Research on Intelligent Vehicle Tracking Control and Energy Consumption Optimization Based on Dilated Convolutional Model Predictive Control by Lanxin Li, Wenhui Pei, Qi Zhang

“…This model was then integrated into a model predictive control (MPC) framework. By solving a nonlinear optimization problem, a dilated convolutional model predictive controller (DCMPC) was developed to enhance tracking accuracy and reduce energy consumption. …”
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Optimizing forest stand aggregation in fragmented stands using graph convolutional networks: A case study in Japan by YangYu You, Hyun Bae Kim, Takuyuki Yoshioka

“…Unlike traditional rule-based methods that rely on fixed thresholds and static adjacency rules, the GCN framework allows stand-level attributes—such as slope, road proximity, and spatial adjacency—to influence each other through a graph structure. …”
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Robust Low-Snapshot DOA Estimation for Sparse Arrays via a Hybrid Convolutional Graph Neural Network by Hongliang Zhu, Hongxi Zhao, Chunshan Bao, Yiran Shi, Wenchao He

“…The C-GNN architecture combines 1D convolutional layers for local spatial feature extraction with graph convolutional layers for global structural learning, effectively capturing both fine-grained and long-range array dependencies. …”
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Breast tumor segmentation in ultrasound using distance-adapted fuzzy connectedness, convolutional neural network, and active contour by Marta Biesok, Jan Juszczyk, Pawel Badura

“…Abstract This study addresses computer-aided breast cancer diagnosis through a hybrid framework for breast tumor segmentation in ultrasound images. …”
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A Deep Learning Model Based on Bidirectional Temporal Convolutional Network (Bi-TCN) for Predicting Employee Attrition by Farhad Mortezapour Shiri, Shingo Yamaguchi, Mohd Anuaruddin Bin Ahmadon

“…In this study, we introduce a deep learning framework based on a Bidirectional Temporal Convolutional Network (Bi-TCN) to predict employee attrition. …”
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Comparative Analysis of Recurrent vs. Temporal Convolutional Autoencoders for Detecting Container Impacts During Quay Crane Handling by Sergej Jakovlev, Tomas Eglynas, Edvinas Pocevicius, Miroslav Voznak, Gediminas Gricius, Valdas Jankunas, Mindaugas Jusis

“…We compare the performance of Recurrent Autoencoders (RAEs) and Temporal Convolutional Autoencoders (TCAEs) using acceleration data collected during quay crane handling. …”
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Optimizing Fire Scene Analysis: Hybrid Convolutional Neural Network Model Leveraging Multiscale Feature and Attention Mechanisms by Shakhnoza Muksimova, Sabina Umirzakova, Mirjamol Abdullaev, Young-Im Cho

“…Extensive experiments on a comprehensive dataset encompassing wildfires, building fires, vehicle fires, and non-fire scenes demonstrate that the proposed framework outperforms existing cutting-edge techniques. …”
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Multi-Domain Feature Incorporation of Lightweight Convolutional Neural Networks and Handcrafted Features for Lung and Colon Cancer Diagnosis by Omneya Attallah

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GAGAN: Enhancing Image Generation Through Hybrid Optimization of Genetic Algorithms and Deep Convolutional Generative Adversarial Networks by Despoina Konstantopoulou, Paraskevi Zacharia, Michail Papoutsidakis, Helen C. Leligou, Charalampos Patrikakis

“…The discriminator, which classifies images as real or fake, is optimized not only through standard backpropagation, but also through a GA framework that evolves its weights via crossover, mutation, and selection processes. …”
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Modeling Higher-Order Interactions in Graphs Through Combinatorial Arc-Transitive Structure Using Graph Convolutional Network by Qingwei Wen

