< Submission Guidelines

Guidelines for Engineering- and Machine Learning-Based Projects

All manuscripts published in JEI must be hypothesis-driven research. Hypotheses are a crucial part of the scientific thinking process, and professional scientific endeavors revolve around posing and testing hypotheses. We believe that it is important for students who publish with JEI to practice rigorous scientific thinking. This means that manuscripts that merely introduce an invention, methods optimization, or machine learning algorithm/model, no matter how impressive it is, are not appropriate for JEI. Here are some common examples of unacceptable “hypotheses” relating to engineering projects:

  • I hypothesize that my invention will work
  • I hypothesize that I can build this invention
  • I hypothesize that my model/algorithm will be accurate
  • I hypothesize that this model/algorithm will be more accurate than other models

In this guide, we will describe a few of the best strategies to convert your engineering- or machine learning-based manuscript into a hypothesis-driven one publishable with JEI. We will use some examples of submissions that we received in the past that either implement one of the included strategies or no longer would be acceptable and provide guidance on how to revise them.

Converting your engineering- or machine learning-based project to a hypothesis-driven manuscript

It is often possible to convert an engineering- or machine learning-based manuscript to a hypothesis-driven one by adding a few experiments, and sometimes just by changing the way it is presented. Here are two strategies to convert manuscripts that involve engineering, machine learning, and optimization projects to also include a clear, experimentally tested hypothesis, with examples drawn from previous JEI submissions.

1. Use your device, algorithm, or model to address a scientific question

This is the best way to use your invention to write a hypothesis-driven manuscript acceptable for JEI publication. Rather than centering your hypothesis on your invention or model, your hypothesis should predict a scientific finding using your invention or model within the methodology of testing your hypothesis. Below are some examples of JEI manuscripts that use their invention/model/optimization to pose a question and perform a series of experiments to test the hypothesis using their invention/model/optimization.

2. Predict a specific factor that will influence the effectiveness of your learning model/invention

If an invention or computational algorithm/model explores a different way to accomplish a goal more efficiently or more accurately, you can design an experiment where you compare the factors that may influence the effectiveness/accuracy of your invention or model. However, you must include the following information for this approach to be acceptable:

  • Predict which factor you hypothesize will specifically lead to more accurate results compared to other tested factors within your device/model.
  • Give concrete reasons about why you think so and back your claim up with previous research or theoretical background.
  • The Results section should focus on the hypothesis of which factor is responsible for highest accuracy, not the accuracy of the model(s) as a whole.
  • Results section should contain statistical analysis to directly compare the results gathered from the independent variables (i.e., the factors you want to compare).

These requirements are necessary to ensure that you have thought critically about your invention/model.

Links to Manuscripts

Feel free to contact the JEI Editorial Staff if you have any more questions about how to write a hypothesis-driven manuscript for JEI. Find the links to the full manuscripts mentioned above, as well as some other acceptable machine learning-based manuscripts below:

Identifying factors, such as low sleep quality, that predict suicidal thoughts using machine learning

Jet optimization using a hybrid multivariate regression model and statistical methods in dimuon collisions

Comparison of three large language models as middle school math tutoring assistants

A comparative analysis of machine learning approaches for prediction of breast cancer

Predicting asthma-related emergency department visits and hospitalizations with machine learning techniques

Evaluating the effectiveness of machine learning models for detecting AI-generated art

Evaluating machine learning algorithms to classify forest tree species through satellite imagery

These guidelines were last updated on June 24, 2024. The primary reason for our new guidelines on computational algorithm manuscripts is that JEI currently does not have the necessary expertise to evaluate the increasing volume of computational manuscripts. We always strive to provide extensive feedback to our student authors so that they can have a positive and educational experience while publishing their (likely) first scientific work. At the moment, we do not feel that we can do this appropriately for all computational manuscripts.