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Series Editorial: Data Science and Artificial Intelligence for Communications
2021
IEEE Communications Magazine
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The performance of deep learning-aided RF fingerprinting for NFC tag identification is evaluated adopting various DNN models, including fully connected layer-based neural network (FNN), convolutional neural ...
Finally, key technical challenges involved in the use of deep learning-based RF fingerprinting for improving security in NFC tag identification are discussed. ...
doi:10.1109/mcom.2021.9446678
fatcat:ft5rs5huuvgwvo6nedlpno6uuy
Authentication and Authorization for Mobile IoT Devices using Bio-features: Recent Advances and Future Trends
[article]
2019
arXiv
pre-print
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Bio-features are fast becoming a key tool to authenticate the IoT devices; in this sense, the purpose of this investigation is to summaries the factors that hinder biometrics models' development and deployment ...
on a large scale, including human physiological (e.g., face, eyes, fingerprints-palm, or electrocardiogram) and behavioral features (e.g., signature, voice, gait, or keystroke). ...
Deep convolutional neural network The deep convolutional neural networks (DCNNs) for face detection was attempted by Ranjan et al. ...
arXiv:1901.09374v1
fatcat:tglfqlpnczhufoutjae6ogunz4
Special Issue on Deep Learning in Biological Image and Signal Processing
2021
IEEE Signal Processing Magazine
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Deep learning of artificial neural networks has emerged as a powerful tool for extracting the relevant information from such data and helping researchers to detect patterns that may be unnoticeable to ...
• Deep learning for biomarker discovery • Generative deep models for disease fingerprints • Annotation-efficient deep learning strategies All topics are to be covered from the perspective of applications ...
• Deep learning for biomarker discovery • Generative deep models for disease fingerprints • Annotation-efficient deep learning strategies All topics are to be covered from the perspective of applications ...
doi:10.1109/msp.2020.3040489
fatcat:qys3d64ajncqpbyq2bl4lnoofm
Special Issue on Deep Learning in Biological Image and Signal Processing
2020
IEEE Signal Processing Magazine
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Deep learning of artificial neural networks has emerged as a powerful tool for extracting the relevant information from such data and helping researchers to detect patterns that may be unnoticeable to ...
• Deep learning for biomarker discovery • Generative deep models for disease fingerprints • Annotation-efficient deep learning strategies All topics are to be covered from the perspective of applications ...
• Deep learning for biomarker discovery • Generative deep models for disease fingerprints • Annotation-efficient deep learning strategies All topics are to be covered from the perspective of applications ...
doi:10.1109/msp.2020.3028720
fatcat:an3exd7lmjadrhv75kzc4ds6vm
AI-Assisted Authentication: State of the Art, Taxonomy and Future Roadmap
[article]
2022
arXiv
pre-print
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With the emerging AI-assisted authentication schemes, our comprehensive survey provides an overall understanding on a high level, which paves the way for future research in this area. ...
In this paper, we focus on the applications of artificial intelligence in authentication, which is used in a wide range of scenarios including facial recognition to access buildings, keystroke dynamics ...
Deep Neural network Deep neural network(DNN) is a specific type of deep learning that uses multiple layers of neural networks. ...
arXiv:2204.12492v1
fatcat:vdeuhy63cvawjdhclricvkq42q
Bioplastic Design using Multitask Deep Neural Networks
[article]
2022
arXiv
pre-print
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In this work, we develop multitask deep neural network property predictors using available experimental data for a diverse set of nearly 23000 homo- and copolymer chemistries. ...
Using the predictors, we identify 14 PHA-based bioplastics from a search space of almost 1.4 million candidates which could serve as potential replacements for seven petroleum-based commodity plastics ...
Figure 1b schematically portrays the architecture of the three concatenation-conditioned multitask predictors: the copolymer fingerprint and selector vector are fed to a feed-forward deep neural network ...
arXiv:2203.12033v1
fatcat:znfrrmhkfncz5hdrf5xy3q2l4y
An Efficient Siamese Network Based Multi-Biometric Key Distribution Protocol for Cloud Data Security
2020
International Journal of Emerging Trends in Engineering Research
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In this work, an authentication based deep learning framework is proposed to improve the feature selection process and error rate on the different biometric feature sets also, a novel key distribution ...
Storage as a Service is one of the functionalities of cloud computing. ...
A deep neural network (DNN) framework implemented on line data and can accomplish control and decision-making steps within the system. ...
doi:10.30534/ijeter/2020/37882020
fatcat:tcfitujfhvbqzetzjvzi3s3zsy
From Big Data to Artificial Intelligence: chemoinformatics meets new challenges
2020
Journal of Cheminformatics
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This editorial highlights the main results presented during the special session of the International Conference on Neural Networks organized by "Big Data in Chemistry" project and draws perspectives on ...
Artificial Intelligence and machine learning, especially neural networks, are increasingly used in the chemical industry, in particular with respect to Big Data. ...
