Articles

Real-Time Monitoring of Kindergarten Safety Using YOLO-11-Based Detection of Children and Adults

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Ensuring the safety and well-being of children in kindergartens requires continuous monitoring of their interactions with caregivers and the surrounding environment, as even short periods of inattentiveness can lead to accidents or unnoticed risky behavior. In this work, we present a computer-vision–based monitoring system that uses an improved YOLO-11 object detection model to localize and classify adults and children in surveillance video streams in real time. Based on the detection results, the system infers whether each child is currently supervised or unsupervised, and whether a child is present near predefined dangerous zones (such as exits, staircases, or other restricted areas) defined in the camera field of view.

To support this task, a custom dataset was created and annotated with bounding boxes for “child” and “adult” classes using both publicly available images and collected video frames from kindergarten-like environments, covering different viewpoints, illumination conditions, and crowd levels. The YOLO-11 model was trained and evaluated using standard detection metrics (precision, recall, F1-score and mAP) on separate training, validation, and test splits. In addition, a simple geometric reasoning module was implemented on top of the detector outputs to derive high-level safety events, such as “unsupervised child in the room” and “child entering a danger zone.”

A prototype implementation demonstrates that the proposed approach can robustly separate adults and children, operate at real-time frame rates on GPU hardware, and automatically flag frames where a child remains alone or moves toward restricted areas, thus providing timely cues for caregivers. These preliminary results confirm the feasibility of applying modern YOLO-family detectors to real-time kindergarten safety monitoring and provide a practical foundation for further extensions toward action recognition (e.g., falling, aggression, social isolation), spatio-temporal behavior analysis, and affective state estimation in early childhood education settings.

EcoCycle: A Deep Learning-Based Waste Categorization and Management System for Sustainable Smart Cities

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Waste management is a critical environmental and economic issue worldwide. Existing waste segregation ac- tivities are inefficient, resulting in high landfill contributions and environmental contamination. In this paper, an artificial intelligence-based waste categorization and management system, EcoCycle, is proposed that utilizes deep learning models like VGG16, ResNet50, and DenseNet121 for automatic classification of waste materials. EcoCycle is equipped with a gamification system based on mobile, a marketplace for recyclables supported by blockchain, and an IoT-based network of intelligent bins for real-time monitoring. Experimental results show 92.36% classification accuracy with DenseNet121, which is improved compared to other implementation results. User survey with 500 users shows a 98% positive effect on user experience and increased awareness about sus- tainability issues. The proposed system contributes significantly towards processes related to circular economies and the goals of smart city initiatives, and it has high global applicability potential for urban waste management systems.

Adapting Deep-Learning in Early Yam Disease Detection Using Lenet-5 (Adam Optimizer) Convolutional Neural Network Architecture to Improve Productivity and Enhance Farmers Social Habits in the Digital Age

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The agriculture sector faces significant challenges due to diseases affecting crop yields, particularly in yam cultivation. This study explores the adaptation of deep learning techniques for early detection of yam diseases using a LeNet-5 Convolutional Neural Network (CNN) architecture optimized with the Adam optimizer. The fam sides considered are; Ardokola, Zing and Mutum Biu in Taraba State, Nigeria. By leveraging advanced image processing and machine learning methodologies, we aim to develop an effective diagnostic tool that empowers farmers to identify and manage diseases promptly, ultimately improving productivity. This research not only enhances the technological capabilities of farmers in the digital age but also promotes better agricultural practices, fostering social habits that encourage knowledge sharing and community engagement. The proposed system is tested on a comprehensive dataset of yam leaf images, demonstrating its ability to accurately detect various disease conditions at 17.84%. Results indicate a significant improvement in recognition accuracy, suggesting that the integration of AI-driven solutions can transform disease management approaches in yam farming, contributing to sustainable agricultural practices and improved livelihoods for farmers.

Virtual Mouse and Keyboard for Computer Interaction by Hand Gestures Using Machine Learning

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Human-computer interaction has changed since the advent of computer technology. Gestures are a useful way to communicate, and the Covid-19 era had an impact on us. Both the keyboard and the mouse are tools used to communicate with computers. Here, we’ve attempted to use hand gestures to interact with the mouse and keyboard. Eventually, get rid of the electronics. Consequently, use a virtual keyboard and your finger to move the mouse cursor. Using different hand gestures, actions like clicking, dragging, and typing data will be carried out.
A webcam is the IOT device required to accomplish this. The output from the camera will be displayed on the system’s screen so that the user can fine-tune it. We employ tools like Python, Media- Pipe, and Open-CV. The Media-Pipe library offers features that improve the model’s effectiveness and is particularly helpful in AI projects. The user will be able to move the computer cursor with various hand motions, type on the virtual keyboard while holding coloured caps or tapes, and left-click and drag objects. In this research, we suggest a hand gesture detection system for a natural human computer interface that can control a virtual mouse and keyboard.

Detection of COVID-19 using Modified VGG Architectures

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COVID-19 has created havoc in the world. This paper aims to study and understand the performance of modified VGG-16 and VGG-19 architectures in detecting COVID-19 using the concept of transfer learning. The algorithm has been validated using a private dataset with normal and COVID-19 positive chest X-ray images.

COVID-19 has created havoc in the world. This paper aims to study and understand the performance of modified VGG-16 and VGG-19 architectures in detecting COVID-19 using the concept of transfer learning. The algorithm has been validated using a private dataset with normal and COVID-19 positive chest X-ray images.