In E-Learning Systems, the automatic detection of the learners’ learning styles provides a concrete way for instructors to personalize the learning to be made available to learners. The ...classification techniques are the most used techniques to automatically detect the learning styles by processing data coming from learner interactions with the system. By using these classification techniques, considerable results are obtained by several approaches with various learning style models. The performance of these approaches varies from one approach to another depending on the data used. However, these approaches have some limitations related to robustness. Indeed, a common feature of these approaches is that they consider only a single course in their trials. Whereas to construct a robust classifier, a representative set of data is crucial. Subsequently, a robust approach for automatically detecting learning styles must take into account the wealth of information to be processed. Therefore, it must consider that the data have to be gathered from learners’ learning behaviors that correspond to several courses. In this paper, we propose a robust classifier which can be able to identify the learning style of the learner in E Learning System. The learning behavior of the learner is captured on varied contexts typically on varied courses appertaining to a specific subject matter. The web usage mining is used for capturing the learners’ behaviors and then, the learning styles are mapped to Felder-Silverman Learning Style Model (FSLSM) categories. Fuzzy C Means (FCM) algorithm is used to cluster the captured learning behavioral data into FSLSM categories. The experiment results show the performance of our approach although the captured data are gathered from the learners’ learning behaviors corresponding to several courses.
Wind energy is a green source of electricity that is growing faster than other renewable energies. However, dependent mainly on wind speed, this source is characterized by the randomness and ...fluctuation that makes challenging optimal management. In order to remedy this inconvenience, it is essential to predict meteorological data or power produced by generators. In this paper, we present a wind power forecasting approach based on regularized extreme learning machine algorithm (R-ELM), particle swarm optimization method (PSO), and AutoEncoder network (AE) so-called AutoEncoder-optimal regularized extreme learning machine (AE-ORELM). Firstly, we train the AE model by the ELM algorithm. Then, the output weights resulting are used as the input weights of the R-ELM model. Furthermore, the PSO method is used to optimally select hyperparameters of the whole model, namely the regularization parameter and the number of hidden nodes in the hidden layer. The simulation results show that the proposed AE-ORELM can achieve better testing accuracy with a faster training time compared to related models.
Live migration of virtual machines proves to be inexorable in providing load balancing among physical devices and allowing scalability and flexibility in resource allocation. The existing approaches ...exhibit different policies, distinct performance characteristics, and side effects such as power consumption and performance degradation. Therefore, determining the most optimal live migration algorithm in certain situations remains an open challenge. In this work, a new prediction-based model to manage the live migration process of VMs is introduced. Our adaptive model dynamically identifies the optimal live migration algorithm for a given performance metric based on a prior diagnosis of the system. The model is developed by considering the assumption of different workloads alongside certain resource constraints for any of the currently available migration algorithms. The proposed model consists of an ensemble-learning strategy that involves linear and non-parametric regression methods to predict six live migration key metrics, provided by the operator and/or the user, for each live migration algorithm. Our model allows considering the best combination which is constituted of the algorithm-metric pair to migrate a VM. The experimental results show that the proposed model allows to significantly alleviate the service level agreement violation rate by between
31
%
and
60
%
, along with decreasing the total CPU time required for the prediction process.
With the development of autonomous vehicles, localization and mapping technologies have become crucial to equip the vehicle with the appropriate knowledge for its operation. In this paper, we extend ...our previous work by prepossessing a localization and mapping architecture for autonomous vehicles that do not rely on GPS, particularly in environments such as tunnels, under bridges, urban canyons, and dense tree canopies. The proposed approach is of two parts. Firstly, a K-means algorithm is employed to extract features from LiDAR scenes to create a local map of each scan. Then, we concatenate the local maps to create a global map of the environment and facilitate data association between frames. Secondly, the main localization task is performed by an adaptive particle filter that works in four steps: (a) generation of particles around an initial state (provided by the GPS); (b) updating the particle positions by providing the motion (translation and rotation) of the vehicle using an inertial measurement device; (c) selection of the best candidate particles by observing at each timestamp the match rate (also called particle weight) of the local map (with the real-time distances to the objects) and the distances of the particles to the corresponding chunks of the global map; (d) averaging the selected particles to derive the estimated position, and, finally, using a resampling method on the particles to ensure the reliability of the position estimation. The performance of the newly proposed technique is investigated on different sequences of the Kitti and Pandaset raw data with different environmental setups, weather conditions, and seasonal changes. The obtained results validate the performance of the proposed approach in terms of speed and representativeness of the feature extraction for real-time localization in comparison with other state-of-the-art methods.
