Thematische Bibliographien / Sklearn

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Auswahl der wissenschaftlichen Literatur zum Thema „Sklearn“

Autor: Grafiati

Veröffentlicht am 28. Juni 2021

Zuletzt aktualisiert am 25. April 2022

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Zeitschriftenartikel zum Thema "Sklearn"

Кубегенова, Айгуль Даулетовна, und Казизат Такуадинович Искаков. „DATA MINING ТЕХНОЛОГИЯСЫНЫҢ КӨМЕГІМЕН, МЕДИЦИНАДА ИНТЕЛЛЕКТУАЛДЫ ТАЛДАУ ЖАСАУ ЖӘНЕ ӘДІСТЕРДІ ҚОЛДАНУ АСПЕКТІЛЕРІ“. Bulletin of Toraighyrov University. Energetics series, Nr. 1.2021 (29.03.2021): 184–95. http://dx.doi.org/10.48081/uovu7003.

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Мақалада деректерді зерттеу, деректерді терең талдау, білім алу, білім базасындағы мәліметтерді өңдеу жолдары, медицина саласында Data Mining технологиясының интелектуалды талдау әдістері мен қолдану аспектілері қарастырылған. АИТВ инфекциясын жұқтырған науқастардың, тобын анықталып аурулар тарихымен талдау жасалды, Модельдер әзірленіп және іс-қимыл алгоритмі құрылып (кіріс деректері), деректерді іздеу әдістері арқылы талдау жасап эксперименттер жүргізілді. Барлық аурулар сандық векторлар жиынтығы ретінде ұсынылып, кластерлерге топтастырылған деректердің бейімділігі сипатталған әдіснамаға сәйкес осы бөлу арқылы Хопкинс статистикасының мәні есептелді. Кластерлеудің өзі sklearn кітапханасының әдеттегі құралдарын қолдану арқылы жүзеге асырылды. Екі өлшемді жазықтықта көп өлшемді деректерді ұсынудың әртүрлі әдістері ұсынылды негізгі компоненттер әдісі, Кохонен желісі және т. б. Кластерлеудің екі түрлі тәсілі қарастырылды: k – орта әдісі (Python тілінің sklearn кітапханасынан Kmeans функциясын қолдана отырып), АВТО-конфигурациясы бар тығыздыққа негізделген кластерлеу әдістері қолданылды (Python тілінің hdbscan кітапханасынан HDBSCAN функциясынан).

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Abugharsa, Azza. „Sentiment Analysis in Poems in Misurata Sub-dialect“. INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY 21 (15.09.2021): 103–14. http://dx.doi.org/10.24297/ijct.v21i.9105.

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Over the recent decades, there has been a significant increase and development of resources for Arabic natural language processing. This includes the task of exploring Arabic Language Sentiment Analysis (ALSA) from Arabic utterances in both Modern Standard Arabic (MSA) and different Arabic dialects. This study focuses on detecting sentiment in poems written in Misurata Arabic sub-dialect spoken in Misurata, Libya. The tools used to detect sentiment from the dataset are Sklearn as well as Mazajak sentiment tool1. Logistic Regression, Random Forest, Naive Bayes (NB), and Support Vector Machines (SVM) classifiers are used with Sklearn, while the Convolutional Neural Network (CNN) is implemented with Mazajak. The results show that the traditional classifiers score a higher level of accuracy as compared to Mazajak which is built on an algorithm that includes deep learning techniques. More research is suggested to analyze Arabic sub-dialect poetry in order to investigate the aspects that contribute to sentiments in these multi-line texts; for example, the use of figurative language such as metaphors.

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Retnoningsih, Endang, und Rully Pramudita. „Mengenal Machine Learning Dengan Teknik Supervised Dan Unsupervised Learning Menggunakan Python“. BINA INSANI ICT JOURNAL 7, Nr. 2 (28.12.2020): 156. http://dx.doi.org/10.51211/biict.v7i2.1422.

