Results 31 to 40 of about 75,080 (277)

Deep Speaker Feature Learning for Text-independent Speaker Verification

, 2017
Recently deep neural networks (DNNs) have been used to learn speaker features. However, the quality of the learned features is not sufficiently good, so a complex back-end model, either neural or probabilistic, has to be used to address the residual ...
Chen, Yixiang, Li, Lantian, Shi, Ying, Tang, Zhiyuan, Wang, Dong   +4 more
core   +1 more source

Repairing Deep Neural Networks: Fix Patterns and Challenges [PDF]

, 2020
Significant interest in applying Deep Neural Network (DNN) has fueled the need to support engineering of software that uses DNNs. Repairing software that uses DNNs is one such unmistakable SE need where automated tools could be beneficial; however, we do
Islam, Md Johirul, Nguyen, Giang, Pan, Rangeet, Rajan, Hridesh, Rajan, Hridesh   +4 more
core   +3 more sources

EP-DNN: A Deep Neural Network-Based Global Enhancer Prediction Algorithm [PDF]

Scientific Reports, 2016
AbstractWe presentEP-DNN, a protocol for predicting enhancers based on chromatin features, in different cell types. Specifically, we use a deep neural network (DNN)-based architecture to extract enhancer signatures in a representative human embryonic stem cell type (H1) and a differentiated lung cell type (IMR90).
Seong Gon Kim, Mrudul Harwani, Ananth Grama, Somali Chaterji   +3 more
openaire   +2 more sources

Joint Optimization of DNN Partition and Continuous Task Scheduling for Digital Twin-Aided MEC Network With Deep Reinforcement Learning

IEEE Access, 2023
Nowadays, mobile application services face the challenges of high speed, low latency and high reliability. The combination of digital twin (DT) technology and mobile edge computing (MEC) network can effectively solve these challenges.
Siyu Yuan, Zhenyu Zhang, Qin Li, Weiyuan Li, Yong Zhang   +4 more
doaj   +1 more source

Stacking-Based Deep Neural Network: Deep Analytic Network for Pattern Classification

, 2019
Stacking-based deep neural network (S-DNN) is aggregated with pluralities of basic learning modules, one after another, to synthesize a deep neural network (DNN) alternative for pattern classification.
Low, Cheng-Yaw, Park, Jaewoo, Teoh, Andrew Beng-Jin   +2 more
core   +1 more source

Malicious Network Traffic Detection Based on Deep Neural Networks and Association Analysis

Sensors, 2020
Anomaly detection systems can accurately identify malicious network traffic, providing network security. With the development of internet technology, network attacks are becoming more and more sourced and complicated, making it difficult for traditional ...
Minghui Gao, Li Ma, Heng Liu, Zhijun Zhang, Zhiyan Ning, Jian Xu   +5 more
doaj   +1 more source

Physics Inspired Deep Neural Networks for Top Quark Reconstruction [PDF]

EPJ Web of Conferences, 2020
Deep neural networks (DNNs) have been applied to the fields of computer vision and natural language processing with great success in recent years. The success of these applications has hinged on the development of specialized DNN architectures that take ...
Greif Kevin, Lannon Kevin
doaj   +1 more source

Comparison of Deep Learning and the Classical Machine Learning Algorithm for the Malware Detection

, 2018
Recently, Deep Learning has been showing promising results in various Artificial Intelligence applications like image recognition, natural language processing, language modeling, neural machine translation, etc.
Rathore, Hemant, Sahay, Sanjay K., Sewak, Mohit   +2 more
core   +1 more source

Deep Learning-Based Channel Estimation for Massive-MIMO With Mixed-Resolution ADCs and Low-Resolution Information Utilization

IEEE Access, 2021
In this paper, we propose two deep-learning based uplink channel estimation approaches that can utilize not only high-resolution-ADC-quantized but also low-resolution-ADC-quantized received pilot signals to improve estimation performance for mixed analog-
Jin Zicheng, Gao Shen, Liu Nan, Pan Zhiwen, You Xiaohu   +4 more
doaj   +1 more source

Deep learning-based modeling method for probabilistic LCF life prediction of turbine blisk

Propulsion and Power Research
Turbine blisk is one of the typical components of gas turbine engines. The fatigue life of turbine blisk directly affects the reliability and safety of both turbine blisk and aeroengine whole-body. To monitor the performance degradation of an aeroengine,
Cheng-Wei Fei, Yao-Jia Han, Jiong-Ran Wen, Chen Li, Lei Han, Yat-Sze Choy   +5 more
doaj   +1 more source