Designing and modeling of a multi-agent adaptive learning system (MAALS) using incremental hybrid case-based reasoning (IHCBR)

Several researches in the field of adaptive learning systems has developed systems and techniques to guide the learner and reduce cognitive overload, making learning adaptation essential to better understand preferences, the constraints and learning habits of the learner. Thus, it is particularly advisable to propose online learning systems that are able to collect and detect information describing the learning process in an automatic and deductive way, and to rely on this information to follow the learner in real time and offer him training according to his dynamic learning pace. This article proposes a multi-agent adaptive learning system to make a real decision based on a current learning situation. This decision will be made by performing a hypride cycle of the Case-Based Reasonning approach in order to follow the learner and provide him with an individualized learning path according to Felder Silverman learning style model and his learning traces to predict his future learning status. To ensure this decision, we assign at each stage of the Incremental Hybrid Case-Based Reasoning at least one active agent performing a particular task and a broker agent that collaborates between the different agents in the system

Energy-Efficient Hybrid K-Means Algorithm for Clustered Wireless Sensor Networks

Energy efficiency is the most critical challenge in wireless sensor network. The transmission energy is the most consuming task in sensor nodes, specifically in large distances. Clustered routing techniques are efficient approaches used to lower the transmission energy and maximize the network’s lifetime. In this paper, a hybrid clustered routing approach is proposed for energy optimization in WSN. This approach is based on K-Means clustering algorithm and LEACH protocol. The simulation results using MATLAB tool have shown that the proposed hybrid approach outperforms LEACH protocol and optimizes the nodes energy and the network lifetime

Performance evaluation of hierarchical clustering protocols with fuzzy C-means

The longevity of the network and the lack of resources are the main problems within the WSN. Minimizing energy dissipation and optimizing the lifespan of the WSN network are real challenges in the design of WSN routing protocols. Load balanced clustering increases the reliability of the system and enhances coordination between different nodes within the network. WSN is one of the main technologies dedicated to the detection, sensing, and monitoring of physical phenomena of the environment. For illustration, detection, and measurement of vibration, pressure, temperature, and sound. The WSN can be integrated into many domains, like street parking systems, smart roads, and industrial. This paper examines the efficiency of our two proposed clustering algorithms: Fuzzy C-means based hierarchical routing approach for homogeneous WSN (F-LEACH) and fuzzy distributed energy efficient clustering algorithm (F-DEEC) through a detailed comparison of WSN performance parameters such as the instability and stability duration, lifetime of the network, number of cluster heads per round and the number of alive nodes. The fuzzy C-means based on hierarchical routing approach is based on fuzzy C-means and low-energy adaptive clustering hierarchy (LEACH) protocol. The fuzzy distributed energy efficient clustering algorithm is based on fuzzy C-means and design of a distributed energy efficient clustering (DEEC) protocol. The technical capability of each protocol is measured according to the studied parameters

Hybrid approach of the fuzzy C-Means and the K-Nearest neighbors methods during the retrieve phase of dynamic case based reasoning for personalized Follow-up of learners in real time

The goal of adaptive learning systems is to help the learner achieve their goals and guide their learning. These systems make it possible to adapt the presentation of learning resources according to learners' needs, characteristics and learning styles, by offering them personalized courses. We propose an approach to an adaptive learning system that takes into account the initial learning profile based on Felder Silverman's learning style model in order to propose an initial learning path and the dynamic change of his behavior during the learning process using the Incremental Dynamic Case Based Reasoning approach to monitor and control its behavior in real time, based on the successful experiences of other learners, to personalize the learning. These learner experiences are grouped into homogeneous classes at the behavioral level, using the Fuzzy C-Means unsupervised machine learning method to facilitate the search for learners with similar behaviors using the supervised machine learning method K- Nearest Neighbors

An energy-efficient clustering protocol using fuzzy logic and network segmentation for heterogeneous WSN

