Omar Suleiman Arabeyyat

Bio: Omar Suleiman Arabeyyat is an academic researcher from Al-Balqa` Applied University. The author has contributed to research in topics: Metaheuristic & Multiple-criteria decision analysis. The author has an hindex of 4, co-authored 7 publications receiving 72 citations.

An efficient weighted distributed clustering algorithm for mobile ad hoc networks

Abstract: Clustering approach is an important research topic for MANETs and widely used in efficient network management, hierarchical routing protocol design, network modeling, Quality of Service, etc. Many researchers' recent focus has been on clustering management which is one of the fundamental problems in mobile ad hoc networks. The main objective of clustering in mobile ad-hoc network environments is how can an optimal clusterhead be elected and how can the optimal number of clusters be achieved through division without degrading the whole network's performance. In this paper, we propose new weighted distributed clustering algorithm, called CBMD. It takes into consideration the parameters: connectivity (C), residual battery power (B), average mobility (M), and distance (D) of the nodes to choose locally optimal clusterheads. The goals of this algorithm are maintaining stable clustering structure with a lowest number of clusters formed, to minimise the overhead for the clustering formation and maintenance and to maximise the lifespan of mobile nodes in the system. Simulation experiments are conducted to evaluate the performance of our algorithm in terms of the number of clusters formed, reaffiliation count and numbers of clusterhead changes. Results show that our algorithm performs better than existing ones and is also tuneable to different kinds of network conditions.

Multiple-Reservoir Scheduling Using β-Hill Climbing Algorithm

Abstract: The multi-reservoir systems optimization problem requires defining a set of rules to recognize the water amount stored and released in accordance with the system constraints. Traditional methods are not suitable for complex multi-reservoir systems with high dimensionality. Recently, metaheuristic-based algorithms such as evolutionary algorithms and local search-based algorithms are successfully used to solve the multi-reservoir systems. β-hill climbing is a recent metaheuristic local search-based algorithm. In this paper, the multi-reservoir systems optimization problem is tackled using β-hill climbing. In order to validate the proposed method, four-reservoir systems used in the literature to evaluate the algorithm are utilized. A comparative evaluation is conducted to evaluate the proposed method against other methods found in the literature. The obtained results show the competitiveness of the proposed algorithm.

The applications of Kaizen methods in project settings: applied study in Jordan

Abstract: The purpose of this paper is to explore the results of implementing the Kaizen approach in a caravan repairing project near the Jordanian–Syrian border in the Zaatari camp.,The study is based on the exploratory qualitative research approach. The data were collected through interviews and on-site observation with employees who were involved with the caravan maintenance project and have adequate knowledge and information about this project. In this process, a fishbone diagram, a quality control tool, is used to recognize and explain a causal-effect relationship under the selected Kaizen theme.,The findings suggest that the Kaizen approach was economical in terms of both money and time. Also, waste elimination can be achieved through a variety of tools and easily combined with the Kaizen approach. Implementing the Kaizen approach is an effective and reliable system that allows for the tackling of all types of inefficiencies in the caravan repairing project.,The findings of this study will help policy makers and managers put together suitable and effective policies that will assist those firms in overcoming the demands of customers and competitors to deliver high quality, inexpensive products in less time through the application of the Kaizen approach. This, in turn, will lead to improved quality, efficiency and productivity in the most cost-effective way. However, these results should not be generalized since they are only confined to the context of caravan repairing project.,Very little research has been done that takes into account the contexts of developing countries. Additionally, most literature presents the use of Kaizen applications only in the manufacturing or production sectors. This study is the first to implement Kaizen as a continuous improvement technique in a caravan repairing project – a job shop industry different from the repetitive batch work environment that is usually associated with implementation of Kaizen. The current research should be of great interest to researchers, managers and professionals who wish to apply Kaizen approach as it is sustainable over time in similar projects.

An integrated multi-criteria decision-making framework for a medical device selection in the healthcare industry

Abstract: Medical devices used in healthcare organizations are costly, and the process of selecting these devices requires considering multiple criteria such as effectiveness and ease of use. Careful selecti...

