h2u的問題，透過圖書和論文來找解法和答案更準確安心。 我們找到下列各種有用的問答集和懶人包

以深度學習演算法應用於資料分析與影像分類

為了解決h2u的問題，作者Haobijam Basanta 這樣論述：

Early recognition and diagnosis of disease are the best health practices in terms of minimizing health risks and limiting the dangers to well-being faced by each individual in their daily life. But most of the patients are daunted or neglected the preventive measures of health and safety challenges

due to a lack of medical knowledge. They have under-recognized problems that can prompt conjunction with other health issues and exacerbate other diseases if not properly managed. Moreover, in the traditional healthcare, paper records are used to maintain the clinical history of the patients, which

leads a greater risk of misplacement or lost and consequently hinder the treatment. So, it is vital to comprehend and recognize the preventive measures that can ensure proper care and improve or maintain quality of their daily life. To condense the risk for complications and assess the causes of un

intended consequences of health risk this study aimed to validate the efficiency of constant monitoring and clinical data analysis of the patients from infants to elderly people by integrating diverse deep learning platforms in real time. The system includes Help-to-You (H2U) that can collect the El

ectronic Health Record (EHR) from the user through a mobile application. The collected clinical data can be monitored and managed seamlessly. Symptom checker which helps the user to define the type of ailments that they may have and suggests the right medical consultation. Voice and gesture recognit

ion platform was used to control the user’s home appliances and gadgets. To ascertain and identify activities that are related to various health and self-care problems, a deep belief network (DBN) was used to monitor unsupervised, diverse daily life activities of patients or elderly people. Simulati

on results showed that the proposed system with DBN outperformed the support vector machines in terms of F1 score and accuracy in identifying daily activities. Uncertainty is an inherent problem of medical diagnosis due to the lack of sufficient evidences to assess a disease. A Fuzzy Analytical Netw

ork Process (FANP) with Transformed Fuzzy Neural Network (TFNN) was proposed to achieve best clinical judgement and precise treatment, especially applicable to enhance the classification accuracy. A benchmark dataset, diabetic retinopathy Debrecen was used to validate the model. Through the process,

the model achieved 100% accuracy. A deep learning model using convolutional neural network (CNN) was designed to detect and classify the severity of ROP diseases which are especially observed in preterm infants with low birth weights. The model was applied to a group of patients at the early stages

of the disease, i.e., stages 1 and 2. The inputs consisted 2205 annotated fundus images and validated by 441 test images with 10-fold cross validations. The preliminary result yielded a high accuracy rates for training and test datasets. Birdwatching is a recreational activity that can provide rela

xation in daily life and promote resilience to face daily challenges. It can also offer health benefits and happiness derived from enjoying nature. Lastly, a CNN with skip connection was adopted to classify 3563 images of 27 endemic birds of Taiwan. The trained model was tested by 713 images, which

achieved average sensitivity, specificity, and accuracy of 93.79%, 96.11%, and 95.37%, respectively. Timely recognition of health issues is mandatory and therefore the methods applied in this study might uplift a perspective on the treatment of patients. This process would impact immense benefit and

improve the clinical data analysis with efficient classification of inherent problem of medical diagnosis.