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).
ردیف | عنوان | نوع |
---|---|---|
1 |
Big data analytics for financial Market volatility forecast based on support vector machine
تجزیه و تحلیل داده های بزرگ برای پیش بینی نوسانات مالی بازار بر اساس دستگاه بردار پشتیبانی-2020 High-frequency data provides a lot of materials and broad research prospects for in-depth research and understanding
on financial market behavior, but the problems solved in the research of high-frequency data are far
less than the problems faced and encountered, and the research value of high-frequency data will be greatly
reduced without solving these problems. Volatility is an important measurement index of market risk, and the
research and forecasting on the volatility of high-frequency data is of great significance to investors, government
regulators and capital markets. To this end, by modelling the jump volatility of high-frequency data, the shortterm
volatility of high-frequency data are predicted. Keywords: Big data | Financial market | Volatility | Support vector machine |
مقاله انگلیسی |
2 |
Prediction of the ground temperature with ANN, LS-SVM and fuzzy LS-SVM for GSHP application
پیش بینی دمای زمین با شبکه های عصبی، LS-SVM و LS-SVM فازی برای استفاده GSHP-2020 Ground source heat pump (GSHP) system has received more and more attentions for its energy-conserving and
environmental-friendly properties. Acquisition of the undisturbed ground temperature is the prerequisite for
designing of GSHP system. Measurement by burying temperature sensors underground is the conventional
means for obtaining the ground temperature data. However, this way is usually time consuming and high investment,
and also easily encounter with certain technical difficulties. The rapid development of intelligent
computation algorithm provides solutions for many realistic difficult problems. Basing on a great number of the
measured data of the ground temperature from two boreholes with 100m depth located in Chongqing, ground
temperature prediction models basing on artificial neural network (ANN) and support vector machine based on
least square (LS-SVM) are established, respectively. And then, two kinds of validation works, i.e., holdout validation
and k-fold validation are conducted toward the two models, respectively. Furthermore, a new method
that correlating fuzzy theory with LS-SVM is proposed to solve the big computation burden problem encountered
by LS-SVM model. By comparing with the above two models, it is concluded that the newly proposed model can
not only improve the calculation speed obviously but also be able to promote the prediction accuracy, especially
superior to the single LS-SVM model. Keywords: Ground temperature | Fuzzy | Support vector machine | Ground source heat pump |
مقاله انگلیسی |
3 |
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 |
4 |
A grid-quadtree model selection method for support vector machines
روش انتخاب مدل شبکه چهارگوش برای ماشینهای بردار پشتیبانی-2020 In this paper, a new model selection approach for Support Vector Machine (SVM), which integrates the quadtree technique with the grid search, denominated grid-quadtree (GQ) is proposed. The developed method is the first in the literature to apply the quadtree for the SVM parameters optimization. The SVM is a machine-learning technique for pattern recognition whose performance relies on its parameters determination. Thus, the model selection problem for SVM is an important field of study and requires expert and intelligent systems to solve it. Real classification data sets involve a huge number of instances and features, and the greater is the training data set dimension, the larger is the cost of a recognition system. The grid search (GS) is the most popular and the simplest method to select parameters for SVM. However, it is time-consuming, which limits its application for big-sized problems. With this in mind, the main idea of this research is to apply the quadtree technique to the GS to make it faster. Hence, this may lower computational time cost for solving problems such as bio-identification, bank credit risk and cancer detection. Based on the asymptotic behaviors of the SVM, it was noticeably observed that the quadtree is able to avoid the GS full search space evaluation. As a consequence, the GQ carries out fewer parameters analysis, solving the same problem with much more efficiency. To assess the GQ performance, ten classification benchmark data set were used. The obtained results were compared with the ones of the traditional GS. The outcomes showed that the GQ is able to find parameters that are as good as the GS ones, executing 78.8124% to 85.8415% fewer operations. This research points out that the adoption of quadtree expressively reduces the computational time of the original GS, making it much more efficient to deal with high dimensional and large data sets. Keywords: Support vector machine | Parameter determination | Quadtree | Grid search |
مقاله انگلیسی |
5 |
Dynamic changes of brain networks during feedback-related processing of reinforcement learning in schizophrenia
تغییرات پویا شبکه های مغزی در طی پردازش مربوط به بازخورد یادگیری تقویت در اسکیزوفرنی-2020 Previous studies have reported that schizophrenia (SZ) patients showed selective reinforcement learning deficits
and abnormal feedback-related event-related potential (ERP) components. However, how the brain networks
and their topological properties evolve over time during transient feedback-related cognition processing in SZ
patients has not been investigated so far. In this paper, using publicly available feedback-related ERP data which
were recorded from SZ patients and healthy controls (HC) when they performed a reinforcement learning task,
we carried out an event-related network analysis where topology of brain functional networks was characterized
with some graph measures including clustering coefficient (C), global efficiency (Eglobal) and local efficiency
(Elocal) on a millisecond timescale. Our results showed that the brain functional networks displayed rapid rearrangements
of topological properties during transient feedback-related cognition process for both two groups.
