با سلام خدمت کاربران در صورتی که با خطای سیستم پرداخت بانکی مواجه شدید از طریق کارت به کارت (6037997535328901 بانک ملی ناصر خنجری ) مقاله خود را دریافت کنید (تا مشکل رفع گردد).
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Rapid discrimination of Salvia miltiorrhiza according to their geographical regions by laser induced breakdown spectroscopy (LIBS) and particle swarm optimization-kernel extreme learning machine (PSO-KELM)
تبعیض سریع miltiorrhiza مریم گلی با توجه به مناطق جغرافیایی خود را با طیف سنجی شکست ناشی از لیزر (LIBS) و یادگیری ماشین افراطی بهینه سازی ازدحام ذرات (PSO-KELM)-2020 Laser-induced breakdown spectroscopy (LIBS) coupled with particle swarm optimization-kernel extreme learning
machine (PSO-KELM) method was developed for classification and identification of six types Salvia miltiorrhiza
samples in different regions. The spectral data of 15 Salvia miltiorrhiza samples were collected by LIBS spectrometer.
An unsupervised classification model based on principal components analysis (PCA) was employed first
for the classification of Salvia miltiorrhiza in different regions. The results showed that only Salvia miltiorrhiza
samples from Gansu and Sichuan Province can be easily distinguished, and the samples in other regions present a
bigger challenge in classification based on PCA. A supervised classification model based on KELM was then
developed for the classification of Salvia miltiorrhiza, and two methods of random forest (RF) and PSO were used
as the variable selection method to eliminate useless information and improve classification ability of the KELM
model. The results showed that PSO-KELM model has a better classification result with a classification accuracy of
94.87%. Comparing the results with that obtained by particle swarm optimization-least squares support vector
machines (PSO-LSSVM) and PSO-RF model, the PSO-KELM model possess the best classification performance. The
overall results demonstrate that LIBS technique combined with PSO-KELM method would be a promising method
for classification and identification of Salvia miltiorrhiza samples in different regions. Keywords: Laser-induced breakdown spectroscopy | Particle swarm optimization | Kernel extreme learning machine | Salvia miltiorrhiza | Classification |
مقاله انگلیسی |
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پیش بینی ورود گردشگران از طریق یادگیری ماشین و شاخص جستجوی اینترنتی
سال انتشار: 2019 - تعداد صفحات فایل pdf انگلیسی: 10 - تعداد صفحات فایل doc فارسی: 38 مطالعات قبلی نشان داده است که داده های آنلاین، مانند پرس وجوهای انجام شده در موتورهای جستجو، یک منبع اطلاعاتی جدید محسوب می شوند که می توانند برای پیش بینی تقاضای گردشگری مورد استفاده قرار گیرند. در این مطالعه، ما چارچوبی را برای این پیش بینی ارائه می دهیم که با استفاده از یادگیری ماشین و شاخص های جستجوی اینترنتی، ورود گردشگران به مکان های محبوب چین را پیش بینی می کند و عملکرد این پیش بینی، را به ترتیب با نتایج جستجوی تولید شده توسط گوگل و بایدو مقایسه می کنیم. این تحقیق، علیت گرانجر و همبستگیِ میانِ شاخص جستجوی اینترنتی و ورود گردشگران به پکن را تایید می کند. نتایج تجربی ما نشان می دهد که عملکردِ پیش-بینیِ مدل های پیشنهادیِ هسته ی ماشین یادگیری افراطی (KELM )، که مجموعه هایی از گردشگران را با شاخص بایدو و شاخص گوگل ادغام می کنند، در مقایسه با مدل های معیار، به میزان قابل توجهی از نظر دقت پیش بینی و قدرت تحلیل ، بهتر بوده اند.
کلمه های کلیدی: پیش بینی تقاضای گردشگری | هسته ی ماشین یادگیری افراطی | جستجوی داده-های پرس وجو | تحلیل داده های بزرگ | شاخص جستجوی ترکیبی. |
مقاله ترجمه شده |
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MARK-ELM: Application of a novel Multiple Kernel Learning framework for improving the robustness of Network Intrusion Detection
MARK-ELM: کاربرد رویکرد چارچوب یادگیری چند هسته ای برای بهبود استحکام تشخیص نفوذ شبکه-2015 Detection of cyber-based attacks on computer networks continues to be a relevant and challenging area
of research. Daily reports of incidents appear in public media including major ex-filtrations of data for the
purposes of stealing identities, credit card numbers, and intellectual property as well as to take control of
network resources. Methods used by attackers constantly change in order to defeat techniques employed
by information technology (IT) teams intended to discover or block intrusions. ‘‘Zero Day’’ attacks whose
‘‘signatures’’ are not yet in IT databases are continually being uncovered. Machine learning approaches
have been widely used to increase the effectiveness of intrusion detection platforms. While some machine
learning techniques are effective at detecting certain types of attacks, there are no known methods that can
be applied universally and achieve consistent results for multiple attack types. The focus of our research is
the development of a framework that combines the outputs of multiple learners in order to improve the
efficacy of network intrusion on data that contains instances of multiple classes of attacks. We have chosen
the Extreme Learning Machine (ELM) as the core learning algorithm due to recent research that suggests
that ELMs are straightforward to implement, computationally efficient and have excellent learning performance characteristics on par with the Support Vector Machine (SVM), one of the most widely used and best
performing machine learning platforms (Liu, Gao, & Li, 2012). We introduce the novel Multiple Adaptive
Reduced Kernel Extreme Learning Machine (MARK-ELM) which combines Multiple Kernel Boosting (Xia
& Hoi, 2013) with the Multiple Classification Reduced Kernel ELM (Deng, Zheng, & Zhang, 2013). We tested
this approach on several machine learning datasets as well as the KDD Cup 99 (Hettich & Bay, 1999) intrusion detection dataset. Our results indicate that MARK-ELM works well for the majority of University of California, Irvine (UCI) Machine Learning Repository small datasets and is scalable for larger datasets. For UCI
datasets we achieved performance similar to the MKBoost Support Vector Machine (SVM) approach. In our
experiments we demonstrate that MARK-ELM achieves superior detection rates and much lower false
alarm rates than other approaches on intrusion detection data
Keywords:
Network Intrusion Detection
KDD Cup 1999
Multiple Kernel Learning
Machine Learning
Extreme Learning Machine
Ensemble Learning
Adaptive Boosting
Cyber security
Multiclass Classification
Kernel Selection
Fractional Polynomial Kernels |
مقاله انگلیسی |