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Special Issue "AI and IoT Enabled Solutions for Healthcare"
A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Intelligent Sensors".
Deadline for manuscript submissions: 31 July 2023 | Viewed by 20048
Special Issue Editors
Interests: machine learning; health informatics; biomedical signal processing; computational biology
Interests: wearable health monitoring; biomedical signal processing; artificial intelligence in healthcare
Interests: signal processing; biomedical signal processing; machine learning; body sensor networking
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Ever-increasing quantities of clinical data are routinely collected concerning all aspects of patient care throughout the patient’s life. Internet of Things (IoT) and smart home technologies provide an opportunity to monitor people in their homes without any disruption in their daily activities. Digital health records offer a rich source of clinical information. Innovations in AI and machine learning can facilitate faster patient monitoring, management, and treatment, and convert a hospital-only treatment pathway into cost-effective combined home-hospital or even outpatient alternatives, which improve the overall quality of healthcare and pave the path for personalised medicine. However, analysing the real-time collected data poses several challenges as the data have significant artefacts due to transmission and recording limitation, are highly imbalanced and incomplete due to subject variabilities and resource limitation, and involve various modalities. Moreover, data labelling is cumbersome and often involves uncertainty. This Special Issue aims to attract innovative and novel machine learning developments and IoT solutions around the challenges in healthcare applications. The Special Issue topics include but are not limited to the following:
- Assistive AI;
- Augmentation techniques including adversarial networks;
- Data imputation;
- Dealing with noisy labels;
- Deep learning;
- Ensemble learning;
- Independent living;
- Interpretable machine learning;
- Learning under uncertainty, noise, and imbalanced data;
- More efficient delivery of healthcare and healthcare applications;
- Multi-modal and heterogeneous data analysis;
- Reinforcement learning;
- Remote/smart monitoring of patients;
- Semi-supervised learning;
- Unsupervised learning;
- Weakly supervised/self-supervised learning.
Dr. Samaneh Kouchaki
Dr. Xiaorong Ding
Prof. Dr. Saeid Sanei
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 2400 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.
- Deep learning
- Health informatics
- Machine learning
- Multi-modal data analysis
- Reinforcement learning
- Remote/smart monitoring