Tree inventory analysis using AI and GIS in Uzbekistan: A case study from Tashkent by Sobirov Ulmas, Alikulova Feruza

“…This study offers a valuable framework for Tashkent and similar cities, contributing to sustainable urban planning and resilience against environmental stressors through data-driven urban forestry practices.…”
Get full text

A Survey of Deep Learning-Driven 3D Object Detection: Sensor Modalities, Technical Architectures, and Applications by Xiang Zhang, Hai Wang, Haoran Dong

“…Through a dual-axis “sensor modality–technical architecture” classification framework, it systematically analyzes detection methods based on RGB cameras, LiDAR, and multimodal fusion. …”
Get full text

SIAT: Pedestrian trajectory prediction via social interaction-aware transformer by Chengdong Wang, Jianming Wang, Wenbo Gao, Lei Guo

“…SIAT’s contributions include improved precision through temporal and spatial processing, deep contextual understanding of pedestrian dynamics, and robustness across various settings. The novel model framework establishes a new benchmark for mixed models in trajectory prediction.…”
Get full text

Improving classifier decision boundaries and interpretability using nearest neighbors by Johannes Schneider, Arianna Casanova

“…Through diverse evaluations using both self-trained and state-of-the-art pre-trained convolutional neural networks, we show that our framework enhances (i) resistance to label noise, (ii) robustness against adversarial attacks, (iii) classification accuracy, and offers novel approaches for (iv) interpretability. …”
Get full text

Fault Diagnosis for Rolling Bearings Under Complex Working Conditions Based on Domain-Conditioned Adaptation by Xu Zhang, Gaoquan Gu

“…Finally, within a structural risk minimization framework, model parameters are iteratively optimized to achieve minimal distribution discrepancy, resulting in an optimal coefficient matrix for fault diagnosis. …”
Get full text

Exploring Low-Cost Platforms for Automatic Chess Digitization by David Mallasén, María José Belda, Alberto A. del Barrio, Fernando Castro, Katzalin Olcoz, Manuel Prieto-Matias

“…In our study, we adapted these techniques specifically for cost-effective single-board computers like the Nvidia Jetson Nano. Our framework combines a swift chessboard detection method with a Convolutional Neural Network for piece recognition. …”
Get full text

High Fidelity Single-Pixel Imaging by Chao Deng, Xuemei Hu, Xiaoxu Li, Jinli Suo, Zhili Zhang, Qionghai Dai

“…Specifically, we can represent the target scene via convolving with a set of statistically learned kernels, with the convolution coefficient matrix being sparse. We introduce the above local prior into conventional SPI framework to promote the final reconstruction quality. …”
Get full text

Automatic detection of teacher behavior in classroom videos using AlphaPose and Faster R-CNN algorithms by Jing Huang, Harwati Hashim, Helmi Norman, Mohammad Hafiz Zaini, Xiaojun Zhang

“…This study proposes an automated classification framework for evaluating teacher behavior in classroom settings by integrating AlphaPose and Faster region-based convolutional neural networks (R-CNN) algorithms. …”
Get full text

Time series image coding classification theory based on Lagrange multiplier method by Wentao Jiang, Ming Zhao, Hongbo Li

“…This rigorous proof framework enhances the theoretical foundation for TSI classification based on image encoding. …”
Get full text

A New and Tested Ionospheric TEC Prediction Method Based on SegED-ConvLSTM by Yuanhang Liu, Yingkui Gong, Hao Zhang, Ziyue Hu, Guang Yang, Hong Yuan

“…We compared our model with traditional image-based models such as convolutional neural networks (CNNs), convolutional long short-term memory networks (ConvLSTMs), a self-attention mechanism-integrated ConvLSTM (SAM-ConvLSTM) model, and one-day predicted ionospheric products (C1PG) provided by the Center for Orbit Determination in Europe (CODE). …”
Get full text

Hybrid Deep Learning for Survival Prediction in Brain Metastases Using Multimodal MRI and Clinical Data by Cristian Constantin Volovăț, Călin Gheorghe Buzea, Diana-Ioana Boboc, Mădălina-Raluca Ostafe, Maricel Agop, Lăcrămioara Ochiuz, Ștefan Lucian Burlea, Dragoș Ioan Rusu, Laurențiu Bujor, Dragoș Teodor Iancu, Simona Ruxandra Volovăț

“…<b>Methods:</b> We propose a novel hybrid deep learning framework that integrates volumetric MRI-derived imaging biomarkers with structured clinical and demographic data to predict overall survival time. …”
Get full text

Analysis and Recommendation of Outdoor Activities for Smart City Users Based on Real-Time Contextual Data by S. R. Mani Sekhar, D. M. Mushtaq Ahmed, G. M. Siddesh

