scholarly journals Study of Deep Learning Methods f or Fingerprint Recognition

2021 ◽  
Vol 10 (3) ◽  
pp. 192-197
Author(s):  
Mamadou Diarra ◽  
◽  
Ayikpa Kacoutchy Jean ◽  
Ballo Abou Bakary ◽  
Kouassi Brou Medard ◽  
...  
Keyword(s):  
Decision Making ◽  
Deep Learning ◽  
High Reliability ◽  
Color Images ◽  
Fingerprint Recognition ◽  
Behavioral Characteristics ◽  
Network System ◽  
Learning Method ◽  
Biometric Systems ◽  
Deep Convolution Network

Biometric systems aim to reliably identify and authenticate an individual using physiological or behavioral characteristics. Traditional systems such as the use of access cards, passwords have shown limitations such as forgotten passwords, stolen cards, etc. As an alternative, biometric systems present themselves as efficient systems with a high reliability due to the physiological characteristics of each individual. This paper focuses on a deep learning method for fingerprint recognition. The described architecture uses a pre-processing phase in which grayscale images are represented on the RGB bands and then merged to obtain color images. On the obtained color images will be extracted the characteristics of the fingerprints textures.The fingerprint images after preprocessing are used in a deep convolution network system for decision making. The method is robust with an accuracy of over 99.43% and 99.53% with the respective variants densenet-201 and ResNet-50.

scholarly journals DeepReco: Deep Learning Based Health Recommender System Using Collaborative Filtering

Computation ◽  
2019 ◽  
Vol 7 (2) ◽  
pp. 25 ◽  
Author(s):  
Abhaya Kumar Sahoo ◽  
Chittaranjan Pradhan ◽  
Rabindra Kumar Barik ◽  
Harishchandra Dubey
Keyword(s):  
Health Care ◽  
Decision Making ◽  
Deep Learning ◽  
Recommender System ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Health Condition ◽  
Healthcare Sector ◽  
Learning Method ◽  
Decision Making Processes

In today’s digital world healthcare is one core area of the medical domain. A healthcare system is required to analyze a large amount of patient data which helps to derive insights and assist the prediction of diseases. This system should be intelligent in order to predict a health condition by analyzing a patient’s lifestyle, physical health records and social activities. The health recommender system (HRS) is becoming an important platform for healthcare services. In this context, health intelligent systems have become indispensable tools in decision making processes in the healthcare sector. Their main objective is to ensure the availability of the valuable information at the right time by ensuring information quality, trustworthiness, authentication and privacy concerns. As people use social networks to understand their health condition, so the health recommender system is very important to derive outcomes such as recommending diagnoses, health insurance, clinical pathway-based treatment methods and alternative medicines based on the patient’s health profile. Recent research which targets the utilization of large volumes of medical data while combining multimodal data from disparate sources is discussed which reduces the workload and cost in health care. In the healthcare sector, big data analytics using recommender systems have an important role in terms of decision-making processes with respect to a patient’s health. This paper gives a proposed intelligent HRS using Restricted Boltzmann Machine (RBM)-Convolutional Neural Network (CNN) deep learning method, which provides an insight into how big data analytics can be used for the implementation of an effective health recommender engine, and illustrates an opportunity for the health care industry to transition from a traditional scenario to a more personalized paradigm in a tele-health environment. By considering Root Square Mean Error (RSME) and Mean Absolute Error (MAE) values, the proposed deep learning method (RBM-CNN) presents fewer errors compared to other approaches.

scholarly journals Deep learning for image-based large-flowered chrysanthemum cultivar recognition

Plant Methods ◽  
2019 ◽  
Vol 15 (1) ◽  
Author(s):  
Zhilan Liu ◽  
Jue Wang ◽  
Ye Tian ◽  
Silan Dai
Keyword(s):  
Decision Making ◽  
Deep Learning ◽  
Recognition Performance ◽  
Commercial Application ◽  
Learning Method ◽  
Key Factors ◽  
Disc Floret ◽  
Model Generalization ◽  
Basic Work ◽  
Human Decision

