L'écriture est une compétence fondamentale au cours de la scolarité primaire, permettant non seulement de communiquer des idées, mais aussi de développer la créativité des élèves. Cet article examine l’importance de l’enseignement de l’écriture aux élèves du primaire dans un contexte plurilingue. Nous proposerons des outils et des méthodes qui favorisent une écriture authentique et confiante.

L'écriture est une compétence fondamentale au cours de la scolarité primaire, permettant non seulement de communiquer des idées, mais aussi de développer la créativité des élèves. Cet article examine l’importance de l’enseignement de l’écriture aux élèves du primaire dans un contexte plurilingue. Nous proposerons des outils et des méthodes qui favorisent une écriture authentique et confiante.

Global sustainability initiatives increasingly rely on innovative technologies to safeguard biodiversity and mitigate environmental impacts. In this paper, we present EcoWatch, a novel framework that leverages Wireless Multimedia Sensor Networks (WMSNs) using LoRaWAN technology for efficient data transmission to enable real-time bird species detection and counting in their natural habitat. EcoWatch combines YOLOv8 You Only Look Once for object detection and Learning to Count Everything (LTCE) for precise object counting at the base station. To address the inherent limitations of WSNs in terms of energy and bandwidth, EcoWatch incorporates a multi-level ROI-based video compression technique. Extensive evaluation demonstrates that EcoWatch significantly reduces energy consumption (up to 58.7%) and data transmission load (by 69.8%) compared to other methods while maintaining acceptable image quality, detection and counting accuracy. Notably, EcoWatch exhibits robust performance across seasons and adapts well to varying environmental conditions, making it a promising solution for real-world ecological monitoring applications.

Global sustainability initiatives increasingly rely on innovative technologies to safeguard biodiversity and mitigate environmental impacts. In this paper, we present EcoWatch, a novel framework that leverages Wireless Multimedia Sensor Networks (WMSNs) using LoRaWAN technology for efficient data transmission to enable real-time bird species detection and counting in their natural habitat. EcoWatch combines YOLOv8 You Only Look Once for object detection and Learning to Count Everything (LTCE) for precise object counting at the base station. To address the inherent limitations of WSNs in terms of energy and bandwidth, EcoWatch incorporates a multi-level ROI-based video compression technique. Extensive evaluation demonstrates that EcoWatch significantly reduces energy consumption (up to 58.7%) and data transmission load (by 69.8%) compared to other methods while maintaining acceptable image quality, detection and counting accuracy. Notably, EcoWatch exhibits robust performance across seasons and adapts well to varying environmental conditions, making it a promising solution for real-world ecological monitoring applications.

Global sustainability initiatives increasingly rely on innovative technologies to safeguard biodiversity and mitigate environmental impacts. In this paper, we present EcoWatch, a novel framework that leverages Wireless Multimedia Sensor Networks (WMSNs) using LoRaWAN technology for efficient data transmission to enable real-time bird species detection and counting in their natural habitat. EcoWatch combines YOLOv8 You Only Look Once for object detection and Learning to Count Everything (LTCE) for precise object counting at the base station. To address the inherent limitations of WSNs in terms of energy and bandwidth, EcoWatch incorporates a multi-level ROI-based video compression technique. Extensive evaluation demonstrates that EcoWatch significantly reduces energy consumption (up to 58.7%) and data transmission load (by 69.8%) compared to other methods while maintaining acceptable image quality, detection and counting accuracy. Notably, EcoWatch exhibits robust performance across seasons and adapts well to varying environmental conditions, making it a promising solution for real-world ecological monitoring applications.

Global sustainability initiatives increasingly rely on innovative technologies to safeguard biodiversity and mitigate environmental impacts. In this paper, we present EcoWatch, a novel framework that leverages Wireless Multimedia Sensor Networks (WMSNs) using LoRaWAN technology for efficient data transmission to enable real-time bird species detection and counting in their natural habitat. EcoWatch combines YOLOv8 You Only Look Once for object detection and Learning to Count Everything (LTCE) for precise object counting at the base station. To address the inherent limitations of WSNs in terms of energy and bandwidth, EcoWatch incorporates a multi-level ROI-based video compression technique. Extensive evaluation demonstrates that EcoWatch significantly reduces energy consumption (up to 58.7%) and data transmission load (by 69.8%) compared to other methods while maintaining acceptable image quality, detection and counting accuracy. Notably, EcoWatch exhibits robust performance across seasons and adapts well to varying environmental conditions, making it a promising solution for real-world ecological monitoring applications.

