Cloud Detection Challenge-Methods and Results by Alessio Barbaro Chisari, Luca Guarnera, Alessandro Ortis, Wladimiro Carlo Patatu, Bruno Casella, Luca Naso, Giuseppe Puglisi, Vincenzo del Zoppo, Mario Valerio Giuffrida, Sebastiano Battiato

“…This paper details the challenge framework, as well as the methodologies proposed by top-performing teams, offering a comparative evaluation of their effectiveness. …”
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Enhanced Magnetic Resonance Imaging-Based Brain Tumor Classification with a Hybrid Swin Transformer and ResNet50V2 Model by Abeer Fayez Al Bataineh, Khalid M. O. Nahar, Hayel Khafajeh, Ghassan Samara, Raed Alazaidah, Ahmad Nasayreh, Ayah Bashkami, Hasan Gharaibeh, Waed Dawaghreh

“…We evaluate the proposed framework using two publicly accessible brain magnetic resonance imaging (MRI) datasets, each including two and four distinct classes, respectively. …”
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SCCA-YOLO: Spatial Channel Fusion and Context-Aware YOLO for Lunar Crater Detection by Jiahao Tang, Boyuan Gu, Tianyou Li, Ying-Bo Lu

“…In this paper, we propose a novel Spatial Channel Fusion and Context-Aware YOLO (SCCA-YOLO) model built upon the YOLO11 framework. Specifically, the Context-Aware Module (CAM) employs a multi-branch dilated convolutional structure to enhance feature richness and expand the local receptive field, thereby strengthening the feature extraction capability. …”
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The Prediction of Sound Insulation for the Front Wall of Pure Electric Vehicles Based on AFWL-CNN by Yan Ma, Jie Yan, Jianjiao Deng, Xiaona Liu, Dianlong Pan, Jingjing Wang, Ping Liu

“…In response to the limitations of traditional experimental and simulation methods in terms of accuracy and efficiency, this paper proposes a convolutional neural network (AFWL-CNN) based on adaptive weighted feature learning. …”
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Enhancing Anomaly Detection in Attributed Networks Using Proximity Preservation and Advanced Embedding Techniques by Wasim Khan, Mohammad Ishrat, Mohammad Nadeem Ahmed, Shafiqul Abidin, Mohammad Husain, Mohd Izhar, Abu Taha Zamani, Mohammad Rashid Hussain, Arshad Ali

“…To address this, we propose a novel approach that combines a Graph Convolution Auto encoder (GCAE) with self-supervised learning, proximity preservation, and adversarial training using Generative Adversarial Networks (GAN). …”
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Computational methods and artificial intelligence-based modeling of magnesium alloys: a systematic review of machine learning, deep learning, and data-driven design and optimizatio... by Hanxuan Wang, Raman Kumar, Raman Kumar, Ashutosh Pattanaik, Rajender Kumar, Rajender Kumar, Ali Saeed Owayez Khawaf Aljaberi, Mayada Ahmed Abass

“…The review highlights the extensive application of models, including Artificial Neural Networks, Convolutional Neural Networks, and hybrid frameworks that combine ML with optimization algorithms or physical simulations. …”
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Data-driven polarimetric approaches fuel computational imaging expansion by Sylvain Gigan

“…Incorporating polarization in computer vision tasks provides new solutions to high-level analytics, in particular when coupled with machine learning frameworks such as convolutional neural networks (CNN). …”
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Multi-Head Graph Attention Adversarial Autoencoder Network for Unsupervised Change Detection Using Heterogeneous Remote Sensing Images by Meng Jia, Xiangyu Lou, Zhiqiang Zhao, Xiaofeng Lu, Zhenghao Shi

“…Our bidirectional adversarial convolutional autoencoder simultaneously aligns features across both domains. …”
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The analysis of motion recognition model for badminton player movements using machine learning by Xuanmin Zhu, Lizhi Liu, Jingshuo Huang, Genyan Chen, Xi Ling, Yanshuo Chen

“…Abstract This study aims to comprehensively analyze and classify the badminton players’ swing actions by combining the theoretical frameworks of quantum mechanics and machine learning. …”
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Ensemble learning for microbiome-based caries diagnosis: multi-group modeling and biological interpretation from salivary and plaque metagenomic data by Fangqiao Wei, Zailong Wu, Guanghui Li, Xiangyu Sun, Xiangru Shi, Lei Tan, Tianxiang Ai, Long Qu, Shuguo Zheng

“…Conclusion The current work provided reliable diagnostic models for early childhood caries, and established a robust computational framework for AI-driven microbiome analysis. This study, by focusing on the characteristics of the oral microbiome, offers novel perspectives for data mining and validation of existing data through the application of AI modelling.…”
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VGGBM-Net: A Novel Pixel-Based Transfer Features Engineering for Automated Coffee Bean Diseases Classification by Muhammad Shadab Alam Hashmi, Azam Mehmood Qadri, Ali Raza, Saleem Ullah, Aseel Smerat, Changgyun Kim, Muhammad Syafrudin, Norma Latif Fitriyani