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Abstrak: Machine learning merupakan sistem yang mampu belajar sendiri untuk memutuskan sesuatu tanpa harus berulangkali diprogram oleh manusia sehingga komputer menjadi semakin cerdas berlajar dari pengalaman data yang dimiliki. Berdasarkan teknik pembelajarannya, dapat dibedakan supervised learning menggunakan dataset (data training) yang sudah berlabel, sedangkan unsupervised learning menarik kesimpulan berdasarkan dataset. Input berupa dataset digunakan pembelajaran mesin untuk menghasilkan analisis yang benar. Permasalahan yang akan diselesaikan bunga iris (iris tectorum) yang memiliki bunga bermaca-macam warna dan memiliki sepal dan petal yang menunjukkan spesies bunga, dibutuhkan metode yang tepat untuk pengelompokan bunga-bunga tersebut kedalam spesiesnya iris-setosa, iris-versicolor atau iris-virginica. Penyelesaian digunakan Python yang menyediakan algoritma dan library yang digunakan membuat machine learning. Penyelesaian dengan teknik supervised learning dipilih algoritma KNN Clasiffier dan teknik unsupervised learning dipilih algoritma DBSCAN Clustering. Hasil yang diperoleh Python menyediakan library yang lengkap numPy, Pandas, matplotlib, sklearn untuk membuat pemrograman machine learning dengan algortima KNN memanggil from sklearn import neighbors termasuk teknik supervised, maupun DBSCAN memanggil from sklearn.cluster import DBSCAN termasuk teknik unsupervised learning. Kemampuan Python memberikan hasil output sesuai input dalam dataset menghasilkan keputusan berupa klasifikasi maupun klusterisasi. Kata kunci: DBSCAN, KNN, machine learning, python. Abstract: Machine learning is a system that is able to learn on its own to decide something without having to be repeatedly programmed by humans so that computers become smarter in learning from the experience of the data they have. Based on the learning technique, supervised learning can be distinguished using a dataset (training data) that is already labeled, while unsupervised learning draws conclusions based on the dataset. The input in the form of a dataset is used by machine learning to produce the correct analysis. The problem to be solved by iris flowers (iris tectorum), which has flowers of various colors and has sepals and petals that indicate the species of flowers, requires an appropriate method for grouping these flowers into iris-setosa, iris-versicolor or iris-virginica species. The solution is used by Python, which provides the algorithms and libraries used to make machine learning. The solution with the supervised learning technique was chosen by the KNN Clasiffier algorithm and the unsupervised learning technique was selected by the DBSCAN Clustering algorithm. The results obtained by Python provide a complete library of numPy, Pandas, matplotlib, sklearn to create machine learning programming with KNN algorithms calling from sklearn import neighbors including supervised techniques, and DBSCAN calling from sklearn.cluster import DBSCAN including unsupervised learning techniques. Python's ability to provide output according to the input in the dataset results in decisions in the form of classification and clustering. Keywords: DBSCAN, KNN, machine learning, python.

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Kulin, N. I., und S. B. Muravyov. „A meta-feature selection method based on the Auto-sklearn framework“. Scientific and Technical Journal of Information Technologies, Mechanics and Optics 21, Nr. 5 (01.10.2021): 702–8. http://dx.doi.org/10.17586/2226-1494-2021-21-5-702-708.

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Vaganov, A. V., Z. V. Pokalyakin und L. A. Khvorova. „Complex solution on solving problems of estimating plant resources by GIS and climate model methods“. Проблемы ботаники южной сибири и монголии 20, Nr. 1 (14.09.2021): 87–91. http://dx.doi.org/10.14258/pbssm.2021018.

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The paper considers the applied aspects of the use of modern information technologies for an accurateassessment of plant resources using GIS and climate model methods. For the most effective achievement of the goals ofintegrated monitoring and assessment of plant resources, the authors discussed and proposed a number of requirementsfor the initial data, factors affecting the change in the area and the results of the assessment of plant resources. As anavailable free analogue of the method for correcting the spatial unevenness of the points of registration of species inSDMtoolbox (ArcGIS), we proposed the DBSCAN clustering method, which is implemented in the Python library sklearn.

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Patel, Sharad, Gurkeerat Singh, Samson Zarbiv, Kia Ghiassi und Jean-Sebastien Rachoin. „Mortality Prediction Using SaO2/FiO2 Ratio Based on eICU Database Analysis“. Critical Care Research and Practice 2021 (08.11.2021): 1–9. http://dx.doi.org/10.1155/2021/6672603.

