2 research outputs found
A Zigbee Based Cost-Effective Home Monitoring System Using WSN
WSNs are vital in a variety of applications, including environmental
monitoring, industrial process control, and healthcare. WSNs are a network of
spatially scattered and dedicated sensors that monitor and record the physical
conditions of the environment.Significant obstacles to WSN efficiency include
the restricted power and processing capabilities of individual sensor nodes and
the issues with remote and inaccessible deployment sites. By maximising power
utilisation, enhancing network effectiveness, and ensuring adaptability and
durability through dispersed and decentralised operation, this study suggests a
comprehensive approach to dealing with these challenges. The suggested
methodology involves data compression, aggregation, and energy-efficient
protocol. Using these techniques, WSN lifetimes can be increased and overall
performance can be improved. In this study we also provide methods to collect
data generated by several nodes in the WSN and store it in a remote cloud such
that it can be processed and analyzed whenever it is required.Comment: Paper has been presented at ICCCNT 2023 and the final version will be
published in IEEE Digital Library Xplor
Enhancing Knee Osteoarthritis severity level classification using diffusion augmented images
This research paper explores the classification of knee osteoarthritis (OA)
severity levels using advanced computer vision models and augmentation
techniques. The study investigates the effectiveness of data preprocessing,
including Contrast-Limited Adaptive Histogram Equalization (CLAHE), and data
augmentation using diffusion models. Three experiments were conducted: training
models on the original dataset, training models on the preprocessed dataset,
and training models on the augmented dataset. The results show that data
preprocessing and augmentation significantly improve the accuracy of the
models. The EfficientNetB3 model achieved the highest accuracy of 84\% on the
augmented dataset. Additionally, attention visualization techniques, such as
Grad-CAM, are utilized to provide detailed attention maps, enhancing the
understanding and trustworthiness of the models. These findings highlight the
potential of combining advanced models with augmented data and attention
visualization for accurate knee OA severity classification.Comment: Paper has been accepted to be presented at ICACECS 2023 and the final
version will be published by Atlantis Highlights in Computer Science (AHCS) ,
Atlantis Press(part of Springer Nature