Hierarchical Transfer Learning with Transformers to Improve Semantic Segmentation in Remote Sensing Land Use by Miaomiao Chen, Lianfa Li

“…Additionally, the scarcity of labeled remote sensing data and domain shift issues adversely impact deep learning model performance. This study proposes a hierarchical transfer learning framework for fine-category semantic segmentation tasks, leveraging the powerful global relationship modeling capabilities of Transformer models to classify land use in Dongpo District, Meishan City, in mainland China. …”
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Enhancing unsupervised learning in medical image registration through scale-aware context aggregation by Yuchen Liu, Ling Wang, Xiaolin Ning, Yang Gao, Defeng Wang

“…Traditional registration algorithms often require significant computational resources due to iterative optimization, while deep learning approaches face challenges in managing diverse deformation complexities and task requirements. …”
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RETRACTED: An Infrared Small Target Detection Method Based on a Weighted Human Visual Comparison Mechanism for Safety Monitoring by Yuanyuan Chen, Huiqian Wang, Yu Pang, Jinhui Han, En Mou, Enling Cao

“…In addition, unlike deep learning, this method is appropriate for small sample sizes and is easy to implement on FPGA hardware.…”
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Comparative Analysis of Traditional and Modern NLP Techniques on the CoLA Dataset: From POS Tagging to Large Language Models by Abdessamad Benlahbib, Achraf Boumhidi, Anass Fahfouh, Hamza Alami

“…In this article, we compare a range of techniques, from traditional methods such as Part-of-Speech (POS) tagging and feature extraction methods like CountVectorizer with Term Frequency-Inverse Document Frequency (TF-IDF) and N-grams, to modern embeddings such as FastText and Embeddings from Language Models (ELMo), as well as deep learning architectures like transformers and Large Language Models (LLMs). …”
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Blockchain-Powered Secure and Scalable Threat Intelligence System With Graph Convolutional Autoencoder and Reinforcement Learning Feedback Loop by Mohamad Khayat, Ezedin Barka, Mohamed Adel Serhani, Farag Sallabi, Khaled Shuaib, Heba M. Khater

“…This paper proposes an approach that integrates secure blockchain technology with data preprocessing, deep learning, and reinforcement learning to enhance threat detection and response capabilities. …”
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Aircraft Detection for Remote Sensing Images Based on Deep Convolutional Neural Networks by Liming Zhou, Haoxin Yan, Yingzi Shan, Chang Zheng, Yang Liu, Xianyu Zuo, Baojun Qiao

“…Aircraft detection for remote sensing images, as one of the fields of computer vision, is one of the significant tasks of image processing based on deep learning. Recently, many high-performance algorithms for aircraft detection have been developed and applied in different scenarios. …”
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Minimal sourced and lightweight federated transfer learning models for skin cancer detection by Vikas Khullar, Prabhjot Kaur, Shubham Gargrish, Anand Muni Mishra, Prabhishek Singh, Manoj Diwakar, Anchit Bijalwan, Indrajeet Gupta

“…Here minimal resource based pre-trained deep learning models including EfficientNetV2S, EfficientNetB3, ResNet50, and NasNetMobile have been used to apply transfer learning on data of shape $$\:\:224\times\:224\times\:3$$ . …”
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Enhancing pediatric congenital heart disease detection using customized 1D CNN algorithm and phonocardiogram signals by Ihtisham Ul Haq, Ghassan Husnain, Yazeed Yasin Ghadi, Nisreen Innab, Masoud Alajmi, Hanan Aljuaid

“…The research highlights the promise of combining modern signal processing with deep learning for efficient CHD screening. The suggested model exhibits outstanding performance yet, issues like dataset variability and noise persist. …”
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Discovery of novel PRMT1 inhibitors: a combined approach using AI classification model and traditional virtual screening by Jungan Zhang, Yixin Ren, Yixin Ren, Yun Teng, Han Wu, Jingsu Xue, Lulu Chen, Xiaoyue Song, Yan Li, Ying Zhou, Zongran Pang, Zongran Pang, Hao Wang, Hao Wang, Hao Wang

“…Although extensive research has been conducted on PRMT1, the reported inhibitors have not successfully passed clinical trials. In this study, deep learning was employed to analyze the characteristics of existing PRMTs inhibitors and to construct a classification model for PRMT1 inhibitors. …”
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Explainability of Subfield Level Crop Yield Prediction Using Remote Sensing by Hiba Najjar, Miro Miranda, Marlon Nuske, Ribana Roscher, Andreas Dengel

“…Crop yield forecasting plays a significant role in addressing growing concerns about food security and guiding decision-making for policymakers and farmers. When deep learning is employed, understanding the learning and decision-making processes of the models, as well as their interaction with the input data, is crucial for establishing trust in the models and gaining insight into their reliability. …”
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Benchmark dataset on feeding intensity of the pearl gentian grouper(Epinephelus fuscoguttatus♀×E. lanceolatus♂) by Haijing Qin, Yunchen Tian, Jianing Quan, Xueqi Cong, Qingfei Li, Jinzhu Sui

