OcularAge: A Comparative Study of Iris and Periocular Images for Pediatric Age Estimation by Naveenkumar G. Venkataswamy, Poorna Ravi, Stephanie Schuckers, Masudul H. Imtiaz

“…This study presents a comparative evaluation of iris and periocular images for estimating the ages of children aged between 4 and 16 years. …”
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Use of gabapentin for neuropathic pain therapy: A view from perspective of evidence-based medicine by O. I. Butranova, S. K. Zyryanov

“…The aim of the study was to analyze the literature sources for pharmacodynamic and pharmacokinetic features of gabapentin, providing its use in patients with neuropathic pain, as well as a comparative evaluation of its efficacy and safety when used in different doses.Materials and methods. …”
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Muographic Image Upsampling with Machine Learning for Built Infrastructure Applications by William O’Donnell, David Mahon, Guangliang Yang, Simon Gardner

“…The civil engineering industry faces a critical need for innovative non-destructive evaluation methods, particularly for ageing critical infrastructure, such as bridges, where current techniques fall short. …”
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SSMM-DS: A semantic segmentation model for mangroves based on Deeplabv3+ with swin transformer by Zhenhua Wang, Jinlong Yang, Chuansheng Dong, Xi Zhang, Congqin Yi, Jiuhu Sun

“…We then employed Swin Transformer as the backbone network, enhancing the capability of global information learning and detail feature extraction. …”
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A CrossMod-Transformer deep learning framework for multi-modal pain detection through EDA and ECG fusion by Jaleh Farmani, Ghazal Bargshady, Stefanos Gkikas, Manolis Tsiknakis, Raul Fernandez Rojas

“…The proposed framework includes a uni-modal approach (FCN-ALSTM-Transformer) comprising a Fully Convolutional Network, Attention-based LSTM, and a Transformer block to integrate features extracted by these models. …”
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Automated Cervical Cytology Image Cell Segmentation Using Enhanced MultiResUNet With DCT and Spectral Domain Attention Mechanisms by S. Resmi, Rimjhim Padam Singh, Kannappan Palaniappan

“…Hence, this work proposes a novel deep multi-residual encoder-decoder network enhanced with spectral domain block and channel attention analyzing Discrete cosine transform (DCT)-based spectral features for efficient and accurate semantic segmentation of cytoplasm and nucleus regions in the complex cytology images of the cervix. …”
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RB-TRNet: a regularity-guided and boundary-aware architecture for toponym recognition from Chinese text by Haigang Sui, Jindi Wang, Xining Zhang, Huihan Ning, Wentao Wang, Lieyun Hu

“…Additionally, an orthogonal space is established after the BiLSTM network to facilitate the learning of different rule features by the two modules. …”
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EEG–fNIRS signal integration for motor imagery classification using deep learning and evidence theory by Mohammed E. Seno, Niladri Maiti, Maulik Patel, Mihirkumar M. Patel, Kalpesh B. Chaudhary, Ashish Pasaya, Babacar Toure

“…For EEG signals, spatiotemporal features are extracted using dual-scale temporal convolution and depthwise separable convolution, and a hybrid attention module is introduced to enhance the network's sensitivity to salient neural patterns. …”
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Machine Learning and Deep Learning Approaches for Fake News Detection: A Systematic Review of Techniques, Challenges, and Advancements by Omar Bashaddadh, Nazlia Omar, Masnizah Mohd, Mohd Nor Akmal Khalid

“…Similarly, the GANM model demonstrated robust performance on the FakeNewsNet dataset by integrating text and social features. Transfer learning and multimodal models that incorporate user behaviour and network information significantly improve detection in diverse, low-resource environments. …”
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Microscopic pore combination type identification of tight sandstone reservoir based on improved swin transformer architecture by Zhenyu Pang, Zhicong Chen, Sijie Lu, Zhenbo Cai, Yuqing Lu, Mengting Peng

“…To address these issues, this study proposes an enhanced Swin Transformer-based neural network architecture, termed SwinLSC (Swin Transformer with Linformer and Self-Adaptive Channel Attention). …”
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Statistical Analysis of Environmental Pollution Impact on Population Morbidity in the Republic of Mari El by P. A. Korotkov, A. B. Trubyanov, A. A. Avdeeva, A. I. Gismieva

