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Tezsezen E, Yigci D, Ahmadpour A, Tasoglu S. AI-Based Metamaterial Design. ACS APPLIED MATERIALS & INTERFACES 2024. [PMID: 38808674 DOI: 10.1021/acsami.4c04486] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/30/2024]
Abstract
The use of metamaterials in various devices has revolutionized applications in optics, healthcare, acoustics, and power systems. Advancements in these fields demand novel or superior metamaterials that can demonstrate targeted control of electromagnetic, mechanical, and thermal properties of matter. Traditional design systems and methods often require manual manipulations which is time-consuming and resource intensive. The integration of artificial intelligence (AI) in optimizing metamaterial design can be employed to explore variant disciplines and address bottlenecks in design. AI-based metamaterial design can also enable the development of novel metamaterials by optimizing design parameters that cannot be achieved using traditional methods. The application of AI can be leveraged to accelerate the analysis of vast data sets as well as to better utilize limited data sets via generative models. This review covers the transformative impact of AI and AI-based metamaterial design for optics, acoustics, healthcare, and power systems. The current challenges, emerging fields, future directions, and bottlenecks within each domain are discussed.
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Affiliation(s)
- Ece Tezsezen
- Graduate School of Science and Engineering, Koç University, Istanbul 34450, Türkiye
| | - Defne Yigci
- School of Medicine, Koç University, Istanbul 34450, Türkiye
| | - Abdollah Ahmadpour
- Department of Mechanical Engineering, Koç University Sariyer, Istanbul 34450, Türkiye
| | - Savas Tasoglu
- Department of Mechanical Engineering, Koç University Sariyer, Istanbul 34450, Türkiye
- Koç University Translational Medicine Research Center (KUTTAM), Koç University, Istanbul 34450, Türkiye
- Bogaziçi Institute of Biomedical Engineering, Bogaziçi University, Istanbul 34684, Türkiye
- Koç University Arçelik Research Center for Creative Industries (KUAR), Koç University, Istanbul 34450, Türkiye
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Khalid N, Qayyum A, Bilal M, Al-Fuqaha A, Qadir J. Privacy-preserving artificial intelligence in healthcare: Techniques and applications. Comput Biol Med 2023; 158:106848. [PMID: 37044052 DOI: 10.1016/j.compbiomed.2023.106848] [Citation(s) in RCA: 11] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2022] [Revised: 03/21/2023] [Accepted: 03/30/2023] [Indexed: 04/14/2023]
Abstract
There has been an increasing interest in translating artificial intelligence (AI) research into clinically-validated applications to improve the performance, capacity, and efficacy of healthcare services. Despite substantial research worldwide, very few AI-based applications have successfully made it to clinics. Key barriers to the widespread adoption of clinically validated AI applications include non-standardized medical records, limited availability of curated datasets, and stringent legal/ethical requirements to preserve patients' privacy. Therefore, there is a pressing need to improvise new data-sharing methods in the age of AI that preserve patient privacy while developing AI-based healthcare applications. In the literature, significant attention has been devoted to developing privacy-preserving techniques and overcoming the issues hampering AI adoption in an actual clinical environment. To this end, this study summarizes the state-of-the-art approaches for preserving privacy in AI-based healthcare applications. Prominent privacy-preserving techniques such as Federated Learning and Hybrid Techniques are elaborated along with potential privacy attacks, security challenges, and future directions.
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Affiliation(s)
- Nazish Khalid
- Information Technology University, Lahore, Pakistan.
| | - Adnan Qayyum
- Information Technology University, Lahore, Pakistan.
| | - Muhammad Bilal
- Big Data Enterprise and Artificial Intelligence Lab (Big-DEAL), University of the West England, Bristol, United Kingdom.
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Rodriguez-Conde I, Campos C, Fdez-Riverola F. Horizontally Distributed Inference of Deep Neural Networks for AI-Enabled IoT. SENSORS (BASEL, SWITZERLAND) 2023; 23:1911. [PMID: 36850508 PMCID: PMC9958567 DOI: 10.3390/s23041911] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/31/2022] [Revised: 02/02/2023] [Accepted: 02/05/2023] [Indexed: 06/18/2023]
Abstract
Motivated by the pervasiveness of artificial intelligence (AI) and the Internet of Things (IoT) in the current "smart everything" scenario, this article provides a comprehensive overview of the most recent research at the intersection of both domains, focusing on the design and development of specific mechanisms for enabling a collaborative inference across edge devices towards the in situ execution of highly complex state-of-the-art deep neural networks (DNNs), despite the resource-constrained nature of such infrastructures. In particular, the review discusses the most salient approaches conceived along those lines, elaborating on the specificities of the partitioning schemes and the parallelism paradigms explored, providing an organized and schematic discussion of the underlying workflows and associated communication patterns, as well as the architectural aspects of the DNNs that have driven the design of such techniques, while also highlighting both the primary challenges encountered at the design and operational levels and the specific adjustments or enhancements explored in response to them.