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Purpose. PaO2 to FiO2 ratio (P/F) is used to assess the degree of hypoxemia adjusted for oxygen requirements. The Berlin definition of Acute Respiratory Distress Syndrome (ARDS) includes P/F as a diagnostic criterion. P/F is invasive and cost-prohibitive for resource-limited settings. SaO2/FiO2 (S/F) ratio has the advantages of being easy to calculate, noninvasive, continuous, cost-effective, and reliable, as well as lower infection exposure potential for staff, and avoids iatrogenic anemia. Previous work suggests that the SaO2/FiO2 ratio (S/F) correlates with P/F and can be used as a surrogate in ARDS. Quantitative correlation between S/F and P/F has been verified, but the data for the relative predictive ability for ICU mortality remains in question. We hypothesize that S/F is noninferior to P/F as a predictive feature for ICU mortality. Using a machine-learning approach, we hope to demonstrate the relative mortality predictive capacities of S/F and P/F. Methods. We extracted data from the eICU Collaborative Research Database. The features age, gender, SaO2, PaO2, FIO2, admission diagnosis, Apache IV, mechanical ventilation (MV), and ICU mortality were extracted. Mortality was the dependent variable for our prediction models. Exploratory data analysis was performed in Python. Missing data was imputed with Sklearn Iterative Imputer. Random assignment of all the encounters, 80% to the training (n = 26690) and 20% to testing (n = 6741), was stratified by positive and negative classes to ensure a balanced distribution. We scaled the data using the Sklearn Standard Scaler. Categorical values were encoded using Target Encoding. We used a gradient boosting decision tree algorithm variant called XGBoost as our model. Model hyperparameters were tuned using the Sklearn RandomizedSearchCV with tenfold cross-validation. We used AUC as our metric for model performance. Feature importance was assessed using SHAP, ELI5 (permutation importance), and a built-in XGBoost feature importance method. We constructed partial dependence plots to illustrate the relationship between mortality probability and S/F values. Results. The XGBoost hyperparameter optimized model had an AUC score of .85 on the test set. The hyperparameters selected to train the final models were as follows: colsample_bytree of 0.8, gamma of 1, max_depth of 3, subsample of 1, min_child_weight of 10, and scale_pos_weight of 3. The SHAP, ELI5, and XGBoost feature importance analysis demonstrates that the S/F ratio ranks as the strongest predictor for mortality amongst the physiologic variables. The partial dependence plots illustrate that mortality rises significantly above S/F values of 200. Conclusion. S/F was a stronger predictor of mortality than P/F based upon feature importance evaluation of our data. Our study is hypothesis-generating and a prospective evaluation is warranted. Take-Home Points. S/F ratio is a noninvasive continuous method of measuring hypoxemia as compared to P/F ratio. Our study shows that the S/F ratio is a better predictor of mortality than the more widely used P/F ratio to monitor and manage hypoxemia.

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Adugna, Tesfaye, Wenbo Xu und Jinlong Fan. „Comparison of Random Forest and Support Vector Machine Classifiers for Regional Land Cover Mapping Using Coarse Resolution FY-3C Images“. Remote Sensing 14, Nr. 3 (25.01.2022): 574. http://dx.doi.org/10.3390/rs14030574.

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The type of algorithm employed to classify remote sensing imageries plays a great role in affecting the accuracy. In recent decades, machine learning (ML) has received great attention due to its robustness in remote sensing image classification. In this regard, random forest (RF) and support vector machine (SVM) are two of the most widely used ML algorithms to generate land cover (LC) maps from satellite imageries. Although several comparisons have been conducted between these two algorithms, the findings are contradicting. Moreover, the comparisons were made on local-scale LC map generation either from high or medium resolution images using various software, but not Python. In this paper, we compared the performance of these two algorithms for large area LC mapping of parts of Africa using coarse resolution imageries in the Python platform by the employing Scikit-Learn (sklearn) library. We employed a big dataset, 297 metrics, comprised of systematically selected 9-month composite FegnYun-3C (FY-3C) satellite images with 1 km resolution. Several experiments were performed using a range of values to determine the best values for the two most important parameters of each classifier, the number of trees and the number of variables, for RF, and penalty value and gamma for SVM, and to obtain the best model of each algorithm. Our results showed that RF outperformed SVM yielding 0.86 (OA) and 0.83 (k), which are 1–2% and 3% higher than the best SVM model, respectively. In addition, RF performed better in mixed class classification; however, it performed almost the same when classifying relatively pure classes with distinct spectral variation, i.e., consisting of less mixed pixels. Furthermore, RF is more efficient in handling large input datasets where the SVM fails. Hence, RF is a more robust ML algorithm especially for heterogeneous large area mapping using coarse resolution images. Finally, default parameter values in the sklearn library work well for satellite image classification with minor/or no adjustment for these algorithms.

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Chen, Hongsong, Caixia Meng und Jingjiu Chen. „DDoS Attack Simulation and Machine Learning-Based Detection Approach in Internet of Things Experimental Environment“. International Journal of Information Security and Privacy 15, Nr. 3 (Juli 2021): 1–18. http://dx.doi.org/10.4018/ijisp.2021070101.