“…This research establishes a highly effective framework for automated coffee bean classification, setting a benchmark for future studies in agricultural image analysis.…”
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Maui: modular analytics of UAS imagery for specialty crop research by Kathleen Kanaley, Maylin J. Murdock, Tian Qiu, Ertai Liu, Schuyler E. Seyram, Dominik Starzmann, Lawrence B. Smart, Kaitlin M. Gold, Yu Jiang

“…Conclusion We present a modular framework to efficiently extract spectral data for specialty crops from UAS imagery. …”
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Enhancing Autism Spectrum Disorder Classification with Lightweight Quantized CNNs and Federated Learning on ABIDE-1 Dataset by Simran Gupta, Md. Rahad Islam Bhuiyan, Sadia Sultana Chowa, Sidratul Montaha, Rashik Rahman, Sk. Tanzir Mehedi, Ziaur Rahman

“…We propose a federated learning (FL) framework to ensure data privacy, which allows decentralized training across different data centers without compromising local data security. …”
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Forward Predicting Chromatic-Optical Parameters of the Mixed Light of White-Red Light-Emitting Diode Configurations Based on Deep Learning Algorithms by Songsheng Lin, Huanting Chen, Yin Zheng, Quanji Xie, Xuehua Shen, Huichuan Lin, Shuo Lin, Yan Li

“…This paper presents a novel deep learning framework that integrates experimental measurements with advanced modeling techniques to predict key optical parameters, including luminous flux, correlated color temperature (CCT), and chromaticity coordinates of white-red light-emitting diodes (LED) configurations under diverse operating conditions. …”
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Deep Multi-Modal Skin-Imaging-Based Information-Switching Network for Skin Lesion Recognition by Yingzhe Yu, Huiqiong Jia, Li Zhang, Suling Xu, Xiaoxia Zhu, Jiucun Wang, Fangfang Wang, Lianyi Han, Haoqiang Jiang, Qiongyan Zhou, Chao Xin

“…To address this, we propose a deep learning framework, Multi-Modal Skin-Imaging-based Information-Switching Network (MDSIS-Net), for end-to-end skin lesion recognition. …”
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Deep learning-based diffusion MRI tractography: Integrating spatial and anatomical information by Yiqiong Yang, Yitian Yuan, Baoxing Ren, Ye Wu, Yanqiu Feng, Xinyuan Zhang

“…To improve the accuracy of streamline propagation predictions, we introduce a novel deep learning framework that integrates image-domain spatial information and anatomical information along tracts, with the former extracted through convolutional layers and the latter modeled via a Transformer-decoder. …”
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Research on multi class pests identification and detection based on fusion attention mechanism with Mask-RCNN-CBAM by Xingwang Wang, Xingwang Wang, Xingwang Wang, Can Hu, Xufeng Wang, Hainie Zha, Xueyong Chen, Shanshan Yuan, Jing Zhang, Jianfeng Liao, Zhangying Ye

“…This study addresses challenges in agricultural pest detection, such as false positives and missed detections in complex environments, by proposing an enhanced Mask-RCNN model integrated with a Convolutional Block Attention Module (CBAM). The framework combines three innovations: (1) a CBAM attention mechanism to amplify pest features while suppressing background noise; (2) a feature-enhanced pyramid network (FPN) for multi-scale feature fusion, enhancing small pest recognition; and (3) a dual-channel downsampling module to minimize detail loss during feature propagation. …”
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Development of an Efficient Grading Model for Maize Seedlings Based on Indicator Extraction in High-Latitude Cold Regions of Northeast China by Song Yu, Yuxin Lu, Yutao Zhang, Xinran Liu, Yifei Zhang, Mukai Li, Haotian Du, Shan Su, Jiawang Liu, Shiqiang Yu, Jiao Yang, Yanjie Lv, Haiou Guan, Chunyu Zhang

“…It provides a robust framework for evaluating seedling quality under low-temperature stress.…”
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Ensemble Streamflow Simulations in a Qinghai–Tibet Plateau Basin Using a Deep Learning Method with Remote Sensing Precipitation Data as Input by Jinqiang Wang, Zhanjie Li, Ling Zhou, Chi Ma, Wenchao Sun

“…These findings highlight that the proposed 1D CNN ensemble simulation framework has great potential to improve streamflow estimations using remote sensing precipitation data as input and may provide new insight into how deep learning methods advance the application of remote sensing in hydrological research.…”
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Deep Learning-Based Super-Resolution of Remote Sensing Images for Enhanced Groundwater Quality Assessment and Environmental Monitoring in Urban Areas by Peng Shu, Rana Waqar Aslam, Iram Naz, Bushra Ghaffar, Dmitry E. Kucher, Abdul Quddoos, Danish Raza, M. Abdullah-Al-Wadud, Rana Muhammad Zulqarnain

“…This study presents a novel deep learning-based super-resolution framework for enhancing remote sensing imagery to assess groundwater quality and environmental conditions in Lahore, Pakistan. …”
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