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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Diagnosis of obstructive sleep apnea with prediction of flow characteristics according to airway morphology automatically extracted from medical images: Computational fluid dynamics and artificial intelligence approach
تشخیص آپنه انسدادی خواب با پیش بینی ویژگی های جریان با توجه به مورفولوژی راه های هوایی به طور خودکار از تصاویر پزشکی استخراج می شود: دینامیک سیالات محاسباتی و رویکرد هوش مصنوعی-2021 Background: Obstructive sleep apnea syndrome (OSAS) is being observed in an increasing number of
cases. It can be diagnosed using several methods such as polysomnography.
Objectives: To overcome the challenges of time and cost faced by conventional diagnostic methods, this
paper proposes computational fluid dynamics (CFD) and machine-learning approaches that are derived
from the upper-airway morphology with automatic segmentation using deep learning.
Method: We adopted a 3D UNet deep-learning model to perform medical image segmentation. 3D UNet prevents the feature-extraction loss that may occur by concatenating layers and extracts the anteroposterior coordination and width of the airway morphology. To create flow characteristics of the upper airway training data, we analyzed the changes in flow characteristics according to the upper-airway morphology using CFD. A multivariate Gaussian process regression (MVGPR) model was used to train the flow characteristic values. The trained MVGPR enables the prompt prediction of the aerodynamic features of the upper airway without simulation. Unlike conventional regression methods, MVGPR can be trained by considering the correlation between the flow characteristics. As a diagnostic step, a support vector machine (SVM) with predicted aerodynamic and biometric features was used in this study to classify patients as healthy or suffering from moderate OSAS. SVM is beneficial as it is easy to learn even with a small dataset, and it can diagnose various flow characteristics as factors while enhancing the feature via the kernel function. As the patient dataset is small, the Monte Carlo cross-validation was used to validate the trained model. Furthermore, to overcome the imbalanced data problem, the oversampling method was applied. Result: The segmented upper-airway results of the high-resolution and low-resolution models present overall average dice coefficients of 0.76±0.041 and 0.74±0.052, respectively. Furthermore, the classification accuracy, sensitivity, specificity, and F1-score of the diagnosis algorithm were 81.5%, 89.3%, 86.2%, and 87.6%, respectively. Conclusion: The convenience and accuracy of sleep apnea diagnosis are improved using deep learning and machine learning. Further, the proposed method can aid clinicians in making appropriate decisions to evaluate the possible applications of OSAS. Keywords: Obstructive sleep apnea syndrome | Auto-segmentation | Upper-airway morphology | Computational fluid dynamics |
مقاله انگلیسی |
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Imbalanced credit risk evaluation based on multiple sampling, multiple kernel fuzzy self-organizing map and local accuracy ensemble
ارزیابی ریسک اعتباری نامتوازن بر اساس نمونه گیری چندگانه ، نقشه خود سازماندهی فازی چند هسته ای و گروه دقت محلی-2020 Credit risk evaluation model is generally regarded as a valid method for business risk management. Although the most of literatures about credit risk evaluation always use class-balanced data as sample sets, the study on class-imbalanced datasets is more suitable for actual situation. This paper proposes a new ensemble model to evaluate class-imbalanced credit risk, which integrates multiple sampling, multiple kernel fuzzy self-organizing map and local accuracy ensemble. To preprocess imbalanced sample sets of credit risk evaluation, multiple sampling approaches (synthetic minority over-sampling technique, under sampling and hybrid sampling) are improved and integrated to acquire balanced datasets. To construct more suitable base classifiers, multiple kernel functions (Gaussian, Polynomial and Sigmoid) respectively are used to improve fuzzy self-organizing map. Then, the balanced sample sets are respectively processed by the improved base classifiers to acquire different prediction results. The local accuracy ensemble method is employed to dynamically synthesize these prediction results to obtain final result. The new ensemble model can further avoid over-fitting and information loss, be more suitable to handle the dataset including different financial indicators, and acquire the stable and satisfactory prediction result for imbalanced credit risk evaluation In the empirical research, this paper adopts the financial data from Chinese listed companies, and makes the comparative analysis with the relative models step by step. The results can prove that the new ensemble model presented by this article has better performance than other methods in terms of evaluating the imbalanced credit risk.© 2020 Elsevier B.V. All rights reserved. Keywords: Credit risk evaluation | Class-imbalanced data | Multiple sampling | Multiple kernel fuzzy self-organizing map | Local accuracy ensemble |
