GastroFuse-Net: an ensemble deep learning framework designed for gastrointestinal abnormality detection in endoscopic images by Sonam Aggarwal, Isha Gupta, Ashok Kumar, Sandeep Kautish, Abdulaziz S. Almazyad, Ali Wagdy Mohamed, Frank Werner, Mohammad Shokouhifar

Subjects: “…deep learning…”
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A Framework for Promoting Passive Breast Cancer Monitoring: Deep Learning as an Interpretation Tool for Breast Thermograms by Mohamad Firouzmand, Keivan Majidzadeh, Maryam Jafari, Shahpar Haghighat, Rezvan Esmaeili, Leila Moradi, Nima Misaghi, Mahsa Ensafi, Fatemeh Batmanghelich, Mohammadreza Keyvanpour, Seyed Vahab Shojaedini

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A file archival integrity check method based on the BiLSTM + CNN model and deep learning by Jinxun Li, Tingjun Wang, Chao Ma, Yunxuan Lin, Qing Yan

“…An updated Archive File Integrity Check Method (AFICM) may solve these issues, and the paper explains it. Deep learning allows the combination of a Bidirectional Long-Short Term Memory (Bi-LSTM) with adaptive gating and an adaptive Temporal Convolutional Neural Network (TCNN) with multi-scale temporal attention. …”
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Two-Step Deep Learning Approach for Estimating Vegetation Backscatter: A Case Study of Soybean Fields by Dong Zhu, Peng Zhao, Qiang Zhao, Qingliang Li, Jinpeng Zhang, Lixia Yang

“…Precisely predicting vegetation backscatter involves various challenges, such as complex vegetation structure, soil–vegetation interaction, and data availability. Deep learning (DL) works as a powerful tool to analyze complex data and approximate the nonlinear relationship between variables, thus exhibiting potential applications in microwave scattering problems. …”
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Deep-learning prediction of cardiovascular outcomes from routine retinal images in individuals with type 2 diabetes by Mohammad Ghouse Syed, Emanuele Trucco, Muthu R. K. Mookiah, Chim C. Lang, Rory J. McCrimmon, Colin N. A. Palmer, Ewan R. Pearson, Alex S. F. Doney, Ify R. Mordi

“…Our aim was to determine whether a deep-learning artificial intelligence (AI) model could be used to predict CVD outcomes from routinely obtained diabetic retinal screening photographs and to compare its performance to a traditional clinical CVD risk score. …”
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Discrimination of Fresh Tobacco Leaves with Different Maturity Levels by Near-Infrared (NIR) Spectroscopy and Deep Learning by Yi Chen, Jun Bin, Congming Zou, Mengjiao Ding

“…Therefore, an objective and reliable discriminant technique for tobacco leaf maturity level based on near-infrared (NIR) spectroscopy combined with a deep learning approach of convolutional neural networks (CNNs) is proposed in this study. …”
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Leveraging Deep Learning and Multimodal Large Language Models for Near-Miss Detection Using Crowdsourced Videos by Shadi Jaradat, Mohammed Elhenawy, Huthaifa I. Ashqar, Alexander Paz, Richi Nayak

“…This study underscores the potential of combining deep learning with MLLMs to enhance traffic safety analysis by integrating near-miss data as a key predictive layer. …”
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Automatic detection, identification and counting of deep-water snappers on underwater baited video using deep learning by Florian Baletaud, Florian Baletaud, Florian Baletaud, Sébastien Villon, Antoine Gilbert, Jean-Marie Côme, Sylvie Fiat, Corina Iovan, Laurent Vigliola

“…To address this issue, we used a Region-based Convolutional Neural Network (Faster R-CNN), a deep learning architecture to automatically detect, identify and count deep-water snappers in BRUVS. …”
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Sensor Image, Anomaly Detection Method for Hydroelectric Dam Structure Using Sensors Measurements and Deep Learning by Van-Phuong Ha, Dinh-Van Nguyen, Trong-Chuong Trinh, Duc-Cuong Quach, Van HuyBui

