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All submitted articles should report original, previously unpublished research results, experimental or theoretical, and will be peer-reviewed.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

Editor-in-Chief

Prof. Lin Huang
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

01ARTEC: Accelerated Reconstruction of High Angular Resolution Diffusion Imaging with Trajectory Error Correction

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

02A Binarized Feature Mapping Technique for Enhancing Squeeze-and-Excitation (SE) Channel Attention Mechanism

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

03SHA-ZA: Advanced Reinforcement Learning for Othello Mastery Using Proximal Policy Optimization

Abstract—This paper introduces SHA-ZA (StrategicHeuristic Agent with Zero-human Advancement), an advancedreinforcement [Click]

04Instance Selection for MI-Support Vector Machines

Abstract—Support Vector Machines (SVM) is a well-known algorithm in machine learning due to its superior performan [Click]

05Hybrid Deep Learning and Genetic Algorithm Approach for Detecting Keratoconus Using Corneal Tomography

Abstract—Nowadays, there are still significant challengesencountered in the accurate diagnosis of various eye diseas [Click]

More Latest Articles

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]

More Articles

Conferences

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What's New

  • 23

    Jan

    Vol.15, No.1 has been published with online version. [Click]

  • 29

    Jul

    Vol.14, No.3 has been published with online version.[Click]

  • 16

    Jan

    IJML adopts Article-by-Article Work Flow from 2024.[Click]
Read more

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E-mail: editor@ijml.org