EEG-Based Multi-Level Mental State Classification Using Partial Directed Coherence and Graph Convolutional Networks: Impact of Binaural Beats on Stress Mitigation by Yara Badr, Fares Al-Shargie, M. N. Afzal Khan, Nour Faris Ali, Usman Tariq, Fadwa Almughairbi, Fabio Babiloni, Hasan Al-Nashash

“…This study addresses limitations in EEG-based stress detection research by developing a novel approach to differentiate multiple mental states in different stress baseline population samples. Utilizing EEG signals, graph convolutional neural networks (GCNs), and binaural beats stimulation (BBs), the research investigates stress detection and reduction in two population sample groups with distinct baselines (group 1: low daily baseline, and group 2: stressed daily baseline). …”
Get full text

Brittleness evaluation of main coal seams in Permian Taiyuan-Shanxi formations, Baode block, Ordos Basin: based on a convolutional neural network method by Qingfeng ZHANG, Ziling LI, Jikun ZHANG, Shuai HAO, Xiaoguang SUN, Yanjie SHANG, Yun ZUO

“…However, the productivity varies greatly among wells, mainly attributed to the strong heterogeneity caused by regional differences in reservoir brittleness. Rock mechanical parameter method is commonly used to evaluate reservoir brittleness. …”
Get full text

Mesoscale Cellular Convection Detection and Classification Using Convolutional Neural Networks: Insights From Long‐Term Observations at ARM Eastern North Atlantic Site by Jingjing Tian, Jennifer Comstock, Andrew Geiss, Peng Wu, Israel Silber, Damao Zhang, Parvathi Kooloth, Ya‐ Chien Feng

“…The analysis of the MCC cases shows clear differences between closed and open MCCs: Closed MCC clouds are characterized by lower cloud tops and bases, shallower cloud geometrical depth, weaker horizontal wind speeds, stronger atmospheric stability, and a more homogeneous liquid water path than open MCCs. …”
Get full text

Change-Guided Difference Interaction Attention Network for Remote Sensing Change Detection by Canbin Hu, Sida Du, Hongyun Chen, Xiaokun Sun, Kailun Liu

“…To address these challenges, we propose the change-guided difference interaction attention network (CGDIANet). …”
Get full text

Predicting Index Trend Using Hybrid Neural Networks with a Focus on Multi-Scale Temporal Feature Extraction in the Tehran Stock Exchange by Mohammad Osoolian, Ali Nikmaram, Mahdi Karimi

“…MethodsThe hybrid neural network architecture that has been put forward integrates the unique capabilities of convolutional neural networks (CNNs) in the realm of feature extraction with the effectiveness of long short-term memory (LSTM) networks in capturing temporal dependencies. …”
Get full text

Synthesis of MR fingerprinting information from magnitude-only MR imaging data using a parallelized, multi network U-Net convolutional neural network by Kiaran P. McGee, Yi Sui, Robert J. Witte, Ananya Panda, Norbert G. Campeau, Thomaz R. Mostardeiro, Nahil Sobh, Umberto Ravaioli, Shuyue (Lucia) Zhang, Kianoush Falahkheirkhah, Nicholas B. Larson, Christopher G. Schwarz, Jeffrey L. Gunter

Get full text

High precision light field image depth estimation via multi‐region attention enhanced network by Jie Li, Wenxuan Yang, Chuanlun Zhang, Heng Li, Xinjia Li, Lin Wang, Yanling Wang, Xiaoyan Wang

“…Firstly, we construct a multi‐region disparity selection module based on angular patch, which selects specific regions for generating angular patch, achieving representative sub‐angular patch by balancing different regions. Secondly, different from traditional guided deformable convolution, the guided optimisation leverages colour prior information to learn the aggregation of sampling points, which enhances the deformable convolution ability by learning deformation parameters and fitting irregular windows. …”
Get full text

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

“…The SHAP analysis identified Normalized Green-Red Difference Index (NGRDI) and plant height as the most influential features for AGB estimation. …”
Get full text

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

“…Optical simulation plays an important role in photonic hardware design flow. The finite-difference time-domain (FDTD) method is widely adopted to solve time-domain Maxwell equations. …”
Get full text

Myoelectric-Based Estimation of Vertical Ground Reaction Force During Unconstrained Walking by a Stacked One-Dimensional Convolutional Long Short-Term Memory Model by Alessandro Mengarelli, Andrea Tigrini, Mara Scattolini, Rami Mobarak, Laura Burattini, Sandro Fioretti, Federica Verdini

Get full text

Application of YOLO11 Model with Spatial Pyramid Dilation Convolution (SPD-Conv) and Effective Squeeze-Excitation (EffectiveSE) Fusion in Rail Track Defect Detection by Weigang Zhu, Xingjiang Han, Kehua Zhang, Siyi Lin, Jian Jin

“…First, the conventional convolutional layers in the YOLO (You Only Look Once) 11backbone network were substituted with the SPD-Conv (Spatial Pyramid Dilation Convolution) module to enhance the model’s detection performance on low-resolution images and small objects. …”
Get full text

