Adaptive systems were supposed to simplify decision-making. Instead of hard-coded rules, engineers built models that could learn from data, respond to change, and improve over time. That promise still ...

A2Z/ ├── Problems/ # Solved problems organized by difficulty │ ├── Easy/ # Easy level problems │ ├── Medium/ # Medium level problems │ └── Hard/ # Hard level problems │ ├── DataStructures/ # Core data ...

Dr. James McCaffrey presents a complete end-to-end demonstration of anomaly detection using k-means data clustering, implemented with JavaScript. Compared to other anomaly detection techniques, ...

Rocky high steep slopes are among the most dangerous disaster-causing geological bodies in large-scale engineering projects, like water conservancy and hydropower projects, railway tunnels, and metal ...

Accra, Oct. 21, GNA — It began as another viral moment in Ghana’s ever-busy social media space. A TikTok user made a short clip of a woman arguing with a man in traffic. Within hours, the video had ...

Accurately identifying fracture zones and their types in strata is of great significance for enhancing oil and gas recovery efficiency. Due to its complicated geological structure and long-term ...

Amsterdam’s struggles with its welfare fraud algorithm show us the stakes of deploying AI in situations that directly affect human lives. What Amsterdam’s welfare fraud algorithm taught me about fair ...

ABSTRACT: Clustering is an unsupervised machine learning technique used to organize unlabeled data into groups based on similarity. This paper applies the K-means and Fuzzy C-means clustering ...

Clustering is an unsupervised machine learning technique used to organize unlabeled data into groups based on similarity. This paper applies the K-means and Fuzzy C-means clustering algorithms to a ...

This study employs an unsupervised machine learning model to analyze the level of prosperity across countries based on the 2023 Legatum Prosperity Index data. The dataset includes various economic and ...