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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41 |
Identifying depression in the National Health and Nutrition Examination Survey data using a deep learning algorithm
شناسایی افسردگی در داده های بررسی آزمایش ملی بهداشت و تغذیه با استفاده از الگوریتم یادگیری عمیق-2019 Background: As depression is the leading cause of disability worldwide, large-scale surveys have been conducted
to establish the occurrence and risk factors of depression. However, accurately estimating epidemiological
factors leading up to depression has remained challenging. Deep-learning algorithms can be applied to assess the
factors leading up to prevalence and clinical manifestations of depression.
Methods: Customized deep-neural-network and machine-learning classifiers were assessed using survey data
from 19,725 participants from the NHANES database (from 1999 through 2014) and 4949 from the South Korea
NHANES (K-NHANES) database in 2014.
Results: A deep-learning algorithm showed area under the receiver operating characteristic curve (AUCs) of 0.91
and 0.89 for detecting depression in NHANES and K-NHANES, respectively. The deep-learning algorithm trained
with serial datasets (NHANES, from 1999 to 2012), predicted the prevalence of depression in the following two
years of data (NHANES, 2013 and 2014) with an AUC of 0.92. Machine learning classifiers trained with NHANES
could further predict depression in K-NHANES. There, logistic regression had the highest performance (AUC,
0.77) followed by deep learning algorithm (AUC, 0.74).
Conclusions: Deep neural-networks managed to identify depression well from other health and demographic
factors in both the NHANES and K-NHANES datasets. The deep-learning algorithm was also able to predict
depression relatively well on new data set—cross temporally and cross nationally. Further research can delineate
the clinical implications of machine learning and deep learning in detecting disease prevalence and progress as
well as other risk factors for depression and other mental illnesses. Keywords: Machine learning | Depression | National Health and Nutrition Examination | Survey | Deep learning |
مقاله انگلیسی |
42 |
Machine-learning assisted coarse-grained model for epoxies over wide ranges of temperatures and cross-linking degrees
یادگیری ماشین به کمک مدل دانه درشت برای epoxies در طیف گسترده ای از درجه حرارت و درجه اتصال متقابل-2019 We present a practical computational framework for the coarse-graining of cross-linked epoxies by developing a machine-learning technique, which integrates molecular dynamics simulations with artificial neural network (ANN) assisted particle swarm optimization (PSO) algorithm. Key features of the framework include two as- pects: (1) determining the bonded interactions via the iterative Boltzmann inversion method to emulate the local structures of the epoxies and, (2) optimizing the nonbonded interaction potentials through the machine- learning approach to reproduce the mechanical properties. Such machine-learning based technique is computa- tionally efficient in searching for the optimal solution of nonbonded potential parameters and enables the CG model to become transferable within a wide range of cross-linking degrees. This is mainly attributed to the fact that ANN can give good predictions based on training database obtained from CG simulations and thus greatly accelerates the PSO algorithm in achieving the optimal solution. On the basis of the DOC-transferable CG model, the cohesive interaction strength is phenomenologically adjusted to preserve the temperature-dependent prop- erties. The CG model allows the mechanical properties of cross-linked epoxies to be predicted with reasonable accuracy over wide ranges of cross-linking degrees and temperature. The proposed framework will become highly beneficial to the design of high performance epoxy-matrix nanocomposites. Keywords: Machine-learning approach | Cross-linked epoxy | Coarse-grained model | Molecular dynamics |
مقاله انگلیسی |
43 |
Autonomic machine learning platform
سکوی یادگیری ماشین خودگردان-2019 Acquiring information properly through machine learning requires familiarity with the available algorithms and
understanding how they work and how to address the given problem in the best possible way. However, even for
machine-learning experts in specific industrial fields, in order to predict and acquire information properly in
different industrial fields, it is necessary to attempt several instances of trial and error to succeed with the
application of machine learning. For non-experts, it is much more difficult to make accurate predictions through
machine learning.