Abstract Background Cultivar recognition is a basic work in flower production, research, and commercial application. Chinese large-flowered chrysanthemum (Chrysanthemum × morifolium Ramat.) is miraculous because of its high ornamental value and rich cultural deposits. However, the complicated capitulum structure, various floret types and numerous cultivars hinder chrysanthemum cultivar recognition. Here, we explore how deep learning method can be applied to chrysanthemum cultivar recognition. Results We propose deep learning models with two networks VGG16 and ResNet50 to recognize large-flowered chrysanthemum. Dataset A comprising 14,000 images for 103 cultivars, and dataset B comprising 197 images from different years were collected. Dataset A was used to train the networks and determine the calibration accuracy (Top-5 rate of above 98%), and dataset B was used to evaluate the model generalization performance (Top-5 rate of above 78%). Moreover, gradient-weighted class activation mapping (Grad-CAM) visualization and feature clustering analysis were used to explore how the deep learning model recognizes chrysanthemum cultivars. Conclusion Deep learning method applied to cultivar recognition is a breakthrough in horticultural science with the advantages of strong recognition performance and high recognition speed. Inflorescence edge areas, disc floret areas, inflorescence colour and inflorescence shape may well be the key factors in model decision-making process, which are also critical in human decision-making.

scholarly journals A novel deep learning approach to extract Chinese clinical entities for lung cancer screening and staging

2021 ◽  
Vol 21 (S2) ◽  
Author(s):  
Huanyao Zhang ◽  
Danqing Hu ◽  
Huilong Duan ◽  
Shaolei Li ◽  
Nan Wu ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Decision Making ◽  
Deep Learning ◽  
Cancer Screening ◽  
Clinical Decision Making ◽  
Lung Cancer Screening ◽  
Clinical Decision ◽  
Experimental Results ◽  
Entity Recognition ◽  
Learning Method

Abstract Background Computed tomography (CT) reports record a large volume of valuable information about patients’ conditions and the interpretations of radiology images from radiologists, which can be used for clinical decision-making and further academic study. However, the free-text nature of clinical reports is a critical barrier to use this data more effectively. In this study, we investigate a novel deep learning method to extract entities from Chinese CT reports for lung cancer screening and TNM staging. Methods The proposed approach presents a new named entity recognition algorithm, namely the BERT-based-BiLSTM-Transformer network (BERT-BTN) with pre-training, to extract clinical entities for lung cancer screening and staging. Specifically, instead of traditional word embedding methods, BERT is applied to learn the deep semantic representations of characters. Following the long short-term memory layer, a Transformer layer is added to capture the global dependencies between characters. Besides, pre-training technique is employed to alleviate the problem of insufficient labeled data. Results We verify the effectiveness of the proposed approach on a clinical dataset containing 359 CT reports collected from the Department of Thoracic Surgery II of Peking University Cancer Hospital. The experimental results show that the proposed approach achieves an 85.96% macro-F1 score under exact match scheme, which improves the performance by 1.38%, 1.84%, 3.81%,4.29%,5.12%,5.29% and 8.84% compared to BERT-BTN, BERT-LSTM, BERT-fine-tune, BERT-Transformer, FastText-BTN, FastText-BiLSTM and FastText-Transformer, respectively. Conclusions In this study, we developed a novel deep learning method, i.e., BERT-BTN with pre-training, to extract the clinical entities from Chinese CT reports. The experimental results indicate that the proposed approach can efficiently recognize various clinical entities about lung cancer screening and staging, which shows the potential for further clinical decision-making and academic research.

scholarly journals Automatic Fault Location Identification and Isolation Method for Smart Distribution Network in Surabaya City

2021 ◽  
Vol 2117 (1) ◽  
pp. 012025
Author(s):  
N H Rohiem ◽  
A Soeprijanto ◽  
O Penangsang ◽  
N P U Putra ◽  
R Defianti ◽  
...  
Keyword(s):  
Deep Learning ◽  
Distribution System ◽  
Fault Location ◽  
Network System ◽  
Learning Method ◽  
Current Profile ◽  
Isolation Method ◽  
Disturbed Areas ◽  
Smart Distribution Network ◽  
The City

