EEG Emotion Recognition Using AttGraph: A Multi-Dimensional Attention-Based Dynamic Graph Convolutional Network by Shuai Zhang, Chengxi Chu, Xin Zhang, Xiu Zhang

“…Results: Through the dynamic weighting of EEG features via a multi-dimensional attention convolution layer, the AttGraph method is able to precisely detect emotional changes and automatically choose the most discriminative features for emotion recognition tasks. …”
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Voice-AttentionNet: Voice-Based Multi-Disease Detection with Lightweight Attention-Based Temporal Convolutional Neural Network by Jintao Wang, Jianhang Zhou, Bob Zhang

“…The results show that our method achieves the most advanced performance with an average classification accuracy of 91.61% on six datasets and is superior to the existing classical models. …”
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DDoS attack detection method based on improved convolutional long short-term memory and three-way decision in SDN. by Haizhen Wang, Xiaojing Yang, Na Jia

“…It is based on the Convolutional Long Short-Term Memory Network (ConvLSTM) and three-way decision (TWD). …”
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Papayafreshnet: a hybrid deep learning framework for non-destructive freshness classification of papayas using convolutional and transformer networks by Ayan Sar, Tanupriya Choudhury, Tanmay Sarkar, Ketan Kotecha

“…The architecture integrates the advantages of Convolutional Neural Networks (CNNs) with sophisticated Transformer-based feature extraction and attention methods. …”
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MarshCover: A Web-based Tool for Estimating Vegetation Coverage in Marsh Images Using Convolutional Neural Networks by Lucas Wayne Welch, Xudong Liu

“…Marsh ecosystems are some of our most important, serving many crucial ecological functions. …”
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A Novel PSO-Based Optimized Lightweight Convolution Neural Network for Movements Recognizing from Multichannel Surface Electromyogram by Xiu Kan, Dan Yang, Le Cao, Huisheng Shu, Yuanyuan Li, Wei Yao, Xiafeng Zhang

“…With the purpose of reducing the structural complexity of the deep neural network, the designed convolution neural network model is mainly composed of three convolution layers and two full connection layers. …”
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Adaptive Graph Convolutional Network with Deep Sequence and Feature Correlation Learning for Porosity Prediction from Well-Logging Data by Long Hao, Xun Wang, Yunlong Dong, Peizhi Zhao, Peifu Han, Xue Li, Fengrui Jing, Chuchu Zhai

“…Recently, deep learning has been introduced for porosity prediction. However, most existing methods focus on either the relationships among features or deep sequences, neglecting the correlation between them. …”
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Heuristic Optimization-Assisted Dilated Convolution Neural Network With Gated Recurrent Unit for Channel Estimation in NOMA-OFDM System by Vijayakumar Kondepogu, Budhaditya Bhattacharyya

“…However, channel estimation remains one of the most important challenges in OFDM based communication. …”
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Multi-Sensor Information Fusion with Multi-Scale Adaptive Graph Convolutional Networks for Abnormal Vibration Diagnosis of Rolling Mill by Rongrong Peng, Changfen Gong, Shuai Zhao

“…First, convolutional neural networks (CNNs) were adopted for the deeper features of multi-sensor signals. …”
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Lung and Colon Cancer Classification Using Multiscale Deep Features Integration of Compact Convolutional Neural Networks and Feature Selection by Omneya Attallah

“…To this end, the present research introduces a CAD system that integrates several lightweight convolutional neural networks (CNNs) with dual-layer feature extraction and feature selection to overcome the aforementioned constraints. …”
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An integrated deep convolutional neural networks framework for the automatic segmentation and grading of glioma tumors using multimodal MRI scans by Otung John Peter Odong, Mohammed Abo-Zahhad, Moataz Abdelwahab

“…Abstract Gliomas are the most prevalent and aggressive primary brain tumors characterized by rapid progressions and infiltration. …”
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Classification of salivary gland biopsies in Sjögren’s syndrome by a convolutional neural network using an auto-machine learning platform by Jorge Álvarez Troncoso, Elena Ruiz-Bravo, Clara Soto Abánades, Alexandre Dumusc, Álvaro López-Janeiro, Thomas Hügle

