Special Issue "Smart Objects and Technologies for Social Good"

A special issue of Future Internet (ISSN 1999-5903). This special issue belongs to the section "Techno-Social Smart Systems".

Deadline for manuscript submissions: closed (20 March 2022) | Viewed by 3814

Special Issue Editor

Dr. Ivan Miguel Serrano Pires
E-Mail Website
Guest Editor
1. Instituto de Telecomunicações, Universidade da Beira Interior, Covilhã, Portugal
2. Polytechnic Institute of Viseu, Viseu, Portugal
Interests: ambient assisted living technologies; health; sensor-based systems; machine learning; mobile innovative technologies
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Special Issue Information

Dear Colleagues,

The aim of this Special Issue is to collect papers related to the design, implementation, deployment, operation, and evaluation of smart objects and technologies for social good. Social good includes products and services that benefit several people with special needs (e.g., older adults), the performance of sports, and young people.

These represent key issues in the implementation of different kinds of solutions in different fields, such as healthcare, safety, sports, environment, democracy, computer science, and human rights.

It is a wide field that is a very challenging subject for different research studies, requiring the integration of classical and innovative methodologies. It also promoted the combination of different knowledge from different sciences.

Both theoretical and application-oriented papers on various aspects of traditional and innovative medicine, intelligent systems and devices, distributed computing, artificial intelligence, data acquisition, data processing, diagnostic, preventive medicine, sensored devices, participatory medicine, big data, precision systems, automation, Internet of Things, and cyber-physical systems are invited.

Dr. Ivan Miguel Serrano Pires
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. 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 1400 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

  • environment sensing, monitoring and preservation
  • health and social care
  • pervasive and ubiquitous services in cloud and IoT
  • smart living and e-health
  • mobile applications and ubiquitous devices in Healthcare and lifestyle training
  • big data analytics for e-health
  • intelligent decision support and data systems in health care, medicine and society
  • cloud computing
  • cyber-physical systems and real-time data collection for social good
  • machine learning applications for social good

Published Papers (4 papers)

