標(biāo)題: Titlebook: Breath Analysis for Medical Applications; David Zhang,Dongmin Guo,Ke Yan Book 2017 Springer Nature Singapore Pte Ltd. 2017 Breath signal D [打印本頁(yè)] 作者: 萌芽的心 時(shí)間: 2025-3-21 17:43
書(shū)目名稱Breath Analysis for Medical Applications影響因子(影響力)
書(shū)目名稱Breath Analysis for Medical Applications影響因子(影響力)學(xué)科排名
書(shū)目名稱Breath Analysis for Medical Applications網(wǎng)絡(luò)公開(kāi)度
書(shū)目名稱Breath Analysis for Medical Applications網(wǎng)絡(luò)公開(kāi)度學(xué)科排名
書(shū)目名稱Breath Analysis for Medical Applications被引頻次
書(shū)目名稱Breath Analysis for Medical Applications被引頻次學(xué)科排名
書(shū)目名稱Breath Analysis for Medical Applications年度引用
書(shū)目名稱Breath Analysis for Medical Applications年度引用學(xué)科排名
書(shū)目名稱Breath Analysis for Medical Applications讀者反饋
書(shū)目名稱Breath Analysis for Medical Applications讀者反饋學(xué)科排名
作者: 擔(dān)憂 時(shí)間: 2025-3-21 20:32
Visionsarbeit von Walt Disney lernen-RFE and other typical algorithms. An ensemble method is further studied to improve the stability of the proposed method. By statistically analyzing the features’ rankings, some knowledge is obtained, which can guide future design of e-noses and feature extraction algorithms.作者: 敵手 時(shí)間: 2025-3-22 03:16 作者: ROOF 時(shí)間: 2025-3-22 07:25 作者: byline 時(shí)間: 2025-3-22 11:56
Feature Selection and Analysis on Correlated Breath Data-RFE and other typical algorithms. An ensemble method is further studied to improve the stability of the proposed method. By statistically analyzing the features’ rankings, some knowledge is obtained, which can guide future design of e-noses and feature extraction algorithms.作者: 補(bǔ)充 時(shí)間: 2025-3-22 16:26 作者: labile 時(shí)間: 2025-3-22 20:46 作者: DECRY 時(shí)間: 2025-3-22 21:25
including acquisition, preprocessing, classification, and ty.This book describes breath signal processing technologies and their applications in medical sample classification and diagnosis. First, it provides a comprehensive introduction to breath signal acquisition methods, based on different kinds作者: invert 時(shí)間: 2025-3-23 04:12
https://doi.org/10.1007/978-3-322-87042-1h analysis device, and of the design of specific pattern recognition algorithm for breath analysis. This is followed by a statement of the objective of the research, a brief summary of the work, and a general outline of the overall structure of the present study.作者: GRAVE 時(shí)間: 2025-3-23 06:51 作者: 一個(gè)攪動(dòng)不安 時(shí)間: 2025-3-23 12:38
Introductionh analysis device, and of the design of specific pattern recognition algorithm for breath analysis. This is followed by a statement of the objective of the research, a brief summary of the work, and a general outline of the overall structure of the present study.作者: granite 時(shí)間: 2025-3-23 17:43
Literature Review approaches like GC which have been used to analyze the compounds of breath and identify several diseases are then described. This is followed by a detailed introduction of current major approaches, e-noses, for breath analysis. The final section gives a short summary of the chapter.作者: Recessive 時(shí)間: 2025-3-23 18:26
A Transfer Learning Approach for Correcting Instrumental Variation and Time-Varying Driftrection algorithms and autoencoder-based transfer learning methods. In particular, it is better than TMTL in the last chapter in datasets with complex drift, at the cost of longer training time and more hyper-parameters.作者: Paradox 時(shí)間: 2025-3-24 02:09 作者: 盡責(zé) 時(shí)間: 2025-3-24 04:36 作者: 內(nèi)向者 時(shí)間: 2025-3-24 08:26
Management aus soziologischer Sichtethods are made convenient to observe and draw intuitive conclusions. They are applied to the breath acquisition system and some useful discoveries about the sensors in the system are made accordingly.作者: senile-dementia 時(shí)間: 2025-3-24 13:39 作者: Inferior 時(shí)間: 2025-3-24 16:37 作者: 四牛在彎曲 時(shí)間: 2025-3-24 19:20
