scholarly journals Framework for user behavioural biometric identification using a multimodal scheme with keystroke trajectory feature and recurrent neural network on a mobile platform

2022 ◽  
Author(s):  
Ka‐Wing Tse ◽  
Kevin Hung
Keyword(s):  
Neural Network ◽  
Recurrent Neural Network ◽  
Mobile Platform ◽  
Biometric Identification

scholarly journals Gait-Based Identification Using Deep Recurrent Neural Networks and Acceleration Patterns

Sensors ◽  
2020 ◽  
Vol 20 (23) ◽  
pp. 6900
Author(s):  
Angel Peinado-Contreras ◽  
Mario Munoz-Organero
Keyword(s):  
Neural Network ◽  
Recurrent Neural Network ◽  
Recurrent Neural Networks ◽  
Deep Neural Network ◽  
Biometric Identification ◽  
Vertical Acceleration ◽  
Acceleration Data ◽  
Deep Recurrent Neural Network ◽  
Dense Output ◽  
User Recognition

This manuscript presents an approach to the challenge of biometric identification based on the acceleration patterns generated by a user while walking. The proposed approach uses the data captured by a smartphone’s accelerometer and gyroscope sensors while the users perform the gait activity and optimizes the design of a recurrent neural network (RNN) to optimally learn the features that better characterize each individual. The database is composed of 15 users, and the acceleration data provided has a tri-axial format in the X-Y-Z axes. Data are pre-processed to estimate the vertical acceleration (in the direction of the gravity force). A deep recurrent neural network model consisting of LSTM cells divided into several layers and dense output layers is used for user recognition. The precision results obtained by the final architecture are above 97% in most executions. The proposed deep neural network-based architecture is tested in different scenarios to check its efficiency and robustness.

Research on behavior recognition based on feature fusion of automatic coder and recurrent neural network

2020 ◽  
Vol 39 (6) ◽  
pp. 8927-8935
Author(s):  
Bing Zheng ◽  
Dawei Yun ◽  
Yan Liang
Keyword(s):  
Neural Network ◽  
Recurrent Neural Network ◽  
Behavior Pattern ◽  
Rapid Development ◽  
Video Data ◽  
Support Vector ◽  
Behavior Recognition ◽  
Learning Methods ◽  
The Impact ◽  
Internet Of Things Technology

Under the impact of COVID-19, research on behavior recognition are highly needed. In this paper, we combine the algorithm of self-adaptive coder and recurrent neural network to realize the research of behavior pattern recognition. At present, most of the research of human behavior recognition is focused on the video data, which is based on the video number. At the same time, due to the complexity of video image data, it is easy to violate personal privacy. With the rapid development of Internet of things technology, it has attracted the attention of a large number of experts and scholars. Researchers have tried to use many machine learning methods, such as random forest, support vector machine and other shallow learning methods, which perform well in the laboratory environment, but there is still a long way to go from practical application. In this paper, a recursive neural network algorithm based on long and short term memory (LSTM) is proposed to realize the recognition of behavior patterns, so as to improve the accuracy of human activity behavior recognition.

Deadlift Recognition and Application based on Multiple Modalities using Recurrent Neural Network

2020 ◽  
Vol 2020 (17) ◽  
pp. 2-1-2-6
Author(s):  
Shih-Wei Sun ◽  
Ting-Chen Mou ◽  
Pao-Chi Chang
Keyword(s):  
Neural Network ◽  
Recurrent Neural Network ◽  
Network Structure ◽  
Inertial Sensors ◽  
Body Movement ◽  
The Body ◽  
Body Parts ◽  
Multimodal Sensing ◽  
Multiple Devices ◽  
Neural Network Structure

To improve the workout efficiency and to provide the body movement suggestions to users in a “smart gym” environment, we propose to use a depth camera for capturing a user’s body parts and mount multiple inertial sensors on the body parts of a user to generate deadlift behavior models generated by a recurrent neural network structure. The contribution of this paper is trifold: 1) The multimodal sensing signals obtained from multiple devices are fused for generating the deadlift behavior classifiers, 2) the recurrent neural network structure can analyze the information from the synchronized skeletal and inertial sensing data, and 3) a Vaplab dataset is generated for evaluating the deadlift behaviors recognizing capability in the proposed method.