“…In the factory-based recirculating water high-density aquaculture environment, images have disadvantages such as uneven contrast and blurring, and there are difficulties in manually extracting image features. Although the deep learning-based fish feeding intensity assessment model has higher recognition accuracy and better robustness, the conventional differentiation of feeding intensity usually relies on manual experience to divide the feeding intensity dataset, which is subjective and uncertain, and the annotation is observed by the aquaculture experienced personnel to increase the labor and time cost. …”
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Meso Hybridized Silk Fibroin Watchband for Wearable Biopotential Sensing and AI Gesture Signaling by Xiao Wang, Changsheng Lu, Zerong Jiang, Guangwei Shao, Jingzhe Cao, Xiang Yang Liu

“…Through smart raining via deep learning, we achieved an unparalleled recognition rate (96% across 20 volunteers of different genders) among other EMG sensing devices. …”
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HFIFNet: Hierarchical Feature Interaction Network With Multiscale Fusion for Change Detection by Mingzhi Han, Tao Xu, Qingjie Liu, Xiaohui Yang, Jing Wang, Jiaqi Kong

“…Change detection (CD) from remote sensing images has been widely used in land management and urban planning. Benefiting from deep learning, numerous methods have achieved significant results in the CD of clearly changed targets. …”
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Dual‐fluorescence imaging and automated trophallaxis detection for studying multi‐nutrient regulation in superorganisms by Lior Baltiansky, Einav Sarafian‐Tamam, Efrat Greenwald, Ofer Feinerman

“…Additionally, our image‐based deep learning algorithm for automatic detection of ant trophallaxis events efficiently yields a detailed record of all food‐transfer interactions. …”
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A graph neural network approach for hierarchical mapping of breast cancer protein communities by Xiao Zhang, Qian Liu

“…Existing approaches are subjective and fail to take information from protein sequences into consideration. Deep learning can automatically learn features from protein sequences and protein–protein interactions for hierarchical clustering. …”
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NavBLIP: a visual-language model for enhancing unmanned aerial vehicles navigation and object detection by Ye Li, Li Yang, Meifang Yang, Fei Yan, Tonghua Liu, Chensi Guo, Rufeng Chen

“…Traditional methods for UAV navigation and object detection have often relied on either handcrafted features or unimodal deep learning approaches. While these methods have seen some success, they frequently encounter limitations in dynamic environments, where robustness and computational efficiency become critical for real-time performance. …”
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Research on new energy power plant network traffic anomaly detection method based on EMD by Danni Liu, Shengda Wang, YutongLi, Ji Du, Jia Li

“…The incorporation of these state-of-the-art convolutional methods into the CNN-GRU model enhances detection capabilities and opens up new avenues for exploration in the realm of anomaly detection based on deep learning. Results The grid deployment of large-scale PV power facilities relies heavily on dependable communication networks. …”
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Artificial Intelligence in Pediatric Epilepsy Detection: Balancing Effectiveness With Ethical Considerations for Welfare by Marina Ramzy Mourid, Hamza Irfan, Malik Olatunde Oduoye

“…Specific methods, like EEG‐based AI for detecting interictal discharges, showed high specificity (93.33%–96.67%) and sensitivity (76.67%–93.33%), while neuroimaging approaches using rs‐fMRI and DTI reached up to 97.5% accuracy in identifying microstructural abnormalities. Deep learning models, such as CNN‐LSTM, have also enhanced seizure detection from video by capturing subtle movement and expression cues. …”
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Novel Speech-Based Emotion Climate Recognition in Peers’ Conversations Incorporating Affect Dynamics and Temporal Convolutional Neural Networks by Ghada Alhussein, Mohanad Alkhodari, Ahsan H. Khandoker, Leontios J. Hadjileontiadis

“…Moreover, there is a distinct improvement when the AD are combined with the TCNN, compared to the baseline deep learning approaches. These results demonstrate the effectiveness of AffECt in speech-based EC recognition, paving the way for many applications, e.g., in patients’ group therapy, negotiations, and emotion-aware mobile applications.…”
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Data-knowledge hybrid driven intelligent prediction method of tunnel excavation profiles geometric deformation by Ya Hu, Jingyi Lu, Jun Zhu, Huixin Zhang, Ying Ren, Jianlin Wu, Jianbo Lai, Heng Zhang, Hongyue Zhao, Xiang Zeng

“…A dynamic data-driven tunnel excavation knowledge extraction method is designed, along with the construction of a multi-process coupled tunnel excavation knowledge base. Additionally, a deep learning-based excavation profile deformation prediction model is developed, integrating the data-knowledge hybrid driven method to improve prediction performance. …”
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