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Кубегенова, Айгуль Даулетовна, and Казизат Такуадинович Искаков. "DATA MINING ТЕХНОЛОГИЯСЫНЫҢ КӨМЕГІМЕН, МЕДИЦИНАДА ИНТЕЛЛЕКТУАЛДЫ ТАЛДАУ ЖАСАУ ЖӘНЕ ӘДІСТЕРДІ ҚОЛДАНУ АСПЕКТІЛЕРІ." Bulletin of Toraighyrov University. Energetics series, no. 1.2021 (March 29, 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 (September 15, 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, and Rully Pramudita. "Mengenal Machine Learning Dengan Teknik Supervised Dan Unsupervised Learning Menggunakan Python." BINA INSANI ICT JOURNAL 7, no. 2 (December 28, 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., and 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, no. 5 (October 1, 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, and 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, no. 1 (September 14, 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, and Jean-Sebastien Rachoin. "Mortality Prediction Using SaO2/FiO2 Ratio Based on eICU Database Analysis." Critical Care Research and Practice 2021 (November 8, 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, and 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, no. 3 (January 25, 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, and 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, no. 3 (July 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, and Alexander Gray. "An ADMM Based Framework for AutoML Pipeline Configuration." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 04 (April 3, 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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ĞAYA, Şükrü, Gizem ŞAHİN, Ergin ĞAYA, Ayfer KOYUNOĞLU, Selami ŞAHİN, and Murat CANPOLAT. "Body Height Estimation In Irrigation Dams With Deep Learning Model." MAS Journal Of Applied Sciences 7, no. 11 (March 10, 2022): 241–48. http://dx.doi.org/10.52520/masjaps.226.
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Dams are one of the most important constructions for our country. The body height of the dams is one of the important factors in the efficiency of the dams. Today, the body height of dams is calculated by engineers. The aim of our study is to calculate the dam height with the deep learning model of artificial intelligence. Modeling was coded with python software. Numpy pandas libraries were used for the analysis of dam data. Matplotlib and seaborn were employed to visualize the data. Sklearn, tensorflow and keras libraries were used for deep learning modeling. Dam data are limited to irrigation dams in Turkey. For data analysis, the altitude, height, volume, area, temperature and precipitation characteristics were taken into consideration. As a result of our study, the dam body height estimation was done by teaching the dam data to the machine through multi-layer artificial neural networks of the deep learning model. The deviation in the body height estimations was found to be higher due to the insufficient data.
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Li, Yang, Yu Shen, Wentao Zhang, Jiawei Jiang, Bolin Ding, Yaliang Li, Jingren Zhou, et al. "VolcanoML." Proceedings of the VLDB Endowment 14, no. 11 (July 2021): 2167–76. http://dx.doi.org/10.14778/3476249.3476270.
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End-to-end AutoML has attracted intensive interests from both academia and industry, which automatically searches for ML pipelines in a space induced by feature engineering, algorithm/model selection, and hyper-parameter tuning. Existing AutoML systems, however, suffer from scalability issues when applying to application domains with large, high-dimensional search spaces. We present VOLCANOML, a scalable and extensible framework that facilitates systematic exploration of large AutoML search spaces. VOLCANOML introduces and implements basic building blocks that decompose a large search space into smaller ones, and allows users to utilize these building blocks to compose an execution plan for the AutoML problem at hand. VOLCANOML further supports a Volcano-style execution model - akin to the one supported by modern database systems - to execute the plan constructed. Our evaluation demonstrates that, not only does VOLCANOML raise the level of expressiveness for search space decomposition in AutoML, it also leads to actual findings of decomposition strategies that are significantly more efficient than the ones employed by state-of-the-art AutoML systems such as auto-sklearn.
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Prabha, T. "MACHINE LEARNING ALGORITHM USED TO BUILD A QSAR MODEL FOR PYRAZOLINE SCAFFOLD AS ANTI-TUBERCULAR AGENT." Journal of Medical pharmaceutical and allied sciences 10, no. 6 (November 15, 2021): 4024–30. http://dx.doi.org/10.22270/jmpas.v10i6.2562.
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Machine learning has become an essential tool for drug research to generate pertinent structural information to design drugs with higher biological activities. In this paper, we used python program language on pyrazoline scaffold, which is collected from diverse literature for the inhibition of Mycobacterium tuberculosis. Pyrazoline, a small molecule scaffold could block the biosynthesis of mycolic acids, resulting in mycobacteria death and leading to anti-tubercular drug discovery. The generated QSAR model afforded the ordinary least squares (OLS) regression as R2 = 0.380, F=4.909, and Q2 =0.303, reg. coef_ developed were of 0.00651593 (molecular weight), -0.0069445 (hydrogen bond acceptor), - 0.07576775 (hydrogen bond donor), -0.239021 (LogP) and reg. intercept of 3.10331589018553 developed through statsmodels.formula module. The support vector machine of the sklearn module generated the model score of 0.6294242262068762, the developed model was cross-validated by using the test set compounds and plotting the linear curve between the predicted and actual pMIC50 value. We have found that the values obtained using this script correlated well and may be useful in the design of a similar group of pyrazoline analogs as anti-tubercular agents.
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Li, Yu-Feng, Hai Wang, Tong Wei, and Wei-Wei Tu. "Towards Automated Semi-Supervised Learning." Proceedings of the AAAI Conference on Artificial Intelligence 33 (July 17, 2019): 4237–44. http://dx.doi.org/10.1609/aaai.v33i01.33014237.
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Automated Machine Learning (AutoML) aims to build an appropriate machine learning model for any unseen dataset automatically, i.e., without human intervention. Great efforts have been devoted on AutoML while they typically focus on supervised learning. In many applications, however, semisupervised learning (SSL) are widespread and current AutoML systems could not well address SSL problems. In this paper, we propose to present an automated learning system for SSL (AUTO-SSL). First, meta-learning with enhanced meta-features is employed to quickly suggest some instantiations of the SSL techniques which are likely to perform quite well. Second, a large margin separation method is proposed to fine-tune the hyperparameters and more importantly, alleviate performance deterioration. The basic idea is that, if a certain hyperparameter owns a high quality, its predictive results on unlabeled data may have a large margin separation. Extensive empirical results over 200 cases demonstrate that our proposal on one side achieves highly competitive or better performance compared to the state-of-the-art AutoML system AUTO-SKLEARN and classical SSL techniques, on the other side unlike classical SSL techniques which often significantly degenerate performance, our proposal seldom suffers from such deficiency.
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A, Prof Swethashree. "Speech Emotion Recognition." International Journal for Research in Applied Science and Engineering Technology 9, no. 8 (August 31, 2021): 2637–40. http://dx.doi.org/10.22214/ijraset.2021.37375.