In this paper, we propose an autonomic machine learning platform which provides the decision factors to be
made during the developing of machine learning applications. In the proposed autonomic machine learning
platform, machine learning processes are automated based on the specification of autonomic levels. This autonomic
machine learning platform can be used to derive a high-quality learning result by minimizing experts’
interventions and reducing the number of design selections that require expert knowledge and intuition. We also
demonstrate that the proposed autonomic machine learning platform is suitable for smart cities which typically
require considerable amounts of security sensitive information. Keywords: Autonomic machine learning platform | Autonomic level | Machine learning | Smart City |
مقاله انگلیسی |
44 |
Differentiation of glioblastoma from solitary brain metastases using radiomic machine-learning classifiers
تمایز گلیوبلاستوما از متاستازهای مغز انفرادی با استفاده از طبقه بندی کننده های یادگیری ماشین رادیولوژی-2019 This study aimed to identify the optimal radiomic machine-learning classifier for differentiating glioblastoma
(GBM) from solitary brain metastases (MET) preoperatively. Four hundred and twelve patients with solitary
brain tumors (242 GBM and 170 solitary brain MET) were divided into training (n=227) and test (n=185)
cohorts. Radiomic features extraction was performed with PyRadiomics software. In the training cohort, twelve
feature selection methods and seven classification methods were evaluated to construct favorable radiomic
machine-learning classifiers. The performance of the classifiers was evaluated using the mean area under the
curve (AUC) and relative standard deviation in percentile (RSD). In the training cohort, thirteen classifiers had
favorable predictive performances (AUC≥0.95 and RSD≤6). In the test cohort, receiver operating characteristic
(ROC) curve analysis revealed that support vector machines (SVM) + least absolute shrinkage and selection
operator (LASSO) (AUC, 0.90) classifiers had the highest prediction efficacy. Furthermore, the clinical performance
of the best classifier was superior to neuroradiologists in accuracy, sensitivity, and specificity. In conclusion,
employing radiomic machine-learning technology could help neuroradiologist in differentiating GBM
from solitary brain MET preoperatively. Keywords: Brain metastases | Glioblastoma | Radiomics | Machine learning |
مقاله انگلیسی |
45 |
Comparison between linear and nonlinear machine-learning algorithms for the classification of thyroid nodules
مقایسه الگوریتمهای یادگیری ماشین خطی و غیرخطی برای طبقه بندی ندولهای تیروئید-2019 Background: A key challenge in thyroid carcinoma is preoperatively diagnosing malignant thyroid nodules. The
purpose of this study was to compare the classification performance of linear and nonlinear machine-learning
algorithms for the evaluation of thyroid nodules using pathological reports as reference standard.
Methods: Ethical approval was obtained for this retrospective analysis, and the informed consent requirement
was waived. A total of 1179 thyroid nodules (training cohort, n=700; validation cohort, n=479) were confirmed
by pathological reports or fine-needle aspiration (FNA) biopsy. The following ultrasonography (US) featu
res were measured for each nodule: size (maximum diameter), margins, shape, aspect ratio, capsule, hypoechoic
halo, composition, echogenicity, calcification pattern, vascularity, and cervical lymph node status. We analyzed
five nonlinear and three linear machine-learning algorithms. The diagnostic performance of each algorithm was
compared by using the area under the curve (AUC) of the receiver operating characteristic curve. We repeated
this process 1000 times to obtain the mean AUC and 95% confidence interval (CI).
Results: Overall, nonlinear machine-learning algorithms demonstrated similar AUCs compared with linear algorithms.
The Random Forest and Kernel Support Vector Machines algorithms achieved slightly greater AUCs in
the validation cohort (0.954, 95% CI: 0.939–0.969; 0.954 95%CI: 0.939–0.969, respectively) than other algorithms.
Conclusions: Overall, nonlinear machine-learning algorithms share similar performance compared with linear
algorithms for the evaluation the malignancy risk of thyroid nodules. Keywords: Thyroid nodule | Ultrasonography | Diagnosis | Machine learning | Area under the curve |
مقاله انگلیسی |
46 |
Gender differences in the diagnostic performance of machine learning coronary CT angiography-derived fractional flow reserve -results from the MACHINE registry
تفاوت های جنسیتی در عملکرد تشخیصی یادگیری دستگاه یادگیری عروق کرونر CT-نتیجه حاصل از کسری جریان کسری ناشی از آنژیوگرافی از رجیستری ماشین-2019 Purpose: This study investigated the impact of gender differences on the diagnostic performance of machine-learning
based coronary CT angiography (cCTA)-derived fractional flow reserve (CT-FFRML) for the detection of lesion-specific
ischemia.
