Special Issue "Recent Advances of Machine Learning Techniques on Smartphones"

A special issue of Future Internet (ISSN 1999-5903). This special issue belongs to the section "Smart System Infrastructure and Applications".

Deadline for manuscript submissions: 20 September 2021.

Special Issue Editors

Dr. Mehdi Sookhak
E-Mail Website
Guest Editor
College of Applied Science & Technology, Illinois State University, Il 61790-5000, USA
Interests: Cloud, Mobile Cloud, and fog computing; Blockchain and its application; Vehicular cloud computing; Smart Vehicles and Connected Vehicles Smart City
Dr. Rishi Saripalle
E-Mail
Guest Editor
College of Applied Science & Technology, Illinois State University, Il 61790-5000, USA
Dr. Mahboobeh Haghparast
E-Mail
Guest Editor
Faculty of Computer Science and Information Technology, University of Malaya, Kuala Lumpur, Wilayah Persekutuan Kuala Lumpur, Malaysia

Special Issue Information

Dear Colleagues,

For the past two decades, smartphones have fundamentally altered the way we live our lives and interact with others. From reading the news to interacting with others, to running a business, we often depend on these small but powerful devices to complete our tasks. Smartphones also allow us to connect with other digital entities (e.g., smartwatches, IoT devices, smart systems, AI assistants, etc.) in multiple ways, and communicate and control with them with ease. Smartphones generate and collect a large amount of data (~60 gigabytes) and this is expected to grow with the explosion of IoT and the highly anticipated arrival of 5G. Currently, in most cases, the generated data is uploaded onto a cloud to extract actionable knowledge by applying various machine learning techniques. However, as smartphones have become powerful computing devices and will continue to improve their computing profile, bringing machine learning techniques and algorithms to the smartphones will boost truly intelligent smartphones.

In light of the above observations, in this Special Issue, we look for original work on machine learning techniques and algorithms on smartphones, addressing particular challenges. On the one hand, conventional machine learning generally uses powerful computing infrastructures (e.g., cloud computing platforms), while smartphones only have limited resources for computations and communications. This suggests that machine learning algorithms or, at least, the implementations of machine learning algorithms, should be revisited for smartphones, which represents a considerable risk and challenge at once. This research area also allows new applications of machine learning and artificial intelligence, opening up new opportunities for smartphones. This Special Issue offers a venue for researchers from both academia and industry to present their solutions for re-designing machine learning algorithms compatible with smartphones, and for building intelligent device by machine learning techniques, possibly revealing new, compelling use cases.

Some of the relevant topics include, but are not limited to the following:

• Machine learning/deep learning techniques for smartphones
• Supervised, unsupervised, and reinforcement learning for smartphones
• Reasoning/learning and techniques applied for smartphone data management
• Machine learning for energy efficiency in the smartphone or its applications
• Evaluation metrics for machine learning algorithms and techniques for smartphones
• Testing platforms or techniques for testing machine learning techniques for smartphones

Dr. Mehdi Sookhak
Dr. Rishi Saripalle
Dr. Mahboobeh Haghparast
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 papers will be 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.

Published Papers (4 papers)

Order results
Result details
Select all
Export citation of selected articles as:

Research

Jump to: Review

Article
An Interoperable UMLS Terminology Service Using FHIR
Future Internet 2020, 12(11), 199; https://doi.org/10.3390/fi12110199 - 16 Nov 2020
Viewed by 845
Abstract
The Unified Medical Language System (UMLS) is an internationally recognized medical vocabulary that enables semantic interoperability across various biomedical terminologies. To use its knowledge, the users must understand its complex knowledge structure, a structure that is not interoperable or is not compliant with [...] Read more.
The Unified Medical Language System (UMLS) is an internationally recognized medical vocabulary that enables semantic interoperability across various biomedical terminologies. To use its knowledge, the users must understand its complex knowledge structure, a structure that is not interoperable or is not compliant with any known biomedical and healthcare standard. Further, the users also need to have good technical skills to understand its inner working and interact with UMLS in general. These barriers might cause UMLS usage concerns among inter-disciplinary users in biomedical and healthcare informatics. Currently, there exists no terminology service that normalizes UMLS’s complex knowledge structure to a widely accepted interoperable healthcare standard and allows easy access to its knowledge, thus hiding its workings. The objective of this research is to design and implement a light-weight terminology service that allows easy access to UMLS knowledge structured using the fast health interoperability resources (FHIR) standard, a widely accepted interoperability healthcare standard. The developed terminology service, named UMLS FHIR, leverages FHIR resources and features, and can easily be integrated into any application to consume UMLS knowledge in the FHIR format without the need to understand UMLS’s native knowledge structure and its internal working. Full article
(This article belongs to the Special Issue Recent Advances of Machine Learning Techniques on Smartphones)
Show Figures