Abstract There are various types of fault that can occur in the distribution system network, so it is necessary to identify the location of the fault and isolate the fault in the area of the fault. The city of Surabaya is in preparation for the development of a smart city, so it is necessary to prepare a smart distribution system network system that can identify locations and isolate disturbed areas automatically. This paper describes the reconfiguration process to improve the value of losses in the system which results in a decrease in the value of total line losses after reconfiguration of 313.46 kW from 8 scenarios and includes the effect of adding solar energy to the existing network. The process of identifying the fault location and the isolation process on the Surabaya distribution system network in this paper uses the deep learning method. The fault location is determined based on the voltage and current profile of each bus in the system, while the isolation process is carried out by opening the switch closest to the fault area. In this process, deep learning can provide accurate fault location and isolation results for 6 fault tests.

Applying Deep Learning to Audit Procedures: An Illustrative Framework

2019 ◽  
Vol 33 (3) ◽  
pp. 89-109 ◽  
Author(s):  
Ting (Sophia) Sun
Keyword(s):  
Decision Making ◽  
Deep Learning ◽  
Data Warehouse ◽  
Visual Recognition ◽  
Prediction Models ◽  
Historical Data ◽  
Structured Data ◽  
Text Understanding ◽  
Audit Data ◽  
Learning Functions

SYNOPSIS This paper aims to promote the application of deep learning to audit procedures by illustrating how the capabilities of deep learning for text understanding, speech recognition, visual recognition, and structured data analysis fit into the audit environment. Based on these four capabilities, deep learning serves two major functions in supporting audit decision making: information identification and judgment support. The paper proposes a framework for applying these two deep learning functions to a variety of audit procedures in different audit phases. An audit data warehouse of historical data can be used to construct prediction models, providing suggested actions for various audit procedures. The data warehouse will be updated and enriched with new data instances through the application of deep learning and a human auditor's corrections. Finally, the paper discusses the challenges faced by the accounting profession, regulators, and educators when it comes to applying deep learning.

scholarly journals Siamese Reconstruction Network: Accurate Image Reconstruction from Human Brain Activity by Learning to Compare

2019 ◽  
Vol 9 (22) ◽  
pp. 4749
Author(s):  
Lingyun Jiang ◽  
Kai Qiao ◽  
Linyuan Wang ◽  
Chi Zhang ◽  
Jian Chen ◽  
...  
Keyword(s):  
Deep Learning ◽  
Human Brain ◽  
Brain Activity ◽  
Feature Space ◽  
Training Data ◽  
Reconstruction Method ◽  
Learning Method ◽  
Training Samples ◽  
Visual Reconstruction ◽  
Relationship Of

Decoding human brain activities, especially reconstructing human visual stimuli via functional magnetic resonance imaging (fMRI), has gained increasing attention in recent years. However, the high dimensionality and small quantity of fMRI data impose restrictions on satisfactory reconstruction, especially for the reconstruction method with deep learning requiring huge amounts of labelled samples. When compared with the deep learning method, humans can recognize a new image because our human visual system is naturally capable of extracting features from any object and comparing them. Inspired by this visual mechanism, we introduced the mechanism of comparison into deep learning method to realize better visual reconstruction by making full use of each sample and the relationship of the sample pair by learning to compare. In this way, we proposed a Siamese reconstruction network (SRN) method. By using the SRN, we improved upon the satisfying results on two fMRI recording datasets, providing 72.5% accuracy on the digit dataset and 44.6% accuracy on the character dataset. Essentially, this manner can increase the training data about from n samples to 2n sample pairs, which takes full advantage of the limited quantity of training samples. The SRN learns to converge sample pairs of the same class or disperse sample pairs of different class in feature space.

A Deep Learning Method for Frame Selection in Videos for Structure from Motion Pipelines

2021 ◽  
Author(s):  
Francesco Banterle ◽  
Rui Gong ◽  
Massimiliano Corsini ◽  
Fabio Ganovelli ◽  
Luc Van Gool ◽  
...  
Keyword(s):  
Deep Learning ◽  
Structure From Motion ◽  
Learning Method ◽  
Frame Selection
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