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

Marktechpost

Explainable AI (xAI) methods, such as saliency maps and attention mechanisms, attempt to clarify these models by highlighting key ECG features. xECGArch uniquely separates short-term (morphological) and long-term (rhythmic) ECG features using two independent Convolutional Neural Networks CNNs.

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Artificial Neural Network: A Comprehensive Guide

Pickl AI

Summary: Artificial Neural Network (ANNs) are computational models inspired by the human brain, enabling machines to learn from data. Introduction Artificial Neural Network (ANNs) have emerged as a cornerstone of Artificial Intelligence and Machine Learning , revolutionising how computers process information and learn from data.

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Neural Network in Machine Learning

Pickl AI

Summary: Neural networks are a key technique in Machine Learning, inspired by the human brain. Different types of neural networks, such as feedforward, convolutional, and recurrent networks, are designed for specific tasks like image recognition, Natural Language Processing, and sequence modelling.

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AI and the future agriculture

IBM Journey to AI blog

” When Guerena’s team first started working with smartphone images, they used convolutional neural networks (CNNs). Well-trained computer vision models produce consistent quantitative data instantly.”

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A Comprehensive Guide on Deep Learning Engineers

Pickl AI

Summary : Deep Learning engineers specialise in designing, developing, and implementing neural networks to solve complex problems. They work on complex problems that require advanced neural networks to analyse vast amounts of data. Hyperparameter Tuning: Adjusting model parameters to improve performance and accuracy.

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Where AI is headed in the next 5 years?

Pickl AI

Machine Learning and Neural Networks (1990s-2000s): Machine Learning (ML) became a focal point, enabling systems to learn from data and improve performance without explicit programming. Techniques such as decision trees, support vector machines, and neural networks gained popularity.

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Classification Algorithm in Machine Learning: A Comprehensive Guide

Pickl AI

Examples include Logistic Regression, Support Vector Machines (SVM), Decision Trees, and Artificial Neural Networks. Lazy Learners These algorithms do not build a model immediately from the training data. Instead, they memorise the training data and make predictions by finding the nearest neighbour.