Over the past few decades, the education sector has achieved impressive advancements by incorporating Artificial Intelligence (AI) into the educational environment. Nevertheless, specific educational processes, particularly educational counseling, still depend on traditional procedures. The current method of conducting group sessions between counselors and students does not offer personalized assistance or individual attention, which can cause stress to students and make it difficult for them to make informed decisions about their coursework and career path. This paper proposes a counseling solution designed to aid high school seniors in selecting appropriate academic paths at the tertiary level. The system utilizes a predictive model that considers academic history and student preferences to determine students’ likelihood of admission to their chosen university and recommends similar alternative universities to provide more opportunities. We developed the model based on data from 500 graduates from 12 public high schools in Morocco, as well as eligibility criteria from 31 institutions and colleges. The counseling system comprises two modules: a recommendation module that uses popularity-based and content-based recommendations and a prediction module that calculates the likelihood of admission using the Huber Regressor model. This model outperformed 13 other machine learning modules, with a low MSE of 0.0017, RMSE of 0.0422, and the highest R-squared value of 0.9306. Finally, the system is accessible through a user-friendly web interface.

Additive manufacturing (AM) technologies are growing more and more in the manufacturing industry; the increase in world energy consumption encourages the quantification and optimization of energy use in additive manufacturing processes. Orientation of the part to be printed is very important for reducing energy consumption. Our work focuses on defining the most appropriate direction for minimizing energy consumption. In this paper, twelve machine learning (ML) algorithms are applied to model energy consumption in the fused deposition modelling (FDM) process using a database of the FDM 3D printing of isovolumetric mechanical components. The adequate predicted model was selected using four performance criteria: mean absolute error (MAE), root mean squared error (RMSE), R-squared (R2), and explained variance score (EVS). It was clearly seen that the Gaussian process regressor (GPR) model estimates the energy consumption in FDM process with high accuracy: R² > 99%, EVS > 99%, MAE < 3.89, and RMSE < 5.8.

Given the recognized advantages of additive manufacturing (AM) printing systems in comparison with conventional subtractive manufacturing systems, AM technology has become increasingly adopted in 3D manufacturing, with usage rates increasing dramatically. This strong growth has had a significant and direct impact not only on energy consumption but also on manufacturing time, which in turn has generated significant costs. As a result, this problem has attracted the attention of industry actors and the research community, and several studies have focused on predicting and reducing energy consumption and additive manufacturing time, which has become one of the main objectives of research in this field. However, there is no effective model yet for predicting and optimizing energy consumption and printing time in a fused deposition modeling (FDM) process while taking into account the correct part orientation that minimizes both of these costs. In this paper, a neural-network-based model has been proposed to solve this problem using experimental data from isovolumetrically shaped mechanical parts. The data will serve as the basis for proposing the appropriate model using a specific methodology based on five performance criteria with the following statistical values: R2-squared > 99%, explained variance > 99%, MAE < 0.99%, MSE < 0.02% and RMSE < 1.36%. These values show just how effective the proposed model will be in estimating energy consumption and FDM printing time, taking into account the best choice of part orientation for the lowest cost. This model provides a global understanding of the primary energy and time requirements for manufacturing while also improving the system’s cost efficiency. The results of this work can be extended and applied to other additive manufacturing processes in future work.

This paper examines online learner engagement, a complex concept encompassing several dimensions (behavioral, emotional, and cognitive) and recognized as a key indicator of learning effectiveness. Engagement involves participation, motivation, persistence, and reflection, facilitating content understanding. Predicting engagement, particularly behavioral engagement, encourages interaction and aids teachers in adjusting their methods. The aim is to develop a predictive model to classify learners based on their engagement, using indicators such as academic outcomes to identify signs of difficulty. This study demonstrates that engagement in quizzes and exams predicts engagement in lessons, promoting personalized learning. We utilized supervised machine learning algorithms to forecast engagement at three levels: quizzes, exams, and lessons, drawing from a Kaggle database. Quiz and exam scores were employed to create predictive models for lessons. The performance of the models was evaluated using classic metrics such as precision, recall, and F1-score. The Decision Tree model emerged as the best performer among those evaluated, achieving 97% and 98.49% accuracy in predicting quiz and exam engagement, respectively. The K-Nearest Neighbors (KNN) and Gradient Boosting models also showed commendable performance, albeit slightly less effective than the Decision Tree. The results indicate a strong correlation between engagement predictions across the three levels. This suggests that engagement in quizzes and exams, known as assessments, is a pertinent indicator of overall engagement. Active learners tend to perform better in these assessments. Early identification of at-risk learners allows for targeted interventions, optimizing their engagement.

We describe The Internet of Things (IoT) as a network of physical objects or "things" embedded with electronics, software, sensors, and network connectivity, which enables these objects to collect and exchange data in real time with the outside world. It therefore assumes an operating system (OS) which is considered as an unavoidable point for good communication between all devices “objects”. For this purpose, this paper presents a comparative study between the popular known operating systems for internet of things . In a first step we will define in detail the advantages and disadvantages of each one , then another part of Interpretation is developed, in order to analyse the specific requirements that an OS should satisfy to be used and determine the most appropriate .This work will solve the problem of choice of operating system suitable for the Internet of things in order to incorporate it within our research team . We describe The Internet of Things (IoT) as a network of physical objects or "things" embedded with electronics, software, sensors, and network connectivity, which enables these objects to collect and exchange data in real time with the outside world. It therefore assumes an operating system (OS) which is considered as an unavoidable point for good communication between all devices “objects”. For this purpose, this paper presents a comparative study between the popular known operating systems for internet of things . In a first step we will define in detail the advantages and disadvantages of each one , then another part of Interpretation is developed, in order to analyse the specific requirements that an OS should satisfy to be used and determine the most appropriate .This work will solve the problem of choice of operating system suitable for the Internet of things in order to incorporate it within our research team . Keywords: Internet of things , network, physical object ,sensors,operating system.