Khalid Shaker

Arabic machine translation: a survey

Although there is no machine learning technique that fully meets human requirements, finding a quick and efficient translation mechanism has become an urgent necessity, due to the differences between the languages spoken in the world’s communities and the vast development that has occurred worldwide, as each technique demonstrates its own advantages and disadvantages. Thus, the purpose of this paper is to shed light on some of the techniques that employ machine translation available in literature, to encourage researchers to study these techniques. We discuss some of the linguistic characteristics of the Arabic language. Features of Arabic that are related to machine translation are discussed in detail, along with possible difficulties that they might present. This paper summarizes the major techniques used in machine translation from Arabic into English, and discusses their strengths and weaknesses.

Dual sequence simulated annealing with round-robin approach for university course timetabling

The university course timetabling problem involves assigning a given number of events into a limited number of timeslots and rooms under a given set of constraints; the objective is to satisfy the hard constraints (essential requirements) and minimize the violation of soft constraints (desirable requirements). In this study we employed a Dual-sequence Simulated Annealing (DSA) algorithm as an improvement algorithm. The Round Robin (RR) algorithm is used to control the selection of neighbourhood structures within DSA. The performance of our approach is tested over eleven benchmark datasets. Experimental results show that our approach is able to generate competitive results when compared with other state-of-the-art techniques.

Incorporating great deluge approach with kempe chain neighbourhood structure for curriculum-based course timetabling problems

Constructing university course timetable is a very difficult task where a set of events has to be scheduled in timeslots and located in suitable rooms. The objective of course timetabling problem is to satisfy the hard constraints and minimize the violation of soft constraints. In this paper, a hybridization of great deluge algorithm with kempe chain neighbourhood structure is employed. The problem is solved in two steps: a graph-based heuristic is used to construct a feasible timetable in the first step, and the improvement is carried out by employing a hybrid approach in the second step. The approach is tested on the curriculum-based course timetabling problems as described in the 2nd International Timetabling Competition (ITC2007). We present the result of our technique in relation to the competition results that show the proposed approach is able to produce promising results for the university course timetabling problem.

Controlling multi algorithms using Round Robin for university course timetabling problem

The university course timetabling problem (CTTP) involves assigning a given number of events into a limited number of timeslots and rooms under a given set of constraint. The objective is to satisfy the hard constraints (essential requirements) and minimise the violation of soft constraints (desirable requirements). In this study, we apply three algorithms to the CTTP problem: Great Deluge, Simulated Annealing and Hill Climbing. We use a Round Robin Scheduling Algorithm (RR) as a strategy to control the application of these three algorithms. The performance of our approach is tested over eleven benchmark datasets: one large, five medium and five small problems. Competitive results have been obtained when compared with other state-of-the-art techniques.

Incorporating great deluge with kempe chain neighbourhood structure for the enrolment-based course timetabling problem

In general, course timetabling refers to assignment processes that assign events (courses) to a given rooms and timeslots subject to a list of hard and soft constraints. It is a challenging task for the educational institutions. In this study we employed a great deluge algorithm with kempe chain neighbourhood structure as an improvement algorithm. The Round Robin (RR) algorithm is used to control the selection of neighbourhood structures within the great deluge algorithm. The performance of our approach is tested over eleven benchmark datasets (representing one large, five medium and five small problems). Experimental results show that our approach is able to generate competitive results when compared with previous available approaches. Possible extensions upon this simple approach are also discussed.

Hybridizing Meta-heuristics Approaches for Solving University Course Timetabling Problems

In this paper we have presented a combination of two meta-heuristics, namely great deluge and tabu search, for solving the university course timetabling problem. This problem occurs during the assignment of a set of courses to specific timeslots and rooms within a working week and subject to a variety of hard and soft constraints. Essentially a set of hard constraints must be satisfied in order to obtain a feasible solution and satisfying as many as of the soft constraints as possible. The algorithm is tested over two databases: eleven enrolment-based benchmark datasets representing one large, five medium and five small problems and curriculum-based datasets used and developed from the International Timetabling Competition, ITC2007 UD2 problems. A new strategy has been introduced to control the application of a set of neighbourhood structures using the tabu search and great deluge. The results demonstrate that our approach is able to produce solutions that have lower penalties on all the small and medium problems in eleven enrolment-based datasets and can produce solutions with comparable results on the curriculum-based datasets with lower penalties on several data instances when compared against other techniques from the literature.

Morphological analysis for rule based machine translation

The Arabic language is a Semitic language and it exhibits systematic but complex morphological structure based on root-pattern design. The aim of the present paper is to propose a transfer-based approach using morphological analysis which induces a syntactic symmetry and morphology to improve machine translation system between Arabic and English. Both languages are highly asymmetrical in terms of morphological structures. Our system supposes segmentation the word in the morphologically rich language into the sequence of prefix (es)-stem-suffix (es). The system identifies morphemes to be merged or deleted in the morphologically rich language to make the desired morphological and syntactic symmetry. The technique applied aims to improve Arabic-to-English translation quality.

The Effect of Neighborhood Structures on Tabu Search Algorithm in Solving University Course Timetabling Problem

The design of course timetables for academic institutions is a very difficult job due to the huge number of possible feasible timetables with respect to the problem size. This process contains lots of constraints that must be taken into account and a large search space to be explored, even if the size of the problem input is not significantly large. Different heuristic approaches have been proposed in the literature in order to solve this kind of problem. One of the efficient solution methods for this problem is tabu search. Different neighborhood structures based on different types of move have been defined in studies using tabu search. In this paper, different neighborhood structures on the operation of tabu search are examined. The performance of different neighborhood structures is tested over eleven benchmark datasets. The obtained results of every neighborhood structures are compared with each other. Results obtained showed the disparity between each neighborhood structures and another in terms of penalty cost.

Training the Neural Networks by Electromagnetism-like Mechanism Based algorithm

Recently, medical data mining has become one of the most popular topics in the data mining community. This is due to the societal importance of the field and also the particular computational challenges posed in this domain of data mining. Early researches concentrated on sequential heuristics and later moved to meta-heuristic approaches due to the ability of these approaches to generate better solutions. The aim of this paper is to introduce the basic principles of a new meta-heuristic algorithm called Electromagnetism-like Mechanism (EMag) for neural network training. EMag simulates the electromagnetism theory of physics by considering each data sample to be an electrical charge. For neural network, EMag simulates the attraction-repulsion mechanism of each weight connection as charge partials to move towards the optimum without being trapped into local minimum. The performance of the proposed algorithm is evaluated in 12 of benchmark classification problems, and the computational results show that the proposed algorithm performs better than the standard back propagation algorithm.

A Dynamic Scatter Search Algorithm for Solving Traveling Salesman Problem

Scatter Search (SS) is a population-based evolutionary metaheuristic algorithm that selects solutions from a specific memory called a reference set (RefSet) to produce other diverse solutions. In this work, a dynamic SS algorithm is proposed to solve the symmetric traveling salesman problem (TSP). To improve the performance of SS, a dynamic RefSet update and a dynamic population update are proposed. To test the performance of the proposed algorithm, computational experiments are carried out on the basis of the benchmark instances of the problem. The computational results show that the performance of the proposed algorithm is effective in solving the TSP.