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Science and Artificial Intelligence conference (S.A.I.ence)

作者： Jetina J. Tsvaki Dmitry O. Tailakov Evgeniy N. Pavlovskiy Novosibirsk State University Novosibirsk Russia Laboratory for Fundamentals of Energy Technologies Kutateladze Institute of Thermophysics SB RAS Novosibirsk Russia Stream Data Analytics and Machine Learning Laboratory Novosibirsk State University Novosibirsk Russia

ISBN: (数字)9780738131115

ISBN: (纸本)9781665415439

Main goal of any industry is to increase productivity which in oil and gas field is to increase reservoir oil asset by producing oil in an effective and economically efficient manner. The objective of the study is to develop a water flood model for oil production enhancement using artificial neural networks and provide a model that maximizes oil production for a given water injection that in turn will extend mature fields life and decrease operational costs. Using the data comprising of daily water injection rates, oil production rates, water production, and gas production from the year 2004 to 2016 for 577 injection wells, 1344 production wells, and 36 events which had occurred during the course. Comparative analysis on the deep neural models such as Multi-Layer Perception, Convolutional Neural Networks, Long Short-Term Memory, and Gated Recurrent Neural Networks are used, and Gated Recurrent Neural Networks outperformed them. To minimize the loss and improve the performance of the water flood model tabular data mix-up was adopted on all the models above. The results showed that the data mixed up Gated Recurrent Neural Network outperformed all the other models. To maximize the oil production Nelder-Mead optimization method was adopted to find appropriate water injection rates. A simple two-layered multi-layer perceptron was used in modeling the nonlinear relationship between water injection and oil production to avoid function complexity.

关键词： Production Oils Numerical models Computational modeling Recurrent neural networks Genetic algorithms data mining

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Deep learning algorithms to classify Fitzpatrick skin types for smartphone based NIRS imaging device 15

Deep learning algorithms to classify Fitzpatrick skin types ...

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Next-Generation Spectroscopic Technologies XV 2023

作者： Leizaola, Daniela Sobhan, Masrur Kaile, Kacie Mondal, Ananda Mohan Godavarty, Anuradha Optical Imaging Laboratory Department of Biomedical Engineering Florida International University 10555 W Flagler St MiamiFL33174 United States Machine Learning and Data Analytics Group Knight Foundation School Computing and Information Sciences Florida International University 11200 SW 8th St MiamiFL33199 United States

ISBN: (数字)9781510661479

ISBN: (纸本)9781510661462

Non-contact imaging modalities for monitoring wound health could supplement the current standard which is a visual inspection by clinicians. Recently, a smartphone oxygenation tool (SPOT) has been developed for physiological imaging of tissue oxygenation changes in response to treatment. However, upon visual inspection of the wound bed and surrounding area, there are variations of pigmentation. Melanin concentration is a highly absorbing chromophore that can impact spatial oxygenation measurements. The objective of this study was to classify the six Fitzpatrick Skin Types (FST) by applying deep learning techniques prior to correcting SPOT’s oxygenation maps. In this IRB-approved study, control subjects were imaged on seven skin locations of varying FST (I-VI) under three different lighting conditions using SPOT device’s camera. A benchmark dataset with samples of 28 × 28 pixel images of human subjects’ feet was developed in three color spaces (R-G-B, Y-Cb-Cr, L-a-b). A deep learning algorithm, specifically a convolutional neural network (CNN), was used to classify skin into six FST (classes). Preliminary results showed the skin types on control subjects’ feet could be classified using deep learning with hyperparameter tuning with accuracies of > 82%. Our ongoing efforts are focused on extensive in-vivo studies on control subjects of FST I-VI on feet towards future implementation of the technology for diabetic wounds and oxygenation mapping using the SPOT device. © 2023 SPIE.

关键词： Smartphones

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167-PFlops deep learning for electron microscopy: From learning physics to atomic manipulation

167-PFlops deep learning for electron microscopy: From learn...

