scholarly journals Вплив роздільної здатності цифрових моделей рельєфу на якість предикативної симуляції ґрунтового покриву

2018 ◽  
Vol 18 (1-2) ◽  
pp. 79-95
Author(s):  
V. R. Cherlіnka
Keyword(s):  
Neural Networks ◽  
Random Forests ◽  
Support Vector

Основною метою математичного експерименту було дослідження впливу роздільної здатності ЦМР на якісні характеристики симулятивних ґрунтових карт, які отримуються шляхом моделювання при використанні типового набору матеріалів, які можуть бути потенційно доступними пересічному ґрунтознавцю чи науковцю в сучасних українських реаліях. При цьому показано, що морфометричні параметри рельєфу та його деривати є надійним базисом предикативного моделювання просторового поширення ґрунтових відмін з достатньо високою точністю, а представлена методика має значну перспективу в науково-виробничих задачах. На основі кореляційного аналізу була здійснена оцінка тісноти зв’язку та ролі згаданих параметрів у мінливості ґрунтового покриву, що з залученням аналізу головних компонент дозволило обрати 9 базових предикторів моделі: абсолютні висоти, топографічний індекс вологості, кількість сонячної радіації на одиницю площі, крутизну схилів, поздовжню та максимальну кривизну топографічної поверхні, акумуляцію, довжину та відстань до водних потоків. Зроблено розширену оцінку якості симулятивних ґрунтових карт при різних значеннях роздільної здатності ЦМР. Встановлено відмінності у якості прогнозних ґрунтових карт при використанні 14 основних типів предикативних алгоритмів та рекомендовано найбільш придатні для такого роду задач моделі, зокрема Decіsіon Trees, Random Forests, та виокремлено деякі з них, які потенційно можуть показувати високі результати, зокрема Bugget Trees, K-Nearest Neіghbors, Support Vector Machіnes та Neural Networks.

Varіatіons іn the predіctіve effіcіency of soіl maps dependіng on the methods of constructіng traіnіng samples of predіcatіve algorіthms

2017 ◽  
Vol 28 (3-4) ◽  
pp. 55-71
Author(s):  
V. R. Cherlіnka
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Random Forests ◽  
Land Surface ◽  
Free Software ◽  
Support Vector ◽  
Data Set ◽  
Surface Parameters ◽  
The Neural Network ◽  
Land Surface Parameters

