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This paper suggests an enhanced machine-learning-based system to guide future stock price decisions. In reality, most existing machine learning systems, such as SEA (Stream Ensemble Algorithm), VFDT (Very Fast Decision Tree ), and online bagging and boosting, keep models updated with only new data and reduce training timeframes to allow working rapidly with the most recent model. However, limited learning times and the exclusion of essential information from previous data may result in a bad performance. When it comes to learning models, our system takes a different approach. It builds several models based on random selections of historical data from the main stock as well as related stocks. The best models are then combined to generate a final, performant model. We performed an empirical study on five Islamic stock market indices. We can say from the results that our system outperforms the existing published algorithms. This framework can contribute then to having an enhanced system that will enable different stakeholders to make rapid decisions based on the forecasted trend of indices.

This work has two principal goals. First, we investigate the asymptotic behavior of a two-group epidemiological model and determine the expression of its basic reproduction number using the dynamical systems approach based on the spectral radius of the relative matrix. Second, we simulate the obtained analytical results using a new deep learning method that associates the ordinary differential equations governing the model to neural networks. A general disease-free equilibrium is considered and sufficient conditions of stability and convergence are formulated. A detailed description of the neural network model used in the simulation is provided. Moreover, the proposed deep learning simulation algorithm is compared to the simulation provided by “odeint”, a function from “SciPy” which is a Python library of mathematical routines.

In this paper, we propose a new formulation of Nash games for solving a general multi-objectives optimization problems. The objective of this approach is to split the optimization variables, allowing us to determine numerically the strategies between two players. The first player minimizes his function cost using the variables of the first table P and the second player, using the second table Q. The original contribution of this work concerns the construction of the two tables of allocations that lead to a Nash equilibrium on the Pareto front. The second proposition of this paper is to find a Nash Equilibrium solution, which coincides with the Kalai--Smorodinsky solution. Two algorithms that calculate P, Q and their associated Nash equilibrium, by using some extension of the normal boundary intersection approach, are tried out successfully. Then, we propose a search engine to look for similar images of a given image based on multiple image representations using Color, Texture and Shape Features.

International audience; The purpose of this work is the study of the qualitative behavior of the homogeneous in space solution of a delay differential equation arising from a model of infection dynamics. This study is mainly based on the monotone dynamical systems theory. Existence and smoothness of solutions are proved, and conditions of asymptotic stability of equilibriums in the sense of monotone dynamical systems are formulated. Then, sufficient conditions of global stability of the nonzero steady state are derived, for the two typical forms of the function f, specifying the efficiency of immune response-mediated virus elimination. Numerical simulations illustrate the analytical results. The obtained theoretical results have been applied, in a context of COVID-19 data calibration, to forecast the immunological behaviour of a real patient.

The purpose of this work is to study the impact of introducing ethical products in banking system. It explores the use of Agent-Based Models (ABM) in order to simulate the evolution of the financial system if we consider ethical principles. The ultimate aim is to catch how, some important economical features such as consumption, saving, production, and investment, can vary following banks’ actions. We are interested also in the interaction between the different agents, and how the behaviour of banks influences the actions adopted by the financial entities. Particular attention will be given to the evolution of ethics in the model and its effects on the decisions of agents.

This article is concerned with the debate around the economic knowledge evolution and the role of ethics in economy. It reports on the 2008 crisis, the research literature reveals two main problems: the efficiency of the economic modeling and the failure of the ethical system.The authors explore the use of the new Dynamic Stochastic General Equilibrium “DSGE” model in the case of Islamic economy, it can enable to develop a new approach, taking into account the criticism of the models used before the crisis, and giving more importance to the ethical principles.The question is to know if the principles of Islamic economy feed into a sustainable economic system.The characteristic of this model lies in the consideration of Islamic principles, namely the abolition of interest rates and their replacement by the rate of return of the capital. In this perspective, it is supposed that the intervention of the monetary authorities is done by an unconventional approach. The model also distinguishes itself by the integration of Zakat. The model is applied in the case of Morocco.The results of simulations show that the introduction of these Islamic principles has no negative effects on the macroeconomic and financial conditions of Morocco and that the stability of the economic system is maintained.

