GaitTriViT and GaitVViT: Transformer-based methods emphasizing spatial or temporal aspects in gait recognition by Hongyun Sheng

“…Moreover, these methods are primarily based on traditional convolutional neural networks (CNNs) due to the dominance of CNNs in computer vision. …”
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Advancements in the use of AI in the diagnosis and management of inflammatory bowel disease by Dalia Braverman-Jaiven, Luigi Manfredi

“…Therefore, algorithms based on Deep Learning (DL) and Convolutional Neural Networks (CNN) for colonoscopy images and videos are growing in popularity, especially for the detection and classification of colorectal polyps. …”
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Dance Movement Recognition Based on Feature Expression and Attribute Mining by Xianfeng Zhai

“…Finally, using the semantic inference and information transfer function of the graph convolution network, the relationship between attribute features and dancer features can be mined and deduced, and more expressive action features can be obtained; thus, high-performance dance motion recognition is realized. …”
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Leaf disease detection and classification in food crops with efficient feature dimensionality reduction. by Khasim Syed, Shaik Salma Asiya Begum, Anitha Rani Palakayala, G V Vidya Lakshmi, Sateesh Gorikapudi

“…Dimensionality reduction techniques are employed to enhance computational performance by reducing the dimensionality of inner layers. Convolutional Neural Networks (CNNs), originally designed to recognize critical image components, now learn features across multiple layers. …”
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Research on Beef Marbling Grading Algorithm Based on Improved YOLOv8x by Jun Liu, Lian Wang, Huafu Xu, Jie Pi, Daoying Wang

“…The model integrates a convolutional neural network (CNN) augmented with an improved attention mechanism and loss function, along with a Region-of-Interest (ROI) preprocessing algorithm to automate the marbling grading process. …”
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High-Precision Qiantang River Water Body Recognition Based on Remote Sensing Image by Hongcui Wang, Yihong Zheng, Ouxiang Chen

“…River water body identification plays an important role in flood monitoring, urban planning, Thus, it attracts more interests of studying and investigating, especially based on remote sensing technology, The traditional NDWI (Normalized Difference Water Index) and MNDWI (Modified Normalized Difference Water Index) methods are widely used, However, these methods need manual intervention to select the threshold, In order to achieve automatic water body recognition, deep learning methods, such as CNN, VGG, Unet etc., are applied, Currently there are few works on the water body identification of Qiantang River, Here, one major challenge for high-precision Qiantang water body recognition is the real complex water body features and complicated geological environment, They are the dense distribution of small water bodies in the Qiantang River Basin, large differences in water body nutrition, and the high complexity of surface environments such as mountains and plains, We investigated two traditional and several deep learning methods and found that WatNet was the most effective model for Qiantang River, This model adopts the structure based on encoder-decoder convolutional network, It uses MobileNetV2 as the encoder, which makes it extract more water feature information while being lightweight and uses ASPP module to capture global multi-scale features in deep layers, Experimental results show that the MIoU and OA (Overall Accuracy) can reach 0. 97 and 0. 99 respectively.…”
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Object Detection Method of Inland Vessel Based on Improved YOLO by Yaoqi Wang, Jiasheng Song, Yichun Wang, Rongjie Wang, Hongyu Chen

“…Then, the Conv of the Backbone network was replaced with DBB to enhance the expression ability of a single convolution and enrich the feature space. …”
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Trustworthiness of Deep Learning Under Adversarial Attacks in Power Systems by Dowens Nicolas, Kevin Orozco, Steve Mathew, Yi Wang, Wafa Elmannai, George C. Giakos

“…In power grids, DL models such as Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) networks are commonly utilized for tasks like state estimation, load forecasting, and fault detection, depending on their ability to learn complex, non-linear patterns in high-dimensional data such as voltage, current, and frequency measurements. …”
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Recent advances in the inverse design of silicon photonic devices and related platforms using deep generative models by Sun Jae Baek, Minhyeok Lee

“…This review examines various deep learning methodologies, including multi-layer perceptrons (MLP), convolutional neural networks (CNN), auto-encoders (AE), Generative Adversarial Networks (GAN), and reinforcement learning (RL) approaches. …”
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An Mcformer encoder integrating Mamba and Cgmlp for improved acoustic feature extraction by Nurmemet Yolwas, Yongchao Li, Lixu Sun, Jian Peng, Zhiwu Sun, Yajie Wei, Yineng Cai

