scholarly journals Deep Learning Methods On Neutron Scattering Data

2020 ◽  
Vol 225 ◽  
pp. 01004
Author(s):  
Guanghan Song ◽  
Lionel Porcar ◽  
Martin Boehm ◽  
Franck Cecillon ◽  
Charles Dewhurst ◽  
...  
Keyword(s):  
Experimental Data ◽  
Deep Learning ◽  
Neutron Scattering ◽  
Human Performance ◽  
Early Stage ◽  
Scientific Information ◽  
Scattering Data ◽  
Learning Methods ◽  
Structure Geometry ◽  
Instrument Configuration

Recently, by using deep learning methods, a computer is able to surpass or come close to matching human performance on image analysis and recognition. This advanced methods could also help extracting features from neutron scattering experimental data. Those data contain rich scientific information about structure and dynamics of materials under investigation. Deep learning could help researchers better understand the link between experimental data and materials properties. Moreover,it could also help to optimize neutron scattering experiment by predicting the best possible instrument configuration. Among all possible experimental methods, we begin our study on the small-angle neutron scattering (SANS) data and by predicting the structure geometry of the sample material at an early stage. This step is a keystone to predict the experimental parameters to properly setup the instrument as well as the best measurement strategy. In this paper, we propose to use transfer learning to retrain a convolutional neural networks (CNNs) based pre rained model to adapt the scattering images classification, which could predict the structure of the materials at an early stage in the SANS experiment. This deep neural network is trained and validated on simulated database, and tested on real scattering images.

Diagnosing breast cancer tumors using stacked ensemble model

2021 ◽  
pp. 1-9
Author(s):  
Ahmet Haşim Yurttakal ◽  
Hasan Erbay ◽  
Türkan İkizceli ◽  
Seyhan Karaçavuş ◽  
Cenker Biçer
Keyword(s):  
Breast Cancer ◽  
Deep Learning ◽  
Medical Imaging ◽  
Early Stage ◽  
False Negative ◽  
Gradient Boosting ◽  
Physical Sign ◽  
Ensemble Model ◽  
Learning Methods ◽  
Dce Mri

Breast cancer is the most common cancer that progresses from cells in the breast tissue among women. Early-stage detection could reduce death rates significantly, and the detection-stage determines the treatment process. Mammography is utilized to discover breast cancer at an early stage prior to any physical sign. However, mammography might return false-negative, in which case, if it is suspected that lesions might have cancer of chance greater than two percent, a biopsy is recommended. About 30 percent of biopsies result in malignancy that means the rate of unnecessary biopsies is high. So to reduce unnecessary biopsies, recently, due to its excellent capability in soft tissue imaging, Dynamic Contrast-Enhanced Magnetic Resonance Imaging (DCE-MRI) has been utilized to detect breast cancer. Nowadays, DCE-MRI is a highly recommended method not only to identify breast cancer but also to monitor its development, and to interpret tumorous regions. However, in addition to being a time-consuming process, the accuracy depends on radiologists’ experience. Radiomic data, on the other hand, are used in medical imaging and have the potential to extract disease characteristics that can not be seen by the naked eye. Radiomics are hard-coded features and provide crucial information about the disease where it is imaged. Conversely, deep learning methods like convolutional neural networks(CNNs) learn features automatically from the dataset. Especially in medical imaging, CNNs’ performance is better than compared to hard-coded features-based methods. However, combining the power of these two types of features increases accuracy significantly, which is especially critical in medicine. Herein, a stacked ensemble of gradient boosting and deep learning models were developed to classify breast tumors using DCE-MRI images. The model makes use of radiomics acquired from pixel information in breast DCE-MRI images. Prior to train the model, radiomics had been applied to the factor analysis to refine the feature set and eliminate unuseful features. The performance metrics, as well as the comparisons to some well-known machine learning methods, state the ensemble model outperforms its counterparts. The ensembled model’s accuracy is 94.87% and its AUC value is 0.9728. The recall and precision are 1.0 and 0.9130, respectively, whereas F1-score is 0.9545.

scholarly journals Development of Decision Support System for Early Stage Diabetes Diagnosis Using Machine Learning and Deep Learning Methods: A Comprehensive Analysis

2021 ◽  
Vol 4 (1) ◽  
pp. 186-194
Author(s):  
Caglar Gurkan ◽  
Sude Kozalioglu ◽  
Merih Palandoken
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Decision Support ◽  
Decision Support System ◽  
Support System ◽  
Naive Bayes ◽  
Early Stage ◽  
Comprehensive Analysis ◽  
Diabetes Diagnosis ◽  
Learning Methods