Urologic studies have reported in several papers that the optimal dose of Bacillus Calmette-Guerin (BCG) immunotherapy in superficial bladder cancer, is still a subject of research. Our main goal from this paper, is to find treatment regimens that minimize the total number of tumors in the presence of a diffusion process. For this, we devise a stochastic model in the form of a nonlinear system of four stochastic differential equations (SDEs) that describe tumor-immune dynamics after BCG instillations. Therefore, we study the existence and the stability results. Then, we introduce a control function in the mathematical model, to represent the dose of BCG intravesical therapy, and we seek its optimal values through the application of a stochastic version of Pontryagin's maximum principle. Finally, we present some numerical simulations using iterative stochastic Runge-Kutta progressive-regressive schemes which we propose for solving the optimality system of the obtained stochastic two-point boundary value problem.

The fractional flow reserve (FFR) provides an efficient quantitative assessment of the severity of a coronary lesion. Our aim is to address the problem of computing non-invasive virtual fractional flow reserve (VFFR). In this paper, we present a preliminary study of the main features of flow over a stenosed coronary arterial portion, in order to enumerate the different factors affecting the VFFR. We adopt a non-Newtonian flow model and we assume that the two-dimensional (2D) domain is rigid in a first place. In a second place, we consider a simplified weakly coupled FSI model in order to take into account the infinitesimal displacements of the upper wall. A 2D finite element solver was implemented using Freefem++. We computed the VFFR profiles with respect to different lesion parameters and compared the results given by the rigid wall model to those obtained for the elastic wall one.

International audience; We propose a game theory approach to simultaneously restore and segment noisy images. We define two players: one is restoration, with the image intensity as strategy, and the other is segmentation with contours as strategy. Cost functions are the classical relevant ones for restoration and segmentation, respectively. The two players play a static game with complete information, and we consider as solution to the game the so-called Nash Equilibrium. For the computation of this equilibrium we present an iterative method with relaxation. The results of numerical experiments performed on some real images show the relevance and efficiency of the proposed algorithm.

In this work, we propose an approximate optimal control formulation of the Cauchy problem for the Stokes system. Here the problem is converted into an optimization one. In order to handle the instability of the solution of this ill-posed problem, a regularization technique is developed. We add a term in the least square function which happens to vanish while the algorithm converges. The efficiency of the proposed method is illustrated by numerical experiments.

International audience; Multiple myeloma (MM) is a genetically complex hematological cancer that is characterized by proliferation of malignant plasma cells in the bone marrow. MM evolves from the clonal premalignant disorder monoclonal gammopathy of unknown significance (MGUS) by sequential genetic changes involving many different genes, resulting in dysregulated growth of multiple clones of plasma cells. The migration, survival, and proliferation of these clones require the direct and indirect interactions with the non-hematopoietic cells of the bone marrow. We develop a hybrid discrete-continuous model of MM development from the MGUS stage. The discrete aspect of the model is observed at the cellular level: cells are represented as individual objects which move, interact, divide, and die by apoptosis. Each of these actions is regulated by intracellular and extracellular processes as described by continuous models. The hybrid model consists of the following submodels that have been simplified from the much more complex state of evolving MM: cell motion due to chemotaxis, intracellular regulation of plasma cells, extracellular regulation in the bone marrow, and acquisition of mutations upon cell division. By extending a previous, simpler model in which the extracellular matrix was considered to be uniformly distributed, the new hybrid model provides a more accurate description in which cytokines are produced by the marrow microenvironment and consumed by the myeloma cells. The complex multiple genetic changes in MM cells and the numerous cell-cell and cytokine-mediated interactions between myeloma cells and their marrow microenviroment are simplified in the model such that four related but evolving MM clones can be studied as they compete for dominance in the setting of intraclonal heterogeneity.

