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پیش بینی قیمت بیت کوین با استفاده از یادگیری ماشین: یک رویکر برای مهندسی ابعاد نمونه
سال انتشار: 2020 - تعداد صفحات فایل pdf انگلیسی: 13 - تعداد صفحات فایل doc فارسی: 32 پس از فراز و فرودهای قیمت های ارزهای رمزنگاری شده در سال های اخیر، بیت کوین به صورت فزاینده ای به عنوان یک دارایی برای سرمایه گذاری در نظر گرفته شده است. به خاطر ماهیت بسیار بی ثبات قیمت بیت کوین، لازم است تا پیش بینی های مناسبی صورت گیرد تا، بر اساس آن، بتوان در مورد سرمایه گذاری تصمیم گیری نمود. با وجودی که تحقیقات جاری برای پیش بینی دقیق تر قیمت بیت کوین از یادگیری ماشین استفاده کرده اند، تعداد اندکی از آنها به امکان استفاده از تکنیک های مختلف مدل سازی برای نمونه هایی با ساختار داده ای و ویژگی های بعدی مختلف توجه کرده اند. به منظور پیش بینی بهای بیت کوین در فرکانس های مختلف با استفاده از تکنیک های یادگیری ماشین، ابتدا قیمت بیت کوین را بر اساس قیمت روزانه و قیمت فرکانس بالا طبقه بندی می کنیم. مجموعه ای از ویژگی های با ابعاد بالا از جمله دارایی و شبکه، معاملات و بازار، توجه و قیمت لحظه ای طلا برای پیش بینی قیمت روزانه بیت کوین استفاده می شود، در حالی که ویژگی های اصلی تجارت که از تبادل ارز رمزنگاری شده حاصل شده اند، برای پیش بینی قیمت در فواصل 5 دقیقه ای استفاده می شوند. روشهای آماری شامل رگرسیون لجستیک و آنالیز افتراقی خطی برای پیش بینی قیمت روزانه بیت کوین با ویژگی های ابعاد بالا، به دقت 66٪ رسیده و از الگوریتم های یادگیری پیچیده تر ماشین پیشی می گیرند. در مقایسه با نتایج مبنا برای پیش بینی قیمت روزانه، با بالاترین دقت در روش های آماری و الگوریتم های یادگیری ماشینی، به ترتیب 66٪ و 3/65٪، به عملکرد بهتری دست پیدا می کنیم. مدلهای یادگیری ماشینی، شامل جنگل تصادفی ،XGBoost، آنالیز افتراقی درجه دو، ماشین بردار پشتیبان و حافظه کوتاه مدت بلند برای پیش بینی قیمت 5 دقیقه ای بیت کوین که دقت آنها به 67.2% رسیده است، از روشهای آماری بهتر هستند. بررسی ما در مورد پیش بینی قیمت بیت کوین را می توان مطالعه ای مقدماتی در مورد اهمیت ابعاد نمونه در تکنیک های یادگیری ماشین در نظر گرفت.
