Using artificial neural network condensation to facilitate adaption of machine learning in medical settings by reducing computational burden (Preprint)

2020 ◽  
Author(s):  
Dianbo Liu
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Third World ◽  
Mortality Prediction ◽  
Neural Net ◽  
Medical Settings ◽  
Hidden Layer ◽  
Applications Of Machine Learning ◽  
Computational Resources ◽  
Developed Nations

BACKGROUND Applications of machine learning (ML) on health care can have a great impact on people’s lives. At the same time, medical data is usually big, requiring a significant amount of computational resources. Although it might not be a problem for wide-adoption of ML tools in developed nations, availability of computational resource can very well be limited in third-world nations and on mobile devices. This can prevent many people from benefiting of the advancement in ML applications for healthcare. OBJECTIVE In this paper we explored three methods to increase computational efficiency of either recurrent neural net-work(RNN) or feedforward (deep) neural network (DNN) while not compromising its accuracy. We used in-patient mortality prediction as our case analysis upon intensive care dataset. METHODS We reduced the size of RNN and DNN by applying pruning of “unused” neurons. Additionally, we modified the RNN structure by adding a hidden-layer to the RNN cell but reduce the total number of recurrent layers to accomplish a reduction of total parameters in the network. Finally, we implemented quantization on DNN—forcing the weights to be 8-bits instead of 32-bits. RESULTS We found that all methods increased implementation efficiency–including training speed, memory size and inference speed–without reducing the accuracy of mortality prediction. CONCLUSIONS This improvements allow the implementation of sophisticated NN algorithms on devices with lower computational resources.

scholarly journals Applying of machine learning in the construction of a voice-controlled interface on the example of a music player

2019 ◽  
Vol 13 ◽  
pp. 302-309
Author(s):  
Jakub Basiakowski
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Convolutional Neural Network ◽  
Feedforward Neural Network ◽  
Hidden Layer ◽  
Music Player ◽  
The Impact

The following paper presents the results of research on the impact of machine learning in the construction of a voice-controlled interface. Two different models were used for the analysys: a feedforward neural network containing one hidden layer and a more complicated convolutional neural network. What is more, a comparison of the applied models was presented. This comparison was performed in terms of quality and the course of training.

scholarly journals Encoding Health Records into Pathway Representations for Deep Learning

2021 ◽  
Author(s):  
Marco Luca Sbodio ◽  
Natasha Mulligan ◽  
Stefanie Speichert ◽  
Vanessa Lopez ◽  
Joao Bettencourt-Silva
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Deep Learning ◽  
Source Code ◽  
Training Dataset ◽  
Health Records ◽  
Learning Tasks ◽  
Patient Pathways ◽  
Computational Resources ◽  
The Impact

There is a growing trend in building deep learning patient representations from health records to obtain a comprehensive view of a patient’s data for machine learning tasks. This paper proposes a reproducible approach to generate patient pathways from health records and to transform them into a machine-processable image-like structure useful for deep learning tasks. Based on this approach, we generated over a million pathways from FAIR synthetic health records and used them to train a convolutional neural network. Our initial experiments show the accuracy of the CNN on a prediction task is comparable or better than other autoencoders trained on the same data, while requiring significantly less computational resources for training. We also assess the impact of the size of the training dataset on autoencoders performances. The source code for generating pathways from health records is provided as open source.

Review and Applications of Machine Learning and Artificial Intelligence in Engineering

2020 ◽  
pp. 1-12
Author(s):  
Melda Yucel ◽  
Gebrail Bekdaş ◽  
Sinan Melih Nigdeli
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Machine Learning ◽  
Artificial Neural Network ◽  
Development Process ◽  
Current Trend ◽  
Engineering Applications ◽  
The Past ◽  
Applications Of Machine Learning ◽  
Basic Features

This chapter presents a summary review of development of Artificial Intelligence (AI). Definitions of AI are given with basic features. The development process of AI and machine learning is presented. The developments of applications from the past to today are mentioned and use of AI in different categories is given. Prediction applications using artificial neural network are given for engineering applications. Usage of AI methods to predict optimum results is the current trend and it will be more important in the future.

scholarly journals Classification of masked image data

PLoS ONE ◽  
2021 ◽  
Vol 16 (7) ◽  
pp. e0254181
Author(s):  
Kamila Lis ◽  
Mateusz Koryciński ◽  
Konrad A. Ciecierski
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Image Data ◽  
Original Data ◽  
Machine Learning Algorithms ◽  
General Data Protection Regulation ◽  
Additional Information ◽  
Classification Of Images ◽  
Applications Of Machine Learning