“…Abstract Currently, attention models based on the Conformer architecture have become mainstream in the field of speech recognition due to their integration of self-attention mechanisms and convolutional networks. …”
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Genome data based deep learning identified new genes predicting pharmacological treatment response of attention deficit hyperactivity disorder by Yilu Zhao, Zhao Fu, Eric J. Barnett, Ning Wang, Kangfuxi Zhang, Xuping Gao, Xiangyu Zheng, Junbin Tian, Hui Zhang, XueTong Ding, Shaoxian Li, Shuyu Li, Qingjiu Cao, Suhua Chang, Yufeng Wang, Stephen V. Faraone, Li Yang

“…The convolutional neural network (CNN) model, using variants with genome-wide P values less than E-02 (5516 SNPs), demonstrated the best performance with mean squared error (MSE) equals 0.012 (Accuracy = 0.83; Sensitivity = 0.90; Specificity = 0.75) in the validation dataset, 0.081 in an independent test dataset (Acc = 0.61, Sensitivity = 0.81; Specificity = 0.26). …”
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Real-Time Waste Detection and Classification Using YOLOv12-Based Deep Learning Model by Mosharof Hossain Dipo, Fahmid Al Farid, Md. Sifti Al Mahmud, Muntasir Momtaz, Shakila Rahman, Jia Uddin, Hezerul Abdul Karim

“…It is coupled with advanced convolutional neural networks (CNNs), which are used for data collection, real-time waste detection, and classification of the proposed framework. …”
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Machine learning applications for early detection of esophageal cancer: a systematic review by Farhang Hosseini, Farkhondeh Asadi, Hassan Emami, Rayan Ebnali Harari

“…Among all ML techniques, methods based on convolutional neural networks (CNN) achieved higher accuracy and sensitivity in the early detection of EC compared to other methods. …”
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An interpretable wheat yield estimation model using an attention mechanism-based deep learning framework with multiple remotely sensed variables by Mingqi Li, Pengxin Wang, Kevin Tansey, Yue Zhang, Fengwei Guo, Junming Liu, Hongmei Li

“…The proposed approach (AM-CNN-LSTM) combined a one-dimensional convolutional neural network (1D-CNN) to capture local dependencies in sequences, the temporal data processing capability of long short-term memory (LSTM), and the interpretability of the AM. …”
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Predicting chronic pain using wearable devices: a scoping review of sensor capabilities, data security, and standards compliance by Johannes C. Ayena, Johannes C. Ayena, Amina Bouayed, Amina Bouayed, Myriam Ben Arous, Myriam Ben Arous, Youssef Ouakrim, Youssef Ouakrim, Karim Loulou, Karim Loulou, Darine Ameyed, Isabelle Savard, Leila El Kamel, Neila Mezghani, Neila Mezghani

“…Random Forest and multilevel models have demonstrated consistent performance, while advanced models like Convolutional Neural Network-Long Short-Term Memory have faced challenges with data quality and computational demands. …”
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Medical Image Segmentation: A Comprehensive Review of Deep Learning-Based Methods by Yuxiao Gao, Yang Jiang, Yanhong Peng, Fujiang Yuan, Xinyue Zhang, Jianfeng Wang

“…In recent years, with the widespread application of Convolutional Neural Networks (CNNs) in computer vision, deep learning-based methods for medical image segmentation have become a focal point of research. …”
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Bridging Theory and Practice: A Review of AI-Driven Techniques for Ground Penetrating Radar Interpretation by Lilong Zou, Ying Li, Kevin Munisami, Amir M. Alani

“…Key findings highlight the success of convolutional neural networks in hyperbola detection, the use of segmentation models for stratigraphic analysis, and the integration of AI with robotic and real-time systems. …”
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An enhanced YOLOv8‐based bolt detection algorithm for transmission line by Guoxiang Hua, Huai Zhang, Chen Huang, Moji Pan, Jiyuan Yan, Haisen Zhao

“…Firstly, the C2f module in the feature extraction network is integrated with the self‐calibrated convolution module, and the model is streamlined by reducing spatial and channel redundancies of the network through the SRU and CUR mechanisms in the module. …”
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Dynamic Scene Segmentation and Sentiment Analysis for Danmaku by Limin Li, Jie Jing, Peng Shi

“…This paper presents a new approach that combines advanced shot segmentation techniques, using Deep Convolutional Neural Networks (DDCNN), with an analysis of feelings based on the MacBERT model. …”
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Lightweight Vehicle Detection Based on Mamba_ViT by Ze Song, Yuhai Wang, Shuobo Xu, Peng Wang, Lele Liu

“…Vehicle detection algorithms are essential for intelligent traffic management and autonomous driving systems. Current vehicle detection algorithms largely rely on deep learning techniques, enabling the automatic extraction of vehicle image features through convolutional neural networks (CNNs). …”
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