The maіn objectіve was to study the іnfluence of the traіnіng dataset on the qualіtatіve characterіstіcs of sіmulatіve soіl maps, whіch are obtaіned through sіmulatіon usіng a typіcal set of materіals that can be potentіally avaіlable for the soіl scіentіst іn modern Ukraіnіan realіtіes. Achіevement of thіs goal was achіeved by solvіng a number of the followіng tasks: a) dіgіtіzіng of cartographіc materіals; b) creatіng DEM wіth a resolutіon equal to 10 m; c) analysіs of dіgіtal elevatіon models and extractіon of land surface parameters; d) generatіon of traіnіng datasets accordіng to the descrіbed methodologіcal approaches; e) creatіon sіmulatіon models of soіl-cover іn R-statіstіc; g) analysіs of the obtaіned results and conclusіons regardіng the optіmal sіze of the traіnіng datasets for predіctіve modelіng of the soіl cover and іts duratіon. As an object was selected a fragment of the terrіtory of Ukraіne (4200×4200 m) wіthіn the lіmіts of Glybotsky dіstrіct of the Chernіvtsі regіon, confіned to the Prut-Sіret іnterfluve (North Bukovyna) wіth contrast geomorphologіcal condіtіons. Thіs area has dіfferent admіnіstratіve subordіnatіon and economіc use but іs covered wіth soіl cartographіc materіals only by 49.43 %. For data processіng were used іnstrumental possіbіlіtіes of free software: geo- rectіfіcatіons of maps materіal – GІS Quantum, dіgіtalіzatіon – Easy Trace, preparatіon of maps morphometrіc parameters – GRASS GІS and buіldіng sіmulatіve soіl maps – R, a language and envіronment for statіstіcal computіng. To create sіmulatіon models of soіl cover, a R-statіstіc scrіpt was wrіtten that іncludes a number of adaptatіons for solvіng set tasks and іmplements the dіfferent types of predіcatіve algorіthms such as: Multіnomіal Logіstіc Regressіon, Decіsіon Trees, Neural Networks, Random Forests, K-Nearest Neіghbors, Support Vector Machіnes and Bagged Trees. To assess the qualіty of the obtaіned models, the Cohen’s Kappa Іndex (?) was used whіch best represents the degree of complіance between the orіgіnal and the sіmulated data. As a benchmark, the usual medіal axes traіnіng dataset of was used. Other study optіons were: medіan-weіghted and randomіzed-weіghted samplіng. Thіs together wіth 7 predіcatіve algorіthms allowed to get 72 soіl sіmulatіons, the analysіs of whіch revealed quіte іnterestіng patterns. Models rankіng by іncreasіng the qualіty of the predіctіon by the kappa of the maіn data set shown, that the MLR algorіthm showed the worst results among others. Next іn ascendіng order are Neural Network, SVM, KNN, BGT, RF, DT. The last three algorіthms refer to the classіfіcatіon and theіr hіgh results іndіcate the greatest suіtabіlіty of such approaches іn sіmulatіon of soіl cover. The sample based on the weіghted medіan dіd not show strong advantages over others, as the results are quіte controversіal. Only іn the case of the neural network and the Bugget Trees the results of the medіan-weіghted sample predіctіon showed a better result vs a sіmple medіan sample and much worse than any varіants of randomіzed traіnіng data. Other algorіthms requіred a dіfferent number of randomіzed poіnts to cross the 90 % kappa: KNN – 25 %; BGT, RF and DT – 90 %. To achіeve 95 % kappa BGT algorіthm requіres 30% traіnіng poіnts of the total, RF – 25 % and DT – 20 %. Decіsіon Trees as a result turned out to be the most powerful algorіthm, whіch was able to sіmulate the dіstrіbutіon of soіl abnormalіtіes from kappa 97.13 % wіth 35 % saturatіon of the traіnіng sample wіth the orіgіnal data. Overall, DT shows a great dіfference between the approaches to selectіng traіnіng data: any medіan falls by 13 % іn front of a sіmple 5 % randomіzed-weіghted set of traіnіng cells and 22 % – about 35 % of the set.

scholarly journals Beyond the Hype: Deep Neural Networks Outperform Established Methods Using A ChEMBL Bioactivity Benchmark Set

2017 ◽  
Author(s):  
Eelke B. Lenselink ◽  
Niels ten Dijke ◽  
Brandon Bongers ◽  
George Papadatos ◽  
Herman W.T. van Vlijmen ◽  
...  
Keyword(s):  
Neural Networks ◽  
Logistic Regression ◽  
Support Vector Machines ◽  
Random Forests ◽  
Deep Neural Networks ◽  
Direct Result ◽  
Support Vector ◽  
Temporal Validation ◽  
Vector Machines ◽  
The Mean

AbstractThe increase of publicly available bioactivity data in recent years has fueled and catalyzed research in chemogenomics, data mining, and modeling approaches. As a direct result, over the past few years a multitude of different methods have been reported and evaluated, such as target fishing, nearest neighbor similarity-based methods, and Quantitative Structure Activity Relationship (QSAR)-based protocols. However, such studies are typically conducted on different datasets, using different validation strategies, and different metrics.In this study, different methods were compared using one single standardized dataset obtained from ChEMBL, which is made available to the public, using standardized metrics (BEDROC and Matthews Correlation Coefficient). Specifically, the performance of Naive Bayes, Random Forests, Support Vector Machines, Logistic Regression, and Deep Neural Networks was assessed using QSAR and proteochemometric (PCM) methods. All methods were validated using both a random split validation and a temporal validation, with the latter being a more realistic benchmark of expected prospective execution.Deep Neural Networks are the top performing classifiers, highlighting the added value of Deep Neural Networks over other more conventional methods. Moreover, the best method (‘DNN_PCM’) performed significantly better at almost one standard deviation higher than the mean performance. Furthermore, Multi task and PCM implementations were shown to improve performance over single task Deep Neural Networks. Conversely, target prediction performed almost two standard deviations under the mean performance. Random Forests, Support Vector Machines, and Logistic Regression performed around mean performance. Finally, using an ensemble of DNNs, alongside additional tuning, enhanced the relative performance by another 27% (compared with unoptimized DNN_PCM).Here, a standardized set to test and evaluate different machine learning algorithms in the context of multitask learning is offered by providing the data and the protocols.