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Aiming at the problem of DDoS attack detection in internet of things (IoT) environment, statistical and machine-learning algorithms are proposed to model and analyze the network traffic of DDoS attack. Docker-based virtualization platform is designed and configured to collect IoT network traffic data. Then the packet-level, flow-level, and second-level network traffic datasets are generated, and the importance of features in different traffic datasets are sorted. By SKlearn and TensorFlow machine-learning software framework, different machine learning algorithms are researched and compared. In packet-level DDoS attack detection, KNN algorithm achieves the best results; the accuracy is 92.8%. In flow-level DDoS attack detection, the voting algorithm achieves the best results; the accuracy is 99.8%. In second-level DDoS attack detection, the RNN algorithm behaves best results; the accuracy is 97.1%. The DDoS attack detection method combined with statistical analysis and machine-learning can effectively detect large-scale DDoS attacks on the internet of things simulation experimental environment.

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Serpuhovitin, Dmitry. „Prospective directions of state support of the national innovation system of Russia“. SHS Web of Conferences 128 (2021): 04009. http://dx.doi.org/10.1051/shsconf/202112804009.

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The article presents an original methodology for selecting the most popular measures of state support of the national innovation system of a country with usage of numerical methods of clustering. The clustering methodology is based on a combination of indexes: Global Innovation Index, Gross Natural Income and Human Development Index. In the lists of countries and their corresponding clusters obtained as a result of empirical analysis, the most demanded measures of state support of the national innovation system were identified on the base of retrospective dynamics of Global Innovation Index indicators characterizing the state support of the national innovation system. For the obtained indicators of the Global Innovation Index, recommendations were given for the direction of development of the national innovation system of Russia. Classical clustering methods were used as analysis instruments: Density-based spatial clustering of applications with noise and K-Means, The Silhouette Coefficient implemented in the sklearn library of Python programming language was used as a quality metric.

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Liu, Sijia, Parikshit Ram, Deepak Vijaykeerthy, Djallel Bouneffouf, Gregory Bramble, Horst Samulowitz, Dakuo Wang, Andrew Conn und Alexander Gray. „An ADMM Based Framework for AutoML Pipeline Configuration“. Proceedings of the AAAI Conference on Artificial Intelligence 34, Nr. 04 (03.04.2020): 4892–99. http://dx.doi.org/10.1609/aaai.v34i04.5926.

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We study the AutoML problem of automatically configuring machine learning pipelines by jointly selecting algorithms and their appropriate hyper-parameters for all steps in supervised learning pipelines. This black-box (gradient-free) optimization with mixed integer & continuous variables is a challenging problem. We propose a novel AutoML scheme by leveraging the alternating direction method of multipliers (ADMM). The proposed framework is able to (i) decompose the optimization problem into easier sub-problems that have a reduced number of variables and circumvent the challenge of mixed variable categories, and (ii) incorporate black-box constraints alongside the black-box optimization objective. We empirically evaluate the flexibility (in utilizing existing AutoML techniques), effectiveness (against open source AutoML toolkits), and unique capability (of executing AutoML with practically motivated black-box constraints) of our proposed scheme on a collection of binary classification data sets from UCI ML & OpenML repositories. We observe that on an average our framework provides significant gains in comparison to other AutoML frameworks (Auto-sklearn & TPOT), highlighting the practical advantages of this framework.

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Mehr Quellen

Dissertationen zum Thema "Sklearn"

Jindra, Jakub. „Detekce stresu“. Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2019. http://www.nusl.cz/ntk/nusl-400971.

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Stress detection based on non-EEG physiological data can be useful for monitoring drivers, pilots, and also for monitoring of people in ordinary situation, where standard EEG monitoring is unsuitable. This work uses Non-EEG database freely available from Physionet. The database contains records of heart rate, saturation of blood oxygen, motion, a conductance of skin and temperature recorded for 3 type of stress alternated with relax state. Two final models were created in this thesis. First model for Binary classification stress/relax, second for classification of 4 different type of psychical state. Best results were reached using model created by decision tree algorithm with 8 features for binary classification and with 8 features for classification of 4 psychical state. Accuracy of final models is aproximately 95 % for binary model and 99 % for classification of 4 psychical state. All algorithms were implemented in Python.

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Alklid, Jonathan. „Time to Strike: Intelligent Detection of Receptive Clients : Predicting a Contractual Expiration using Time Series Forecasting“. Thesis, Linnéuniversitetet, Institutionen för datavetenskap och medieteknik (DM), 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:lnu:diva-106217.

