Python, NumPy, SciPy und Matplotlib – in a nutshell

Frochte, Jörg

doi:10.3139/9783446459977.003

Cited by 4 publications

(4 citation statements)

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“…In this study, the following most widely used supervised learning methods were investigated for their suitability for functional kidney classification: support vector machine (SVM), random forest (RF), k-nearest-neighbor (kNN), logistic regression (LOG), and naive bayes (BAY). A detailed description of the principles of the methods is described in [ 30 ], among others. Classifiers were implemented in python (python 3.8.8, Python Software Foundation, Wilmington, DE, USA) using the sklearn and numpy libraries .…”

Section: Methodsmentioning

confidence: 99%

Preoperative Function Assessment of Ex Vivo Kidneys with Supervised Machine Learning Based on Blood and Urine Markers Measured during Normothermic Machine Perfusion

Markgraf

Malberg

2022

Biomedicines

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Establishing an objective quality assessment of an organ prior to transplantation can help prevent unnecessary discard of the organ and reduce the probability of functional failure. In this regard, normothermic machine perfusion (NMP) offers new possibilities for organ evaluation. However, to date, few studies have addressed the identification of markers and analytical tools to determine graft quality. In this study, function and injury markers were measured in blood and urine during NMP of 26 porcine kidneys and correlated with ex vivo inulin clearance behavior. Significant differentiation of kidneys according to their function could be achieved by oxygen consumption, oxygen delivery, renal blood flow, arterial pressure, intrarenal resistance, kidney temperature, relative urea concentration, and urine production. In addition, classifications were accomplished with supervised learning methods and histological analysis to predict renal function ex vivo. Classificators (support vector machines, k-nearest-neighbor, logistic regression and naive bayes) based on relevant markers in urine and blood achieved 75% and 83% accuracy in the validation and test set, respectively. A correlation between histological damage and function could not be detected. The measurement of blood and urine markers provides information of preoperative renal quality, which can used in future to establish an objective quality assessment.

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Section: Methodsmentioning

confidence: 99%

Preoperative Function Assessment of Ex Vivo Kidneys with Supervised Machine Learning Based on Blood and Urine Markers Measured during Normothermic Machine Perfusion

Markgraf

Malberg

2022

Biomedicines

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“…Since the number of weights correspond to the degrees of freedom of the FFNN, an l 2 regularization is used to avoid an overfitting of more complex FFNNs. For this purpose, another hyperparameter λ ≥ 0 is introduced so that the model complexity is taken into account in the optimization problem 35 :…”

Section: Methodsmentioning

confidence: 99%

Physical-oriented and machine learning-based emission modeling in a diesel compression ignition engine: Dimensionality reduction and regression

Mohammad

Rezaei

Hayduk

et al. 2022

International Journal of Engine Research

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The development of internal combustion engines is affected by the exhaust gas emissions legislation and the striving to increase performance. This demands for engine-out emission models that can be used for engine optimization for real driving emission controls. The prediction capability of physically and data-driven engine-out emission models is influenced by the system inputs, which are specified by the user and can lead to an improved accuracy with increasing number of inputs. Thereby the occurrence of irrelevant inputs becomes more probable, which have a low functional relation to the emissions and can lead to overfitting. Alternatively, data-driven methods can be used to detect irrelevant and redundant inputs. In this work, thermodynamic states are modeled based on 772 stationary measured test bench data from a commercial vehicle diesel engine. Afterward, 37 measured and modeled variables are led into a data-driven dimensionality reduction. For this purpose, approaches of supervised learning, such as lasso regression and linear support vector machine, and unsupervised learning methods like principal component analysis and factor analysis are applied to select and extract the relevant features. The selected and extracted features are used for regression by the support vector machine and the feedforward neural network to model the NOx, CO, HC, and soot emissions. This enables an evaluation of the modeling accuracy as a result of the dimensionality reduction. Using the methods in this work, the 37 variables are reduced to 25, 22, 11, and 16 inputs for NOx, CO, HC, and soot emission modeling while maintaining the accuracy. The features selected using the lasso algorithm provide more accurate learning of the regression models than the extracted features through principal component analysis and factor analysis. This results in test errors RMSETe for modeling NOx, CO, HC, and soot emissions 19.22 ppm, 6.46 ppm, 1.29 ppm, and 0.06 FSN, respectively.

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“…The software module, which is visualized in Figure 11, includes a subclass that includes reuse, performance, testing, privacy, and security. For software testing, the main point was to verify that the code was running correctly by testing the code under known conditions and checking that the results were as expected [36]. Visual analytic and interactive visualizations offer a higher degree of freedom for users for feature filtering, sorting patterns according to different interestingness measures, templating, and providing details on demand.…”

Section: ) Softwarementioning

confidence: 99%

A Novel Software Engineering Approach Toward Using Machine Learning for Improving the Efficiency of Health Systems

2020

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Recently, machine learning has become a hot research topic. Therefore, this study investigates the interaction between software engineering and machine learning within the context of health systems. We proposed a novel framework for health informatics: the framework and methodology of software engineering for machine learning in health informatics (SEMLHI). The SEMLHI framework includes four modules (software, machine learning, machine learning algorithms, and health informatics data) that organize the tasks in the framework using a SEMLHI methodology, thereby enabling researchers and developers to analyze health informatics software from an engineering perspective and providing developers with a new road map for designing health applications with system functions and software implementations. Our novel approach sheds light on its features and allows users to study and analyze the user requirements and determine both the function of objects related to the system and the machine learning algorithms that must be applied to the dataset. Our dataset used in this research consists of real data and was originally collected from a hospital run by the Palestine government covering the last three years. The SEMLHI methodology includes seven phases: designing, implementing, maintaining and defining workflows; structuring information; ensuring security and privacy; performance testing and evaluation; and releasing the software applications. INDEX TERMS Health dataset analysis, machine learning, methodology, software development management, software engineering.

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Python, NumPy, SciPy und Matplotlib – in a nutshell

Cited by 4 publications

References 0 publications

Preoperative Function Assessment of Ex Vivo Kidneys with Supervised Machine Learning Based on Blood and Urine Markers Measured during Normothermic Machine Perfusion

Preoperative Function Assessment of Ex Vivo Kidneys with Supervised Machine Learning Based on Blood and Urine Markers Measured during Normothermic Machine Perfusion

Physical-oriented and machine learning-based emission modeling in a diesel compression ignition engine: Dimensionality reduction and regression

A Novel Software Engineering Approach Toward Using Machine Learning for Improving the Efficiency of Health Systems

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