AFN-Net: Adaptive Fusion Nucleus Segmentation Network Based on Multi-Level U-Net by Ming Zhao, Yimin Yang, Bingxue Zhou, Quan Wang, Fu Li

“…This DSCOM effectively preserves high-resolution information and improves the segmentation accuracy of small targets and boundary regions through multi-level convolution operations and channel optimization. Finally, we proposed an Adaptive Fusion Loss Module (AFLM) that effectively balances different lossy targets by dynamically adjusting weights, thereby further improving the model’s performance in segmentation region consistency and boundary accuracy while maintaining classification accuracy. …”
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Infrared image super resolution with structure prior from uncooled infrared readout circuit by Kai Che, Jian Lv, Jia Wei, Jiayuan Gong, Longcheng Que, Yun Zhou

“…Acknowledging the continuity of temperature information within the image and the correlation between adjacent pixel regions, we develop a Compact Convolution Block (CCB) that incorporates a U-shape Spatial Channel Attention Block (USCAB) to extract local features before the RCTB. …”
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Research on Mine-Personnel Helmet Detection Based on Multi-Strategy-Improved YOLOv11 by Lei Zhang, Zhipeng Sun, Hongjing Tao, Meng Wang, Weixun Yi

“…The proposed improvements are realized through three key aspects: Firstly, the traditional convolution is replaced with GSConv, which significantly enhances feature extraction capabilities while simultaneously reducing computational costs. …”
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DCW-YOLO: An Improved Method for Surface Damage Detection of Wind Turbine Blades by Li Zou, Anqi Chen, Chunzi Li, Xinhua Yang, Yibo Sun

“…Firstly, Dynamic Separable Convolution (DSConv) is introduced into the C2f module of YOLOv8, allowing the model to more effectively focus on the geometric structural details associated with damage on WTBs. …”
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Small Target Detection Algorithm for UAV Aerial Images Based on Improved YOLOv7-tiny by ZHANG Guanghua, LI Congfa, LI Gangying, LU Weidang

“…ObjectiveUAVs provide advantages such as easy control, low cost, and good performance, and efficiently perform tasks in diverse sites and complex environments. …”
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Geographic origin discrimination and quantification of phenolic compounds and moisture in Artemisia argyi folium using NIRS and chemometrics by Lifei Hu, Yifan Wang, Xin Wu, Yuanyuan Shan, Fengxiao Zhu, Fan Zhang, Qiang Yang, Mingxing Liu

“…This NIRS-based approach achieved a higher detection efficiency and lower cost compared to conventional methods and thus provides a rapid and efficient solution for the geographic traceability and quantitative evaluation of AAF.…”
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Identifying defective casting products using hierarchical defect recognition architecture: A computer vision approach by Quoc Bao Diep

“…This paper proposes a novel approach for identifying defective casting products using a custom convolutional neural network architecture named Hierarchical Defect Recognition Architecture (HiDraNet). …”
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Predicting per capita expenditure using satellite imagery and transfer learning: A case study of east Java province, Indonesia by Heri Kuswanto, Wahidatul Wardah Al Maulidiyah, Widhianingsih Tintrim Dwi Ary, Yudistira Ashadi

“…The satellite images are processed using a transfer learning approach that employs a pretrained Convolutional Neural Network (CNN) model with VGG-16 architecture as a feature extractor. …”
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A Comprehensive Investigation of Fraud Detection Behavior in Federated Learning by Sun Rui

“…The comparison involves three machine learning models - Artificial Neural Networks (ANN), Random Forest (RF), and Convolutional Neural Networks (CNN) - to assess their efficacy in the FL context. …”
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Evaluation of Peroxide Value and Free Fatty Acid Content in Coconut Oil and Palm Oil under Different Heating Temperature Treatments using Reflectance–Fluorescence-based computer vi... by Luluk Oktaviana, Wahyunanto Agung Nugroho, Dimas Firmanda Al Riza Al Riza

“…A classification model was developed using a Convolutional Neural Network (CNN) algorithm to automatically extract color features contributing to oil quality classification. …”
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Advanced Hydro-Informatic Modeling Through Feedforward Neural Network, Federated Learning, and Explainable AI for Enhancing Flood Prediction by Shahariar Hossain Mahir, Md Tanjum An Tashrif, Md Ahsan Karim, Dipanjali Kundu, Anichur Rahman, Md. Amir Hamza, Fahmid Al Farid, Abu Saleh Musa Miah, Sarina Mansor

