Yogasana classification using Deep Neural Network: A Unique Approach by Rishi Raj, Rajesh Mukherjee, Bidesh Chakraborty

“…In this research work, we have automatically identified different Yogasanas from images using a deep neural network. For our work we have considered 10 very popular categories of Yogasana and built a dataset of volume 800 images. …”
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Human Activity Recognition Using Graph Structures and Deep Neural Networks by Abed Al Raoof K. Bsoul

“…This research presents a novel HAR system combining graph structures with deep neural networks to capture both spatial and temporal patterns in activities. …”
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Learning Automata Based Incremental Learning Method for Deep Neural Networks by Haonan Guo, Shilin Wang, Jianxun Fan, Shenghong Li

“…In this paper, we proposed an effective incremental training method based on learning automata for deep neural networks. The main thought is to train a deep model with dynamic connections which can be either “activated” or “deactivated” on different datasets of the incremental training stages. …”
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Learning From High-Cardinality Categorical Features in Deep Neural Networks by Mustafa Murat Arat

Subjects: “…deep neural networks…”
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Steel surface defect detection and segmentation using deep neural networks by Sara Ashrafi, Sobhan Teymouri, Sepideh Etaati, Javad Khoramdel, Yasamin Borhani, Esmaeil Najafi

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DWNet: Dual-Window Deep Neural Network for Time Series Prediction by Jin Fan, Yipan Huang, Ke Zhang, Sen Wang, Jinhua Chen, Baiping Chen

“…In this paper, we propose a dual-window deep neural network (DWNet) to predict time series. The dual-window mechanism allows the model to mine multigranularity dependencies of time series, such as local information obtained from a short sequence and global information obtained from a long sequence. …”
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SBS Content Detection for Modified Asphalt Using Deep Neural Network by Zhixiang Wang, Jiange Li, Zhengqi Zhang, Youxiang Zuo

“…This study proposes a prediction model for accurately detecting styrene-butadiene-styrene (SBS) content in modified asphalt using the deep neural network (DNN). Traditional methods used for evaluating the SBS content are inaccurate and complicated because they are prone to produce errors by manual computation. …”
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DEEP NEURAL NETWORK-BASED APPROACH FOR COMPUTING SINGULAR VALUES OF MATRICES by Diyari A. Hassan

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Analysis of Consumer Behavior Data Based on Deep Neural Network Model by Shijiao Yuan

“…In order to improve the accuracy of sample classification and maximize the output function, genetic algorithm is used to optimize the samples. A deep neural network structure algorithm is proposed to classify customer transaction data samples. …”
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An Intelligent Health Monitoring Model Based on Fuzzy Deep Neural Network by Tianye Xing, Yidan Wang, Yingxue Liu, Qi Wu, Rong Ma, Xiaoling Shang

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Well Control Optimization of Waterflooding Oilfield Based on Deep Neural Network by Lihui Tang, Junjian Li, Wenming Lu, Peiqing Lian, Hao Wang, Hanqiao Jiang, Fulong Wang, Hongge Jia

“…This paper proposes a new method of a well control optimization method based on a multi-input deep neural network. This method takes the production history data of the reservoir as the main input and the saturation field as the auxiliary input and establishes a multi-input deep neural network for learning, forming a production dynamic prediction model instead of conventional numerical simulators. …”
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Assessing the value of deep neural networks for postoperative complication prediction in pancreaticoduodenectomy patients. by Mikkel Bonde, Alexander Bonde, Haytham Kaafarani, Andreas Millarch, Martin Sillesen

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Assessing the value of deep neural networks for postoperative complication prediction in pancreaticoduodenectomy patients by Mikkel Bonde, Alexander Bonde, Haytham Kaafarani, Andreas Millarch, Martin Sillesen

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The Application of Speech Synthesis Technology Based on Deep Neural Network in Intelligent Broadcasting by Jihong Yang

“…To improve the sound quality of speech synthesis technology in intelligent broadcasting, a deep neural network-based method is proposed. It also proved the effectiveness of the DNN discrimination s/u/v and completed the conversion of the HMM synthesis spectrum parameter to original speech. …”
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Image Recommendation Algorithm Combined with Deep Neural Network Designed for Social Networks by Shaohui Du, Zhenghan Chen, Haoyan Wu, Yihong Tang, YuanQing Li

“…In recent years, deep neural networks have achieved great success in many fields, such as computer vision and natural language processing. …”
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Compressing fully connected layers of deep neural networks using permuted features by Dara Nagaraju, Nitin Chandrachoodan

“…Abstract Modern deep neural networks typically have some fully connected layers at the final classification stages. …”
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Implementation of Genetic Algorithm Integrated with the Deep Neural Network for Estimating at Completion Simulation by Karrar Raoof Kareem Kamoona, Cenk Budayan

“…In this research, a relatively new intelligent model called deep neural network (DNN) is proposed to calculate the EAC. …”
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Device Modeling Based on Cost-Sensitive Densely Connected Deep Neural Networks by Xiaoying Tang, Zhiqiang Li, Lang Zeng, Hongwei Zhou, Xiaoxu Cheng, Zhenjie Yao

“…The DenseDNN model with cost-sensitive learning exhibits better performance than traditional deep neural networks (DNN) with various widths and depths, with a prediction error below 1.62%. …”
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A multiobjective continuation method to compute the regularization path of deep neural networks by Augustina Chidinma Amakor, Konstantin Sonntag, Sebastian Peitz

“…Sparsity is a highly desired feature in deep neural networks (DNNs) since it ensures numerical efficiency, improves the interpretability (due to the smaller number of relevant features), and robustness. …”
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Electromagnetic signal modulation recognition technology based on lightweight deep neural network by Sicheng ZHANG, Yun LIN, Ya TU, Shiwen Mao

“…In response to the trend that in the 6th generation wireless (6G) era,mobile communications and artificial intelligence will be closely integrated,and a huge number of edge intelligent signal processing nodes will be deployed,an efficient and intelligent electromagnetic signal recognition model was proposed,which could be deployed on resource-constrained edge devices.The constellation diagram of electromagnetic signal was firstly drawn to visualize electromagnetic signal as a two-dimensional image,and color the constellation diagram according to the normalized point density to achieve feature enhancement.Then,a binary deep neural network was adopted to recognize the colored constellation diagrams.It was shown that the approach can guarantee a high recognition accuracy,which significantly reduced the model storage and calculation costs.For verification,the proposed approach was applied to the problem of electromagnetic signal modulation recognition.The experiment uses eight commonly used digital modulation signals and selects additive white Gaussian noise as the channel environment.The experimental results show that the scheme can achieve a comprehensive recognition rate of 96.1% under the noise condition of -6～6 dB,while the size of the network model is only 166 KB.Further,the execution time,when executed on a Raspberry Pi 4B,is only 290 ms.Compared to a full-precision network of the same scale,the accuracy is increased by 0.6%,the model is reduced to <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML"> <mfrac> <mtext>1</mtext> <mrow> <mtext>26</mtext><mo>.…”
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