Monitoring Population Phenology of Asian Citrus Psyllid Using Deep Learning by Maria Bibi, Muhammad Kashif Hanif, Muhammad Umer Sarwar, Muhammad Irfan Khan, Shouket Zaman Khan, Casper Shikali Shivachi, Asad Anees

“…Multiple linear regression, random forest regressor, and deep neural network approaches were compared to predict population dynamics of Asian citrus psyllid. …”
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FABLDroid: Malware detection based on hybrid analysis with factor analysis and broad learning methods for android applications by Kazım Kılıç, İsmail Atacak, İbrahim Alper Doğru

“…Our method is based on a lightweight deep neural network architecture based on broad learning to reveal hidden factors to detect Android malware. …”
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Multi‐subband fusion algorithm based on autoencoder by Yilin Jiang, Liting Zhang, Li Li, Jinxin Li, Yaozu Yang

“…Abstract A novel algorithm for multi‐subband signal fusion achieves performance superior to traditional all‐pole model, matrix pencil algorithm and deep‐neural‐network (Deep neural network (DNN)). The method uses a deep‐learning autoencoder more fully described as a multi‐subband fusion autoencoder (MSFAE). …”
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EEG analysis of speaking and quiet states during different emotional music stimuli by Xianwei Lin, Xinyue Wu, Zefeng Wang, Zhengting Cai, Zihan Zhang, Guangdong Xie, Lianxin Hu, Laurent Peyrodie

“…In the construction of EEG classification models, the classification performance of deep neural network algorithms is superior to other machine learning algorithms.…”
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Research on Airport Target Recognition under Low-Visibility Condition Based on Transfer Learning by Jiajun Li, Yongzhong Wang, Yuexin Qian, Tianyi Xu, Kaiwen Wang, Liancheng Wan

“…According to the results, the dark channel algorithm has the best image defogging enhancement effect, and the GoogLeNet deep neural network has the highest target recognition rate.…”
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Designing Channel Attention Fully Convolutional Networks with Neural Architecture Search for Customer Socio-Demographic Information Identification Using Smart Meter Data by Zhirui Luo, Qingqing Li, Ruobin Qi, Jun Zheng

“…Our results show that the deep neural network architectures designed automatically by our proposed method significantly outperform all baseline methods in addressing the socio-demographic questions investigated in our study.…”
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Computer Vision-assisted Wireless Channel Simulation for Millimeter Wave Human Motion Recognition by Zhenyu REN, Chenqing JI, Chao YU, Wanli CHEN, Rui WANG

“…The Doppler spectrograms obtained from the simulation can be used to train deep neural network for real wireless human motion recognition. …”
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Deep learning technology: enabling safe communication via the internet of things by Ramiz Salama, Hitesh Mohapatra, Tuğşad Tülbentçi, Fadi Al-Turjman

“…Specifically, the Cuda-deep neural network (Cu-DNN), Cuda-bidirectional long short-term memory (Cu-BLSTM), and Cuda-gated recurrent unit (Cu-DNNGRU) classifiers are utilized for effective threat detection. …”
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A deep learning approach for predicting the antenna pointing error caused by transmission faults with simulation data by Lihui Chen, Song Xue, Peiyuan Lian, Qian Xu, Meng Wang, Congsi Wang

“…Finally, labeled fault data and antenna pointing errors have been put into the deep neural network model for training to obtain the prediction model for predicting antenna axis error. …”
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Sentiment analysis of telecom official micro-blog users based on LSTM deep learning model by Xin CAI, Jingsheng LOU

“…As an internet media,China Telecom official micro-blog is an important channel for the company to publish information and get feedback from users.Users’ comments on telecom official micro-blog messages reflect different attitudes towards telecom brand,products and services.The message content and comment data of the micro-blog was crawled,and the Word2vec was used to express the text information after data cleaning,and the deep learning platform was chosen to carry out the positive and negative emotional classification of the user interaction text based on the LSTM deep neural network model,and sentiment analysis of telecom official micro-blog users was realized.…”
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Emotional State Analysis Model of Humanoid Robot in Human-Computer Interaction Process by Boxin Peng

“…In view of the shortcomings of traditional methods, this study designed an emotion analysis model based on deep neural network to detect the emotion of interactive objects and built an open-domain dialogue system of humanoid robot. …”
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Precise and interpretable neural networks reveal epigenetic signatures of aging across youth in health and disease by David Martínez-Enguita, Thomas Hillerton, Julia Åkesson, Daniel Kling, Maria Lerm, Mika Gustafsson

“…We then retrieved their age-specific epigenetic signatures of aging and examined their functional enrichments in age-associated biological processes.ResultsWe introduce NCAE-CombClock, a novel highly precise (R2 = 0.978, mean absolute error = 1.96 years) deep neural network age clock integrating data-driven DNAm embeddings and established CpG age markers. …”
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Speaker verification method based on deep information divergence maximization by Chen CHEN, Yafeng RONG, Chaoqun JI, Deyun CHEN, Yongjun HE

