scholarly journals A Novel Approach to Face Verification Based on Second-Order Face-Pair Representation

Complexity ◽  
2018 ◽  
Vol 2018 ◽  
pp. 1-10 ◽  
Author(s):  
Qiang Hua ◽  
Chunru Dong ◽  
Feng Zhang
Keyword(s):  
Binary Classification ◽  
Metric Learning ◽  
Second Order ◽  
Classification Model ◽  
Face Verification ◽  
Unified Framework ◽  
Face Pair ◽  
Face Representation ◽  
Representation Method ◽  
Pair Representation

Face representation and matching are two essential issues in face verification task. Various approaches have been proposed focusing on these two issues. However, few of them addressed the joint optimal solutions of these two issues in a unified framework. In this paper, we present a second-order face representation method for face pair and a unified face verification framework, in which the feature extractors and the subsequent binary classification model design can be selected flexibly. Our contributions can be summarized in the following aspects. First, a novel face-pair representation method that employs the second-order statistical property of the face pairs is proposed, which retains more information compared to the existing methods. Second, a flexible binary classification model, which differs from the conventionally used metric learning, is constructed based on the new face-pair representation. Finally, we verify that our proposed face-pair representation can benefit from large training datasets. All the experiments are carried out on Labeled Face in the Wild (LFW) to verify the algorithm’s effectiveness against challenging uncontrolled conditions.

scholarly journals Anomaly Identification during Polymerase Chain Reaction for Detecting SARS-CoV-2 Using Artificial Intelligence Trained from Simulated Data

Molecules ◽  
2020 ◽  
Vol 26 (1) ◽  
pp. 20
Author(s):  
Reynaldo Villarreal-González ◽  
Antonio J. Acosta-Hoyos ◽  
Jaime A. Garzon-Ochoa ◽  
Nataly J. Galán-Freyle ◽  
Paola Amar-Sepúlveda ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Big Data ◽  
Real Time ◽  
Binary Classification ◽  
Simulated Data ◽  
Classification Model ◽  
Rt Pcr ◽  
Simon Bolivar ◽  
Polymerase Chain ◽  
Available Information

Real-time reverse transcription (RT) PCR is the gold standard for detecting Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), owing to its sensitivity and specificity, thereby meeting the demand for the rising number of cases. The scarcity of trained molecular biologists for analyzing PCR results makes data verification a challenge. Artificial intelligence (AI) was designed to ease verification, by detecting atypical profiles in PCR curves caused by contamination or artifacts. Four classes of simulated real-time RT-PCR curves were generated, namely, positive, early, no, and abnormal amplifications. Machine learning (ML) models were generated and tested using small amounts of data from each class. The best model was used for classifying the big data obtained by the Virology Laboratory of Simon Bolivar University from real-time RT-PCR curves for SARS-CoV-2, and the model was retrained and implemented in a software that correlated patient data with test and AI diagnoses. The best strategy for AI included a binary classification model, which was generated from simulated data, where data analyzed by the first model were classified as either positive or negative and abnormal. To differentiate between negative and abnormal, the data were reevaluated using the second model. In the first model, the data required preanalysis through a combination of prepossessing. The early amplification class was eliminated from the models because the numbers of cases in big data was negligible. ML models can be created from simulated data using minimum available information. During analysis, changes or variations can be incorporated by generating simulated data, avoiding the incorporation of large amounts of experimental data encompassing all possible changes. For diagnosing SARS-CoV-2, this type of AI is critical for optimizing PCR tests because it enables rapid diagnosis and reduces false positives. Our method can also be used for other types of molecular analyses.

ID: 3492451 VALIDATION OF A BINARY CLASSIFICATION MODEL USING A DEEP CONVOLUTIONAL NEURAL NETWORK FOR WIRELESS CAPSULE ENDOSCOPY

2021 ◽  
Vol 93 (6) ◽  
pp. AB201
Author(s):  
Sang Hoon Kim ◽  
Youngbae Hwang ◽  
Dong Jun Oh ◽  
Ji Hyung Nam ◽  
Ki Bae Kim ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Capsule Endoscopy ◽  
Binary Classification ◽  
Wireless Capsule Endoscopy ◽  
Deep Convolutional Neural Network ◽  
Classification Model ◽  
Wireless Capsule

Joint Bayesian guided metric learning for end-to-end face verification

2018 ◽  
Vol 275 ◽  
pp. 560-567 ◽  
Author(s):  
Di Chen ◽  
Chunyan Xu ◽  
Jian Yang ◽  
Jianjun Qian ◽  
Yuhui Zheng ◽  
...  
Keyword(s):  
Metric Learning ◽  
Face Verification ◽  
End To End

scholarly journals Constructive Learning of Deep Neural Networks for Bigdata Analysis