Data classification is one of the most commonly used applications of machine learning. The are many developed algorithms that can work in various environments and for different data distributions that perform this task with excellence. Classification algorithms, just like other machine learning algorithms have one thing in common: in order to operate on data, they must see the data. In the present world, where concerns about privacy, GDPR (General Data Protection Regulation), business confidentiality and security are growing bigger and bigger; this requirement to work directly on the original data might become, in some situations, a burden. In this paper, an approach to the classification of images that cannot be directly accessed during training has been made. It has been shown that one can train a deep neural network to create such a representation of the original data that i) without additional information, the original data cannot be restored, and ii) that this representation—called a masked form—can still be used for classification purposes. Moreover, it has been shown that classification of the masked data can be done using both classical and neural network-based classifiers.

scholarly journals Predicting Runtime in HPC Environments for an Efficient Use of Computational Resources

2021 ◽  
Author(s):  
Mariza Ferro ◽  
Vinicius P. Klôh ◽  
Matheus Gritz ◽  
Vitor de Sá ◽  
Bruno Schulze
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Linear Regression ◽  
Decision Tree ◽  
High Performance ◽  
Performance Metrics ◽  
Scientific Applications ◽  
Computing Systems ◽  
Computational Resources ◽  
Performance Computing

Understanding the computational impact of scientific applications on computational architectures through runtime should guide the use of computational resources in high-performance computing systems. In this work, we propose an analysis of Machine Learning (ML) algorithms to gather knowledge about the performance of these applications through hardware events and derived performance metrics. Nine NAS benchmarks were executed and the hardware events were collected. These experimental results were used to train a Neural Network, a Decision Tree Regressor and a Linear Regression focusing on predicting the runtime of scientific applications according to the performance metrics.

scholarly journals Conservation laws in a neural network architecture: Enforcing the atom balance of a Julia-based photochemical model (v0.2.0)

2021 ◽  
Author(s):  
Patrick Obin Sturm ◽  
Anthony S. Wexler
Keyword(s):  
Neural Network ◽  
Conservation Laws ◽  
Network Architecture ◽  
Neural Network Architecture ◽  
Machine Precision ◽  
Emissions Scenarios ◽  
Hidden Layer ◽  
Computational Resources ◽  
Atmospheric Phenomena ◽  
Insight Into

Abstract. Models of atmospheric phenomena provide insight into climate, air quality, and meteorology, and provide a mechanism for understanding the effect of future emissions scenarios. To accurately represent atmospheric phenomena, these models consume vast quantities of computational resources. Machine learning (ML) techniques such as neural networks have the potential to emulate compute-intensive components of these models to reduce their computational burden. However, such ML surrogate models may lead to nonphysical predictions that are difficult to uncover. Here we present a neural network architecture that enforces conservation laws. Instead of simply predicting properties of interest, a physically interpretable hidden layer within the network predicts fluxes between properties which are subsequently related to the properties of interest. As an example, we design a physics-constrained neural network surrogate model of photochemistry using this approach and find that it conserves atoms as they flow between molecules to machine precision, while outperforming a naïve neural network in terms of accuracy and non-negativity of concentrations.

scholarly journals RESEARCH OF APPLICATIONS OF MACHINE LEARNING ALGORITHMS IN IMPROVING OPC SOLUTIONS

2020 ◽  
Author(s):  
Pavel Tryasoguzov ◽  
Georgiy Teplov ◽  
Alexey Kuzovkov
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Random Forest ◽  
Network Models ◽  
Machine Learning Algorithms ◽  
Gradient Boosting ◽  
Neural Network Models ◽  
Machine Learning Methods ◽  
Applications Of Machine Learning ◽  
Topological Drawing

In this paper the effectiveness of machine learning methods for solving OPC problems was consider. The task was to determine the direction of displacement and the amount of displacement of the boundary of the segment of the topological drawing. The generated training database was used to train regression, random forest, gradient boosting, and feedforward convolutional neural network models.

scholarly journals PEMODELAN PENGARUH SENTIMEN PUBLIK DI TWITTER TERKAIT POLITIK TERHADAP KURS RUPIAH DAN INDEKS HARGA SAHAM DENGAN PENDEKATAN MACHINE LEARNING

2021 ◽  
Vol 2020 (1) ◽  
pp. 989-999
Author(s):  
Epan Mareza Primahendra ◽  
Budi Yuniarto
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Support Vector Machine ◽  
Big Data ◽  
Least Square ◽  
Support Vector ◽  
Hidden Layer ◽  
Hidden Neurons

