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Electronics
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Swarm Intelligence, Fuzzy Logic, and Cloud Computing in Applications for...
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Swarm Intelligence, Fuzzy Logic, and Cloud Computing in Applications for IoT and Information Retrieval

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

Deadline for manuscript submissions: 15 April 2025 | Viewed by 915

Special Issue Editors

Prof. Dr. Jacek M. Czerniak

E-Mail Website
Guest Editor
Faculty of Computer Sciences, Kazimierz Wielki University, Chodkiewicza 30, 85-064 Bydgoszcz, Poland
Interests: artificial intelligence; swarm intelligence; fuzzy logic; ordered fuzzy numbers; information retrieval; machine learning; deep learning; metaheuristics; bioinspired optimization algorithms; internet of things; big data; cloud computing; computer vision
Dr. Mauro Iacono

E-Mail Website
Guest Editor
Dipartimento di Elettronica, Informazione e Bioingegneria, Politecnico di Milano, 20133 Milano, Italy
Interests: computer systems modeling
Special Issues, Collections and Topics in MDPI journals
Dr. Tibor Vince

E-Mail Website
Guest Editor
Department of Theoretical and Industrial Electrical Engineering, Faculty of Electrical Engineering and Informatics, Technical University of Košice, 042 00 Košice, Slovakia
Interests: industrial electronic engineering; industrial application; IoT; simulation and modeling applications; automated measurement systems
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This Special Issue will focus on the applications of artificial intelligence (AI) in the Internet of Things (IoT) and information retrieval. The following are some of the specific areas that will be covered:

  • Swarm intelligence: the use of AI algorithms inspired by the behavior of insects or animals.
  • Examples of application in IoT and information retrieval:

    IoT: Swarm intelligence can be used to control, optimize, and coordinate large numbers of IoT devices. For example, it can be used to manage traffic in smart cities or to control the operation of smart grids.

    Information retrieval: Swarm intelligence can be used to improve the accuracy and efficiency of information retrieval. For example, it can be used to identify relevant documents in a large corpus or to rank search results.

  • Fuzzy logic: the use of fuzzy logic to model and make decisions in uncertain environments.
  • Examples of application in IoT and information retrieval:

    IoT: Fuzzy logic can be used to handle uncertainty in IoT data. For example, it can be used to estimate the battery level of a wearable device or to predict the failure of an IoT device.

    Information retrieval: Fuzzy logic can be used to improve the relevance of search results. For example, it can be used to take into account the user's context when ranking search results.

  • Cloud computing: The use of cloud computing as a platform for AI applications.
  • Examples of application in IoT and information retrieval:

    IoT: Cloud computing can be used to store and process data from IoT devices. This can help to reduce the cost and complexity of IoT deployments.

    Information retrieval: Cloud computing can be used to scale up AI-powered information retrieval systems. This can help to meet the needs of large-scale applications, such as those used by e-commerce companies or social media platforms.

In addition to these specific areas, this Special Issue will also cover the following:

  • IoT: The use of AI for monitoring and controlling IoT devices.
  • Information retrieval: the use of AI for indexing and searching for information.

This Special Issue will also include interviews with experts, book reviews, and a summary of the main findings from the published articles.

This Special Issue is intended to be a valuable resource for researchers, engineers, and practitioners interested in AI in IoT and information retrieval. It will also contribute to the wider dissemination of knowledge about AI among the general public.

The scope of this Special Issue is broad and covers a variety of topics related to the applications of AI in IoT and information retrieval. The specific areas listed above are intended to provide a starting point for the discussion, but this Special Issue is not limited to these topics.

Additional topics:

  • The use of AI for security and privacy in IoT and information retrieval systems.
  • The ethical implications of the use of AI in IoT and information retrieval systems.
  • The future of AI in IoT and information retrieval.

We look forward to receiving your contributions. 

Prof. Dr. Jacek M. Czerniak
Dr. Mauro Iacono
Dr. Tibor Vince
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. 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

  • AI
  • swarm intelligence
  • fuzzy logic
  • cloud computing
  • optimization
  • information retrieval
  • IoT

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

Published Papers (1 paper)

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Research

17 pages, 4492 KiB  
Article
Fast and Accurate Detection of Dim and Small Targets for Smart Micro-Light Sight
by Jia Wei, Kai Che, Jiayuan Gong, Yun Zhou, Jian Lv, Longcheng Que, Hu Liu and Yuanbin Len
Electronics 2024, 13(16), 3301; https://doi.org/10.3390/electronics13163301 - 20 Aug 2024
Viewed by 575
Abstract
To deal with low recognition accuracy and large time-consumption for dim, small targets in a smart micro-light sight, we propose a lightweight model DS_YOLO (dim and small target detection). We introduce the adaptive channel convolution module (ACConv) to reduce computational redundancy while maximizing [...] Read more.
To deal with low recognition accuracy and large time-consumption for dim, small targets in a smart micro-light sight, we propose a lightweight model DS_YOLO (dim and small target detection). We introduce the adaptive channel convolution module (ACConv) to reduce computational redundancy while maximizing the utilization of channel features. To address the misalignment problem in multi-task learning, we also design a lightweight dynamic task alignment detection head (LTD_Head), which utilizes GroupNorm to improve the performance of detection head localization and classification, and shares convolutions to make the model lightweight. Additionally, to improve the network’s capacity to detect small-scale targets while maintaining its generalization to multi-scale target detection, we extract high-resolution feature map information to establish a new detection head. Ultimately, the incorporation of the attention pyramid pooling layer (SPPFLska) enhances the model’s regression accuracy. We conduct an evaluation of the proposed algorithm DS_YOLO on four distinct datasets: CityPersons, WiderPerson, DOTA, and TinyPerson, achieving a 66.6% mAP on the CityPersons dataset, a 4.3% improvement over the original model. Meanwhile, our model reduces the parameter count by 33.3% compared to the baseline model. Full article
(This article belongs to the Special Issue Swarm Intelligence, Fuzzy Logic, and Cloud Computing in Applications for IoT and Information Retrieval)
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