A text dataset of fire door defects for pre-delivery inspections of apartments during the construction stageFigshare by Seunghyeon Wang, Sungkon Moon, Ikchul Eum, Dongjin Hwang, Jaejun Kim

Get full text

Impact of Input Image Resolution on Deep Learning Performance for Side-Scan Sonar Classification: An Accuracy–Efficiency Analysis by Xing Du, Yongfu Sun, Yupeng Song, Wanqing Chi, Lifeng Dong, Xiaolong Zhao

Get full text

MFH-LPI: based on multi-view similarity networks fusion and hypergraph learning for long non-coding RNA-protein interactions prediction by Zengwei Xing, Shaoyou Yu, Shuzu Liao, Peng Wang, Bo Liao

“…Finally, we predict LPIs using a multilayer graph convolutional network (GCN) combined with a fully connected (FC) layer. …”
Get full text

YOLO-RD: A Road Damage Detection Method for Effective Pavement Maintenance by Wei Wang, Xiaoru Yu, Bin Jing, Ziqi Tang, Wei Zhang, Shengyu Wang, Yao Xiao, Shu Li, Liping Yang

“…Road damage detection is crucial for ensuring road safety and minimizing maintenance costs. However, detecting small damage, managing complex backgrounds, and identifying irregular damage shapes remain significant challenges. …”
Get full text

deepBBQ: A Deep Learning Approach to the Protein Backbone Reconstruction by Justyna D. Kryś, Maksymilian Głowacki , Piotr Śmieja , Dominik Gront

“…Extensive comparison with similar programs shows that our solution is accurate and cost-efficient. The deepBBQ program is available as part of the open-source bioinformatics toolkit Bioshell and is free for download and the documentation is available online.…”
Get full text

Encrypted traffic identification method based on deep residual capsule network with attention mechanism by Guozhen SHI, Kunyang LI, Yao LIU, Yongjian YANG

“…With the improvement of users’ security awareness and the development of encryption technology, encrypted traffic has become an important part of network traffic, and identifying encrypted traffic has become an important part of network traffic supervision.The encrypted traffic identification method based on the traditional deep learning model has problems such as poor effect and long model training time.To address these problems, the encrypted traffic identification method based on a deep residual capsule network (DRCN) was proposed.However, the original capsule network was stacked in the form of full connection, which lead to a small model coupling coefficient and it was impossible to build a deep network model.The DRCN model adopted the dynamic routing algorithm based on the three-dimensional convolutional algorithm (3DCNN) instead of the fully-connected dynamic routing algorithm, to reduce the parameters passed between each capsule layer, decrease the complexity of operations, and then build the deep capsule network to improve the accuracy and efficiency of recognition.The channel attention mechanism was introduced to assign different weights to different features, and then the influence of useless features on the recognition results was reduced.The introduction of the residual network into the capsule network layer and the construction of the residual capsule network module alleviated the gradient disappearance problem of the deep capsule network.In terms of data pre-processing, the first 784byte of the intercepted packets was converted into images as input of the DRCN model, to avoid manual feature extraction and reduce the labor cost of encrypted traffic recognition.The experimental results on the ISCXVPN2016 dataset show that the accuracy of the DRCN model is improved by 5.54% and the training time of the model is reduced by 232s compared with the BLSTM model with the best performance.In addition, the accuracy of the DRCN model reaches 94.3% on the small dataset.The above experimental results prove that the proposed recognition scheme has high recognition rate, good performance and applicability.…”
Get full text

AngleCam: Predicting the temporal variation of leaf angle distributions from image series with deep learning by Teja Kattenborn, Ronny Richter, Claudia Guimarães‐Steinicke, Hannes Feilhauer, Christian Wirth

“…Here, we present AngleCam, a deep learning‐based approach to predict leaf angle distributions from horizontal photographs acquired with low‐cost timelapse cameras. AngleCam is based on pattern recognition with convolutional neural networks and trained with leaf angle distributions obtained from visual interpretation of more than 2500 plant photographs across different species and scene conditions. …”
Get full text

Automatic assessment of lower limb deformities using high-resolution X-ray images by Reyhaneh Rostamian, Masoud Shariat Panahi, Morad Karimpour, Alireza Almasi Nokiani, Ramin Jafarzadeh Khaledi, Hadi Ghattan Kashani

“…Methods The proposed approach uses a Convolutional Neural Network (CNN) that receives the raw X-ray image as input and produces the coordinates of the landmarks. …”
Get full text

COVID-19 Artificial Intelligence Diagnosis Using Only Cough Recordings by Jordi Laguarta, Ferran Hueto, Brian Subirana

