About IJML

Former Title: International Journal of Machine Learning and Computing (ISSN: 2010-3700)

International Journal of Machine Learning (IJML) is an international academic open access journal which gains a foothold in Singapore, Asia and opens to the world. It aims to promote the integration of machine learning. The focus is to publish papers on state-of-the-art machine learning. Submitted papers will be reviewed by technical committees of the Journal and Association. The audience includes researchers, managers and operators for machine learning and computing as well as designers and developers.

All submitted articles should report original, previously unpublished research results, experimental or theoretical, and will be peer-reviewed. Articles submitted to the journal should meet these criteria and must not be under consideration for publication elsewhere. Manuscripts should follow the style of the journal and are subject to both review and editing.

General Information

  • E-ISSN: 2972-368X
  • Abbreviated Title: Int. J. Mach. Learn.
  • Frequency: Quaterly
  • DOI: 10.18178/IJML
  • Editor-in-Chief: Dr. Lin Huang
  • Executive Editor:  Ms. Cherry L. Chen
  • Abstracing/Indexing: Inspec (IET), Google Scholar, Crossref, ProQuest, Electronic Journals LibraryCNKI.
  • E-mail: ijml@ejournal.net
  • APC: 500USD


Metropolitan State University of Denver, USA
It's my honor to take on the position of editor in chief of IJML. We encourage authors to submit papers concerning any branch of machine learning.

Latest Articles

01Performance Analysis of Machine Learning Models in Solar Energy Forecasting

Abstract—Energy is essential to facilitate the social andeconomic growth of a society But this energy generation [Click]

02Resilience Evaluation of Automakers after 2008 Financial Crisis by UMAP

Abstract—Under various risks, companies are required to be resilient, so that they can flexibly respond to change [Click]

03Prediction of Mental Health: Heuristic Subjective Well-Being Model on Perceived Stress Scale Using Machine Learning Algorithms

Abstract—More research is being done to find out how well-being can be predicted using well-designed models To [Click]

04JoyBot: RASA-Trained Chatbots to Provide Mental Health Assistance for Australians

Abstract—The project aims to boost the mental health of Australians using RASA-trained chatbots JoyBot was traine [Click]

05Employing the Exponentiated Magnitude Spectrogram in the Deep Learning-Based Mask Estimation for Speech Enhancement

Abstract—The objective of speech enhancement (SE) is to alleviate various types of distortion (noise, channel effe [Click]

06Deep-Racing: An Embedded Deep Neural Network (EDNN) Model to Predict the Winning Strategy in Formula One Racing

Abstract—This paper presents an embedded deep neural network model to predict the driver rank and the optimum pi [Click]

Most cited papers

01Effect of Drop and Rebuilt Operator for Solving the Biobjective Obnoxious p-Median Problem
Méziane Aïder, Aida-Ilham Azzi, and Mhand Hifi*

Abstract—In this paper, we solve the bi-objective obnoxious with a population-based method The designed algorithm [Click]

02Relaxed Training Procedure for a Binary Neural Network
Jiazhen Xi and Hiroyuki Yamauchi*

Abstract—Binary neural networks (BNNs) have drawn much attention recently because they possess the most promising [Click]

03Application of Classification Methods in Forecasting Broadband Internet Subscribers Leaving the Network
Dong-Ho Le and Van-Dung Hoang*

Abstract—The cancellation of subscribers is always a matter of special concern for service providers in general a [Click]

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