Computing and Embedded Artificial Intelligence

A special issue of Information (ISSN 2078-2489). This special issue belongs to the section "Artificial Intelligence".

Deadline for manuscript submissions: closed (31 July 2023) | Viewed by 8601

Special Issue Editor


E-Mail Website
Guest Editor
Faculdade de Engenharia, Universidade do Porto, 4099-002 Porto, Portugal
Interests: artificial intelligence; data mining; machine learning; pattern recognition; simulation; intelligent transport systems
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This Special Issue will include excellent academic achievements of selected papers submitted to the 2021 International Symposium on Computing and Artificial Intelligence (ISCAI).

The conference’s quality of attendees and topics is at an impressively high level. Moreover, the organization and venue of the ISCAI and co-located conferences are world-class. The conferences in Beijing in 2021 will allow for an international gathering of Asian and other international participants. The high academic standards of the conference ensure a consistent level of quality in academic work and related discourse. ISCAI is a premier conference for sharing advances in computer science and artificial intelligence.

Authors of invited papers should be aware that the final submitted manuscript must provide a minimum of 50% new content and not exceed 30% copy/paste from the proceedings paper.

Prof. Dr. João Manuel R. S. Tavares
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. Information 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

  • artificial intelligence
  • computing
  • autonomic
  • reasoning

Published Papers (4 papers)

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

Research

21 pages, 10765 KiB  
Article
Enabled Artificial Intelligence (AI) to Develop Sehhaty Wa Daghty App of Self-Management for Saudi Patients with Hypertension: A Qualitative Study
by Adel Alzahrani, Valerie Gay and Ryan Alturki
Information 2023, 14(6), 334; https://doi.org/10.3390/info14060334 - 15 Jun 2023
Viewed by 1685
Abstract
(1) Background: The prevalence of uncontrolled hypertension is rising all across the world, making it a concern for public health. The usage of mobile health applications has resulted in a number of positive outcomes for the management and control of hypertension. (2) Objective: [...] Read more.
(1) Background: The prevalence of uncontrolled hypertension is rising all across the world, making it a concern for public health. The usage of mobile health applications has resulted in a number of positive outcomes for the management and control of hypertension. (2) Objective: The study’s primary goal is to explain the steps to create a hypertension application (app) that considers cultural and social standards in Saudi Arabia, motivational features, and the needs of male and female Saudi citizens. (3) Methods: This study reports the emerged features and content needed to be adapted or developed in health apps for hypertension patients during an interactive qualitative analysis focus group activity with (n = 5) experts from the Saudi Ministry of Health. A gap analysis was conducted to develop an app based on a deep understanding of user needs with a patient-centred approach. (4) Results: Based on the participant’s reviews in this study, the app was easy to use and can help Saudi patients to control their hypertension, the design was interactive, motivational features are user-friendly, and there is a need to consider other platforms such as Android and Blackberry in a future version. (5) Conclusions: Mobile health apps can help Saudis change their unhealthy lifestyles. Target users, usability, motivational features, and social and cultural standards must be considered to meet the app’s aim. Full article
(This article belongs to the Special Issue Computing and Embedded Artificial Intelligence)
Show Figures

Figure 1

16 pages, 387 KiB  
Article
Fast Training Set Size Reduction Using Simple Space Partitioning Algorithms
by Stefanos Ougiaroglou, Theodoros Mastromanolis, Georgios Evangelidis and Dionisis Margaris
Information 2022, 13(12), 572; https://doi.org/10.3390/info13120572 - 10 Dec 2022
Cited by 1 | Viewed by 1035
Abstract
The Reduction by Space Partitioning (RSP3) algorithm is a well-known data reduction technique. It summarizes the training data and generates representative prototypes. Its goal is to reduce the computational cost of an instance-based classifier without penalty in accuracy. The algorithm keeps on dividing [...] Read more.
The Reduction by Space Partitioning (RSP3) algorithm is a well-known data reduction technique. It summarizes the training data and generates representative prototypes. Its goal is to reduce the computational cost of an instance-based classifier without penalty in accuracy. The algorithm keeps on dividing the initial training data into subsets until all of them become homogeneous, i.e., they contain instances of the same class. To divide a non-homogeneous subset, the algorithm computes its two furthest instances and assigns all instances to their closest furthest instance. This is a very expensive computational task, since all distances among the instances of a non-homogeneous subset must be calculated. Moreover, noise in the training data leads to a large number of small homogeneous subsets, many of which have only one instance. These instances are probably noise, but the algorithm mistakenly generates prototypes for these subsets. This paper proposes simple and fast variations of RSP3 that avoid the computationally costly partitioning tasks and remove the noisy training instances. The experimental study conducted on sixteen datasets and the corresponding statistical tests show that the proposed variations of the algorithm are much faster and achieve higher reduction rates than the conventional RSP3 without negatively affecting the accuracy. Full article
(This article belongs to the Special Issue Computing and Embedded Artificial Intelligence)
Show Figures