Today, insurers may use the yield curve as an indicator evaluation of the profit or the performance of their portfolios; therefore, they modeled it by one class of model that has the ability to fit and forecast the future term structure of interest rates. This class of model is the Nelson-Siegel-Svensson model. Unfortunately, many authors have reported a lot of difficulties when they want to calibrate the model because the optimization problem is not convex and has multiple local optima. In this context, we implement a hybrid Particle Swarm optimization and Nelder Mead algorithm in order to minimize by least squares method, the difference between the zero-coupon curve and the NSS curve., {"references":["Bliss R. and Fama E. The Information in Long Maturity Forward Rates\nAmerican Economic Review,Vol. 77 , 680.692.1987.","Bonnin F., Planchet F. and Juillard M Application of stochastic techniques\nfor the prospective analysis of the fiscal impact of interest rate\nrisk-Example on financial expenses of a complex bond debt French\nBulletin of Actuarial,vol. 11, no 21,2011.","Diebold F.X. and LI C Forecasting the term structure of government bond\nyields. Journal of Econometrics,vol. 130, no 2, 337.364,2006.","Diebold F. X.and Rudebusch G. D.and Borag[caron]an Aruoba S The\nmacroeconomy and the yield curve: A dynamic latent factor approach.\nJournal of Econometrics Vol.131,309-338, 2006.","Gilli. M. and Grosse.S and Schumann. E. Calibrating the\nNelson-Siegel-Svensson Model. Computational Optimization Methods in\nStatistics, Econometrics and Finance, 2010.","Gimeno. R. and Nave, Juan Migue. Genetic Algorithm Estimation of\nInterest Rate Term Structure.. Banco de Espana Research Paper No.\nWP-0634,2010.","Hautsch N. E and Ou Y. Analyzing interest rate risk: Stochastic volatility\nin the term structure of government bond yields. Journal of Banking and\nFinance Vol.36, 2988-3007,2012.","Kao Y.T., E. Zahara, and I. W. Kao A hybridized approach to data\nclustering. Expert Systems with Applications, vol. 34, no. 3, pp.\n17541762, 2008.","Kennedy, J. and Eberhart, R. Particle Swarm Optimization In Proceedings\nof IEEE International Conference on Neural Network, volume IV, 1995.\n[10] Koduru P., S. Das, S. M. Welch, J. L. Roe. Fuzzy Dominance\nBased Multi-objective GA-Simplex Hybrid Algorithms Applied to Gene\nNetwork Models Lecture Notes in Computer Science: Proceedings of the\nGenetic and Evolutionary Computing Conference, Seattle, Washington,\n(Eds. Kalyanmoy Deb et al.), Springer-Verlag, Vol 3102, pp. 356-367,\n2004.\n[11] Liu.A, and Yang M.T A New Hybrid Nelder-Mead Particle Swarm\nOptimization for Coordination Optimization of Directional Overcurrent\nRelays. Hindawi Publishing Corporation Mathematical Problems in\nEngineering Volume 2012, Article ID 456047, 18 pages,2012.\n[12] McCulloch J.H The Tax-Adjusted Yield Curve The Journal of Finance\nVol.30, 811-830,1975.\n[13] Nelson C. and Siegel A. F Parsimonious Modeling of Yield Curves\nJournal of Business, Vol. 60, 473.489,1987.\n[14] Svensson L. E. O Estimating and Interpreting Forward Interest Rates:\nSweden 1992-1994 IMFWorking Paper , 1.49,1994.\n[15] Vasicek O. A. and Fong H. G. Term Structure Modeling Using\nExponential Splines. Journal of Finance,Vol. 73,339.348,1982."]}

In this paper we present a new hybrid method, called the SASP method. The purpose of this method is the hybridization of the simulated annealing (SA) with the descent method, where we estimate the gradient using simultaneous perturbation. Firstly, the new hybrid method finds a local minimum using the descent method, then SA is executed in order to escape from the currently discovered local minimum to a better one, from which the descent method restarts a new local search, and so on until convergence. The new hybrid method can be widely applied to a class of global optimization problems for continuous functions with constraints. Experiments on 30 benchmark functions, including high dimensional functions, show that the new method is able to find near optimal solutions efficiently. In addition, its performance as a viable optimization method is demonstrated by comparing it with other existing algorithms. Numerical results improve the robustness and efficiency of the method presented.

This study investigates the effects of the input parameter uncertainties (organ conductivities, boundary data, etc.) on the electrocardiography (ECG) imaging problem. These inputs are very important for the construction of the torso potential for the forward problem and for the non-invasive electrical potential on the heart surface in the case of the inverse problem. We propose a new stochastic formulation that allows us to combine both sources of errors. We formulate the forward and inverse stochastic problems by considering the input parameters as random fields and a stochastic optimal control formulation. In order to quantify multiple independent sources of uncertainties on the forward and inverse solutions, we attribute suitable probability density functions for each randomness source and apply stochastic finite elements based on generalized polynomial chaos method. The efficiency of this approach to solve the forward and inverse ECG problems and the usability to quantify the effect of organ conductivity and epicardial boundary data uncertainties in the torso are demonstrated by a number of numerical simulations on a two-dimensional computational mesh of a realistic torso geometry.

