Deep Learning and Biomedical Engineering

Deep Learning ◽

Real World ◽

Biomedical Engineering ◽

Input Data ◽

Reference Source ◽

Data Input ◽

Essential Information

Deep learning (DL) is a method of machine learning, as running over artificial neural networks, which has a structure above the standards to deal with large amounts of data. That is generally because of the increasing amount of data, input data sizes, and of course, greater complexity of objective real-world problems. Performed research studies in the associated literature show that the DL currently has a good performance among considered problems and it seems to be a strong solution for more advanced problems of the future. In this context, this chapter aims to provide some essential information about DL and its applications within the field of biomedical engineering. The chapter is organized as a reference source for enabling readers to have an idea about the relation between DL and biomedical engineering.

Convolutional Neural Network

10.4018/978-1-6684-2408-7.ch077 ◽

2022 ◽

pp. 1559-1575

Author(s):

Mário Pereira Véstias

Keyword(s):

Neural Network ◽

Machine Learning ◽

Neural Networks ◽

Deep Learning ◽

Convolutional Neural Network ◽

Machine Learning Algorithms ◽

Training Data ◽

Machine Learning Model ◽

Machine learning is the study of algorithms and models for computing systems to do tasks based on pattern identification and inference. When it is difficult or infeasible to develop an algorithm to do a particular task, machine learning algorithms can provide an output based on previous training data. A well-known machine learning model is deep learning. The most recent deep learning models are based on artificial neural networks (ANN). There exist several types of artificial neural networks including the feedforward neural network, the Kohonen self-organizing neural network, the recurrent neural network, the convolutional neural network, the modular neural network, among others. This article focuses on convolutional neural networks with a description of the model, the training and inference processes and its applicability. It will also give an overview of the most used CNN models and what to expect from the next generation of CNN models.

Artificial Neural Networks and Machine Learning – ICANN 2019: Deep Learning

10.1007/978-3-030-30484-3 ◽

2019 ◽

Keyword(s):

Machine Learning ◽

Neural Networks ◽

Deep Learning ◽

ACCURACY IMPROVING OF PRE-TRAINED NEURAL NETWORKS BY FINE TUNING

EurasianUnionScientists ◽

10.31618/esu.2413-9335.2021.5.82.1231 ◽

2021 ◽

Vol 5 (1(82)) ◽

pp. 26-28

Author(s):

D. Кonarev ◽

А. Gulamov

Keyword(s):

Machine Learning ◽

Neural Networks ◽

Pattern Recognition ◽

Input Data ◽

Fine Tuning ◽

Methods of accuracy improving of pre-trained networks are discussed. Images of ships are input data for the networks. Networks are built and trained using Keras and TensorFlow machine learning libraries. Fine tuning of previously trained convoluted artificial neural networks for pattern recognition tasks is described. Fine tuning of VGG16 and VGG19 networks are done by using Keras Applications. The accuracy of VGG16 network with finetuning of the last convolution unit increased from 94.38% to 95.21%. An increase is only 0.83%. The accuracy of VGG19 network with fine-tuning of the last convolution unit increased from 92.97% to 96.39%, which is 3.42%.

From Reflex to Reflection: Two Tricks AI Could Learn from Us

Philosophies ◽

10.3390/philosophies4020027 ◽

2019 ◽

Vol 4 (2) ◽

pp. 27

Author(s):

Jean-Louis Dessalles

Keyword(s):

Machine Learning ◽

Neural Networks ◽

Deep Learning ◽

Cognitive Processes ◽

Real World ◽

Machine Learning Techniques ◽

Human Intervention ◽

Real World Data ◽

World Data ◽

Learning Techniques

Deep learning and other similar machine learning techniques have a huge advantage over other AI methods: they do function when applied to real-world data, ideally from scratch, without human intervention. However, they have several shortcomings that mere quantitative progress is unlikely to overcome. The paper analyses these shortcomings as resulting from the type of compression achieved by these techniques, which is limited to statistical compression. Two directions for qualitative improvement, inspired by comparison with cognitive processes, are proposed here, in the form of two mechanisms: complexity drop and contrast. These mechanisms are supposed to operate dynamically and not through pre-processing as in neural networks. Their introduction may bring the functioning of AI away from mere reflex and closer to reflection.