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Research

Article
First Steps of Asthma Management with a Personalized Ontology Model
Future Internet 2022, 14(7), 190; https://doi.org/10.3390/fi14070190 - 22 Jun 2022
Viewed by 298
Abstract
Asthma is a chronic respiratory disease characterized by severe inflammation of the bronchial mucosa. Allergic asthma is the most common form of this health issue. Asthma is classified into allergic and non-allergic asthma, and it can be triggered by several factors such as [...] Read more.
Asthma is a chronic respiratory disease characterized by severe inflammation of the bronchial mucosa. Allergic asthma is the most common form of this health issue. Asthma is classified into allergic and non-allergic asthma, and it can be triggered by several factors such as indoor and outdoor allergens, air pollution, weather conditions, tobacco smoke, and food allergens, as well as other factors. Asthma symptoms differ in their frequency and severity since each patient reacts differently to these triggers. Formal knowledge is selected as one of the most promising solutions to deal with these challenges. This paper presents a new personalized approach to manage asthma. An ontology-driven model supported by Semantic Web Rule Language (SWRL) medical rules is proposed to provide personalized care for an asthma patient by identifying the risk factors and the development of possible exacerbations. Full article
(This article belongs to the Special Issue Smart Objects and Technologies for Social Good)
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Article
Forecasting Students Dropout: A UTAD University Study
Future Internet 2022, 14(3), 76; https://doi.org/10.3390/fi14030076 - 28 Feb 2022
Viewed by 921
Abstract
In Portugal, the dropout rate of university courses is around 29%. Understanding the reasons behind such a high desertion rate can drastically improve the success of students and universities. This work applies existing data mining techniques to predict the academic dropout mainly using [...] Read more.
In Portugal, the dropout rate of university courses is around 29%. Understanding the reasons behind such a high desertion rate can drastically improve the success of students and universities. This work applies existing data mining techniques to predict the academic dropout mainly using the academic grades. Four different machine learning techniques are presented and analyzed. The dataset consists of 331 students who were previously enrolled in the Computer Engineering degree at the Universidade de Trás-os-Montes e Alto Douro (UTAD). The study aims to detect students who may prematurely drop out using existing methods. The most relevant data features were identified using the Permutation Feature Importance technique. In the second phase, several methods to predict the dropouts were applied. Then, each machine learning technique’s results were displayed and compared to select the best approach to predict academic dropout. The methods used achieved good results, reaching an F1-Score of 81% in the final test set, concluding that students’ marks somehow incorporate their living conditions. Full article
(This article belongs to the Special Issue Smart Objects and Technologies for Social Good)
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Article
S4 Features and Artificial Intelligence for Designing a Robot against COVID-19—Robocov
Future Internet 2022, 14(1), 22; https://doi.org/10.3390/fi14010022 - 06 Jan 2022
Cited by 1 | Viewed by 879
Abstract
Since the COVID-19 Pandemic began, there have been several efforts to create new technology to mitigate the impact of the COVID-19 Pandemic around the world. One of those efforts is to design a new task force, robots, to deal with fundamental goals such [...] Read more.
Since the COVID-19 Pandemic began, there have been several efforts to create new technology to mitigate the impact of the COVID-19 Pandemic around the world. One of those efforts is to design a new task force, robots, to deal with fundamental goals such as public safety, clinical care, and continuity of work. However, those characteristics need new products based on features that create them more innovatively and creatively. Those products could be designed using the S4 concept (sensing, smart, sustainable, and social features) presented as a concept able to create a new generation of products. This paper presents a low-cost robot, Robocov, designed as a rapid response against the COVID-19 Pandemic at Tecnologico de Monterrey, Mexico, with implementations of artificial intelligence and the S4 concept for the design. Robocov can achieve numerous tasks using the S4 concept that provides flexibility in hardware and software. Thus, Robocov can impact positivity public safety, clinical care, continuity of work, quality of life, laboratory and supply chain automation, and non-hospital care. The mechanical structure and software development allow Robocov to complete support tasks effectively so Robocov can be integrated as a technological tool for achieving the new normality’s required conditions according to government regulations. Besides, the reconfiguration of the robot for moving from one task (robot for disinfecting) to another one (robot for detecting face masks) is an easy endeavor that only one operator could do. Robocov is a teleoperated system that transmits information by cameras and an ultrasonic sensor to the operator. In addition, pre-recorded paths can be executed autonomously. In terms of communication channels, Robocov includes a speaker and microphone. Moreover, a machine learning algorithm for detecting face masks and social distance is incorporated using a pre-trained model for the classification process. One of the most important contributions of this paper is to show how a reconfigurable robot can be designed under the S3 concept and integrate AI methodologies. Besides, it is important that this paper does not show specific details about each subsystem in the robot. Full article
(This article belongs to the Special Issue Smart Objects and Technologies for Social Good)
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Article
Application of Machine Learning Techniques to Predict a Patient’s No-Show in the Healthcare Sector
Future Internet 2022, 14(1), 3; https://doi.org/10.3390/fi14010003 - 22 Dec 2021
Cited by 4 | Viewed by 1126
Abstract
The health sector faces a series of problems generated by patients who miss their scheduled appointments. The main challenge to this problem is to understand the patient’s profile and predict potential absences. The goal of this work is to explore the main causes [...] Read more.
The health sector faces a series of problems generated by patients who miss their scheduled appointments. The main challenge to this problem is to understand the patient’s profile and predict potential absences. The goal of this work is to explore the main causes that contribute to a patient’s no-show and develop a prediction model able to identify whether the patient will attend their scheduled appointment or not. The study was based on data from clinics that serve the Unified Health System (SUS) at the University of Vale do Itajaí in southern Brazil. The model obtained was tested on a real collected dataset with about 5000 samples. The best model result was performed by the Random Forest classifier. It had the best Recall Rate (0.91) and achieved an ROC curve rate of 0.969. This research was approved and authorized by the Ethics Committee of the University of Vale do Itajaí, under opinion 4270,234, contemplating the General Data Protection Law. Full article
(This article belongs to the Special Issue Smart Objects and Technologies for Social Good)
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