An approach to arousal disorder classification using deformable convolution and adaptive multiscale features in EEG signals by Andia Foroughi, Fardad Farokhi, Fereidoun Nowshiravan Rahatabad, Alireza Kashaninia

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A path aggregation network with deformable convolution for visual object detection by Chengming Rao, Zunhao Hu, QiMing Zhao, Min Shan, Li Mao

“…In this article, we propose a novel neck that can perform effective fusion of multi-scale features for a single-stage object detector. This neck, named the deformable convolution and path aggregation network (DePAN), is an integration of a path aggregation network with a deformable convolution block added to the feature fusion branch to improve the flexibility of feature point sampling. …”
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Short term prediction of photovoltaic power with time embedding temporal convolutional networks by Jingxin Wang, Guohan Li, Jin Gu, Zhengyi Xu, Xinrong Chen, Jianming Wei

“…Finally, a fusion strategy integrates spatial and temporal features through a DCNN with residual connections and BiLSTM, enabling effective modeling of complex data relationships. …”
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Identification method for wheel/rail tread defects based on integrated partial convolutional network by CHENG Xiang, HE Jing, ZHANG Changfan, JIA Lin

“…To address this challenge, an integrated partial convolutional network (I-PCNet) method was proposed for identifying wheel-rail tread defects. …”
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Secondary Operation Risk Assessment Method Integrating Graph Convolutional Networks and Semantic Embeddings by Pengyu Zhu, Youwei Li, Peidong Xu, Ping Li, Zhenbing Zhao, Gang Li

“…To address this issue, this paper proposes a hybrid model that integrates graph convolutional networks (GCNs) with semantic embedding techniques. …”
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Power Load Data Completion Method Based on Integrated Graph Convolutional Variational Transformer by YAN Li, HU Hailin, SHI Lei, WU Qinzheng, LÜ Tianguang, XU Yingdong, ZHANG Wenbin, WANG Gaozhou

“…[Methods] To this end， this paper proposes a new power load missing data completion model based on an integrated graph convolutional variational transformer （IGCVT） network. …”
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Linear pattern detection of building groups by integrating dynamic snake convolution with YOLO11 by Xiao Wang, Yue Wu, Longfei Cui, Haizhong Qian, Bohao Li, Xu Wang

“…This paper introduces the YOLO11 object detection model to achieve the detection of building groups with linear patterns by integrating the dynamic snake convolution (DSC) which is used to enhance the feature extraction capability. …”
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A Multimodal Machine Learning Model in Pneumonia Patients Hospital Length of Stay Prediction by Anna Annunziata, Salvatore Cappabianca, Salvatore Capuozzo, Nicola Coppola, Camilla Di Somma, Ludovico Docimo, Giuseppe Fiorentino, Michela Gravina, Lidia Marassi, Stefano Marrone, Domenico Parmeggiani, Giorgio Emanuele Polistina, Alfonso Reginelli, Caterina Sagnelli, Carlo Sansone

“…Specifically, our approach uses the following: (i) feature extraction from chest CT scans via a convolutional neural network (CNN), (ii) their integration with clinically relevant tabular data from patient exams, refined through a feature selection system to retain only significant predictors. …”
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Spatio-temporal transformer and graph convolutional networks based traffic flow prediction by Jin Zhang, Yimin Yang, Xiaoheng Wu, Sen Li

“…Additionally, the model integrates the periodic features of traffic flow data to further enhance prediction accuracy. …”
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DBSANet: A Dual-Branch Semantic Aggregation Network Integrating CNNs and Transformers for Landslide Detection in Remote Sensing Images by Yankui Li, Wu Zhu, Jing Wu, Ruixuan Zhang, Xueyong Xu, Ye Zhou

“…This study proposes a dual-branch semantic aggregation network (DBSANet) by integrating ResNet and a Swin Transformer. A Feature Fusion Module (FFM) is designed to effectively integrate semantic information extracted from the ResNet and Swin Transformer branches. …”
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Novel algorithm for multifocus image fusion: integration of convolutional neural network and partial differential equation by Gargi J Trivedi, Rajesh Sanghvi