“…Recent advancements in flood prediction research include the development of robust, accurate, and low-cost flood models designed for urban deployment. …”
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A Radiomic-based model to predict the depth of myometrial invasion in endometrial cancer on ultrasound images by Francesca Arezzo, Annarita Fanizzi, Rosanna Mancari, Emiliano Cocco, Samantha Bove, Maria Colomba Comes, Mariangela Gianciotta, Giorgia Lanza, Salvatore Lopez, Gerardo Cazzato, Erica Silvestris, Elsa Vitale, Enrico Vizza, Gennaro Cormio, Raffaella Massafra, Vera Loizzi

“…After a pre-processing phase of ultrasound images, a pre-trained Inception-V3 convolutional neural network (CNN) was used as features extractor. …”
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Early Detection and Classification of Diabetic Retinopathy: A Deep Learning Approach by Mustafa Youldash, Atta Rahman, Manar Alsayed, Abrar Sebiany, Joury Alzayat, Noor Aljishi, Ghaida Alshammari, Mona Alqahtani

“…The proposed model utilizes six pre-trained convolutional neural networks (CNNs): EfficientNetB3, EfficientNetV2B1, RegNetX008, RegNetX080, RegNetY006, and RegNetY008. …”
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Efficient Side-Tuning for Remote Sensing: A Low-Memory Fine-Tuning Framework by Haichen Yu, Wenxin Yin, Hanbo Bi, Chongyang Li, Yingchao Feng, Wenhui Diao, Xian Sun

“…To reduce memory requirements and training costs, this article proposes a low-memory fine-tuning framework, called efficient side-tuning (EST), for remote sensing downstream tasks. …”
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GaussianMix: Rethinking Receptive Field for Efficient Data Augmentation by A. F. M. Shahab Uddin, Maryam Qamar, Jueun Mun, Yuje Lee, Sung-Ho Bae

“…Studies suggest that a convolutional neural network’s receptive field follows a Gaussian distribution, with central pixels being more influential. …”
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FraudGNN-RL: A Graph Neural Network With Reinforcement Learning for Adaptive Financial Fraud Detection by Yiwen Cui, Xu Han, Jiaying Chen, Xinguang Zhang, Jingyun Yang, Xuguang Zhang

“…We propose a novel GNN architecture, Temporal-Spatial-Semantic Graph Convolution (TSSGC), which simultaneously captures temporal patterns, spatial relationships, and semantic information in transaction data. …”
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Predicting Software Perfection Through Advanced Models to Uncover and Prevent Defects by Tariq Shahzad, Sunawar Khan, Tehseen Mazhar, Wasim Ahmad, Khmaies Ouahada, Habib Hamam

“…Software defect prediction is a critical task in software engineering, enabling organizations to proactively identify and address potential issues in software systems, thereby improving quality and reducing costs. In this study, we evaluated and compared various machine learning models, including logistic regression (LR), random forest (RF), support vector machines (SVMs), convolutional neural networks (CNNs), and eXtreme Gradient Boosting (XGBoost), for software defect prediction using a combination of diverse datasets. …”
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Towards rigorous dataset quality standards for deep learning tasks in precision agriculture: A case study exploration by A. Carraro, G. Saurio, F. Marinello

“…Deep Learning (DL) through Convolutional Neural Networks (CNNs) has emerged as a critical player in classifying plant diseases from images. …”
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Scheme Analysis for Enhancing Autonomous Driving Based on Computer Vision by Chen Xinyi, Luo Binbin, Xu Ziyue

“…Thesis adopted both literature review and technical analysis, focusing on recent developments in key technologies such as image processing, hybrid convolutional neural network (CNN)-transformer models, object detection, and multi-sensor fusion. …”
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Aeroengine Remaining Life Prediction Using Feature Selection and Improved SE Blocks by Hairui Wang, Shijie Xu, Guifu Zhu, Ya Li

“…To reduce computational costs and improve prediction performance, we use random forest to evaluate the feature importance of sensor data. …”
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