IEEE

Classification of Epileptic Seizures by Simple Machine Learning Techniques: Application to Animals’ Electroencephalography Signals

Abstract: Detection and prediction of the onset of seizures are among the most challenging problems in epilepsy diagnostics and treatment. Small electronic devices capable of doing that will improve ...
Trains on MSN

Startup's drones use high-tech to find coupler problems in yards

HOUSTON — Tech startup Unspace was founded in 2020. Since 2022, it has been advancing machine learning in the field of machine vision to improve rail safety and operational efficiency, a journey that ...
AHA/ASA Journals

Contrastive Machine Learning to Quantify Hypertensive Multiorgan Damage and Identify New Disease Phenotypes: A Multinational Multimodal Study

BACKGROUND: Hypertension induces structural and functional damage in multiple organs. Evidence of subclinical damage ...
Scientific Research Publishing

Deep Reinforcement Learning for Phishing Detection with Transformer-Based Semantic Features ()

Phishing is a form of cybercrime in which people are deceived into exposing their personal information which can result in ...

IIT Roorkee opens admissions for the 11th batch of its post graduate certificate in data science, machine learning & generative AI

Eight-month live online programme by CEC, IIT Roorkee equips professionals to build applied expertise across Python, machine ...
IEEE

Comparing Automated Machine Learning Against an Off-the-Shelf Pattern-Based Classifier in a Class Imbalance Problem: Predicting University Dropout

Abstract: When facing a classification problem, data science practitioners must search through an armory of methods. Often, practitioners are tempted to use off-the-shelf classifiers, including ...
holisticai

Bias Mitigation Strategies and Techniques for Classification Tasks

The bias problem in classification tasks and the different strategies used for bias mitigation. How these strategies are grouped into categories and a brief introduction of the most representative ...
C&EN

Addressing Imbalanced Classification Problems in Drug Discovery and Development Using Random Forest, Support Vector Machine, AutoGluon-Tabular, and H2O AutoML

The classification models built on class imbalanced data sets tend to prioritize the accuracy of the majority class, and thus, the minority class generally has a higher misclassification rate.
Scientific Research Publishing

Hand, D. J., & Till, R. J. (2001). A Simple Generalisation of the Area Under the ROC Curve for Multiple Class Classification Problems. Machine Learning, 45, 171-186.

ABSTRACT: The survival of agroforestry tree species in sub-Saharan Africa is essential for sustainable livelihoods, particularly in the semi-arid environment. Drought in the Agroecological zones (AEZ) ...
LinkedIn

Converting Regression Problems into Classification Problems in Machine Learning: A Learner's Guide

Machine learning encompasses a variety of techniques tailored to solve different types of problems. Two primary categories of supervised learning are regression and classification. Regression focuses ...
LinkedIn

K-Nearest Neighbors (KNN) Algorithm for Classification: Real-world Applications and Examples

K-Nearest Neighbors (KNN) is a simple yet effective supervised machine learning algorithm used for both regression and classification tasks. The algorithm works by finding the K nearest data points in ...