scholarly journals Using A Low-Cost Sensor Array and Machine Learning Techniques to Detect Complex Pollutant Mixtures and Identify Likely Sources

Sensors ◽  
2019 ◽  
Vol 19 (17) ◽  
pp. 3723 ◽  
Author(s):  
Jacob Thorson ◽  
Ashley Collier-Oxandale ◽  
Michael Hannigan
Keyword(s):  
Neural Networks ◽  
Linear Regression ◽  
Multiple Linear Regression ◽  
Random Forests ◽  
Regression Models ◽  
Low Cost ◽  
Classification Model ◽  
Support Vector ◽  
Classification Models ◽  
Pollutant Mixtures

An array of low-cost sensors was assembled and tested in a chamber environment wherein several pollutant mixtures were generated. The four classes of sources that were simulated were mobile emissions, biomass burning, natural gas emissions, and gasoline vapors. A two-step regression and classification method was developed and applied to the sensor data from this array. We first applied regression models to estimate the concentrations of several compounds and then classification models trained to use those estimates to identify the presence of each of those sources. The regression models that were used included forms of multiple linear regression, random forests, Gaussian process regression, and neural networks. The regression models with human-interpretable outputs were investigated to understand the utility of each sensor signal. The classification models that were trained included logistic regression, random forests, support vector machines, and neural networks. The best combination of models was determined by maximizing the F1 score on ten-fold cross-validation data. The highest F1 score, as calculated on testing data, was 0.72 and was produced by the combination of a multiple linear regression model utilizing the full array of sensors and a random forest classification model.

An Efficient Hybrid Forecasting Approach for Wind Speed Time Series

2017 ◽  
Vol 7 (9) ◽  
pp. 13
Author(s):  
Bhargavi Munnaluri ◽  
K. Ganesh Reddy
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Wind Speed ◽  
Fossil Fuels ◽  
Renewable Energy Sources ◽  
Support Vector ◽  
Hybrid Forecasting ◽  
Artificial Neural ◽  
Future Utilization

Wind forecasting is one of the best efficient ways to deal with the challenges of wind power generation. Due to the depletion of fossil fuels renewable energy sources plays a major role for the generation of power. For future management and for future utilization of power, we need to predict the wind speed.  In this paper, an efficient hybrid forecasting approach with the combination of Support Vector Machine (SVM) and Artificial Neural Networks(ANN) are proposed to improve the quality of prediction of wind speed. Due to the different parameters of wind, it is difficult to find the accurate prediction value of the wind speed. The proposed hybrid model of forecasting is examined by taking the hourly wind speed of past years data by reducing the prediction error with the help of Mean Square Error by 0.019. The result obtained from the Artificial Neural Networks improves the forecasting quality.

scholarly journals The Budapest Amyloid Predictor and Its Applications

Biomolecules ◽  
2021 ◽  
Vol 11 (4) ◽  
pp. 500
Author(s):  
László Keresztes ◽  
Evelin Szögi ◽  
Bálint Varga ◽  
Viktor Farkas ◽  
András Perczel ◽  
...  
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Support Vector ◽  
User Needs ◽  
Computational Technique ◽  
Linear Support Vector Machine ◽  
Β Sheets ◽  
Artificial Neural ◽  
Amorphous Aggregation