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Abstract: Speech Emotion Recognition, abbreviated as SER, the act of trying to identify a person's feelings and relationships. Affected situations from speech. This is because the truth often reflects the basic feelings of tone and tone of voice. Emotional awareness is a fast-growing field of research in recent years. Unlike humans, machines do not have the power to comprehend and express emotions. But human communication with the computer can be improved by using automatic sensory recognition, accordingly reducing the need for human intervention. In this project, basic emotions such as peace, happiness, fear, disgust, etc. are analyzed signs of emotional expression. We use machine learning techniques such as Multilayer perceptron Classifier (MLP Classifier) which is used to separate information provided by groups to be divided equally. Coefficients of Mel-frequency cepstrum (MFCC), chroma and mel features are extracted from speech signals and used to train MLP differentiation. By accomplishing this purpose, we use python libraries such as Librosa, sklearn, pyaudio, numpy and audio file to analyze speech patterns and see the feeling. Keywords: Speech emotion recognition, mel cepstral coefficient, neural artificial network, multilayer perceptrons, mlp classifier, python.
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Bharambe, Prof Pallavi, Bhargav Bagul, Shreyas Dandekar, and Prerna Ingle. "Used Car Price Prediction using Different Machine Learning Algorithms." International Journal for Research in Applied Science and Engineering Technology 10, no. 4 (April 30, 2022): 773–78. http://dx.doi.org/10.22214/ijraset.2022.41300.
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Abstract: A car price prediction has been a high-interest research area, as it needed recognizable effort and knowledge of the field expert. This paper mainly focuses on working of three different kind regression algorithms which are used to predict price of a used car. In this project, We have Considered number of distinct attributes which are examined for the reliable and accurate prediction. To build a model for predicting the price of used cars we have used three different kinds of machine learning techniques which comes under supervised machine learning type of algorithm which are linear regression, lasso regression and ridge regression respectively. we have used Python libraries to design GUI for our project and some other machine learning related libraries like Numpy, Pandas, Sklearn etc. we have calculated and compared the accuracies of three machine learning algorithms. The accuracies for linear regression, lasso and Ridge regression were 83.65%, 87.09% and 84.00% respectively. The final main price is predicted according to lasso regression as it gives highest accuracy amongst three different algorithms. Keywords: car price prediction, machine learning, Regression techniques, linear regression, lasso regression, ridge regression
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Charamba, Lívia Vieira Carlini, Rayany Magali da Rocha Santana, Graziele Elisandra do Nascimento, Bruno Vieira Carlini Charamba, Maiara Celine de Moura, Luana Cassandra Breitenbach Barroso Coelho, Julierme Gomes Correia de Oliveira, Marta Maria Menezes Bezerra Duarte, and Daniella Carla Napoleão. "Application of the advanced oxidative process on the degradation of the green leaf and purple açaí food dyes with kinetic monitoring and artificial neural network modelling." Water Science and Technology 78, no. 5 (August 29, 2018): 1094–103. http://dx.doi.org/10.2166/wst.2018.391.
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Abstract The study evaluated the advanced oxidative processes concerning the degradation of green leaf and purple açaí dyes, as well as the prediction of data through artificial neural networks (ANNs). It was verified that percentage of degradation on the wavelengths (λ) of 215, 248, 523 and 627 nm was 5.95, 49.99, 98.17 and 95.99%, respectively, when UV/H2O2 action and UV-C radiation was applied. A non-linear kinetic model proposed by Chan and Chu presented a good fit to the experimental data, reaching an R2 value between 0.978 and 0.999, for the studied λ. Within the ANN simulations through Statistica 6.0, the multilayer perceptron (MLP) (3-9-4) presented a better fit to the experimental data. However, higher values of R² were obtained when utilizing the sklearn package with Python language and an MLP (4-5-4) model. Assays with Staphylococcus aureus and Staphylococcus pyogenes bacteria isolates were performed and it was verified that after employing the UV/H2O2 process, there was a decrease in the toxicity of the solution of dyes. In evaluating S. aureus toxicity, normal growth was observed. However, for S. pyogenes bacteria, it was found that when using the UV/H2O2 process, toxicity was evidenced at post-treatment solution concentrations of 100, 70 and 50%.
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Rakhimova, D., and A. Turganbayeva. "SEMANTIC ANALYSIS OF THE KAZAKH LANGUAGE BASED ON THE APPROACH OF NEURAL NETWORKS." PHYSICO-MATHEMATICAL SERIES 5, no. 333 (October 15, 2020): 68–75. http://dx.doi.org/10.32014/2020.2518-1726.84.
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This paper provides an overview of existing modern methods and software approaches for semantic analysis. Based on the research done, it was revealed that, for the semantic analysis of text resources, an approach based on machine learning is most used. This article presents the developed algorithm for the semantic analysis of the text in the Kazakh language. The paper also presents a software solution to this approach implemented in the Python programming language. The vector representation of words was obtained by machine learning based on the corpus, which is 1 million sentences in the Kazakh language. In the software implementation, well-known libraries such as gensim, matplotlib, sklearn, numpy, etc. were used. Based on a set of semantically related pairs of words, an ontology for a specific document is built, which is formed during the operation of a neural network. The paper presents the results of the experiments in the graphical form of a set of words. The novelty of the proposed approach lies in the identification of semantic close words in meaning in texts in the Kazakh language. This work contributes to solving problems in machine translation systems, information retrieval, as well as in analysis and processing systems in the Kazakh language.
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Nur, M. Adnan. "Perbandingan Levenshtein Distance Dan Jaro-Winkler Distance Untuk Koreksi Kata Dalam Preprocessing Analisis Sentimen Pengguna Twitter." Jurnal Fokus Elektroda : Energi Listrik, Telekomunikasi, Komputer, Elektronika dan Kendali) 6, no. 2 (June 30, 2021): 88. http://dx.doi.org/10.33772/jfe.v6i2.17751.
