Explainable Artificial Intelligence for IoT and Smart Systems

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Networks".

Deadline for manuscript submissions: closed (20 August 2024) | Viewed by 1495

Special Issue Editors


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Guest Editor
Department of Mathematics, Embry-Riddle Aeronautical University, Daytona Beach, FL, USA
Interests: artificial intelligence; cyber-physical systems
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Department of Computer Science, University of Tennessee at Martin, Martin, TN 38238, USA
Interests: machine learning; unmanned aerial vehicles; wireless networking
Special Issues, Collections and Topics in MDPI journals

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Department of Computer Science, Bowling Green State University, Ohio, AL 36117, USA
Interests: machine learning; transfer learning; biomedical informatics
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Guest Editor
Department of Electrical Engineering and Computer Science, Embry-Riddle Aeronautical University, Daytona Beach, FL 32114-3900, USA
Interests: adaptive/statistical signal processing; independent component analysis; wireless communications
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Guest Editor
College of Aviation, Embry-Riddle Aeronautical University, Daytona Beach, FL 32114-3900, USA
Interests: artificial intelligence; machine learning; agent based simulation; human factors; unmanned systems
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Special Issue Information

Dear Colleagues,

The rapid growth of Artificial Intelligence (AI) and Internet of Things (IoT) technologies has led to the development of numerous smart systems, with applications in agriculture, healthcare, transportation, and manufacturing. As these systems become increasingly complex, there is a growing need for more transparency, interpretability, and accountability in their decision-making processes. Explainable AI (XAI) aims to address this need by providing clear and understandable explanations for the inner workings of AI models. This Special Issue will focus on the latest advancements in XAI for IoT and smart systems and include novel methods, applications, and case studies. The Special Issue welcomes original research articles, reviews, and case studies on topics including, but not limited to:

  • Novel XAI techniques and algorithms for IoT and smart systems;
  • Applications of XAI in precision agriculture, smart cities, healthcare, and transportation;
  • Interpretability and transparency of AI models in IoT environments;
  • Ethical considerations and challenges in deploying XAI for IoT and smart systems;
  • Case studies on the impact of XAI on decision-making in IoT applications;
  • Human-computer interaction and user experience in XAI-enabled IoT applications;
  • Integration of XAI with other AI techniques, such as reinforcement learning and transfer learning, in IoT environments;
  • Benchmarking, evaluation, and comparison of XAI methods for IoT and smart systems.

We also welcome original research using XAI for the performance enhancement of existing neural networks.

Submission Guidelines:

Authors are encouraged to submit their original, unpublished research articles that have not been submitted to any other journal. All submissions must adhere to the Electronics journal's author guidelines and should be submitted through the journal's online submission system. Please indicate that your submission is intended for the special issue on "Advancements in Explainable Artificial Intelligence for IoT and Smart Systems" during the submission process.

We look forward to receiving your contributions.

Technical Committee Members:

  1. Dr. Hong Liu, Embry-Riddle Aeronautical University
  2. Dr. Wei Li, Auburn University at Montgomery
  3. Dr. Xiaomin Li, Zhongkai University of Agriculture and Engineering
  4. Dr. Yingying Ren, Central South University

Dr. Yongxin Liu
Dr. Jian Wang
Dr. Shuteng Niu
Prof. Dr. Thomas Yang
Prof. Dr. Dahai Liu
Guest Editors

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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. Electronics 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

  • explainable artificial intelligence
  • IoT
  • smart systems

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Published Papers (1 paper)

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Research

14 pages, 1123 KiB  
Article
LIME-Mine: Explainable Machine Learning for User Behavior Analysis in IoT Applications
by Xiaobo Cai, Jiajin Zhang, Yue Zhang, Xiaoshan Yang and Ke Han
Electronics 2024, 13(16), 3234; https://doi.org/10.3390/electronics13163234 - 15 Aug 2024
Viewed by 728
Abstract
In Internet of Things (IoT) applications, user behavior is influenced by factors such as network structure, user activity, and location. Extracting valuable patterns from user activity traces can lead to the development of smarter, more personalized IoT applications and improved user experience. This [...] Read more.
In Internet of Things (IoT) applications, user behavior is influenced by factors such as network structure, user activity, and location. Extracting valuable patterns from user activity traces can lead to the development of smarter, more personalized IoT applications and improved user experience. This paper proposes a LIME-based user behavior preference mining algorithm that leverages Explainable AI (XAI) techniques to interpret user behavior data and extract user preferences. By training a black-box neural network model to predict user behavior using LIME and approximating predictions with a local linear model, we identify key features influencing user behavior. This analysis reveals user behavioral patterns and preferences, such as habits at specific times, locations, and device states. Incorporating user behavioral information into the resource scheduling process, combined with a feedback mechanism, establishes an active discovery network of user demand. Our approach, utilizing edge computing capabilities, continuously fine-tunes and optimizes resource scheduling, actively adapting to user perceptions. Experimental results demonstrate the effectiveness of feedback control in satisfying diverse user resource requests, enhancing user satisfaction, and improving system resource utilization. Full article
(This article belongs to the Special Issue Explainable Artificial Intelligence for IoT and Smart Systems)
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