“…The autoML platform utilized (Giotto, L2F, Lausanne Switzerland) employed a Convolutional Neural Network (CNN) architecture (ResNet-152) for the training and validation phases, using a dataset of 172 slides. …”
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One-Dimensional Convolutional Neural Network for Automated Kimchi Cabbage Downy Mildew Detection Using Aerial Hyperspectral Images by Yang Lyu, Lukas Wiku Kuswidiyanto, Pingan Wang, Hyun-Ho Noh, Hee-Young Jung, Xiongzhe Han

“…Overall, the results of this study revealed that the red-edge band reliably signaled the presence of downy mildew, and the 1D-InceptionNet model demonstrated the most effective performance for automatic disease detection.…”
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Porosity Analysis and Thermal Conductivity Prediction of Non-Autoclaved Aerated Concrete Using Convolutional Neural Network and Numerical Modeling by Alexey N. Beskopylny, Evgenii M. Shcherban’, Sergey A. Stel’makh, Diana Elshaeva, Andrei Chernil’nik, Irina Razveeva, Ivan Panfilov, Alexey Kozhakin, Emrah Madenci, Ceyhun Aksoylu, Yasin Onuralp Özkılıç

“…This paper considers the process of analyzing the visual structure of non-autoclaved aerated concrete products, namely their porosity, using the YOLOv11 convolutional neural network, with a subsequent prediction of one of the most important properties—thermal conductivity. …”
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Enhancing Clinical Assessment of Skin Ulcers with Automated and Objective Convolutional Neural Network-Based Segmentation and 3D Analysis by Rosanna Cavazzana, Angelo Faccia, Aurora Cavallaro, Marco Giuranno, Sara Becchi, Chiara Innocente, Giorgia Marullo, Elia Ricci, Jacopo Secco, Enrico Vezzetti, Luca Ulrich

“…This study introduces a method for automatically detecting and segmenting skin ulcers using a Convolutional Neural Network and two-dimensional images. …”
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Leveraging commonality across multiple tissue slices for enhanced whole slide image classification using graph convolutional networks by Sakonporn Noree, Willmer Rafell Quinones Robles, Young Sin Ko, Mun Yong Yi

“…While deep learning approaches have shown promise in WSI analysis, they mostly overlook potential common patterns across different slices of the original tissue. …”
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A Novel Forest Dynamic Growth Visualization Method by Incorporating Spatial Structural Parameters Based on Convolutional Neural Network by Linlong Wang, Huaiqing Zhang, Kexin Lei, Tingdong Yang, Jing Zhang, Zeyu Cui, Rurao Fu, Hongyan Yu, Baowei Zhao, Xianyin Wang

“…Current visual methods of forest dynamic growth mostly focus on the plot or stand level, which cannot express the morphological and structural characteristics of individual trees, as well as their statistical linkages, and causes each tree in the stand to grow at the same rate. …”
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Research on Arc Fault Detection Based on Conditional Batch Normalization Convolutional Neural Network with Cost-Sensitive Multi-Feature Extraction by Xin Ning, Tianli Ding, Hongwei Zhu

“…Although existing studies have made progress in improving the accuracy of their detection, most methods have not proposed effective solutions that address the cost-sensitive problem of feature selection. …”
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Predicting deep-seated landslide displacement on Taiwan's Lushan through the integration of convolutional neural networks and the Age of Exploration-Inspired Optimizer by J.-S. Chou, H.-M. Nguyen, H.-P. Phan, K.-L. Wang

“…The novel framework evaluates machine learning, time series deep learning, and convolutional neural networks (CNNs), identifying the most effective models to be enhanced by the Age of Exploration-Inspired Optimizer (AEIO) algorithm. …”
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Image-based soft drink type classification and dietary assessment system using deep convolutional neural network with transfer learning by Rubaiya Hafiz, Mohammad Reduanul Haque, Aniruddha Rakshit, Mohammad Shorif Uddin

“…To perform experimentation a dataset is built containing ten most consumed soft drinks in Bangladesh using images from the ImageNet dataset, internet sources and also self-capturing. …”
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