Ice volume and thickness of all Scandinavian glaciers and ice caps by Thomas Frank, Ward Jan Jacobus van Pelt

“…The ice flow model used is the instructed glacier model (Jouvet and Cordonnier, 2023, Journal of Glaciology 1–15), a generic physics-informed deep-learning emulator that models higher-order ice flow with high-computational efficiency. …”
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Deciphering the Role of Artificial Intelligence in Medical Sciences: An Update by Siddhant Khanna, Merajul Haque Siddiqui, Shalini Bhushan, Rahul Saxena

“…It’s related techniques (machine to deep learning) are frequently employed in disease diagnosis, treatment procedure, and in the evaluation of its side effect. …”
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Deep CNN ResNet-18 based model with attention and transfer learning for Alzheimer's disease detection by Sofia Biju Francis, Sofia Biju Francis, Jai Prakash Verma

“…This design is based on analyses of existing deep learning architectures and feature extraction techniques. …”
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Code-Switching ASR for Low-Resource Indic Languages: A Hindi-Marathi Case Study by Hemant Palivela, Meera Narvekar, David Asirvatham, Shashi Bhushan, Vinay Rishiwal, Udit Agarwal

“…This work critically evaluates current methods and proposes improvements using modern deep-learning techniques to address the primary challenges in developing efficient ASR models for Hindi and Marathi. …”
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Development of a Guidance System for an Agricultural Wheeled Robotic Platform in Row Crop Fields by Hossein Behfar, Fahime Hashemi, Arezu Nobakht

“…Modern tools such as sensors, imagery cameras, and deep learning enable farmers to identify and control weeds, pests, and diseases in real-time. …”
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Temporal integration of ResNet features with LSTM for enhanced skin lesion classification by Sasmita Padhy, Sachikanta Dash, Naween Kumar, Shailendra Pratap Singh, Gyanendra Kumar, Poonam Moral

“…This research combines ResNet-50 for spatial feature extraction with Long Short-Term Memory (LSTM) networks for temporal analysis and introduces an innovative hybrid deep learning model, Residual network-Long Short-Term Memory (R-LSTM50). …”
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Forecasting of Global Ionosphere Maps With Multi‐Day Lead Time Using Transformer‐Based Neural Networks by Chung‐Yu Shih, Cissi Ying‐tsen Lin, Shu‐Yu Lin, Cheng‐Hung Yeh, Yu‐Ming Huang, Feng‐Nan Hwang, Chia‐Hui Chang

“…Recently, data‐driven approaches, such as deep learning, have therefore surged as means for TEC prediction. …”
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Forecasting Ionospheric foF2 Using Bidirectional LSTM and Attention Mechanism by Jun Tang, Dengpan Yang, Mingfei Ding

“…Abstract The critical frequency of ionospheric F2 layer (foF2) is an important ionospheric characteristic parameter. In this paper, a deep learning model based on Bidirectional long short‐term memory (BiLSTM) and attention mechanism is implemented for predicting the foF2 parameter. …”
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Text-Based Price Recommendation System for Online Rental Houses by Lujia Shen, Qianjun Liu, Gong Chen, Shouling Ji

“…In this paper, we analyzed the relationship between the description of each listing and its price, and proposed a text-based price recommendation system called TAPE to recommend a reasonable price for newly added listings. We used deep learning techniques (e.g., feedforward network, long short-term memory, and mean shift) to design and implement TAPE. …”
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Attention-based interactive multi-level feature fusion for named entity recognition by Yiwu Xu, Yun Chen

“…Recently, Deep Neural Networks (DNNs) have been extensively applied to NER tasks owing to the rapid development of deep learning technology. However, despite their advancements, these models fail to take full advantage of the multi-level features (e.g., lexical phrases, keywords, capitalization, suffixes, etc.) of entities and the dependencies between different features. …”
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Tuning a SAM-Based Model With Multicognitive Visual Adapter to Remote Sensing Instance Segmentation by Linghao Zheng, Xinyang Pu, Su Zhang, Feng Xu