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Melanoma Skin Cancer Recognition with a Convolutional Neural Network and Feature Dimensions Reduction with Aquila Optimizer by Jalaleddin Mohamed, Necmi Serkan Tezel, Javad Rahebi, Raheleh Ghadami

“…This study aims to develop a novel classification system for melanoma detection that integrates Convolutional Neural Networks (CNNs) for feature extraction and the Aquila Optimizer (AO) for feature dimension reduction, improving both computational efficiency and classification accuracy. …”
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Enhanced Coronary Artery Disease Classification Through Feature Engineering and One-Dimensional Convolutional Neural Network by Atitaya Phoemsuk, Vahid Abolghasemi

“…Our findings confirm that the proposed model exhibits outstanding performance, highlighting the effectiveness of our integrated feature engineering approach with the CNN model.…”
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Early breast cancer detection in CT scans using convolutional neural bidirectional feature pyramid network by Tahani Jaser Alahmadi, Adeel Ahmed, Amjad Rehman, Abeer Rashad Mirdad, Bayan Al Ghofaily, Shehryar Ali

“…In this article, we proposed a framework called convolutional neural bidirectional feature pyramid network, which integrates multi-scale feature extraction and bidirectional feature fusion for breast cancer detection in CT scans. …”
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S2-IFNet: A spatial-semantic information fusion network integrated with boundary feature enhancement for forest land extraction from Sentinel-2 data by Junyang Xie, Mengyao Zhang, Hao Wu, Anqi Lin, Marcos Adami, Abdul Rashid Mohamed Shariff, Yahui Guo

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A Lightweight Single-Image Super-Resolution Method Based on the Parallel Connection of Convolution and Swin Transformer Blocks by Tengyun Jing, Cuiyin Liu, Yuanshuai Chen

“…Specifically, through a parallel structure of channel feature-enhanced convolution and Swin Transformer, the network extracts, enhances, and fuses the local and global information. …”
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Enhanced Crack Detection in Composite Plates: Integrating Haar Wavelet Transform with Convolutional Neural Networks by Mehrparvar Marmar, Majak Jüri, Karjust Kristo

“…In order to ensure structural integrity, detecting cracks, as a common structural flaw, is crucial. …”
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Lightweight Transformer traffic scene semantic segmentation algorithm integrating multi-scale depth convolution by Gang XIE, Quanyi WANG, Xinlin XIE, Jian’an WANG

“…Aiming at the problems of discontinuous segmentation of thin strip objects that were easy to blend into the surrounding background and a large number of model parameters in the semantic segmentation algorithm of traffic scenes, a lightweight Transformer traffic scene semantic segmentation algorithm integrating multi-scale depth convolution was proposed.First, a multi-scale strip feature extraction module (MSEM) was constructed based on deep convolution to enhance the representation ability of thin strip target features at different scales.Secondly, a spatial detail auxiliary module (SDAM) was designed using the convolutional inductive bias feature in the shallow network to compensate for the loss of deep spatial detail information to optimize object edge segmentation.Finally, an asymmetric encoding-decoding network based on the Transformer-CNN framework (TC-AEDNet) was proposed.The encoder combined Transformer and CNN to alleviate the loss of detail information and reduce the amount of model parameters; while the decoder adopted a lightweight multi-level feature fusion design to further model the global context.The proposed algorithm achieves the mean intersection over union (mIoU) of 78.63% and 81.06% respectively on the Cityscapes and CamVid traffic scene public datasets.It can achieve a trade-off between segmentation accuracy and model size in traffic scene semantic segmentation and has a good application prospect.…”
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SpikeAtConv: an integrated spiking-convolutional attention architecture for energy-efficient neuromorphic vision processing by Wangdan Liao, Fei Chen, Fei Chen, Changyue Liu, Weidong Wang, Weidong Wang, Weidong Wang, Hongyun Liu, Hongyun Liu

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Detecting small seamounts in multibeam data using convolutional neural networks by Tobias Ziolkowski, Colin W. Devey, Agnes Koschmider

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