Yaygın olarak görülen hastalıklardan biri olan diyabetin prevalansı her yıl artmaktadır. Diyabet hastalığı erken teşhis edilmezse kalp ve damar hastalıklarına, böbrek hastalığına, körlüğe, sinir hasarlarına, felce ve organ yetmezliklerine neden olabilir. Ayrıca bu diyabet hastaları için yapılacak sağlık harcamalarının da 2040 yılında 802 milyon dolar olacağı tahmin edilmektedir. Bu durumlar göz önünde bulundurulduğunda diyabet tanısı için yapılacak çalışmalar oldukça önemlidir. Bu çalışmada, diyabet tanısı için bir karar destek sistemi geliştirmek amacıyla karar ağaçları, k-en yakın komşu, lojistik regresyon, Naive Bayes, rastgele orman, destek vektör makineleri gibi makine öğrenmesi ve çok katmanlı algılayıcı (ÇKA), evrişimli sinir ağları (ESA), tekrarlayan sinir ağları (RNN) tasarımları olan Basit RNN, Uzun Kısa Dönem Bellek Ağları (LSTM), Geçitli Tekrarlayan Birim (GRU), İki Yönlü Uzun Kısa Dönem Bellek Ağları (BiLSTM), İki Yönlü Geçitli Tekrarlayan Birim (BiGRU), ESA ve RNN hibrit modelleri olan ESA+Simple RNN, ESA+LSTM, ESA+GRU, ESA+BiLSTM ve ESA+BiGRU gibi derin öğrenme yöntemleri kullanılmıştır. Makine öğrenmesi tabanlı sınıflandırıcılar içerisinde en yüksek sınıflandırma performansını %98.10 doğruluk oranı ve %98.00 F1- skoru ile DVM elde etmiştir. Derin öğrenme tabanlı sınıflandırıcılar içerisinde en yüksek sınıflandırma performansını %99.50 doğruluk oranı ve %99.30 F1- skoru ile ESA+BiGRU hibrit modeli elde etmiştir. Genel analizde ise, derin öğrenme tabanlı sınıflandırıcıların makine öğrenmesi tabanlı sınıflandırıcılara göre daha iyi performans göstermiştir. Ek olarak CNN ve RNN tasarımlarının hibrit modelleri, yalın modellere göre daha iyi sınıflandırma performansına sahiptir.

Abstract WP437: Where Are We Located Now With Artificial Neural Network? Application of Deep Learning for the Development of Culturally Sensitive Social Support Interventions for Dementia Caregivers

Stroke ◽  
2020 ◽  
Vol 51 (Suppl_1) ◽  
Author(s):  
Sunmoo Yoon ◽  
Dante Tipiani ◽  
Peter Broadwell ◽  
Jose Luchsigner
Keyword(s):  
Social Support ◽  
African American ◽  
Deep Learning ◽  
Vascular Dementia ◽  
Early Stage ◽  
Disease Diagnosis ◽  
Culturally Sensitive ◽  
Health Science ◽  
Learning Methods ◽  
Dementia Caregivers

Introduction: Little is known about culturally sensitive interventions for Hispanic and African American family caregivers for persons living with vascular dementia. Applications of deep-learning neural networks have expanded rapidly in recent years, from the first uses in character recognition (1989) to the high-profile AlphaGo (2016) from Google DeepMind. The purpose of this study is to visualize topics from health literature that use deep learning, exploring the techniques’ ability to provide a foundation for developing culturally sensitive Twitter-based social support interventions for Hispanic and African American dementia caregivers. Methods: The corpus of deep learning literature was extracted from 506 studies mentioning deep learning from PubMed library. Text mining was conducted using AutoMap to identify topics and semantic relations from the corpus. Results were visualized as network clusters of frequently applied topics and isolated topics. Results: Literature applying deep learning are limited to 1) imaging and radiology, 2) genomics, 3) cancer (N=506 studies). Topics on dementia and drug discovery were identified as an emerging area for the adaptation of deep learning techniques (Figure 1). Only seven journals in the PubMed library were identified as having published more than 10 studies applying deep learning methods. Conclusion: Despite its popularity in art and science research, deep learning is at an early stage of adoption in health science, largely focused on limited topics, e.g., imaging and disease diagnosis. Applying deep learning methods may provide insights for developing culturally sensitive Twitter-based interventions for Hispanic and African American dementia caregivers. Implementing education on applying deep learning to imaging and vascular-dementia prediction may be a good start to adapt, deepen and broaden the scope of health analytics to improve vascular dementia patients’ health outcomes.

Diagnostics of metal samples using the results of experimental and theoretical study of positively charged microparticles formed upon sample destruction

2018 ◽  
Vol 84 (10) ◽  
pp. 23-28
Author(s):  
D. A. Golentsov ◽  
A. G. Gulin ◽  
Vladimir A. Likhter ◽  
K. E. Ulybyshev
Keyword(s):  
Experimental Data ◽  
Early Stage ◽  
Experimental Studies ◽  
Material Model ◽  
Total Charge ◽  
Cross Sectional ◽  
Practical Applications ◽  
Mechanical And Electrical Properties ◽  
Order Of Magnitude ◽  
Using Data

Destruction of bodies is accompanied by formation of both large and microscopic fragments. Numerous experiments on the rupture of different samples show that those fragments carry a positive electric charge. his phenomenon is of interest from the viewpoint of its potential application to contactless diagnostics of the early stage of destruction of the elements in various technical devices. However, the lack of understanding the nature of this phenomenon restricts the possibility of its practical applications. Experimental studies were carried out using an apparatus that allowed direct measurements of the total charge of the microparticles formed upon sample rupture and determination of their size and quantity. The results of rupture tests of duralumin and electrical steel showed that the size of microparticles is several tens of microns, the particle charge per particle is on the order of 10–14 C, and their amount can be estimated as the ratio of the cross-sectional area of the sample at the point of discontinuity to the square of the microparticle size. A model of charge formation on the microparticles is developed proceeding from the experimental data and current concept of the electron gas in metals. The model makes it possible to determine the charge of the microparticle using data on the particle size and mechanical and electrical properties of the material. Model estimates of the total charge of particles show order-of-magnitude agreement with the experimental data.

Data Science in Economics: Comprehensive Review of Advanced Machine Learning and Deep Learning Methods

2020 ◽  
Author(s):  
Saeed Nosratabadi ◽  
Amir Mosavi ◽  
Puhong Duan ◽  
Pedram Ghamisi ◽  
Filip Ferdinand ◽  
...  
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Data Science ◽  
Learning Methods ◽  
Comprehensive Review
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