Overview of Image Enhancement
Image enhancement is the process of making images clearer, sharper, and more vivid for better interpretation by human viewers or for use in other automated image processing techniques. The main goal of image enhancement is to modify certain attributes of an image to make it more suitable for a particular task and a specific observer.
Image enhancement techniques are useful in a variety of fields, including medicine, astronomy, remote sensing, and surveillance, as
Knowledge Graph Completion is a task in which computers predict unseen relationships between two already known entities or predict the tail entity when the head entity and the query relationship are known. Knowledge graphs are collections of triples that represent entities and relationships among them.
What is a Knowledge Graph?
A knowledge graph is a collection of interconnected triples that represent real-world objects and their relationships. Each triple consists of three parts: a head ent
Learning-to-Rank: Using Machine Learning to Build Ranking Models
If you've ever searched for something on Google or scrolled through a news feed on social media, you've benefited from learning-to-rank. Learning-to-rank is the application of machine learning to build ranking models. Ranking models are used to sort information in order of relevance or importance. Therefore, they are essential in information retrieval and news feeds applications.
What are Ranking Models?
Ranking models are soph
What is Link Prediction?
Link prediction is a task in graph and network analysis that aims to predict missing or future connections in a network. In simpler terms, it is a method used to predict relationships that are likely to exist between objects in a network.
How Does Link Prediction Work?
Link prediction works by analyzing the connections between nodes in a partially observed network. Nodes are any objects, individuals or entities that are connected in the network. By studying the exist
Outlier Detection: Identifying Anomalous Data Points
Outlier detection is a tool used to identify unusual data points in a given set. These anomalous instances are different from other points and can provide important insights into the dataset. For example, outlier detection can be used in the security field to identify potential threats, or in manufacturing to detect parts that are likely to fail. Outlier detection is a core task of data mining and is widely used in many applications.
The Im
Unsupervised Anomaly Detection: Understanding the Basics
In today's technological landscape, large amounts of data are generated every second. This data is generally characterized into normal and abnormal data. Normal data is what is considered as the standard or regular data, while abnormal data are events or objects that are rare or outside the norm. Detecting anomalies in large data sets is very important because they can cause harm, lower the accuracy of models, and lead to data breaches. T