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Algorithms
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AI-Driven Solutions for Smart Systems in Engineering, Computing, Education, and...
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AI-Driven Solutions for Smart Systems in Engineering, Computing, Education, and Society

A special issue of Algorithms (ISSN 1999-4893). This special issue belongs to the section "Evolutionary Algorithms and Machine Learning".

Deadline for manuscript submissions: 31 October 2025 | Viewed by 770

Special Issue Editors

Dr. Jaafar Alghazo

E-Mail Website
Guest Editor
Software Engineering and Information Technology Management, University of Minnesota Crookston, Crookston, MN 56716, USA
Interests: machine learning; artificial intelligence; image processing; Internet of things (IoT)
Special Issues, Collections and Topics in MDPI journals
Dr. Ghazanfar Latif

E-Mail Website
Guest Editor
Department of Computing Science, Thompson Rivers University, Kamloops, BC V2C 0C8, Canada
Interests: machine learning; artificial intelligence; image processing; Internet of Things (IoT); robotics

Special Issue Information

Dear Colleagues,

This Special Issue explores the rapidly expanding role of artificial intelligence (AI) and machine learning (ML) in enabling smart, adaptive systems across engineering, computing, education, and societal domains.

As emerging technologies increasingly intersect with critical sectors such as healthcare, education, sustainability, industry, and social systems, researchers are developing sophisticated AI-driven solutions that address real-world challenges through automation, prediction, personalization, and optimization.

This Special Issue invites original research, reviews, and case studies highlighting innovative system designs, architectures, models, algorithms, and data-driven frameworks. Topics include smart sensors, predictive analytics, intelligent interfaces, and autonomous systems, with a strong emphasis on interdisciplinary collaboration and practical impact.

Dr. Jaafar Alghazo
Dr. Ghazanfar Latif
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. Algorithms is an international peer-reviewed open access monthly 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 1800 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
  • machine learning
  • smart systems
  • internet of things (IoT)
  • intelligent computing
  • human-centered design
  • sustainable technologies
  • digital health
  • educational technology
  • applied data science
  • quantum computing
  • large language models (LLMs)
  • digital twin technology
  • cloud computing, edge computing, and big data
  • cybersecurity in intelligent systems
  • privacy in intelligence systems
  • smart cities and urban development
  • renewable and green technologies
  • digital economy and SMART Enterprise
  • business intelligence and data analytics
  • software engineering
  • sustainability

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (2 papers)

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27 pages, 2928 KiB  
Article
ML-RASPF: A Machine Learning-Based Rate-Adaptive Framework for Dynamic Resource Allocation in Smart Healthcare IoT
by Wajid Rafique
Algorithms 2025, 18(6), 325; https://doi.org/10.3390/a18060325 - 29 May 2025
Viewed by 403
Abstract
The growing adoption of the Internet of Things (IoT) in healthcare has led to a surge in real-time data from wearable devices, medical sensors, and patient monitoring systems. This latency-sensitive environment poses significant challenges to traditional cloud-centric infrastructures, which often struggle with unpredictable [...] Read more.
The growing adoption of the Internet of Things (IoT) in healthcare has led to a surge in real-time data from wearable devices, medical sensors, and patient monitoring systems. This latency-sensitive environment poses significant challenges to traditional cloud-centric infrastructures, which often struggle with unpredictable service demands, network congestion, and end-to-end delay constraints. Consistently meeting the stringent QoS requirements of smart healthcare, particularly for life-critical applications, requires new adaptive architectures. We propose ML-RASPF, a machine learning-based framework for efficient service delivery in smart healthcare systems. Unlike existing methods, ML-RASPF jointly optimizes latency and service delivery rate through predictive analytics and adaptive control across a modular mist–edge–cloud architecture. The framework formulates task provisioning as a joint optimization problem that aims to minimize service latency and maximize delivery throughput. We evaluate ML-RASPF using a realistic smart hospital scenario involving IoT-enabled kiosks and wearable devices that generate both latency-sensitive and latency-tolerant service requests. Experimental results demonstrate that ML-RASPF achieves up to 20% lower latency, 18% higher service delivery rate, and 19% reduced energy consumption compared to leading baselines. Full article
(This article belongs to the Special Issue AI-Driven Solutions for Smart Systems in Engineering, Computing, Education, and Society)
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Review

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30 pages, 893 KiB  
Review
A Comprehensive Review and Benchmarking of Fairness-Aware Variants of Machine Learning Models
by George Raftopoulos, Nikos Fazakis, Gregory Davrazos and Sotiris Kotsiantis
Algorithms 2025, 18(7), 435; https://doi.org/10.3390/a18070435 - 16 Jul 2025
Viewed by 154
Abstract
Fairness is a fundamental virtue in machine learning systems, alongside with four other critical virtues: Accountability, Transparency, Ethics, and Performance (FATE + Performance). Ensuring fairness has been a central research focus, leading to the development of various mitigation strategies in the literature. These [...] Read more.
Fairness is a fundamental virtue in machine learning systems, alongside with four other critical virtues: Accountability, Transparency, Ethics, and Performance (FATE + Performance). Ensuring fairness has been a central research focus, leading to the development of various mitigation strategies in the literature. These approaches can generally be categorized into three main techniques: pre-processing (modifying data before training), in-processing (incorporating fairness constraints during training), and post-processing (adjusting outputs after model training). Beyond these, an increasingly explored avenue is the direct modification of existing algorithms, aiming to embed fairness constraints into their design while preserving or even enhancing predictive performance. This paper presents a comprehensive survey of classical machine learning models that have been modified or enhanced to improve fairness concerning sensitive attributes (e.g., gender, race). We analyze these adaptations in terms of their methodological adjustments, impact on algorithmic bias and ability to maintain predictive performance comparable to the original models. Full article
(This article belongs to the Special Issue AI-Driven Solutions for Smart Systems in Engineering, Computing, Education, and Society)
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