In this paper we present a new hybrid method, called SASP method. We propose the hybridization of two methods, the simulated annealing (SA), which belong to the class of global optimization based on the principles of thermodynamics, and the descent method were we estimate the gradient using the simultaneous perturbation. This hybrid method gives better results. We use the Normal Boundary Intersection approach (NBI) based on the SASP method to solve a portfolio optimization problem. Such problem is a multi-objective optimization problem, in order to solve this problem we use three statistical quantities: the expected value, the variance and the Conditional Value-at-Risk (CVaR). The purpose of this work is to find the efficient boundary of the considered multi-objective problem using the NBI method based on the SASP method.

The image's restoration is an essential step in medical imaging. Several Filters are developped to remove noise, the most interesting are filters who permits to denoise the image preserving semantically important structures. One class of recent adaptive denoising methods is the nonlinear Partial Differential Equations who knows currently a significant success. This work deals with mathematical study for a proposed nonlinear evolution partial differential equation for image processing. The existence and the uniqueness of the solution are established. Using a finite differences method we experiment the validity of the proposed model and we illustrate the efficiently of the method using some medical images. The Signal to Noise Ration (SNR) number is used to estimate the quality of the restored images.

This paper suggests a decoupled method in order to approximate a free boundary separating the depletion region and the charge neutrality region in a field effect transistor of MESFET type. In order to do that, a simplified drift-diffusion model is used. A decoupled algorithm is presented in (Math. Comput. Simulat. 1998; 47(6):531–539). In this work an existence theorem is proved, and the study of the convergence of the previous algorithm is presented with some numerical results. Copyright © 2005 John Wiley & Sons, Ltd.

International audience; Electrocardiography imaging (ECGI) is a new non invasive technology used for heart diagnosis. It allows to construct the electrical potential on the heart surface only from measurement on the body surface and some geometrical informations of the torso. The purpose of this work is twofold: First, we propose a new formulation to calculate the distribution of the electric potential on the heart, from measurements on the torso surface. Second, we study the influence of the errors and uncertainties on the conductivity parameters, on the ECGI solution. We use an optimal control formulation for the mathematical formulation of the problem with a stochastic diffusion equation as a constraint. The descretization is done using stochastic Galerkin method allowing to separate random and de-terministic variables. The optimal control problem is solved using a conjugate gradient method where the gradient of the cost function is computed with an ad-joint technique. The efficiency of this approach to solve the inverse problem and the usability to quantify the effect of conductivity uncertainties in the torso are demonstrated through a number of numerical simulations on a 2D geometrical model. Our results show that adding ±50% uncertainties in the fat conductivity does not alter the inverse solution, whereas adding ±50% uncertainties in the lung conductivity affects the reconstructed heart potential by almost 50%.

This article advocates the multiple benefits of applying probabilistic approaches to capital budgeting through enriching the deterministic framework with a stochastic modeling of main impacting inputs (including a methodology for selecting the most important inputs to be modeled stochastically). The essential limitations of the deterministic capital budgeting methodology are presented: behavioral biases (optimism, asymmetric probability distribution, etc.), incomplete view of the risk return profile, neglecting real options (be it to evaluate a project or to reshape it in a value creation perspective), portfolio diversification impact, etc. Through some selected examples, we illustrate how each of these limitations can be mitigated thanks to probabilistic approaches leading to a better decision-making process and ultimately more value creation.

This paper involves developing financial utility function that considers compliance to a certain qualitative characteristic and studies the impact on market equilibrium prices, should this criterion be Sharia compliance, fair-trade, environmental, social and governance principles or other ethical aspect. The goal is to show that individual utility can depend on other parameters than wealth and risk aversion, that therefore influence equilibrium market prices. This has been done by examining a possible utility function that takes into account individual sensitivity to the criterion and the intrinsic quality of compliance of this parameter. In order to prove the effectiveness of the proposed utility function, a simulation is made using agent-based approach with NetLogo platform. Upon examination of the impact of these parameters, it becomes clear that compliance to a qualitative characteristic would impact individual utility, supply and demand and result in equilibrium prices. This research highlights the importance of ethical arguments on individual decision making and how markets behave to this.