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Pada analisis sentimen pengguna twitter dibutuhkan tahap preprocessing sebelum mengklasifikasikan sentimen. Preprocessing digunakan untuk menyaring kata yang dianggap perlu untuk kebutuhan klasifikasi. Kesalahan penulisan pada tweet merupakan suatu permasalahan dalam tahap preprocessing yang tentunya mempengaruhi tingkat akurasi klasifikasi. Berdasarkan hal tersebut dibutuhkan proses tambahan pada preprocessing untuk melakukan koreksi kesalahan penulisan kata. Pada penelitian ini, penulis membandingkan kinerja metode levenshtein distance dan jaro-winkler distance dalam melakukan koreksi kesalahan penulisan kata. Penelitian ini diawali dengan melakukan survei literatur untuk mengidentifikasi masalah. Selanjutnya melakukan studi pustaka untuk menentukan objek dan parameter yang dibutuhkan dalam merancang dan memodelkan data serta perangkat lunak. Perangkat lunak dikembangkan menggunakan bahasa pemrograman python dengan beberapa library sastrawi, levenshtein, pyjarowinkler dan sklearn. Perangkat lunak ini dibangun untuk memudahkan dalam melihat kinerja metode yang digunakan. Pengujian dilakukan menggunakan confusion matrix dengan 10 fold cross validation. Pengujian melibatkan pengukuran kinerja levenshtein distance jika ditempatkan sebelum dan sesudah proses stemming. Begitupula untuk metode jaro-winkler distance juga ditempatkan sebelum dan sesudah proses stemming dalam preprocessing. Dari hasil pengujian diperoleh nilai accuracy, recall dan f1score dari metode levenshtein distance lebih baik dibandingkan jaro-winkler distance. Penerapan koreksi kata dengan metode levenshtein distance juga meningkatkan accuracy, recall dan f1score jika dibandingkan tanpa koreksi kata pada preprocessing. Penempatan koreksi kata pada tahap preprocessing dari hasil pengujian menunjukan posisi setelah proses stemming lebih baik dari penempatan koreksi kata sebelum proses stemming
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Padmanabhan, Meghana, Pengyu Yuan, Govind Chada, and Hien Van Nguyen. "Physician-Friendly Machine Learning: A Case Study with Cardiovascular Disease Risk Prediction." Journal of Clinical Medicine 8, no. 7 (July 18, 2019): 1050. http://dx.doi.org/10.3390/jcm8071050.
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Machine learning is often perceived as a sophisticated technology accessible only by highly trained experts. This prevents many physicians and biologists from using this tool in their research. The goal of this paper is to eliminate this out-dated perception. We argue that the recent development of auto machine learning techniques enables biomedical researchers to quickly build competitive machine learning classifiers without requiring in-depth knowledge about the underlying algorithms. We study the case of predicting the risk of cardiovascular diseases. To support our claim, we compare auto machine learning techniques against a graduate student using several important metrics, including the total amounts of time required for building machine learning models and the final classification accuracies on unseen test datasets. In particular, the graduate student manually builds multiple machine learning classifiers and tunes their parameters for one month using scikit-learn library, which is a popular machine learning library to obtain ones that perform best on two given, publicly available datasets. We run an auto machine learning library called auto-sklearn on the same datasets. Our experiments find that automatic machine learning takes 1 h to produce classifiers that perform better than the ones built by the graduate student in one month. More importantly, building this classifier only requires a few lines of standard code. Our findings are expected to change the way physicians see machine learning and encourage wide adoption of Artificial Intelligence (AI) techniques in clinical domains.
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Caplescu, Raluca Dana, Ana-Maria Panaite, Daniel Traian Pele, and Vasile Alecsandru Strat. "Will they repay their debt? Identification of borrowers likely to be charged off." Management & Marketing. Challenges for the Knowledge Society 15, no. 3 (September 1, 2020): 393–409. http://dx.doi.org/10.2478/mmcks-2020-0023.
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AbstractRecent increase in peer-to-peer lending prompted for development of models to separate good and bad clients to mitigate risks both for lenders and for the platforms. The rapidly increasing body of literature provides several comparisons between various models. Among the most frequently employed ones are logistic regression, Support Vector Machines, neural networks and decision tree-based models. Among them, logistic regression has proved to be a strong candidate both because its good performance and due to its high explainability. The present paper aims to compare four pairs of models (for imbalanced and under-sampled data) meant to predict charged off clients by optimizing F1 score. We found that, if the data is balanced, Logistic Regression, both simple and with Stochastic Gradient Descent, outperforms LightGBM and K-Nearest Neighbors in optimizing F1 score. We chose this metric as it provides balance between the interests of the lenders and those of the platform. Loan term, debt-to-income ratio and number of accounts were found to be important positively related predictors of risk of charge off. At the other end of the spectrum, by far the strongest impact on charge off probability is that of the FICO score. The final number of features retained by the two models differs very much, because, although both models use Lasso for feature selection, Stochastic Gradient Descent Logistic Regression uses a stronger regularization. The analysis was performed using Python (numpy, pandas, sklearn and imblearn).
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Kang, Chunyan, Dandan Wang, Xiuzhi Zhang, Lingxiao Wang, Fengxiang Wang, and Jie Chen. "Construction and Validation of a Lung Cancer Diagnostic Model Based on 6-Gene Methylation Frequency in Blood, Clinical Features, and Serum Tumor Markers." Computational and Mathematical Methods in Medicine 2021 (June 26, 2021): 1–7. http://dx.doi.org/10.1155/2021/9987067.
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Lung cancer has a high mortality rate. Promoting early diagnosis and screening of lung cancer is the most effective way to enhance the survival rate of lung cancer patients. Through computer technology, a comprehensive evaluation of genetic testing results and basic clinical information of lung cancer patients could effectively diagnose early lung cancer and indicate cancer risks. This study retrospectively collected 70 pairs of lung cancer tissue samples and normal human tissue samples. The methylation frequencies of 6 genes (FHIT, p16, MGMT, RASSF1A, APC, DAPK) in lung cancer patients, the basic clinical information, and tumor marker levels of these patients were analyzed. Then, the python package “sklearn” was employed to build a support vector machine (SVM) classifier which performed 10-fold cross-validation to construct diagnostic models that could identify lung cancer risk of suspected cases. Receiver operation characteristic (ROC) curves were drawn, and the performance of the combined diagnostic model based on several factors (clinical information, tumor marker level, and methylation frequency of 6 genes in blood) was shown to be better than that of models with only one pathological feature. The AUC value of the combined model was 0.963, and the sensitivity, specificity, and accuracy were 0.900, 0.971, and 0.936, respectively. The above results revealed that the diagnostic model based on these features was highly reliable, which could screen and diagnose suspected early lung cancer patients, contributing to increasing diagnosis rate and survival rate of lung cancer patients.
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Paldino, Gian Marco, Jacopo De Stefani, Fabrizio De Caro, and Gianluca Bontempi. "Does AutoML Outperform Naive Forecasting?" Engineering Proceedings 5, no. 1 (July 5, 2021): 36. http://dx.doi.org/10.3390/engproc2021005036.
