Models & Optimisation and Mathematical Analysis Journal


This journal concerns both the national and international scientific community and will be primarily focusing on Models and Optimisation of Systems. Systems will be utilized in different applications for example, Web technologies, Information Systems, Decision Systems, Embedded Systems, Control-command Systems and Real-time Systems. Space of journal is also dedicated to mathematical analysis like functional spaces, polynomial computing etc. MOMAjournal reserves a space for research work in the field of the history of mathematics and the didactics of mathematics.







An optimization approach for task scheduling in cloud computing environments

علي بلقاسم, 

Résumé: The evolution of the tech world has boosted the power of the cloud more than ever due to the changing ways of software use and has made them mere services. Since the services are paid per use, reducing cost is an important issue to focus on. Consequently, task scheduling is considered one of the predominant topics to overcome this issue. In this paper, we suggest a new hybrid heuristic algorithm based on particle swarm optimization and Electromagnetism Meta-heuristic Algorithm (PSO-EMA). PSO-EMA aims to minimize the cost of using virtual machines. The simulation results demonstrate that the proposed algorithm is better than the Max-min, FCFS, PSO, EMA, and DSOS methods in terms of cost.

Mots clés: cloud computing ; resource allocation ; cost ; task Scheduling ; EMA algorithm

Application of Artificial Neural Networks Models in Diabetes Mellitus Classification

حداد فاطمة الزهرة,  بن بوراس محمد أمين, 

Résumé: Diabetes mellitus is one of the most worrying chronic diseases. It results from disturbances in blood sugar levels causing many complications and sometimes leads to death. International Diabetes Federation has stated that the number of diabetics is rising and could up to 642 million in 2040. The rapid development of information technology has imposed new advanced methods entitled “learning machine” or “artificial intelligence techniques” which have led to impressive results in the medical field. Based on this background, this study contributes to classify diabetes, by the mean applied of artificial neural networks ‘ANN’ method. The idea is based on the application of ANN on the pima Indian diabetes database on 3 cases, according to the number and the type of selected features "attributes". The results show high accuracy of the model with all attributes (92.3%) and without Diastolic blood pressure (92.6%). The proposed ANN model composed by two hidden layers ensures better predictability in data learning and yields data prediction values better than the ones published in previous studies.

Mots clés: Diabetes Mellitus ; Artificial Neural Networks (ANN) ; Pima Indian Diabetes (PID) ; Classification