Real-Time Pipeline Fault Detection in Water Distribution Networks Using You Only Look Once v8 DOI Creative Commons
Eric Michael, Essa Q. Shahra, Shadi Basurra

et al.

Sensors, Journal Year: 2024, Volume and Issue: 24(21), P. 6982 - 6982

Published: Oct. 30, 2024

Detecting faulty pipelines in water management systems is crucial for ensuring a reliable supply of clean water. Traditional inspection methods are often time-consuming, costly, and prone to errors. This study introduces an AI-based model utilizing images detect pipeline defects, focusing on leaks, cracks, corrosion. The YOLOv8 employed object detection due its exceptional performance detecting objects, segmentation, pose estimation, tracking, classification. By training large dataset labeled images, the effectively learns identify visual patterns associated with faults. Experiments conducted real-world demonstrate that significantly outperforms traditional accuracy. also exhibits robustness various environmental conditions such as lighting changes, camera angles, occlusions, diverse scenarios. efficient processing time enables real-time fault large-scale distribution networks implementing this offers numerous advantages systems. It reduces dependence manual inspections, thereby saving costs enhancing operational efficiency. Additionally, facilitates proactive maintenance through early faults, preventing loss, contamination, infrastructure damage. results from three experiments indicate Experiment 1 achieves commendable mAP50 90% pipes, overall 74.7%. In contrast, 3 superior performance, achieving 76.1%. research presents promising approach improving reliability sustainability using image analysis.

Language: Английский

How Artificial Intelligence and Generative AI Is Revolutionizing the Fashion Industry DOI

B. Uma Maheswari,

G. Painguzhali,

Viswanath Ananth

et al.

Advances in business strategy and competitive advantage book series, Journal Year: 2025, Volume and Issue: unknown, P. 281 - 316

Published: Jan. 31, 2025

Artificial intelligence (AI) is transforming the fashion industry by generating innovative designs and predicting future trends. Technology coupled with AI optimizing not just design manufacturing in this sector but also shopping experience of consumers. Generative one such advancement that learns from big datasets, captures patterns, generates new content. Virtual-Tryon, AI-powered designs, image recognition 3D scanning are being implemented extensively industry. Interactive mirrors personalized stylists ensure colour, palettes fit precisely suited for customers. The paper focuses on benefits applications product development, using Adversarial Networks (GANs) other models creating outfits images related to fashion. This article explores various approaches designing clothing, digital transformations underway domain possibilities generative integration into

Language: Английский

Citations

0
Advanced Diagnosis of Cardiac and Respiratory Diseases from Chest X-Ray Imagery Using Deep Learning Ensembles DOI Creative Commons

Hemal Nakrani,

Essa Q. Shahra, Shadi Basurra

et al.

Journal of Sensor and Actuator Networks, Journal Year: 2025, Volume and Issue: 14(2), P. 44 - 44

Published: April 18, 2025

Chest X-ray interpretation is essential for diagnosing cardiac and respiratory diseases. This study introduces a deep learning ensemble approach that integrates Convolutional Neural Networks (CNNs), including ResNet-152, VGG19, EfficientNet, Vision Transformer (ViT), to enhance diagnostic accuracy. Using the NIH dataset, methodology involved comprehensive preprocessing, data augmentation, model optimization techniques address challenges such as label imbalance feature variability. Among individual models, VGG19 exhibited strong performance with Hamming Loss of 0.1335 high accuracy in detecting Edema, while ViT excelled classifying certain conditions like Hernia. Despite strengths meta-model achieved best overall performance, 0.1408 consistently higher ROC-AUC values across multiple diseases, demonstrating its superior capability handle complex classification tasks. robust framework underscores potential reliable precise disease detection, offering significant improvements over traditional methods. The findings highlight value integrating diverse architectures complexities multi-label chest classification, providing pathway more accurate, scalable, accessible tools clinical practice.

Language: Английский

Citations

0
Real-Time Pipeline Fault Detection in Water Distribution Networks Using You Only Look Once v8 DOI Creative Commons
Eric Michael, Essa Q. Shahra, Shadi Basurra

et al.

Sensors, Journal Year: 2024, Volume and Issue: 24(21), P. 6982 - 6982

Published: Oct. 30, 2024

Detecting faulty pipelines in water management systems is crucial for ensuring a reliable supply of clean water. Traditional inspection methods are often time-consuming, costly, and prone to errors. This study introduces an AI-based model utilizing images detect pipeline defects, focusing on leaks, cracks, corrosion. The YOLOv8 employed object detection due its exceptional performance detecting objects, segmentation, pose estimation, tracking, classification. By training large dataset labeled images, the effectively learns identify visual patterns associated with faults. Experiments conducted real-world demonstrate that significantly outperforms traditional accuracy. also exhibits robustness various environmental conditions such as lighting changes, camera angles, occlusions, diverse scenarios. efficient processing time enables real-time fault large-scale distribution networks implementing this offers numerous advantages systems. It reduces dependence manual inspections, thereby saving costs enhancing operational efficiency. Additionally, facilitates proactive maintenance through early faults, preventing loss, contamination, infrastructure damage. results from three experiments indicate Experiment 1 achieves commendable mAP50 90% pipes, overall 74.7%. In contrast, 3 superior performance, achieving 76.1%. research presents promising approach improving reliability sustainability using image analysis.

Language: Английский

Citations

3