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The availability of massive amounts of temporal data opens new perspectives of knowledge extraction and automated decision making for companies and practitioners. However, learning forecasting models from data requires a knowledgeable data science or machine learning (ML) background and expertise, which is not always available to end-users. This gap fosters a growing demand for frameworks automating the ML pipeline and ensuring broader access to the general public. Automatic machine learning (AutoML) provides solutions to build and validate machine learning pipelines minimizing the user intervention. Most of those pipelines have been validated in static supervised learning settings, while an extensive validation in time series prediction is still missing. This issue is particularly important in the forecasting community, where the relevance of machine learning approaches is still under debate. This paper assesses four existing AutoML frameworks (AutoGluon, H2O, TPOT, Auto-sklearn) on a number of forecasting challenges (univariate and multivariate, single-step and multi-step ahead) by benchmarking them against simple and conventional forecasting strategies (e.g., naive and exponential smoothing). The obtained results highlight that AutoML approaches are not yet mature enough to address generic forecasting tasks once compared with faster yet more basic statistical forecasters. In particular, the tested AutoML configurations, on average, do not significantly outperform a Naive estimator. Those results, yet preliminary, should not be interpreted as a rejection of AutoML solutions in forecasting but as an encouragement to a more rigorous validation of their limits and perspectives.
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MOCHURAD, LESIA, and ANDRII ILKIV. "A NOVEL METHOD OF MEDICAL CLASSIFICATION USING PARALLELIZATION ALGORITHMS." Computer systems and information technologies, no. 1 (April 14, 2022): 23–31. http://dx.doi.org/10.31891/csit-2022-1-3.
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Methods of machine learning in the medical field are the subject of significant ongoing research, which mainly focuses on modeling certain human actions, thought processes or disease recognition. Other applications include biomedical systems, which include genetics and DNA analysis. The purpose of this paper is the implementation of machine learning methods – Random Forest and Decision Tree, further parallelization of these algorithms to achieve greater accuracy of classification and reduce the time of training of these classifiers in the field of medical data processing, determining the presence of human cardiovascular disease. The paper conducts research using machine learning methods for data processing in medicine in order to improve the accuracy and execution time using parallelization algorithms. Classification is an important tool in today's world, where big data is used to make various decisions in government, economics, medicine, and so on. Researchers have access to vast amounts of data, and classification is one of the tools that helps them understand data and find certain patterns in it. The paper used a dataset consisting of records of 70000 patients and containing 12 attributes. Analysis and preliminary data preparation were performed. The Random Forest algorithm is parallelized using the sklearn library functional. The time required to train the model was reduced by 4.4 times when using 8 parallel streams, compared with sequential training. This algorithm is also parallelized based on CUDA. As a result, the time required to train the model was reduced by 83.4 times when using this technology on the GPU. The paper calculates the acceleration and efficiency coefficients, as well as provides a detailed comparison with a sequential algorithm.
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van Eeden, Wessel A., Chuan Luo, Albert M. van Hemert, Ingrid V. E. Carlier, Brenda W. Penninx, Klaas J. Wardenaar, Holger Hoos, and Erik J. Giltay. "Predicting the 9-year course of mood and anxiety disorders with automated machine learning: A comparison between auto-sklearn, naïve Bayes classifier, and traditional logistic regression." Psychiatry Research 299 (May 2021): 113823. http://dx.doi.org/10.1016/j.psychres.2021.113823.
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Syah, Herwin, and Arita Witanti. "ANALISIS SENTIMEN MASYARAKAT TERHADAP VAKSINASI COVID-19 PADA MEDIA SOSIAL TWITTER MENGGUNAKAN ALGORITMA SUPPORT VECTOR MACHINE (SVM)." Jurnal Sistem Informasi dan Informatika (Simika) 5, no. 1 (February 19, 2022): 59–67. http://dx.doi.org/10.47080/simika.v5i1.1411.
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This research was conducted to find information about the tendency of Indonesian people regarding the Covid-19 vaccination. The method that the author uses is by collecting data from Twitter social media using the API key provided by Twitter. The process of collecting data using a Python application with several libraries such as tweepy, pandas, numpy and nltk. After the data is crawled, then the data is cleaned with several data cleaning processes such as remove username, remove url, lower case, remove stopwords and lemmatize. Then the results are labeled with the textblob and sklearn libraries. then the data is analyzed using the Support Vector Machine (SVM) algorithm with the best comparison being 20 testing data and 80 training data or as many as 942 testing data and 3766 training data, the prediction results for testing data are f1 score 0.93, accuracy score 0.88, precision score 0.88 and a recall score of 0.99. The results showed that from 4,078 tweet data, there were 2,525 positive sentiments (43.0%), 771 negative sentiments (16.4%), and 1,912 neutral sentiments (40.6%). The results of 80% (3766) of training data and 20% (942) of test data obtained an accuracy score of 73.6%. From this study, it can be concluded that the tendency of Indonesian people when sampling data is taken is more accepting (positive responses) to government policies regarding the Covid-19 vaccination program. In the future, it is hoped that there will be a library that supports text data processing such as regional languages, because researchers found that during the data cleaning process there was a lot of word elimination, because many regional languages were used by the Indonesian people in writing on social media.
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Howard, Derek, Marta M. Maslej, Justin Lee, Jacob Ritchie, Geoffrey Woollard, and Leon French. "Transfer Learning for Risk Classification of Social Media Posts: Model Evaluation Study." Journal of Medical Internet Research 22, no. 5 (May 13, 2020): e15371. http://dx.doi.org/10.2196/15371.
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Background Mental illness affects a significant portion of the worldwide population. Online mental health forums can provide a supportive environment for those afflicted and also generate a large amount of data that can be mined to predict mental health states using machine learning methods. Objective This study aimed to benchmark multiple methods of text feature representation for social media posts and compare their downstream use with automated machine learning (AutoML) tools. We tested on datasets that contain posts labeled for perceived suicide risk or moderator attention in the context of self-harm. Specifically, we assessed the ability of the methods to prioritize posts that a moderator would identify for immediate response. Methods We used 1588 labeled posts from the Computational Linguistics and Clinical Psychology (CLPsych) 2017 shared task collected from the Reachout.com forum. Posts were represented using lexicon-based tools, including Valence Aware Dictionary and sEntiment Reasoner, Empath, and Linguistic Inquiry and Word Count, and also using pretrained artificial neural network models, including DeepMoji, Universal Sentence Encoder, and Generative Pretrained Transformer-1 (GPT-1). We used Tree-based Optimization Tool and Auto-Sklearn as AutoML tools to generate classifiers to triage the posts. Results The top-performing system used features derived from the GPT-1 model, which was fine-tuned on over 150,000 unlabeled posts from Reachout.com. Our top system had a macroaveraged F1 score of 0.572, providing a new state-of-the-art result on the CLPsych 2017 task. This was achieved without additional information from metadata or preceding posts. Error analyses revealed that this top system often misses expressions of hopelessness. In addition, we have presented visualizations that aid in the understanding of the learned classifiers. Conclusions In this study, we found that transfer learning is an effective strategy for predicting risk with relatively little labeled data and noted that fine-tuning of pretrained language models provides further gains when large amounts of unlabeled text are available.