مقاله انگلیسی |
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Dynamic imbalanced business credit evaluation based on Learn++ with sliding time window and weight sampling and FCM with multiple kernels
ارزیابی اعتبار نامتعادل پویای تجاری بر اساس یادگیری ++ با پنجره زمانی کشویی و نمونه برداری از وزن و FCM با چندین هسته-2020 A good model of business credit evaluation is an important tool for risk management. Although the dynamic imbalanced data flow is more consistent with the form of collected
financial data in the actual situation, existing studies seldom research financial data as this
form. This paper proposes a new ensemble model for dynamic imbalanced business credit
evaluation based on the improved Learn++ and fuzzy c-means (FCM). To handle dynamic
imbalanced financial data, Learn++ is improved by using a sliding time window (STW)
and weight sampling (WS). This method is termed Learn++.STW-WS. STW can divide data
with the same concept into the same dataset to solve the problem of concept drift which
characteristic in dynamic data. Additionally, WS can redistribute the weights for samples of
different classes to resolve the issue of imbalance. To satisfy the demand of Learn++.STWWS on the prediction accuracy of a base classifier, FCM is improved by multiple kernels
(MK), and is designated as MK-FCM. Several kernel functions are integrated to construct
MK by the mean method, and MK is adopted to improve the calculation method of distances among points for FCM. Therefore, this new ensemble model can solve the problems
of dynamic data and imbalanced classes at the same time. In the empirical research, financial data from Chinese listed companies are selected to evaluate business credit risk,
and the associated models are adopted to make comparative analysis. The experiment results can fully demonstrate the good performance of the new ensemble model in terms of
handling dynamic imbalanced financial data. Keywords: Business credit evaluation | Dynamic imbalanced financial data | Ensemble model | Learn++ | Fuzzy c-means |
مقاله انگلیسی |
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Internet of energy-based demand response management scheme for smart homes and PHEVs using SVM
اینترنت برنامه پاسخگویی به تقاضای مبتنی بر انرژی برای خانه های هوشمند و PHEV با استفاده از SVM-2020 The usage of information and communication technology (ICT) in the power sector has led to the
emergence of smart grid (SG). The connected loads in SG are able to communicate their consumption data
to the grid using ICT and thus forming a large Internet of Energy (IoE) network. However, various issues
such as–increasing demand–supply gap, grid instability, and deteriorating quality of service persist in this
network which degrade its performance. These issues can be handled in an efficient way by managing the
demand response (DR) of different types of loads. For this purpose, cloud computing can be leveraged to
gather the data generated in IoE network and perform analytics to manage DR. Working in this direction,
a novel scheme to handle the DR of smart homes (SHs) and plug-in hybrid electric vehicles (PHEVs) is
presented in this paper. The proposed scheme is based on analyzing the demand of these users at the cloud
server for flattening the overall load profile of grid. This scheme is divided into two hierarchical stages
which work as follows. In the first stage, the residential and PHEV users are identified whose demands
can be regulated. This task is achieved with the help of a binary-class support vector machine (SVM) which
uses Gaussian kernel function to classify these users. In the next stage, the load in SHs is curtailed on the
basis of a pre-defined rule-base after analyzing the consumption data of various devices; whereas PHEVs
are managed by controlling their charging rates. The efficacy of proposed scheme has been tested on PJM
benchmark data and Open Energy Information dataset. The simulation results prove that the proposed
scheme is effective in maintaining the overall load profile of SG by managing the DR of SHs and PHEV
users. Keywords: Data analytics | Demand response | Plug-in hybrid electric vehicles | Smart grid | Smart homes | Support vector machine |
مقاله انگلیسی |
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A low-cost and high-speed hardware implementation of spiking neural network