Molecular Generation Targeting Desired Electronic Properties via Deep Generative Models

2019 ◽  
Author(s):  
Qi Yuan ◽  
Alejandro Santana-Bonilla ◽  
Martijn Zwijnenburg ◽  
Kim Jelfs
Keyword(s):  
Neural Network ◽  
Electronic Properties ◽  
Transfer Learning ◽  
Recurrent Neural Network ◽  
Chemical Space ◽  
Generative Models ◽  
Molecular Features ◽  
Donor Acceptor ◽  
Homo Lumo ◽  
Training Sets

<p>The chemical space for novel electronic donor-acceptor oligomers with targeted properties was explored using deep generative models and transfer learning. A General Recurrent Neural Network model was trained from the ChEMBL database to generate chemically valid SMILES strings. The parameters of the General Recurrent Neural Network were fine-tuned via transfer learning using the electronic donor-acceptor database from the Computational Material Repository to generate novel donor-acceptor oligomers. Six different transfer learning models were developed with different subsets of the donor-acceptor database as training sets. We concluded that electronic properties such as HOMO-LUMO gaps and dipole moments of the training sets can be learned using the SMILES representation with deep generative models, and that the chemical space of the training sets can be efficiently explored. This approach identified approximately 1700 new molecules that have promising electronic properties (HOMO-LUMO gap <2 eV and dipole moment <2 Debye), 6-times more than in the original database. Amongst the molecular transformations, the deep generative model has learned how to produce novel molecules by trading off between selected atomic substitutions (such as halogenation or methylation) and molecular features such as the spatial extension of the oligomer. The method can be extended as a plausible source of new chemical combinations to effectively explore the chemical space for targeted properties.</p>

IMPLEMENTASI CHATBOT PADA PENDAFTARAN MAHASISWA BARU MENGGUNAKAN RECURRENT NEURAL NETWORK

2019 ◽  
Vol 24 (2) ◽  
pp. 91-101
Author(s):  
Tjut Awaliyah Zuraiyah ◽  
Dian Kartika Utami ◽  
Degi Herlambang
Keyword(s):  
Neural Network ◽  
Recurrent Neural Network

Chatbot adalah perangkat lunak yang dapat berkomunikasi dengan manusia menggunakan bahasa alami. Model percakapan menggunakan kecerdasan buatan agar mampu memahami ucapan pengguna dan memberi tanggapan yang relevan dengan masalah yang dibahas oleh pengguna. Pendaftaran mahasiswa baru memerlukan banyak informasi mengenai prosedur pendaftaran di perguruan tinggi. Website pendaftaran online di Universitas Pakuan masih sebatas berisi informasi umum. Penelitian ini bertujuan untuk membuat suatu aplikasi Chatbot otomatis yang dapat berkomunikasi dengan manusia mengenai informasi pendaftaran mahasiswa baru di Universitas Pakuan menggunakan Recurrent Neural Network (RNN) untuk klasifikasi teks. Aplikasi Chatbot diimplementasikan menggunakan bahasa pemrograman Python dan Telegram API. Tahapan pada implementasi Chatbot terdiri dari preprocessing, transformasi data ke format .JSON, pelatihan data, bag of word dan full connection. Pengujian aplikasi Chatbot menggunakan data sebanyak 251 kalimat pertanyaan tentang pendaftaran mahasiswa baru di Universitas Pakuan. Hasil pengujian menunjukkan bahwa Chatbot dapat menjawab pertanyaan mengenai pendaftaran mahasiswa baru dengan akurasi sebesar 88%, presisi sebesar 95% dan recall sebesar 92%.

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