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Angarita-Zapata, Juan S., Gina Maestre-Gongora, and Jenny Fajardo Calderín. "A Bibliometric Analysis and Benchmark of Machine Learning and AutoML in Crash Severity Prediction: The Case Study of Three Colombian Cities." Sensors 21, no. 24 (December 16, 2021): 8401. http://dx.doi.org/10.3390/s21248401.
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Traffic accidents are of worldwide concern, as they are one of the leading causes of death globally. One policy designed to cope with them is the design and deployment of road safety systems. These aim to predict crashes based on historical records, provided by new Internet of Things (IoT) technologies, to enhance traffic flow management and promote safer roads. Increasing data availability has helped machine learning (ML) to address the prediction of crashes and their severity. The literature reports numerous contributions regarding survey papers, experimental comparisons of various techniques, and the design of new methods at the point where crash severity prediction (CSP) and ML converge. Despite such progress, and as far as we know, there are no comprehensive research articles that theoretically and practically approach the model selection problem (MSP) in CSP. Thus, this paper introduces a bibliometric analysis and experimental benchmark of ML and automated machine learning (AutoML) as a suitable approach to automatically address the MSP in CSP. Firstly, 2318 bibliographic references were consulted to identify relevant authors, trending topics, keywords evolution, and the most common ML methods used in related-case studies, which revealed an opportunity for the use AutoML in the transportation field. Then, we compared AutoML (AutoGluon, Auto-sklearn, TPOT) and ML (CatBoost, Decision Tree, Extra Trees, Gradient Boosting, Gaussian Naive Bayes, Light Gradient Boosting Machine, Random Forest) methods in three case studies using open data portals belonging to the cities of Medellín, Bogotá, and Bucaramanga in Colombia. Our experimentation reveals that AutoGluon and CatBoost are competitive and robust ML approaches to deal with various CSP problems. In addition, we concluded that general-purpose AutoML effectively supports the MSP in CSP without developing domain-focused AutoML methods for this supervised learning problem. Finally, based on the results obtained, we introduce challenges and research opportunities that the community should explore to enhance the contributions that ML and AutoML can bring to CSP and other transportation areas.
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Li, Kai-Yun, Niall G. Burnside, Raul Sampaio de Lima, Miguel Villoslada Peciña, Karli Sepp, Victor Henrique Cabral Pinheiro, Bruno Rucy Carneiro Alves de Lima, Ming-Der Yang, Ants Vain, and Kalev Sepp. "An Automated Machine Learning Framework in Unmanned Aircraft Systems: New Insights into Agricultural Management Practices Recognition Approaches." Remote Sensing 13, no. 16 (August 12, 2021): 3190. http://dx.doi.org/10.3390/rs13163190.
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The recent trend of automated machine learning (AutoML) has been driving further significant technological innovation in the application of artificial intelligence from its automated algorithm selection and hyperparameter optimization of the deployable pipeline model for unraveling substance problems. However, a current knowledge gap lies in the integration of AutoML technology and unmanned aircraft systems (UAS) within image-based data classification tasks. Therefore, we employed a state-of-the-art (SOTA) and completely open-source AutoML framework, Auto-sklearn, which was constructed based on one of the most widely used ML systems: Scikit-learn. It was combined with two novel AutoML visualization tools to focus particularly on the recognition and adoption of UAS-derived multispectral vegetation indices (VI) data across a diverse range of agricultural management practices (AMP). These include soil tillage methods (STM), cultivation methods (CM), and manure application (MA), and are under the four-crop combination fields (i.e., red clover-grass mixture, spring wheat, pea-oat mixture, and spring barley). Furthermore, they have currently not been efficiently examined and accessible parameters in UAS applications are absent for them. We conducted the comparison of AutoML performance using three other common machine learning classifiers, namely Random Forest (RF), support vector machine (SVM), and artificial neural network (ANN). The results showed AutoML achieved the highest overall classification accuracy numbers after 1200 s of calculation. RF yielded the second-best classification accuracy, and SVM and ANN were revealed to be less capable among some of the given datasets. Regarding the classification of AMPs, the best recognized period for data capture occurred in the crop vegetative growth stage (in May). The results demonstrated that CM yielded the best performance in terms of classification, followed by MA and STM. Our framework presents new insights into plant–environment interactions with capable classification capabilities. It further illustrated the automatic system would become an important tool in furthering the understanding for future sustainable smart farming and field-based crop phenotyping research across a diverse range of agricultural environmental assessment and management applications.
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Pradhan, Biswajeet, Maher Ibrahim Sameen, Husam A. H. Al-Najjar, Daichao Sheng, Abdullah M. Alamri, and Hyuck-Jin Park. "A Meta-Learning Approach of Optimisation for Spatial Prediction of Landslides." Remote Sensing 13, no. 22 (November 10, 2021): 4521. http://dx.doi.org/10.3390/rs13224521.
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Optimisation plays a key role in the application of machine learning in the spatial prediction of landslides. The common practice in optimising landslide prediction models is to search for optimal/suboptimal hyperparameter values in a number of predetermined hyperparameter configurations based on an objective function, i.e., k-fold cross-validation accuracy. However, the overhead of hyperparameter optimisation can be prohibitive, especially for computationally expensive algorithms. This paper introduces an optimisation approach based on meta-learning for the spatial prediction of landslides. The proposed approach is tested in a dense tropical forested area of Cameron Highlands, Malaysia. Instead of optimising prediction models with a large number of hyperparameter configurations, the proposed approach begins with promising configurations based on several basic and statistical meta-features. The proposed meta-learning approach was tested based on Bayesian optimisation as a hyperparameter tuning algorithm and random forest (RF) as a prediction model. The spatial database was established with a total of 63 historical landslides and 15 conditioning factors. Three RF models were constructed based on (1) default parameters as suggested by the sklearn library, (2) parameters suggested by the Bayesian optimisation (BO), and (3) parameters suggested by the proposed meta-learning approach (BO-ML). Based on five-fold cross-validation accuracy, the Bayesian method achieved the best performance for both the training (0.810) and test (0.802) datasets. The meta-learning approach achieved slightly lower accuracies than the Bayesian method for the training (0.769) and test (0.800) datasets. Similarly, based on F1-score and area under the receiving operating characteristic curves (AUROC), the models with optimised parameters either by the Bayesian or meta-learning methods produced more accurate landslide susceptibility assessment than the model with the default parameters. In the present approach, instead of learning from scratch, the meta-learning would begin with hyperparameter configurations optimal for the most similar previous datasets, which can be considerably helpful and time-saving for landslide modelings.
