Background Annually, 4% of the global population undergoes non-cardiac surgery, with 30% of those patients having at least ...

Telecom Fraud Detection: SMS Spam Classifier built with Python, Scikit-learn, and Streamlit. Achieves ~98% accuracy using TF-IDF + Naive Bayes. Includes EDA, fraud trend visualization, and real-time ...

Introduction Application of artificial intelligence (AI) tools in the healthcare setting gains importance especially in the domain of disease diagnosis. Numerous studies have tried to explore AI in ...

Implement Neural Network in Python from Scratch ! In this video, we will implement MultClass Classification with Softmax by making a Neural Network in Python from Scratch. We will not use any build in ...

Comprehensive genomic testing in routine cancer care pathways has created the need to interpret the consequences of somatic (acquired) genomic variants beyond the currently well-characterised driver ...

Abstract: Various deep learning-based methods have greatly improved hyperspectral image (HSI) classification performance, but these models are sensitive to noisy training labels. Human annotation on ...

Objectives: Current approaches to objective measurement of sleep disturbances in children overlook the period prior to sleep, or the settling down time. Using machine learning techniques, we ...

Early prediction of acute respiratory distress syndrome (ARDS) after liver transplantation (LT) facilitates timely intervention. We aimed to develop a predictor of post-LT ARDS using machine learning ...

Hands-on coding of a multiclass neural network from scratch, with softmax and one-hot encoding. #Softmax #MulticlassClassification #PythonAI Trump announces two new national holidays, including one on ...

In the ever-evolving landscape of artificial intelligence, machine learning continues to push the boundaries of understanding non-human communication. A groundbreaking study published in iScience ...