“…In the future, it is proposed to use additional, more objective and integral evaluation of environmental quality, for example, the fluctuating asymmetry of bilateral features of biological objects.…”
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Assessing Revisit Risk in Emergency Department Patients: Machine Learning Approach by Wang-Chuan Juang, Zheng-Xun Cai, Chia-Mei Chen, Zhi-Hong You

“…A 5-year dataset consisting of 184,687 ED visits, along with 324,111 historical electronic health records and the associated medical notes, was obtained from Kaohsiung Veterans General Hospital, a tertiary medical center in Taiwan, to evaluate the proposed system. ResultsThe evaluation results indicate that incorporating convolutional neural network–based feature extraction from unstructured ED physician narrative notes, combined with structured vital signs and demographic data, significantly enhances predictive performance. …”
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Training strategies for semi-supervised remote sensing image captioning by Qimin Cheng, Haojun Cheng, Linfeng Yuan, Yingjie Du, Saifei Tu, Yuqi Xu

“…Specifically, we propose a Weakly Supervised Enhanced Noisy Teacher-Student Network (WENTS) to prevent over-reliance on specific features and improve generalization. …”
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Photovoltaic panel defect detection algorithm based on infrared imaging and improved YOLOv8 by Jingdong Wang, Zhu Cheng

“…First, a multi-channel squeeze-and-excitation network is introduced to improve feature extraction capabilities and is integrated into the neck network. …”
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Representation Enhancement-Based Proximal Policy Optimization for UAV Path Planning and Obstacle Avoidance by Xiangxiang Huang, Wei Wang, Zhaokang Ji, Bin Cheng

“…OMI reduces collision under partially observable conditions by separately extracting perception features and state features through an embedding network and feeding the extracted features to a gated recurrent unit (GRU) to enhance observation memory. …”
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Improving Medical Image Quality Using a Super-Resolution Technique with Attention Mechanism by Dong Yun Lee, Jang Yeop Kim, Soo Young Cho

“…The proposed model incorporates a convolutional self-attention block that combines channel and spatial attention to emphasize important image features. Channel attention uses global average pooling and fully connected layers to enhance high-frequency features within channels. …”
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CAPSE-ViT: A Lightweight Framework for Underwater Acoustic Vessel Classification Using Coherent Spectral Estimation and Modified Vision Transformer by Najamuddin NAJAMUDDIN, Usman Ullah SHEIKH, Ahmad Zuri SHA’AMERI

“…Vessel frequency spectra is first computed through the CAPSE analysis, capturing key machinery characteristics. Further, the features are processed via a vision transformer (ViT) network. …”
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Design and Performance Verification of Deep Learning-Based River Flood Prediction System Design and Digital Twin-Based Its Application by Heesang Eom, Younghun Kim, Jongho Paik

“…To address the problems of rapid runoff and complex terrain, a deep learning-based hybrid model is proposed that integrates a Convolutional Neural Network (CNN) for spatial feature extraction and a Recurrent Neural Network (RNN) with Long Short-Term Memory (LSTM) units for temporal sequence modeling. …”
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Adapting to evolving MRI data: A transfer learning approach for Alzheimer’s disease prediction by Rosanna Turrisi, Sarthak Pati, Giovanni Pioggia, Gennaro Tartarisco

“…The General Approach involves distinct feature extraction and classification steps, using Radiomic features and TL-based features evaluated with six classifiers. …”
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Multimodal consumer choice prediction using EEG signals and eye tracking by Syed Muhammad Usman, Shehzad Khalid, Aimen Tanveer, Ali Shariq Imran, Muhammad Zubair

“…Class imbalance is handled by employing the Synthetic Minority Over-sampling Technique (SMOTE). Handcrafted features, including statistical and wavelet features, and automated features from Convolutional Neural Network and Long Short-Term Memory (CNN-LSTM), have been extracted and concatenated to generate a feature space representation. …”
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