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نتیجه جستجو - e-Learning

تعداد مقالات یافته شده: 90
ردیف عنوان نوع
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
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