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Verburgt, C., K. A. Dunn, J. P. Bielawski, A. R. Otley, M. B. Heyman, W. Sunseri, D. Shouval, et al. "P711 Stool microbiome communities predict remission in pediatric Crohn’s disease patients even after start of treatment." Journal of Crohn's and Colitis 16, Supplement_1 (January 1, 2022): i608. http://dx.doi.org/10.1093/ecco-jcc/jjab232.832.
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Abstract Background Early relapse in children with Crohn’s Disease (CD) after reaching remission is associated with a more severe disease course that significantly impairs quality of life. We have previously shown that a Bayesian approach predicted clinical course in children with CD following the first year after diagnosis with high accuracy when ensuring samples were truly treatment-naïve. Here, we aimed to assess the impact on the accuracy when taking a broader timeframe of stool collection to baseline, in order to facilitate use in clinical trials and eventually daily practice. Methods We selected de novo paediatric CD patients with PCDAI>10 from the RISK cohort with baseline stool samples within 14 days after start of induction treatment. We assessed if they sustained remission at 12 months (PCDAI≤10). Using QIIME2 sequences were demultiplexed, joined, and denoised (deblur) to obtain amplicon sequence variants (ASVs). The ASVs were classified (classify-sklearn) using a pre-trained SILVA database. We used hierarchical Bayesian model for microbial community structure (BioMiCo), previously trained on treatment-naïve stool samples to predict treatment outcomes at 6 months according to baseline gut microbiome differences. Results Patient metadata and 16S rRNA amplicon data were available from 197 stool samples of newly diagnosed paediatric CD patients as part of the RISK cohort. Previous analysis of 42 truly treatment-naïve samples lead to prediction of samples in patients maintaining remission without early treatment escalation and those that did not in 81% and 75% (AUC=0.79). In this analysis, we selected 13 samples of children with PCDAI that were taken within 14 days after start of induction therapy. PCDAI varied from 15–47.5 at baseline. Therapy regimens started within the first 14 days were EEN, 5ASA, corticosteroids, immunomodulators and antibiotics. The Bayesian model predicted 12-month outcome of patients that maintained remission with a positive predictive value of 75% and negative predictive value of 60% (AUC 0.76). Conclusion Using treatment-naïve faecal samples only, a Bayesian approach predicted clinical course in treatment-naïve children with CD over the first year after diagnosis with high accuracy. When taking a broader timeframe of stool collection after start of treatment, the accuracy of the model decreased only slightly. Further exploration of microbiome signatures and potential use in practice should therefore emphasize the importance of treatment naivety of samples, but not necessarily rule them out for prediction of treatment outcome.
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Schäfer, C., and G. Keyßer. "Syndrom der blauen Skleren." Zeitschrift für Rheumatologie 67, no. 3 (April 13, 2008): 237–38. http://dx.doi.org/10.1007/s00393-008-0275-8.
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VATANSEVER, Mustafa, Erdem DİNÇ, and Ayça SARI. "Scleral Rupture in a Patient Who Has Senile Scleral Plaque After Blunt Trauma: Case Report." Turkiye Klinikleri Journal of Ophthalmology 26, no. 2 (2017): 128–31. http://dx.doi.org/10.5336/ophthal.2015-47313.
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Rosalina, Clara, Erwan Sugiatno, and Haryo Mustiko. "Pembuatan Obturator Mata pada Pasien dengan Kehilangan Mata Akibat Cacat Bawaan." Majalah Kedokteran Gigi Indonesia 17, no. 1 (June 30, 2010): 35. http://dx.doi.org/10.22146/majkedgiind.16017.
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Kasus kehilangan mata pada pasien dapat menimbulkan masalah fungsi dan estetik. Salah satu cara yang dapat dilakukan untuk memperbaiki masalah estetik adalah dengan membuatkan protesa mata kepada pasien tersebut. Tujuan pembuatan obturator mata pada pasien yang kehilangan mata adalah untuk membantu pasien dalam memperbaiki estetik. Pasien wan ita usia 35 tahun datang ke klinik Prostodonsia RSGM FKG UGM dengan kondisi kehilangan mata sebelah kanan yang merupakan cacat bawaan. Pemeriksaan wajah menunjukkan muka asimetris. Pada mata kanan tampak adanya cheloid yang timbul setelah operasi pengangkatan bola mata. Perawatan dilakukan dengan pembuatan protesa mata non fabricated dengan tahap-tahap: pencetakan mata dengan sendok cetak mata perorangan dan pengisian hasil cetakan terdiri dari dua bagian, yang pertama diisi dengan gips keras sampai bagian terlebar dari cetakan dasar soket dan dibuat tiga retensi sebagai kunci, kedua sampai menutupi seluruh hasil cetakan. Pembuatan model malam sklera, mencoba pola malam sklera dan packing model malam sklera. Oef/asking dan polishing untuk membuat sklera akrilik, mencoba sklera akrilik dan penentuan lokasi diameter iris, melukis iris dan pupil, penyelesaian protesa mata, packing sklera dan iris, def/asking dan polishing untuk membuat protesa mata serta insersi protesa mata. Kontrol setelah 2 minggu menunjukkan hasil yang baik, tidak ada keluhan rasa sakit, tidak ada peradangan, volume dan frekuensi air mata menjadi berkurang jumlah dan frekuensinya.
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Ressel, Lena, Anna Lipke, Katrin Elsharkawi‐Welt, Thorsten Peters, Anca Sindrilaru, and Karin Scharffetter‐Kochanek. "Hyperpigmentierung der Haut und Skleren." JDDG: Journal der Deutschen Dermatologischen Gesellschaft 19, no. 3 (March 2021): 460–64. http://dx.doi.org/10.1111/ddg.14420_g.
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ÇİFTCİ, Süleyman, Umut DAĞ, Eyüp DOĞAN, and Salim AKDEMİR. "Implant Motility in Two-Scleral Flaps Evisceration." Turkiye Klinikleri Journal of Ophthalmology 24, no. 4 (2015): 260–64. http://dx.doi.org/10.5336/ophthal.2015-46295.
