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- Descrizione:
- Internet of Things (IoT), a strong integration of radio frequency identifier (RFID), wireless devices, and sensors, has provided a difficult yet strong chance to shape existing systems into intelligent ones. Many new applications have been created in the last few years. As many as a million objects are anticipated to be linked together to form a network that can infer meaningful conclusions based on raw data. This means any IoT system is heterogeneous when it comes to the types of devices that are used in the system and how they communicate with each other. In most cases, an IoT network can be described as a layered network, with multiple tiers stacked on top of each other. IoT network performance improvement typically focuses on a single layer. As a result, effectiveness in one layer may rise while that of another may fall. Ultimately, the achievement issue must be addressed by considering improvements in all layers of an IoT network, or at the very least, by considering contiguous hierarchical levels. Using a parallel and clustered architecture in the device layer, this paper examines how to improve the performance of an IoT network’s controller layer. A particular clustered architecture at the device level has been shown to increase the performance of an IoT network by 16% percent. Using a clustered architecture at the device layer in conjunction with a parallel architecture at the controller layer boosts performance by 24% overall.
- Parola chiave:
- Layered Networking, Parallel Architectures, Device Level Clustering, Internet of Things, Topology Binding, Performance Optimization, and Radio-frequency Identification
- Soggetto:
- Artificial Intelligence and Data Science
- Creatore:
- Singh, Ninni , Roy, Sudipta , Saad, Ismail , Rahebi, Javad , Farzamnia, Ali , Gunjan, Vinit Kumar , Davanam, Ganesh , and Kallam, Suresh
- Owner:
- n.sakthivel@jioinstitute.edu.in
- Editore:
- MDPI
- luogo:
- Switzerland
- Lingua:
- English
- Data caricata:
- 11-02-2023
- Data modificata:
- 16-02-2023
- data di creazione:
- 01-11-2022
- Rights Statement Tesim:
- In Copyright
- License Tesim:
- All rights reserved
- Resource Type:
- Article
- Identifier:
- 10.3390/en15228738
-
- Descrizione:
- The global healthcare sector continues to grow rapidly and is reflected as one of the fastestgrowing sectors in the fourth industrial revolution (4.0). The majority of the healthcare industry still uses labor-intensive, time-consuming, and error-prone traditional, manual, and manpower-based methods. This review addresses the current paradigm, the potential for new scientific discoveries, the technological state of preparation, the potential for supervised machine learning (SML) prospects in various healthcare sectors, and ethical issues. The effectiveness and potential for innovation of disease diagnosis, personalized medicine, clinical trials, non-invasive image analysis, drug discovery, patient care services, remote patient monitoring, hospital data, and nanotechnology in various learning-based automation in healthcare along with the requirement for explainable artificial intelligence (AI) in healthcare are evaluated. In order to understand the potential architecture of non-invasive treatment, a thorough study of medical imaging analysis from a technical point of view is presented. This study also represents new thinking and developments that will push the boundaries and increase the opportunity for healthcare through AI and SML in the near future. Nowadays, SML-based applications require a lot of data quality awareness as healthcare is data-heavy, and knowledge management is paramount. Nowadays, SML in biomedical and healthcare developments needs skills, quality data consciousness for data-intensive study, and a knowledge-centric health management system. As a result, the merits, demerits, and precautions need to take ethics and the other effects of AI and SML into consideration. The overall insight in this paper will help researchers in academia and industry to understand and address the future research that needs to be discussed on SML in the healthcare and biomedical sectors.
- Parola chiave:
- Healthcare, Precision Medicine, Artificial Intelligence, Computer Vision, Deep Learning, Medical Imaging, XAI, and Supervised Learning
- Soggetto:
- Artificial Intelligence and Data Science
- Creatore:
- Roy, Sudipta , Lim, Se-Jung , and Meena, Tanushree
- Collaboratore:
- Jio Institute, CVMI-Computer Vision in Medical Imaging Project
- Owner:
- n.sakthivel@jioinstitute.edu.in
- Editore:
- MDPI
- luogo:
- Switzerland and India
- Lingua:
- English
- Data caricata:
- 10-02-2023
- Data modificata:
- 16-02-2023
- data di creazione:
- 01-10-2022
- Rights Statement Tesim:
- In Copyright
- License Tesim:
- All rights reserved
- Resource Type:
- Article
- Identifier:
- 10.3390/diagnostics12102549
-
3. Bone Fracture Detection Using Deep Supervised Learning from Radiological Images: A Paradigm Shift
- Descrizione:
- Bone diseases are common and can result in various musculoskeletal conditions (MC). An estimated 1.71 billion patients suffer from musculoskeletal problems worldwide. Apart from musculoskeletal fractures, femoral neck injuries, knee osteoarthritis, and fractures are very common bone diseases, and the rate is expected to double in the next 30 years. Therefore, proper and timely diagnosis and treatment of a fractured patient are crucial. Contrastingly, missed fractures are a common prognosis failure in accidents and emergencies. This causes complications and delays in patients’ treatment and care. These days, artificial intelligence (AI) and, more specifically, deep learning (DL) are receiving significant attention to assist radiologists in bone fracture detection. DL can be widely used in medical image analysis. Some studies in traumatology and orthopaedics have shown the use and potential of DL in diagnosing fractures and diseases from radiographs. In this systematic review, we provide an overview of the use of DL in bone imaging to help radiologists to detect various abnormalities, particularly fractures. We have also discussed the challenges and problems faced in the DL-based method, and the future of DL in bone imaging.
- Parola chiave:
- Deep Learning, Fractures, Bone Imaging, Artificial Intelligence, Radiology, and Computer Vision
- Soggetto:
- Data Science and Artificial Intelligence
- Creatore:
- Roy, Sudipta and Meena, Tanushree
- Collaboratore:
- Jio Institute CVMI-Computer Vision in Medical Imaging Project
- Owner:
- n.sakthivel@jioinstitute.edu.in
- Editore:
- MDPI
- luogo:
- Switzerland
- Lingua:
- English
- Data caricata:
- 10-02-2023
- Data modificata:
- 16-02-2023
- data di creazione:
- 01-10-2022
- Rights Statement Tesim:
- In Copyright
- License Tesim:
- All rights reserved
- Resource Type:
- Article
- Identifier:
- 10.3390/diagnostics12102420