Method: Five centers enrolled 351 patients (73.5% male) with 525 vessels in the MACHINE (Machine leArning Based
CT angiograpHy derIved FFR: a Multi-ceNtEr) registry. CT-FFRML and invasive FFR≤0.80 were considered hemodynamically
significant, whereas cCTA luminal stenosis ≥50% was considered obstructive. The diagnostic performance
to assess lesion-specific ischemia in both men and women was assessed on a per-vessel basis.
Results: In total, 398 vessels in men and 127 vessels in women were included. Compared to invasive FFR, CT-FFRML
reached a sensitivity, specificity, positive predictive value, and negative predictive value of 78% (95%CI 72–84), 79%
(95%CI 73–84), 75% (95%CI 69–79), and 82% (95%CI: 76–86) in men vs. 75% (95%CI 58–88), 81 (95%CI 72–89),
61% (95%CI 50–72) and 89% (95%CI 82–94) in women, respectively. CT-FFRML showed no statistically significant
difference in the area under the receiver-operating characteristic curve (AUC) in men vs. women (AUC: 0.83 [95%CI
0.79–0.87] vs. 0.83 [95%CI 0.75–0.89], p=0.89). CT-FFRML was not superior to cCTA alone [AUC: 0.83 (95%CI:
0.75–0.89) vs. 0.74 (95%CI: 0.65–0.81), p=0.12] in women, but showed a statistically significant improvement in
men [0.83 (95%CI: 0.79–0.87) vs. 0.76 (95%CI: 0.71–0.80), p=0.007].
Conclusions: Machine-learning based CT-FFR performs equally in men and women with superior diagnostic
performance over cCTA alone for the detection of lesion-specific ischemia. Keywords: Coronary artery disease | Machine learning | Spiral computed tomography | Fractional flow reserve |
مقاله انگلیسی |
47 |
Troodon: A machine-learning based load-balancing application scheduler for CPU–GPU system
Troodon: یک برنامه زمانبندی برنامه تعادل بار بر مبنای یادگیری ماشین برای سیستم CPU-GPU-2019 Heterogeneous computing machines consisting of a CPU and one or more GPUs are increasingly being
used today because of their higher performance-cost ratio and lower energy consumption. To program
such heterogeneous systems, OpenCL has become an industry standard due to the portability across
various computing architectures. To exploit the computing capabilities of heterogeneous systems, application
developers are porting their cluster and Cloud applications using OpenCL. With the increasing
number of such applications, the use of shared accelerating computing devices (such as CPUs and
GPUs) should be managed using an efficient load-balancing scheduling heuristic capable of reducing
execution time, increasing throughput with high device utilization. Mostly, the OpenCL applications
are suited (execute faster) on a specific computing device (CPU or GPU) and with varying data-sizes
the speedup obtained by an application on the suitable device varies too. Applications’ mapping
to computing devices without considering device suitability and obtainable speedup on a suitable
device leads to sub-optimal execution time, lower throughput and load imbalance. Therefore, an
application scheduler should consider both the device-suitability and speedup variation for scheduling
decisions leading to a reduction in execution time and an increase in throughput. In this paper,
we present a novel load-balancing scheduling heuristic named as Troodon that considers machinelearning
based device-suitability model that classify OpenCL applications into either CPU suitable
or GPU suitable. Moreover, a speedup predictor that predicts the amount of speedup that jobs
will obtain when executed on a suitable device is also part of the Troodon. Troodon incorporates
the E-OSched scheduling mechanism to map jobs on CPU and GPUs in a load balanced way. This
results in reduced applications execution time, increased system throughput, and improved device
utilization. We evaluate the proposed scheduler using a large number of data-parallel applications
and compared with several other state-of-the-art scheduling heuristics. The experimental evaluation
has demonstrated that the proposed scheduler outperformed the existing heuristics and reduced the
application execution time up to 38% with increased system throughput and device utilization. Keywords: Heterogeneous system | Scheduling | Device suitability | Load-balancing | Machine learning |
مقاله انگلیسی |
48 |
Algorithmic sign prediction and covariate selection across eleven international stock markets
پیش بینی علائم الگوریتمی و انتخاب متغیرها در یازده بورس بین المللی سهام-2019 I investigate whether an expert system can be used for profitable long-term asset management. The trad- ing strategy of the expert system needs to be based on market predictions. To this end, I generate binary predictions of the market returns by using statistical and machine-learning algorithms. The methods used include logistic regressions, regularized logistic regressions and similarity-based classification. I test the methods in a contemporary data set involving data from eleven developed markets. Both statistical and economic significance of the results are considered. As an ensemble, the results seem to indicate that there is some degree of mild predictability in the stock markets. Some of the results obtained are highly significant in the economic sense, featuring annualized excess returns of 3.1% (France), 2.9% (Netherlands) and 0.8% (United States). However, statistically significant results are seldom found. Consequently, the re- sults do not completely invalidate the efficient-market hypothesis. Keywords: Stock market indices | S&P 500 | Sign prediction | Efficient-market hypothesis | Regularized regression | Similarity-based classification |
مقاله انگلیسی |
49 |
Differentiation of spinal metastases originated from lung and other cancers using radiomics and deep learning based on DCE-MRI
تمایز متاستازهای ستون فقرات ناشی از سرطانهای ریه و سایر سرطانها با استفاده از رادیولوژی و یادگیری عمیق بر اساس DCE-MRI-2019 Purpose: To differentiate metastatic lesions in the spine originated from primary lung cancer and other cancers
using radiomics and deep learning, compared to traditional hot-spot ROI analysis.
