Mohammed Aldasht

Palestine Polytechnic University · College of Information Technology and Computer Engineering

Feature selection (FS) is a pre-processing step that aims to eliminate the redundant and less-informative features to enhance the performance of data mining techniques. It is also considered as one of the key success factors for classification problems in high-dimensional datasets. This paper proposes an efficient wrapper feature selection method b...

Feature selection is a key success factor for classification problems with high dimensional and large datasets. In this paper, we introduce an approach for enhancing classification performance of high dimensional datasets using a combination of genetic algorithms for feature selection and One-class SVM for classification. The proposed approach is s...

Evolutionary algorithms provide mechanisms that can achieve efficient exploration for complex design spaces. Also, they constitute an efficient tool for identifying the best alternatives to implement the solution of a certain problem. In this research, we use Particle Swarm Optimization (PSO) to find the best alternatives for the distributed load b...

Evolutionary Algorithm (EA) provides a mechanism that can achieve efficient exploration for design spaces. Thus, it constitutes an efficient tool for identifying the best alternatives to implement the solution of a certain problem. In this work, EA is implemented to solve the university course scheduling problem and a real data from Palestine Polyt...

This paper presents a new heuristic based on evolutionary algorithms and applied to the university course scheduling problem, where a feasible and comfort time tables are required. Here, the idea is to use an evolution program which is a stochastic optimization strategy similar to genetic algorithms. The main difference is that evolutionary program...

The dynamic load balancing techniques, practically, do not assume any information about the tasks to be executed at compilation time. Parameters like execution time or communication time are unknown at compilation time. These techniques are used to distribute the computation tasks of an application between different processors at execution time to...

Evolutionary algorithms provide ways to explore wide search spaces. Thus, it is possible to get some conclusions about the characteristics of these spaces in order to aid in the determination of the best alternatives to solve the problem at hand. We have applied a genetic algorithm to assess the problem of distributed load balancing in parallel pro...

In this paper we implement an AR Model for prediction based on ANNs (Cross-over Predic-tion Model) in PVM ("Parallel Virtual Machine") and MPI ("Message Passing Interface"), in order to reduce computation time. PVM permits an hetero-geneous collection of Unix computers networked to-gether to be viewed by our program as a simple par-allel computer....

Evolutionary algorithms provide mechanisms that can achieve efficient exploration for design spaces. This way, they constitute an efficient tool for identifying the best alternatives to implement the solution of a certain problem. In this work we apply the genetic algorithms to the field of distributed load balancing in parallel computers. We have...

The size of images in image processing considered a critical point in processing the images, so we must process the large size of images in small time es-pecially in medical applications.In this paper, we present a new design of parallelizing Sobel edge de-tection algorithm in order to decrease the computa-tion time. The parallel algorithm is imple...

Hidden Markov models widely used as tool for sequential data modeling, and it were used many times in data clustering. In this work a HMM were employed to build a new space representation as feature extraction for protein sequences. Where each sequence described by a vector of its similarities respect to a predetermined set of other objects. K-mean...