歡迎來到演算法、軟體和硬體三位一體的未來世界論文書籍站
靜宜大學 資訊應用與科技管理碩士在職專班 劉志俊所指導 呂文惠的 基於毫米波雷達技術的智慧洗手品質監控系統 （2021），提出Eai camera關鍵因素是什麼，來自於毫米波雷達、深度學習、卷積神經網路、洗手、洗手品質監控。
而第二篇論文國立臺灣科技大學 機械工程系 林其禹、蘇順豐所指導 Hafiz Abbad Ur Rehman的 甲狀腺疾病診斷的深度學習和機器學習方法研究 （2021），提出因為有 Deep Learning、Thyroid、Medical Imaging、Healthcare、Machine Learning、Iridology的重點而找出了 Eai camera的解答。
基於毫米波雷達技術的智慧洗手品質監控系統
為了解決Eai camera 的問題，作者呂文惠 這樣論述：
洗手是預防感染與防止傳染病傳播的第一道防線，但能做到WHO建議正確洗手動作要求的使用者人數比例很低。毫米波雷達辨識具有非接觸性的優點，在全球防疫狀況下顯得特別重要。本文提出結合毫米波雷達與深度學習技術的非接觸式智慧洗手品質監控系統，初步實驗結果顯示對三種WHO建議洗手動作的辨識準確率達99.48%。希望未來毫米波雷達智慧洗手品質監控系統能對提高一般民眾與醫護人員正確洗手品質有所助益。
甲狀腺疾病診斷的深度學習和機器學習方法研究
為了解決Eai camera 的問題，作者Hafiz Abbad Ur Rehman 這樣論述：
This dissertation focuses on the use of deep learning and machine learning techniques to diagnose Thyroid Disease. Those techniques have recently made unprecedented progress and exhibit incredible abilities to discover intricate structures from high dimensional data. Deep learning approaches have a
chieved state-of-the-art performances by a significant margin for many computer vision tasks in various cases. The contributions of this dissertation are stated in three folds. First, a fast screening approach leveraging deep learning is proposed to solve the thyroid nodules problem. Second, the imp
ortance and effectiveness of medical data fusion are illustrated in developing machine learning classifiers. Third, the potential use of the deep learning model and iridology can address medical images for the thyroid problems. In particular, we investigate deep learning and machine learning ways of
addressing classification, detection, segmentation of medical images for thyroid disease. For classification, a newly developed dataset has been used with fewer attributes for the early prediction of thyroid disease to allow doctors to get more precise and accurate results in less time. For detecti
on and classification, the motivation is to provide a quick, supportive, non-invasive system for physicians to screen thyroid disorder through human iris images. For the segmentation, to distinguish between healthy tissues and the thyroid nodule region, an automated deep learning technique is used f
or detecting and segmenting thyroid nodules in ultrasound images. Experimental results demonstrate that the developed computational methods in this study are effective and efficient in learning from medical imaging data.