Shuffle window transformer DeepLabV3+: a lightweight convolutional neural network and transformer based hybrid semantic segmentation network by Yane Li, Zhichao Chen, Hongxia Qi, Ming Fan, Lihua Li

“…To address this, this paper proposes a semantic segmentation network based on a hybrid architecture of convolutional neural network and Transformer, named shuffle window transformer DeeplabV3+ (SWT-DeepLabV3+). …”
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Swin-ReshoUnet: A Seismic Profile Signal Reconstruction Method Integrating Hierarchical Convolution, ORCA Attention, and Residual Channel Attention Mechanism by Jie Rao, Mingju Chen, Xiaofei Song, Chen Xie, Xueyang Duan, Xiao Hu, Senyuan Li, Xingyue Zhang

“…The encoder uses a hierarchical convolution module to build a multi-scale feature pyramid, enhancing cross-scale geological signal representation. …”
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Melt Density Monitoring of Extruder Extrusion Process Based on Multi-source Data Fusion and Convolutional Long Short-term Memory Neural Network by Binbin ZHANG, Zhuyun CHEN, Fei ZHANG, Gang JIN

“…Ensuring precise control over melt density is imperative for achieving desired product characteristics and maintaining process stability in polymer blending operations.Methods The research proposes a novel methodological framework that integrates multi-source data fusion with a convolutional long short-term memory (LSTM) neural network architecture to address this challenge. …”
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Unobtrusive Sleep Posture Detection Using a Smart Bed Mattress with Optimally Distributed Triaxial Accelerometer Array and Parallel Convolutional Spatiotemporal Network by Zhuofu Liu, Gaohan Li, Chuanyi Wang, Vincenzo Cascioli, Peter W. McCarthy

“…Additionally, we have constructed a Parallel Convolutional Spatiotemporal Network (PCSN) by integrating Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), and Bidirectional Long Short-Term Memory (Bi-LSTM) modules. …”
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Research on an Intelligent Sedimentary Microfacies Recognition Method Based on Convolutional Neural Networks Within the Sequence Stratigraphy of Well Logging Curve Image Groups by Xinyi Yuan, Xidong Wang, Shutian Wang, Feng Tian, Zichun Yang

“…Traditional facies interpretation methods, reliant on manual log-response parameter analysis, are constrained by interpreter subjectivity, reservoir heterogeneity, and inefficiencies in resolving thin interbedded sequences and concealed fluvial sand bodies—issues marked by high interpretive ambiguity, prolonged cycles, and elevated costs. This study focuses on the Lower Cretaceous Yaojia Formation Member 1 (K2y1) in the satellite oilfield of the Songliao Basin, integrating sequence stratigraphy into a machine learning framework to propose an innovative convolutional neural network (CNN)-based facies recognition method using log-curve image groups by graphically transforming five log curves and establishing a CNN model that correlates log responses with microfacies. …”
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An Improved Decision Support System for Identification of Abnormal EEG Signals Using a 1D Convolutional Neural Network and Savitzky-Golay Filtering by Unmesh Shukla, Geetika Jain Saxena, Manish Kumar, Anil Singh Bafila, Amit Pundir, Sanjeev Singh

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CCDR: Combining Channel-Wise Convolutional Local Perception, Detachable Self-Attention, and a Residual Feedforward Network for PolSAR Image Classification by Jianlong Wang, Bingjie Zhang, Zhaozhao Xu, Haifeng Sima, Junding Sun

“…In the task of PolSAR image classification, effectively utilizing convolutional neural networks and vision transformer models with limited labeled data poses a critical challenge. …”
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Lightweight convolutional neural networks using nonlinear Lévy chaotic moth flame optimisation for brain tumour classification via efficient hyperparameter tuning by Amin Abdollahi Dehkordi, Mehdi Neshat, Alireza Khosravian, Menasha Thilakaratne, Ali Safaa Sadiq, Seyedali Mirjalili

“…Abstract Deep convolutional neural networks (CNNs) have seen significant growth in medical image classification applications due to their ability to automate feature extraction, leverage hierarchical learning, and deliver high classification accuracy. …”
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YOLOv9-AAG: Distinguishing Birds and Drones in Infrared and Visible Light Scenarios by Peichun Suo, Jincan Zhu, Qingyang Zhou, Weili Kou, Xiuyan Wang, Wen Suo

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Toward Reliable Post-Disaster Assessment: Advancing Building Damage Detection Using You Only Look Once Convolutional Neural Network and Satellite Imagery by César Luis Moreno González, Germán A. Montoya, Carlos Lozano Garzón

