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Computational biology: deep learning

William Jones, Kaur Alasoo, Dmytro Fishman, Leopold Parts
2017 Emerging Topics in Life Sciences  
Now, ideas for constructing and training networks and even off-the-shelf models have been adapted from the rapidly developing machine learning subfield to improve performance in a range of computational  ...  This exciting class of methods, based on artificial neural networks, quickly became popular due to its competitive performance in prediction problems.  ...  Acknowledgements We thank Oliver Stegle for the comments on the text.  ... 
doi:10.1042/etls20160025 pmid:33525807 pmcid:PMC7289034 fatcat:qnw2yndsp5aqlnxxshtaipzctu

Deep Physiological Arousal Detection in a Driving Simulator Using Wearable Sensors

Aaqib Saeed, Stojan Trajanovski, Maurice van Keulen, Jan van Erp
2017 2017 IEEE International Conference on Data Mining Workshops (ICDMW)  
Our results show that a 7-layers convolutional neural network trained on raw physiological signals (such as heart rate, skin conductance and skin temperature) outperforms a baseline neural network and  ...  On this dataset, we evaluated a range of deep neural network models for representation learning as an alternative to handcrafted feature extraction.  ...  The convolutional neural network trained on raw physiological signals (i.e. heart rate, skin conductance and skin temperature) outperformed baseline neural network and denoising autoencoder models with  ... 
doi:10.1109/icdmw.2017.69 dblp:conf/icdm/SaeedTKE17 fatcat:vvsv22kd5vhzpgdy6xu776lfsm

Data Analytics in Smart Healthcare: The Recent Developments and Beyond

Miltiadis D. Lytras, Kwok Tai Chui, Anna Visvizi
2019 Applied Sciences  
The concepts of the smart city and the Internet of Things (IoT) have been facilitating the rollout of medical devices and systems to capture valuable information of humanity.  ...  In recent decade, retardation of the adoption of data analytics algorithms and systems in healthcare has been decreasing, and there is tremendous growth in data analytics research on healthcare data.  ...  Work Application Methodology [7] Prediction of inpatient violence incidents Recurrent neural network; convolutional neural network; neural network; Naïve Bayes; support vector machine; decision tree  ... 
doi:10.3390/app9142812 fatcat:gf2iionncjhd5prphkdkiknoky

Stress detection using deep neural networks

Russell Li, Zhandong Liu
2020 BMC Medical Informatics and Decision Making  
Developing robust methods for the rapid and accurate detection of human stress is of paramount importance.  ...  Results The deep convolutional neural network achieved 99.80% and 99.55% accuracy rates for binary and 3-class classification, respectively.  ...  [10] used a recurrent neural network that analyzed speech to detect stress.  ... 
doi:10.1186/s12911-020-01299-4 pmid:33380334 fatcat:hspx35nvsbcnncsdv6pqrhyeve

Cancer Diagnosis Using Deep Learning: A Bibliographic Review

Khushboo Munir, Hassan Elahi, Afsheen Ayub, Fabrizio Frezza, Antonello Rizzi
2019 Cancers  
Boltzmann's machine (RBM), stacked autoencoders (SAE), convolutional autoencoders (CAE), recurrent neural networks (RNNs), long short-term memory (LTSM), multi-scale convolutional neural network (M-CNN  ...  ), multi-instance learning convolutional neural network (MIL-CNN).  ...  Recurrent Neural Networks (RNNs) Recurrent neural networks are a powerful model of sequential data [126] .  ... 
doi:10.3390/cancers11091235 pmid:31450799 pmcid:PMC6770116 fatcat:ktuuttdu6zc7phj3mahp5yynxq

