The paper "Intelligent Velocity Control of Mobile Robots Using Fuzzy and Supervised Machine Learning"

The paper "Intelligent Velocity Control of Mobile Robots Using Fuzzy and Supervised Machine Learning" was published by VSB - Technical University of Ostrava in proceedings of conference GIS Ostrava 2021 Advances in Localization and Navigation on March 17-19, 2021. This paper proposes an intelligent technique for velocity control of a wheeled mobile robot by simultaneously using a fuzzy controller and a supervised machine learning (SML) algorithm. The technique is suitable for flexible leader-follower formation control on straight paths where a follower robot maintains a safe but flexible distance from a leader robot. The fuzzy controller determines the ultimate distance of the follower with respect to the leader from the measurements of two ultrasonic sensors. The SML algorithm calculates an appropriate velocity for the follower based on the ultimate distance. Simulations showed the effectiveness of the proposed technique in adjusting the follower robot's velocity in order to maintain a flexible formation with the leader robot.

You could find more information about the paper and the conference via the link 1 and link 2, respectively.

The paper "A Double-controller Fuzzy Scheme for Intelligent Resource Discovery on IaaS Cloud Systems"

The paper "A Double-controller Fuzzy Scheme for Intelligent Resource Discovery on IaaS Cloud Systems" was published by International Journal of Networking and Virtual Organisations on 17 Jun 2021. This paper proposes a double-controller fuzzy scheme for intelligent resource discovery in IaaS cloud systems, called DOCFIR. This scheme applies two fuzzy controllers to perform the intelligent resource discovery across the network. The first controller determines the number of virtual machines in the deployment phase based on the most important characteristics of the physical machines. The second controller discovers the appropriate computing resources for the user's job in the service phase based on characteristics of the physical machines and user requirements. The simulation results show that the proposed scheme surpasses some of the existing related works in terms of the number of completed jobs and success rate.

You could find more information about this paper via the link.

The paper "A Knowledge and Intelligent-based Strategy for Resource Discovery on IaaS Cloud Systems"

The paper "A Knowledge and Intelligent-based Strategy for Resource Discovery on IaaS Cloud Systems" was published by International Journal of Grid and Utility Computing on 30 Apr 2021. This paper proposes a knowledge and intelligent-based strategy for resource discovery in IaaS cloud systems, called KINRED. It uses a fuzzy system, a Multi-Criteria Decision Making (MCDM) controller and an artificial neural node to discover suitable resources under various changes on network metrics. The suggested fuzzy system uses hardware specifications of the computing resources in which CPU speed, CPU core, memory, disk, the number of virtual machines and utilisation rate are considered as inputs, and hardware type is considered as output of the system. The suggested MCDM controller makes proper decisions based on users' requirements in which CPU speed, CPU core, memory, and disk are assumed as inputs, and job type is assumed as output of the controller. Furthermore, the artificial neural node selects the computing resource having the highest success rate based on both outputs of the fuzzy system and MCDM controller. Simulation results show that the proposed strategy surpasses some of the existing related works in terms of the number of successful jobs, system throughput and service price.

You could find more information about this paper via the link.