Chapter 8. Machine Learning, Deep Learning—Artificial Neural Networks

Big Data Analytics Methods ◽

10.1515/9781547401567-009 ◽

2019 ◽

pp. 155-184

Keyword(s):

Machine Learning ◽

Neural Networks ◽

Deep Learning ◽

Offline Handwritten Signature Authentication with Conditional Deep Convolutional Generative Adversarial Networks

10.5753/eniac.2021.18277 ◽

2021 ◽

Author(s):

David C. Yonekura ◽

Elloá B. Guedes

Keyword(s):

Machine Learning ◽

Neural Networks ◽

Deep Learning ◽

Real World ◽

Generative Adversarial Networks ◽

Equal Error Rate ◽

Training Time ◽

Adversarial Networks ◽

Handwritten Signature ◽

State Of Art

Handwritten signature authentication systems are important in many real world scenarios to avoid frauds. Thanks to Deep Learning, state-of-art solutions have been proposed to this problem by making use of Convolutional Neural Networks, but other models in this Machine Learning subarea are still to be further explored. In this perspective, the present article introduces a Conditional Deep Convolutional Generative Adversarial Networks (cDCGAN) approach whose experimental results in a realistic dataset with skilled forgeries have Equal Error Rate (EER) of 18.53% and balanced accuracy of 87.91%. These results validate a writerdependent cDCGAN-based solution to the signature authentication problem in a real world scenario where no forgeries are available nor required in training time.

Encyclopedia of Information Science and Technology, Fifth Edition - Advances in Information Quality and Management ◽

Convolutional Neural Network

10.4018/978-1-7998-3479-3.ch002 ◽

2021 ◽

pp. 12-26

Author(s):

Mário Pereira Véstias

Keyword(s):

Neural Network ◽

Machine Learning ◽

Neural Networks ◽

Deep Learning ◽

Convolutional Neural Network ◽

Machine Learning Algorithms ◽

Training Data ◽

Machine Learning Model ◽

Machine learning is the study of algorithms and models for computing systems to do tasks based on pattern identification and inference. When it is difficult or infeasible to develop an algorithm to do a particular task, machine learning algorithms can provide an output based on previous training data. A well-known machine learning model is deep learning. The most recent deep learning models are based on artificial neural networks (ANN). There exist several types of artificial neural networks including the feedforward neural network, the Kohonen self-organizing neural network, the recurrent neural network, the convolutional neural network, the modular neural network, among others. This article focuses on convolutional neural networks with a description of the model, the training and inference processes and its applicability. It will also give an overview of the most used CNN models and what to expect from the next generation of CNN models.

Deep learning as a tool for ecology and evolution

10.32942/osf.io/nt3as ◽

2021 ◽

Author(s):

Marek L. Borowiec ◽

Paul Frandsen ◽

Rebecca Dikow ◽

Alexander McKeeken ◽

Gabriele Valentini ◽

...

Keyword(s):

Machine Learning ◽

Neural Networks ◽

Population Genetics ◽

Deep Learning ◽

Language Processing ◽

Predictive Modeling ◽

Input Data ◽

Autonomous Driving ◽

Behavioral Studies ◽

Inference Methods

Deep learning is driving recent advances behind many everyday technologies, including those relying on speech and image recognition, natural language processing, and autonomous driving. It is also gaining popularity in biology, where it has been used for automated species identification, environmental monitoring, behavioral studies, DNA sequencing, and population genetics and phylogenetics, among other applications. Deep learning relies on artificial neural networks for predictive modeling and excels at recognizing complex patterns. Operating within the machine learning paradigm, deep learning can be viewed as an alternative to likelihood-based inference methods. It has desirable properties, including good performance and scaling with increasing complexity, while posing unique challenges such as sensitivity to bias in input data. In this review we provide a gentle introduction to deep learning, review its applications in ecology and evolution, and discuss its limitations and efforts to overcome them. We also provide a practical primer for biologists interested in including deep learning in their toolkit and identify its possible future applications.

A Machine Learning Approach as a Surrogate for a Finite Element Analysis: Status of Research and Application to One Dimensional Systems

Sensors ◽

10.3390/s21051654 ◽

2021 ◽

Vol 21 (5) ◽

pp. 1654

Author(s):

Poojitha Vurtur Badarinath ◽

Maria Chierichetti ◽

Fatemeh Davoudi Kakhki

Keyword(s):

Machine Learning ◽

Neural Networks ◽

Finite Element Analysis ◽

Finite Element ◽

Stress Distribution ◽

Maintenance Scheduling ◽

Element Analysis ◽

One Dimensional ◽

Current maintenance intervals of mechanical systems are scheduled a priori based on the life of the system, resulting in expensive maintenance scheduling, and often undermining the safety of passengers. Going forward, the actual usage of a vehicle will be used to predict stresses in its structure, and therefore, to define a specific maintenance scheduling. Machine learning (ML) algorithms can be used to map a reduced set of data coming from real-time measurements of a structure into a detailed/high-fidelity finite element analysis (FEA) model of the same system. As a result, the FEA-based ML approach will directly estimate the stress distribution over the entire system during operations, thus improving the ability to define ad-hoc, safe, and efficient maintenance procedures. The paper initially presents a review of the current state-of-the-art of ML methods applied to finite elements. A surrogate finite element approach based on ML algorithms is also proposed to estimate the time-varying response of a one-dimensional beam. Several ML regression models, such as decision trees and artificial neural networks, have been developed, and their performance is compared for direct estimation of the stress distribution over a beam structure. The surrogate finite element models based on ML algorithms are able to estimate the response of the beam accurately, with artificial neural networks providing more accurate results.