INTELLIGENT CONTROLLING THE GRIPPING FORCE OF AN OBJECT BY TWO COMPUTER-CONTROLLED COOPERATIVE ROBOTS

This paper presents a Multiple Adaptive Neuro-Fuzzy Inference System (MANFIS)-based method for regulating the handling force of a common object. The foundation of this method is the prediction of the inverse dynamics of a cooperative robotic system made up of two 3-DOF robotic manipulators. Considering the no slip in contact between the tool and the object, an object is moved. to create and feed the MANFIS database, the inverse kinematics and dynamic equations of motion for the closed chain of motion for both arms are established in Matlab. Results from a SimMechanic simulation are given to demonstrate how well the suggested ANFIS controller works. Several manipulated object movements covering the shared workspace of the two manipulator arms are used to test the proposed control strategy

RETAKAFUL CONTRIBUTIONS MODEL USING MACHINE LEARNING TECHNIQUES

Driven by the need to manage risk by the newly created Moroccan Takaful operators, the Moroccan Insurance and Social Welfare Control Authority has authorized the Central Reinsurance Company to create a ReTakaful window for the purpose of reinsuring Takaful operations. Nevertheless, the main challenge is determining the appropriate ReTakaful model for the Moroccan Islamic insurance sector by ensuring compliance with Shariah. With this in mind, this article aims to determine the optimal ReTakaful contributions model for the Moroccan Takaful industry via Machine Learning algorithms. We select the best model by comparing the performance of each algorithm. The achieved results of this study demonstrate the potential of using Machine Learning algorithms to compute ReTakaful contributions that are more suitable for Takaful operators and more optimal for the ReTakaful operator

Nonlinear elliptic problems involving the n-Laplacian with maximal growth

In this article, we study the limit case of some elliptic problems involving nonlinearities having the maximal growth with Dirichlet boundary conditions. We apply a result by Ricceri [12] to prove the existence of multiple nontrivial solutions using Trudinger-Moser estimates

Optimization of Surplus Reinsurance Treaty using the Conditional Tail Expectation

In this work, we propose a new optimization strategy for reinsurance using the genetic algorithms. This approach is to determine an optimal structure of a "surplus" reinsurance contract by finding the optimal cession rates through an optimization model which is based on the minimization of the Conditional Tail Expectation (CTE) risk measure under the constraint of technical benefit. This approach can be seen as a decision support tool that can be used by managers to minimize the actuarial risk and maximize the technical benefit in the insurance company