“…<italic>Methods:</italic> We developed an AI speech processing framework that leverages acoustic biomarker feature extractors to pre-screen for COVID-19 from cough recordings, and provide a personalized patient saliency map to longitudinally monitor patients in real-time, non-invasively, and at essentially zero variable cost. Cough recordings are transformed with Mel Frequency Cepstral Coefficient and inputted into a Convolutional Neural Network (CNN) based architecture made up of one Poisson biomarker layer and 3 pre-trained ResNet50's in parallel, outputting a binary pre-screening diagnostic. …”
Get full text

YOLO-LSD: A Lightweight Object Detection Model for Small Targets at Long Distances to Secure Pedestrian Safety by Ming-An Chung, Sung-Yun Chai, Ming-Chun Hsieh, Chia-Wei Lin, Kai-Xiang Chen, Shang-Jui Huang, Jun-Hao Zhang

“…The proposed model integrates the C3C2 and the new Efficient Layer Aggregation Network - Convolutional Block Attention Module(ELAN-CBAM) modules to improve the efficiency of feature extraction while reducing computational overhead. …”
Get full text

Compressive strength prediction of fly ash/slag-based geopolymer concrete using EBA-optimised chemistry-informed interpretable deep learning model by Yang Yu, Iman Munadhil Abbas Al-Damad, Stephen Foster, Ali Akbar Nezhad, Ailar Hajimohammadi

“…However, optimising GPC's compressive strength (CS) often requires costly and time-consuming experimental trials. This study develops a deep learning (DL) model based on convolutional neural networks (CNN) to predict the CS of FA/GGBS-based GPC. …”
Get full text

A data-driven approach for predicting remaining intra-surgical time and enhancing operating room efficiency by Saleem Ramadan, Mohammad Abu-Shams, Sameer Al-Dahidi, Ibrahim Odeh, Najat Almasarwah

“…Purpose: Efficient scheduling in Operating Rooms (ORs) is essential for optimizing corresponding costs and enhancing customer satisfaction in healthcare systems. …”
Get full text

An explainable hybrid feature aggregation network with residual inception positional encoding attention and EfficientNet for cassava leaf disease classification by M. Sundara Srivathsan, S. Alden Jenish, K. Arvindhan, R. Karthik

Get full text

CNN for Computer Vision tasks by Р. Ковальчук, О. Польшакова

“…The article covers essential methods such as convolutional, pooling, fully connected, and inception layers. …”
Get full text

Real-World Steam Powerplant Boiler Tube Leakage Detection Using Hybrid Deep Learning by Salman Khalid, Muhammad Muzammil Azad, Heung Soo Kim

“…The detection of boiler water-wall tube leakage in steam power plants is essential to prevent efficiency loss, unexpected shutdowns, and costly repairs. This study proposes a hybrid deep learning approach that combines convolutional neural networks (CNNs) with a support vector machine (SVM) classifier to allow early and accurate leak detection. …”
Get full text

A Deep Learning Method for the Automated Mapping of Archaeological Structures from Geospatial Data: A Case Study of Delos Island by Pavlos Fylaktos, George P. Petropoulos, Ioannis Lemesios

“…The integration of artificial intelligence (AI), specifically through convolutional neural networks (CNNs), is paving the way for significant advancements in archaeological research. …”
Get full text

BPFun: a deep learning framework for bioactive peptide function prediction using multi-label strategy by transformer-driven and sequence rich intrinsic information by Lun Zhu, Hao Sun, Sen Yang

“…Meanwhile, adopting data augmentation to solve the problem of data imbalance. We combine convolutional networks of different scales and Bi-LSTM layers to obtain high-level feature vectors of different features. …”
Get full text

Deep Learning-Based Performance Prediction of Electric Submersible Pumps Under Viscous and Gas–Liquid Flow Conditions by Haiwen Zhu, Hong Yu, Qiang Sun, Qiuchen Wang, Haorong Jing, Rakhymzhan Abdikadyrov

Get full text

Securing Urban Landscape: Cybersecurity Mechanisms for Resilient Smart Cities by Qiang Lyu, Sujuan Liu, Zhouyuan Shang

“…This article explores a novel approach to enhancing cybersecurity in smart cities by integrating Convolutional Neural Networks (CNNs) with Genetic Algorithms (GAs). …”
Get full text

Bringing Intelligence to SAR Missions: A Comprehensive Dataset and Evaluation of YOLO for Human Detection in TIR Images by Mostafa Rizk, Israa Bayad

Get full text