| 1. |
|
|
| 2. |
|
|
| 3. |
- Elmusrati, M, et al.
(författare)
-
Wireless automation : opportunities and challenges
- 2007
-
Ingår i: Finnish Automation Days.
-
Konferensbidrag (refereegranskat)abstract
- In this paper we discuss the opportunities of replacing wirelines by wireless connections in automation systems.However, there are several challenges inherently associated with these opportunities. One of the major challengesis how to select a proper wireless connection protocol that achieves at least the minimum requirements ofthe automation system. These system requirements are discussed in the paper. Some wireless communicationsystems which could be used for wireless automation are briefly revised. Finally, we discuss the applicability ofcontemporary wireless protocols for wireless automation and whether a new wireless protocol needs to be definedfor wireless automation systems. The augmentation of wireless technology to automation systems will improvethe performance of those systems and also many new applications may be defined.
|
|
| 4. |
|
|
| 5. |
|
|
| 6. |
|
|
| 7. |
|
|
| 8. |
|
|
| 9. |
- Alabi, RO, et al.
(författare)
-
Deep Machine Learning for Oral Cancer: From Precise Diagnosis to Precision Medicine
- 2021
-
Ingår i: Frontiers in oral health. - : Frontiers Media SA. - 2673-4842. ; 2, s. 794248-
-
Tidskriftsartikel (refereegranskat)abstract
- Oral squamous cell carcinoma (OSCC) is one of the most prevalent cancers worldwide and its incidence is on the rise in many populations. The high incidence rate, late diagnosis, and improper treatment planning still form a significant concern. Diagnosis at an early-stage is important for better prognosis, treatment, and survival. Despite the recent improvement in the understanding of the molecular mechanisms, late diagnosis and approach toward precision medicine for OSCC patients remain a challenge. To enhance precision medicine, deep machine learning technique has been touted to enhance early detection, and consequently to reduce cancer-specific mortality and morbidity. This technique has been reported to have made a significant progress in data extraction and analysis of vital information in medical imaging in recent years. Therefore, it has the potential to assist in the early-stage detection of oral squamous cell carcinoma. Furthermore, automated image analysis can assist pathologists and clinicians to make an informed decision regarding cancer patients. This article discusses the technical knowledge and algorithms of deep learning for OSCC. It examines the application of deep learning technology in cancer detection, image classification, segmentation and synthesis, and treatment planning. Finally, we discuss how this technique can assist in precision medicine and the future perspective of deep learning technology in oral squamous cell carcinoma.
|
|
| 10. |
|
|
