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1 |
Open data mining for Taiwan’s dengue epidemic
داده کاوی گسترش اپیدمی تب دانگ تایوان-2018 By using a quantitative approach, this study examines the applicability of data mining technique to discover
knowledge from open data related to Taiwan’s dengue epidemic. We compare results when Google trend data are
included or excluded. Data sources are government open data, climate data, and Google trend data. Research
findings from analysis of 70,914 cases are obtained. Location and time (month) in open data show the highest
classification power followed by climate variables (temperature and humidity), whereas gender and age show
the lowest values. Both prediction accuracy and simplicity decrease when Google trends are considered (re
spectively 0.94 and 0.37, compared to 0.96 and 0.46). The article demonstrates the value of open data mining in
the context of public health care.
Keywords: Open data ، Data mining ، Dengue epidemic ، Google trend ، Simplicity |
مقاله انگلیسی |
2 |
Traffic noise and pavement distresses: Modelling and assessment of input parameters influence through data mining techniques
سر و صدای ترافیکی و ناراحتی های پیاده رو : مدل سازی و ارزیابی پارامترهای ورودی تاثیر از طریق تکنیک های داده کاوی -2018 Traffic noise affects greatly health and well-being of people, consequently the knowledge and control of the
factors affecting it is very important. In this study models to predict tyre-pavement noise acoustic and psy
choacoustic indicators based on type of pavement, texture, pavement distresses and speed were developed and
used to assess the importance of each factor. By applying data mining techniques, in particular artificial neural
networks and support vector machines, models with good predictive capacity of both acoustic and psychoa
coustic noise indicators were obtained, constituting a precious tool to reduce the tyre-pavement noise. Moreover,
the proposed models allowed for the assessment of the influence of the input parameters controlling noise such
as: type of pavement, texture, speed and pavement distresses for the first time. It was found that pavement
distresses and, as expected, speed influence strongly tyre-pavement noise. In this way it is clearly shown that
preventive maintenance of road pavements by authorities, which eliminates distresses, can have an important
effect on tyre-road noise, promoting the well-being of the populations.
Keywords: Tyre-pavement noise ، Acoustic and psychoacoustic indicators ، Pavement distresses ، Data mining ، Support vector machines ، Artificial neural networks |
مقاله انگلیسی |
3 |
Data mining to online galvanic current of zinc/copper
داده کاوی به جریان گالوانیزیکی آنلاین روی / مس-2018 The galvanic current of a zinc/copper atmospheric corrosion monitor exposed to outdoor conditions is analysed
to evaluate the corrosivity of the atmospheric environment. It is essential to develop effective and efficient
models for the monitored corrosion current in order to uncover the underlying mechanism of the corrosion
process. In this paper, we propose a new variable, the corrosion index, to quantify the corrosivity of the at
mospheric environment. The time series of galvanic current is treated statistically to predict the corrosion index
via a hidden Markov model. The prediction model performs favourably on the online corrosion data in terms of
efficiency and accuracy.
Keywords: Galvanic current ، Corrosion index ، Hidden Markov model ، Atmospheric corrosion |
مقاله انگلیسی |
4 |
Analysis of factors influencing tunnel deformation in loess deposits by data mining: A deformation prediction model
تحلیل و بررسی عوامل موثر بر تغییر شکل تونل در لس سپرده توسط داده کاوی : مدل پیش بینی تغییر شکل-2018 Due to the special properties of loess, the deformation of tunnels constructed in loess is generally large and easily
induced. To control deformation during construction, the degree of influence of multiple factors on tunnel de
formation is analyzed by data mining and a deformation prediction model is established, based on tunnels along
the Menghua railway of China. Both objective environment and manual operation are considered. The sur
rounding rock level, groundwater condition, burial depth, excavation method and support close time are se
lected as the main factors influencing tunnel deformation. The influence degree of each factor is calculated
through mining statistical data collected from the project. Finally, using influencing factors as evaluation in
dices, a Rough set-extension model for predicting loess tunnel deformation is established and tested. Results
obtained via the prediction model are in good agreement with field observations. The study quantifies the
influence degree of each selected factor on deformation of the loess tunnel, which in turn can help in de
formation control efforts. Moreover, the Rough set-extension model realizes a multi-criteria prediction of the
loess tunnels deformation and provides a practical guide for construction of similar projects.
