Remove Deep Learning Remove Explainable AI Remove Neural Network
article thumbnail

XElemNet: A Machine Learning Framework that Applies a Suite of Explainable AI (XAI) for Deep Neural Networks in Materials Science

Marktechpost

Deep learning has made advances in various fields, and it has made its way into material sciences as well. From tasks like predicting material properties to optimizing compositions, deep learning has accelerated material design and facilitated exploration in expansive materials spaces. Check out the Paper.

article thumbnail

ImandraX: A Breakthrough in Neurosymbolic AI Reasoning and Automated Logical Verification

Unite.AI

The company has built a cloud-scale automated reasoning system, enabling organizations to harness mathematical logic for AI reasoning. With a strong emphasis on developing trustworthy and explainable AI , Imandras technology is relied upon by researchers, corporations, and government agencies worldwide.

professionals

Sign Up for our Newsletter

This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.

article thumbnail

xECGArch: A Multi-Scale Convolutional Neural Network CNN for Accurate and Interpretable Atrial Fibrillation Detection in ECG Analysis

Marktechpost

Deep learning methods excel in detecting cardiovascular diseases from ECGs, matching or surpassing the diagnostic performance of healthcare professionals. Explainable AI (xAI) methods, such as saliency maps and attention mechanisms, attempt to clarify these models by highlighting key ECG features.

article thumbnail

Top 10 Explainable AI (XAI) Frameworks

Marktechpost

The increasing complexity of AI systems, particularly with the rise of opaque models like Deep Neural Networks (DNNs), has highlighted the need for transparency in decision-making processes. The post Top 10 Explainable AI (XAI) Frameworks appeared first on MarkTechPost. Image Source 10.

article thumbnail

This AI Paper Introduces XAI-AGE: A Groundbreaking Deep Neural Network for Biological Age Prediction and Insight into Epigenetic Mechanisms

Marktechpost

Neural network-based methods in estimating biological age have shown high accuracy but lack interpretability, prompting the development of a biologically informed tool for interpretable predictions in prostate cancer and treatment resistance. The most noteworthy result was probably obtained for the pan-tissue dataset.

article thumbnail

easy-explain: Explainable AI for YoloV8

Towards AI

It uses one of the best neural network architectures to produce high accuracy and overall processing speed, which is the main reason for its popularity. Layer-wise Relevance Propagation (LRP) is a method used for explaining decisions made by models structured as neural networks, where inputs might include images, videos, or text.

article thumbnail

Enhancing AI Transparency and Trust with Composite AI

Unite.AI

Composite AI is a cutting-edge approach to holistically tackling complex business problems. These techniques include Machine Learning (ML), deep learning , Natural Language Processing (NLP) , Computer Vision (CV) , descriptive statistics, and knowledge graphs. Decision trees and rule-based models like CART and C4.5