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Affiliation(s)
- Ivan Rodriguez-Conde
- Department of Computer Science, University of Arkansas at Little Rock, 2801 South University Avenue, Little Rock, AR 72204, USA
| | - Celso Campos
- Department of Computer Science, ESEI—Escuela Superior de Ingeniería Informática, Universidade de Vigo, 32004 Ourense, Spain
| | - Florentino Fdez-Riverola
- CINBIO, Department of Computer Science, ESEI—Escuela Superior de Ingeniería Informática, Universidade de Vigo, 32004 Ourense, Spain
- SING Research Group, Galicia Sur Health Research Institute (IIS Galicia Sur), SERGAS-UVIGO, 36213 Vigo, Spain
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Qayyum A, Janjua MU, Qadir J. Making federated learning robust to adversarial attacks by learning data and model association. Comput Secur 2022. [DOI: 10.1016/j.cose.2022.102827] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/03/2022]
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Rasheed K, Qayyum A, Ghaly M, Al-Fuqaha A, Razi A, Qadir J. Explainable, trustworthy, and ethical machine learning for healthcare: A survey. Comput Biol Med 2022; 149:106043. [PMID: 36115302 DOI: 10.1016/j.compbiomed.2022.106043] [Citation(s) in RCA: 24] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2022] [Revised: 08/15/2022] [Accepted: 08/20/2022] [Indexed: 12/18/2022]
Abstract
With the advent of machine learning (ML) and deep learning (DL) empowered applications for critical applications like healthcare, the questions about liability, trust, and interpretability of their outputs are raising. The black-box nature of various DL models is a roadblock to clinical utilization. Therefore, to gain the trust of clinicians and patients, we need to provide explanations about the decisions of models. With the promise of enhancing the trust and transparency of black-box models, researchers are in the phase of maturing the field of eXplainable ML (XML). In this paper, we provided a comprehensive review of explainable and interpretable ML techniques for various healthcare applications. Along with highlighting security, safety, and robustness challenges that hinder the trustworthiness of ML, we also discussed the ethical issues arising because of the use of ML/DL for healthcare. We also describe how explainable and trustworthy ML can resolve all these ethical problems. Finally, we elaborate on the limitations of existing approaches and highlight various open research problems that require further development.
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Affiliation(s)
- Khansa Rasheed
- IHSAN Lab, Information Technology University of the Punjab (ITU), Lahore, Pakistan.
| | - Adnan Qayyum
- IHSAN Lab, Information Technology University of the Punjab (ITU), Lahore, Pakistan.
| | - Mohammed Ghaly
- Research Center for Islamic Legislation and Ethics (CILE), College of Islamic Studies, Hamad Bin Khalifa University (HBKU), Doha, Qatar.
| | - Ala Al-Fuqaha
- Information and Computing Technology Division, College of Science and Engineering, Hamad Bin Khalifa University (HBKU), Doha, Qatar.
| | - Adeel Razi
- Turner Institute for Brain and Mental Health, Monash University, Clayton, Australia; Monash Biomedical Imaging, Monash University, Clayton, Australia; Wellcome Centre for Human Neuroimaging, UCL, London, United Kingdom; CIFAR Azrieli Global Scholars program, CIFAR, Toronto, Canada.
| | - Junaid Qadir
- Department of Computer Science and Engineering, College of Engineering, Qatar University, Doha, Qatar.
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Optimized convolutional neural network architectures for efficient on-device vision-based object detection. Neural Comput Appl 2021. [DOI: 10.1007/s00521-021-06830-w] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
Abstract
AbstractConvolutional neural networks have pushed forward image analysis research and computer vision over the last decade, constituting a state-of-the-art approach in object detection today. The design of increasingly deeper and wider architectures has made it possible to achieve unprecedented levels of detection accuracy, albeit at the cost of both a dramatic computational burden and a large memory footprint. In such a context, cloud systems have become a mainstream technological solution due to their tremendous scalability, providing researchers and practitioners with virtually unlimited resources. However, these resources are typically made available as remote services, requiring communication over the network to be accessed, thus compromising the speed of response, availability, and security of the implemented solution. In view of these limitations, the on-device paradigm has emerged as a recent yet widely explored alternative, pursuing more compact and efficient networks to ultimately enable the execution of the derived models directly on resource-constrained client devices. This study provides an up-to-date review of the more relevant scientific research carried out in this vein, circumscribed to the object detection problem. In particular, the paper contributes to the field with a comprehensive architectural overview of both the existing lightweight object detection frameworks targeted to mobile and embedded devices, and the underlying convolutional neural networks that make up their internal structure. More specifically, it addresses the main structural-level strategies used for conceiving the various components of a detection pipeline (i.e., backbone, neck, and head), as well as the most salient techniques proposed for adapting such structures and the resulting architectures to more austere deployment environments. Finally, the study concludes with a discussion of the specific challenges and next steps to be taken to move toward a more convenient accuracy–speed trade-off.
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