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Found 14 papers, 2 papers with code
Efficient and Concise Explanations for Object Detection with Gaussian-Class Activation Mapping Explainer
To address the challenges of providing quick and plausible explanations in Explainable AI (XAI) for object detection models, we introduce the Gaussian Class Activation Mapping Explainer (G-CAME).
Achieving Pareto Optimality using Efficient Parameter Reduction for DNNs in Resource-Constrained Edge Environment
The results of the Caltech-101 image classification show that our model has a better test accuracy (76. 21%) than Xception (75. 89%), uses less memory on average (847. 9MB) than Xception (874. 6MB), and has faster training and inference times.
LangXAI: Integrating Large Vision Models for Generating Textual Explanations to Enhance Explainability in Visual Perception Tasks
LangXAI is a framework that integrates Explainable Artificial Intelligence (XAI) with advanced vision models to generate textual explanations for visual recognition tasks.
Explainable artificial intelligence
Explainable Artificial Intelligence (XAI)
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Towards Precision Cardiovascular Analysis in Zebrafish: The ZACAF Paradigm
Finally, we applied this approach to assess the cardiovascular function in nrap mutant zebrafish, a model of cardiomyopathy.
XAI-Enhanced Semantic Segmentation Models for Visual Quality Inspection
Visual quality inspection systems, crucial in sectors like manufacturing and logistics, employ computer vision and machine learning for precise, rapid defect detection.
Enhancing the Fairness and Performance of Edge Cameras with Explainable AI
The rising use of Artificial Intelligence (AI) in human detection on Edge camera systems has led to accurate but complex models, challenging to interpret and debug.
Developing a Resource-Constraint EdgeAI model for Surface Defect Detection
The results of our experiment show that our model has a remarkable performance with a test accuracy of 73. 45% without pre-training.
Evaluating Multi-Global Server Architecture for Federated Learning
We posit that implementing multiple global servers in federated learning can enhance efficiency by capitalizing on local collaborations and aggregating knowledge, and the error tolerance in regard to communication failure in the single server framework would be handled.
ECAvg: An Edge-Cloud Collaborative Learning Approach using Averaged Weights
The local (edge) models are then updated with the weights of the global (server) model.
Reducing Intraspecies and Interspecies Covariate Shift in Traumatic Brain Injury EEG of Humans and Mice Using Transfer Euclidean Alignment
We tested the robustness of this transfer learning technique on various rule-based classical machine learning models as well as the EEGNet-based deep learning model by evaluating on different datasets, including human and mouse data in a binary classification task of detecting individuals with versus without traumatic brain injury (TBI).
TransferD2: Automated Defect Detection Approach in Smart Manufacturing using Transfer Learning Techniques
Our proposed approach can be applied in defect detection applications where insufficient data is available for training a model and can be extended to identify imperfections in new unseen data.
A Novel ECG Denoising Scheme Using the Ensemble Kalman Filter
In this paper, a novel method using Ensemble Kalman Filter (EnKF) is developed for denoising ECG signals.
Deep learning-based framework for cardiac function assessment in embryonic zebrafish from heart beating videos
Zebrafish is a powerful and widely-used model system for a host of biological investigations including cardiovascular studies and genetic screening.
A Raspberry Pi-based Traumatic Brain Injury Detection System for Single-Channel Electroencephalogram
The increasing affordability and reduction in size of relatively high-performance computing systems combined with promising results from TBI related machine learning research make it possible to create compact and portable systems for early detection of TBI.