کلمات کلیدی: مهندسی ابعاد نمونه | اصل Occam’s Razor | پیش بینی قیمت بیت کوین | الگوریتم های یادگیری ماشین |
مقاله ترجمه شده |
2 |
پیش بینی قیمت بیت کوین با استفاده از یادگیری ماشین: یک رویکر برای مهندسی ابعاد نمونه
سال انتشار: 2020 - تعداد صفحات فایل pdf انگلیسی: 13 - تعداد صفحات فایل doc فارسی: 32 پس از فراز و فرودهای قیمت های ارزهای رمزنگاری شده در سال های اخیر، بیت کوین به صورت فزاینده ای به عنوان یک دارایی برای سرمایه گذاری در نظر گرفته شده است. به خاطر ماهیت بسیار بی ثبات قیمت بیت کوین، لازم است تا پیش بینی های مناسبی صورت گیرد تا، بر اساس آن، بتوان در مورد سرمایه گذاری تصمیم گیری نمود. با وجودی که تحقیقات جاری برای پیش بینی دقیق تر قیمت بیت کوین از یادگیری ماشین استفاده کرده اند، تعداد اندکی از آنها به امکان استفاده از تکنیک های مختلف مدل سازی برای نمونه هایی با ساختار داده ای و ویژگی های بعدی مختلف توجه کرده اند. به منظور پیش بینی بهای بیت کوین در فرکانس های مختلف با استفاده از تکنیک های یادگیری ماشین، ابتدا قیمت بیت کوین را بر اساس قیمت روزانه و قیمت فرکانس بالا طبقه بندی می کنیم. مجموعه ای از ویژگی های با ابعاد بالا از جمله دارایی و شبکه، معاملات و بازار، توجه و قیمت لحظه ای طلا برای پیش بینی قیمت روزانه بیت کوین استفاده می شود، در حالی که ویژگی های اصلی تجارت که از تبادل ارز رمزنگاری شده حاصل شده اند، برای پیش بینی قیمت در فواصل 5 دقیقه ای استفاده می شوند. روشهای آماری شامل رگرسیون لجستیک و آنالیز افتراقی خطی برای پیش بینی قیمت روزانه بیت کوین با ویژگی های ابعاد بالا، به دقت 66٪ رسیده و از الگوریتم های یادگیری پیچیده تر ماشین پیشی می گیرند. در مقایسه با نتایج مبنا برای پیش بینی قیمت روزانه، با بالاترین دقت در روش های آماری و الگوریتم های یادگیری ماشینی، به ترتیب 66٪ و 3/65٪، به عملکرد بهتری دست پیدا می کنیم. مدلهای یادگیری ماشینی، شامل جنگل تصادفی ،XGBoost، آنالیز افتراقی درجه دو، ماشین بردار پشتیبان و حافظه کوتاه مدت بلند برای پیش بینی قیمت 5 دقیقه ای بیت کوین که دقت آنها به 67.2% رسیده است، از روشهای آماری بهتر هستند. بررسی ما در مورد پیش بینی قیمت بیت کوین را می توان مطالعه ای مقدماتی در مورد اهمیت ابعاد نمونه در تکنیک های یادگیری ماشین در نظر گرفت.
کلمات کلیدی: مهندسی ابعاد نمونه | اصل Occam’s Razor | پیش بینی قیمت بیت کوین | الگوریتم های یادگیری ماشین |
مقاله ترجمه شده |
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Comparative analysis of surface water quality prediction performance and identification of key water parameters using different machine learning models based on big data
تجزیه و تحلیل مقایسه ای عملکرد پیش بینی کیفیت آب سطحی و شناسایی پارامترهای کلیدی آب با استفاده از مدل های مختلف یادگیری ماشین بر اساس داده های بزرگ-2020 The water quality prediction performance of machine learning models may be not only dependent on the
models, but also dependent on the parameters in data set chosen for training the learning models.
Moreover, the key water parameters should also be identified by the learning models, in order to further
reduce prediction costs and improve prediction efficiency. Here we endeavored for the first time to
compare the water quality prediction performance of 10 learning models (7 traditional and 3 ensemble
models) using big data (33,612 observations) from the major rivers and lakes in China from 2012 to 2018,
based on the precision, recall, F1-score, weighted F1-score, and explore the potential key water parameters
for future model prediction. Our results showed that the bigger data could improve the performance
of learning models in prediction of water quality. Compared to other 7 models, decision tree
(DT), random forest (RF) and deep cascade forest (DCF) trained by data sets of pH, DO, CODMn, and NH3
eN had significantly better performance in prediction of all 6 Levels of water quality recommended by
Chinese government. Moreover, two key water parameter sets (DO, CODMn, and NH3eN; CODMn, and
NH3eN) were identified and validated by DT, RF and DCF to be high specificities for perdition water
quality. Therefore, DT, RF and DCF with selected key water parameters could be prioritized for future
water quality monitoring and providing timely water quality warning. Keywords: Water quality prediction | Machine learning models | Ensemble methods | Deep cascade forest | The key water parameters |
مقاله انگلیسی |
4 |
Machine Learning Models can Detect Aneurysm Rupture and Identify Clinical Features Associated with Rupture
مدلهای یادگیری ماشینی می توانند پارگی آنوریسم را تشخیص دهند و ویژگیهای بالینی مرتبط با پارگی را شناسایی کنند-2019 - BACKGROUND: Machine learning (ML) has been
increasingly used in medicine and neurosurgery. We
sought to determine whether ML models can distinguish
ruptured from unruptured aneurysms and identify features
associated with rupture.
