Objective Cardiovascular diseases (CVD) remain the leading cause of mortality globally, necessitating early risk identification to improve prevention and management strategies. Traditional risk ...
In this video, we will study Supervised Learning with Examples. We will also look at types of Supervised Learning and its applications. Supervised learning is a type of Machine Learning which learns ...
ABSTRACT: Over the past decade, financial fraud has emerged as a critical challenge in rural communities, where limited financial literacy, weak digital infrastructure, and social vulnerabilities make ...
ABSTRACT: Egg loss is one of the major problems in the egg hatching industry. This study aims to support farmers in optimizing their egg hatch through the development of a prediction model. This is to ...
Abstract: Malware poses a significant threat to network and information system security, particularly in industrial Internet of Things (IIoT) environments, where embedded systems and edge devices ...
This model was trained and tested on a 70%/30% split (train/test result cohort), achieving an area under the receiver operator curve on the test set of 0.866 (95% CI, 0.857 to 0.875). Assigning a ...
A Perceptron is an algorithm used for supervised learning of binary classifiers.Given a sample, the neuron classifies it by assigning a weight to its features. To accomplish this a Perceptron ...
Implementing a simple percepron and training it with gradient descent optimizer for linear regression task with out any frameworks such as Keras etc. The first one is 1.py which is a simple ...
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