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In recent years with the advances in Machine Learning and Artificial Intelligence, the demand for ever smarter automation solutions could seem insatiable. One such demand was identified by Fortnox AB, but undoubtedly shared by many other industries dealing with contractual services, who were looking for an intelligent solution capable of predicting the expiration date of a contractual period. As there was no clear evidence suggesting that Machine Learning models were capable of learning the patterns necessary to predict a contract's expiration, it was deemed desirable to determine subject feasibility while also investigating whether it would perform better than a commonplace rule-based solution, something that Fortnox had already investigated in the past. To do this, two different solutions capable of predicting a contractual expiration were implemented. The first one was a rule-based solution that was used as a measuring device, and the second was a Machine Learning-based solution that featured Tree Decision classifier as well as Neural Network models. The results suggest that Machine Learning models are indeed capable of learning and recognizing patterns relevant to the problem, and with an average accuracy generally being on the high end. Unfortunately, due to a lack of available data to use for testing and training, the results were too inconclusive to make a reliable assessment of overall accuracy beyond the learning capability. The conclusion of the study is that Machine Learning-based solutions show promising results, but with the caveat that the results should likely be seen as indicative of overall viability rather than representative of actual performance.

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Konečný, Antonín. „Využití umělé inteligence v technické diagnostice“. Master's thesis, Vysoké učení technické v Brně. Fakulta strojního inženýrství, 2021. http://www.nusl.cz/ntk/nusl-443221.

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The diploma thesis is focused on the use of artificial intelligence methods for evaluating the fault condition of machinery. The evaluated data are from a vibrodiagnostic model for simulation of static and dynamic unbalances. The machine learning methods are applied, specifically supervised learning. The thesis describes the Spyder software environment, its alternatives, and the Python programming language, in which the scripts are written. It contains an overview with a description of the libraries (Scikit-learn, SciPy, Pandas ...) and methods — K-Nearest Neighbors (KNN), Support Vector Machines (SVM), Decision Trees (DT) and Random Forests Classifiers (RF). The results of the classification are visualized in the confusion matrix for each method. The appendix includes written scripts for feature engineering, hyperparameter tuning, evaluation of learning success and classification with visualization of the result.

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Dimopoulos, Vasileios Verfasser], und Martin [Akademischer Betreuer] [Spitzer. „Langzeitergebnisse skleral-fixierter Hinterkammer-Intraokularlinsen-Implantation mit der knotenlosen modifizierten Z-Naht-Technik / Vasileios Dimopoulos ; Betreuer: Martin Spitzer“. Hamburg : Staats- und Universitätsbibliothek Hamburg, 2019. http://d-nb.info/1192442725/34.

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Dimopoulos, Vasileios [Verfasser], und Martin [Akademischer Betreuer] Spitzer. „Langzeitergebnisse skleral-fixierter Hinterkammer-Intraokularlinsen-Implantation mit der knotenlosen modifizierten Z-Naht-Technik / Vasileios Dimopoulos ; Betreuer: Martin Spitzer“. Hamburg : Staats- und Universitätsbibliothek Hamburg, 2019. http://d-nb.info/1192442725/34.

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Schulz, Udo [Verfasser]. „Photoablation an der Sklera mit dem 308-nm Excimer-Laser zur kontrollierten fistulierenden OP / Udo Schulz“. Berlin : Medizinische Fakultät Charité - Universitätsmedizin Berlin, 2014. http://d-nb.info/1046832654/34.

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Hoell, Bernadette [Verfasser]. „Herstellung eines Modells zur refraktiven Chirurgie der Sklera beim Menschen anhand von Nahttechnik beziehungsweise physikalischer Einwirkung / Bernadette Hoell“. Greifswald : Universitätsbibliothek Greifswald, 2013. http://d-nb.info/1031485074/34.

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Körber, Nicole. „Ein neuer therapeutischer Ansatz zur vorbeugenden Behandlung der pathologischen Myopie - Einfluss des skleralen Riboflavin/Blaulicht Cross-Linkings auf das Augenwachstum junger Kaninchen“. Doctoral thesis, Universitätsbibliothek Leipzig, 2017. http://nbn-resolving.de/urn:nbn:de:bsz:15-qucosa-220353.