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Tuncer, İbrahim, Süleyman Kaynak, Eyyüp Karahan, and Mehmet Özgür Zengin. "Yırtıklı Retina Dekolmanında Ameliyat Mikroskopu Altında Yapılan Skleral Çökertme Cerrahisi ile Ameliyat Mikroskopu Kullanılmadan Yapılan Skleral Çökertme Cerrahisinin Karşılaştırılması." Türk Oftalmoloji Dergisi 44, no. 2 (May 5, 2014): 175–78. http://dx.doi.org/10.4274/tjo.24392.
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Szurman, P., and K. Gekeler. "Sekundäre Intraokularlinsenimplantation von Sklera-nahtfixierten Intraokularlinsen." Der Ophthalmologe 111, no. 3 (February 20, 2014): 217–23. http://dx.doi.org/10.1007/s00347-013-2847-5.
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Herzog, Michael, and Jürgen Sengebusch. "Ich sehe … was?" Deutsche Heilpraktiker-Zeitschrift 13, no. 06 (September 2018): 47–48. http://dx.doi.org/10.1055/a-0638-5701.
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SummaryGelbfärbung der Sklera, Hautzeichen und Deformierung der Hand: diese Symptome können verschiedene Ursachen haben. Treten sie jedoch gemeinsam auf, steht ein bestimmtes Krankheitsbild im Verdacht.
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DEMİRKILINÇ BİLER, Elif, Suzan GÜVEN YILMAZ, Kutay ANDAÇ, and Halil ATEŞ. "Our Late Post-operative Results of Small Flap Trabeculectomy." Turkiye Klinikleri Journal of Ophthalmology 24, no. 1 (2015): 6–11. http://dx.doi.org/10.5336/ophthal.2014-39792.
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Einecke, Dirk. "Hilfe für Sklero- dermie-Patienten mit Lungenfibrose." MMW - Fortschritte der Medizin 162, no. 13 (July 2020): 70. http://dx.doi.org/10.1007/s15006-020-0702-6.
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Klett, A., and R. Guthoff. "Deckung von Orbitaimplantaten mit muskelgestielter autologer Sklera." Der Ophthalmologe 100, no. 6 (June 2003): 449–52. http://dx.doi.org/10.1007/s00347-003-0836-9.
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Wittmann, G. "54/w mit Dyspnoe, gelben Skleren und dunklem Urin." Der Internist 62, S4 (August 26, 2021): 482–85. http://dx.doi.org/10.1007/s00108-021-01104-y.
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Ali, Omer M., Sahil S. Nalawade, Yin Xi, Ben Wagner, Alexander Mazal, Shane Ahlers, Syed M. Rizvi, et al. "A Radiomic Machine Learning Model to Predict Treatment Response to Methotrexate and Survival Outcomes in Primary Central Nervous System Lymphoma (PCNSL)." Blood 136, Supplement 1 (November 5, 2020): 29–30. http://dx.doi.org/10.1182/blood-2020-141941.
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Introduction: Primary CNS lymphomas (PCNSL) are heterogeneous, aggressive, extra-nodal non-Hodgkin lymphomas limited to the neuraxis. Published response rates to high-dose methotrexate (MTX) based induction regimens for PCNSL range from 35-78%. However, >50% of patients relapse and have a median survival of 2 months without additional treatment. Our ability to prognosticate outcomes is limited to clinical models like the International Extranodal Lymphoma Study Group (IELSG) score and Memorial Sloan-Kettering Cancer Center (MSKCC) classifier. There is an urgent need to develop improved biologic and radiologic predictive models for PCNSL to facilitate therapeutic advances. We hypothesize that a machine learning model using advanced magnetic resonance imaging (MRI) tumor characteristics will improve the accuracy of clinical models to predict response to MTX and survival outcomes. Methods: Data from patients with PCNSL treated at UT Southwestern and Parkland Health and Hospital System hospitals from 2008-2020 (n=95) were collected. An analytical dataset of 61 patients was selected based on the availability of T1 postcontrast (T1c) and T2w FLAIR MR images. A subset of 47 patients was used to evaluate MTX treatment response. Expert neuroradiologists drew regions of interest (ROIs) on the multiparametric MR images including whole tumor (consisting of edema + enhancing tumor + necrosis), enhancing tumor and necrosis (Figure 1). Response to methotrexate-based induction was defined per the International Primary CNS Lymphoma Collaborative Group (IPCG) criteria. For overall- and progression-free survival (OS and PFS) analysis, short (≤1 year) and long-term (>1 year) survivor groups were defined. A support vector machine (SVM) network was used for predicting treatment response to MTX and for predicting the OS groups. A Multinomial Naive Bayes (MNB) network was used for predicting the PFS groups. PyRadiomics package was used to extract 106 texture-based features from the combination of each MR image and tumor ROI. A total of 642 features were extracted from the imaging parameters. Clinical features including age, race, performance status, MSKCC class, IELSG score, histology, delay from 1st MRI to start of treatment, induction and consolidation treatments used were included in the analysis. Feature reduction methodology based on the feature importance derived from the gradient boost model was applied to reduce the number of features. 17 features (imaging = 14, clinical = 3) were used for predicting OS/PFS and 7 features (imaging = 5, clinical = 2) were used for predicting treatment response to MTX. Networks utilizing only clinical features were analyzed for comparison. The sklearn package in python was used for the machine learning analysis. 5-Fold cross validation was performed to generalize the network performance. Results: Baseline wclinical characteristics of the study population is shown in Table 1. Table 2 lists the accuracy, F1 score, sensitivity, specificity, positive predictive value, negative predictive value and area under the curve (AUC) values averaged for the 5-fold cross validation. The SVM network achieved a mean testing accuracy of 81.1 ± 12.3% for predicting the treatment response to MTX-based induction. Sensitivity, specificity and AUC values were 90.5 ± 13.1%, 63.3 ± 22.1% and 0.81 ± 0.14 respectively. The SVM and the MNB network achieved mean testing accuracies of 80.3 ± 11.4% and 83.3 ± 11.8% for predicting the long and short survival groups in OS and PFS respectively. Sensitivity, specificity and AUC values for the SVM and MNB networks were 79.3 ± 6.5%, 80.5 ± 16.5% and 0.86 ± 0.12 and 85.3 ± 12.9%, 81.9 ± 11.8% and 0.86 ± 0.13 respectively. The accuracy values for predicting treatment response to MTX, OS and PFS using only the clinical features were 61.6 ± 9.2%, 59.1 ± 16.4% and 62.1 ± 17.5% respectively. Conclusion: This machine learning model boosted the accuracy (≥20%) over currently validated clinical models alone in predicting response to methotrexate-based therapies and survival outcomes in PCNSL. The current analysis is limited by the small sample size, and we plan to statistically test this model across a larger dataset and report results at the meeting. Our preliminary results suggest that machine learning based radiomic analysis may predict biologic aggressiveness in PCNSL and has the potential to be integrated in clinical predictive tools and design of clinical trials. Disclosures Awan: Blueprint medicines: Consultancy; Celgene: Consultancy; Sunesis: Consultancy; Karyopharm: Consultancy; MEI Pharma: Consultancy; Astrazeneca: Consultancy; Genentech: Consultancy; Dava Oncology: Consultancy; Kite Pharma: Consultancy; Gilead Sciences: Consultancy; Pharmacyclics: Consultancy; Janssen: Consultancy; Abbvie: Consultancy. Desai:Boston Scientific: Consultancy, Other: Trial Finding.