More importantly, we found that SZ patients exhibited significantly reduced theta-band (time window of
170–350 ms after stimuli onset) brain functional connectivity strength, Eglobal, Elocal and C in response to negative
feedback stimuli compared to HC group. The network based statistic (NBS) analysis detected one significantly
decreased theta-band subnetwork in SZ patients mainly involving in frontal-occipital and temporal-occipital
connections compared to HC group. In addition, clozapine treatment seemed to greatly reduce theta-band power
and topological measures of brain networks in SZ patients. Finally, the theta-band power, graph measures and
functional connectivity were extracted to train a support vector machine classifier for classification of HC from
SZ, or Cloz + SZ or Cloz- SZ, and a relatively good classification accuracy of 84.48%, 89.47% and 78.26% was
obtained, respectively. The above results suggested a less optimal organization of theta-band brain network in SZ
patients, and studying the topological parameters of brain networks evolve over time during transient feedbackrelated
processing could be useful for understanding the pathophysiologic mechanisms underlying reinforcement
learning deficits in SZ patients. Keywords: Event-related network analysis | Support vector machine | Graph measures | Reinforcement learning | Schizophrenia |
مقاله انگلیسی |
6 |
Optimization of sizing and frequency control in battery/supercapacitor hybrid energy storage system for fuel cell ship
بهینه سازی اندازه و کنترل فرکانس در سیستم ذخیره انرژی هیبریدی باتری / ابررساننده برای حامل سوخت-2020 The fuel cell is generally coupled with the hybrid energy storage system (HESS) to improve power system
dynamic performance and prolong the fuel cell lifetime. Therefore, the sizing of HESS and design of
energy management strategy (EMS) have already become key research points. Based on support vector
machine and frequency control, a novel EMS is proposed. As the sizing of HESS and the design of energy
management strategy have a strong inner link, a multi-objective optimization method for the HESS and
EMS is proposed. After that, simulations are used to compare the performance of the optimal hybrid
power system. Compared with the different hybrid power system structures, the optimal HESS can meet
power demand and reduce the cost of the energy storage device. Compared with the rule-based energy
management strategy, the energy consumption of the optimal hybrid power system reduces 5.4%, and
improves power quality and prolongs the device life. The results indicate that the proposed method can
achieve excellent performance and is easily applied. Keywords: Hybrid ship | Energy management strategy | Hybrid energy storage system | Whale optimization algorithm (WOA) | Support vector machine (SVM) |
مقاله انگلیسی |
7 |
A machine-learning-based prediction model of fistula formation after interstitial brachytherapy for locally advanced gynecological malignancies
یک مدل پیش بینی مبتنی بر یادگیری ماشینی از تشکیل فیستول پس از براکی تراپی بینابینی برای بدخیمی های ژنتیکی بومی محلی-2019 PURPOSE: External beam radiotherapy combined with interstitial brachytherapy is commonly
used to treat patients with bulky, advanced gynecologic cancer. However, the high radiation dose
needed to control the tumor may result in fistula development. There is a clinical need to identify
patients at high risk for fistula formation such that treatment may be managed to prevent this toxic
side effect. This work aims to develop a fistula prediction model framework using machine learning
based on patient, tumor, and treatment features.
METHODS AND MATERIALS: This retrospective study included 35 patients treated at our
institution using interstitial brachytherapy for various gynecological malignancies. Five patients
developed rectovaginal fistula and two developed both rectovaginal and vesicovaginal fistula. For
each patient, 31 clinical features of multiple data types were collected to develop a fistula prediction
framework. A nonlinear support vector machine was used to build the prediction model. Sequential
backward feature selection and sequential floating backward feature selection methods were used to
determine optimal feature sets. To overcome data imbalance issues, the synthetic minority oversampling
technique was used to generate synthetic fistula cases for model training.
RESULTS: Seven mixed data features were selected by both sequential backward selection and
sequential floating backward selection methods. Our prediction model using these features achieved
a high prediction accuracy, that is, 0.904 area under the curve, 97.1% sensitivity, and 88.5% specificity.
CONCLUSIONS: A machine-learningebased prediction model of fistula formation has been
developed for patients with advanced gynecological malignancies treated using interstitial brachytherapy.
