scholarly journals AI-Track-tive: open source software for automated recognition and counting of surface semi-tracks using computer vision (artificial intelligence)

2021 ◽  
Author(s):  
Simon Nachtergaele ◽  
Johan De Grave
Keyword(s):  
Artificial Intelligence ◽  
Computer Vision ◽  
Open Source ◽  
Open Source Software ◽  
Automated Recognition

scholarly journals AI-Track-tive: open-source software for automated recognition and counting of surface semi-tracks using computer vision (artificial intelligence)

Geochronology ◽  
2021 ◽  
Vol 3 (1) ◽  
pp. 383-394
Author(s):  
Simon Nachtergaele ◽  
Johan De Grave
Keyword(s):  
Artificial Intelligence ◽  
Neural Networks ◽  
Computer Vision ◽  
Open Source ◽  
Open Source Software ◽  
Deep Neural Networks ◽  
Detection Algorithm ◽  
Fission Tracks ◽  
Automated Recognition ◽  
Automatic Counting

Abstract. A new method for automatic counting of etched fission tracks in minerals is described and presented in this article. Artificial intelligence techniques such as deep neural networks and computer vision were trained to detect fission surface semi-tracks on images. The deep neural networks can be used in an open-source computer program for semi-automated fission track dating called “AI-Track-tive”. Our custom-trained deep neural networks use the YOLOv3 object detection algorithm, which is currently one of the most powerful and fastest object recognition algorithms. The developed program successfully finds most of the fission tracks in the microscope images; however, the user still needs to supervise the automatic counting. The presented deep neural networks have high precision for apatite (97 %) and mica (98 %). Recall values are lower for apatite (86 %) than for mica (91 %). The application can be used online at https://ai-track-tive.ugent.be (last access: 29 June 2021), or it can be downloaded as an offline application for Windows.

Overview of Multi-Factor Prediction Using Deep Neural Networks, Machine Learning, and Their Open-Source Software

2022 ◽  
pp. 1-28
Author(s):  
Richard S. Segall
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Machine Learning ◽  
Neural Networks ◽  
Open Source ◽  
Open Source Software ◽  
Limited Time ◽  
Software Company ◽  
Data Capacity ◽  
Network Software

This chapter first provides an overview with examples of what neural networks (NN), machine learning (ML), and artificial intelligence (AI) are and their applications in biomedical and business situations. The characteristics of 29 types of neural networks are provided including their distinctive graphical illustrations. A survey of current open-source software (OSS) for neural networks, neural network software available for free trail download for limited time use, and open-source software (OSS) for machine learning (ML) are provided. Characteristics of artificial intelligence (AI) technologies for machine learning available as open source are discussed. Illustrations of applications of neural networks, machine learning, and artificial intelligence are presented as used in the daily operations of a large internationally-based software company for optimal configuration of their Helix Data Capacity system.

scholarly journals A Case Study: Crowd Sourced AI Programming [Research in Progress; Abstract]

10.28945/4516 ◽  
2020 ◽  
Author(s):  
Christine Bakke
Keyword(s):  
Artificial Intelligence ◽  
Experiential Learning ◽  
Software Development ◽  
Open Source ◽  
Undergraduate Students ◽  
Open Source Software ◽  
Real World ◽  
Instructional Materials ◽  
Future Research ◽  
Software Projects

Aim/Purpose: To examine crowd-sourced programming as an experiential learning, instructional medium. The goal is to provide real-time, real-world, artificial intelligence programming without textbook instructional materials. Background: Open source software has resulted in loosely knit communities of global software developers that work together on a software project. Taking open source software development to another level, current trends have expanded into crowd sourced development of Artificial Intelligence (AI). This project explored the use of Amazon Alexa’s tools and web resources to learn AI software development. Methodology: This project incorporated experiential and inquiry educational methods that combined direct experience with crowd-sourced programming while requiring students to take risks, solve problems, be creative, make mistakes and resolve them. The instructor facilitated the learning experience through weekly meetings and structured reports that focused on goal setting and analysis of problems. This project is part of ongoing research into small group creative works research that provides students with real-world coding experience. Contribution: Undergraduate students successfully programmed an introductory level social bot using experiential learning methods and a crowd-sourced programming project (Amazon Alexa social bot). Findings: A of the experience and findings will be included with final paper release summary Recommendations for Practitioners: Crowd sourced programming provides opportunities and can be harnessed for semester long coding projects to develop student programming skills through direct involvement in real open sourced projects. Recommendation for Researchers: There is a high rate of failure associated with software projects, yet pro-gramming courses continue to be taught as they have been for decades. More research needs to be done and instructional materials developed for the undergraduate level that use real programming projects. Can we improve the rate of success for software projects by requiring expe-riential education in our courses? Impact on Society: Crowd-sourced programming is an opportunity for students to learn to program and build their portfolio with real world experience. Students participating in crowd-sourced programming are involved in creative works research and gain experience developing real-world software. Future Research: Future research will explore experiential learning such as crowd-sourced and other open source programming opportunities for undergraduate students to participate in real software development.

scholarly journals A Survey of Open Source Statistical Software (OSSS) and Their Data Processing Functionalities

2021 ◽  
Vol 12 (1) ◽  
pp. 1-20
Author(s):  
Gao Niu ◽  
Richard S. Segall ◽  
Zichen Zhao ◽  
Zhijian Wu
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Big Data ◽  
Statistical Analysis ◽  
Open Source ◽  
Open Source Software ◽  
Free Software ◽  
Statistical Software ◽  
Artificial Intelligence Application ◽  
Intelligence Application

This paper discusses the definitions of open source software, free software and freeware, and the concept of big data. The authors then introduce R and Python as the two most popular open source statistical software (OSSS). Additional OSSS, such as JASP, PSPP, GRETL, SOFA Statistics, Octave, KNIME, and Scilab, are also introduced in this paper with function descriptions and modeling examples. They further discuss OSSS's capability in artificial intelligence application and modeling and Popular OSSS-based machine learning libraries and systems. The paper intends to provide a reference for readers to make proper selections of open source software when statistical analysis tasks are needed. In addition, working platform and selective numerical, descriptive and analysis examples are provided for each software. Readers could have a direct and in-depth understanding of each software and its functional highlights.

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