Electric vehicles (EVs) are a sustainable transportation solution with environmental benefits and energy efficiency. However, their popularity has raised challenges in locating appropriate charging ...stations, especially in cities with limited infrastructure and dynamic charging demands. To address this, we propose a multi-agent deep deterministic policy gradient (MADDPG) method for optimal EV charging station recommendations, considering real-time traffic conditions. Our approach aims to minimize total travel time in a stochastic environment for efficient smart transportation management. We adopt a centralized learning and decentralized execution strategy, treating each region of charging stations as an individual agent. Agents cooperate to recommend optimal charging stations based on various incentive functions and competitive contexts. The problem is modeled as a Markov game, suitable for analyzing multi-agent decisions in stochastic environments. Intelligent transportation systems provide us with traffic information, and each charging station feeds relevant data to the agents. Our MADDPG method is challenged with a substantial number of EV requests, enabling efficient handling of dynamic charging demands. Simulation experiments compare our method with DDPG and deterministic approaches, considering different distributions and EV numbers. The results highlight MADDPG’s superiority, emphasizing its value for sustainable urban mobility and efficient EV charging station scheduling.
This paper introduces a novel supervised classification method based on dynamic clustering (DC) and K-nearest neighbor (KNN) learning algorithms, denoted DC-KNN. The aim is to improve the accuracy of ...a classifier by using a DC method to discover the hidden patterns of the apriori groups of the training set. It provides a partitioning of each group into a predetermined number of subgroups. A new objective function is designed for the DC variant, based on a trade-off between the compactness and separation of all subgroups in the original groups. Moreover, the proposed DC method uses adaptive distances which assign a set of weights to the variables of each cluster, which depend on both their intra-cluster and inter-cluster structure. DC-KNN performs the minimization of a suitable objective function. Next, the KNN algorithm takes into account objects by assigning them to the label of subgroups. Furthermore, the classification step is performed according to two KNN competing algorithms. The proposed strategies have been evaluated using both synthetic data and widely used real datasets from public repositories. The achieved results have confirmed the effectiveness and robustness of the strategy in improving classification accuracy in comparison to alternative approaches.
Motivated by the rapid and current progress to develop intelligent image-guided intervention tools, we aim in this paper to present, a recent review of a specific family of deep learning-based ...medical image registration methods which is Transformer-based methods. Within the revolution of Transformers in the domain of Natural Language Processing, Vision Transformers (ViTs) have been widely investigated in many applications of medical imaging including image registration. The main goal of this work, is to survey the different models proposed in the literature to tackle the task of medical image registration using ViTs and classify them according different viewpoints by focusing on the strategy of the use of Transformer in each method. For this purpose, twenty-nine papers published between 2021 and 2024 have been reported and discussed in this study. Furthermore, to provide a comprehensive guidance to the researchers in the field, we give an overview of the different attention mechanisms and variants of Transformer used in registration architectures. A concise summary of reviewed works, datasets, evaluation metrics as well as challenges with future directions have been introduced in this paper.
Convolutional Neural Networks (CNNs) are a kind of artificial neural network designed to extract features and find out patterns for tasks such as segmentation, recognizing objects, and drawing up ...classification. Within a CNNs architecture, pooling operations are used until the number of parameters and the computational complexity are reduced. Numerous papers have focused on investigating the impact of pooling on the performance of Convolutional Neural Networks (CNNs), leading to the development of various pooling models. Recently, a fuzzy pooling operation based on type-1 fuzzy sets was introduced to cope with the local imprecision of the feature maps. However, in fuzzy set theory, it is not always accurate to assume that the degree of non-membership of an element in a fuzzy set is simply the complement of the degree of membership. This is due to the potential existence of a hesitation degree, which implies a certain level of uncertainty. To overcome this limitation, intuitionistic fuzzy sets (IFS) were introduced to incorporate the concept of a degree of hesitation. In this paper, we introduce a novel pooling operation based on intuitionistic fuzzy sets to incorporate the degree of hesitation heretofore neglected by a fuzzy pooling operation based on classical fuzzy sets, and we investigate its performance in the context of image classification. Intuitionistic pooling is performed in four steps: bifuzzification (by the transformation of data through the use of membership and non-membership maps), first aggregation (through the transformation of the IFS into a standard fuzzy set, second aggregation (through the transformation and use of a sum operator), and the defuzzification of feature map neighborhoods by using a max operator. IFS pooling is used for the construction of an intuitionistic pooling layer that can be applied as a drop-in replacement for the current, fuzzy (type-1) and crisp, pooling layers of CNN architectures. Various experiments involving multiple datasets demonstrate that an IFS-based pooling can enhance the classification performance of a CNN. A benchmarking study reveals that this significantly outperforms even the most recent pooling models, especially in stochastic environments.
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