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 499

Special Issue Editors


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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

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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

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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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Published Papers (1 paper)

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Research

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 329
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
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