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A deep learning method based on multi-scale fusion for noise-resistant coal-gangue recognition by Qingjun Song, Shirong Sun, Qinghui Song, Bingrui Wang, Zihao Liu, Haiyan Jiang

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Retracted: A Deep Learning Approach for a Source Code Detection Model Using Self-Attention by null Complexity

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A Deep Learning Filter that Blocks Phishing Campaigns Using Intelligent English Text Recognition Methods by Yonghui Tang, Fei Wu

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Deep Learning in the Fast Lane: A Survey on Advanced Intrusion Detection Systems for Intelligent Vehicle Networks by Mohammed Almehdhar, Abdullatif Albaseer, Muhammad Asif Khan, Mohamed Abdallah, Hamid Menouar, Saif Al-Kuwari, Ala Al-Fuqaha

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Identifying Incident Causal Factors to Improve Aviation Transportation Safety: Proposing a Deep Learning Approach by Tianxi Dong, Qiwei Yang, Nima Ebadi, Xin Robert Luo, Paul Rad

“…This paper focuses on constructing deep-learning-based models to identify causal factors from incident reports. …”
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An Enhanced Drone Technology for Detecting the Human Object in the Dense Areas Using a Deep Learning Model by Mohamad Reda A. Refaai, Dhruva R. Rinku, I. Thamarai, null S. Meera, Naresh Kumar Sripada, Simon Yishak

“…Meanwhile, growing movement of the deep learning techniques in computer vision offers an interesting perspective into the project’s objective. …”
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Enhancing deep learning methods for brain metastasis detection through cross-technique annotations on SPACE MRI by Tassilo Wald, Benjamin Hamm, Julius C. Holzschuh, Rami El Shafie, Andreas Kudak, Balint Kovacs, Irada Pflüger, Bastian von Nettelbladt, Constantin Ulrich, Michael Anton Baumgartner, Philipp Vollmuth, Jürgen Debus, Klaus H. Maier-Hein, Thomas Welzel

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Automatic detection of concrete cracks from images using Adam-SqueezeNet deep learning model by Lin Wang

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Advanced Machine Learning and Deep Learning Approaches for Estimating the Remaining Life of EV Batteries—A Review by Daniel H. de la Iglesia, Carlos Chinchilla Corbacho, Jorge Zakour Dib, Vidal Alonso-Secades, Alfonso J. López Rivero

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Detection and Prediction of HMS from Drinking Water by Analysing the Adsorbents from Residuals Using Deep Learning by Sanjay Kumar Suman, N. Arivazhagan, L. Bhagyalakshmi, Himanshu Shekhar, P. Shanmuga Priya, T. Helan Vidhya, Sushma S. Jagtap, Gouse Baig Mohammad, Shubhangi Digamber Chikte, S. Chandragandhi, Alazar Yeshitla

“…With modern technologies, this could be possible by enabling the carbon adsorbents to adsorb the pollutions via deep learning strategies. In this paper, we develop a model on detection and prediction of presence of HMs from drinking water by analysing the adsorbents from residuals using deep learning. …”
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Longitudinal Screening for Diabetic Retinopathy in a Nationwide Screening Program: Comparing Deep Learning and Human Graders by Jirawut Limwattanayingyong, Variya Nganthavee, Kasem Seresirikachorn, Tassapol Singalavanija, Ngamphol Soonthornworasiri, Varis Ruamviboonsuk, Chetan Rao, Rajiv Raman, Andrzej Grzybowski, Mike Schaekermann, Lily H. Peng, Dale R. Webster, Christopher Semturs, Jonathan Krause, Rory Sayres, Fred Hersch, Richa Tiwari, Yun Liu, Paisan Ruamviboonsuk

“…To evaluate diabetic retinopathy (DR) screening via deep learning (DL) and trained human graders (HG) in a longitudinal cohort, as case spectrum shifts based on treatment referral and new-onset DR. …”
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