In this article, we’ll go through an overview of Artificial Intelligence (AI) libraries in Java.
Some theoretical knowledge of AI would be helpful in understanding the use of these libraries.
AI is a very wide field, so we will be focusing on some of the most popular fields today, like Natural Language Processing, Machine Learning, and Neural Networks. In the end, we’ll see some interesting AI challenges where we can practice our understanding of AI.
2. Expert Systems
2.1. Apache Jena
Apache Jena is an open-source Java framework for building semantic web and linked data applications from RDF data. It provides an API to extract data from and write to RDF graphs.
2.2. PowerLoom Knowledge Representation and Reasoning System
d3web is an open-source reasoning engine for developing, testing, and applying problem-solving knowledge onto a given problem situation, with many algorithms already included.
Eye is an open-source reasoning engine for performing semi-backward reasoning.
Tweety is a collection of Java frameworks for logical aspects of AI and knowledge representation.
OptaPlanner is a Java-based constraint solver. It can serve a number of use-cases like vehicle routing, employee rostering, maintenance scheduling, and school timetabling, to name a few.
3. Neural Networks
Neuroph is an lightweight Java framework for neural network creation. It comes with an open-source Java library and a GUI editor for quickly creating Java neural network components
Deeplearning4j is a deep learning library for the JVM, and it also provides an API for neural network creation.
4. Natural Language Processing
4.1. Apache OpenNLP
Apache OpenNLP is an open-source Natural Language Processing Java library. It features an API for use cases like Named Entity Recognition, Sentence Detection, POS tagging and Tokenization.
4.2. Stanford CoreNLP
Stanford CoreNLP is a popular Java NLP framework that provides various tools for performing NLP tasks.
5. Machine Learning
RapidMiner is a data science platform that provides various machine learning algorithms through GUI and Java API. It has a big community with many tutorials and extensive documentation.
Weka is a collection of machine learning algorithms for data mining tasks. It provides tools for a number of use cases like data clustering and association rules mining visualization.
5.3. Encog Machine Learning Framework
Encong is a Java machine learning framework that supports many ML algorithms. It’s developed by Jeff Heaton from Heaton Research.
5.4. Deep Java Library (DJL)
Deep Java Library is an open-source library developed by AWS Labs. It provides an intuitive, framework-independent Java API for training and testing learning models.
6. Genetic Algorithms
Jenetics is an advanced genetic algorithm written in Java. It provides a clear separation of the genetic algorithm concepts.
6.2. Watchmaker Framework
Watchmaker Framework is a framework for implementing genetic algorithms in Java.
6.3. ECJ 23
ECJ 23 is a Java-based research framework with strong algorithmic support for genetic algorithms. It is highly flexible, with most of the settings being dynamically determined at runtime.
6.4. Java Genetic Algorithms Package (JGAP)
JGAP is a genetic programming component provided as a Java framework.
Eva is a simple Java OOP evolutionary algorithm framework.
7. Automatic Programming
7.1. Spring Roo
Acceleo is an open-source code generator for Eclipse that generates code from EMF models defined from any metamodel (UML, SysML, and others).
We can find many online challenges and competitions related to AI. Here’s a list of some competitions where we can train and test our skills:
AI is a very wide field that is evolving at a rapid rate. In this article, we presented various Java AI frameworks that can make our applications better and more innovative.