SDNet: Sandwich Decoder Network for Waterbody Segmentation in Remote Sensing Imagery by Hao Ni, Jianfeng Li, Chenxu Wang, Zhiquan Zhou, Xinsheng Wang

“…Although self-attention (SA) and convolutional neural networks have demonstrated strong modeling capabilities on RS image recognition, their efficiency and expressiveness still deserve further optimization. …”
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NPI-WGNN: A Weighted Graph Neural Network Leveraging Centrality Measures and High-Order Common Neighbor Similarity for Accurate ncRNA–Protein Interaction Prediction by Fatemeh Khoushehgir, Zahra Noshad, Morteza Noshad, Sadegh Sulaimany

“…Predicting ncRNA–protein interactions (NPIs) is essential for understanding regulatory roles in cellular processes and disease mechanisms, yet experimental methods are costly and time-consuming. In this study, we propose NPI-WGNN, a novel weighted graph neural network model designed to enhance NPI prediction by incorporating topological insights from graph structures. …”
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Enhancing FMCW Radar Gesture Classification With Physically Interpretable Data Augmentation by Alessandra Fusco, Zain Amir Zaman, Souvik Hazra, Lorenzo Servadei, Robert Wille

“…By simulating variations in hand gesture distance, angle, duration, and noise, the framework improves model generalization while reducing the need for extensive and costly data collection. The augmentation techniques employed in this research include time scaling, range and angle transformation, and noise injection, effectively simulating different gesture speeds, orientations, distances, and interference levels. …”
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Dynamic Optimization of Recurrent Networks for Wind Speed Prediction on Edge Devices by Laeeq Aslam, Runmin Zou, Ebrahim Shahzad Awan, Sayyed Shahid Hussain, Muhammad Asim, Samia Allaoua Chelloug, Mohammed A. ELAffendi

“…Server-dependent machine learning models, commonly deployed in wind farms, prove infeasible for domestic systems due to high costs and energy demands. While edge computing offers a viable alternative, current WSP methods prioritize hyperparameter optimization without constraining model size (MS), resulting in memory-intensive architectures incompatible with resource-limited devices. …”
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CINNAMON-GUI: Revolutionizing Pap Smear Analysis with CNN-Based Digital Pathology Image Classification [version 1; peer review: 2 approved] by Luca Zammataro

“…However, existing commercial platforms are often costly and restrictive, limiting customization and scalability. …”
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AI-Based Solar Panel Detection and Monitoring Using High-Resolution Drone Imagery by Raheleh Parsaeifar, Mojtaba Valinejadshoubi, Anthony Le Guen, Fernando Valdivieso

“…The developed model improves efficiency and accuracy in solar panel monitoring, reduces operational costs, and adapts to various geographic and environmental conditions. …”
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Enhancing the Performance of YOLOv9t Through a Knowledge Distillation Approach for Real-Time Detection of Bloomed Damask Roses in the Field by Farhad Fatehi, Hossein Bagherpour, Jafar Amiri Parian

“…To carry out agricultural operation, particularly for picking fully bloomed Damask roses, using harvesting robots offers significant potential to reduce labor costs while simultaneously improving crop quality. …”
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Enhanced detection of Mpox using federated learning with hybrid ResNet-ViT and adaptive attention mechanisms by V. Maheskumar, R. Vijayarajeswari

“…Traditional diagnostic methods, including visual examination and PCR tests, face limitations such as misdiagnoses, high costs, and unavailability in resource-limited areas. …”
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Dynamic atrous attention and dual branch context fusion for cross scale Building segmentation in high resolution remote sensing imagery by Yaohui Liu, Shuzhe Zhang, Xinkai Wang, Rui Zhai, Hu Jiang, Lingjia Kong

“…Abstract Building segmentation of high-resolution remote sensing images using deep learning effectively reduces labor costs, but still faces the key challenges of effectively modeling cross-scale contextual relationships and preserving fine spatial details. …”
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Interpretable multi-instance heterogeneous graph network learning modelling CircRNA-drug sensitivity association prediction by Mengting Niu, Chunyu Wang, Yaojia Chen, Quan Zou, Ximei Luo