“…The evaluation results indicate the proposed method surpasses other deep learning algorithms and verify its effectiveness and generalization.…”
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A pediatric emergency prediction model using natural language process in the pediatric emergency department by Arum Choi, Chohee Kim, Jisu Ryoo, Jangyeong Jeon, Sangyeon Cho, Dongjoon Lee, Junyeong Kim, Changhee Lee, Woori Bae

“…Abstract This study developed a predictive model using deep learning (DL) and natural language processing (NLP) to identify emergency cases in pediatric emergency departments. …”
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Explainable attention based breast tumor segmentation using a combination of UNet, ResNet, DenseNet, and EfficientNet models by Shokofeh Anari, Soroush Sadeghi, Ghazaal Sheikhi, Ramin Ranjbarzadeh, Malika Bendechache

“…Abstract This study utilizes the Breast Ultrasound Image (BUSI) dataset to present a deep learning technique for breast tumor segmentation based on a modified UNet architecture. …”
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New Heuristics Method for Malicious URLs Detection Using Machine Learning by Maher Kassem Hasan

“…., Further works on deep learning models emphasized their potentials. In our study, the optimized Random Forest model in our case showed the best performance, and its training accuracy was 99%, while validation accuracy was 90.5%, also logistic Regression and SVM achieved training accuracy was 89.31%, while validation accuracy was 90.5%. …”
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LASSO–MOGAT: a multi-omics graph attention framework for cancer classification by Fadi Alharbi, Aleksandar Vakanski, Murtada K. Elbashir, Mohanad Mohammed

“…This article introduces Least Absolute Shrinkage and Selection Operator–Multi-omics Gated Attention (LASSO–MOGAT), a novel graph-based deep learning framework that integrates messenger RNA, microRNA, and DNA methylation data to classify 31 cancer types. …”
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TBF-YOLOv8n: A Lightweight Tea Bud Detection Model Based on YOLOv8n Improvements by Wenhui Fang, Weizhen Chen

“…To solve the problem of the high computational complexity of deep learning detection models, we developed the Tea Bud DSCF-YOLOv8n (TBF-YOLOv8n)lightweight detection model. …”
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Transferable Targeted Adversarial Attack on Synthetic Aperture Radar (SAR) Image Recognition by Sheng Zheng, Dongshen Han, Chang Lu, Chaowen Hou, Yanwen Han, Xinhong Hao, Chaoning Zhang

“…Deep learning models have been widely applied to synthetic aperture radar (SAR) target recognition, offering end-to-end feature extraction that significantly enhances recognition performance. …”
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On the Effect of the Patient Table on Attenuation in Myocardial Perfusion Imaging SPECT by Tamino Huxohl, Gopesh Patel, Wolfgang Burchert

“…Abstract Background The topic of the effect of the patient table on attenuation in myocardial perfusion imaging (MPI) SPECT is gaining new relevance due to deep learning methods. Existing studies on this effect are old, rare and only consider phantom measurements, not patient studies. …”
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A novel knowledge distillation framework for enhancing small object detection in blurry environments with unmanned aerial vehicle-assisted images by Sayed Jobaer, Xue-song Tang, Yihong Zhang, Gaojian Li, Foysal Ahmed

“…Abstract Deep learning-based object detectors excel on mobile devices but often struggle with blurry images that are common in real-world scenarios, like unmanned aerial vehicle (UAV)-assisted images. …”
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Triple-attentions based salient object detector for strip steel surface defects by Li Zhang, Xirui Li, Yange Sun, Huaping Guo

“…Abstract Accurate detection of surface defects on strip steel is essential for ensuring strip steel product quality. Existing deep learning based detectors for strip steel surface defects typically strive to iteratively refine and integrate the coarse outputs of the backbone network, enhancing the models’ ability to express defect characteristics. …”
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