Resistance Spot Welding Defect Detection Based on Visual Inspection: Improved Faster R-CNN Model by Weijie Liu, Jie Hu, Jin Qi

Subjects: Get full text

Identifikasi Tanaman Obat Indonesia Melalui Citra Daun Menggunakan Metode Convolutional Neural Network (CNN) by Budi Setiyono, Muhammad Riv’an Arif, Qonita Qurratu Aini, Theophil Henry Soegianto, Jasti Ohanna, Ricky Andrean Fernanda Gunawan, Ayu Putri Rizkia

“…Penelitian ini menunjukkan bahwa, CNN berhasil digunakan untuk mengidentifikasi tanaman herbal. …”
Get full text

A hybrid CNN-LSTM model with adaptive instance normalization for one shot singing voice conversion by Assila Yousuf, David Solomon George

Subjects: Get full text

A novel ensemble model for fall detection: leveraging CNN and BiLSTM with channel and temporal attention by Sarita Sahni, Sweta Jain, Sri Khetwat Saritha

Get full text

A file archival integrity check method based on the BiLSTM + CNN model and deep learning by Jinxun Li, Tingjun Wang, Chao Ma, Yunxuan Lin, Qing Yan

Get full text

Reducing lead requirements for wearable ECG: Chest lead reconstruction with 1D-CNN and Bi-LSTM by Kazuki Hebiguchi, Hiroyoshi Togo, Akimasa Hirata

Get full text

Residual Life Prediction of SA-CNN-BILSTM Aero-Engine Based on a Multichannel Hybrid Network by Yonghao He, Changjun Wen, Wei Xu

“…In this paper, a multichannel hybrid network is proposed; this network is a combination of the one-dimensional convolutional neural network (1D-CNN), the bidirectional long short-term memory network (BiLSTM), and the self-attention mechanism. …”
Get full text

一种新的基于CNN调度信元的输入缓冲ATM交换结构 by 陈金山, 韦岗

Get full text

High-performance traffic volume prediction: An evaluation of RNN, GRU, and CNN for accuracy and computational trade-offs by Pranolo Andri, Saifullah Shoffan, Bella Utama Agung, Wibawa Aji Prasetya, Bastian Muhammad, Hardiyanti P Cicin

“…This study addresses these challenges by evaluating the effectiveness of three deep learning architectures— Recurrent Neural Network (RNN), Gated Recurrent Unit (GRU), and Convolutional Neural Network (CNN)—in forecasting hourly traffic volume on Interstate 94. …”
Get full text

Predicting vasovagal reactions to needles from video data using 2D-CNN with GRU and LSTM. by Judita Rudokaite, Sharon Ong, Itir Onal Ertugrul, Mart P Janssen, Elisabeth Huis In 't Veld

“…We compared 5 different sequences of videos-45, 30, 20, 10 and 5 seconds to test the shortest video duration required to predict VVR levels. We used 2D-CNN with LSTM and GRU to predict continuous VVR scores and to classify discrete (low and high) VVR values obtained during the blood donation. …”
Get full text

DHCT-GAN: Improving EEG Signal Quality with a Dual-Branch Hybrid CNN–Transformer Network by Yinan Cai, Zhao Meng, Dian Huang

Get full text

A Reconfigurable Coarse-to-Fine Approach for the Execution of CNN Inference Models in Low-Power Edge Devices by Auangkun Rangsikunpum, Sam Amiri, Luciano Ost

“…In this regard, this work proposes an FPGA-based dynamic reconfigurable coarse-to-fine (C2F) inference of CNN models, aiming to increase power efficiency and flexibility. …”
Get full text

Studying Forgetting in Faster R-CNN for Online Object Detection: Analysis Scenarios, Localization in the Architecture, and Mitigation by Baptiste Wagner, Denis Pellerin, Sylvain Huet

Subjects: Get full text

A Wi-Fi sensing method for complex continuous human activities based on CNN-BiGRU by Yang LIU, Anming DONG, Jiguo YU, Kai ZHAO, You ZHOU

“…Human activity sensing based on Wi-Fi channel state information (CSI) has an important application prospect in future intelligent interaction scenarios such as virtual reality, intelligent games, and the metaverse.Accurate sensing of complex and continuous human activities is an important challenge for Wi-Fi sensing.Convolutional neural network (CNN) has the ability of spatial feature extraction but is poor at modeling the temporal features of the data.While long short-term memory (LSTM) network or gated recurrent unit (GRU) network, which are suitable for modeling time-series data, neglect learning spatial features of data.In order to solve this problem, an improved CNN that integrates bidirectional gated recurrent unit (BiGRU) network was proposed.The bi-directional feature extraction ability of BiGRU was used to capture the correlation and dependence of the front and back information in the time series data.The extraction of the spatiotemporal features of the time series CSI data was realized, and then the mapping relationship between the action and the CSI data was present.Thus the recognition accuracy of the complex continuous action was improved.The proposed network structure was tested with basketball actions.The results show that the recognition accuracy of this method is above 95% under various conditions.Compared with the traditional multi-layer perceptron (MLP), CNN, LSTM, GRU, and attention based bidirectional long short-term memory (ABLSTM) baseline methods, the recognition accuracy has been improved by 1%～20%.…”
Get full text

Diabetic retinopathy grading using curvelet CNN with optimized SSO activations and wavelet-based image enhancement by N. Mohana Suganthi, M. Arun

Subjects: Get full text

A CNN-LSTM Phase Compensation Method for Unidirectional Two-way Radio Frequency Transmission System by Jiahui Cheng, Zhengkang Wang, Yaojun Qiao, Hao Gao, Chenxia Liu, Zhuoze Zhao, Jie Zhang, Baodong Zhao, Bin Luo, Song Yu

“…A convolutional neural network combined with long short-term memory (CNN-LSTM) phase compensation method (PCM) is proposed and demonstrated, where CNN is employed to extract spatial features, and LSTM is used to capture temporal features and realize the long-term predictions of residual phase fluctuations. …”
Get full text

A Spatiotemporal Feature Extraction Technique Using Superlet-CNN Fusion for Improved Motor Imagery Classification by Neha Sharma, Manoj Sharma, Amit Singhal, Nuzhat Fatema, Vinay Kumar Jadoun, Hasmat Malik, Asyraf Afthanorhan

Get full text

A Joint Network of 3D-2D CNN Feature Hierarchy and Pyramidal Residual Model for Hyperspectral Image Classification by Hongwei Wei, Yufan Wang, Yu Sun, Jianfeng Zheng, Xiaodong Yu

Subjects: Get full text

Breast Cancer Detection Using Deep Learning by ahmed Abed Maeedi, Dalal Abdulmohsin Hammood, Shatha Mezher Hasan

Subjects: “…breast cancer, deep learning, cnn, neural networks, hybrid lstm-cnn.…”
Get full text

Enhancing pediatric congenital heart disease detection using customized 1D CNN algorithm and phonocardiogram signals by Ihtisham Ul Haq, Ghassan Husnain, Yazeed Yasin Ghadi, Nisreen Innab, Masoud Alajmi, Hanan Aljuaid

“…This research presents a tailored one-dimensional convolutional neural network (1D-CNN) for the classification of phonocardiogram (PCG) signals into normal or abnormal categories, providing an automated and efficient solution for congenital heart disease (CHD) diagnosis. …”
Get full text