Keywords: Loess tunnel ، Influencing factors ، Extension theory ، Rough set ، Deformation prediction |
مقاله انگلیسی |
5 |
Monitoring soil lead and zinc contents via combination of spectroscopy with extreme learning machine and other data mining methods
نظارت بر سرب خاک و محتوای روی از طریق ترکیبی از طیف سنجی با دستگاه یادگیری شدید و دیگر روش های داده کاوی-2018 In order to limit pollution risk and develop proper remediation strategies, soil quality has to be controlled by
rapid and sustainable monitoring measures. Visible and near-infrared reflectance spectroscopy (VisNIR) is an
attractive surrogate to time-consuming and costly classical soil assessment protocols. It highly depends on se
lecting appropriate data mining methods for regression analysis. In this study, performance of a state of the art
learning algorithm called extreme learning machine (ELM), was evaluated through comparing with the other
calibration methods proposed in the literature for predicting lead (Pb) and Zinc (Zn) concentrations. Solid
samples collected from a mine waste dump (n = 120) were scanned using a Fieldspec3 portable spectro
radiometer with a measurement range of (350–2500 nm) in a laboratory. Transformation of the reflectance
spectra to absorbance was followed by three pre-processing scenarios including Savitzky-Golay smoothing (SG),
first derivative (FD) and second derivative (SD). Partial Least Square Regression (PLSR), Support Vector Machine
(SVM) and neural networks with two learning algorithms models (back propagation and extreme learning
machine), were calibrated on spectral features selected by genetic algorithm, and then applied to predict soil
metal concentrations. The best prediction accuracy was obtained by FD-ELM method with R2p, RMSEp, con
cordance correlation coefficient and RPD values of 0.93, 63.01, 0.98 and 5.92 for Pb and 0.87, 167.90, 0.91 and
5.62 for Zn, respectively. Study of the prediction mechanism proved that element sorption by spectrally active
Fe-oxide and clay contents of the soil was the major mechanism by which the spectrally featureless Pb and Zn
ions can be predicted. The spatial patterns of predicted toxic elements showed that FD-ELM had the most si
milarity with those maps obtained by interpolating measured values. Over all, it is concluded that reflectance
spectroscopy combined with the ELM algorithm is a rapid, inexpensive and accurate tool for indirect evaluation
of Pb and Zn and mapping their spatial distribution in dumpsite soils of Sarcheshmeh copper mine.
Keywords: Mine waste dump ، Toxic elements ، Spectroscopy ، Binding mechanism ، Extreme learning machine ، Geostatistical interpolation |
مقاله انگلیسی |
6 |
A Novel Approach for the Prediction of Treadmill Test in Cardiology using Data Mining Algorithms implemented as a Mobile Application
یک رویکرد جدید برای پیش بینی تست تردمیل در قلب و عروق با استفاده از الگوریتم های داده کاوی به عنوان یک برنامه کاربردی موبایل-2018 Objective: To develop a mobile app called “TMT Predict” to predict the results of Treadmill Test(TMT),
using Data Mining techniques applied to a clinical dataset using minimal clinical attributes. To
prospectively test the results of the app in realtime to TMT and correlate with Coronary Angiogram
results.
Methods: In this study, instead of statistics, Data mining approach has been utilised for the prediction of
the results of TMT by analysing the clinical records of 1000 Cardiac patients. This research employed the
Decision Tree algorithm, a new modified version of K-Nearest Neighbour (KNN) algorithm, K-Sorting &
Searching (KSS). Furthermore, Curve Fitting Mathematical Technique was used to improve the Accuracy.
The system used six clinical attributes such as Age, Gender, BMI, Dyslipidemia, Diabetes mellitus and
Systemic hypertension. An Android app called “TMT Predict” was developed, wherein all three inputs
were combined and analysed. The final result is based on the dominating values of the three results. The
App was further tested prospectively in 300 patients to predict the results of TMT and correlate with
Coronary angiography.
Results: The accuracy of predicting the result of a TMT using Data Mining algorithms, Decision Tree and K
Sorting & Searching (KSS) were 73% and 78% respectively. The mathematical method Curve Fitting
predicted with 82% Accuracy. The accuracy of the mobile app “TMT Predict”, improved to 84%. Age-wise
analysis of the results show that the accuracy of the app dips when the age is more than 60 years
indicating that there may be other factors like retirement stress that may have to be included. This gives
scope for future research also. In the prospective study, the Positive and Negative predictive values of the
App for the results of TMT and Coronary Angiogram were found to be 40% and 83% for TMT and 52% and
80% for Coronary Angiogram. The Negative Predictive value of the app was high, indicating that it is a
good screening tool to rule out CAHD.
Conclusion: “TMT Predict” is a simple user-friendly android app, which uses six simple clinical attributes
to predict the results of TMT. The app has a high negative predictive value indicating that it is a useful tool
to rule out CAHD. The “TMT Predict” could be a future digital replacement for the manual TMTas an initial
screening tool to rule out CAHD.
Keywords: Cardiology ، Treadmill test (TMT) ، Pattern recognition ، K-Nearest neighbour (KNN) ، K-Sorting & Searching (KSS) ، Curve fitting |
مقاله انگلیسی |
7 |
Opportunistic mining of top-n high utility patterns
معدن فرصت طلب از بالا-N الگوهای مفید بالا-2018 Mining high utility patterns is an important data mining problem that is formulated as
finding patterns whose utilities are no less than a threshold. As the mining results are
very sensitive to such a threshold, it is difficult for users to specify an appropriate one.
An alternative formulation of the problem is to find the top-n high utility patterns. How
ever, the second formulation is more challenging because the corresponding threshold is
unknown in advance and the solution search space becomes even larger. When there are
very long patterns prior algorithms simply cannot work to mine top-n high utility patterns
even for very small n.