Classifying Behaviours in Videos with Recurrent Neural Networks

Javier Abellan-Abenza, Alberto Garcia-Garcia, Sergiu Oprea, David Ivorra-Piqueres, Jose Garcia-Rodriguez
2017 International Journal of Computer Vision and Image Processing  
In particular, convolutional neural networks are used to detect features in the video images, meanwhile Recurrent Neural Networks are used to analyze these features and predict the possible activity in  ...  This article describes how the human activity recognition in videos is a very attractive topic among researchers due to vast possible applications.  ...  Combining several convolutional and pooling layers in parallel is a very good approach for detecting features.  ... 
doi:10.4018/ijcvip.2017100101 fatcat:heywstulmnaypikhjcv4q5ravu

SELECTED APPLICATIONS OF DEEP NEURAL NETWORKS IN SKIN LESION DIAGNOSTIC

Magdalena Michalska
2021 Informatyka Automatyka Pomiary w Gospodarce i Ochronie Środowiska  
The article provides an overview of selected applications of deep neural networks in the diagnosis of skin lesions from human dermatoscopic images, including many dermatological diseases, including very  ...  The described algorithms have been widely used in the diagnosis of skin lesions. The effectiveness, specificity, and accuracy of classifiers were compared and analysed based on available datasets.  ...  Next step is multimodal deep learning framework for automatic malignat melanoma and begin lesions detection by combining deep convolutional neural networks.  ... 
doi:10.35784/iapgos.2804 fatcat:c5zhukafsnesln6sdmevueskqe

Analysis of Big Data Technology for Health Care Services [article]

Dinesh Samuel Sathia Raj, Vijayakumar V, Bharat Rawal, Longzhi Yang
2019 arXiv   pre-print
Deep learning and other big data technologies have over time become very powerful and accurate. There are algorithms and models developed that have near human accuracy in their task.  ...  In health care, the amount of data available is massive and hence, these technologies have a great scope in health care.  ...  Convolutional Neural Networks Convolutional Neural Networks(CNNs) are deep neural networks that are particularly efficient at analyzing images.  ... 
arXiv:1909.03029v1 fatcat:ee4ljnkxkjhs5aa7ufwjympimm

Neural Network Model for Artifacts Marking in EEG Signals

Olga Komisaruk, Evgeny Nikulchev
2021 International Journal of Advanced Computer Science and Applications  
As a result, the performance rates for different ML methods were obtained. The neural network model based on U-net architecture with recurrent networks elements was developed.  ...  The series of experiments were conducted to investigate the performance of different neural networks architectures for the task of artifact detection.  ...  The most frequent methods for time series analysis are such architectures as convolutional neural network and recurrent neural network. • Non-linear classifier [13] .  ... 
doi:10.14569/ijacsa.2021.0121204 fatcat:j63dn3xnn5budbf36bivhczxbq

MALIGNANT SKIN CANCER DETECTION USING CONVOLUTIONAL NEURAL NETWORKING

Rachakonda Hrithik Sagar, Abhishek Bingi, Aashray Pola, Krishna Sai Raj Goud, Tuiba Ashraf, Subrata Sahana
2021 International Journal of Technical Research & Science  
layer, then compiling Convolutional neural networks and fitting the CNN model to a dataset.  ...  Traditionally classification algorithms are Convolutional neural networking which Consists of initialization, adding a convolutional layer, summing pooling layer, summing flattening layer, summing a dense  ...  Agarap" written in this paper "An Architecture Combining Convolutional Neural Network (CNN) and Support Vector Machine (SVM) for Image Classification" [4] has proven that Convolutional neural networking  ... 
doi:10.30780/ijtrs.v06.i05.001 fatcat:4yikb3vubbbf5nqulafmejyzqy

Gesture Recognition with a Convolutional Long Short-Term Memory Recurrent Neural Network