This model may be clinically impactful pending refinement and validation in a larger series. Keywords: Machine learning | Support vector machine | Interstitial brachytherapy | Gynecologic cancer |
مقاله انگلیسی |
8 |
Deep learning facilitates the diagnosis of adult asthma
تسهیلات یادگیری عمیق در تشخیص آسم بزرگسالان-2019 Background: We explored whether the use of deep learning to model combinations of symptom-physical
signs and objective tests, such as lung function tests and the bronchial challenge test, would improve
model performance in predicting the initial diagnosis of adult asthma when compared to the conventional
machine learning diagnostic method.
Methods: The data were obtained from the clinical records on prospective study of 566 adult outpatients
who visited Kindai University Hospital for the first time with complaints of non-specific respiratory
symptoms. Asthma was comprehensively diagnosed by specialists based on symptom-physical
signs and objective tests. Model performance metrics were compared to logistic analysis, support vector
machine (SVM) learning, and the deep neural network (DNN) model.
Results: For the diagnosis of adult asthma based on symptom-physical signs alone, the accuracy of the
DNN model was 0.68, whereas that for the SVM was 0.60 and for the logistic analysis was 0.65. When
adult asthma was diagnosed based on symptom-physical signs, biochemical findings, lung function tests,
and the bronchial challenge test, the accuracy of the DNN model increased to 0.98 and was significantly
higher than the 0.82 accuracy of the SVM and the 0.94 accuracy of the logistic analysis.
Conclusions: DNN is able to better facilitate diagnosing adult asthma, compared with classical machine
learnings, such as logistic analysis and SVM. The deep learning models based on symptom-physical signs
and objective tests appear to improve the performance for diagnosing adult asthma Keywords: Artificial intelligence | Asthma | Deep learning | Diagnosis | Support vector machine |
مقاله انگلیسی |
9 |
A deep feature mining method of electronic nose sensor data for identifying beer olfactory information
یک روش استخراج عمیق از داده های حسگر بینی الکترونیکی برای شناسایی اطلاعات بویایی آبجو-2019 In this work, a deep feature mining method for electronic nose (E-nose) sensor data based on the convolutional
neural network (CNN) was proposed in combination with a support vector machine (SVM) to identify beer
olfactory information. According to the characteristics of E-nose sensor data, the structure and parameters of the
CNN was designed. By means of convolution and pooling operations, the beer olfaction features were extracted
automatically. Meanwhile, the SVM replaced the full connection layer of the CNN to enhance the generalization
ability of the model, and two important parameters affecting the classification performance of the SVM were
optimized based on an improved particle swarm optimization (PSO). The results indicated that the CNN-SVM
model achieved deep feature automatic extraction of beer olfactory information, and a good classification
performance of 96.67% was obtained in the testing set. This study shows that the CNN-SVM can be used as an
effective tool for high precision intelligent identification of beer olfactory information Keywords: Electronic nose | Feature mining | Convolutional neural network | Support vector machine | Beer |
مقاله انگلیسی |
10 |
A machine learning approach for traffic-noise annoyance assessment
یک روش یادگیری ماشین برای تخمین آزار سر و صدای ترافیک-2019 In this study, models for predicting traffic-noise annoyance based on noise perception, noise exposure
levels, and demographics were developed. By applying machine-learning techniques, in particular artificial
neural networks (ANN), support vector machines (SVM) and multiple linear regressions (MLR), the
traffic-noise annoyance models were obtained, and the error rates compared. A traffic noise map and
the estimation of noise exposure for the case study area were developed. Although, it is quite evident that
subjective noise perception and predicted noise exposure levels strongly influence traffic-noise annoyance,
traditional statistical models fail to produce accurate predictions. Therefore, a machine-learning
approach was applied, which showed a better performance in terms of error rates and the coefficient
of determination (R2). The best results for predicting traffic-noise annoyance were obtained with the
ANN model, obtaining 42% and 35% error reduction in training subsets compared to the MRL and SVM
models, respectively. For testing subsets, the error reductions were 24% and 19% for the corresponding
models. The coefficient of determination R2 increased 3.8 and 2.3 times using ANN compared to MRL
and SVM models in training subsets respectively, and 1.7 times (in both MRL and SVM models) for testing
subsets. In this way, the applied methodology can be used as a reliable and more accurate tool for determining
the impact of transportation noise in urban context, promoting the well-being of the population
and the creation of suitable public policy. Keywords: Noise annoyance | Traffic noise | Machine-learning | Artificial neural networks | Support vector machine |
مقاله انگلیسی |