A Comprehensive Guide for Deploying Artificial Intelligence with Java

Posted By : Himani Mishra | 28-Jan-2020

 

 

Artificial Intelligence:

 

Artificial intelligence is a branch of computer science that strives to build smart machines. Some of the tasks that can be accomplished with an AI-based machine can be speech recognition, learning, planning, problem-solving. Artificial Intelligence is closely connected with search algorithms, generative programming, and the use of artificial neural networks. With algorithmic advancements gaining pace, businesses are beginning to automate key operations effectively with expansive artificial intelligence services

 

Artificial Intelligence using Java:

Java in the artificial intelligence domain may be more than useful. This is versatile used for making multi-robot systems, sensor networks, and machine learning suites. AI in Java has many benefits: 

  •  Easy use
  •  Debugging ease
  •  Simplified work with large-scale projects
  •  Facilitated visualization
  •  Better user interaction etc.

Following are some of the AI libraries in Java-

 

1.Machine Learning

 

a) Weka:

 

Under AI, machine learning development services involve specific algorithms and statistics to train models to perform certain tasks without human intervention. Weka is an ML library that is a combination of machine learning algorithms for data mining. This can be used directly to a dataset or asked from your own Java code. It includes tools for data classification, pre-processing, regression, clustering, association rules, and visualization.

 

b) Java-ML:

 

It is an open-source Java framework that provides various machine learning algorithms specifically for programmers.

 

c) RapidMiner:

 

It is a data science platform that supports various machine and deep-learning algorithms through its GUI and Java API.


 

2. Neural Network library

 

a) Deeplearning4j:

 

This is a distributed, Open source, and commercial-grade deep-learning library for JVM.

 

b) Neuroph:

 

Neuroph is a lightweight neural network framework in java to develop common neural network architectures. It simplifies the development of neural networks by presenting Java neural network library and GUI tool that helps to create, train and save neural networks.


 

3. Natural Language Processing(NLP)

 

a) Apache OpenNLP:

 

This is a machine-learning toolkit for processing natural language like-text.

 

b) Stanford CoreNLP:

 

Stanford CoreNLP is the most famous Java natural-language processing framework. It gives various tools for NLP tasks.

 

4. Rules-Based System

 

a) Tweety:

 

Tweety is a collection of numerous Java libraries that add strategies to different areas of artificial intelligence.

 

b) Drools:

 

This is a business rules management system supported by Red Hat.

 

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