Deep learning classification of drainage crossings based on high-resolution DEM-derived geomorphological information by Michael Edidem, Bill Xu, Ruopu Li, Di Wu, Banafsheh Rekabdar, Guangxing Wang

“…The study culminates in explainable artificial intelligence (XAI) for evaluating those most critical image segments responsible for characterizing drainage crossings.…”
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Innovative approaches for skin disease identification in machine learning: A comprehensive study by Kuldeep Vayadande, Amol A. Bhosle, Rajendra G. Pawar, Deepali J. Joshi, Preeti A. Bailke, Om Lohade

“…Investigate the effectiveness and performance of several algorithms, such as the flexible k-nearest neighbor, the sturdy support vector machine (SVM), and the complex convolutional neural networks (CNNs), advanced techniques for automated skin disease detection encompass deep learning methods such as recurrent neural networks (RNNs) for sequential data processing, generative adversarial networks (GANs) for generating synthetic data, and attention mechanisms for focusing on relevant image regions by means of a thorough examination of the most recent studies. …”
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Advances in Remote Sensing and Deep Learning in Coastal Boundary Extraction for Erosion Monitoring by Marc-André Blais, Moulay A. Akhloufi

“…The presented algorithms range from basic convolutional networks to encoder–decoder architectures and attention mechanisms. …”
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Challenges in AI-driven multi-omics data analysis for Oncology: Addressing dimensionality, sparsity, transparency and ethical considerations by Maryem Ouhmouk, Shakuntala Baichoo, Mounia Abik

“…These methods have advanced the handling of missing data and dimensionality, outperforming traditional approaches. However, most reviewed models remain at the proof-of-concept stage, with limited clinical validation or real-world deployment.…”
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The Accuracy of Algorithms Used by Artificial Intelligence in Cephalometric Points Detection: A Systematic Review by Júlia Ribas-Sabartés, Meritxell Sánchez-Molins, Nuno Gustavo d’Oliveira

“…CNN have proven to be the most effective AI system for detecting cephalometric points in radiographic images. …”
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Multiple-Feature Construction for Image Segmentation Based on Genetic Programming by David Herrera-Sánchez, José-Antonio Fuentes-Tomás, Héctor-Gabriel Acosta-Mesa, Efrén Mezura-Montes, José-Luis Morales-Reyes

“…Image segmentation is one of the most used techniques for medical support to identify regions of interest in different organs. …”
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Performance and clinical implications of machine learning models for detecting cervical ossification of the posterior longitudinal ligament: a systematic review by Wongthawat Liawrungrueang, Sung Tan Cho, Watcharaporn Cholamjiak, Peem Sarasombath, Nattaphon Twinprai, Prin Twinprai, Inbo Han

“…The ML and DL models demonstrated high diagnostic performance, with accuracy rates ranging from 69.6% to 98.9% and AUC values up to 0.99. Convolutional neural networks and random forest models were the most used approaches. …”
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A novel feature extractor based on constrained cross network for detecting sleep state by Chenlei Tian, Fei Song

“…Feature Derivation Module leverages dilated convolutions, gated recurrent units, and attention mechanisms to construct new features in batches. …”
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Educational Psychology Analysis Method for Extracting Students’ Facial Information Based on Image Big Data by Maoyue Zhang

“…At present, most of the research on academic emotions focuses on the concept, current situation, and relevance. …”
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SwinD-Net: a lightweight segmentation network for laparoscopic liver segmentation by Shuiming Ouyang, Baochun He, Huoling Luo, Fucang Jia

“…In the practical setting of most hospitals, where powerful computing resources are lacking, these models cannot meet the real-time computational demands. …”
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Well logging super-resolution based on fractal interpolation enhanced by BiLSTM-AMPSO by Jian Han, Yu Deng, Bing Zheng, Zhimin Cao

“…In order to address these challenges, geophysical logging is one of the most important data for characterizing target reservoir model. …”
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Flow Field Analysis and Development of a Prediction Model Based on Deep Learning by Yingjie Yu, Xiufeng Zhang, Lucai Wang, Rui Tian, Xiaobin Qian, Dongdong Guo, Yanwei Liu

“…The velocity of ocean currents significantly affects the trajectory prediction of ocean drifters and the safe navigation of intelligent vessels. Currently, most ocean current predictions focus on time-based forecasts at specific fixed points. …”
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Unraveling trends in schistosomiasis: deep learning insights into national control programs in China by Qing Su, Cici Xi Chen Bauer, Robert Bergquist, Zhiguo Cao, Fenghua Gao, Zhijie Zhang, Yi Hu

“…This study explored the progress of the 2 most recent national schistosomiasis control programs in an endemic area along the Yangtze River in China. …”
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An enhanced deep learning-based feature extraction framework for moving object detection by Upasana Panigrahi, Prabodh Kumar Sahoo, Manoj Kumar Panda, Aswini Kumar Samantaray, Ganapati Panda

“…Various methods across the globe are developed by the researcher for change detection. However, most current methods require improvement in the challenging datasets. …”
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Application of Ontology Matching Algorithm Based on Linguistic Features in English Pronunciation Quality Evaluation by Shan Zhu

“…Traditional English classroom teaching is difficult to meet the oral learning needs of most learners. Thanks to the continuous advancement of speech processing technology, computer-assisted language learning systems are becoming more intelligent, not only pointing out learners’ pronunciation errors but also assessing their overall pronunciation level. …”
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FBI-Net: Frequency-Based Image Forgery Localization via Multitask Learning With Self-Attention by A-Rom Gu, Ju-Hyeon Nam, Sang-Chul Lee

“…Our proposed network adopts a fully convolutional encoder-decoder architecture, consisting of three encoders sharing parameters, a bridge attention module, and two output streams in the decoder. …”
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Enhancing Periodontal Bone Loss Diagnosis Through Advanced AI Techniques by Nader Nabil Fouad Rezallah, George Sherif, Ahmed Z. Abdelkarim, Shereen Afifi

“…Based on our extensive research, we concluded that convolutional neural networks (CNNs) are the most effective type of neural network for addressing our problem. …”
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Improving the accuracy of prediction models for small datasets of Cytochrome P450 inhibition with deep learning by Elpri Eka Permadi, Reiko Watanabe, Kenji Mizuguchi

“…Notably, the multitask models with data imputation demonstrated significant improvement in CYP inhibition prediction over the single-task models. Using the most accurate prediction models, we evaluated the inhibitory activity of approved drugs against CYP2B6 and CYP2C8. …”
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MTFSR: Multitemporal and Spatial Feature Reconstruction Denoising Network for Remote Sensing Change Detection by YeKai Cui, Peng Duan, Jinjiang Li

“…With the widespread application of convolutional neural networks (CNNs) in remote sensing (RS) technologies, change detection (CD) has attracted increasing attention in environmental monitoring research. …”
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Mastitis Classification in Dairy Cows Using Weakly Supervised Representation Learning by Soo-Hyun Cho, Mingyung Lee, Wang-Hee Lee, Seongwon Seo, Dae-Hyun Lee

“…The proposed method employed a structure where the classifier branches from the latent space of a 1D-convolutional autoencoder, enabling representation learning of milking data to be conducted from the perspective of reconstructing the original information and detecting mastitis. …”
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