“…It contributes to the growing field of Smart Cities by introducing a scalable and adaptable framework that harnesses the power of deep learning to improve urban living. …”
Get full text

A practical guide for nephrologist peer reviewers: evaluating artificial intelligence and machine learning research in nephrology by Yanni Wang, Wisit Cheungpasitporn, Hatem Ali, Jianbo Qing, Charat Thongprayoon, Wisit Kaewput, Karim M. Soliman, Zhengxing Huang, Min Yang, Zhongheng Zhang

“…Overfitting in AI is driven by small patient cohorts faced with thousands of candidate features; our framework spotlights this imbalance and offers concrete remedies. …”
Get full text

DualCMNet: a lightweight dual-branch network for maize variety identification based on multi-modal feature fusion by Xinhua Bi, Hao Xie, Ziyi Song, Jinge Li, Chang Liu, Xiaozhu Zhou, Helong Yu, Chunguang Bi, Ming Zhao

“…Additionally, existing multimodal methods face high computational complexity, making it difficult to balance accuracy and efficiency.MethodsBased on multi-modal data from 11 maize varieties, this paper presents DualCMNet, a novel dual-branch deep learning framework that utilizes a one-dimensional convolutional neural network (1D-CNN) for hyperspectral data processing and a MobileNetV3 network for spatial feature extraction from images. …”
Get full text

Fine-Grained Classification of Pressure Ulcers and Incontinence-Associated Dermatitis Using Multimodal Deep Learning: Algorithm Development and Validation Study by Alexander Brehmer, Constantin Seibold, Jan Egger, Khalid Majjouti, Michaela Tapp-Herrenbrück, Hannah Pinnekamp, Vanessa Priester, Michael Aleithe, Uli Fischer, Bernadette Hosters, Jens Kleesiek

“…ConclusionsOur multimodal deep learning framework effectively differentiates between PUs and IAD, achieving high accuracy and outperforming human wound care experts. …”
Get full text

Advanced Multi-Level Ensemble Learning Approaches for Comprehensive Sperm Morphology Assessment by Abdulsamet Aktas, Taha Cap, Gorkem Serbes, Hamza Osman Ilhan, Hakkı Uzun

“…<b>Results:</b> The proposed ensemble framework was evaluated using the Hi-LabSpermMorpho dataset, which contains 18 distinct sperm morphology classes. …”
Get full text

Wheat disease recognition method based on the SC-ConvNeXt network model by Tianliang Dong, Xiao Ma, Bin Huang, Wenyu Zhong, Qingan Han, Qinghai Wu, You Tang

“…Abstract When utilizing convolutional neural networks for wheat disease identification, the training phase typically requires a substantial amount of labeled data. …”
Get full text

Optimizing 5G resource allocation with attention-based CNN-BiLSTM and squeeze-and-excitation architecture by Anfal Musadaq Rayyis, Mohammad Maftoun, Maryam Khademi, Emrah Arslan, Silvia Gaftandzhieva

“…Traditional machine learning models struggle to capture intricate temporal dependencies and handle imbalanced data distributions, limiting their effectiveness in real-world applications.MethodsTo overcome these limitations, this study presents an innovative deep learning-based framework that combines a convolutional layer with squeeze-and-excitation block, bidirectional long short-term memory, and a self-attention mechanism for resource allocation prediction. …”
Get full text

Vision Foundation Model Guided Multimodal Fusion Network for Remote Sensing Semantic Segmentation by Chen Pan, Xijian Fan, Tardi Tjahjadi, Haiyan Guan, Liyong Fu, Qiaolin Ye, Ruili Wang

“…Specifically, the framework incorporates a cross-modal collaborative network for feature embedding that blends a convolutional neural network and vision transformer to simultaneously capture both local information and long-range dependencies from the input modalities. …”
Get full text

A deep learning model for predicting systemic lupus erythematosus-associated epitopes by Jiale He, Zixia Liu, Xiaopo Tang

“…This study proposes a hybrid deep learning architecture that synergistically integrates handcrafted biochemical features with data-driven deep sequence modeling to improve the identification of SLE-associated epitopes. Methods The framework comprises six interconnected components: (1) handcrafted feature extraction encoding biochemical and physicochemical attributes; (2) an embedding layer for dense sequence representation; (3) a Convolutional Neural Network (CNN) branch that captures local patterns from handcrafted features; (4) a Long Short-Term Memory branch for learning temporal dependencies in sequence data; (5) a scaled dot-product attention-based fusion module that integrates complementary information from both branches; and (6) a Multi-Layer Perceptron for final classification. …”
Get full text