Global sustainability initiatives increasingly rely on innovative technologies to safeguard biodiversity and mitigate environmental impacts. In this paper, we present EcoWatch, a novel framework that leverages Wireless Multimedia Sensor Networks (WMSNs) using LoRaWAN technology for efficient data transmission to enable real-time bird species detection and counting in their natural habitat. EcoWatch combines YOLOv8 You Only Look Once for object detection and Learning to Count Everything (LTCE) for precise object counting at the base station. To address the inherent limitations of WSNs in terms of energy and bandwidth, EcoWatch incorporates a multi-level ROI-based video compression technique. Extensive evaluation demonstrates that EcoWatch significantly reduces energy consumption (up to 58.7%) and data transmission load (by 69.8%) compared to other methods while maintaining acceptable image quality, detection and counting accuracy. Notably, EcoWatch exhibits robust performance across seasons and adapts well to varying environmental conditions, making it a promising solution for real-world ecological monitoring applications.

This paper presents a novel metaheuristic binary crow search algorithm (CSA) designed for positive-unlabeled (PU) learning, a paradigm where only positive and unlabeled data are available, with applications in many diversified fields, such as medical diagnosis and fraud detection. The algorithm represent a useful adaptation of CSA, itself inspired by the food-hiding behavior of crows. The proposed BiCSA-PUL (binary crow search algorithm for positive-unlabeled learning) selects reliable negative samples from unlabeled data using binary vectors, and updates positions employing Hamming distance, guided by a modified F1-score, as fitness function. The algorithm was tested on 30 samples from 10 diverse datasets, outperforming seven state-of-the-art PU learning methods. The results reveal that BiCSA-PUL provides a robust and efficient approach for PU learning, significantly improving fitness and reliability. This work opens new avenues for applying metaheuristic optimization methods to challenging classification problems with limited labeled data. The main limitations are the potentially time-intensive process of parameters tuning and reliance on initial sampling.

This paper presents a novel metaheuristic binary crow search algorithm (CSA) designed for positive-unlabeled (PU) learning, a paradigm where only positive and unlabeled data are available, with applications in many diversified fields, such as medical diagnosis and fraud detection. The algorithm represent a useful adaptation of CSA, itself inspired by the food-hiding behavior of crows. The proposed BiCSA-PUL (binary crow search algorithm for positive-unlabeled learning) selects reliable negative samples from unlabeled data using binary vectors, and updates positions employing Hamming distance, guided by a modified F1-score, as fitness function. The algorithm was tested on 30 samples from 10 diverse datasets, outperforming seven state-of-the-art PU learning methods. The results reveal that BiCSA-PUL provides a robust and efficient approach for PU learning, significantly improving fitness and reliability. This work opens new avenues for applying metaheuristic optimization methods to challenging classification problems with limited labeled data. The main limitations are the potentially time-intensive process of parameters tuning and reliance on initial sampling.

This paper presents a novel metaheuristic binary crow search algorithm (CSA) designed for positive-unlabeled (PU) learning, a paradigm where only positive and unlabeled data are available, with applications in many diversified fields, such as medical diagnosis and fraud detection. The algorithm represent a useful adaptation of CSA, itself inspired by the food-hiding behavior of crows. The proposed BiCSA-PUL (binary crow search algorithm for positive-unlabeled learning) selects reliable negative samples from unlabeled data using binary vectors, and updates positions employing Hamming distance, guided by a modified F1-score, as fitness function. The algorithm was tested on 30 samples from 10 diverse datasets, outperforming seven state-of-the-art PU learning methods. The results reveal that BiCSA-PUL provides a robust and efficient approach for PU learning, significantly improving fitness and reliability. This work opens new avenues for applying metaheuristic optimization methods to challenging classification problems with limited labeled data. The main limitations are the potentially time-intensive process of parameters tuning and reliance on initial sampling.

This paper presents a novel metaheuristic binary crow search algorithm (CSA) designed for positive-unlabeled (PU) learning, a paradigm where only positive and unlabeled data are available, with applications in many diversified fields, such as medical diagnosis and fraud detection. The algorithm represent a useful adaptation of CSA, itself inspired by the food-hiding behavior of crows. The proposed BiCSA-PUL (binary crow search algorithm for positive-unlabeled learning) selects reliable negative samples from unlabeled data using binary vectors, and updates positions employing Hamming distance, guided by a modified F1-score, as fitness function. The algorithm was tested on 30 samples from 10 diverse datasets, outperforming seven state-of-the-art PU learning methods. The results reveal that BiCSA-PUL provides a robust and efficient approach for PU learning, significantly improving fitness and reliability. This work opens new avenues for applying metaheuristic optimization methods to challenging classification problems with limited labeled data. The main limitations are the potentially time-intensive process of parameters tuning and reliance on initial sampling.