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Dr. Antonio Coronato

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Guest Editor

Centro di Ricerche sulle Tecnologie ICT per la Salute ed il Benessere, Università Giustino Fortunato, 82100 Benevento, Italy
Interests: ambient intelligence; artificial intelligence; reinforcement learning; self-learning; healthcare intelligent services

Dr. Muddasar Naeem

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Guest Editor

Artificial Intelligence & Robotics Laboratory, Giustino Fortunato University, 82100 Benevento, Italy
Interests: application of AI methods; reinforcement learning in communication and healthcare
Special Issues, Collections and Topics in MDPI journals

Dr. Mitja Luštrek

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Guest Editor

Jožef Stefan Institute, 1000 Ljubljana, Slovenia
Interests: ambient intelligence; interpretation of sensor data; application of AI in healthcare; machine learning; decision support
Special Issues, Collections and Topics in MDPI journals

Dr. ‪Vito Janko‬

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Guest Editor

Jožef Stefan Institute, 1000 Ljubljana, Slovenia
Interests: Ambient Intelligence; Energy-efficient context recognition; Machine learning; COVID-19 modelling

Special Issue Information

Dear Colleagues,

Advances in artificial intelligence, machine learning, robotics and sensors (ranging from Internet of Things (IoT)-enabled contactless sensors to wearables) have given rise to a new generation of ambient intelligence (AmI) applications.

With the aging of the population, healthcare is becoming increasingly important, and thus approaches to ensure a high quality of healthcare that is also affordable and accesible are urgently sought. This Special Issue is intended to report on applications in the healthcare domain that benefit from the integration of AmI technologies in a domestic environment, employing sensors to obtain a more complete picture of patients—or citizens before they become patients—and offering better healthcare services. In this context, we are envisaging contributions covering one or more of the following topics:

Intelligent monitoring of vital signs at home and on the go;
Behavior analysis of patients, seniors and others;
Identification and prevention of critical condition;
Health-promoting interventions triggered by sensors;
Ambient assisted living and active assisted living;
Smart sensors and sensing devices;
Wireless sensor networks (WSNs) for patient monitoring and healthy aging;
Methodologies and tools for the rapid integration of WSNs, IoT and AI in health monitoring;
Monitoring systems within the medical devices contest;
AmI security and privacy models for healthcare applications.

Dr. Antonio Coronato
Dr. Muddasar Naeem
Dr. Mitja Luštrek
Dr. ‪Vito Janko‬
Guest Editors

Manuscript Submission Information

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Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 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 (2 papers)

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Research

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26 pages, 1356 KiB

Open AccessArticle

Deep Learning Hybrid Techniques for Brain Tumor Segmentation

by Khushboo Munir, Fabrizio Frezza and Antonello Rizzi

Sensors 2022, 22(21), 8201; https://doi.org/10.3390/s22218201 - 26 Oct 2022

Cited by 8 | Viewed by 2948

Abstract

Medical images play an important role in medical diagnosis and treatment. Oncologists analyze images to determine the different characteristics of deadly diseases, plan the therapy, and observe the evolution of the disease. The objective of this paper is to propose a method for the detection of brain tumors. Brain tumors are identified from Magnetic Resonance (MR) images by performing suitable segmentation procedures. The latest technical literature concerning radiographic images of the brain shows that deep learning methods can be implemented to extract specific features of brain tumors, aiding clinical diagnosis. For this reason, most data scientists and AI researchers work on Machine Learning methods for designing automatic screening procedures. Indeed, an automated method would result in quicker segmentation findings, providing a robust output with respect to possible differences in data sources, mostly due to different procedures in data recording and storing, resulting in a more consistent identification of brain tumors. To improve the performance of the segmentation procedure, new architectures are proposed and tested in this paper. We propose deep neural networks for the detection of brain tumors, trained on the MRI scans of patients’ brains. The proposed architectures are based on convolutional neural networks and inception modules for brain tumor segmentation. A comparison of these proposed architectures with the baseline reference ones shows very interesting results. MI-Unet showed a performance increase in comparison to baseline Unet architecture by

7.5 %

in dice score,

23.91 %

insensitivity, and

7.09 %

in specificity. Depth-wise separable MI-Unet showed a performance increase by

10.83 %

in dice score,

2.97 %

in sensitivity, and

12.72 %

in specificity as compared to the baseline Unet architecture. Hybrid Unet architecture achieved performance improvement of

9.71 %

in dice score,

3.56 %

in sensitivity, and

12.6 %

in specificity. Whereas the depth-wise separable hybrid Unet architecture outperformed the baseline architecture by

15.45 %

in dice score,

20.56 %

in sensitivity, and

12.22 %

in specificity. Full article

(This article belongs to the Special Issue Ambient Intelligence in Healthcare)

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Review

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41 pages, 915 KiB

Open AccessReview

Application of Reinforcement Learning and Deep Learning in Multiple-Input and Multiple-Output (MIMO) Systems

by Muddasar Naeem, Giuseppe De Pietro and Antonio Coronato

Sensors 2022, 22(1), 309; https://doi.org/10.3390/s22010309 - 31 Dec 2021

Cited by 26 | Viewed by 5826

Abstract

The current wireless communication infrastructure has to face exponential development in mobile traffic size, which demands high data rate, reliability, and low latency. MIMO systems and their variants (i.e., Multi-User MIMO and Massive MIMO) are the most promising 5G wireless communication systems technology due to their high system throughput and data rate. However, the most significant challenges in MIMO communication are substantial problems in exploiting the multiple-antenna and computational complexity. The recent success of RL and DL introduces novel and powerful tools that mitigate issues in MIMO communication systems. This article focuses on RL and DL techniques for MIMO systems by presenting a comprehensive review on the integration between the two areas. We first briefly provide the necessary background to RL, DL, and MIMO. Second, potential RL and DL applications for different MIMO issues, such as detection, classification, and compression; channel estimation; positioning, sensing, and localization; CSI acquisition and feedback, security, and robustness; mmWave communication and resource allocation, are presented. Full article

(This article belongs to the Special Issue Ambient Intelligence in Healthcare)

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