Open Source Ruby Artificial Intelligence Software - Page 2

A tool for segmenting objects of interest in images, namely creating masks, and storing them for a set of images. It may provide some automatic/interactive segmentation for certain classes of objects. For computer vision / machine learning applications.

Apollo3D is a team in RoboCup soccer simulation 3D league. The target is to build a nice team with wonderful skills and good cooperation among players.

This project is aimed to the development of a framework for alife contests: the main rules of life, a graphical interface and some examples of elementary microorganisms (more complex survival strategies are developed by the contestants).

COLDIC is an integrated lexicographic platform for creation and management of electronic lexica. Generates itself automatically from a DTD with the database schema and provides human interfaces (query and insert tools) and machine interfaces(webservices)

Powerful command line and configuration file parsing for C++, Python, Ruby and Java (others to come). This tool provides many features, such as separate treatment for options, variables, and flags, unrecognized object detection, prefixes and much more.

Decision support system. Using user preference method

Eigenfocus is an AI-powered personal knowledge management system that uses embeddings and semantic search to help users organize and retrieve ideas across documents. Designed for researchers and creatives, it enables deep linking between notes and supports querying based on meaning rather than keywords.

ExPo: Exchange Portal is an online financial trading exchange platform designed to run controlled scientific trading experiments between human traders and automated trader robots.

Video processing and computer vision library for GNU/Linux offering interfaces to do image- and video-I/O with ImageMagick/Magick++, Xine, firewire digital camera (DC1394), and video for linux (V4L2). Note that this version of HornetsEye is deprecated. HornetsEye now is released as multiple packages on RubyGems.org. The source code is available on Github.com (see https://wedesoft.github.io/hornetseye-doc/ for more information).

A Ruby library with a powerful, high-level, general artificial intelligence engine. Sample implementations include a chatterbox and a mud bot.

Acording to Wikipedia chattetbot is: "a computer program designed to simulate an intelligent conversation with one or more human users via auditory or textual methods, primarily for engaging in small talk". Main objective of project L.I.D.I.A was to create fully operational chatterbot implementing AIML with working dataset. AIML interpreter, is developed in Ruby 1.8. Two datasets were developed. First called "Lidia" - your personal assistant, and second called "Mr. Grape F." which was able to simulate dietician.

Learner lets you teach a computer in plain English. You can teach both simple statements and ways of reasoning. Learner will ask you follow-up and new questions. See http://teach-computers.org (not yet active).

This Ruby program takes in a topology specification for an artificial neural network and emits optimized C code (using SSE intrinsics) that implements fast forward and backward propagation for that specific topology.

mcp-ui is a software development kit designed to bring interactive user interface capabilities to applications built on the Model Context Protocol (MCP). It enables developers to create rich, dynamic UI components that can be delivered from an MCP server and rendered seamlessly by a compatible client. Instead of returning only text responses, tools can provide structured UI resources such as HTML or remote-rendered components, allowing more engaging and functional interactions. mcp-ui introduces a standardized approach where tools and their associated interfaces are linked through metadata, enabling clients to automatically discover and display the correct UI. It includes both client-side and server-side SDKs, making it possible to define UI elements on the backend and handle user interactions on the frontend. It supports multiple programming environments, including TypeScript, Python, and Ruby, broadening its accessibility for developers.

Simulator of virtual animals made up of biological neural networks for research in the Computational Neuroscience field.

P53 Cancer Rescue Project, University of California, Irvine , Samuel A. Danziger, Christopher Wassman, Faezeh Salehi Amiri, Roberta Baronio, Linda Hall, Rainer K. Brachmann, G. Wesley Hatfield, Peter Kaiser, Richard H. Lathrop

Platform for Agents as Web Services

Prastava (hindi for "suggestion") - a generic open source recommendation system built entirely in ruby. This system gives recommendations to users on the basis of their past likings (ratings of items) and an item file(for content based similarity).

This is a recommendation system built in ruby which is able to generate recommendations for user inputted data (a text file and a ratings matrix). It works on a hybrid model of collaborative filtering and content based filtering.

Optical Character Recognition (OCR) utility

A Ruby implementation of something RoboCode-like. Provides a game world in which AI(ish) bots can be pitted against each other. The world and the user-created bots are implemented in Ruby.

Design and develop Recommendation and Adaptive Prediction Engines to address eCommerce opportunities. Build a portfolio of engines by creating and porting algorithms from multiple disciplines to a usable form. Try to solve NetFlix and other challenges.

A client side browser implementation of Tom Ray's Tierra Artificial Life simulator (http://en.wikipedia.org/wiki/Tierra_(computer_simulation)).

This is implementation of CommitBERT: Commit Message Generation Using Pre-Trained Programming Language Model. CommitBERT is accepted in ACL workshop : NLP4Prog. Have you ever hesitated to write a commit message? Now get a commit message from Artificial Intelligence! CodeBERT: A Pre-Trained Model for Programming and Natural Languages introduces a pre-trained model in a combination of Program Language and Natural Language(PL-NL). It also introduces the problem of converting code into natural language (Code Documentation Generation). We can use CodeBERT to create a model that generates a commit message when code is added. However, most code changes are not made only by add of the code, and some parts of the code are deleted. We plan to slowly conquer languages that are not currently supported. To run this project, you need a flask-based inference server (GPU) and a client (commit module). If you don't have a GPU, don't worry, you can use it through Google Colab.

Adopting Web2.0-wisdom of crowds, Generate New Generation of Image Semantic Search Engine Based on XML Technology, RDF knowledge warehouse.