Implementing Artificial Intelligence in Predicting Metrics for Characterizing Laser Propagation in Atmospheric Turbulence

2019 ◽  
Vol 141 (12) ◽  
Author(s):  
Diego Alberto Lozano Jimenez ◽  
V. M.Krushnarao Kotteda ◽  
Vinod Kumar ◽  
V. S. Rao Gudimetla
Keyword(s):  
Machine Learning ◽  
Laser Beam ◽  
Atmospheric Turbulence ◽  
Language Processing ◽  
Large Scale ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Outer Scale ◽  
Laser Propagation ◽  
Turbulence Effects

The effects of a laser beam propagating through atmospheric turbulence are investigated using the phase screen approach. Turbulence effects are modeled by the Kolmogorov description of the energy cascade theory, and outer scale effect is implemented by the von Kármán refractive power spectral density. In this study, we analyze a plane wave propagating through varying atmospheric horizontal paths. An important consideration for the laser beam propagation of long distances is the random variations in the refractive index due to atmospheric turbulence. To characterize the random behavior, statistical analysis of the phase data and related metrics are examined at the output signal. We train three different machine learning algorithms in tensorflow library with the data at varying propagation lengths, outer scale lengths, and levels of turbulence intensity to predict statistical parameters that describe the atmospheric turbulence effects on laser propagation. tensorflow is an interface for demonstrating machine learning algorithms and an implementation for executing such algorithms on a wide variety of heterogeneous systems, ranging from mobile devices such as phones and tablets to large-scale distributed systems and thousands of computational devices such as GPU cards. The library contains a wide variety of algorithms including training and inference algorithms for deep neural network models. Therefore, it has been used for deploying machine learning systems in many fields including speech recognition, computer vision, natural language processing, and text mining.

Essentiality of Machine Learning Algorithms for Big Data Computation

2016 ◽  
pp. 156-167
Author(s):  
Manjunath Thimmasandra Narayanapppa ◽  
T. P. Puneeth Kumar ◽  
Ravindra S. Hegadi
Keyword(s):  
Machine Learning ◽  
Big Data ◽  
Large Scale ◽  
Learning Algorithms ◽  
Big Data Analytics ◽  
Machine Learning Algorithms ◽  
Real Time Analysis ◽  
Large Scale Data ◽  
Computational Environment ◽  
Large Scale Data Processing

Recent technological advancements have led to generation of huge volume of data from distinctive domains (scientific sensors, health care, user-generated data, finical companies and internet and supply chain systems) over the past decade. To capture the meaning of this emerging trend the term big data was coined. In addition to its huge volume, big data also exhibits several unique characteristics as compared with traditional data. For instance, big data is generally unstructured and require more real-time analysis. This development calls for new system platforms for data acquisition, storage, transmission and large-scale data processing mechanisms. In recent years analytics industries interest expanding towards the big data analytics to uncover potentials concealed in big data, such as hidden patterns or unknown correlations. The main goal of this chapter is to explore the importance of machine learning algorithms and computational environment including hardware and software that is required to perform analytics on big data.

scholarly journals Towards scaling Twitter for digital epidemiology of birth defects

2019 ◽  
Vol 2 (1) ◽  
Author(s):  
Ari Z. Klein ◽  
Abeed Sarker ◽  
Davy Weissenbacher ◽  
Graciela Gonzalez-Hernandez
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Language Processing ◽  
Birth Defects ◽  
Birth Defect ◽  
Learning Algorithms ◽  
Class Imbalance ◽  
Machine Learning Algorithms ◽  
Supervised Machine Learning ◽  
Svm Classifier

Abstract Social media has recently been used to identify and study a small cohort of Twitter users whose pregnancies with birth defect outcomes—the leading cause of infant mortality—could be observed via their publicly available tweets. In this study, we exploit social media on a larger scale by developing natural language processing (NLP) methods to automatically detect, among thousands of users, a cohort of mothers reporting that their child has a birth defect. We used 22,999 annotated tweets to train and evaluate supervised machine learning algorithms—feature-engineered and deep learning-based classifiers—that automatically distinguish tweets referring to the user’s pregnancy outcome from tweets that merely mention birth defects. Because 90% of the tweets merely mention birth defects, we experimented with under-sampling and over-sampling approaches to address this class imbalance. An SVM classifier achieved the best performance for the two positive classes: an F1-score of 0.65 for the “defect” class and 0.51 for the “possible defect” class. We deployed the classifier on 20,457 unlabeled tweets that mention birth defects, which helped identify 542 additional users for potential inclusion in our cohort. Contributions of this study include (1) NLP methods for automatically detecting tweets by users reporting their birth defect outcomes, (2) findings that an SVM classifier can outperform a deep neural network-based classifier for highly imbalanced social media data, (3) evidence that automatic classification can be used to identify additional users for potential inclusion in our cohort, and (4) a publicly available corpus for training and evaluating supervised machine learning algorithms.

