Technology Networks

Predictive Modeling for Compound Prioritization

Explore predictive modeling for compound prioritization, including in silico screening, toxicology models, and lead selection ...
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 ...
LinkedIn

Demystifying the SVM Algorithm in Machine Learning

Machine learning offers numerous algorithms, but SVM stands out for its robustness and versatility. At its core, SVM focuses on finding the optimal hyperplane that separates data points from different ...
Nature

Prediction of under-five mortality using supervised machine learning algorithms in the 23 sub-Sharan African countries

Under-five mortality remains a critical public health concern in Sub-Saharan Africa (SSA), ranges from 59 to 230 per 1000 live births in different nations of SSA. Despite substantial efforts to reduce ...
Frontiers

Editorial: Machine learning algorithms for brain imaging: new frontiers in neurodiagnostics and treatment

The field of neuroimaging has undergone profound transformation in recent years, driven primarily by rapid advances in machine learning (ML), and especially deep learning (DL), techniques. These ...
Scientific Research Publishing

Smith, J. (2019) Heart Disease Dataset.

ABSTRACT: Cardiovascular diseases (CVDs) are the leading cause of death worldwide, accounting for millions of deaths each year according to the World Health Organization (WHO). Early detection of ...
IEEE

The Impact of Feature Scaling in Machine Learning: Effects on Regression and Classification Tasks

Abstract: This study addresses the lack of comprehensive evaluations of feature scaling by systematically assessing 12 techniques, including less common methods such as VAST and Pareto, in 14 machine ...
IEEE

Support Vector Machine-Integrated High-Definition MCPWM Technique for Improving Power Quality in Multilevel Inverters

Abstract: What if machine learning could predict inverter harmonics before prototyping? Conventional pulse width modulation (PWM) techniques in cascaded H-bridge (CHB) multilevel inverters (MLIs) ...
Frontiers

Postpartum depression risk prediction using explainable machine learning algorithms

Postpartum depression (PPD) is a common and serious mental health complication after childbirth, with potential negative consequences for both the mother and her infant. This study aimed to develop an ...
GitHub

Prediction of stroke using Support Vector Machine (SVM) and Decision Tree (DTREE).

The stroke deprives person's brain of oxygen and nutrients, which can cause brain cells to die. Numerous works have been carried out for predicting various diseases by comparing the performance of ...
Nature

Enhancing anomaly detection in IoT-driven factories using Logistic Boosting, Random Forest, and SVM: A comparative machine learning approach

Three machine learning algorithms—Logistic Boosting, Random Forest, and Support Vector Machines (SVM)—were evaluated for anomaly detection in IoT-driven industrial environments. A real-world dataset ...
LinkedIn

Support Vector Machine (SVM) Algorithm

Support Vector Machine (SVM) is a supervised machine learning algorithm used for classification and regression tasks. It tries to find the best boundary known as hyperplane that separates different ...