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Yusmaidi, Yusmaidi, Rakhmi Rafie, and Annisa Permatasari. "Karakteristik Pasien Ikterus Obstruktif Et Causa Batu Saluran Empedu." Jurnal Ilmiah Kesehatan Sandi Husada 11, no. 1 (June 30, 2020): 328–33. http://dx.doi.org/10.35816/jiskh.v11i1.277.
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Ikterus Obstruktif merupakan ikterus yang disebabkan oleh obstruksi empedu. Batu saluran empedu merupakan salah satu penyebab ikterus obstruktif yang paling sering.Tujuan penelitian ini adalah mengetahui karakteristik pasien ikterus obstruktif et causa batu saluran empedu di RSUD Dr.H. Abdul Moeloek Bandar Lampung Tahun 2017-2018. Jenis penelitian ini adalah deskriptif retrospektif menggunakan data sekunder dari rekam medik. Sampel penelitian ini menggunakan teknik Purposive Sampling. Sampel penelitian ini sebanyak 35 pasien. Kasus terbanyak menyerang pada kelompok umur 46-65 tahun sebanyak 18 pasien (51,4%)dan 22 pasien (62,9%) pada perempuan. Keluhan utama dan pemeriksaan fisik terbanyak pada sklera dan kulit ikterik sebanyak 100%. Dari hasil pemeriksaan laboratorium dan usg didapatkan sebanyak 51,4% leukosit normal dan gambaran pelebaran saluran empedu intrahepatik sebanyak 85,7%. Penatalaksanaan dilakukan tindakan operatif sebanyak 57,1%. Karakteristik terbanyak ditemukan pada kelompok umur 46-65 tahun, jenis kelamin perempuan dengan keluhan utama dan pemeriksaan fisik yaitu sklera dan kulit ikterik, leukosit normal, gambaran usg terdapat pelebaran saluran empedu intrahepatik, dan dilakukan tindakan operatif.
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Reich, Mike. "Scleres of Alcyonacea (Anthozoa: Octocorallia) from a Silurian Geschiebe of northern Germany." Neues Jahrbuch für Geologie und Paläontologie - Monatshefte 2002, no. 9 (August 14, 2002): 551–61. http://dx.doi.org/10.1127/njgpm/2002/2002/551.
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Bandlitz, Stefan. "Current status of the topography of the sclera: a literature review." Optometry & Contact Lenses 2, no. 3 (March 30, 2022): 81–90. http://dx.doi.org/10.54352/dozv.qaox4941.
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Purpose. Although a major part of the anterior surface of the eyeball is formed by the sclera, there is little confirmed data on topography of the sclera compared to the cornea. The goal of this paper was to provide an update of relevant literature published on anatomy and topography of the anterior sclera. Methods. A systematic literature search was conducted in PubMed using the key words “scleral” and “topography.” 66 of the 310 papers dealt with the topic and were used for this present review. In addition, 5 articles from the Germanlanguage non-peer-reviewed literature were included. Results. Several studies have demonstrated the utility of modern measurement and examination techniques, such as optical coherence tomography (OCT), Scheimpflug imaging, or Fourier-based profilometry in the assessment of scleral parameters. These allow a more comprehensive understanding of the structure and shape of the anterior sclera, simplified contact lens selection, prediction of lens fit, and assessment of changes in scleral topography. The shape of the sclera can be influenced by factors such as age, refraction, accommodation, convergence, thickness, stiffness, modulus of elasticity, intraocular pressure, keratoconus and contact lens wear. Conclusion. A comprehensive understanding of the biometric properties and topography of the sclera of the anterior segment of the eye is an essential component for both the design and fitting of scleral as well as soft contact lenses. Keywords Sclera, topography, anatomy, Scheimpflug, profilometry, optical coherence tomography
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Ghasemi Nikmanesh, Forusan. "Transparenz um jeden Preis: Phänomen Auge." Deutsche Heilpraktiker-Zeitschrift 13, no. 01 (February 2018): 12–17. http://dx.doi.org/10.1055/s-0043-124114.
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SummaryDas Auge entsteht im Rahmen der Embryonalentwicklung als vorgeschobener Teil des Gehirns aus dem Ekto- und Mesoderm. Sklera, Gefäß- und Netzhaut sowie feinjustierte Druck- und Perfusionsverhältnisse sorgen für eine äußerst stabile Struktur und Funktion – und sind doch angreifbar. Nah- und Fernbeziehungen zu anderen Strukturen erklären, warum bei vielen neurologischen, intestinalen und immunologischen Erkrankungen das Auge mitbeteiligt ist.
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Müllhaupt. "Ikterus – Differentialdiagnostische Überlegungen." Praxis 93, no. 21 (May 1, 2004): 898–903. http://dx.doi.org/10.1024/0369-8394.93.21.898.
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Mannigfaltige Ursachen können zu einer Hyperbilirubinämie führen, die sich klinisch mit einer Gelbverfärbung der Skleren, Haut und Schleimhäute manifestiert. Neben einer sorgfältigen Anamnese und Untersuchung ist die Bestimmung der Leberwerte der entscheidende Schritt zur Abklärung eines Ikterus. Bei einer isolierten Hyperbilirubinämie ist nach Ausschluss einer Hämolyse in erster Linie an einen Morbus Gilbert-Meulengracht zu denken. Sind die Leberwerte erhöht, kann anhand des Verhältnisses von Transaminasen zu Cholestaseenzymen zwischen einem hepatozellulären oder cholestatischen Ikterus unterschieden werden und die weiteren Abklärungen entsprechend geplant werden.
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Küçükevcilioğlu, Murat, and Yusuf Uysal. "Diğer Yönlerden Normal Olan Bir Gözde Çift Konjenital Ön Skleral Stafilom." Türk Oftalmoloji Dergisi 43, no. 3 (June 5, 2013): 211–12. http://dx.doi.org/10.4274/tjo.43.42650.
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