Figure 1

27 pages, 7120 KiB  
Communication
A Simplistic and Cost-Effective Design for Real-World Development of an Ambient Assisted Living System for Fall Detection and Indoor Localization: Proof-of-Concept
by Nirmalya Thakur and Chia Y. Han
Information 2022, 13(8), 363; https://doi.org/10.3390/info13080363 - 29 Jul 2022
Cited by 5 | Viewed by 3083
Abstract
Falls, highly common in the constantly increasing global aging population, can have a variety of negative effects on their health, well-being, and quality of life, including restricting their capabilities to conduct activities of daily living (ADLs), which are crucial for one’s sustenance. Timely [...] Read more.
Falls, highly common in the constantly increasing global aging population, can have a variety of negative effects on their health, well-being, and quality of life, including restricting their capabilities to conduct activities of daily living (ADLs), which are crucial for one’s sustenance. Timely assistance during falls is highly necessary, which involves tracking the indoor location of the elderly during their diverse navigational patterns associated with different activities to detect the precise location of a fall. With the decreasing caregiver population on a global scale, it is important that the future of intelligent living environments can detect falls during ADLs while being able to track the indoor location of the elderly in the real world. Prior works in these fields have several limitations, such as the lack of functionalities to detect falls and indoor locations in a simultaneous manner, high cost of implementation, complicated design, the requirement of multiple hardware components for deployment, and the necessity to develop new hardware for implementation, which make the wide-scale deployment of such technologies challenging. To address these challenges, this work proposes a cost-effective and simplistic design paradigm for an ambient assisted living system that can capture multimodal components of user behaviors during ADLs that are necessary for performing fall detection and indoor localization in a simultaneous manner in the real-world. Proof-of-concept results from real-world experiments are presented to uphold the effective working of the system. The findings from two comparative studies with prior works in this field are also presented to uphold the novelty of this work. The first comparative study shows how the proposed system outperforms prior works in the areas of indoor localization and fall detection in terms of the effectiveness of its software design and hardware design. The second comparative study shows that the cost of the development of this system is the lowest as compared to prior works in these fields, which involved real-world development of the underlining systems, thereby upholding its cost-effective nature. Full article
(This article belongs to the Special Issue Computing and Embedded Artificial Intelligence)
Show Figures

Figure 1

16 pages, 1354 KiB  
Article
Operational Rule Extraction and Construction Based on Task Scenario Analysis
by Xinye Zhao, Chao Wang, Peng Cui and Guangming Sun
Information 2022, 13(3), 144; https://doi.org/10.3390/info13030144 - 9 Mar 2022
Cited by 1 | Viewed by 2003
Abstract
Changes in the information age have induced the necessity for a more efficient and effective self-decision-making requirement. A method of extracting and constructing naval operations decision-making rules based on scenario analysis is proposed. The template specifications of Event Condition Action (ECA) rules are [...] Read more.
Changes in the information age have induced the necessity for a more efficient and effective self-decision-making requirement. A method of extracting and constructing naval operations decision-making rules based on scenario analysis is proposed. The template specifications of Event Condition Action (ECA) rules are defined, and a consistency detection method of ECA rules based on SWRL is proposed. The logical relationships and state transitions of the naval operational process is analyzed in detail, and the association of objects, events, and behaviors is realized. Finally, the operation of the proposed methods is illustrated through an example process, showing the method can effectively solve the problems of self-decision-making rule extraction and construction among naval battlefield decision environment, and avoid relying on artificial intelligence, which may have brought some uncertain factors. Full article
(This article belongs to the Special Issue Computing and Embedded Artificial Intelligence)
Show Figures

Figure 1

Back to TopTop