A review on optimization algorithm for deep learning method in bioinformatics field

Yousoff, Siti Noorain Mohmad; Baharin, Amirah; Abdullah, Afnizanfaizal

doi:10.1109/iecbes.2016.7843542

Cited by 8 publications

(2 citation statements)

References 24 publications

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“…This evaluation will produce an error value that will lead to the adjustment of connected weight working backward from the output layer to the hidden layer and to the input layer until the output is close to the expected result. This procedure is referred to as backpropagation [46,47].…”

Section: Deep Learning Methodsmentioning

confidence: 99%

Deep Learning Intervention for Health Care Challenges: Some Biomedical Domain Considerations

Igbe

Liu

et al. 2019

JMIR Mhealth Uhealth

135

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The use of deep learning (DL) for the analysis and diagnosis of biomedical and health care problems has received unprecedented attention in the last decade. The technique has recorded a number of achievements for unearthing meaningful features and accomplishing tasks that were hitherto difficult to solve by other methods and human experts. Currently, biological and medical devices, treatment, and applications are capable of generating large volumes of data in the form of images, sounds, text, graphs, and signals creating the concept of big data. The innovation of DL is a developing trend in the wake of big data for data representation and analysis. DL is a type of machine learning algorithm that has deeper (or more) hidden layers of similar function cascaded into the network and has the capability to make meaning from medical big data. Current transformation drivers to achieve personalized health care delivery will be possible with the use of mobile health (mHealth). DL can provide the analysis for the deluge of data generated from mHealth apps. This paper reviews the fundamentals of DL methods and presents a general view of the trends in DL by capturing literature from PubMed and the Institute of Electrical and Electronics Engineers database publications that implement different variants of DL. We highlight the implementation of DL in health care, which we categorize into biological system, electronic health record, medical image, and physiological signals. In addition, we discuss some inherent challenges of DL affecting biomedical and health domain, as well as prospective research directions that focus on improving health management by promoting the application of physiological signals and modern internet technology.

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Section: Deep Learning Methodsmentioning

confidence: 99%

Deep Learning Intervention for Health Care Challenges: Some Biomedical Domain Considerations

Igbe

Liu

et al. 2019

JMIR Mhealth Uhealth

135

View full text Add to dashboard Cite

show abstract

“…RNN maps the previous input background information to the target vector that can be processed by an internal sequence in memory. 27,28 Accordingly, the RNN is ideal for un-segmented, continuous handwriting recognition and speech recognition. The benefit of this model is the recognition of continuous data series.…”

Section: Recurrent Neural Network (Rnn)mentioning

confidence: 99%

A robust optimized convolutional neural network model for human activity recognition using sensing devices

Ata

Francies

2022

Concurrency and Computation

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Human activity recognition has become an expansive field of interest in recent years, both in academic and industrial research. Human Activity recognition (HAR) is concerning the prediction of person's movement or action such as walking, standing, sitting, up and downstairs, etc. Convolutional neural network (CNN) is a key component with deep learning. The main objective of this study is to design and implement an activity recognition algorithm in the state of the art of deep learning systems which accomplish superlative performance to detect human activities. Accordingly, an extensive comparison has been developed between different deep learning algorithms such as classical (CNN) models and Recurrent Neural Network (RNN) models with respect to the major human activities. Furthermore, a robust (CNN) deep learning model has been built up and proposed in order to enhance the recognition precision of human activities. This proposed model uses raw data acquired from a set of inertial sensor and exploring numerous human achas been built up and proposedtivities; sitting, standing, jogging, walking, and etc. Experimental results show that the precision of the proposed deep structure has achieved 97.5% with respect to the NAdam optimizer which would be considered as the most effectively recognizer compared to other deep learning architectures.

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Deep Learning for Medical Informatics and Public Health

Shastry¹,

Sanjay

Lakshmi³

et al. 2022

Studies in Big Data

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A review on optimization algorithm for deep learning method in bioinformatics field

Cited by 8 publications

References 24 publications

Deep Learning Intervention for Health Care Challenges: Some Biomedical Domain Considerations

Deep Learning Intervention for Health Care Challenges: Some Biomedical Domain Considerations

A robust optimized convolutional neural network model for human activity recognition using sensing devices

Deep Learning for Medical Informatics and Public Health

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