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Die Arbeit umreißt das Krankheitsbild der Myopie (Kurzsichtigkeit) und deren unterschiedliche Ausprägungen, im Speziellen der progressiven und pathologischen Myopie. Hierbei wird ein Einblick in die Symptomatik, die anatomischen Ursachen und die heutigen medizinischen Interventionen gegeben. Hierdurch wird die Problematik einer zu „weichen“ Sklera (Lederhaut des Auges) und des damit einhergehenden fortschreitenden Augenwachstums deutlich. Im Zentrum der Arbeit steht ein neuer therapeutischer Ansatz zur vorbeugenden Behandlung der pathologischen Myopie; das Riboflavin/Blaulicht Cross-Linking der Sklera des Kaninchenauges. Dessen Wirkungsweise ermöglicht die biomechanische Versteifung von kollagenem Gewebe. Aus diesem Sachverhalt ergibt sich die Fragestellung der Arbeit: Ist das sklerale Riboflavin/Blaulicht Cross Linking geeignet das Augenwachstum im Tiermodell (junge Kaninchen) verträglich zu hemmen? Operationsbeeinflussende Parameter wie die Riboflavin-Durchdringungsdauer der Sklera und die sklerale Lichtdurchlässigkeit werden untersucht und für die Optimierung der Operationsmethode herangezogen und diskutiert. Zur Einschätzung des Versuchsansatzes werden die im Methodikteil dargelegten Anwendungen an adulten und jungen Kaninchen/Kaninchenaugen durchgeführt. In Tierversuchen wird die Schadensschwelle in Abhängigkeit der Blaulichtintensität, des Alters und der Pigmentierung untersucht, wobei histologische, immunhistochemische und elektronenmikroskopische Verfahren angewendet werden. Der inhibitorische Einfluss des Riboflavin/Blaulicht Cross-Linkings auf das Augenwachstum kann im Jungtiermodell durch verschiedene metrische Verfahren und MRT-Untersuchungen belegt werden.

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Cunietti, Stefano. „Identify popular hotspots through the analysis of movement patterns from social networks in rural areas: Case study of the Borbera Valley in North Italy“. Master's thesis, 2022. http://hdl.handle.net/10362/135045.

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Dissertation submitted in partial fulfilment of the requirements for the Degree of Master of Science in Geospatial Technologies
Social networks are now an increasingly used tool, but analysis possibilities have not yet been fully exploited. In particular, the extraction of information from users' profiles and their processing could give different information. In this work we will focus on the possibilities of using this information to analyse the patterns of rural spaces. The work will be carried out through a review of the available bibliography on the topic, the construction of an application, and the subsequent analysis of the data extracted through the application. Based on the findings, suggestions are made about the intensity of people within an area or the changes that have occurred in social activities.

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Santos, Rodrigo Daniel Garrilha. „Construction of machine learning models to predict pharmacology properties of molecules“. Master's thesis, 2019. http://hdl.handle.net/10451/41459.

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Tese de mestrado, Bioinformática e Biologia Computacional, Universidade de Lisboa, Faculdade de Ciências, 2019
O processo de desenvolvimento de drogas é altamente condicionado pela qualidade dos modelos com os quais se realiza a seleção dos primeiros compostos. Este trabalho procurou avaliar vários metodologias e descobrir qual a melhor abordagem para a construção de modelos de QSAR (relação quantitativa estrutura-propriedade/atividade) usando um conjunto grande de problemas. Usando um banco de dados de modelação de problemas desenvolvidos no projeto de pesquisa MIMED, 500 conjunto de dados foram extraídos de forma a serem usados para a construção de modelos QSAR. Quarenta metodologias diferentes, resultantes na combinação de quatro algoritmos de machine learning, dois fingerprints e cinco valores de bits, foram usados para fazer os modelos. Com o uso destas metodologias forma criados 18000 modelos, dos quais após análise surgiu a abordagem que melhor generaliza os modelos. Esta é a combinação dos seguintes parâmetros: random forest without maximum depth com Extended-Connectivity Fingerprints de raio 2 usando 2048 bits. Esta abordagem após validação construiu modelos com valores RMSE (Root Mean Square Error) de 0.17 e valores PVE (Proportion of Variance Explained) de 0.63. Por fim, procurou-se otimizar o processo de construção de modelos QSAR com a utilização da técnica de feature selection. Daqui resultou uma redução no conjunto de variáveis utilizadas pelo algoritmo resultando na construção de modelos mais robustos, mantendo o mesmo desempenho, RMSE de 0.17 e PVE de 0.59. Por fim a metodologia escolhida foi comparada com uma abordagem construída usando KNIME de forma a ter a perceção do fitness dos modelos construídos.
The drug development process is highly conditioned by the quality of the mathematical models with which the first compounds are selected. In this work, we tried to evaluate various methods and find out which are the best parameters for building Quantitative Structure-Activity Relationship (QSAR) models using a large set of problems. Using a database of modelling problems developed within the research project MIMED, 500 datasets were extracted to be used for building QSAR models. Forty different methodologies, resulting from the combination of four machine learning algorithms, two fingerprints and five bit values, were used to make the models. Using these methodologies, 18000 models were created, from which after analysis came the approach that best generalizes the models. This is the combination of the following parameters: random forest without maximum depth with Extended-Connectivity Fingerprints of radius 2 using 2048 bits. This approach after validation builds models with Root Mean Square Error (RMSE) values of 0.17 and Proportion of Variance Explained (PVE) values of 0.63. After the choice of the methodology, we tried to optimize the process of building QSAR models using the feature selection technique. This resulted in a reduction in the set of variables used by the algorithm resulting in the construction of more robust models, maintaining the same performance, RMSE of 0.17 and PVE of 0.59. Finally, the chosen methodology was compared with an approach built using KNIME to have the perception of the fitness of the built models.

