The purpose of this paper is to study the dynamics of fisheries resources with reserve area, in the presence of bird predators. Aquatic area under investigation is divided into two areas : one for the free fishing and other limited for any type of fishing. In the project harvesting system according to the modified E effort is considered, which depends on the effect of resource density. The local and stability criteria, the overall stability and instability are established for the project model. Finally, the theoretical results are illustrated by numerical simulations in the last section.

Radio Cognitive (RC) is the new concept introduced to improve spectrum utilization in wireless communication and present important research field to resolve the spectrum scarcity problem. The powerful ability of CR to change and adapt its transmit parameters according to environmental sensed parameters, makes CR as the leading technology to manage spectrum allocation and respond to QoS provisioning. In this paper, we assume that the radio environment has been sensed and that the SU specifies QoS requirements of the wireless application. We use genetic algorithm (GA) and propose crossover method called Combined Single-Heuristic Crossover. The weighted sum multi-objective approach is used to combine performance objectives functions discussed in this paper and BER approximate formula is considered.

A. KAILILT*, N. M R A N I~, M. MLIHA TOUATI~, S. CHOUKRI~ and N. ELALAMI~ Fractional Order Control methods were applied to a three-axis reaction wheels satellite attitude control system. To show the advantages of this method, a comparative study between a Linear Quadratic Regulator and a Fractional Order Control was established through two principal fractional control laws. The aim is to establish an efJicient control law which satisJies a given speczjication and maintains su-cient stability and accuracy even under the strong efects of intrinsic parameters uncertainties, and also external perturbations.

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In this paper, the problem of global state regulation via output feedback is investigated to study a structured fishing model, in order to stabilize its states around a non-trivial equilibrium. In our case, the two stages of the juvenile and adults ages of fish population are considered. In order to apply the tools of automatic control to our model, the fishing effort is used as a control term, the age classes as a states and the quantity of captured fish per unit of effort as a measured output. A Lyapunov function is adapted to study the stability and stabilization of the studied system around the non-trivial steady states. Numerical example demonstrates the effectiveness and the convergence of the states to the equilibrium.

This paper presents a new algorithm for computing the transfer function from state equations for linear system. This algorithm employs an approximation method, which uses Krylov subspace techniques for linear system. We have focused on the Lanczos-based using the properties of Schur complemnts. This approach reduces the computation of transfer function from state equation for linear system.

In this paper, we use Lyapunov approach to study the stability of an exploited fish population system. The model considered is structured in n age classes and includes a nonlinear polynomial stock-recruitment relationship. Lyapunov candidate functions is investigated and sufficient conditions for global asymptotic stability of the studied polynomial system are proposed to allow computational implementation. The results obtained extend our previous study focusing on the solution to the same problem solved using other tools of control engineering. The advantage of the proposed approach is that the derived conditions proving the stability of the studied systems can be presented as in inequality in general and feasibility tests. The obtained results are tested by numerical examples.

Coronary Artery Disease (CAD) is one of the most common cardiac diseases worldwide and causes disability and economic burden. It is the world's leading and most serious cause of mortality, with approximately 80% of deaths reported in low-and middle-income countries. The preferred and most precise diagnostic tool for CAD is angiography, but it is invasive, expensive, and technically demanding. However, the research community is increasingly interested in the computeraided diagnosis of CAD via the utilization of machine learning (ML) methods. The purpose of this work is to present an ediagnosis tool based on ML algorithms that can be used in a smart healthcare monitoring system. We applied the most accurate machine learning methods that have shown superior results in the literature to different medical datasets such as RandomForest, XGboost, MultilayerPerceptron, J48, AdaBoost, NaiveBayes, LogitBoost, KNN. Every single classifier can be efficient on a different dataset. Thus, an ensemble model using majority voting was designed to take advantage of the wellperformed single classifiers, Ensemble learning aims to combine the forecasts of multiple individual classifiers to achieve higher performance than individual classifiers in terms of precision, specificity, sensitivity, and accuracy; furthermore, we have benchmarked our proposed model with the most efficient and wellknown ensemble models, such as Bagging, Stacking methods based on the cross-validation technique, The experimental results confirm that the ensemble majority voting approach based on the top three classifiers: MultilayerPerceptron, RandomForest, and AdaBoost, achieves the highest accuracy of 88,12% and outperforms all other classifiers. This study demonstrates that the majority voting ensemble approach proposed above is the most accurate machine learning classification approach for the prediction and detection of coronary artery disease.