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2018 International Conference for High Performance Computing, Networking, Storage, and Analysis, SC 2018

作者： Patton, Robert M. Travis Johnston, J. Young, Steven R. Schuman, Catherine D. March, Don D. Potok, Thomas E. Rose, Derek C. Lim, Seung-Hwan Karnowski, Thomas P. Ziatdinov, Maxim A. Kalinin, Sergei V. Oak Ridge National Laboratory Oak RidgeTN37831-6085 United States Computational Data Analytics Group United States Geographic Information Science and Technology Group United States Imaging Signals and Machine Learning Group United States Institute for Functional Imaging of Materials United States Center for Nanophase Materials Sciences United States

ISBN: (纸本)9781538683842

An artificial intelligence system called MENNDL, which used 25,200 NVIDIA Volta GPUs on Oak Ridge National laboratory's Summit machine, automatically designed an optimal deep learning network in order to extract structural information from raw atomic-resolution microscopy data. In a few hours, MENNDL creates and evaluates millions of networks using a scalable, parallel, asynchronous genetic algorithm augmented with a support vector machine to automatically find a superior deep learning network topology and hyper-parameter set than a human expert can find in months. For the application of electron microscopy, the system furthers the goal of improving our understanding of the electron-beam-matter interactions and real-time image-based feedback, which enables a huge step beyond human capacity towards nanofabricating materials automatically. MENNDL has been scaled to the 4,200 available nodes of Summit achieving a measured 152.5 PFlops, with an estimated sustained performance of 167 PFlops when the entire machine is available. © 2018 IEEE.

关键词： Genetic algorithms

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machine learning algorithms to classify Fitzpatrick skin types during tissue oxygenation mapping

Machine learning algorithms to classify Fitzpatrick skin typ...

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Clinical and Translational Biophotonics, Translational 2022

作者： Kaile, Kacie Sobhan, Masrur Mondal, Ananda Godavarty, Anuradha Department of Biomedical Engineering Optical Imaging Laboratory Florida International University 10555 West Flagler St MiamiFL33174 United States Machine Learning and Data Analytics Group Knight Foundation School of Computing and Information Sciences Florida International University 11200 SW 8th St MiamiFL33199 United States

A machine learning approach is implemented to label different skin tones towards melanin correction of tissue oxygenation maps, when using a smartphone based near-infrared (NIR) imaging device. © 2022 Optica Publ... 详细信息

ISBN: (纸本)9781557528209

关键词： Tissue

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A Deep Feedforward Neural Network and Shallow Architectures Effectiveness Comparison: Flight Delays Classification Perspective 21

A Deep Feedforward Neural Network and Shallow Architectures ...

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Proceedings of the 5th International Conference on Advances in Artificial Intelligence

作者： Desmond Bala Bisandu Mohammed Salih Homaid irene Moulitsas Salvatore Filippone Department of Computational Engineering Sciences Cranfield University UK and Machine Learning and Data Analytics Laboratory Digital Aviation Research and Technology Centre (DARTeC) Cranfield University UK Department of Computational Engineering Sciences Cranfield University UK and Machine Learning and Data Analytics Laboratory Digital Aviation Research and Technology Centre(DARTeC) Cranfield University UK Department of Computational Engineering Sciences Cranfield University UK and Machine learning and data analytics laboratory digital aviation research and technology centre(DARTeC) Cranfield University UK Department of Civil and Computer Engineering Universita di Roma Italy and Department of Computational Engineering Sciences Cranfield Univeristy United Kingdom

ISBN: (纸本)9781450390699

Flight delays have negatively impacted the socio-economics state of passengers, airlines and airports, resulting in huge economic losses. Hence, it has become necessary to correctly predict their occurrences in decision-making because it is important for the effective management of the aviation industry. Developing accurate flight delays classification models depends mostly on the air transportation system complexity and the infrastructure available in airports, which may be a region-specific issue. However, no specific prediction or classification model can handle the individual characteristics of all airlines and airports at the same time. Hence, the need to further develop and compare predictive models for the aviation decision system of the future cannot be over-emphasised. In this research, flight on-time data records from the United State Bureau of Transportation Statistics was employed to evaluate the performances of Deep Feedforward Neural Network, Neural Network, and Support Vector machine models on a binary classification problem. The research revealed that the models achieved different accuracies of flight delay classifications. The Support Vector machine had the worst average accuracy than Neural Network and Deep Feedforward Neural Network in the initial experiment. The Deep Feedforward Neural Network outperformed Support Vector machines and Neural Network with the best average percentage accuracies. Going further to investigate the Deep Feedforward Neural Network architecture on different parameters against itself suggest that training a Deep Feedforward Neural Network algorithm, regardless of data training size, the classification accuracy peaks. We examine which number of epochs works best in our flight delay classification settings for the Deep Feedforward Neural Network. Our experiment results demonstrate that having many epochs affects the convergence rate of the model; unlike when hidden layers are increased, it does not ensure better or higher ac