اجرای سخت افزار کم هزینه و پر سرعت شبکه عصبی اسپایک -2020 Spiking neural network (SNN) is a neuromorphic system based on the information process and store procedure of biological neurons. In this paper, a low-cost and high-speed implementation for a spiking neural network based on FPGA is proposed. The LIF (Leaky-Integrate–Fire) neuron model and tempotron supervised learning rules are used to construct the SNN which can be applied to the classification of pictures. A combined circuit instead of lookup table implementation method is proposed to realize the complex computing of kernel function in LIF neuron model. In addition, this work replaces the multi- plication operations in the weights training with the arithmetic shift, which can speed up the training efficiency and reduce the consumption of computing resources. Experimental results based on Vertix-7 FPGA shows that the classification accuracy is approximately 96% and the average time for classifying a sample is 0.576 us at the maximum frequency 178 MHz which achieves approximately 908,578 speedup compared with the software implementation on Matlab. Keywords: Spiking neural network | Neurons | Hardware implementation | Speed-up | Leaky-Integrate–Fire | Tempotron supervised learning rules |
مقاله انگلیسی |
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A novel spatio-temporal wind power forecasting framework based on multi-output support vector machine and optimization strategy
چارچوب پیش بینی نیروی باد مکانی و مکانی رمان بر اساس ماشین بردار پشتیبانی چند خروجی و استراتژی بهینه سازی-2020 The integration of a large number of wind farms poses big challenges to the secure and
economical operation of power systems, and ultra-short-term wind power forecasting is an
effective solution. However, traditional approaches can only predict an individual wind farm
power at a time and ignore the spatio-temporal correlation of wind farms. In this paper, a novel
ultra-short-term forecasting framework based on spatio-temporal (ST) analysis, multi-output
support vector machine (MSVM) and grey wolf optimizer (GWO) which defined
ST-GWO-MSVM model is proposed to predict the output wind power from multiple wind farms;
the ST-GWO-MSVM model includes data analysis stage, parameters optimization stage, and
modeling stage. In the data analysis stage, the person correlation coefficient and partial
autocorrelation function are used to analyze the spatio-temporal correlation of wind power. In the
parameters optimization stage, to avoid obtaining the unreliable forecasting results due to the
parameters are chosen empirically, the GWO algorithm is used to optimize the kernel function
parameters of the MSVM model. In the modeling stage, an innovative forecasting model with
optimal parameter of MSVM is proposed to predict the output wind power of 15 wind farms.
Results show that the performance of ST-GWO-MSVM is better than other benchmark models in
terms of multiple-error metrics including fractional bias, direction accuracy, and improvement
percentages. Keywords: wind power forecasting | Spatio-temporal correlation | Multi-output support vector machine | Grey wolf optimizer | Combined forecasting approaches |
مقاله انگلیسی |
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Monitoring tip-based nanomachining process by time series analysis using support vector machine
نظارت بر فرآیند نانوماشینه مبتنی بر نوک بوسیله آنالیز سری زمانی با استفاده از دستگاه بردار پشتیبانی-2019 In this paper, time-series data analysis and pattern recognition using a multi-class support vector machine (SVM)
were studied to monitor the state changes of the AFM tip-based nanomachining process with respect to the
machining performance and tip wear. Time series data (i.e. machining force from the process), which has
transient, nonlinear, and non-stationary characteristics, was collected by a data acquisition system. Three status
detection features including the maximum force, peak-to-peak force value, and the variance of the collected
lateral machining force, were extracted to classify the state of the nanomachining process. Directed Acyclic
Graph Support Vector Machines (DAGSVM) with a Gaussian Radial Basis Kernel Function (RBF Kernel) was
constructed to identify the different process states. Using this multi-class SVM, the machining process and the tip
wear can be classified into three regions, which are effective machining with a sharp tip, transition region and
bad/no machining with severe tip wear. The experimental data showed that the accuracy of the SVM was over
94.73% in both binary and ternary classifications, which confirmed that the SVM-based pattern recognition
technology via time series data could successfully monitor the tip wear and process performance for tip-based
nanomachining process. Keywords: AFM tip-based nanomachining | Process monitoring | Tip wear detection | Time series data | Support vector machine |
مقاله انگلیسی |
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A new similarity measure for collaborative filtering based recommender systems
یک اندازه گیری شباهت جدید برای سیستم های توصیه گر مبتنی بر فیلتر مشترک-2019 The objective of a recommender system is to provide customers with personalized recommendations
while selecting an item among a set of products (movies, books, etc.). The collaborative filtering is
the most used technique for recommender systems. One of the main components of a recommender
system based on the collaborative filtering technique, is the similarity measure used to determine the
set of users having the same behavior with regard to the selected items. Several similarity functions
have been proposed, with different performances in terms of accuracy and quality of recommendations.