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Bücher zum Thema "Sklearn"

Trappenberg, Thomas P. Fundamentals of Machine Learning. Oxford University Press, 2019. http://dx.doi.org/10.1093/oso/9780198828044.001.0001.

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Machine learning is exploding, both in research and for industrial applications. This book aims to be a brief introduction to this area given the importance of this topic in many disciplines, from sciences to engineering, and even for its broader impact on our society. This book tries to contribute with a style that keeps a balance between brevity of explanations, the rigor of mathematical arguments, and outlining principle ideas. At the same time, this book tries to give some comprehensive overview of a variety of methods to see their relation on specialization within this area. This includes some introduction to Bayesian approaches to modeling as well as deep learning. Writing small programs to apply machine learning techniques is made easy today by the availability of high-level programming systems. This book offers examples in Python with the machine learning libraries sklearn and Keras. The first four chapters concentrate largely on the practical side of applying machine learning techniques. The book then discusses more fundamental concepts and includes their formulation in a probabilistic context. This is followed by chapters on advanced models, that of recurrent neural networks and that of reinforcement learning. The book closes with a brief discussion on the impact of machine learning and AI on our society.

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Morgan, Chris. Milan Sklenar: Photographs. Behaviour Publishing, 2000.

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Zervopoulou, Tasoula. Ta Sklera Chronia Tes Phaies. Ekdotikos Organismos Livane, 2002.

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Sklenar, Zdenek. Sklenar: Poznamky o zivote a dile (Prace galerie). Krajska galerie v Hradci Kralove, 1989.

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Schaupp, Walter, und Wolfgang Kröll, Hrsg. Spannungsfeld Pflege. Nomos Verlagsgesellschaft mbH & Co. KG, 2020. http://dx.doi.org/10.5771/9783748909507.

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Although legally protected as a profession in its own right, nursing is still subject to tensions between doctors, patients and their relatives. This conference volume addresses images of nursing and related ethical principles on the one hand, and discusses the specific challenges of the daily nursing routine on the other. One of its important contributions is devoted to the inherent potential for violence in nursing care. Walter Schaupp was a professor of moral theology at the Faculty of Catholic Theology at Karl Franzens University in Graz, and Wolfgang Kröll was a professor of anaesthesiology and intensive care medicine at the Medical University of Graz. In their academic activities, both continue to deal with questions of medical ethics, among other things. With contributions by Christina Tax; Sabine Ruppert; Werner Hauser; Monique Weissenberger-Leduc; Hartmann Jörg Hohensinner, Christina Peyker; Angelika Feichtner; Andrea Schober; Renate Skledar, Wolfgang Kröll.

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Buchteile zum Thema "Sklearn"

Komer, Brent, James Bergstra und Chris Eliasmith. „Hyperopt-Sklearn“. In Automated Machine Learning, 97–111. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-05318-5_5.

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Feurer, Matthias, Aaron Klein, Katharina Eggensperger, Jost Tobias Springenberg, Manuel Blum und Frank Hutter. „Auto-sklearn: Efficient and Robust Automated Machine Learning“. In Automated Machine Learning, 113–34. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-05318-5_6.

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Fabris, Fabio, und Alex A. Freitas. „Analysing the Overfit of the Auto-sklearn Automated Machine Learning Tool“. In Machine Learning, Optimization, and Data Science, 508–20. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-37599-7_42.

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Angarita-Zapata, Juan S., Antonio D. Masegosa und Isaac Triguero. „General-Purpose Automated Machine Learning for Transportation: A Case Study of Auto-sklearn for Traffic Forecasting“. In Information Processing and Management of Uncertainty in Knowledge-Based Systems, 728–44. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-50143-3_57.

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Bergua, Antonio. „Sklera“. In Das menschliche Auge in Zahlen, 45–47. Berlin, Heidelberg: Springer Berlin Heidelberg, 2017. http://dx.doi.org/10.1007/978-3-662-47284-2_9.

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Grehn, Franz. „Lederhaut (Sklera)“. In Augenheilkunde, 189–95. Berlin, Heidelberg: Springer Berlin Heidelberg, 2019. http://dx.doi.org/10.1007/978-3-662-59154-3_9.