Literature Reviewto the present study. Following a brief introductory overview of the field, the chapter first presents the development of breath analysis. Traditional approaches like GC which have been used to analyze the compounds of breath and identify several diseases are then described. This is followed by a de作者: dearth 時(shí)間: 2025-3-25 02:56
A Novel Breath Acquisition System Designkers using equipments such as gas chromatography (GC) and electronic nose (e-nose). GC is very accurate but is expensive, time consuming, and non-portable. E-nose has the advantages of low-cost and easy operation, but is not particular for analyzing breath odor and hence has a limited application in作者: Accrue 時(shí)間: 2025-3-25 05:36 作者: Anticoagulants 時(shí)間: 2025-3-25 10:28 作者: cleaver 時(shí)間: 2025-3-25 14:39
Improving the Transfer Ability of Prediction Modelseployment of e-noses, especially when the cost of sample collection is high. In this chapter, the transfer ability of prediction models is improved in two simple yet effective steps. First, windowed piecewise direct standardization (WPDS) is used to standardize the slave device, i.e., to transform t作者: Cognizance 時(shí)間: 2025-3-25 19:43 作者: glisten 時(shí)間: 2025-3-25 22:15
A Transfer Learning Approach for Correcting Instrumental Variation and Time-Varying Driftmodel and correct these influential factors explicitly with the help of transfer samples. It generates drift-corrected and discriminative representation of the original data, which can then be applied to various prediction algorithms. Experimental results show that DCAE outperforms typical drift cor作者: 付出 時(shí)間: 2025-3-26 00:38
Drift Correction Using Maximum Independence Domain Adaptation unsupervised domain adaptation approaches. Maximum independence domain adaptation (MIDA) is proposed in this chapter for unsupervised drift correction. MIDA borrows the definition of domain features in the last chapter and learns features which have maximal independence with them, so as to reduce t作者: 寬敞 時(shí)間: 2025-3-26 05:12 作者: 惡心 時(shí)間: 2025-3-26 11:19
Breath Sample Identification by Sparse Representation-Based Classification system in tandem with certain data evaluation algorithm offers an approach to analyze the compositions of breath. Currently, most algorithms rely on the generally designed pattern recognition techniques rather than considering the specific characteristics of data. They may not be suitable for odor 作者: 上腭 時(shí)間: 2025-3-26 12:47 作者: 圓桶 時(shí)間: 2025-3-26 20:49
Breath Signal Analysis for Diabeticse with abnormal concentrations and the concentrations rise gradually with patients’ blood glucose values. Therefore, the acetone in human breath can be used to monitor the development of diabetes. This chapter investigates the potential of breath signals analysis as a way for blood glucose monitorin作者: neutralize 時(shí)間: 2025-3-26 23:12 作者: falsehood 時(shí)間: 2025-3-27 02:06
https://doi.org/10.1007/978-981-10-4322-2Breath signal Diagnosis; Signal Acquisition; Sensor Selection and Fusion; Signal Preprocessing; Pattern 作者: BARGE 時(shí)間: 2025-3-27 06:03 作者: OFF 時(shí)間: 2025-3-27 11:12
https://doi.org/10.1007/978-3-322-87042-1for the focus of the work is then explained, highlighting the importance of the breath analysis used in disease diagnosis, of the development of breath analysis device, and of the design of specific pattern recognition algorithm for breath analysis. This is followed by a statement of the objective o作者: 頭腦冷靜 時(shí)間: 2025-3-27 16:23
Management aus soziologischer Sichtto the present study. Following a brief introductory overview of the field, the chapter first presents the development of breath analysis. Traditional approaches like GC which have been used to analyze the compounds of breath and identify several diseases are then described. This is followed by a de作者: 執(zhí)拗 時(shí)間: 2025-3-27 20:05 作者: 不透氣 時(shí)間: 2025-3-27 22:39 作者: 正式演說(shuō) 時(shí)間: 2025-3-28 04:11
