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Artificial Intelligence and Digital Technology
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Artificial Intelligence and Digital Technology

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: closed (31 August 2023) | Viewed by 839

Special Issue Editors

Dr. Asad Masood Khattak

E-Mail Website
Guest Editor
College of Technological Innovation, Zayed University, Abu Dhabi, United Arab Emirates
Interests: semantic web; data mining; context-aware computing; secure computing; smart cities
Special Issues, Collections and Topics in MDPI journals
Dr. Wajahat Ali Khan

E-Mail Website
Guest Editor
College of Science and Engineering, University of Derby, Derby, UK
Interests: artificial intelligence; semantic web; health informatics
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Artificial Intelligence (AI) “the theory and development of computer systems able to perform tasks normally requiring human intelligence” has been used in transportation, entertainment, sports, education, banking, and healthcare for quite some time now and has facilitated in the automation and has enhanced the quality in the above mentioned fields. In parallel, new Digital Technologies (DT) are supporting the raising living standards and increasing the labour productivity in industries, healthcare, education, and agriculture. For example, DT can increase productivity by reducing machine downtime. However, the outcomes of digitalization depends on how DTs are governed.

AI and DTs are linked together and working in tendam to create new opportunities, improve the existing technologies/services, and fulfill the emerging needs. AI powered systems can better respond to new information or unexpected changes compared to standard automated systems. AI powered systems can predict maintenance needs, work more quickly, precisely, and consistently. Digital Transformation (DX) is an important initiative many organizations have undertaken the transformation. However, AI is the key towards DX while efficiently and intelligently utilizing DTs in any organization. DT supporting DX can better be achieved with proper implementation and utilization of AI. Research work supporting the above topics are the focus of this Special Issue to extend the research and development work in the field of Machine Learning, Deep Learning, Data Science, Artificial Intelligance, Digital Technologies, and Digital Transformation.

This Special Issue will accept high-quality original research papers and review papers in the overlapping fields of:

  • Artificial Intelligence and Digital Technologies
  • AI in Digital Transformation
  • DT in Digital Transformation
  • AI and DT in Health Informatics
  • AI and DT in Entertainment, Sports, Banking, Telecommunication, Transportation, and Education
  • AI, Data Science, and Computational Intelligence
  • AI, Machine Learning, and Deep Learning
  • AI, Machine Learning, and Deep Learning Applications
  • AI, Ontologies, and Semantics
  • AI and DT in/for Interoperability
  • AI and DT in Blackchain

Dr. Asad Masood Khattak
Dr. Wajahat Ali Khan
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. Applied Sciences 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

  • artificial intelligence
  • digital technology
  • digital transformation
  • machine/deep learning
  • data science
  • health informatics
  • semantics
  • recommender systems

Published Papers (1 paper)

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Research

20 pages, 648 KiB  
Article
High-Dimensional Data Analysis Using Parameter Free Algorithm Data Point Positioning Analysis
by S. M. F. D. Syed Mustapha
Appl. Sci. 2024, 14(10), 4231; https://doi.org/10.3390/app14104231 - 16 May 2024
Viewed by 310
Abstract
Clustering is an effective statistical data analysis technique; it has several applications, including data mining, pattern recognition, image analysis, bioinformatics, and machine learning. Clustering helps to partition data into groups of objects with distinct characteristics. Most of the methods for clustering use manually [...] Read more.
Clustering is an effective statistical data analysis technique; it has several applications, including data mining, pattern recognition, image analysis, bioinformatics, and machine learning. Clustering helps to partition data into groups of objects with distinct characteristics. Most of the methods for clustering use manually selected parameters to find the clusters from the dataset. Consequently, it can be very challenging and time-consuming to extract the optimal parameters for clustering a dataset. Moreover, some clustering methods are inadequate for locating clusters in high-dimensional data. To address these concerns systematically, this paper introduces a novel selection-free clustering technique named data point positioning analysis (DPPA). The proposed method is straightforward since it calculates 1-NN and Max-NN by analyzing the data point placements without the requirement of an initial manual parameter assignment. This method is validated using two well-known publicly available datasets used in several clustering algorithms. To compare the performance of the proposed method, this study also investigated four popular clustering algorithms (DBSCAN, affinity propagation, Mean Shift, and K-means), where the proposed method provides higher performance in finding the cluster without using any manually selected parameters. The experimental finding demonstrated that the proposed DPPA algorithm is less time-consuming compared to the existing traditional methods and achieves higher performance without using any manually selected parameters. Full article
(This article belongs to the Special Issue Artificial Intelligence and Digital Technology)
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