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

“…Therefore, investigating LPI prediction is critical for elucidating disease mechanisms and identifying potential biomarkers and therapeutic targets. Given the high costs and limited efficiency of traditional biological methods, developing cost-effective and accurate computational models for LPI prediction becomes essential. …”
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Insulator Defect Detection Algorithm Based on Improved YOLOv11n by Junmei Zhao, Shangxiao Miao, Rui Kang, Longkun Cao, Liping Zhang, Yifeng Ren

“…Key innovations include a redesigned C3k2 module that incorporates multidimensional dynamic convolutions (ODConv) for improved feature extraction, the introduction of Slimneck to reduce model complexity and computational cost, and the application of the WIoU loss function to optimize anchor box handling and to accelerate convergence. …”
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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.…”
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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.…”
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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. …”
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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. …”
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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. …”
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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. …”
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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. …”
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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. …”
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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

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

“…Among the identified limitations are the complexity of tuning hyperparameters and computational costs associated with increasing the network's depth and the size of training data. …”
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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. …”
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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. …”
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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

“…Traditional experimental identification methods are time-consuming, laborious and costly. To overcome these problems, we adopt a computational biology approach and propose a new model BPFun based on deep learning, which can predict seven functions including anticancer, antibacterial, antihypertensive and so on. …”
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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

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

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Complex-Valued CNN Nonlinear Equalization Enabled 36-Tbit&#x002F;s (45&#x00D7;800-Gbit&#x002F;s) WDM Transmission Over 3150 Km Using Silicon-Based IC-TROSA by Yuhan Gong, Xiaoshuo Jia, Ying Zhu, Kailai Liu, Ming Luo, Jin Tao, Zhixue He, Chao Li, Zichen Liu, Yan Li, Jian Wu, Chao Yang

“…The growing Internet traffic urgently needs large-capacity and cost-effective optical transmissions. To maintain system performance under low-cost conditions, the silicon-based integrated coherent transmit and receive optical sub-assembly (IC-TROSA) and the complex-valued convolutional neural network (CVCNN) algorithm provide an effective solution for high-capacity and long-distance WDM optical transmission. …”
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Automatic detection and classification process for concrete defects in deteriorating buildings based on image data by Suhyun Kang, Seungho Kim, Sangyong Kim

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