| Autor: |
Rajesh E; School of Computing Science and Engineering, Galgotias University, Greater Noida 203201, India., Basheer S; School of Computing Science and Engineering, Galgotias University, Greater Noida 203201, India., Dhanaraj RK; School of Computing Science and Engineering, Galgotias University, Greater Noida 203201, India., Yadav S; School of Computing Science and Engineering, Galgotias University, Greater Noida 203201, India., Kadry S; Department of Applied Data Science, Noroff University College, 4612 Kristiansand, Norway.; Artificial Intelligence Research Center (AIRC), Ajman University, Ajman 346, United Arab Emirates.; Department of Electrical and Computer Engineering, Lebanese American University, Byblos P.O. Box 13-5053, Lebanon., Khan MA; Department of Computer Science, HITEC University, Taxila 47080, Pakistan., Kim YJ; Department of Computer Science, Hanyang University, Seoul 04763, Republic of Korea., Cha JH; Department of Computer Science, Hanyang University, Seoul 04763, Republic of Korea. |
| Abstrakt: |
The rapid increase in Internet technology and machine-learning devices has opened up new avenues for online healthcare systems. Sometimes, getting medical assistance or healthcare advice online is easier to understand than getting it in person. For mild symptoms, people frequently feel reluctant to visit the hospital or a doctor; instead, they express their questions on numerous healthcare forums. However, predictions may not always be accurate, and there is no assurance that users will always receive a reply to their posts. In addition, some posts are made up, which can misdirect the patient. To address these issues, automatic online prediction (OAP) is proposed. OAP clarifies the idea of employing machine learning to predict the common attributes of disease using Never-Ending Image Learner with an intelligent analysis of disease factors. Never-Ending Image Learner predicts disease factors by selecting from finite data images with minimum structural risk and efficiently predicting efficient real-time images via machine-learning-enabled M-theory. The proposed multi-access edge computing platform works with the machine-learning-assisted automatic prediction from multiple images using multiple-instance learning. Using a Never-Ending Image Learner based on Machine Learning, common disease attributes may be predicted online automatically. This method has deeper storage of images, and their data are stored per the isotropic positioning. The proposed method was compared with existing approaches, such as Multiple-Instance Learning for automated image indexing and hyper-spectrum image classification. Regarding the machine learning of multiple images with the application of isotropic positioning, the operating efficiency is improved, and the results are predicted with better accuracy. In this paper, machine-learning performance metrics for online automatic prediction tools are compiled and compared, and through this survey, the proposed method is shown to achieve higher accuracy, proving its efficiency compared to the existing methods. |
