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نتیجه جستجو - مقاله رایگان داده کاوی 2018

تعداد مقالات یافته شده: 152
ردیف عنوان نوع
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
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