Wireless sensor networks have become an emerging research area due to their importance in the present industrial application. The enlargement of network lifetime is the major limitation in WSN. Several routing protocols study the extension of lifespan in WSN. Routing protocols significantly influence on the global of energy consumption for sensors in WSN. It is essential to correct the energy efficiency performance of routing protocol in order to improve the lifetime. The protocols based on clustering are the most routing protocols in WSN to reduce energy consumption. The protocols dedicate to WSN have demonstrated their limitation in expanding the lifetime of the network. In this paper, we present Hybrid SEP protocol : Multi-zonal Fuzzy logic heterogeneous Clustering based on Stable Election Protocol (FMZ-SEP). The FMZ-SEP characterizes by four parameters: WSN segmentation (splitting the WSN into the triangle zones ), the Subtractive Clustering Method to determine a correct number of clusters, the FCM and the SEP protocol. The FMZ-SEP prolong the stability period and extend the lifetime. The simulation results point out that the stability period of FMZ-SEP. FMZ-SEP protocol outperforms of MZ-SEP, FSEP and SEP protocol by improving the network lifetime and the stability period

Our System IDCBR-MAS: from the Modelisation by AUML to the Implementation under JADE Platform

This paper presents our work in the field of Intelligent Tutoring System (ITS), in fact there is still the problem of knowing how to ensure an individualized and continuous learners follow-up during learning process, indeed among the numerous methods proposed, very few systems concentrate on a real time learners follow-up. Our work in this field develops the design and implementation of a Multi-Agents System Based on Dynamic Case Based Reasoning which can initiate learning and provide an individualized follow-up of learner. This approach involves 1) the use of Dynamic Case Based Reasoning to retrieve the past experiences that are similar to the learner’s traces (traces in progress), and 2) the use of Multi-Agents System. Our Work focuses on the use of the learner traces. When interacting with the platform, every learner leaves his/her traces on the machine. The traces are stored in database, this operation enriches collective past experiences. The traces left by the learner during the learning session evolve dynamically over time; the case-based reasoning must take into account this evolution in an incremental way. In other words, we do not consider each evolution of the traces as a new target, so the use of classical cycle Case Based reasoning in this case is insufficient and inadequate. In order to solve this problem, we propose a dynamic retrieving method based on a complementary similarity measure, named Inverse Longest Common Sub-Sequence (ILCSS). Through monitoring, comparing and analyzing these traces, the system keeps a constant intelligent watch on the platform, and therefore it detects the difficulties hindering progress, and it avoids possible dropping out. The system can support any learning subject. To help and guide the learner, the system is equipped with combined virtual and human tutors

Patent Analysis Overview

International audienc

Implementation of an Adaptive Learning System based on Agents and Web Services

Today, the integration of web services and agent technology into Internet applications has attracted the attention of many researchers, so that these applications allow a web service to call an agent service and vice versa. Web services are emerging and promising technologies for the development, deploy-ment and integration of the Internet applications and the use of agents makes them dynamic and automatic, they can provide updates when there is new infor-mation available and improve the qualities of web services by exploiting the ca-pacities and the characteristics of agents. In this context, we propose a prototype of a multi-agent adaptive learning system based on Incremental Hybrid Case Based Reasoning in order to support the learner in his learning process by offer-ing him a learning path adapted to his profile and predict his future learning. This support will be achieved through the execution of a hybrid cycle of Case Based Reasoning which brings together a set of agents collaborating and interacting with each other to provide specific services

Hybrid Approach for Wind Turbines Power Curve Modeling Founded on Multi-Agent System and Two Machine Learning Algorithms, K-Means Method and the K-Nearest Neighbors, in the Retrieve Phase of the Dynamic Case Based Reasoning

Wind turbine power curve (WTPC) plays an important role for energy assessment, power forecasting and condition monitoring. The WTPC captures the nonlinear relationship between wind speed and output power. Many modeling approaches have been proposed by researches to improve the WTPC model performance. In this paper, we present a hybrid approach of wind turbines power curve modeling based on Case Based Reasoning approach, multi agent system, the K-Means unsupervised machine learning method, and then the supervised machine learning algorithm, which is the K-Nearest Neighbors KNN method. The both of the Machine Learning algorithms, K-means and KNN, are used in the retrieve step of the Dynamic Case Based Reasoning (DCBR) cycle to facilitate the search of wind turbines with similar characteristics to our target case. These wind turbines are first grouped into homogeneous classes and then sorted on the basis of a feature similarity measure using the K-Nearest Neighbors supervised machine learning method. Finally, a set of WTPC with similar characteristics of the target case are proposed