A New 1D Chaotic Map and $\beta$ -Hill Climbing for Generating Substitution-Boxes

Abstract: One-dimensional (1-D) chaotic maps have been considered as prominent pseudo-random source for the design of different cryptographic primitives. They have the advantages of simplicity, easy to implement, and low computation. This paper proposes a new 1-D discrete-chaotic map which holds better dynamical behavior, lyapunov exponent, bifurcation, and larger chaotic range compared with the chaotic logistic map. We propose a method to construct cryptographically efficient substitution-boxes (S-boxes) using an improved chaotic map and $\beta $ -hill climbing search technique. S-boxes are used in block ciphers as nonlinear components to bring strong confusion and security. Constructing optimal S-boxes has been a prominent topic of interest for security experts. To begin, the anticipated method generates initial S-box using the improved chaotic map. Then, $\beta $ -hill climbing search is applied to obtain notable configuration of S-box that optimally satisfies the fitness function. The simulation results are compared with some recent S-boxes approaches to demonstrate that the proposed approach is more proficient in generating strong nonlinear component of block encryption systems.

Computer Methods and Programs in Biomedicine

Abstract: Background and objective: Automatic detection and classification of the masses in mammograms are still a big challenge and play a crucial role to assist radiologists for accurate diagnosis. In this paper, we propose a novel Computer-Aided Diagnosis (CAD) system based on one of the regional deep learning techniques, a ROI-based Convolutional Neural Network (CNN) which is called You Only Look Once (YOLO). Although most previous studies only deal with classification of masses, our proposed YOLO-based CAD system can handle detection and classification simultaneously in one framework. Methods: The proposed CAD system contains four main stages: preprocessing of mammograms, feature extraction utilizing deep convolutional networks, mass detection with confidence, and finally mass classification using Fully Connected Neural Networks (FC-NNs). In this study, we utilized original 600 mammo- grams from Digital Database for Screening Mammography (DDSM) and their augmented mammograms of 2,400 with the information of the masses and their types in training and testing our CAD. The trained YOLO-based CAD system detects the masses and then classifies their types into benign or malignant. Results: Our results with five-fold cross validation tests show that the proposed CAD system detects the mass location with an overall accuracy of 99.7%. The system also distinguishes between benign and malignant lesions with an overall accuracy of 97%. Conclusions: Our proposed system even works on some challenging breast cancer cases where the masses exist over the pectoral muscles or dense regions.

Integration of continuous improvement strategies with Industry 4.0: a systematic review and agenda for further research

Abstract: The purpose of this paper is to provide a review of the history, trends and needs of continuous improvement (CI) and Industry 4.0. Four strategies are reviewed, namely, Lean, Six Sigma, Kaizen and Sustainability.,Digitalization and CI practices contribute to a major transformation in industrial practices. There exists a need to amalgamate Industry 4.0 technologies with CI strategies to ensure significant benefits. A systematic literature review methodology has been followed to review CI strategy and Industry 4.0 papers (n = 92).,Various frameworks of Industry 4.0, their advantages and disadvantages were explored. A conceptual framework integrating CI strategies and Industry 4.0 is being presented in this paper.,The benefits and practical application of the developed framework has been presented.,The article is an attempt to review CI strategies with Industry 4.0. A conceptual framework for the integration is also being presented.

A non-convex economic load dispatch problem with valve loading effect using a hybrid grey wolf optimizer

Abstract: Economic load dispatch (ELD) is a crucial problem in the power system which is tackled by distributing the required generation power through a set of units to minimize the fuel cost required. This distribution is subject to two main constraints: (1) equality and inequality related to power balance and power output, respectively. In the optimization context, ELD is formulated as a non-convex, nonlinear, constrained optimization problem which cannot be easily solved using calculus-based techniques. Several optimization algorithms have been adapted. Due to the complexity nature of ELD search space, the theoretical concepts of these optimization algorithms have been modified or hybridized. In this paper, the grey wolf optimizer (GWO) which is a swarm intelligence is hybridized with $$\beta$$ -hill climbing optimizer ( $$\beta$$ HC) which is a local search algorithm, to improve convergence properties. GWO is very powerful in a wide search, while $$\beta$$ HC is very powerful in deep search. By combining the wide and deep search ability in a single optimization framework, the balance between the exploration and exploitation is correctly managed. The proposed hybrid algorithm is named $$\beta$$ -GWO which is evaluated using five different test cases of ELD problems: 3 generating units with 850 MW; 13 generating units with 1800 MW; 13 generating units with 2520 MW; 40 generating units with 10,500 MW; and 80 generating units with 21,000 MW. $$\beta$$ -GWO is comparatively measured using 49 comparative methods. The results obtained by $$\beta$$ -GWO outperform others in most test cases. In conclusion, the proposed $$\beta$$ -GWO is proved to be a powerful method for ELD problem or for any other similar problems in the power system domain.