Scheduling Timetables for courses in the big departments in the universities is a very hard problem and is often be solved by many previous works although results are partially optimal. This work implements the principle of an evolutionary algorithm by using genetic theories to solve the timetabling problem to get a random and full optimal timetable with the ability to generate a multi-solution timetable for each stage in the collage. The major idea is to generate course timetables automatically while discovering the area of constraints to get an optimal and flexible schedule with no redundancy through the change of a viable course timetable. The main contribution in this work is indicated by increasing the flexibility of generating optimal timetable schedules with different copies by increasing the probability of giving the best schedule for each stage in the campus with the ability to replace the timetable when needed. The Evolutionary Algorithm (EA) utilized in this paper is the Genetic Algorithm (GA) which is a common multi-solution metaheuristic search based on the evolutionary population that can be applied to solve complex combinatorial problems like timetabling problems. In this work, all inputs: courses, teachers, and time acted by one array to achieve local search and combined this acting of the timetable by using the heuristic crossover to ensure that the essential conditions are not broken. The result of this work is a flexible scheduling system, which shows the diversity of all possible timetables that can be created depending on user conditions and needs.
Implementation of TSFS (Transposition, Substitution, Folding, and Shifting) algorithm as an encryption algorithm in database security had limitations in character set and the number of keys used. The proposed cryptosystem is based on making some enhancements on the phases of TSFS encryption algorithm by computing the determinant of the keys matrices which affects the implementation of the algorithm phases. These changes showed high security to the database against different types of security attacks by achieving both goals of confusion and diffusion.
String matching is seen as one of the essential problems in computer science. A variety of computer applications provide the string matching service for their end users. The remarkable boost in the number of data that is created and kept by modern computational devices influences researchers to obtain even more powerful methods for coping with this problem. In this research, the Quick Search string matching algorithm are adopted to be implemented under the multi-core environment using OpenMP directive which can be employed to reduce the overall execution time of the program. English text, Proteins and DNA data types are utilized to examine the effect of parallelization and implementation of Quick Search string matching algorithm on multi-co
... Show MoreIn this paper, the methods of weighted residuals: Collocation Method (CM), Least Squares Method (LSM) and Galerkin Method (GM) are used to solve the thin film flow (TFF) equation. The weighted residual methods were implemented to get an approximate solution to the TFF equation. The accuracy of the obtained results is checked by calculating the maximum error remainder functions (MER). Moreover, the outcomes were examined in comparison with the 4th-order Runge-Kutta method (RK4) and good agreements have been achieved. All the evaluations have been successfully implemented by using the computer system Mathematica®10.
This paper focuses on developing a self-starting numerical approach that can be used for direct integration of higher-order initial value problems of Ordinary Differential Equations. The method is derived from power series approximation with the resulting equations discretized at the selected grid and off-grid points. The method is applied in a block-by-block approach as a numerical integrator of higher-order initial value problems. The basic properties of the block method are investigated to authenticate its performance and then implemented with some tested experiments to validate the accuracy and convergence of the method.
Finding communities of connected individuals in complex networks is challenging, yet crucial for understanding different real-world societies and their interactions. Recently attention has turned to discover the dynamics of such communities. However, detecting accurate community structures that evolve over time adds additional challenges. Almost all the state-of-the-art algorithms are designed based on seemingly the same principle while treating the problem as a coupled optimization model to simultaneously identify community structures and their evolution over time. Unlike all these studies, the current work aims to individually consider this three measures, i.e. intra-community score, inter-community score, and evolution of community over
... Show MoreAmong the metaheuristic algorithms, population-based algorithms are an explorative search algorithm superior to the local search algorithm in terms of exploring the search space to find globally optimal solutions. However, the primary downside of such algorithms is their low exploitative capability, which prevents the expansion of the search space neighborhood for more optimal solutions. The firefly algorithm (FA) is a population-based algorithm that has been widely used in clustering problems. However, FA is limited in terms of its premature convergence when no neighborhood search strategies are employed to improve the quality of clustering solutions in the neighborhood region and exploring the global regions in the search space. On the
... Show MoreThe current study aims to
1) identify the prevalent Enneagram personality types and the academic self among the sixth-grade students of the distinguished secondary schools; 2) the differences of relationship between the Enneagram personality types and the academic self-concept based on gender; 3) identify the correlation between the Enneagram personality types and the academic self-concept; and 4) identify the contribution of each type of personality types to the academic self-concept. To this end, two instrument were used to collect the data: Riso-Hudson’s scale,1995 was administered to measure enneagram types which had translated to the Arabic language by ( al-yassery,2004), the other instrument was
... Show Morehe assignment model represents a mathematical model that aims at expressing an important problem facing enterprises and companies in the public and private sectors, which are characterized by ensuring their activities, in order to take the appropriate decision to get the best allocation of tasks for machines or jobs or workers on the machines that he owns in order to increase profits or reduce costs and time As this model is called multi-objective assignment because it takes into account the factors of time and cost together and hence we have two goals for the assignment problem, so it is not possible to solve by the usual methods and has been resorted to the use of multiple programming The objectives were to solve the problem of
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