International Journal of Machine Learning and Networked Collaborative Engineering (IJMLNCE) ISSN 2581-3242 is a quarterly published, Open Access, peer-reviewed, international journal focuses on publishing authentic and unpublished quality research papers. This is a scientific journal which aims to promote quality and innovative research works, not limited to but focuses on the area of machine learning, collaborative engineering and allied areas. We publish original research articles, review articles and technical notes.
The journal is registered with crossref 10.30991/IJMLNCE. Each accepted manuscript shall be assigned a unique digital object identifier for identification. The abbreviated key title for our journal allocated by ISSN is Int. j. mach. learn. networked collab. eng. The journal appears in popular indexes like BASE (Bielefeld Academic Search Engine), CrossRef, CiteFactor, DRJI, Google Scholar, Index Copernicus, J-Gate, Portico, PKP-Index, ROAD, Scilit, Socolar. Our journals manuscript is also visible in ReserachGate & Kudos.
In this present era, the subjects like machine intelligence, machine learning and its associated domains are the first choice for the researchers and the industry peoples. In the past decade, the use of machine learning and its domain has drawn ample attention of the people which has generated a number of applications related to that area and hence it has become a very popular choice for the present day researchers. Machine intelligence or machine learning is also working towards improving the life style of the human beings nowadays. These are being used in various sectors like Healthcare, Space, Automation, Aviation industries and even in academia.
With this insight, we are inviting you to consider IJMLNCE to publish your work. The Journal solicits latest research work carried out by researchers, academicians and industry practitioners in the domain of machine learning and collaborative engineering. It maintains a high standard in the term of similarity check and quality of paper. The originality of the work is highly appreciated. Each paper will be reviewed by the expert reviewers and in a multilayer approach. The journal editorial board believes in high ethical practices.
The authors are invited to submit their unpublished manuscripts online only. The author will get final decision of manuscript within 06-12 weeks from the date of submission of manuscripts. Once the manuscript gets accepted, author will be asked to complete the copyright agreement. An e-mail communication is also sent to the author mail after verification of incorporation of all reviewers’ comments in term of their final camera-ready-paper; it will be published to the upcoming journal’s volumes. There are no publication fees; no article processing cost for publishing articles in this journal and the journal is open access.
Vol 2 No 03 (2018): Volume No 02 Issue No 03
The following manuscript is included in Volume No 02, Issue No 03
Smart Advisor: An Intelligent Inventory Prediction Based On Regression Model.
Forecasting Ventricular Deviation in Monitoring of Live ECG Signal.
Cow Behavior Monitoring Using a Multidimensional Acceleration Sensor and Multiclass SVM.
Machine Learning Approach for User Account Identification with Unwanted Information and Data.
Internet of Things and Healthcare Technologies: A Valuable Synergy from Design to Implementation
International Journal of Machine Learning and Networked Collaborative Engineering (IJMLNCE)
ISSN 2581-3242 (Online)
Frequency : Quarterly | Establishment : 2017
DOI : https://doi.org/10.30991/IJMLNCE.
Indexing : BASE (Bielefeld Academic Search Engine), CrossRef, CiteFactor, DRJI, Google Scholar, Index Copernicus, J-Gate, PKP-Index, ROAD, Scilit, Socolar.
CMR Institute of Technology (Autonomous), Hyderabad, Telangana, India
Universidad de Oviedo , Spain
Copyright All articles published by International Journal of Machine Learning and Networked Collaborative Engineering (IJMLNCE) ISSN 2581-3242 are licensed under the Attribution-NonCommercial-ShareAlike 4.0 This license lets others remix, tweak, and build upon your work non-commercially, as long as they credit you and license their new creations under the identical terms.