- METHODS: We performed a retrospective review of
patients with intracranial aneurysms detected on vascular
imaging at our institution between 2002 and 2018. The
dataset was used to train 3 ML models (random forest,
linear support vector machine [SVM], and radial basis
function kernel SVM). Relative contributions of individual
predictors were derived from the linear SVM model.
- RESULTS: Complete data were available for 845 aneurysms
in 615 patients. Ruptured aneurysms (n [ 309, 37%)
were larger (mean 6.51 mm vs. 5.73 mm; P [ 0.02) and
more likely to be in the posterior circulation (20% vs.
11%; P < 0.001) than unruptured aneurysms. Area under
the receiver operating curve was 0.77 for the linear SVM,
0.78 for the radial basis function kernel SVM models, and
0.81 for the random forest model. Aneurysm location and
size were the 2 features that contributed most significantly
to the model. Posterior communicating artery,
anterior communicating artery, and posterior inferior
cerebellar artery locations were most highly associated
with rupture, whereas paraclinoid and middle cerebral
artery locations had the strongest association with
unruptured status.
-CONCLUSIONS: ML models are capable of accurately
distinguishing ruptured from unruptured aneurysms and
identifying features associated with rupture. Consistent
with prior studies, location and size show the strongest
association with aneurysm rupture. Key words : Aneurysm | Aneurysm rupture | Artificial intelligence | Machine learning | Subarachnoid hemorrhage |
مقاله انگلیسی |
5 |
Machine learning models for solvent effects on electric double layer capacitance
مدلهای یادگیری ماشینی برای تأثیرات حلال بر خازن دو لایه برقی-2019 The role of solvent molecules in electrolytes for supercapacitors, representing a fertile ground for improving
the capacitive performance of supercapacitors, is complicated and has not been well understood.
Here, a combined method is applied to study the solvent effects on capacitive performance. To identify
the relative importance of each solvent variable to the capacitance, five machine learning (ML) models
were tested for a set of collected experimental data, including support vector regression (SVR), multilayer
perceptions (MLP), M5 model tree (M5P), M5 rule (M5R) and linear regression (LR). The performances of
these ML models are ranked as follows: M5P > M5R > MLP > SVR > LR. Moreover, the classical density
functional theory (CDFT) is introduced to yield more microscopic insights into the conclusion derived
from ML models. This method, by combining machine learning, experimental and molecular modeling,
could potentially be useful for predicting and enhancing the performance of electric double layer capacitors
(EDLCs). Keywords: Solvent effects | Electric double layer capacitance | Machine learning | Classical density functional theory |
مقاله انگلیسی |
6 |
Comprehensive analysis of machine learning models for prediction of sub-clinical mastitis: Deep Learning and Gradient-Boosted Trees outperform other models
تجزیه و تحلیل جامع مدلهای یادگیری ماشین برای پیش بینی ورم پستان تحت بالینی: یادگیری عمیق و رشد شیب درختان نسبت به سایر مدلها-2019 Sub-clinical bovine mastitis decreases milk quality and production. Moreover, sub-clinical
mastitis leads to the use of antibiotics with consequent increased risk of the emergence of
antibiotic-resistant bacteria. Therefore, early detection of infected cows is of great
importance. The Somatic Cell Count (SCC) day-test used for mastitis surveillance, gives data
that fluctuate widely between days, creating questions about its reliability and early
prediction power. The recent identification of risk parameters of sub-clinical mastitis based
on milking parameters by machine learning models is emerging as a promising new tool to
enhance early prediction of mastitis occurance. To develop the optimal approach for early
sub-clinical mastitis prediction, we implemented 2 steps: (1) Finding the best statistical
models to accurately link patterns of risk factors to sub-clinical mastitis, and (2) Extending
this application from the farms tested to new farms (method generalization). Herein, we
applied various machine learning-based prediction systems on a big milking dataset to
uncover the best predictive models of sub-clinical mastitis. Data from 364,249 milking
instances were collected by an electronic automated in-line monitoring system where milk
volume, lactose concentration, electrical conductivity (EC), protein concentration, peak flow
and milking time for each sample were measured. To provide a platform for the application
of the models developed to other farms, the Z transformation approach was employed.