“…Using traditional biomedical experiments to discover and confirm sensitivity relationships is not only time-consuming but also costly. Therefore, developing an effective method to accurately predict new associations between circRNAs and drug sensitivity is crucial and urgent. …”
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Advancing smart communities with a deep learning framework for sustainable resource management. by Yongyan Zhao

“…The framework leverages long short-term memory (LSTM) networks for temporal data, convolutional neural networks (CNNs) for spatial analysis, and autoencoders for anomaly detection. …”
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Human fall direction recognition in the indoor and outdoor environment using multi self-attention RBnet deep architectures and tree seed optimization by Awais Khan, Jung-Yeon Kim, Chomyong Kim, Muhammad Attique Khan, Hyojin Shin, Jiyoung Woo, Yunyoung Nam

“…To optimize feature selection and improve classification performance while reducing computational costs, we employed the tree seed algorithm on the self-attention features of 7-RBNet and 9-RBNet self-attention models. …”
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Predicting the Imbalanced Impact of Drugs on Microbial Abundance Using Multi-View Learning and Data Augmentation by Bei Zhu, Haoyang Yu, Bingxue Du, Hui Yu, Jianyu Shi

“…Traditional Microbe-Drug Association (MDA) determination through biological assays is time-consuming and costly. With the accumulation of MDA data, computational methods have become a promising approach to infer potential MDAs. …”
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A Deep Learning-Based Diagnostic Framework for Shaft Earthing Brush Faults in Large Turbine Generators by Katudi Oupa Mailula, Akshay Kumar Saha

“…This work enables the early detection of brush degradation, thereby reducing unplanned downtime and maintenance costs in power generation facilities.…”
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GhostConv+CA-YOLOv8n: a lightweight network for rice pest detection based on the aggregation of low-level features in real-world complex backgrounds by Fei Li, Yang Lu, Qiang Ma, Shuxin Yin, Rui Zhao

“…Existing approaches suffer from two critical limitations: (1) inadequate feature representation under occlusion and scale variations, and (2) excessive computational costs for edge deployment. To overcome these limitations, this paper introduces GhostConv+CA-YOLOv8n, a lightweight object detection framework was proposed, which incorporates several innovative features: GhostConv replaces standard convolutional operations with computationally efficient ghost modules in the YOLOv8n’s backbone structure, reducing parameters by 40,458 while maintaining feature richness; a Context Aggregation (CA) module is applied after the large and medium-sized feature maps were output by the YOLOv8n’s neck structure. …”
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Prediction of 123I-FP-CIT SPECT Results from First Acquired Projections Using Artificial Intelligence by Wadi’ Othmani, Arthur Coste, Dimitri Papathanassiou, David Morland

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Toward Adaptive Unsupervised and Blind Image Forgery Localization with ViT-VAE and a Gaussian Mixture Model by Haichang Yin, KinTak U, Jing Wang, Wuyue Ma

“…Second, we introduce convolutional neural networks (CNNs) to adaptively estimate the mixing coefficients, enabling an end-to-end architecture while significantly lowering computational costs. …”
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A novel attention-based deep learning model for improving sentiment classification after the case of the 2023 Kahramanmaras/Turkey earthquake on Twitter by Serpil Aslan, Muhammed Yildirim

“…These vectorized inputs are then processed by a hybrid model integrating convolutional neural networks (CNNs) and recurrent neural networks (RNNs) with a global attention mechanism. …”
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Severity classification and disposition prediction using ensemble learning for home-based patient management with adequate decision making by Amjad El Khatib, Chaima Ben Abdallah, Ahmed Nait Sidi Moh

“…Traditional methods relying on subjective clinical judgments can lead to inconsistencies, ED overcrowding, and increased healthcare costs. This study proposes a dual-phase support model integrating severity classification using the Emergency Severity Index and patient disposition prediction through a stacking ensemble learning (EL) approach. …”
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Construction and scheduling optimization of renewable energy consumption forecasting system for twisted tire porcelain manufacturing industry based on deep learning by Fangrong Liao, Runlin Ran, Jiayi Zhang

“…However, the current manual sorting method is inefficient, time-consuming, labour-intensive and costly, so exploring an intelligent sorting scheme is urgent. …”
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