The amyloid state of proteins is widely studied with relevance to neurology, biochemistry, and biotechnology. In contrast with nearly amorphous aggregation, the amyloid state has a well-defined structure, consisting of parallel and antiparallel β-sheets in a periodically repeated formation. The understanding of the amyloid state is growing with the development of novel molecular imaging tools, like cryogenic electron microscopy. Sequence-based amyloid predictors were developed, mainly using artificial neural networks (ANNs) as the underlying computational technique. From a good neural-network-based predictor, it is a very difficult task to identify the attributes of the input amino acid sequence, which imply the decision of the network. Here, we present a linear Support Vector Machine (SVM)-based predictor for hexapeptides with correctness higher than 84%, i.e., it is at least as good as the best published ANN-based tools. Unlike artificial neural networks, the decisions of the linear SVMs are much easier to analyze and, from a good predictor, we can infer rich biochemical knowledge. In the Budapest Amyloid Predictor webserver the user needs to input a hexapeptide, and the server outputs a prediction for the input plus the 6 × 19 = 114 distance-1 neighbors of the input hexapeptide.

414 Deep Neural Networks: A Survey Tool for Obstructive Sleep Apnea Prediction

SLEEP ◽  
2021 ◽  
Vol 44 (Supplement_2) ◽  
pp. A164-A164
Author(s):  
Pahnwat Taweesedt ◽  
JungYoon Kim ◽  
Jaehyun Park ◽  
Jangwoon Park ◽  
Munish Sharma ◽  
...  
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Obstructive Sleep Apnea ◽  
Sleep Apnea ◽  
Deep Neural Networks ◽  
Support Vector ◽  
Learning Models ◽  
Obstructive Sleep ◽  
Screening Questionnaires ◽  
Machine Learning Models

Abstract Introduction Obstructive sleep apnea (OSA) is a common sleep-related breathing disorder with an estimation of one billion people. Full-night polysomnography is considered the gold standard for OSA diagnosis. However, it is time-consuming, expensive and is not readily available in many parts of the world. Many screening questionnaires and scores have been proposed for OSA prediction with high sensitivity and low specificity. The present study is intended to develop models with various machine learning techniques to predict the severity of OSA by incorporating features from multiple questionnaires. Methods Subjects who underwent full-night polysomnography in Torr sleep center, Texas and completed 5 OSA screening questionnaires/scores were included. OSA was diagnosed by using Apnea-Hypopnea Index ≥ 5. We trained five different machine learning models including Deep Neural Networks with the scaled principal component analysis (DNN-PCA), Random Forest (RF), Adaptive Boosting classifier (ABC), and K-Nearest Neighbors classifier (KNC) and Support Vector Machine Classifier (SVMC). Training:Testing subject ratio of 65:35 was used. All features including demographic data, body measurement, snoring and sleepiness history were obtained from 5 OSA screening questionnaires/scores (STOP-BANG questionnaires, Berlin questionnaires, NoSAS score, NAMES score and No-Apnea score). Performance parametrics were used to compare between machine learning models. Results Of 180 subjects, 51.5 % of subjects were male with mean (SD) age of 53.6 (15.1). One hundred and nineteen subjects were diagnosed with OSA. Area Under the Receiver Operating Characteristic Curve (AUROC) of DNN-PCA, RF, ABC, KNC, SVMC, STOP-BANG questionnaire, Berlin questionnaire, NoSAS score, NAMES score, and No-Apnea score were 0.85, 0.68, 0.52, 0.74, 0.75, 0.61, 0.63, 0,61, 0.58 and 0,58 respectively. DNN-PCA showed the highest AUROC with sensitivity of 0.79, specificity of 0.67, positive-predictivity of 0.93, F1 score of 0.86, and accuracy of 0.77. Conclusion Our result showed that DNN-PCA outperforms OSA screening questionnaires, scores and other machine learning models. Support (if any):

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