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Plange, Niklas. „Hornhaut, Sklera“. In Springer-Lehrbuch, 161–92. Berlin, Heidelberg: Springer Berlin Heidelberg, 2017. http://dx.doi.org/10.1007/978-3-662-52801-3_12.

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Grehn, Franz. „Lederhaut (Sklera)“. In Augenheilkunde, 143–48. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-11333-8_9.

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Grehn, Franz. „Lederhaut (Sklera)“. In Augenheilkunde, 155–60. Berlin, Heidelberg: Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/978-3-662-05918-0_8.

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Freyler, Heinrich. „Lederhaut = Sklera“. In Augenheilkunde, 170–76. Vienna: Springer Vienna, 1985. http://dx.doi.org/10.1007/978-3-7091-2264-8_9.

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Konferenzberichte zum Thema "Sklearn"

Komer, Brent, James Bergstra und Chris Eliasmith. „Hyperopt-Sklearn: Automatic Hyperparameter Configuration for Scikit-Learn“. In Python in Science Conference. SciPy, 2014. http://dx.doi.org/10.25080/majora-14bd3278-006.

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Zhu, Xuebin, Wangzhou Lin, Jiang Su und Zhenghong Yu. „Adaptive optimization modeling of district warehouse heating network based on Sklearn“. In 2021 Third International Conference on Electronics and Communication, Network and Computer Technology, herausgegeben von Md Khaja Mohiddin, Siting Chen und Said Fathy EL-Zoghdy. SPIE, 2022. http://dx.doi.org/10.1117/12.2628651.

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Tang, Linyun, Yue Li, Guo Yue und Deliang Li. „Blackhole -A flying paddle algorithm platform and its actual application to quantify the financial sklearn framework“. In 2021 2nd International Conference on Big Data Economy and Information Management (BDEIM). IEEE, 2021. http://dx.doi.org/10.1109/bdeim55082.2021.00062.

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Hu, Brian, Evan Gunnell und Yu Sun. „Smart Tab Predictor: A Chrome Extension to Assist Browser Task Management using Machine Learning and Data Analysis“. In 10th International Conference on Natural Language Processing (NLP 2021). Academy and Industry Research Collaboration Center (AIRCC), 2021. http://dx.doi.org/10.5121/csit.2021.112318.

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The outbreak of the Covid 19 pandemic has forced most schools and businesses to use digital learning and working. Many people have repetitive web browsing activities or encounter too many open tabs causing slowness in surfing the websites. This paper presents a tab predictor application, a Chrome browser extension that uses Machine Learning (ML) to predict the next URL to open based on the time and frequency of current and previous tabs. Nowadays, AI technology has expanded in people’s daily lives like self-driving cars and assistive-type robots. The AI ML module in our application is more basic and is built using Python and Scikit-Learn (Sklearn) machine learning libraries. We use JavaScript and Chrome API to collect the browser tab data and store it in a Firebase Cloud Firestore. The ML module then loads data from the Firebase, trains datasets to adapt to a user’s patterns, and predicts URLs to recommend opening new URLs. For Machine Learning, we compare three ML models and select the Random Forest Classifier. We also apply SMOTE (Synthetic Minority Oversampling Technique) to make the data-set more balanced, thus improving the prediction accuracy. Both manual tests and Cross Validation are performed to verify the predicted URLs. As a result, using the Smart Tab Predictor application will help students and business workers manage the web browser tabs more efficiently in their daily routine for online classes, online meetings, and other websites.

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Berichte der Organisationen zum Thema "Sklearn"

Qi, Fei, Zhaohui Xia, Gaoyang Tang, Hang Yang, Yu Song, Guangrui Qian, Xiong An, Chunhuan Lin und Guangming Shi. A Graph-based Evolutionary Algorithm for Automated Machine Learning. Web of Open Science, Dezember 2020. http://dx.doi.org/10.37686/ser.v1i2.77.

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As an emerging field, Automated Machine Learning (AutoML) aims to reduce or eliminate manual operations that require expertise in machine learning. In this paper, a graph-based architecture is employed to represent flexible combinations of ML models, which provides a large searching space compared to tree-based and stacking-based architectures. Based on this, an evolutionary algorithm is proposed to search for the best architecture, where the mutation and heredity operators are the key for architecture evolution. With Bayesian hyper-parameter optimization, the proposed approach can automate the workflow of machine learning. On the PMLB dataset, the proposed approach shows the state-of-the-art performance compared with TPOT, Autostacker, and auto-sklearn. Some of the optimized models are with complex structures which are difficult to obtain in manual design.

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