Following this, various prediction systems [Deep Learning (DL), Naïve Bayes, Generalized
Liner Model, Logistic Regression, Decision Tree, Gradient-Boosted Tree (GBT) and Random
Forest] were applied to the non-transformed milking dataset and to a Z-standardized dataset.
ROC (Receiver Operating Characteristics Curve), AUC (Area Under The Curve), and high
accuracy demonstrated the high sensitivity of GBT and DL in detecting sub-clinical mastitis.
GBT was the most accurate model (accuracy of 84.9%) in prediction of sub-clinical bovine
mastitis. These data demonstrate how these models could be applied for prediction of subclinical
mastitis in multiple bovine herds regardless of the size and sampling techniques. |
مقاله انگلیسی |
7 |
Comprehensive analysis of machine learning models for prediction of sub-clinical mastitis: Deep Learning and Gradient-Boosted Trees outperform other models
تجزیه و تحلیل جامع مدلهای یادگیری ماشینی برای پیش بینی ماستیت تحت بالینی: یادگیری عمیق و رشد شیب درختان نسبت به سایر مدلها-2019 Sub-clinical bovine mastitis decreases milk quality and production. Moreover, sub-clinical mastitis leads to the
use of antibiotics with consequent increased risk of the emergence of antibiotic-resistant bacteria. Therefore,
early detection of infected cows is of great importance. The Somatic Cell Count (SCC) day-test used for mastitis
surveillance, gives data that fluctuate widely between days, creating questions about its reliability and early
prediction power. The recent identification of risk parameters of sub-clinical mastitis based on milking parameters
by machine learning models is emerging as a promising new tool to enhance early prediction of mastitis
occurrence. To develop the optimal approach for early sub-clinical mastitis prediction, we implemented 2 steps:
(1) Finding the best statistical models to accurately link patterns of risk factors to sub-clinical mastitis, and (2)
Extending this application from the farms tested to new farms (method generalization). Herein, we applied
various machine learning-based prediction systems on a big milking dataset to uncover the best predictive
models of sub-clinical mastitis. Data from 364,249 milking instances were collected by an electronic automated
in-line monitoring system where milk volume, lactose concentration, electrical conductivity (EC), protein concentration,
peak flow and milking time for each sample were measured. To provide a platform for the application
of the models developed to other farms, the Z transformation approach was employed. Following this, various
prediction systems [Deep Learning (DL), Naïve Bayes, Generalized Liner Model, Logistic Regression, Decision
Tree, Gradient-Boosted Tree (GBT) and Random Forest] were applied to the non-transformed milking dataset and
to a Z-standardized dataset. ROC (Receiver Operating Characteristics Curve), AUC (Area Under The Curve), and
high accuracy demonstrated the high sensitivity of GBT and DL in detecting sub-clinical mastitis. GBT was the
most accurate model (accuracy of 84.9%) in prediction of sub-clinical bovine mastitis. These data demonstrate
how these models could be applied for prediction of sub-clinical mastitis in multiple bovine herds regardless of
the size and sampling techniques. Keywords: Machine learning | Mastitis |
مقاله انگلیسی |
8 |
Evaluation of temperature-based machine learning and empirical models for predicting daily global solar radiation
ارزیابی یادگیری ماشین مبتنی بر دما و مدلهای تجربی برای پیش بینی تابش روزانه خورشیدی جهانی-2019 Accurate global solar radiation data are fundamental information for the allocation and design of solar energy
systems. The current study compared different machine learning and empirical models for global solar radiation
prediction only using air temperature as inputs. Four machine learning models, e.g., hybrid mind evolutionary
algorithm and artificial neural network model, original artificial neural network, random forests and wavelet
neural network, as well as four empirical temperature-based models (Hargreaves-Samani model, Bristow-
Campbell model, Jahani model, and Fan model) were applied for prediction of daily global solar radiation in
temperate continental regions of China. The results indicated the hybrid mind evolutionary algorithm and artificial
neural network model provided better estimations, compared with the existing machine learning and
empirical models. Thus, the temperature-based hybrid model is highly recommended to predict global solar
radiation in temperate continental regions of China when only air temperature data are available. Combining the