Acknowledgements This study was partially funded by the European Union's Horizon 2020 research and innovation program under the Marie Skłodowska-Curie Innovative Training Network European Industrial Doctorate ...
doi:10.1186/s13321-020-00475-y
pmid:33339533
fatcat:3bpwkzpzxfbo5i4psmdawp7xsq
Skin sensitizer classification using dual-input machine learning model
2020
Chem-Bio Informatics Journal
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Herein, we performed a study on DNN-and GBDT-based modeling to investigate their potential for use in predicting skin sensitizers. ...
Recently, several machine learning approaches, such as the gradient boosting decision tree (GBDT) and deep neural networks (DNNs), have been applied to chemical reactivity prediction, showing remarkable ...
Among classification algorithms, deep neural network (DNN), which is a type of artificial neural network with more than one layer, has been used to predict chemical reactivity, and won various competitions ...
doi:10.1273/cbij.20.54
fatcat:mlz5zji7xbfyxf7pepmzuhjcoi
TEMPLATE CONSERVATION METHODOLOGIES FOR MULTIMODAL BIOMETRIC WITH LSTM NEURAL NETWORK
2022
Indian Journal of Computer Science and Engineering
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In this work, we present a privacy-preserving multi-modal biometric system that uses LSTM (Long Short-Term Memory) neural networks for classification. ...
This work proposes two template preservation methods, bio-hash, and simple hash, to develop a secure architecture. ...
Feature classification based on LSTM neural network range A matching process is a comparison between the query's features with the database storage. ...
doi:10.21817/indjcse/2022/v13i1/221301020
fatcat:klsztglzsfb7zevrck6ljrz3um
FDFNet : A Secure Cancelable Deep Finger Dorsal Template Generation Network Secured via. Bio-Hashing
[article]
2018
arXiv
pre-print
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Later Bio-Hashing, a technique based on assigning a tokenized random number to each user, has been used to hash the features extracted from FDFNet. ...
In this work, we have proposed a secure cancelable finger dorsal template generation network (learning domain specific features) secured via. Bio-Hashing. ...
Conclusion and Future Scope In this paper, we proposed a cancelable finger dorsal template generation technique based on deep-feature extraction using our novel architecture FDFNet and stateof-the-art ...
arXiv:1812.05308v1
fatcat:jemghbxdvzdbvpuitxbzvenkd4
A Lightweight Convolutional Neural Network with Representation Self-challenge for Fingerprint Liveness Detection
2022
Computers Materials & Continua
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Aiming at filling this gap, this paper designs a lightweight multi-scale convolutional neural network method, and further proposes a novel hybrid spatial pyramid pooling block to extract abundant features ...
The vast majority of the FLD methods directly employ convolutional neural network (CNN), and rarely pay attention to the problem of overparameterization and over-fitting of models, resulting in large calculation ...
Multi-scale lightweight network. A multi-scale parallel neural network is proposed for the fingerprint liveness detection task. ...
doi:10.32604/cmc.2022.027984
fatcat:mbo2amzshjelvbl5zfzh6h37pe
Reaching Data Confidentiality and Model Accountability on the CalTrain
[article]
2018
arXiv
pre-print
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In this paper, we introduce CALTRAIN, a Trusted Execution Environment (TEE) based centralized multi-party collaborative learning system that simultaneously achieves data confidentiality and model accountability ...
It also limits DCL's model accountability, which is key to backtracking the responsible "bad" training data instances/contributors. ...
We fully built a prototype of CALTRAIN based on Darknet [22] , an open source neural network implementation in C and CUDA. ...
arXiv:1812.03230v1
fatcat:boywhcunwfcybj6ze2dapfhbgq
A deep learning framework for high-throughput mechanism-driven phenotype compound screening
[article]
2020
biorxiv/medrxiv
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In this study, we propose a mechanism-driven neural network-based method named DeepCE (Deep Chemical Expression) which utilizes graph convolutional neural network to learn chemical representation and multi-head ...
Target-based high-throughput compound screening dominates conventional one-drug-one-gene drug discovery process. ...
Pearson correlation scores of vanilla neural network and kNN trained on training sets generated by filtering unreliable experiments with different APC thresholds
( a ) a Per cell-specific profile, across ...
doi:10.1101/2020.07.19.211235
pmid:32743586
pmcid:PMC7386506
fatcat:cwfqrhiq6rc3lfkkpghicylfu4
A Survey of Biometric Recognition Using Deep Learning
2020
EAI Endorsed Transactions on Energy Web
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The paper starts with biometric basics, transfer learning in deep biometrics, an overview of convolutional neural networks, and then survey work. ...
Since biometrics deals with a person's traits, it mainly involves supervised learning and may exploit deep learning effectiveness in other similar fields. ...
Abdullah (44) worked on fingerprint recognition through ANN, and they proposed a supervised recurrent neural network (RNN). ...
doi:10.4108/eai.27-10-2020.166775
fatcat:aihqgrk6pvbxpa54umpemr4s7a
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