About IJMLC

International Journal of Machine Learning and Computing (IJMLC) 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 and computing. The focus is to publish papers on state-of-the-art machine learning and computing. 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All submitted articles should report original, previously unpublished research results, experimental or theoretical, and will be peer-reviewed.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.

Important Notice: IJMLC will only accept new submissions through online submission system


General Information

  • ISSN: 2010-3700 (Online)
  • Abbreviated Title: Int. J. Mach. Learn. Comput.
  • Frequency: Bimonthly
  • DOI: 10.18178/IJMLC
  • Editor-in-Chief: Dr. Lin Huang
  • Executive Editor:  Ms. Cherry L. Chen
  • Abstracing/Indexing: Inspec (IET), Google Scholar, Crossref, ProQuest, Electronic Journals Library.
  • E-mail: ijmlc@ejournal.net

Editor-in-Chief

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

Latest Articles

01MellisAI - An AI Generated Music Composer Using RNN-LSTMs
N. Hari Kumar, P. S Ashwin, and Haritha Ananthakrishnan

Abstract—The art of composing music can be automated with deep learning along with the knowledge of a few implicit heuristics. In this proposed paper, we aim at building a model that composes Carnatic oriented contemporary tune,[Click]

02Efficient Machine Learning Methods for Hard Disk Drive Yield Prediction Improvement
Anusara Hirunyawanakul, Nittaya Kerdprasop, and Kittisak Kerdprasop

Abstract—Deployment of machine learning techniques are prevailing in world-wide problem solving. Hard disk drive manufacturing is another prominent field seeking for the application of these knowledge intensive techniques.[Click]

03Monte-Carlo Based Reinforcement Learning (MCRL)
Muath Alrammal and Munir Naveed

Abstract—This paper presents a Monte-Carlo based Reinforcement Learning approach called MCRL. MCRL is applied in different domains to construct context-aware model for mobile computing. For mobile devices, we present MCRL, [Click]

04The CAN FD Vehicle Network System with Machine Learning and Scheduling Algorithms
Yung-Hoh Sheu, Cheng-Yo Huang, Chen-Yu Yang, and Yi-Hong Lin

Abstract—The controller area network with flexible datarate (CAN FD) inherits the primary features of a controller area network (CAN); thus, exploring the possibility of establishing a hybrid CAN and CAN FD network is essential. [Click]

05Stock Performance Classification in Stock Exchange of Thailand (SET) by Using Supervised Machine Learning Model
Chayanant Kosol and Punnamee Sachakamol

Abstract—Most investors decide to invest in a stock market in order to win from an inflation. And, Financial Statement is the top tool that Thai investors have been using a financial statement to support their buying/selling decision in the stock market[Click]

Most cited papers

01Wavelet Based Image Coding via Image Component Prediction Using Neural Networks
Takuma Takezawa and Yukihiko Yamashita

Abstract— In the process of wavelet based image coding, it is possible to enhance the performance by applying p [Click]

02A Comparative Analysis Using Different Machine Learning: An Efficient Approach for Measuring Accuracy of Face Recognition
Muhammad Shakeel Faridi, Muhammad Azam Zia, Zahid Javed, Imran Mumtaz, and Saqib Ali

Abstract—Feature extracting and training module can be done by using face recognition neural learning techniques [Click]

03Prediction of Employee Attrition Using Machine Learning and Ensemble Methods
Aseel Qutub, Asmaa Al-Mehmadi, Munirah Al-Hssan, Ruyan Aljohani, and Hanan S. Alghamdi

Abstract—Employees are the most valuable resources for any organization The cost associated with professional trai [Click]

04Machine Learning Versus Deep Learning Performances on the Sentiment Analysis of Product Reviews
Pumrapee Poomka, Nittaya Kerdprasop, and Kittisak Kerdprasop

Abstract—At this current digital era, business platforms havebeen drastically shifted toward online stores on inter [Click]

05Boosted Supervised Intensional Learning Supported by Unsupervised Learning
A. C. M. Fong and G. Hong

Abstract—Traditionally, supervised machine learning (ML) algorithms rely heavily on large sets of annotated data T [Click]

06An Ensemble Framework for Spam Detection on Social Media Platforms
Junzhang Wang, Diwen Xue, and Karen Shi

Abstract—As various review sites grow in popularity andbegin to hold more sway in consumer preferences, spamdetect [Click]

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