An Enhanced Approach Using AGS Network for Skin Cancer Classification by Hwanyoung Lee, Seeun Cho, Jiyoon Song, Hoyoung Kim, Youjin Shin

“…It was evaluated using eight deep learning classifiers—GoogLeNet, DenseNet201, ResNet50, MobileNet V3, EfficientNet B0, ViT, EfficientNet V2, and Swin Transformers—on the HAM10000 dataset. …”
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Rice Identification and Spatio-Temporal Changes Based on Sentinel-1 Time Series in Leizhou City, Guangdong Province, China by Kaiwen Zhong, Jian Zuo, Jianhui Xu

“…In this study, we utilized multi-temporal Sentinel-1 data to develop a method for rapidly extracting the range of rice fields using a threshold segmentation approach and employed a U-Net deep learning model to delineate the distribution of rice fields. …”
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Artificial Intelligence and Postpartum Hemorrhage by Sam J Mathewlynn, Mohammadreza Soltaninejad, Sally L Collins, Yang Pan

“…Recently, there has been a surge in interest in using artificial intelligence (AI), including machine learning and deep learning, across many areas of health care. This article explores the application of AI in tackling PPH, including predictive modeling and risk stratification. …”
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Comparative Analysis of Edge Detection Operators Using a Threshold Estimation Approach on Medical Noisy Images with Different Complexities by Vladimir Maksimovic, Branimir Jaksic, Mirko Milosevic, Jelena Todorovic, Lazar Mosurovic

“…The obtained results are compared with the most popular techniques for edge detection using deep learning like AlexNet, ResNet, VGGNet, MobileNetv2, and Inceptionv3. …”
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Edge-Aware Dual-Task Image Watermarking Against Social Network Noise by Hao Jiang, Jiahao Wang, Yuhan Yao, Xingchen Li, Feifei Kou, Xinkun Tang, Limei Qi

“…In the era of widespread digital image sharing on social media platforms, deep-learning-based watermarking has shown great potential in copyright protection. …”
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Modeling and designing enhancers by introducing and harnessing transcription factor binding units by Jiaqi Li, Pengcheng Zhang, Xi Xi, Liyang Liu, Lei Wei, Xiaowo Wang

“…Here we propose the concept of transcription factor binding unit (TFBU) to modularly model enhancers by quantifying the impact of context sequences surrounding TFBSs using deep learning models. Based on this concept, we develop DeepTFBU, a comprehensive toolkit for enhancer design. …”
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Benchmarking protein language models for protein crystallization by Raghvendra Mall, Rahul Kaushik, Zachary A. Martinez, Matt W. Thomson, Filippo Castiglione

“…Several in silico deep learning methods have been developed to overcome the high attrition rate, cost of experiments and extensive trial-and-error settings, for predicting the crystallization propensities of proteins based on their sequences. …”
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When Remote Sensing Meets Foundation Model: A Survey and Beyond by Chunlei Huo, Keming Chen, Shuaihao Zhang, Zeyu Wang, Heyu Yan, Jing Shen, Yuyang Hong, Geqi Qi, Hongmei Fang, Zihan Wang

“…Most deep-learning-based vision tasks rely heavily on crowd-labeled data, and a deep neural network (DNN) is usually impacted by the laborious and time-consuming labeling paradigm. …”
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Enhanced CATBraTS for Brain Tumour Semantic Segmentation by Rim El Badaoui, Ester Bonmati Coll, Alexandra Psarrou, Hykoush A. Asaturyan, Barbara Villarini

“…Automatic brain tumour segmentation tools in computer vision have integrated powerful deep learning architectures to enable accurate tumour boundary delineation. …”
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Medium-term load forecasting with Power Market Survey: GEPCO case study by Sheeza Mariam Nawaz, Ubaid Ahmed, Adil Amin, Syed Afraz Hussain Shah, Anzar Mahmood

“…The time horizon for MTLF ranges from a few weeks to one year and it has applications in energy management and planning. The deep-learning networks (DLNs), proposed in recent years, have a black-box nature, which reduces the interpretability of the results. …”
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OCT-based diagnosis of glaucoma and glaucoma stages using explainable machine learning by Md Mahmudul Hasan, Jack Phu, Henrietta Wang, Arcot Sowmya, Michael Kalloniatis, Erik Meijering