2020 ◽  
Vol 9 (12) ◽  
pp. 311-322
Author(s):  
Soha Abd Mohamed El-Moamen ◽  
Marghany Hassan Mohamed ◽  
Mohammed F. Farghally
Keyword(s):  
Neural Network ◽  
Lung Cancer ◽  
Binary Classification ◽  
Network Models ◽  
Classification Model ◽  
Neural Network Models ◽  
Constructive Learning ◽  
The Neural Network ◽  
Rapid Pace ◽  
Better Than

The need for tracking and evaluation of patients in real-time has contributed to an increase in knowing people’s actions to enhance care facilities. Deep learning is good at both a rapid pace in collecting frameworks of big data healthcare and good predictions for detection the lung cancer early. In this paper, we proposed a constructive deep neural network with Apache Spark to classify images and levels of lung cancer. We developed a binary classification model using threshold technique classifying nodules to benign or malignant. At the proposed framework, the neural network models training, defined using the Keras API, is performed using BigDL in a distributed Spark clusters. The proposed algorithm has metrics AUC-0.9810, a misclassifying rate from which it has been shown that our suggested classifiers perform better than other classifiers.

Face Representation Method Using Pixel-to-Vertex Map (PVM) for 3D Model Based Face Recognition

2006 ◽  
pp. 21-28 ◽  
Author(s):  
Taehwa Hong ◽  
Hagbae Kim ◽  
Hyeonjoon Moon ◽  
Yongguk Kim ◽  
Jongweon Lee ◽  
...  
Keyword(s):  
Face Recognition ◽  
3D Model ◽  
Face Representation ◽  
Model Based ◽  
Representation Method ◽  
Vertex Map

scholarly journals Binary classification model based on machine learning algorithm for the DC serial arc detection in electric vehicle battery system

2019 ◽  
Vol 12 (1) ◽  
pp. 112-119 ◽  
Author(s):  
Kun Xia ◽  
Haotian Guo ◽  
Sheng He ◽  
Wei Yu ◽  
Jingjun Xu ◽  
...  
Keyword(s):  
Machine Learning ◽  
Electric Vehicle ◽  
Learning Algorithm ◽  
Binary Classification ◽  
Classification Model ◽  
Machine Learning Algorithm ◽  
Battery System ◽  
Model Based ◽  
Electric Vehicle Battery

scholarly journals Long-Term Impacts of Selective Logging on Amazon Forest Dynamics from Multi-Temporal Airborne LiDAR

2019 ◽  
Vol 11 (6) ◽  
pp. 709 ◽  
Author(s):  
Ekena Rangel Pinagé ◽  
Michael Keller ◽  
Paul Duffy ◽  
Marcos Longo ◽  
Maiza dos-Santos ◽  
...  
Keyword(s):  
Laser Scanning ◽  
Forest Canopy ◽  
Binary Classification ◽  
Forest Degradation ◽  
Selective Logging ◽  
Ecological Impacts ◽  
Airborne Lidar ◽  
Classification Model ◽  
Amazon Forest ◽  
Gap Formation

Forest degradation is common in tropical landscapes, but estimates of the extent and duration of degradation impacts are highly uncertain. In particular, selective logging is a form of forest degradation that alters canopy structure and function, with persistent ecological impacts following forest harvest. In this study, we employed airborne laser scanning in 2012 and 2014 to estimate three-dimensional changes in the forest canopy and understory structure and aboveground biomass following reduced-impact selective logging in a site in Eastern Amazon. Also, we developed a binary classification model to distinguish intact versus logged forests. We found that canopy gap frequency was significantly higher in logged versus intact forests even after 8 years (the time span of our study). In contrast, the understory of logged areas could not be distinguished from the understory of intact forests after 6–7 years of logging activities. Measuring new gap formation between LiDAR acquisitions in 2012 and 2014, we showed rates 2 to 7 times higher in logged areas compared to intact forests. New gaps were spatially clumped with 76 to 89% of new gaps within 5 m of prior logging damage. The biomass dynamics in areas logged between the two LiDAR acquisitions was clearly detected with an average estimated loss of −4.14 ± 0.76 MgC ha−1 y−1. In areas recovering from logging prior to the first acquisition, we estimated biomass gains close to zero. Together, our findings unravel the magnitude and duration of delayed impacts of selective logging in forest structural attributes, confirm the high potential of airborne LiDAR multitemporal data to characterize forest degradation in the tropics, and present a novel approach to forest classification using LiDAR data.

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