Special Issues
Table of Content

Advanced Data Science Technology for Intelligent Decision Systems

Submission Deadline: 30 November 2024 View: 144 Submit to Special Issue

Guest Editors

Dr. Quanwang Wu, Chongqing University, China
Dr. Yishui Zhu, Chang'an University, China
Dr. Jialei Liu, Hubei University of Arts and Science, China
Dr. Jabar Mahmood, University of Sialkot, Pakistan

Summary

Data science is an interdisciplinary field that combines statistics, scientific methods, mathematics, and algorithms to extract knowledge and meaningful insights from structured and unstructured data. As an emerging discipline, data science aggregates several research topics including data analytics, data security, privacy, data mining, databases, etc. 


Intelligent decision systems make extensive use of artificial intelligence (AI) techniques to serve decision makers like a sophisticated human consultant: gathering and analysing evidence, identifying and diagnosing problems, proposing possible courses of action and evaluating such proposed actions. Data science plays a critical role in building knowledge bases for such systems by extracting, representing and reasoning knowledge. Furthermore, the extrapolated knowledge from typically large data sets can be generalized to solve problems in a wide range of application domains.


This Special Issue highlights the state-of-the-art data science technologies for intelligent decision systems. It also discusses and exchanges recent innovations, developments, and challenges in knowledge representation, automated reasoning, and hybrid intelligent decision systems, e.g., applying the knowledge base, big data, machine learning in industry, engineering, science, automation & robotics, business & finance. This special issue is intended for multidisciplinary scientists with a research interest in advanced data science technologies and intelligent decision systems covering the applications of artificial intelligence, machine learning, deep learning, etc.



Keywords

Artificial Intelligence for Engineering Applications
Data Mining and Knowledge Discovery
Domain Analysis and Knowledge Modeling
Decision Support Systems
Data Engineering
Database technology for AI
Expert systems
Intelligent Information Systems
Intelligent Computing Systems
Knowledge Representation
Machine Learning for Data Science
Social Network and Information Diffusion

Published Papers


  • Open Access

    ARTICLE

    Development of a Novel Noise Reduction Algorithm for Smart Checkout RFID System in Retail Stores

    Shazielya Shamsul, Mohammed A. H. Ali, Salwa Hanim Abdul-Rashid, Atef M. Ghaleb, Fahad M. Alqahtani
    CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2024.049257
    (This article belongs to the Special Issue: Advanced Data Science Technology for Intelligent Decision Systems)
    Abstract This paper presents a smart checkout system designed to mitigate the issues of noise and errors present in the existing barcode and RFID-based systems used at retail stores’ checkout counters. This is achieved by integrating a novel AI algorithm, called Improved Laser Simulator Logic (ILSL) into the RFID system. The enhanced RFID system was able to improve the accuracy of item identification, reduce noise interference, and streamline the overall checkout process. The potential of the system for noise detection and elimination was initially investigated through a simulation study using MATLAB and ILSL algorithm. Subsequently, it More >

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