Management aus soziologischer Sichtection, some insight behind the performance of different sensor arrays can be obtained. Thus, we can know more about the sensors, which could help us with the selection work in turn. In this chapter, we focus on the evaluation of sensor performance instead of particular sensor selection techniques. 作者: GENUS 時(shí)間: 2025-3-28 08:19 作者: 憤憤不平 時(shí)間: 2025-3-28 12:26 作者: 津貼 時(shí)間: 2025-3-28 14:54
Ikujiro Nonaka,Ichiro Yamaguchimodel and correct these influential factors explicitly with the help of transfer samples. It generates drift-corrected and discriminative representation of the original data, which can then be applied to various prediction algorithms. Experimental results show that DCAE outperforms typical drift cor作者: 吵鬧 時(shí)間: 2025-3-28 20:45
https://doi.org/10.1007/978-981-16-6851-7 unsupervised domain adaptation approaches. Maximum independence domain adaptation (MIDA) is proposed in this chapter for unsupervised drift correction. MIDA borrows the definition of domain features in the last chapter and learns features which have maximal independence with them, so as to reduce t作者: Efflorescent 時(shí)間: 2025-3-28 23:48 作者: Generosity 時(shí)間: 2025-3-29 05:49 作者: 游行 時(shí)間: 2025-3-29 07:34
Visionen: Das Unm?gliche denkenith the blood glucose level of patients. Therefore, the acetone in human breath can be used to monitor the development of diabetes. In this chapter, we introduce a breath analysis system to measure acetone in human breath, and therefore to evaluate the blood glucose levels of diabetics. The system s作者: somnambulism 時(shí)間: 2025-3-29 13:29
Unternehmer sind die besseren Mathematiker,e with abnormal concentrations and the concentrations rise gradually with patients’ blood glucose values. Therefore, the acetone in human breath can be used to monitor the development of diabetes. This chapter investigates the potential of breath signals analysis as a way for blood glucose monitorin作者: PRE 時(shí)間: 2025-3-29 18:57 作者: Conducive 時(shí)間: 2025-3-29 22:18 作者: 準(zhǔn)則 時(shí)間: 2025-3-30 01:14 作者: chastise 時(shí)間: 2025-3-30 05:43
and potential medical applications of computerized breath diagnosis. It is a valuable interdisciplinary resource for researchers, professionals and postgraduate students working in various fields, including breath diagnosis, signal processing, pattern recognition, and biometrics..978-981-13-5106-8978-981-10-4322-2作者: 連鎖 時(shí)間: 2025-3-30 10:41 作者: adduction 時(shí)間: 2025-3-30 14:23
https://doi.org/10.1007/978-3-322-87042-1 the responses of all the sensors in the system, and . can be regarded as the weight vectors for these sensors which indicate the contribution weight of each sensor. Accordingly, it is possible to determine which sensor has a greater contribution in classifying the two classes. A series of experimen作者: 易彎曲 時(shí)間: 2025-3-30 17:17
https://doi.org/10.1007/978-3-031-11637-7s are adopted to collect a dataset, which contains pure chemicals and breath samples. Experiments show that WPDS outperforms previous methods in the sense of standardization error and prediction accuracy; SEMI consistently enhances the accuracy of the master model applied to standardized slave data.作者: absolve 時(shí)間: 2025-3-30 22:24
Ikujiro Nonaka,Ichiro Yamaguchil feature-level drift correction algorithms and typical labeled-sample-based MTL methods, with few transfer samples needed. TMTL is a practical algorithm framework which can greatly enhance the robustness of sensor systems with complex drift.作者: 正面 時(shí)間: 2025-3-31 03:01 作者: Callus 時(shí)間: 2025-3-31 08:27
Unternehmer sind die besseren Mathematiker, and “not controlled”, respectively. The experimental results show that the accuracy to classify the diabetes samples can be up?to 68.66%. The current prediction correct rates are not quite high, but the results are promising because it provides a possibility of noninvasive blood glucose measurement作者: myalgia 時(shí)間: 2025-3-31 10:09 作者: 召集 時(shí)間: 2025-3-31 16:51
