Early Warning Model of Sports Injury Based on RBF Neural Network Algorithm by Fuxing He

“…This paper analyzes the source of sports risk and the main injury factors, designs the sports injury estimation model based on big data analysis, establishes a new assessment model based on RBF neural network, and builds the big data network environment required for the model operation by improving the topological structure, combining big data and deep neural network. In the built environment, the risk assessment of sports injury can be completed by determining the risk source and identifying the risk factors. …”
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Anomaly Detection in Moving Crowds through Spatiotemporal Autoencoding and Additional Attention by Biao Yang, Jinmeng Cao, Rongrong Ni, Ling Zou

“…We propose an anomaly detection approach by learning a generative model using deep neural network. A weighted convolutional autoencoder- (AE-) long short-term memory (LSTM) network is proposed to reconstruct raw data and perform anomaly detection based on reconstruction errors to resolve the existing challenges of anomaly detection in complicated definitions and background influence. …”
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Forecasting Volatility of Stock Index: Deep Learning Model with Likelihood-Based Loss Function by Fang Jia, Boli Yang

“…In this paper, we use deep neural network (DNN) and long short-term memory (LSTM) model to forecast the volatility of stock index. …”
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Research on Personal Loan Default Risk Assessment Based on Machine Learning by Liu Guangsen

“…Among them, the Deep Neural Network has the best overall performance compared to the other three machine learning models. …”
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Mobile Performance Intelligent Evaluation of IoT Networks Based on DNN by Zhen Tang, Xiaobin Fu, Pingping Xiao

“…We also investigate the outage probability (OP) performance and derive OP expressions. Employing the deep neural network (DNN), an OP intelligent prediction algorithm is proposed. …”
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Deep learning for stage prediction in neuroblastoma using gene expression data by Aron Park, Seungyoon Nam

“…Taking the International Neuroblastoma Staging System stages as multiple classes, we designed a deep neural network using the gene expression patterns and stages of neuroblastoma patients. …”
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Development of Deep Convolutional Neural Network with Adaptive Batch Normalization Algorithm for Bearing Fault Diagnosis by Chao Fu, Qing Lv, Hsiung-Cheng Lin

“…The performance results verify that the proposed model is superior to Support Vector Machine with Fast Fourier Transform (FFT-SVM) and Multilayer Perceptron with Fast Fourier Transform (FFT-MLP) models and Deep Neural Network with Fast Fourier Transform (FFT-DNN).…”
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Investigating the Quality of DermaMNIST and Fitzpatrick17k Dermatological Image Datasets by Kumar Abhishek, Aditi Jain, Ghassan Hamarneh

“…However, while large datasets play a crucial role in the development of reliable deep neural network models, the quality of data therein and their correct usage are of paramount importance. …”
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Optimization of Photonic Nanocrystals for Invisibility Using Artificial Intelligence by Z. Dorrani

“…Therefore, this paper employs the deep neural network architecture ResNet to optimize photonic crystals. …”
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Neural processing of naturalistic audiovisual events in space and time by Yu Hu, Yalda Mohsenzadeh

“…Comparing neural representations to a two-branch deep neural network model highlighted the necessity of early cross-modal connections to build a biologically plausible model of audiovisual perception. …”
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SCRD-Net: A Deep Convolutional Neural Network Model for Glaucoma Detection in Retina Tomography by Hua Wang, Jingfei Hu, Jicong Zhang

“…Early and accurate diagnosis of glaucoma is critical for avoiding human vision deterioration and preventing blindness. A deep-neural-network model has been developed for the diagnosis of glaucoma based on Heidelberg retina tomography (HRT), called “Seeking Common Features and Reserving Differences Net” (SCRD-Net) to make full use of the HRT data. …”
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Acoustic Model with Multiple Lexicon Types for Indonesian Speech Recognition by Taufik Fuadi Abidin, Alim Misbullah, Ridha Ferdhiana, Laina Farsiah, Muammar Zikri Aksana, Hammam Riza

“…The quality of the dataset was evaluated using a deep neural network. The time delay neural network (TDNN) was used to build the acoustic model by applying the alignment data from the Gaussian mixture model-hidden Markov model (GMM-HMM). …”
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Performance Prediction for Higher Education Students Using Deep Learning by Shuping Li, Taotang Liu

“…The proposed method used deep neural network in prediction by extracting informative data as a feature with corresponding weights. …”
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Image Reconstruction for High-Performance Electrical Capacitance Tomography System Using Deep Learning by Yanpeng Zhang, Deyun Chen

“…But the ECT system still requires improvements in the quality of image reconstruction given its importance of great significance to obtain the reliability and usefulness of measurement results. The deep neural network is used in this study to extract new features and to update the number of nodes and hidden layers in the system. …”
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Quantitative Detection of Financial Fraud Based on Deep Learning with Combination of E-Commerce Big Data by Jian Liu, Xin Gu, Chao Shang

“…This method builds a deep neural network model with multiple hidden layers, learns the characteristic expression of the data, and fully depicts the rich internal information of the data, thereby improving the accuracy of financial fraud detection. …”
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Exploiting Interslice Correlation for MRI Prostate Image Segmentation, from Recursive Neural Networks Aspect by Qikui Zhu, Bo Du, Baris Turkbey, Peter Choyke, Pingkun Yan

“…To tackle this problem, in this paper, we propose a deep neural network with bidirectional convolutional recurrent layers for MRI prostate image segmentation. …”
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Research and DSP Implementation of Speech Enhancement Technology Based on Dynamic Mixed Features and Adaptive Mask by Jie Yang, Yachun Tang

“…Then, an improved deep neural network model is designed to effectively improve the speech enhancement performance. …”
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Hyperspectral Image Classification Based on Attentional Residual Networks by Ning Wang, Xin Pan, Xiaoling Luo, Xiaojing Gao

“…Firstly, the residual network is used to extract the features of hyperspectral images, and the quality of feature extraction is effectively improved by solving the problem of gradient disappearance and gradient explosion in deep neural network training. Then, the Attention Module (AM) is introduced to optimize the feature extraction process, so that the model can focus on the important regions in the image. …”
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Building Arabic Speech Recognition System Using HuBERT Model and Studying the Sources of Errors [Arabic] by Rima Sbih, Assef Jafar, Ali Kazem

“…This paper presents the development of a speech recognition system for the Arabic language that can handle continuous speech and a large number of words, independent of the speaker, using deep neural network models trained by self-supervised learning. …”
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Detection freezing of gait (FOG) in Parkinson's patients using wearable sensors and deep learning by Maryam Talebvand, Amir Lakizadeh, Faranak Fotouhi

“…The proposed methoddetects FOG by providing a deep neural network architecture based on two-way short-term memory networks (BDL-FOG). …”
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