In this paper, we propose a new simple and efficient similarity measure. Its mathematical expression is
determined through the following paper contributions: 1) transforming some intuitive and qualitative
conditions, that should be satisfied by the similarity measure, into relevant mathematical equations
namely: the integral equation, the linear system of differential equations and a non-linear system
and 2) resolving the equations to achieve the kernel function of the similarity measure. The extensive
experimental study driven on a benchmark datasets shows that the proposed similarity measure is very
competitive, especially in terms of accuracy, with regards to some representative similarity measures
of the literature. Keywords: Recommendation systems | Collaborative filtering | Neighborhood based CF | Similarity measure |
مقاله انگلیسی |
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Parameters estimation in Ebola virus transmission dynamics model based on machine learning
برآورد پارامترها در مدل دینامیک انتقال ویروس ابولا بر اساس یادگیری ماشین-2019 This paper presents the application of machine learning to parameter estimation in biomathematical
model. The background of Ebola disease was introduced, including the
structure and morphology of the virus, the causes of disease, the mode of transmission,
prevention and control measures. Meanwhile, it is essential to present the mechanism
of this method, the application and calculation process, and the parameters. Compared
with other methods, this method can not only obtain more accurate parameter values
based on fewer and scattered data, but also estimate the parameters appearing anywhere
in the partial differential equation, and automatically filter arbitrary noise data through
Gaussian priori hypothesis. Keywords: Ebola | Probabilistic machine learning | Multi-output Gaussian process | Kernel function |
مقاله انگلیسی |
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Syntactic based approach for grammar question retrieval
دیدگاه مبتنی بر نحو برای بازیابی سوال گرامری -2018 With the popularity of online educational platforms, English learners can learn and practice no matter where they are and what they do. English grammar is one of the important components in learning English. To learn English grammar effectively, it requires students to practice questions containing focused grammar knowledge. In this paper, we study a novel problem of retrieving English grammar questions with similar grammatical focus. Since the grammatical focus similarity is different from textual similarity or sentence syntactic similarity, existing approaches cannot be applied directly to our problem. To address this problem, we propose a syntactic based approach for English grammar question retrieval which can retrieve related grammar questions with similar grammatical focus effectively. In the proposed syntactic based approach, we first propose a new syntactic tree, namely parse-key tree, to capture English grammar questions’ grammatical focus. Next, we propose two kernel functions, namely relaxed tree kernel and part-of-speech order kernel, to compute the similarity between two parse-key trees of the query and grammar questions in the collection. Then, the retrieved grammar questions are ranked according to the similarity between the parse-key trees. In addition, if a query is submitted together with answer choices, conceptual similarity and textual similarity are also incorporated to further improve the retrieval accuracy. The performance results have shown that our proposed approach outperforms the state-of-the-art methods based on statistical analysis and syntactic analysis.
keywords: Grammar question retrieval |Syntactic tree |Relaxed tree kernel |POS order kernel |
مقاله انگلیسی |