Soft Computing and Machine Intelligence

ISSN:2765-4273

SCMI journal is an international peer-reviewed open-access journal devoted to soft computing, machine intelligence, and engineering research, published bi-annually online.

SCMI

ISSN: 2765-4273

Soft Computing and Machine Intelligence Journal provide an international forum to report the latest developments in soft computing, machine intelligence, and engineering research.


Open Access

Free for readers, authors or their institutions pay article processing charges (APC).

Rapid Publication

First decision provided to authors approximately 2 weeks after submission; acceptance to publication is undertaken in 2 weeks.

Peer-review

Peer-review process is single-blind, meaning that the author does not know the identity of the reviewer, but the reviewer knows the identity of the author.
Yung-Cheol Byun

Editor-in-Chief
Dr. Yung-Cheol Byun

Department of Computer Engineering, Jeju National University, Jeju-si , South Korea

✉ ycb@jejunu.ac.kr

News & Announcements

Latex template of SCMI journal is now available on Overleaf.

Potential guest editors are welcomed to suggest Research Topics and Special Issue proposals.

We welcome researchers in the field of Soft Computing & Machine Intelligence to join our Editorial Board
Apply for Special Issue

We welcome teams of potential guest editors and early career researchers to suggest Research Topics and Special Issue proposals.

Join our Team

We welcome researchers in the field of Soft Computing & Machine Intelligence to join our Editorial Board. 

Recent Articles

SCMIJ
Towards Effective Analysis and Tracking of Mozilla and Eclipse Defects using Machine Learning Models based on Bugs Data

Author(s): Zohaib Hassan (a)*, Naeem Iqbal (a) and Abnash Zaman (b)

(a) FSRA&IT Solutions Providing Organization Peshawar, Pakistan
(b) Faculty of Bioinformatics Shaheed Benazeer Bhutto Women University Peshawar, Pakistan
* Corresponding author ✉: Dev.zohaibmrt@hotmail.com

SCMIJ
Analyzing the Performance of User Generated Contents in B2B Firms Based on Big Data and Machine Learning

Zeinab Shahbazi (a) and Yung-cheol Byun(a)*

(a) Department of Computer Engineering, Jeju National University, Jeju 63243, Korea
* Corresponding author ✉: ycb@jejunu.ac.kr

Abstract: Social media platforms act as a significant role in human life in recent decades. Marketing scholars show interest in the field of big data based on user-generated content from social media platforms. However, maximum user-generated content is conducted in terms of business-to-consumer (B2C) context to improve the knowledge differences in business to business (B2B) area. The dataset used in the proposed system collects from the Twitter platform. The extracted information is related to eight years of stock data related to 407 companies. Similarly, machine learning techniques are applied to predict data performance. The result of machine learning is converted to the monthly panel dataset. Based on the analysis results, user-generated contents have a considerable impact on companies, showing the differences between B2B and B2C firms. The generated results show that B2C performance is higher and more reliable than B2B. In this process, the consumer's positive response does not affect the stock data performance.

DOI:In Process
SCMIJ
Using machine learning algorithms for housing price prediction: The case of Islamabad housing data

Author(s): Imran(a)*, Umar Zaman (b), Muhammad Waqar(a) and Atif Zaman (a).

(a)Department of Computer Science, Bahria University Islamabad, Pakistan
(b)Department of Computer Science, Iqra University Islamabad, Pakistan
* Corresponding author ✉: imranjejunu@gmail.com 

Soft computing and Machine intelligence
Soft computing and Machine intelligence
Soft computing and Machine intelligence
Soft computing and Machine intelligence
Soft computing and Machine intelligence
Soft computing and Machine intelligence
Soft computing and Machine intelligence
SCMI Journal

Soft Computing and
Machine Intelligence Journal 

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  • ✆: +82 (0) 10 7490 1947 
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