Eleni Tsironi, Pablo V. A. Barros, Stefan Wermter
2016 The European Symposium on Artificial Neural Networks  
Inspired by the adequacy of convolutional neural networks in implicit extraction of visual features and the efficiency of Long Short-Term Memory Recurrent Neural Networks in dealing with long-range temporal  ...  dependencies, we propose a Convolutional Long Short-Term Memory Recurrent Neural Network (CNNLSTM) for the problem of dynamic gesture recognition.  ...  , this paper proposes a Convolutional Long Short-Term Memory recurrent neural network (CNNLSTM) for the task of dynamic gesture recognition.  ... 
dblp:conf/esann/TsironiBW16 fatcat:ttfhdzdygnblni3hlwf2slfoom

Recurrent Residual Convolutional Neural Network based on U-Net (R2U-Net) for Medical Image Segmentation [article]

Md Zahangir Alom, Mahmudul Hasan, Chris Yakopcic, Tarek M. Taha, and Vijayan K. Asari
2018 arXiv   pre-print
In this paper, we propose a Recurrent Convolutional Neural Network (RCNN) based on U-Net as well as a Recurrent Residual Convolutional Neural Network (RRCNN) based on U-Net models, which are named RU-Net  ...  Second, feature accumulation with recurrent residual convolutional layers ensures better feature representation for segmentation tasks.  ...  CONCLUSION AND FUTURE WORKS In this paper, we proposed an extension of the U-Net architecture using Recurrent Convolutional Neural Networks and Recurrent Residual Convolutional Neural Networks.  ... 
arXiv:1802.06955v5 fatcat:lcg67b3wffea7ik7j6kzqbsbnu

Automatic Face and Hijab Segmentation using Convolutional Network

Dina M. Madkour, Computer Engineering Depart, Arab Academy for Science, Technology and Maritime Transport, Cairo, EGYPT, Ahmed Madani, Mohamed Waleed Fakhr, Computer Engineering Depart, Arab Academy for Science, Technology and Maritime Transport, Cairo, EGYPT, Computer Engineering Depart, Arab Academy for Science, Technology and Maritime Transport, Cairo, EGYPT
2019 International Journal of Integrated Engineering  
In this paper, the proposed model uses fully convolutional network (FCN) to make semantic segmentation into skin, veil and background.  ...  Accurate image segmentation plays an important role in portrait editing, face beautification, human identification, hairstyle identification, airport Surveillance system and many other computer vision  ...  Convolutional neural networks perform a series of convolutions and pooling operations during feature detection and extraction [17] .  ... 
doi:10.30880/ijie.2019.11.07.008 fatcat:wbxwx2a3zrenxao4qivgpp5mhy

Delineation of Skin Strata in Reflectance Confocal Microscopy Images using Recurrent Convolutional Networks with Toeplitz Attention [article]

Alican Bozkurt, Kivanc Kose, Jaume Coll-Font, Christi Alessi-Fox, Dana H. Brooks, Jennifer G. Dy, Milind Rajadhyaksha
2017 arXiv   pre-print
In this study, we use a recurrent neural network with attention on convolutional network features.  ...  Reflectance confocal microscopy (RCM) is an effective, non-invasive pre-screening tool for skin cancer diagnosis, but it requires extensive training and experience to assess accurately.  ...  Recent studies have demonstrated that RCM imaging is highly sensitive (90 − 100 %) and specific (70 − 90 %) for detecting skin cancers [11] by expert visual inspection.  ... 
arXiv:1712.00192v1 fatcat:hjlgqomie5ghpcjhtqvxlfipe4

Gesture Recognition using CNN and RNN

2020 International journal of recent technology and engineering  
This system is implemented using the deep learning models such as the Convolution Neural Network (CNN) and the Recurrent Neural Network (RNN).  ...  HCI allows computers to capture and interpret human gestures as commands. A real-time Hand Gesture Recognition System is implemented and is used for operating electronic appliances.  ...  CONCLUSION Thus the Gesture Recognition System for controlling electronic appliances is implemented using the Deep learning model by combining the Convolution Neural Network and Recurrent Neural Network  ... 
doi:10.35940/ijrte.b3417.079220 fatcat:m6qjotzg4rcmla3ca73wdkwsq4
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