Graphical abstract

Article
From Symptom Tracking to Contact Tracing: A Framework to Explore and Assess COVID-19 Apps
Future Internet 2020, 12(9), 153; https://doi.org/10.3390/fi12090153 - 08 Sep 2020
Cited by 3 | Viewed by 1729
Abstract
Smartphone applications related to coronavirus disease 2019 (COVID-19) continue to emerge and evolve, but despite a wide variety of different app functions, there has yet to be a comprehensive study of what the most prevalent publicly available apps provide, and there exists no [...] Read more.
Smartphone applications related to coronavirus disease 2019 (COVID-19) continue to emerge and evolve, but despite a wide variety of different app functions, there has yet to be a comprehensive study of what the most prevalent publicly available apps provide, and there exists no standardized evaluation system for end users to determine the safety and efficacy of an app before they download it. Furthermore, limited oversight means that the rapidly growing space creates challenges for end users trying to find a relevant app. We adapted the M-Health Index and Navigation Database (MIND) from apps.digitalpsych.org that previously has been used to evaluate mental health applications to guide the assessment of COVID apps. Using this framework, we conducted a thorough analysis of the top-100 returned coronavirus apps on two separate dates a month apart to understand the clinical utility and features of COVID-19 apps and how these change in a short period of time. We ultimately identified a significant turnover rate, as well as privacy concerns around lack of privacy policies and disclosure of personal information. Our research offers insight into the current status of COVID-19 apps and provides a comprehensive and adaptable framework to help individuals assess the growing number of such digital tools in the wake of the pandemic. Full article
(This article belongs to the Special Issue Recent Advances of Machine Learning Techniques on Smartphones)
Show Figures

Figure 1

Article
Human Psychophysiological Activity Estimation Based on Smartphone Camera and Wearable Electronics
Future Internet 2020, 12(7), 111; https://doi.org/10.3390/fi12070111 - 01 Jul 2020
Cited by 2 | Viewed by 1781
Abstract
This paper presents a study related to human psychophysiological activity estimation based on a smartphone camera and sensors. In recent years, awareness of the human body, as well as human mental states, has become more and more popular. Yoga and meditation practices have [...] Read more.
This paper presents a study related to human psychophysiological activity estimation based on a smartphone camera and sensors. In recent years, awareness of the human body, as well as human mental states, has become more and more popular. Yoga and meditation practices have moved from the east to Europe, the USA, Russia, and other countries, and there are a lot of people who are interested in them. However, recently, people have tried the practice but would prefer an objective assessment. We propose to apply the modern methods of computer vision, pattern recognition, competence management, and dynamic motivation to estimate the quality of the meditation process and provide the users with objective information about their practice. We propose an approach that covers the possibility of recognizing pictures of humans from a smartphone and utilizes wearable electronics to measure the user’s heart rate and motions. We propose a model that allows building meditation estimation scores based on these parameters. Moreover, we propose a meditation expert network through which users can find the coach that is most appropriate for him/her. Finally, we propose the dynamic motivation model, which encourages people to perform the practice every day. Full article
(This article belongs to the Special Issue Recent Advances of Machine Learning Techniques on Smartphones)
Show Figures

Figure 1

Review

Jump to: Research

Review
Tourist Recommender Systems Based on Emotion Recognition—A Scientometric Review
Future Internet 2021, 13(1), 2; https://doi.org/10.3390/fi13010002 - 24 Dec 2020
Viewed by 1643
Abstract
Recommendation systems have overcome the overload of irrelevant information by considering users’ preferences and emotional states in the fields of tourism, health, e-commerce, and entertainment. This article reviews the principal recommendation approach documents found in scientific databases (Elsevier’s Scopus and Clarivate Web of [...] Read more.
Recommendation systems have overcome the overload of irrelevant information by considering users’ preferences and emotional states in the fields of tourism, health, e-commerce, and entertainment. This article reviews the principal recommendation approach documents found in scientific databases (Elsevier’s Scopus and Clarivate Web of Science) through a scientometric analysis in ScientoPy. Research publications related to the recommenders of emotion-based tourism cover the last two decades. The review highlights the collection, processing, and feature extraction of data from sensors and wearables to detect emotions. The study proposes the thematic categories of recommendation systems, emotion recognition, wearable technology, and machine learning. This paper also presents the evolution, trend analysis, theoretical background, and algorithmic approaches used to implement recommenders. Finally, the discussion section provides guidelines for designing emotion-sensitive tourist recommenders. Full article
(This article belongs to the Special Issue Recent Advances of Machine Learning Techniques on Smartphones)
Show Figures

Figure 1

Back to TopTop