Individualized spatial network predictions using Siamese convolutional neural networks: A resting-state fMRI study of over 11,000 unaffected individuals. by Reihaneh Hassanzadeh, Rogers F Silva, Anees Abrol, Mustafa Salman, Anna Bonkhoff, Yuhui Du, Zening Fu, Thomas DeRamus, Eswar Damaraju, Bradley Baker, Vince D Calhoun

“…To do this, we develop a deep Siamese framework comprising three-dimensional convolution neural networks for contrastive learning based on individual-level spatial maps estimated via a fully automated fMRI independent component analysis approach. …”
Get full text

Multimodal feature fusion-based graph convolutional networks for Alzheimer's disease stage classification using F-18 florbetaben brain PET images and clinical indicators. by Gyu-Bin Lee, Young-Jin Jeong, Do-Young Kang, Hyun-Jin Yun, Min Yoon

“…The usage ratio of these different modal data and edge assignment threshold were tuned by setting them as hyperparameters. …”
Get full text

AirQuaNet: A Convolutional Neural Network Model With Multi-Scale Feature Learning and Attention Mechanisms for Air Quality-Based Health Impact Prediction by Sreeni Chadalavada, Suleyman Yaman, Abdulkadir Sengur, Ravinesh C. Deo, Abdul Hafeez-Baig, Tracy Kolbe-Alexander, Niranjana Sampathila, U. Rajendra Acharya

“…The MSCBs employ four parallel 1D convolutional layers with different kernel sizes, enabling the model to extract multi-scale features critical for learning patterns in complex environmental data. …”
Get full text

RLK-YOLOv8: multi-stage detection of strawberry fruits throughout the full growth cycle in greenhouses based on large kernel convolutions and improved YOLOv8 by Lei He, Lei He, Lei He, Dasheng Wu, Dasheng Wu, Dasheng Wu, Xinyu Zheng, Xinyu Zheng, Xinyu Zheng, Fengya Xu, Fengya Xu, Fengya Xu, Shangqin Lin, Shangqin Lin, Shangqin Lin, Siyang Wang, Siyang Wang, Siyang Wang, Fuchuan Ni, Fang Zheng

“…Firstly, it utilizes the large kernel convolution network RepLKNet as the backbone to enhance the extraction of features from targets and complex backgrounds. …”
Get full text

Multi-scale conv-attention U-Net for medical image segmentation by Peng Pan, Chengxue Zhang, Jingbo Sun, Lina Guo

“…The AC module dynamically adjusts the convolutional kernel through an adaptive convolutional layer. …”
Get full text

FAULT DIAGNOSIS OF ROLLING BEARINGS BASED ON CHANNEL AND SPATIAL RECONSTRUCTION NETWORKS by ZHOU Tao, YAO DeChen, YANG JianWei

“…Since the fault vibration data collected in real engineering may be accompanied by noise，traditional diagnostic models are difficult to identify fault categories，to address this problem，a rolling bearing fault diagnosis research method based on channel and spatial reconstruction and progressive convolutional neural networks （CSRP-CNN） was proposed.The model utilizes channel and spatial reconstruction convolution （CSConv） to reduce the redundant information of channels and space in fault features，and reduces the complexity and computation to improve the performance； using convolutional block attention module （CBAM），attention enhancement operation was carried out in the channel and spatial dimensions to make the model pay attention to important fault feature information； and progressive convolutional network structure was used in the shallow layer of the network，which will fuse the previous fault feature information fused with the current input to obtain richer feature information.The performance of CSRP-CNN was evaluated by two different datasets of Case Western Reserve University（CWRU）and machinery fault simulator magnum（MFS-MG）.After the noise and ablation tests，it is verified that CSRP-CNN has strong robustness and the effects of CSConv，CBAM and progressive convolutional neural network（PCNN） on the model noise immunity performance.…”
Get full text

Fault diagnosis of rolling bearing based on channel and spatial reconstruction networks by ZHOU Tao, YAO Dechen, YANG Jianwei

“…To address this problem, a rolling bearing fault diagnosis research method based on channel and spatial reconstruction and progressive convolutional neural networks (CSRP-CNN) was proposed. …”
Get full text

Multitemporal Difference and Dynamic Optimization Framework for Multiscale Motion Satellite Video Super-Resolution by Qian Zhao, Youming Guo, Lei Min, Changhui Rao

“…Based on the extracted temporal difference data, we further develop a temporal differences-guided dynamic routing optimization module (T-DROM) to extract multiscale motion information. …”
Get full text

Automated Loudness Growth Prediction From EEG Signals Using Autoencoder and Multi-Target Regression by D. Rama Harshita, Nitya Tiwari, Himanshu Padole, K. S. Nataraj

“…The extracted features are mapped to psychoacoustic loudness growth estimates using a multi-target regression model based on a convolutional neural network. An ablation study was conducted to analyze the impact of different autoencoder configurations on feature extraction performance. …”
Get full text