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All submitted articles should report original, previously unpublished research results, experimental or theoretical, and will be peer-reviewed.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.
  • DOI: 10.18178/IJML
  • Frequency: Quarterly
  • Average Days to Accept: 68 days
  • Acceptance Rate: 27%
  • Editor-in-Chief: Dr. Lin Huang
  • Executive Editor:  Ms. Cherry L. Chen
  • DOI: 10.18178/IJML
  • Abstracing/Indexing: Google Scholar, Crossref, ProQuest, Electronic Journals LibraryCNKI.
  • E-mail: editor@ijml.org
  • APC: 500USD


Editor-in-Chief

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

01A Systematic Review of Satellite Image Classification
Zhiyan Liu and Haining Zhang

Abstract—With the rapid development of remote sensing technology, high-resolution satellite images play an essential [Click]

02Forecasting Carbon Price in China Using an ARIMA Integrated with ACNN-LSTM and XGBoost
Yu Hao, Ailing Gu, Chengtuo Lie, and Jiayan Lin

Abstract—China& 39;s carbon price (i e carbon emission trading price) is an important part of China& 39;s carbon [Click]

03Neural Networks to Better Identify Actions and Scenarios for Improving Energy Performance of Existing Buildings
Aboudoul-Manaf Issifou, Smain Femmam, Nadia Femmam, and Samia Nefti-Meziani

Abstract—In a sustainable development approach focused onthree pillars, reducing the ecological footprint of anthrop [Click]

04Melanoma Detection Using Convolutional Neural Network
Pooja Illangarathne, Nethari Jayasinghe, Sharith Rodrigo, Kanishka Hewageegana, and Prasad Wimalaratne

Abstract—Melanoma, a severe kind of skin cancer, requires early identification to enhance patient outcomes This p [Click]

05ARTEC: Accelerated Reconstruction of High Angular Resolution Diffusion Imaging with Trajectory Error Correction
Ashutosh Vaish, Anubha Gupta, and Ajit Rajwade

Abstract—Diffusion Magnetic Resonance Imaging (dMRI) isbeing increasingly used to study neural connectivity of brain [Click]

06A Binarized Feature Mapping Technique for Enhancing Squeeze-and-Excitation (SE) Channel Attention Mechanism
Wu Shaoqing and Hiroyuki Yamauchi

Abstract—Representing the weight in the network with only 1bit contributes to saving of the required memory footp [Click]

Most cited papers

01Borderline Active Learning: Transactional Records in Alert-Feedback System
Bokyung Amy Kwon and Kyungtae Kang

Abstract—Transactional records often exhibit highly imbalanced patterns, which can hinder the performance of data-dr [Click]

02Optimizing the Topology of Transformer Networks Using Modified Clonal Selection Algorithm: A Bio-Inspired Immunocomputing Approach
Ashish Kharel and Devinder Kaur

Abstract—This paper proposes the optimization of theTransformer model for analysis of sequential data using amodifi [Click]

03The Effect of Long Short-Term Memory Forecasting with Varied Time Frames
Pongsakorn Teerarassamee, Ratiporn Chanklan, Kittisak Kerdprasop, and Nittaya Kerdprasop

Abstract—This study explores the application of Long Short-Term Memory (LSTM) networks to predict the price of Bi [Click]

04Prediction of CD4 T-Lymphocyte Count via Machine Learning for HIV-positive Patients
Saad Lamjadli, Oumayma Ouedrhiri, Ikram Souli, Zouhair Elamrani Abou Elassad, Oumayma Banouar, Safa Machraoui, Moulay Yassine Belghali, Raja Hazime, Noura Tassi, Said Raghay, Brahim Admou

Abstract—The World Health Organization recommends routine immunological and virologic monitoring for all patients wi [Click]

05Optimizing Neural Network Compilation via Adaptive Workflow with AutoTVM
Yu-Hsiang Chen, Tay-Jyi Lin, Juin-Ming Lu, Tien-Fu Chen

Abstract—With the development of deep neural networks, network compilation plays as an important role for achievin [Click]

06Software Defect Prediction Based on Tree-structured Parzen Estimator Using Machine Learning Classifiers
Faiza Khan, Sultan Almari, Muhammad Haseeb Khan, and Summrina Kanwal

Abstract—Software testing is the most significant task in software development and it takes maximum amount of tim [Click]

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