hybrid model with future air temperature forecasts, we can get the accurate information of future solar radiation,
which is of great importance to management and operation of solar energy systems. Keywords: Global solar radiation | Forecast | Empirical models | Machine learning models | Temperate continental regions |
مقاله انگلیسی |
9 |
Machine learning models accurately predict ozone exposure during wildfire events
دقت پیش بینی مدلهای یادگیری ماشین با قرار گرفتن در معرض ازن در حوادث آتش سوزی-2019 Epidemiologists use prediction models to downscale (i.e., interpolate) air pollution exposure where
monitoring data is insufficient. This study compares machine learning prediction models for groundlevel
ozone during wildfires, evaluating the predictive accuracy of ten algorithms on the daily 8-hour
maximum average ozone during a 2008 wildfire event in northern California. Models were evaluated
using a leave-one-location-out cross-validation (LOLO CV) procedure to account for the spatial and
temporal dependence of the data and produce more realistic estimates of prediction error. LOLO CV
avoids both the well-known overly optimistic bias of k-fold cross-validation on dependent data and the
conservative bias of evaluating prediction error over a coarser spatial resolution via leave-k-locations-out
CV. Gradient boosting was the most accurate of the ten machine learning algorithms with the lowest
LOLO CV estimated root mean square error (0.228) and the highest LOLO CV bR
2 (0.677). Random forest was the second best performing algorithm with an LOLO CV bR
2 of 0.661. The LOLO CV estimates of predictive accuracy were less optimistic than 10-fold CV estimates for all ten models. The difference in
estimated accuracy between the 10-fold CV and LOLO CV was greater for more flexible models like
gradient boosting and random forest. The order of estimated model accuracy depended on the choice of
evaluation metric, indicating that 10-fold CV and LOLO CV may select different models or sets of covariates
as optimal, which calls into question the reliability of 10-fold CV for model (or variable) selection.
These prediction models are designed for interpolating ozone exposure, and are not suited to inferring
the effect of wildfires on ozone or extrapolating to predict ozone in other spatial or temporal domains.
This is demonstrated by the inability of the best performing models to accurately predict ozone during
2007 southern California wildfires. Keywords: Air pollution | Exposure model | Machine learning | Ozone | Wildfire |
مقاله انگلیسی |
10 |
Design of machine learning models with domain experts for automated sensor selection for energy fault detection
طراحی مدلهای یادگیری ماشینی با کارشناسان دامنه برای انتخاب سنسور خودکار برای تشخیص خطای انرژی-2019 Data-driven techniques that extract insights from sensor data reduce the cost of improving system energy performance
through fault detection and system health monitoring. To lower cost barriers to widespread deployment,
a methodology is proposed that takes advantage of existing sensor data, encodes expert knowledge about
the application system to create ‘virtual sensors’, and applies statistical and mathematical methods to reduce the
time required for manual configurations. The approach combines sensor data points with encoded expert
knowledge that is generic to the application system but independent of a particular deployment, thereby reducing
the need to tailor to individual deployments. This paper not only presents a method that detects faults
from measured energy data, but also (1) describes an engagement method with experts in the energy system
domain to identify data, (2) integrates domain knowledge with the data, (3) automatically selects from among
the large pool of potential input data, and (4) uses machine learning to automatically build a data-driven fault
detection model. Demonstration on a commercial building chiller plant shows that only a small number of
virtual sensors is necessary for fault detection with high accuracy rates. This corresponds to the use of only five
out of 52 original sensor data points features. With as few as four features, classification F1 scores exceed 90% on
the training set and 80% on the testing set. The results are implementable and realizable using off-the-shelf tools.
The goal is to design with domain experts an energy monitoring system that can be configured once and then
widely deployed with little additional cost or effort Keywords: Machine learning | Domain knowledge | Time series | Fault detection | Anomaly detection | Energy savings | Energy efficiency |
مقاله انگلیسی |