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Future Internet
Special Issues
Intelligent Decision Support Systems and Prediction Models in IoT-Based Scenarios
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Intelligent Decision Support Systems and Prediction Models in IoT-Based Scenarios

A special issue of Future Internet (ISSN 1999-5903). This special issue belongs to the section "Internet of Things".

Deadline for manuscript submissions: 30 September 2024 | Viewed by 3506

Special Issue Editors

Dr. Mario Casillo

E-Mail Website
Guest Editor
Department of Industrial Engineering, University of Salerno, 84084 Fisciano, Italy
Interests: machine learning; context aware recommender systems; data mining; ontology; IoT
Dr. Marco Lombardi

E-Mail Website
Guest Editor
Department of Industrial Engineering, University of Salerno, 84084 Fisciano, Italy
Interests: context aware computing; recommender systems; knowledge management; IoT; big data
Special Issues, Collections and Topics in MDPI journals
Dr. Domenico Santaniello

E-Mail Website
Guest Editor
Department of Industrial Engineering, University of Salerno, 84084 Fisciano, Italy
Interests: artificial intelligence; context awareness; situation awareness; IoT; big data; cultural heritage preservation
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

In the Internet of Things era, the continuous exchange of information between devices and humans drives us toward data overloading. This phenomenon can be contrasted with systems that take advantage of contextual information. Context awareness and situational awareness can lead to the design of systems that leverage vast amounts of data to prevent phenomena and assist users in the decision-making process.

This Special Issue's current leading topics are but not limited to:

  • Intelligent decision support systems and prediction systems
  • Context-aware recommender systems
  • Methods and systems for situation awareness
  • Machine learning theory, methodology and algorithms
  • Knowledge-based systems
  • Human–computer interactions
  • Pervasive/ubiquitous computing and applications
  • Digital twin technologies

Researchers are invited to submit their manuscripts to this Special Issue and contribute their models, proposals, reviews, and studies.

Dr. Mario Casillo
Dr. Marco Lombardi
Dr. Domenico Santaniello
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. Future Internet 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 1600 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

  • decision support systems
  • prediction systems
  • artificial intelligence
  • machine learning
  • context awareness
  • situation awareness
  • recommender systems
  • e-service personalization
  • knowledge management
  • IoT
  • big data
  • digital twin

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

Published Papers (3 papers)

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Research

17 pages, 1606 KiB  
Article
Evaluating Convolutional Neural Networks and Vision Transformers for Baby Cry Sound Analysis
by Samir A. Younis, Dalia Sobhy and Noha S. Tawfik
Future Internet 2024, 16(7), 242; https://doi.org/10.3390/fi16070242 - 7 Jul 2024
Viewed by 741
Abstract
Crying is a newborn’s main way of communicating. Despite their apparent similarity, newborn cries are physically generated and have distinct characteristics. Experienced medical professionals, nurses, and parents are able to recognize these variations based on their prior interactions. Nonetheless, interpreting a baby’s cries [...] Read more.
Crying is a newborn’s main way of communicating. Despite their apparent similarity, newborn cries are physically generated and have distinct characteristics. Experienced medical professionals, nurses, and parents are able to recognize these variations based on their prior interactions. Nonetheless, interpreting a baby’s cries can be challenging for carers, first-time parents, and inexperienced paediatricians. This paper uses advanced deep learning techniques to propose a novel approach for baby cry classification. This study aims to accurately classify different cry types associated with everyday infant needs, including hunger, discomfort, pain, tiredness, and the need for burping. The proposed model achieves an accuracy of 98.33%, surpassing the performance of existing studies in the field. IoT-enabled sensors are utilized to capture cry signals in real time, ensuring continuous and reliable monitoring of the infant’s acoustic environment. This integration of IoT technology with deep learning enhances the system’s responsiveness and accuracy. Our study highlights the significance of accurate cry classification in understanding and meeting the needs of infants and its potential impact on improving infant care practices. The methodology, including the dataset, preprocessing techniques, and architecture of the deep learning model, is described. The results demonstrate the performance of the proposed model, and the discussion analyzes the factors contributing to its high accuracy. Full article
(This article belongs to the Special Issue Intelligent Decision Support Systems and Prediction Models in IoT-Based Scenarios)
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16 pages, 2459 KiB  
Article
Software-Bus-Toolchain (SBT): Introducing a Versatile Method for Quickly Implementing (I)IoT-Scenarios
by Simon D. Duque Anton
Future Internet 2024, 16(7), 237; https://doi.org/10.3390/fi16070237 - 3 Jul 2024
Viewed by 645
Abstract
The Internet of Things (IoT) has become ubiquitous. IoT devices are applied in a multitude of applications, e.g., in smart home scenarios, building automation, smart energy and smart cities, healthcare, and industrial environments. Fast and efficient implementation and roll-out of IoT devices is [...] Read more.
The Internet of Things (IoT) has become ubiquitous. IoT devices are applied in a multitude of applications, e.g., in smart home scenarios, building automation, smart energy and smart cities, healthcare, and industrial environments. Fast and efficient implementation and roll-out of IoT devices is a critical factor for successs and acceptance of IoT devices. At the same time, the variety of hardware platforms that can be used for IoT applications, as well as the number of IoT orchestration platforms is increasing. Finding the right combination of tooling and hardware is not trivial, but essential for building applications that provide value. In this work, a Software-Bus-Toolchain (SBT) is introduced that encapsulates firmware design, data point definition, and communication protocol usage. Furthermore, an IoT control platform is provided to control and evaluate the IoT modules. Thus, using the SBT, solely the business logic has to be designed, while the hardware-design is automated to a high degree. Usage of the Zephyr framework allows the interchange of hardware modules, while interfaces provide easy adaption of data points and communication capabilities. The implementation of interfaces to the IoT-platform as well as to the communication layer provides a universal usage of logic and data elements. The SBT is evaluated in two application scenarios, where its flexible nature is shown. Full article
(This article belongs to the Special Issue Intelligent Decision Support Systems and Prediction Models in IoT-Based Scenarios)
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16 pages, 1641 KiB  
Article
Enabling End-User Development in Smart Homes: A Machine Learning-Powered Digital Twin for Energy Efficient Management
by Luca Cotti, Davide Guizzardi, Barbara Rita Barricelli and Daniela Fogli
Future Internet 2024, 16(6), 208; https://doi.org/10.3390/fi16060208 - 14 Jun 2024
Viewed by 715
Abstract
End-User Development has been proposed over the years to allow end users to control and manage their Internet of Things-based environments, such as smart homes. With End-User Development, end users are able to create trigger-action rules or routines to tailor the behavior of [...] Read more.
End-User Development has been proposed over the years to allow end users to control and manage their Internet of Things-based environments, such as smart homes. With End-User Development, end users are able to create trigger-action rules or routines to tailor the behavior of their smart homes. However, the scientific research proposed to date does not encompass methods that evaluate the suitability of user-created routines in terms of energy consumption. This paper proposes using Machine Learning to build a Digital Twin of a smart home that can predict the energy consumption of smart appliances. The Digital Twin will allow end users to simulate possible scenarios related to the creation of routines. Simulations will be used to assess the effects of the activation of appliances involved in the routines under creation and possibly modify them to save energy consumption according to the Digital Twin’s suggestions. Full article
(This article belongs to the Special Issue Intelligent Decision Support Systems and Prediction Models in IoT-Based Scenarios)
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