“…However, current automated diagnostic approaches on Glaucoma diagnosis solely rely on black-box deep learning models, lacking explainability and trustworthiness. …”
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Improved Intelligent Condition Monitoring with Diagnostic Indicator Selection by Urszula Jachymczyk, Paweł Knap, Krzysztof Lalik

“…By applying the proposed method, it was possible to successfully filter out redundant features, enabling simpler machine learning (ML) models to match or even surpass the performance of more complex deep learning (DL) architectures. The best results were achieved by a deep neural network trained on the full dataset, with accuracy, precision, recall, and F1 score of 97.30%, 97.23%, 97.23%, and 97.23%, respectively, while the top-performing ML model (a voting classifier trained on the reduced dataset) attained scores of 97.13%, 96.99%, 96.95%, and 96.94%. …”
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Artificial Intelligence in Diagnosis and Management of Nail Disorders: A Narrative Review by Vishal Gaurav, Chander Grover, Mehul Tyagi, Suman Saurabh

“…In healthcare, AI encompasses various subfields, including machine learning, deep learning, natural language processing, and expert systems. …”
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A Study of Lithosphere–Ionosphere Seismic Precursors from Detecting Gamma-Ray and Total Electron Content Anomalies Prior to the 2018 M_L6.2 Hualien Earthquake in Eastern... by Ching-Chou Fu, Hau-Kun Jhuang, Yi-Ying Ho, Tsung-Che Tsai, Lou-Chuang Lee, Cheng-Horng Lin, Ching-Ren Lin, Vivek Walia, I-Te Lee

“…Positive and negative TEC anomalies were observed in Taiwan on 20–21 January and 5 February, respectively, and may be considered as ionospheric precursors to the earthquake. Additionally, deep-learning techniques applied to TEC data facilitate the detection of ionospheric precursors associated with the Hualien earthquake, enabling forecasts of an approaching seismic event. …”
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Seasonal Tree Height Dynamic Estimation Using Multi-source Remotely Sensed Data in Shenzhen by Hang Song, Xuemei Zhang, Ting Hu, Jinglei Liu, Bing Xu

“…By integrating these features with machine learning, deep learning, and optimization methods, we dynamically estimated tree heights in Shenzhen during summer and winter from 2018 to 2023 and validated seasonal and regional scalability. …”
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DGFEG: Dynamic Gate Fusion and Edge Graph Perception Network for Remote Sensing Change Detection by Shengning Zhou, Genji Yuan, Zhen Hua, Jinjiang Li

“…Benefiting from continuous innovations in deep learning algorithms, the accuracy of building change detection (BCD) in remote sensing (RS) has significantly improved. …”
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Enhancing Disaster Situation Awareness Through Multimodal Social Media Data: Evidence from Typhoon Haikui by Songfeng Gao, Tengfei Yang, Yuning Xu, Naixia Mou, Xiaodong Wang, Hao Huang

“…We employed the ERNIE natural language processing model to complement the Deeplab v3+ deep learning image semantic segmentation model for extracting disaster damage information from social media. …”
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Machine learning assisted plasmonic metascreen for enhanced broadband absorption in ultra-thin silicon films by Waqas W. Ahmed, Haicheng Cao, Changqing Xu, Mohamed Farhat, Muhammad Amin, Xiaohang Li, Xiangliang Zhang, Ying Wu

“…To facilitate the device design and improve light absorption without increasing the thickness of the active layer, we develop a deep learning framework, which learns to map from the absorption spectra to the design space. …”
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Multimodal heterogeneous graph fusion for automated obstructive sleep apnea-hypopnea syndrome diagnosis by Haoyu Wang, Xihe Qiu, Bin Li, Xiaoyu Tan, Jingjing Huang

“…It demonstrated superior diagnostic performance compared to conventional machine learning models and existing deep learning approaches, effectively integrating domain knowledge with data-driven learning to produce explainable representations and robust generalization capabilities, which can potentially be utilized for clinical use. …”
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An efficient convolution neural network method for copy-move video forgery detection by Mohamed Meselhy Eltoukhy, Faisal S. Alsubaei, Akram M. Mortda, Khalid M. Hosny

“…The general representation powers of deep learning models and the connection of multiple forensic characteristics are still not fully explored. …”
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