关键词： deep learning Support Vector machine flight delays Classification deep neural network

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Miscellaneous EEG Preprocessing and machine learning for Pilots' Mental States Classification: Implications 22

Miscellaneous EEG Preprocessing and Machine Learning for Pil...

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Proceedings of the 6th International Conference on Advances in Artificial Intelligence

作者： Ibrahim Mohammad Alreshidi Irene Moulitsas Karl W. Jenkins Centre for Computational Engineering Sciences Cranfield University UK and Machine Learning and Data Analytics Laboratory Digital Aviation Research and Technology Centre (DARTeC) UK Centre for Computational Engineering Sciences Cranfield University UK

ISBN: (纸本)9781450396943

Higher cognitive process efforts may result in mental exhaustion, poor performance, and long-term health issues. An EEG-based methods for detecting a pilot's mental state have recently been created utilizing machine learning algorithms. EEG signals include a significant noise component, and these approaches either ignore this or use a random mix of preprocessing techniques to reduce noise. In the absence of uniform preprocessing procedures for cleaning, it would be impossible to compare the efficacy of machine learning models across research, even if they employ data obtained from the same experiment. In this study, we intend to evaluate how preprocessing approaches affect the performance of machine learning models. To do this, we concentrated on fundamental preprocessing techniques, such as a band-pass filter and independent component analysis. Using a publicly accessible actual physiological dataset gathered from a pilot who was exposed to a variety of mental events, we explore the influence of these preprocessing strategies on two machine learning models, SVMs and ANNs. Our findings indicate that the performance of the models is unaffected by preprocessing techniques. Moreover, our findings indicate that the models were able to anticipate the mental states from merged data collected in two environments. These findings demonstrate the necessity for a standardized methodological framework for the application of machine learning models to EEG inputs.

关键词： machine learning EEG preprocessing Mental states classification

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End-to-end simultaneous learning of single-particle orientation and 3D map reconstruction from cryo-electron microscopy data

arXiv

引用

arXiv 2021年

作者： Nashed, Youssef S.G. Poitevin, Frederic Gupta, Harshit Woollard, Geoffrey Kagan, Michael Yoon, Chuck Ratner, Daniel Machine Learning Initiative SLAC National Accelerator Laboratory United States Department of LCLS Data Analytics SLAC National Accelerator Laboratory United States Department of Mathematics University of British Columbia Canada Fundamental Physics Directorate SLAC National Accelerator Laboratory United States

Cryogenic electron microscopy (cryo-EM) provides images from different copies of the same biomolecule in arbitrary orientations. Here, we present an end-to-end unsupervised approach that learns individual particle orientations from cryo-EM data while reconstructing the average 3D map of the biomolecule, starting from a random initialization. The approach relies on an auto-encoder architecture where the latent space is explicitly interpreted as orientations used by the decoder to form an image according to the linear projection model. We evaluate our method on simulated data and show that it is able to reconstruct 3D particle maps from noisy- and CTF-corrupted 2D projection images of unknown particle orientations. © 2021, CC BY.

关键词： Biomolecules

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Reproducing kernel Hilbert space, Mercer's theorem, eigenfunctions, Nyström method, and use of kernels in machine learning: Tutorial and survey

arXiv

引用

arXiv 2021年

作者： Ghojogh, Benyamin Ghodsi, Ali Karray, Fakhri Crowley, Mark Department of Electrical and Computer Engineering Machine Learning Laboratory University of Waterloo WaterlooON Canada Department of Statistics and Actuarial Science David R. Cheriton School of Computer Science Data Analytics Laboratory University of Waterloo WaterlooON Canada Department of Electrical and Computer Engineering Centre for Pattern Analysis and Machine Intelligence University of Waterloo WaterlooON Canada