Methods: In a retrospective review of clinical spinal MRI database with a dynamic contrast enhanced (DCE)
sequence, a total of 61 patients without prior cancer diagnosis and later confirmed to have metastases (30 lung;
31 non-lung cancers) were identified. For hot-spot analysis, a manual ROI was placed to calculate three heuristic
parameters from the wash-in, maximum, and wash-out phases in the DCE kinetics. For each case, the 3D tumor
mask was generated by using the normalized-cut algorithm. Radiomics analysis was performed to extract histogram
and texture features from three DCE parametric maps. Deep learning was performed using these maps as
inputs into a conventional convolutional neural network (CNN), as well as using all 12 sets of DCE images into a
convolutional long short term memory (CLSTM) network.
Results: For hot-spot ROI analysis, mean wash-out slope was 0.25 ± 10% for lung metastases and
−9.8 ± 12.9% for other tumors. CHAID classification using a wash-out slope of −6.6% followed by wash-in
enhancement ratio of 98% achieved a diagnostic accuracy of 0.79. Radiomics analysis using features representing
tumor heterogeneity only reached the highest accuracy of 0.71. Classification using CNN achieved a
mean accuracy of 0.71 ± 0.043, whereas a CLSTM improved accuracy to 0.81 ± 0.034.
Conclusions: DCE-MRI machine-learning analysis methods have potential to predict lung cancer metastases in
the spine, which may be used to guide subsequent workup for confirmed diagnosis. Keywords: DCE-MRI | Radiomics | Deep learning | Spinal metastases |
مقاله انگلیسی |
50 |
Clustering suicides: A data-driven, exploratory machine learning approach
خودکشی های خوشه ای: یک رویکرد یادگیری ماشین اکتشافی مبتنی بر داده ها-2019 Methods of suicide have received considerable attention in suicide research. The common approach to
differentiate methods of suicide is the classification into “violent” versus “non-violent” method.
Interestingly, since the proposition of this dichotomous differentiation, no further efforts have been
made to question the validity of such a classification of suicides. This study aimed to challenge the
traditional separation into “violent” and “non-violent” suicides by generating a cluster analysis with a
data-driven, machine learning approach. In a retrospective analysis, data on all officially confirmed
suicides (N = 77,894) in Austria between 1970 and 2016 were assessed. Based on a defined distance metric
between distributions of suicides over age group and month of the year, a standard hierarchical clustering
method was performed with the
five most frequent suicide methods. In cluster analysis, poisoning
emerged as distinct from all other methods – both in the entire sample as well as in the male subsample.
Violent suicides could be further divided into sub-clusters: hanging, shooting, and drowning on the one
hand and jumping on the other hand. In the female sample, two different clusters were revealed –
hanging and drowning on the one hand and jumping, poisoning, and shooting on the other. Our datadriven
results in this large epidemiological study confirmed the traditional dichotomization of suicide
methods into “violent” and “non-violent” methods, but on closer inspection “violent methods” can be
further divided into sub-clusters and a different cluster pattern could be identified for women, requiring
further research to support these refined suicide phenotypes. Keywords: Suicide | Suicide methods | Machine-learning | Violent suicide | Cluster analysis |
مقاله انگلیسی |