This paper aims to model the impact of retail consumers’ behavior on a new banking dual market featuring both conventional and Islamic banking products. To build the model, we conduct an empirical qualitative and quantitative survey on Moroccan market consumers in order to appraise their preferences with regard to banking products’ attributes. Then, we use conjoint analysis method to determine the consumers’ decision function. We run market simulations on a Multi-Agents Simulation platform and analyze the results. Our findings indicate that in new dual markets, and under a range of assumptions, it is predicted that Islamic banks will face excess liquidity while conventional banks will be exposed to liquidity shortage.

The volatility is considered constant in Black and Scholes model. However, this implausible assumption leads to an undervaluation of options. We try to remediate to this drawback considering a more realistic model where the volatility is a piecewise constant function of time. We introduce a jump indicator to locate iteratively discontinuities of volatility and use an optimization process to estimate volatility values. We compare our results with regularization method (Aboulaich & Medarhri, 2013) and "AutoRegressive Conditional Heteroskedasticity" ARCH method (Engle, 1982).

This work gives a study of the Regier’s model by using the shape optimization techniques. Two formulations of this model are proposed with appropriate boundary conditions of the MESFET transistor. In each formulation the existence of a free boundary, separating the depletion and neutrality of charge regions, is proved with some hypothesis. The shape gradient is calculated for each formulation to approach the solution. To approximate the free boundary, two algorithms are presented. Numerical results obtained by implementing the last algorithm prove that this shape optimization techniques provide a reasonably smooth free boundary.

The known pseudo-concentration model is a generalization of the classical model of two immiscible fluids when the interface between the two fluids is not a sufficiently regular curve. Besides, it provides efficient and robust numerical methods. The aim of this article is to prove existence of solutions to a mathematical model, based on the pseudo-concentration function model, for the filling of shallow molds with polymers. Numerical methods and numerical simulations with comparison with experimental results have been presented in [6]; [7]. The proposed model is 2-D, the chemical reactivity of the fluid is accounted with the conversion rate satisfying a Kamal-Sourour model, and the temperature is not considered. We prove the existence of a renormalized solution to the mathematical model, and an analysis of time stability is carried out illustrating that the proposed model is suitable for describing the polymer state.

Resume Dans cette Note, nous montrons l'existence et l'unicite de la solution d'un probleme de frontiere libre formule en un probleme d'optimisation de forme. La demonstration de ce resultat est basee sur le principe de Schauder [6].

Electrical potentials in a junction field transistor can be calculated using a simplified model based on a complete depletion assumption. This gives rise to a free boundary problem. We show here how we can approximate this problem with a quasi‐variational inequality technique and the shape optimization method. A detailed analysis of these methods is presented. Using some numerical experiments we compare our results with the solution of the discrete drift‐diffusion system, accomplished with a Gummel‐like algorithm. The numerical results suggest that the methods proposed here work successfully and that the shape optimization technique provides a reasonably free boundary without excessive iterations.

This paper presents the numerical approach of the free boundary by using shape optimization method. The numerical simulation field effect transistor MESFET is possible with the Laplace–Poisson model, which introduces two regions, respectively, known by “charge neutrality region” and “depletion region”, separated by some free boundary.

International audience; In order to solve numerically the constant elasticity of variance (CEV) model for pricing of European call option, we propose in thiswork the Stochastic Runge-Kutta method. We compare the obtained results using this approache, with those given by the Monte Carlo method in Broadie-Kaya [4]. Further, we demonstrate the faster convergence rate of the error obtained by the proposed method. Finally a comparative numerical study is done using different values of the coefficient of elasticity

International audience; The purpose of this work is to present a model for portfolio multi-optimization, in which distributions are compared on the basis of tow statistics: the expected value and the Conditional Value-at-Risk (CVaR), to solve such a problem many authors have developed several algorithms, in this work we propose to find the efficient boundary by using the Normal Boundary Intersection approach (NBI) based on our proposed hybrid method SASP, since the considered problem is multi-objective, then we find the Kalai-smorodinsky solution.