Kurs Rupiah dan indeks harga saham (IHS) berpengaruh terhadap perekonomian Indonesia. Pergerakan kurs Rupiah dan IHS dipengaruhi oleh, informasi publik, kondisi sosial, dan politik. Kejadian politik banyak menimbulkan sentimen dari masyarakat. Sentimen tersebut banyak disampaikan melalui media sosial terutama Twitter. Twitter merupakan sumber big data yang jika datanya tidak dimanfaatkan akan menjadi sampah. Pengumpulan data dilakukan pada periode 26 September 2019 - 27 Oktober 2019. Pola jumlah tweets harian yang sesuai dengan pergerakan kurs Rupiah dan IHS mengindikasikan bahwa terdapat hubungan antara sentimen di Twitter terkait situasi politik terhadap kurs Rupiah dan IHS. Penelitian ini menggunakan pendekatan machine learning dengan algoritma Neural Network dan Least Square Support Vector Machine. Penelitian ini bertujuan untuk mengetahui pengaruh sentimen terhadap kurs Rupiah dan IHS sekaligus mengkaji kedua algoritmanya. Hasilnya menjelaskan bahwa model terbaik untuk estimasi IHS yaitu NN dengan 1 hidden layer dan 2 hidden neurons. Modelnya menunjukan bahwa terdapat pengaruh antara sentimen tersebut terhadap IHS karena volatilitas estimasi IHS sudah cukup mengikuti pola pergerakan IHS aktual. Model terbaik untuk estimasi kurs Rupiah yaitu LSSVM. Pola pergerakan estimasi kurs Rupiah cenderung stagnan di atas nilai aktual. Ini mengindikasikan bahwa modelnya masih belum memuaskan dalam mengestimasi pengaruh sentimen publik terhadap kurs Rupiah.

scholarly journals Gradient boosting for the prediction of gas chromatographic retention indices

2019 ◽  
Vol 19 (6) ◽  
pp. 630-635
Author(s):  
Dmitriy D. Matyushin ◽  
Anastasia Yu. Sholokhova ◽  
Aleksey K. Buryak
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Retention Indices ◽  
Gradient Boosting ◽  
Chromatographic Retention ◽  
Relative Deviation ◽  
The Neural Network ◽  
Gas Chromatographic Retention Indices ◽  
Chromatographic Retention Indices ◽  
Hidden Layer

The estimation of gas chromatographic retention indices based on compounds structures is an importantproblem. Predicted retention indices can be used in a mass spectral library search for the identificationof unknowns. Various machine learning methods are used for this task, but methods based on decisiontrees, in particular gradient boosting, are not used widely. The aim of this work is to examine the usability ofthis method for the retention index prediction. 177 molecular descriptors computed with Chemistry Development Kit are used as the input representation of a molecule. Random subsets of the whole NIST 17 database are used as training, test and validation sets. 8000 trees with 6 leaves each are used. A neural network with one hidden layer (90 hidden nodes) is used for the comparison. The same data sets and the set of descriptors are used for the neural network and gradient boosting. The model based on gradient boosting outperforms the neural network with one hidden layer for subsets of NIST 17 and for the set of essential oils.The performance of this model is comparable or better than performance of other modern retention prediction models. The average relative deviation is ~3.0%, the median relative deviation is ~1.7% for subsets of NIST 17. The median absolute deviation is ~34 retention index units. Only non-polar liquid stationary phases (such as polydimethylsiloxane, 5% phenyl 95% polydimethylsiloxane, squalane) are considered. Errors obtained with different machine learning algorithms and with the same representation of the molecule strongly correlate with each other.

scholarly journals Predicting Fatalities in Air Accidents using CHAID XGBoost Generalized Linear Model Neural Network and Ensemble Models of Machine Learning

2020 ◽  
Vol 9 (3S) ◽  
pp. 35-39
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Linear Model ◽  
Generalized Linear Model ◽  
Historical Data ◽  
Random Trees ◽  
Ensemble Model ◽  
Human Beings ◽  
Hidden Layer ◽  
Learning Principles

The study examines the historical data of about 4700 air crashes all over the world since the first recorded air crash of 1908. Given the immense impact on human beings as well as companies, the study aimed at utilizing Machine Learning principles for predicting fatalities. The train-test partition used was 75-25. Employing the IBM SPSS Modeler, the machine learning models used included CHAID model, Neural Network, Generalized Linear Model, XGBoost, Random Trees and the Ensemble model to predict fatalities in air crashes. The best results (90.6% accuracy) were achieved through Neural Network with one hidden layer. The results presented also include comparison of the predicted versus observed results for the test data.

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