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Feature Papers in Smart Sensing and Intelligent Sensors 2025
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Feature Papers in Smart Sensing and Intelligent Sensors 2025

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Intelligent Sensors".

Deadline for manuscript submissions: 31 December 2025 | Viewed by 720

Special Issue Editor

Prof. Dr. Antonio Fernández-Caballero

E-Mail Website
Guest Editor
Instituto de Investigación en Informática de Albacete, Universidad de Castilla-La Mancha, 02071 Albacete, Spain
Interests: pattern recognition; human–computer interaction; affective computing; computer vision; multi-sensor fusion
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

We are pleased to announce that is now compiling a collection of papers submitted by the Editorial Board Members (EBMs) of our section and outstanding scholars in this research field. We welcome contributions and recommendations from the EBMs.

The aim of this Special Issue is to publish a set of papers that showcase the most insightful and influential original articles or reviews where our section’s EBMs discuss key topics in the field. We expect these papers to be widely read and highly influential within the field. All papers in this Special Issue will be collected into a printed edition after the deadline and will be carefully promoted.

We would also like to take this opportunity to call on more scholars to join so that we can work together to further develop this exciting field of research. Potential topics include, but are not limited to, the following:

  • Sensor signal processing;
  • Deep learning/machine learning;
  • Data processing/science;
  • Computer vision;
  • Integrated circuits;
  • Human–robot/machine/computer interactions;
  • Artificial intelligence;
  • Intelligent instrumentation;
  • Intelligent control;
  • Intelligent portable platforms;
  • Intelligent computing;
  • Wireless sensor networks (WSNs);
  • Smart sensor networks;
  • Intelligent environmental monitoring;
  • Smart cities;
  • Smart home/home automation;
  • Smart manufacturing and industry;
  • Smart energy management/smart grids;
  • Smart agriculture;
  • Smart health monitoring;
  • E-health/M-health;
  • Intelligent emotion recognition;
  • Smart building/smart civil infrastructure;
  • Smart/precision farming;
  • Blockchain 5G/6G.

Prof. Dr. Antonio Fernández-Caballero
Guest Editor

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. Sensors 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 2600 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

  • intelligent sensors
  • smart sensing
  • artificial intelligence
  • sensing systems
  • sensor data

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  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
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Further information on MDPI's Special Issue policies can be found here.

Published Papers (2 papers)

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Research

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18 pages, 737 KiB  
Article
Mobile Game Evaluation Method Based on Data Mining of Affective Time Series
by Jeremi K. Ochab, Paweł Węgrzyn, Przemek Witaszczyk, Dominika Drążyk and Grzegorz J. Nalepa
Sensors 2025, 25(9), 2756; https://doi.org/10.3390/s25092756 - 26 Apr 2025
Viewed by 157
Abstract
Our work is positioned at the intersection of game data science, affective gaming, and the implementation of multimodal body sensors analysis. We propose an original method of evaluating the quality of a class of video games based on the emotional reactions of players. [...] Read more.
Our work is positioned at the intersection of game data science, affective gaming, and the implementation of multimodal body sensors analysis. We propose an original method of evaluating the quality of a class of video games based on the emotional reactions of players. Game developers ask why some games are more profitable (MP games) than others (LP games). An intuitively convincing hypothesis is often put forward: MP games evoke more positive emotions and hence are sustainably engaging. Our main hypothesis is that test players who can clearly distinguish between MP game and LP game in relatively short test sessions are more reliable in scoring games and valuable to keep track of their emotions. From a random group of test players, we selected players with such abilities. We analyzed their affective spectra and obtained a fairly clear confirmation that the selected players showed more positive and less negative emotions in MP games than in LP ones. We can reasonably expect these players to be focused on playing in the test session, and their emotions may really indicate the strengths of MP games over LP games. We present the results of the experimental evaluation of our method conducted with with a leading game company in Poland. Full article
(This article belongs to the Special Issue Feature Papers in Smart Sensing and Intelligent Sensors 2025)
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Review

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38 pages, 2189 KiB  
Review
Advanced Deep Learning and Machine Learning Techniques for MRI Brain Tumor Analysis: A Review
by Rim Missaoui, Wided Hechkel, Wajdi Saadaoui, Abdelhamid Helali and Marco Leo
Sensors 2025, 25(9), 2746; https://doi.org/10.3390/s25092746 - 26 Apr 2025
Viewed by 296
Abstract
A brain tumor is the result of abnormal growth of cells in the central nervous system (CNS), widely considered as a complex and diverse clinical entity that is difficult to diagnose and cure. In this study, we focus on current advances in medical [...] Read more.
A brain tumor is the result of abnormal growth of cells in the central nervous system (CNS), widely considered as a complex and diverse clinical entity that is difficult to diagnose and cure. In this study, we focus on current advances in medical imaging, particularly magnetic resonance imaging (MRI), and how machine learning (ML) and deep learning (DL) algorithms might be combined with clinical assessments to improve brain tumor diagnosis. Due to its superior contrast resolution and safety compared to other imaging methods, MRI is highlighted as the preferred imaging modality for brain tumors. The challenges related to brain tumor analysis in different processes including detection, segmentation, classification, and survival prediction are addressed along with how ML/DL approaches significantly improve these steps. We systematically analyzed 107 studies (2018–2024) employing ML, DL, and hybrid models across publicly available datasets such as BraTS, TCIA, and Figshare. In the light of recent developments in brain tumor analysis, many algorithms have been proposed to accurately obtain ontological characteristics of tumors, enhancing diagnostic precision and personalized therapeutic strategies. Full article
(This article belongs to the Special Issue Feature Papers in Smart Sensing and Intelligent Sensors 2025)
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