“…This research addresses the fundamental challenge of learning robust representations in heterophilic graphs by introducing Higher-Order Graph Convolutional Network (HiGCN), a novel framework that effectively models both homophilic and heterophilic interactions through a structured Petal-Complex (PC) model. …”
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Single Fringe Phase Retrieval for Translucent Object Measurements Using a Deep Convolutional Generative Adversarial Network by Jiayan He, Yuanchang Huang, Juhao Wu, Yadong Tang, Wenlong Wang

“…The network primarily relies on a generative adversarial network framework, with significant enhancements implemented in the generator network, including integrating the U-net++ architecture, Resnet101 as the backbone network for feature extraction, and a multilevel attention module for fully utilizing the high-level features of the source image. …”
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Prediction of Residual Life of Rolling Bearings Based on Multi-Scale Improved Temporal Convolutional Network (MITCN) Model by Keru Xia, Qi Li, Luyuan Han, Zhaohui Ren, Hengfa Luo

“…The model adopts the framework of a time convolution network and has good ability to extract time series information. …”
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ADMM-TransNet: ADMM-Based Sparse-View CT Reconstruction Method Combining Convolution and Transformer Network by Sukai Wang, Xueqin Sun, Yu Li, Zhiqing Wei, Lina Guo, Yihong Li, Ping Chen, Xuan Li

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Enhancing Surveillance Video Abnormal Behavior Detection Using Deep Convolutional Generative Adversarial Network With GRU Model by Setegn Asnakew Kasegn, Ronald Waweru Mwangi, Michael Kimwele, Surafel Lemma Abebe

“…This includes recent generative methods that use deep convolutional generative adversarial networks (DCGAN). …”
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MVBNSleepNet: A Multi-View Brain Network-Based Convolutional Neural Network for Neonatal Sleep Staging by Ligang Zhou, Minghui Liu, Xia Hu, Laishuan Wang, Yan Xu, Chen Chen, Wei Chen

“…<italic>Methods:</italic> We propose MVBNSleepNet, a multi-view brain network-based convolutional neural network. The framework integrates a multi-view brain network (MVBN) to characterize brain functional connectivity from linear temporal correlation, information-theoretic, and phase-dynamics perspectives, providing comprehensive spatial topological information. …”
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Monte Carlo-Based Strategy for Assessing the Impact of EEG Data Uncertainty on Confidence in Convolutional Neural Network Classification by Pierre Sedi Nzakuna, Vincenzo Gallo, Vincenzo Paciello, Aime Lay-Ekuakille, Angelo Kuti Lusala

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Weakly Supervised Semantic Segmentation of Mangrove Ecosystem Using Sentinel-1 SAR and Deep Convolutional Neural Networks by Arsalan Ghorbanian, Ardalan Ghorbanian, Seyed Ali Ahmadi, Ali Mohammadzadeh, Amin Naboureh

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Federated learning-based hybrid convolutional recurrent neural network for multi-class intrusion detection in IoT networks by Prabu Selvam, P. Karthikeyan, S. Manochitra, A. V. L. N. Sujith, T. Ganesan, Rajaram Ayyasamy, Mohammed Shuaib, Shadab Alam, A. Rajendran

“…For multi-class intrusion detection in IoT networks, this study proposed an novel hybrid convolutional recurrent neural network (CRNN) model based on federated learning. …”
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Adaptive spatial-channel feature fusion and self-calibrated convolution for early maize seedlings counting in UAV images by Zhenyuan Sun, Zhenyuan Sun, Zhenyuan Sun, Zhi Yang, Zhi Yang, Yimin Ding, Yimin Ding, Boyan Sun, Boyan Sun, Saiju Li, Saiju Li, Zhen Guo, Zhen Guo, Lei Zhu, Lei Zhu

“…RC-Dino introduces two innovative components: a novel self-calibrating convolutional layer named RSCconv and an adaptive spatial feature fusion module called ASCFF. …”
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Deep Learning Techniques in DICOM Files Classification: A Systematic Review by Mabirizi, Vicent, Kawuma, Simon, Natumanya, Deborah, Wasswa, William

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