get stuck in local optima easily. Therefore, a modified migrating bird optimization algorithm is proposed to solve post enrolment-based course timetabling. An improved neighbourhood sharing mechanism is used with the aim of escaping from local optima. Besides that, iterated local search is selected to be hybridized with the migrating bird optimization in order to further enhance its exploitation ability. The proposed method was tested using Socha’s benchmark datasets. The experimental results show that the proposed method outperformed the basic MBO and it is capable of producing comparable results as compared with existing methods that have been presented in literature. Indeed, the proposed method is capable of addressing university course timetabling problem and promising results were obtained

Soft lens detection in iris image using lens boundary analysis and pattern recognition approach

Recent studies have demonstrated that the soft lens wearing during iris recognition has indicated the increase of false reject rate. It denies the strong belief that the soft lens wearing will cause no performance degradation. Therefore, it is a necessity for an iris recognition system to be able to detect the presence of soft lens prior to iris recognition. As a first step towards soft lens detection, this study proposed a method for segmenting the soft lens boundary in iris images. However, segmenting the soft lens boundary is a very challenging task due to its marginal contrast. Besides, the flash lighting effect during the iris image enrolment has caused the image to suffer from inconsistent illumination. In addition, the visibility condition of the soft lens boundary may be discerned as a bright or dark ridge as a result of the flash lighting. Three image enhancement techniques were therefore proposed in order to enhance the contrast of the soft lens boundary and to provide an even distribution of intensities across the image. A method called summed-histogram has been incorporated as a solution to classify the visibility condition of the soft lens boundary automatically. The visibility condition of the ridge is used to determine the directional directive magnitude by the ridge detection algorithm. The proposed method was evaluated with Notre Dame Contact Lens Detection 2013 database. Results showed that the proposed method has successfully segment the soft lens boundary with an accuracy of over 92%

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

Fusion Iris and Periocular Recognitions in Non-Cooperative Environment

The performance of iris recognition in non-cooperative environment can be negatively impacted when the resolution of the iris images is low which results in failure to determine the eye center, limbic and pupillary boundary of the iris segmentation. Hence, a combination with periocular features is suggested to increase the authenticity of the recognition system. However, the texture feature of periocular can be easily affected by a background complication while the colour feature of periocular is still limited to spatial information and quantization effects. This happens due to different distances between the sensor and the subject during the iris acquisition stage as well as image size and orientation. The proposed method of periocular feature extraction consists of a combination of rotation invariant uniform local binary pattern to select the texture features and a method of color moment to select the color features. Besides, a hue-saturation-value channel is selected to avoid loss of discriminative information in the eye image. The proposed method which consists of combination between texture and colour features provides the highest accuracy for the periocular recognition with more than 71.5% for the UBIRIS.v2 dataset and 85.7% for the UBIPr dataset. For the fusion recognitions, the proposed method achieved the highest accuracy with more than 85.9% for the UBIRIS.v2 dataset and 89.7% for the UBIPr dataset

Contact lens classification by using segmented lens boundary features

Recent studies have shown that the wearing of soft lens may lead to performance degradation with the increase of false reject rate. However, detecting the presence of soft lens is a non-trivial task as its texture that almost indiscernible. In this work, we proposed a classification method to identify the existence of soft lens in iris image. Our proposed method starts with segmenting the lens boundary on top of the sclera region. Then, the segmented boundary is used as features and extracted by local descriptors. These features are then trained and classified using Support Vector Machines. This method was tested on Notre Dame Cosmetic Contact Lens 2013 database. Experiment showed that the proposed method performed better than state of the art methods

Contact lens classification by using segmented lens boundary features

A recent studies have shown that the wearing of soft lens may lead to performance degradation with the increase of false reject rate. However, detecting the presence of soft lens is a non-trivial task as its texture that almost indiscernible. In this work, we proposed a classification method to identify the existence of soft lens in iris image. Our proposed method starts with segmenting the lens boundary on top of the sclera region. Then, the segmented boundary is used as features and extracted by local descriptors. These features are then trained and classified using Support Vector Machines. This method was tested on Notre Dame Cosmetic Contact Lens 2013 database. Experiment showed that the proposed method performed better than state of the art method

Automated examination timetabling solution construction using fuzzy approach

Examination timetabling is of much interest and concern to academic institutions. The basic problem is to allocate a timeslot for all exams within a limited number of permitted timeslots in order to find a conflict free timetable. This assignment process is subject to ‘hard’ constraints which must be satisfied in order to get a feasible timetable, such as no student is required to sit two exams at the same time. In addition, it is also important to build a good quality examination timetable that considers not only the administration requirements, but also takes into account lecturers’ and students’ preferences. These requirements are generally considered as ‘soft’ constraints which are desirable (but not essential) to satisfy. As reported by Burke et al. (1996), these requirements vary from one academic institution to another