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Special Issue Editors

Dr. Shadi Alzu'Bi

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Guest Editor

Department of Computer Science Faculty of Science and Information Technology, Al-Zaytoonah University of Jordan, Amman, Jordan
Interests: intelligent systems; smart cities; business intelligence; data science

Dr. Ala Mughaid

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Guest Editor

Faculty of Prince Al-Hussein Bin Abdallah II For Information Technology, Hashemite University, Zarqa, Jordan
Interests: could computing; cyber security; image processing; virtual reality; data mining

Special Issue Information

Dear Colleagues,

Intelligent decision support systems are computerized systems that help organizations and businesses make better decisions by providing them with big data, analytics, and insights. They help in business planning, monitoring, and processes improvements in a sustainable way by providing access to large amounts of data and real-time analytics. Intelligent decision support systems can also support the automation of decision-making processes, making them faster and more efficient. As a result, businesses can reduce their costs and improve their processes, ensuring sustainability and success. Creating sustainable cities requires businesses to rethink their processes and operations. Improving business processes can help reduce their environmental impact and create a more sustainable future. Companies should focus on automating the decision-making process using artificial intelligence techniques to increase the efficiency of their operations. Businesses should build robust decision-making criteria based on the available big business data to create a sense of sustainability and support for their initiatives. By implementing these changes, businesses can help create a more sustainable and smart future for everyone.

This Special Issue will examine the application of intelligent decision support systems (IDSSs) to enhance business process improvement. This issue will explore the current state of the art with regard to IDSSs, as well as potential areas for future research and development.

IDSSs are an increasingly important tool in the business world. They are used to support decision makers in a wide range of industries, from finance and banking to manufacturing and retail. IDSSs can help businesses to streamline processes, to improve customer satisfaction, and to increase profitability.

This Special Issue will provide a platform for researchers and practitioners to share their experiences and insights about the use of IDSSs for business process improvement. Topics to be covered include but are not limited to the following:

Artificial intelligence in IDSS;
Big business data analytics;
Overview of IDSS technologies and their applications in business processes;
Strategies for successful implementation of an IDSS;
Benchmarking of various IDSS solutions;
Case studies on the use of IDSSs in business process improvement;
Methods for evaluating the performance of an IDSS;
Future trends and challenges in IDSS research.

This Special Issue will be situated in the existing literature on decision support systems, artificial intelligence, and business process management. It will provide a platform for the discussion and exchange of ideas about the potential of IDSSs in enhancing business process improvement.

Dr. Shadi Alzu'Bi
Dr. Ala Mughaid
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sustainability is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

intelligent systems
business decision making
business intelligence
data science
big business data
sustainable businesses
AI in business applications

Published Papers (1 paper)

Research

14 pages, 4217 KiB

Open AccessArticle

An Intelligent Decision Support System Based on Multi Agent Systems for Business Classification Problem

by Mais Haj Qasem, Mohammad Aljaidi, Ghassan Samara, Raed Alazaidah, Ayoub Alsarhan and Mohammed Alshammari

Sustainability 2023, 15(14), 10977; https://doi.org/10.3390/su151410977 - 13 Jul 2023

Cited by 9 | Viewed by 1177

Abstract

The development of e-systems has given consumers and businesses access to a plethora of information, which has complicated the process of decision making. Document classification is one of the main decisions that any business adopts in their decision making to categorize documents into groups according to their structure. In this paper, we combined multi-agent systems (MASs), which is one of the IDSS systems, with Bayesian-based classification to filter out the specialization, collaboration, and privacy of distributed business sources to produce an efficient distributed classification system. Bayesian classification made use of MAS to eliminate distributed sources’ specialization and privacy. Therefore, incorporating the probabilities of various sources is a practical and swift solution to such a problem, where this method works the same when all the data are merged into a single source. Each intelligent agent can collaborate and ask for help from other intelligent agents in classifying cases that are difficult to classify locally. The results demonstrate that our proposed technique is more accurate than those of the non-communicated classification, where the results proved the ability of the utilized productive distributed classification system. Full article

(This article belongs to the Special Issue Intelligent Decision Support Systems in Business Process Improvement)

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