IEEE

Introduction to clustering unsupervised machine learning algorithms applied to power quality disturbances

Abstract: Unsupervised machine learning algorithms, such as clustering and anomaly detection, work by identifying patterns and anomalies in data without the need for labeled training data. These ...
InfoQ

Anthropic Study: AI Coding Assistance Reduces Developer Skill Mastery by 17%

Anthropic research shows developers using AI assistance scored 17% lower on comprehension tests when learning new coding libraries, though productivity gains were not statistically significant. Those ...
Analytics Insight

Best Machine Learning Bootcamps in 2026 to Start Your AI Career

Overview:Machine learning bootcamps focus on deployment workflows and project-based learning outcomes.IIT and global programs provide flexible formats for appli ...
Open Heart

Enhanced cardiovascular disease risk prediction using integrated machine learning models: a study from the UK Biobank cohort

Objective Cardiovascular diseases (CVD) remain the leading cause of mortality globally, necessitating early risk identification to improve prevention and management strategies. Traditional risk ...
TMCnet

Data Normalization vs. Standardization: What's the Difference and When to Use Each

Data Normalization vs. Standardization is one of the most foundational yet often misunderstood topics in machine learning and ...
GitHub

TfELM : Extreme Learning Machines Framework with Python and TensorFlow

This framework provides a comprehensive set of tools and utilities for implementing and experimenting with Extreme Learning Machines using Python and TensorFlow. ELMs are a type of machine learning ...
IEEE

Net-Zero Scheduling of Multi-Energy Building Energy Systems: A Learning-Based Robust Optimization Approach With Statistical Guarantees

Abstract: Buildings produce a significant share of greenhouse gas (GHG) emissions, making homes and businesses a major factor in climate change. To address this critical challenge, this paper explores ...