This paper proposes a novel algorithm for mining top-n high utility patterns that are
long. The proposed algorithm adopts an opportunistic pattern growth approach and pro
poses five opportunistic strategies for scalably maintaining shortlisted patterns, for effi
ciently computing utilities, and for estimating tight upper bounds to prune search space.
Extensive experiments show that the proposed algorithm is 1 to 3 orders of magnitude
more efficient than the state-of-the-art top-n high utility pattern mining algorithms, and
it is even up to 2 orders of magnitude faster than high utility pattern mining algorithms
that are tuned with an optimal threshold.
Keywords: Utility mining ، Pattern mining ، High utility patterns ، Frequent patterns ، Top-n interesting patterns |
مقاله انگلیسی |
8 |
Mining massive hierarchical data using a scalable probabilistic graphical model
استخراج داده های سلسله مراتبی عظیم با استفاده از یک احتمال احتمالی مقیاس پذیرمدل گرافیکی-2018 Probabilistic Graphical Models (PGM) are very useful in the fields of machine learning
and data mining. The crucial limitation of those models, however, is their scalability. The
Bayesian Network, which is one of the most common PGMs used in machine learning and
data mining, demonstrates this limitation when the training data consists of random vari
ables, in which each of them has a large set of possible values. In the big data era, one
could expect new extensions to the existing PGMs to handle the massive amount of data
produced these days by computers, sensors and other electronic devices. With hierarchi
cal data - data that is arranged in a treelike structure with several levels - one may see
hundreds of thousands or millions of values distributed over even just a small number of
levels. When modeling this kind of hierarchical data across large data sets, unrestricted
Bayesian Networks may become infeasible for representing the probability distributions.
In this paper, we introduce an extension to Bayesian Networks that can handle massive
sets of hierarchical data in a reasonable amount of time and space. The proposed model
achieves high precision and high recall when used as a multi-label classifier for the anno
tation of mass spectrometry data. On another data set of 1.5 billion search logs provided
by CareerBuilder.com, the model was able to predict latent semantic relationships among
search keywords with high accuracy.
Keywords: Probabilistic model ، Mass spectrometry annotation ، Big data ، Large scale machine learning ، Smantic discovery |
مقاله انگلیسی |
9 |
Mining massive hierarchical data using a scalable probabilistic graphical model
استخراج داده های سلسله مراتبی عظیم با استفاده از یک مدل گرافیکی احتمالی مقیاس پذیر-2018 Probabilistic Graphical Models (PGM) are very useful in the fields of machine learning
and data mining. The crucial limitation of those models, however, is their scalability. The
Bayesian Network, which is one of the most common PGMs used in machine learning and
data mining, demonstrates this limitation when the training data consists of random vari
ables, in which each of them has a large set of possible values. In the big data era, one
could expect new extensions to the existing PGMs to handle the massive amount of data
produced these days by computers, sensors and other electronic devices. With hierarchi
cal data - data that is arranged in a treelike structure with several levels - one may see
hundreds of thousands or millions of values distributed over even just a small number of
levels. When modeling this kind of hierarchical data across large data sets, unrestricted
Bayesian Networks may become infeasible for representing the probability distributions.
In this paper, we introduce an extension to Bayesian Networks that can handle massive
sets of hierarchical data in a reasonable amount of time and space. The proposed model
achieves high precision and high recall when used as a multi-label classifier for the anno
tation of mass spectrometry data. On another data set of 1.5 billion search logs provided
by CareerBuilder.com, the model was able to predict latent semantic relationships among
search keywords with high accuracy.
Keywords: Probabilistic model ، Mass spectrometry annotation ، Big data ، Large scale machine learning ، Smantic discovery |
مقاله انگلیسی |
10 |
Mining maximal frequent patterns in transactional databases and dynamic data streams: A spark-based approach
معادن حداکثر الگوهای مکرر در پایگاه داده های معاملاتی و جریان داده های پویا: رویکرد مبتنی بر جرقه-2018 Mining maximal frequent patterns (MFPs) in transactional databases (TDBs) and dynamic
data streams (DDSs) is substantially important for business intelligence. MFPs, as the smallest set of patterns, help to reveal customers’ purchase rules and market basket analysis (MBA). Although, numerous studies have been carried out in this area, most of them
extend the main-memory based Apriori or FP-growth algorithms. Therefore, these approaches are not only unscalable but also lack parallelism. Consequently, ever increasing big data sources requirements cannot be met. In addition, mining performance in some
existing approaches degrade drastically due to the presence of null transactions. We, therefore, proposed an efficient way to mining MFPs with Apache Spark to overcome these issues. For the faster computation and efficient utilization of memory, we utilized a prime
number based data transformation technique, in which values of individual transaction
have been preserved. After removing null transactions and infrequent items, the resulting
transformed dataset becomes denser compared to the original distributions. We tested our
proposed algorithms in both real static TDBs and DDSs. Experimental results and performance analysis show that our approach is efficient and scalable to large dataset sizes.
Keywords: Big data ، Transactional databases ، Dynamic data streams ، Null transactions ، Prime number theory ، Data mining ، Apache Spark ، Maximal frequent patterns |
مقاله انگلیسی |