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Unlocking Gait Analysis Beyond the Gait Lab: High-Fidelity Replication of Knee Kinematics Using Inertial Motion Units and a Convolutional Neural Network by Stefano A. Bini, MD, Nicholas Gillian, PhD, Thomas A. Peterson, PhD, Richard B. Souza, PhD, PT, Brooke Schultz, MS, ACE-CPT, Wojciech Mormul, MS, Marek K. Cichoń, MS, Agnieszka Barbara Szczotka, MS, Ivan Poupyrev, PhD

“…Sequence to sequence convolutional neural networks were trained to map IMU data to three motion capture kinematic outputs: right knee angle, right knee angular velocity, and right hip angle. …”
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Deep convolutional neural network based archimedes optimization algorithm for heart disease prediction based on secured IoT enabled health care monitoring system by Sureshkumar S, Santhosh Babu A. V, Joseph James S, Maranco M

“…Finally, the Deep Convolutional Neural Network-based Archimedes Optimization (DCNN-AO) algorithm classifies the normal and abnormal classes of heart disease. …”
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Enhanced Blade Fault Diagnosis Using Hybrid Deep Learning: A Comparative Analysis of Traditional Machine Learning and 1D Convolutional Transformer Architecture by Syed Asad Imam, Meng Hee Lim, Ahmed Mohammed Abdelrhman, Iftikhar Ahmad, Mohd Salman Leong

“…Early fault detection and diagnosis are vital and significant concerns for preventing these incidents and are particularly crucial in gas turbines and compressors to avoid costly downtime and maintain optimal plant performance. …”
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Chiller power consumption forecasting for commercial building based on hybrid convolution neural networks-long short-term memory model with barnacles mating optimizer by Mohd Herwan Sulaiman, Zuriani Mustaffa

“…This paper presents an innovative approach using a hybrid Convolutional Neural Network-Long Short-Term Memory (CNN-LSTM) model optimized by the Barnacles Mating Optimizer (BMO). …”
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Conv1D-LSTM: Autonomous Breast Cancer Detection Using a One-Dimensional Convolutional Neural Network With Long Short-Term Memory by Mitanshi Rastogi, Meenu Vijarania, Neha Goel, Akshat Agrawal, Cresantus N. Biamba, Celestine Iwendi

“…While traditional cancer detection strategies are at times effective, they typically require costly and time-intensive methods for implementation. …”
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Predictive modelling employing machine learning, convolutional neural networks (CNNs), and smartphone RGB images for non-destructive biomass estimation of pearl millet (Pennisetum... by Faten Dhawi, Abdul Ghafoor, Norah Almousa, Sakinah Ali, Sara Alqanbar

“…This study employed a transfer learning approach using pre-trained convolutional neural networks (CNNs) alongside shallow machine learning algorithms (Support Vector Regression, XGBoost, Random Forest Regression) to estimate AGB. …”
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PIC2O-Sim: A physics-inspired causality-aware dynamic convolutional neural operator for ultra-fast photonic device time-domain simulation by Pingchuan Ma, Haoyu Yang, Zhengqi Gao, Duane S. Boning, Jiaqi Gu

“…PIC2O-Sim features a causality-aware dynamic convolutional neural operator as its backbone model that honors the space–time causality constraints via careful receptive field configuration and explicitly captures the permittivity-dependent light propagation behavior via an efficient dynamic convolution operator. …”
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A multi-attention TCN based electricity price forecasting method considering high proportion of wind power fluctuation by LI Zikai, YANG Bo, ZHOU Zhongtang, LI Xin, CHEN Fengwei, JIAO Runhai

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A lightweight multi-path convolutional neural network architecture using optimal features selection for multiclass classification of brain tumor using magnetic resonance images by Amreen Batool, Yung-Cheol Byun

“…Pre-trained models like AlexNet, Residual Networks (ResNet), and Inception V3 are effective but has high computational costs due to trainable parameters. Therefore, a lightweight Multi -path Convolutional Neural Network (M-CNN) is introduced to extract features using varying convolutional filters at each convolutional layer. …”
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YOLOv8n-GBE: A Hybrid YOLOv8n Model With Ghost Convolutions and BiFPN-ECA Attention for Solar PV Defect Localization by Likitha Reddy Yeddula, Archana Pallakonda, Rayappa David Amar Raj, Rama Muni Reddy Yanamala, K. Krishna Prakasha, Mallempati Sunil Kumar

“…The outcomes confirm that reliable PV fault localization under a range of operating situations may be achieved by combining deep feature fusion, lightweight attention, and efficient convolution.…”
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