“…To solve the problem that the nonlinear relationship between speaker representations cannot be accurately captured in speaker verification, an objective function based on depth information divergence maximization was proposed.It could implicitly represent the nonlinear relationship between speaker representations by calculating the similarity between their distributions.Under the supervision of the optimization goal of maximizing the statistical correlation, the deep neural network was optimized towards the direction that the within-class data was more compact and the between-class data were far away from each other, and finally the discrimination of deep speaker representation space could be effectively improved.Experimental results show that compared with other deep learning methods, the relative EER of the proposed method is reduced by 15.80% at most, which significantly improves the system performance.…”
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Deep reinforcement learning based resource allocation algorithm in cellular networks by Xiaomin LIAO, Shaohu YAN, Jia SHI, Zhenyu TAN, Zhongling ZHAO, Zan LI

“…In order to solve multi-objective optimization problem,a resource allocation algorithm based on deep reinforcement learning in cellular networks was proposed.Firstly,deep neural network (DNN) was built to optimize the transmission rate of cellular system and to complete the forward transmission process of the algorithm.Then,the Q-learning mechanism was utilized to construct the error function,which used energy efficiency as the rewards.The gradient descent method was used to train the weights of DNN,and the reverse training process of the algorithm was completed.The simulation results show that the proposed algorithm can determine optimization extent of optimal resource allocation scheme with rapid convergence ability,it is obviously superior to the other algorithms in terms of transmission rate and system energy consumption optimization.…”
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Physics Informed by Deep Learning: Numerical Solutions of Modified Korteweg-de Vries Equation by Yuexing Bai, Temuer Chaolu, Sudao Bilige

“…In this paper, with the aid of symbolic computation system Python and based on the deep neural network (DNN), automatic differentiation (AD), and limited-memory Broyden-Fletcher-Goldfarb-Shanno (L-BFGS) optimization algorithms, we discussed the modified Korteweg-de Vries (mkdv) equation to obtain numerical solutions. …”
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Res-DNN based signal detection algorithm for end-to-end MIMO systems by Guoquan LI, Yonghai XU, Jinzhao LIN, Zhengwen HUANG

“…Deep learning can improve the effect of signal detection by extracting the inherent characteristics of wireless communication data.To solve the tradeoff between the performance and complexity of MIMO system signal detection, an end-to-end MIMO system signal detection scheme based on deep learning was proposed.The encoder and the decoder based on residual deep neural network replace the transmitter and the receiver of the wireless communication system respectively, and they were trained in an end-to-end manner as a whole.Firstly, the features of the input data were extracted by encoder, then the communication model was established and was sent to the zero forcing detector for preliminary detection.Finally, the detection signal was reconstructed through the decoder.Simulation results show that the proposed detection scheme is superior to the same type of algorithm, and the detection performance is significantly better than that of the MMSE detection algorithm at the expense of a certain time complexity.…”
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Adversarial examples detection method based on boundary values invariants by Fei YAN, Minglun ZHANG, Liqiang ZHANG

“…Nowadays,deep learning has become one of the most widely studied and applied technologies in the computer field.Deep neural networks(DNNs) have achieved greatly noticeable success in many applications such as image recognition,speech,self-driving and text translation.However,deep neural networks are vulnerable to adversarial examples that are generated by perturbing correctly classified inputs to cause DNN modes to misbehave.A boundary check method based on traditional programs by fitting the distribution to find the invariants in the deep neural network was proposed and it use the invariants to detect adversarial examples.The selection of training sets was irrelevant to adversarial examples.The experiment results show that proposed method can effectively detect the current adversarial example attacks on LeNet,vgg19 model,Mnist,Cifar10 dataset,and has a low false positive rate.…”
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Research on Management Model Based on Deep Learning by Yuting Zhao

“…Proper management models lead to strategies and decisions help to success organization. Deep neural network (DNN) is proposed to make good prediction for organization for increasing the cost and reduce risk in companies and institutions. …”
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Moving target defense against adversarial attacks by Bin WANG, Liang CHEN, Yaguan QIAN, Yankai GUO, Qiqi SHAO, Jiamin WANG

“…Deep neural network has been successfully applied to image classification, but recent research work shows that deep neural network is vulnerable to adversarial attacks.A moving target defense method was proposed by means of dynamic switching model with a Bayes-Stackelberg game strategy, which could prevent an attacker from continuously obtaining consistent information and thus blocked its construction of adversarial examples.To improve the defense effect of the proposed method, the gradient consistency among the member models was taken as a measure to construct a new loss function in training for improving the difference among the member models.Experimental results show that the proposed method can improve the moving target defense performance of the image classification system and significantly reduce the attack success rate against the adversarial examples.…”
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Multi-channel QoT prediction method in wide-area optical backbone network based on ensemble learning by Xiaochuan SUN, Zhigang LI, Minghui ZHANG, Guan GUI

“…Due to the fact that in dynamic wide-area optical backbone network the accuracies of the existing prediction methods were insufficient,a novel prediction method on quality of transmission (QoT) of optical channel was proposed based on ensemble learning theory.Firstly,under the framework of stacked ensemble learning,a base-learner including five multilayer perceptron (MLP) model was built,which could achieve homomorphic ensemble learning of sample data through parallel combination.Subsequently,the new training set fused from the predicted results of the preceding base-learner was used to training the meta-learner composed of a single MLP.The simulation results show that compared with the used deep neural network,the proposed method can obtain a more excellent nonlinear approximation in the scenarios of the single-channel and multi-channels,and the prediction accuracies have the improvements of 1.93% and 3.82% respectively.…”
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