This is a tutorial and survey paper on kernels, kernel methods, and related fields. We start with reviewing the history of kernels in functional analysis and machine learning. Then, Mercer kernel, Hilbert and Banach spaces, Reproducing Kernel Hilbert Space (RKHS), Mercer's theorem and its proof, frequently used kernels, kernel construction from distance metric, important classes of kernels (including bounded, integrally positive definite, universal, stationary, and characteristic kernels), kernel centering and normalization, and eigenfunctions are explained in detail. Then, we introduce types of use of kernels in machine learning including kernel methods (such as kernel support vector machines), kernel learning by semi-definite programming, Hilbert-Schmidt independence criterion, maximum mean discrepancy, kernel mean embedding, and kernel dimensionality reduction. We also cover rank and factorization of kernel matrix as well as the approximation of eigenfunctions and kernels using the Nyström method. This paper can be useful for various fields of science including machine learning, dimensionality reduction, functional analysis in mathematics, and mathematical physics in quantum mechanics. Copyright © 2021, The Authors. All rights reserved.

关键词： Functional analysis

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Multidimensional scaling, Sammon mapping, and Isomap: Tutorial and survey

arXiv

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arXiv 2020年

Multidimensional Scaling (MDS) is one of the first fundamental manifold learning methods. It can be categorized into several methods, i.e., classical MDS, kernel classical MDS, metric MDS, and non-metric MDS. Sammon mapping and Isomap can be considered as special cases of metric MDS and kernel classical MDS, respectively. In this tutorial and survey paper, we review the theory of MDS, Sammon mapping, and Isomap in detail. We explain all the mentioned categories of MDS. Then, Sammon mapping, Isomap, and kernel Isomap are explained. Out-of-sample embedding for MDS and Isomap using eigenfunctions and kernel mapping are introduced. Then, Nystrom approximation and its use in landmark MDS and landmark Isomap are introduced for big data embedding. We also provide some simulations for illustrating the embedding by these methods. Copyright © 2020, The Authors. All rights reserved.

关键词： Embeddings

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KKT conditions, first-order and second-order optimization, and distributed optimization: Tutorial and survey

arXiv

引用

arXiv 2021年

作者： Ghojogh, Benyamin Ghodsi, Ali Karray, Fakhri Crowley, Mark Department of Electrical and Computer Engineering Machine Learning Laboratory University of Waterloo WaterlooON Canada Department of Statistics and Actuarial Science & David R. Cheriton School of Computer Science Data Analytics Laboratory University of Waterloo WaterlooON Canada Department of Electrical and Computer Engineering Centre for Pattern Analysis and Machine Intelligence University of Waterloo WaterlooON Canada

This is a tutorial and survey paper on Karush-Kuhn-Tucker (KKT) conditions, first-order and second-order numerical optimization, and distributed optimization. After a brief review of history of optimization, we start with some preliminaries on properties of sets, norms, functions, and concepts of optimization. Then, we introduce the optimization problem, standard optimization problems (including linear programming, quadratic programming, and semidefinite programming), and convex problems. We also introduce some techniques such as eliminating inequality, equality, and set constraints, adding slack variables, and epigraph form. We introduce Lagrangian function, dual variables, KKT conditions (including primal feasibility, dual feasibility, weak and strong duality, complementary slackness, and stationarity condition), and solving optimization by method of Lagrange multipliers. Then, we cover first-order optimization including gradient descent, line-search, convergence of gradient methods, momentum, steepest descent, and backpropagation. Other first-order methods are explained, such as accelerated gradient method, stochastic gradient descent, mini-batch gradient descent, stochastic average gradient, stochastic variance reduced gradient, AdaGrad, RMSProp, and Adam optimizer, proximal methods (including proximal mapping, proximal point algorithm, and proximal gradient method), and constrained gradient methods (including projected gradient method, projection onto convex sets, and Frank-Wolfe method). We also cover non-smooth and `1 optimization methods including lasso regularization, convex conjugate, Huber function, soft-thresholding, coordinate descent, and subgradient methods. Then, we explain second-order methods including Newton’s method for unconstrained, equality constrained, and inequality constrained problems. We explain the interior-point method, barrier methods, Wolfe conditions for line-search, fast solving system of equations (including decomposition methods a

关键词： Quadratic programming

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