Machine Learning in Gifted Education: A Demonstration Using Neural Networks

2019 ◽  
Vol 63 (4) ◽  
pp. 243-252 ◽  
Author(s):  
Jaret Hodges ◽  
Soumya Mohan
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Supervised Learning ◽  
Language Processing ◽  
Gifted Education ◽  
Learning Algorithms ◽  
Simulated Data ◽  
Machine Learning Algorithms ◽  
Automated Driving ◽  
The Neural Network

Machine learning algorithms are used in language processing, automated driving, and for prediction. Though the theory of machine learning has existed since the 1950s, it was not until the advent of advanced computing that their potential has begun to be realized. Gifted education is a field where machine learning has yet to be utilized, even though one of the underlying problems of gifted education is classification, which is an area where learning algorithms have become exceptionally accurate. We provide a brief overview of machine learning with a focus on neural networks and supervised learning, followed by a demonstration using simulated data and neural networks for classification issues with a practical explanation of the mechanics of the neural network and associated R code. Implications for gifted education are then discussed. Finally, the limitations of supervised learning are discussed. Code used in this article can be found at https://osf.io/4pa3b/

scholarly journals Computerized Answer Grading

2021 ◽  
Vol 9 (VI) ◽  
pp. 618-619
Author(s):  
Anurag Langan
Keyword(s):  
Machine Learning ◽  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Computer Technology ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Grade Student ◽  
Processing Techniques

Grading student answers is a tedious and time-consuming task. A study had found that almost on average around 25% of a teacher's time is spent in scoring the answer sheets of students. This time could be utilized in much better ways if computer technology could be used to score answers. This system will aim to grade student answers using the various Natural Language processing techniques and Machine Learning algorithms available today.

scholarly journals Compendiums of Cancer Transcriptome for Machine Learning Applications

2018 ◽  
Author(s):  
Su Bin Lim ◽  
Swee Jin Tan ◽  
Wan-Teck Lim ◽  
Chwee Teck Lim
Keyword(s):  
Machine Learning ◽  
Large Scale ◽  
Meta Analysis ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Data Reuse ◽  
Human Cancers ◽  
Cancer Transcriptome ◽  
Cancer Types ◽  
Data Source

AbstractBackgroundThere exist massive transcriptome profiles in the form of microarray, enabling reuse. The challenge is that they are processed with diverse platforms and preprocessing tools, requiring considerable time and informatics expertise for cross-dataset or cross-cancer analyses. If there exists a single, integrated data source consisting of thousands of samples, similar to TCGA, data-reuse will be facilitated for discovery, analysis, and validation of biomarker-based clinical strategy.FindingsWe present 11 merged microarray-acquired datasets (MMDs) of major cancer types, curating 8,386 patient-derived tumor and tumor-free samples from 95 GEO datasets. Highly concordant MMD-derived patterns of genome-wide differential gene expression were observed with matching TCGA cohorts. Using machine learning algorithms, we show that clinical models trained from all MMDs, except breast MMD, can be directly applied to RNA-seq-acquired TCGA data with an average accuracy of 0.96 in classifying cancer. Machine learning optimized MMD further aids to reveal immune landscape of human cancers critically needed in disease management and clinical interventions.ConclusionsTo facilitate large-scale meta-analysis, we generated a newly curated, unified, large-scale MMD across 11 cancer types. Besides TCGA, this single data source may serve as an excellent training or test set to apply, develop, and refine machine learning algorithms that can be tapped to better define genomic landscape of human cancers.

scholarly journals Natural language processing and recurrent network models for identifying genomic mutation-associated cancer treatment change from patient progress notes

JAMIA Open ◽  
2019 ◽  
Vol 2 (1) ◽  
pp. 139-149 ◽  
Author(s):  
Meijian Guan ◽  
Samuel Cho ◽  
Robin Petro ◽  
Wei Zhang ◽  
Boris Pasche ◽  
...  
Keyword(s):  
Machine Learning ◽  
Natural Language Processing ◽  
Natural Language ◽  
Cancer Patients ◽  
Language Processing ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Free Text ◽  
Treatment Change ◽  
Progress Notes

Abstract Objectives Natural language processing (NLP) and machine learning approaches were used to build classifiers to identify genomic-related treatment changes in the free-text visit progress notes of cancer patients. Methods We obtained 5889 deidentified progress reports (2439 words on average) for 755 cancer patients who have undergone a clinical next generation sequencing (NGS) testing in Wake Forest Baptist Comprehensive Cancer Center for our data analyses. An NLP system was implemented to process the free-text data and extract NGS-related information. Three types of recurrent neural network (RNN) namely, gated recurrent unit, long short-term memory (LSTM), and bidirectional LSTM (LSTM_Bi) were applied to classify documents to the treatment-change and no-treatment-change groups. Further, we compared the performances of RNNs to 5 machine learning algorithms including Naive Bayes, K-nearest Neighbor, Support Vector Machine for classification, Random forest, and Logistic Regression. Results Our results suggested that, overall, RNNs outperformed traditional machine learning algorithms, and LSTM_Bi showed the best performance among the RNNs in terms of accuracy, precision, recall, and F1 score. In addition, pretrained word embedding can improve the accuracy of LSTM by 3.4% and reduce the training time by more than 60%. Discussion and Conclusion NLP and RNN-based text mining solutions have demonstrated advantages in information retrieval and document classification tasks for unstructured clinical progress notes.

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