Artificial Intelligence (AI) is based on algorithms that allow machines to make decisions for humans. This technology enhances the users' experience in various ways. Several studies have been conducted in the field of education to solve the problem of student orientation and performance using various Machine Learning (ML) algorithms. The main goal of this article is to predict Moroccan students' performance in the region of Guelmim Oued Noun using an intelligent system based on neural networks, one of the best data mining techniques that provided us with the best results.
The IoT is a growing new approach that has been defined as a global network of devices and machines capable of reliably communicating with each other without human intervention. It is one of the essential technologies in any field, such as medicine and attracts great attention in the future. It is applied in several areas that have achieved success. However, the power and the addition of connected objects to technology is based on the fact that its objects can establish several tasks: communicate, analyze, process and manage data in a parallel manner, which is very difficult in terms of energy consumption. Therefore, the problems related to consumption slow down considerably the evolution and the fast deployment of this high technology.Therefore, it is necessary to create a new lightweight and robust mechanism, which ensures the minimization of the consumption of the objects and makes these objects efficient and less costly while being adapted to the capacities of objects and technologies.That is why our paper aims to address this significant problem and present the role of energy consumption, which is essential in deploying successful IoT products and services and presenting the IoT categories for applications. First, we propose a method that minimizes energy consumption and meets our need through three essential steps: firstly, to study the existing methods to minimize energy consumption. Next, based on these methods, we create a new concept using the data flows. Finally, we implement our solution in an intelligent parking lot to carry out our approach and describe our design steps and conclude with the result of our study and make an interpretation that summarizes our work.
This paper deals with detecting fetal electrocardiogram FECG signals from single-channel abdominal lead.It is based on the Convolutional Neural Network (CNN) combined with advanced mathematical methods, such as Independent Component Analysis (ICA), Singular Value Decomposition (SVD), and a dimension-reduction technique like Nonnegative Matrix Factorization (NMF). Due to the highly disproportionate frequency of the fetus's heart rate compared to the mother's, the time-scale representation clearly distinguishes the fetal electrical activity in terms of energy. Furthermore, we can disentangle the various components of fetal ECG, which serve as inputs to the CNN model to optimize the actual FECG signal, denoted by FECGr, which is recovered using the SVD-ICA process. The findings demonstrate the efficiency of this innovative approach, which may be deployed in real-time.
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