In this paper, three main approaches (univariate, multivariate and multistep) for electricity consumption forecasting have been investigated. In fact, three major algorithms (XGBOOST, LSTM and SARIMA) have been evaluated in each approach with the main aim to figure out which one performs the best in forecasting electricity consumption. The motivation behind this work is to assess the forecasting accuracy and the computational time/complexity for an embedded forecasting and model training at the smart meter level. Moreover, we investigate the deployment of the most efficient model in our platform for an online electricity consumption forecasting. This solution will serve for deploying predictive control solutions for efficient energy management in buildings. As a proof of concept, an already existing public dataset has been used. These data were mainly collected thanks to the usage of already deployed sensors. These provide accurate data related to occupancy (e.g., presence) as well as contextual data (e.g., disaggregated electricity consumption of equipment). Experiments have been conducted and the results showed the effectiveness of these algorithms, used in each approach, for short-term electricity consumption forecasting. This has been proved by performance evaluation and error calculations. The obtained results mainly shed light on the challenging trade-off between embedded forecasting model training and processing for being deployed in smart meters for electricity consumption forecasting.

The classification of brain tumors using MRI scans is critical for accurate diagnosis and effective treatment planning, though it poses significant challenges due to the complex and varied characteristics of tumors, including irregular shapes, diverse sizes, and subtle textural differences. Traditional convolutional neural network (CNN) models, whether handcrafted or pretrained, frequently fall short in capturing these intricate details comprehensively. To address this complexity, an automated approach employing Particle Swarm Optimization (PSO) has been applied to create a CNN architecture specifically adapted for MRI-based brain tumor classification. PSO systematically searches for an optimal configuration of architectural parameters—such as the types and numbers of layers, filter quantities and sizes, and neuron numbers in fully connected layers—with the objective of enhancing classification accuracy. This performance-driven method avoids the inefficiencies of manual design and iterative trial and error. Experimental results indicate that the PSO-optimized CNN achieves a classification accuracy of 99.19%, demonstrating significant potential for improving diagnostic precision in complex medical imaging applications and underscoring the value of automated architecture search in advancing critical healthcare technology.

In a Wireless Sensor Network (WSN), nodes are battery powered and then they are energy constrained. Network lifetime is the most important characteristics of performance for these networks. In this paper, we propose routing protocols that take into account the battery residual energy in sensor nodes and the energy required for transmission along the path toward the sink, which allows the distribution of energy load among the whole network nodes. Simulation results show that the network lifetimes increases up to 50% for the first node die and up to 114% for the last node die over comparable schemes like MLER protocol.

View the large amount of data presented in the web pages, there is a great focus of different organizations and companies, to gather this information to use them in their best interest. This is achieved through Web mining which uses the techniques and algorithms of Data Mining to extract information and knowledge directly from the Web. The Web data mining is not only focused on the achievement of commercial information, but it is also used by various organization to make the good predictions and decisions in a different areas. The present paper deals with primary discussion of web mining, it focuses on a short presentation of web data mining its techniques, tools and applications. Then, we will propose an approach for the cross-analysis between the skills acquired in university training and the skills sought by employers in Morocco.

The automatic traffic sign detection and recognition has been converted to a real challenge for high performance of computer vision and machine learning techniques. It is an important issue, in particular for vehicle safety applications. It is usually tackled in three stages: detection, feature extraction and classification. We focus in this work on the second stage of the process, namely traffic sign feature extraction with applying the Block-Based Local Binary Pattern (LBP) method. This paper investigates the use of two normalization techniques: min-max and z-score. These techniques are incorporated in the SVM prediction model. For comparison purposes we also implemented the block-Based LBP with the K-Nearest Neighbor (K-NN) classifier. The evaluation of the proposed approach on the German Traffic Sign Recognition Benchmark Dataset (GTSRD) proved that the normalization of SVM input space can significantly influence the higher accuracy performance of the classification procedure. Extensive and comparative experiments have been conducted to evaluate our proposed method.