A Review into Data Science and Its Approaches in Mechanical Engineering

• URL: http://arxiv.org/abs/2012.15358v1
• Date: Wed, 30 Dec 2020 23:05:29 GMT
• Title: A Review into Data Science and Its Approaches in Mechanical Engineering
• Authors: Ashkan Yousefi Zadeh, Meysam Shahbazy
• Abstract summary: This article briefly introduced data science and reviewed its methods. In the introduction, different definitions of data science and its background in technology reviewed. Some researches in the mechanical engineering area that used data science methods in their studies are reviewed.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Nowadays it is inevitable to use intelligent systems to improve the performance and optimization of different components of devices or factories. Furthermore, it's so essential to have appropriate predictions to make better decisions in businesses, medical studies, and engineering studies, etc. One of the newest and most widely used of these methods is a field called Data Science that all of the scientists, engineers, and factories need to learn and use in their careers. This article briefly introduced data science and reviewed its methods, especially it's usages in mechanical engineering and challenges and ways of developing data science in mechanical engineering. In the introduction, different definitions of data science and its background in technology reviewed. In the following, data science methodology which is the process that a data scientist needs to do in its works been discussed. Further, some researches in the mechanical engineering area that used data science methods in their studies, are reviewed. Eventually, it has been discussed according to the subjects that have been reviewed in the article, why it is necessary to use data science in mechanical engineering researches and projects.

Related papers

• An Autonomous Large Language Model Agent for Chemical Literature Data Mining [60.85177362167166]
  We introduce an end-to-end AI agent framework capable of high-fidelity extraction from extensive chemical literature. Our framework's efficacy is evaluated using accuracy, recall, and F1 score of reaction condition data.
  arXiv  Detail & Related papers  (2024-02-20T13:21:46Z)
• Defining data science: a new field of inquiry [0.0]
  Modern data science is in its infancy. Emerging slowly since 1962 and rapidly since 2000, it is one of the most active, powerful, and rapidly evolving 21st century innovations. Due to its value, power, and applicability, it is emerging in over 40 disciplines, hundreds of research areas, and thousands of applications. This research addresses this data science multiple definitions challenge by proposing the development of coherent, unified definition based on a data science reference framework.
  arXiv  Detail & Related papers  (2023-06-28T12:58:42Z)
• Symmetry-Informed Geometric Representation for Molecules, Proteins, and Crystalline Materials [66.14337835284628]
  We propose a platform, coined Geom3D, which enables benchmarking the effectiveness of geometric strategies. Geom3D contains 16 advanced symmetry-informed geometric representation models and 14 geometric pretraining methods over 46 diverse datasets.
  arXiv  Detail & Related papers  (2023-06-15T05:37:25Z)
• A Vision for Semantically Enriched Data Science [19.604667287258724]
  Key areas such as utilizing domain knowledge and data semantics are areas where we have seen little automation. We envision how leveraging "semantic" understanding and reasoning on data in combination with novel tools for data science automation can help with consistent and explainable data augmentation and transformation.
  arXiv  Detail & Related papers  (2023-03-02T16:03:12Z)
• Maximizing information from chemical engineering data sets: Applications to machine learning [61.442473332320176]
  We identify four characteristics of data arising in chemical engineering applications that make applying classical artificial intelligence approaches difficult. For each of these data characteristics, we discuss applications where these data characteristics arise and show how current chemical engineering research is extending the fields of data science and machine learning to incorporate these challenges.
  arXiv  Detail & Related papers  (2022-01-25T01:25:45Z)
• Human-in-the-Loop Disinformation Detection: Stance, Sentiment, or Something Else? [93.91375268580806]
  Both politics and pandemics have recently provided ample motivation for the development of machine learning-enabled disinformation (a.k.a. fake news) detection algorithms. Existing literature has focused primarily on the fully-automated case, but the resulting techniques cannot reliably detect disinformation on the varied topics, sources, and time scales required for military applications. By leveraging an already-available analyst as a human-in-the-loop, canonical machine learning techniques of sentiment analysis, aspect-based sentiment analysis, and stance detection become plausible methods to use for a partially-automated disinformation detection system.
  arXiv  Detail & Related papers  (2021-11-09T13:30:34Z)
• Data science and Machine learning in the Clouds: A Perspective for the Future [0.0]
  Data driven science (the so called fourth science paradigm) is going to be the driving force in research and innovation. Huge amount of data to be processed under this new paradigm will be a major concern in the future. One will strongly require cloud based services in all the aspects of these computations.
  arXiv  Detail & Related papers  (2021-09-02T17:36:24Z)
• Data Science Methodologies: Current Challenges and Future Approaches [0.0]
  Lack of vision and clear objectives, a biased emphasis on technical issues, a low level of maturity for ad-hoc projects are among these challenges. Few methodologies offer a complete guideline across team, project and data & information management. We propose a conceptual framework containing general characteristics that a methodology for managing data science projects with a holistic point of view should have.
  arXiv  Detail & Related papers  (2021-06-14T10:34:50Z)
• Data-Driven Aerospace Engineering: Reframing the Industry with Machine Learning [49.367020832638794]
  The aerospace industry is poised to capitalize on big data and machine learning. Recent trends will be explored in context of critical challenges in design, manufacturing, verification and services.
  arXiv  Detail & Related papers  (2020-08-24T22:40:26Z)
• A Data Scientist's Guide to Streamflow Prediction [55.22219308265945]
  We focus on the element of hydrologic rainfall--runoff models and their application to forecast floods and predict streamflow. This guide aims to help interested data scientists gain an understanding of the problem, the hydrologic concepts involved, and the details that come up along the way.
  arXiv  Detail & Related papers  (2020-06-05T08:04:37Z)
• The Big Three: A Methodology to Increase Data Science ROI by Answering the Questions Companies Care About [0.0]
  Companies may be achieving only a third of the value they could be getting from data science in industry applications. We propose a methodology for categorizing and answering 'The Big Three' questions (what is going on, what is causing it, and what actions can I take that will optimize what I care about) using data science.
  arXiv  Detail & Related papers  (2020-02-12T21:25:56Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.