Deep Learning for Traffic Scene Understanding: A Review by Parya Dolatyabi, Jacob Regan, Mahdi Khodayar

“…The paper synthesizes insights from a broad range of studies, tracing the evolution from traditional image processing methods to sophisticated DL techniques, such as Convolutional Neural Networks (CNNs) and Generative Adversarial Networks (GANs). …”
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ML‐UrineQuant: A machine learning program for identifying and quantifying mouse urine on absorbent paper by Warren G. Hill, Bryce MacIver, Gary A. Churchill, Mariana G. DeOliveira, Mark L. Zeidel, Marcelo Cicconet

“…We have developed a machine learning algorithm based on Region‐based Convolutional Neural Networks (Mask‐RCNN) that was trained in object recognition to detect and quantitate urine spots across a broad range of sizes—ML‐UrineQuant. …”
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Beyond encryption: How deep learning can break microcontroller security through power analysis by Ismail Negabi, Smail Ait El Asri, Samir El Adib, Naoufal Raissouni

“…This paper investigates the application of convolutional neural networks (CNNs) for power analysis attacks (PAAs) on cryptographic systems, specifically targeting resource-constrained devices like microcontrollers. …”
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Prediction of post-treatment recurrence in early-stage breast cancer using deep-learning with mid-infrared chemical histopathological imaging by Abigail Keogan, Thi Nguyet Que Nguyen, Pascaline Bouzy, Nicholas Stone, Karin Jirstrom, Arman Rahman, William M. Gallagher, Aidan D. Meade

“…A number of deep learning models were employed, with champion models employing two-dimensional and two-dimensional-separable convolutional networks found to have predictive performance of a ROC AUC of approximately 0.64, which compares well to other clinically used prognostic assays in this space. …”
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The integration of artificial intelligence in assisted reproduction: a comprehensive review by Pragati Kakkar, Shruti Gupta, Kasmiria Ioanna Paschopoulou, Ilias Paschopoulos, Ioannis Paschopoulos, Vassiliki Siafaka, Orestis Tsonis

“…AI's capacity for precise image-based analysis, leveraging convolutional neural networks, stands out as a promising avenue. …”
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Chinese Mathematical Knowledge Entity Recognition Based on Linguistically Motivated Bidirectional Encoder Representation from Transformers by Wei Song, He Zheng, Shuaiqi Ma, Mingze Zhang, Wei Guo, Keqing Ning

“…In order to improve the accuracy of mathematical knowledge entity recognition and provide effective support for subsequent functionalities, this paper adopts the latest pre-trained language model, LERT, combined with a Bidirectional Gated Recurrent Unit (BiGRU), Iterated Dilated Convolutional Neural Networks (IDCNNs), and Conditional Random Fields (CRFs), to construct the LERT-BiGRU-IDCNN-CRF model. …”
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3D reconstruction for unconstrained image collections using Gaussian Splatting with foundation model by Shuowen Huang, Qingwu Hu, Pengcheng Zhao, Mingyao Ai, Xujie Zhang, Wenwu Ou, Linze Li

“…Achieving high-quality rendering with Gaussian Splatting for unconstrained image collections is critical for advancing 3D reconstruction. Currently, the methods using a single multi-layer perceptron and convolutional neural network to predict distractors in input images often suffer from errors and is insufficient in rendering details for appearance modeling. …”
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DBSF: dual-branch boundary-supplemented framework for photovoltaic power station extraction by Bo Yu, Cheng Chen, Fang Chen, Huichen Zhao, Lei Wang

“…DBSF features a dual-branch architecture that integrates convolutional neural networks (CNNs) for local feature extraction and transformers for global feature learning. …”
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Intelligent Fault Diagnosis of Inter-Turn Short Circuit Faults in PMSMs for Agricultural Machinery Based on Data Fusion and Bayesian Optimization by Mingsheng Wang, Wuxuan Lai, Hong Zhang, Yang Liu, Qiang Song

“…In this article, a multi-source data-fusion algorithm based on convolutional neural networks (CNNs) has been proposed for the early fault diagnosis of ITSCs. …”
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Microbiology in the era of artificial intelligence: transforming medical and pharmaceutical microbiology by Virna-Maria Tsitou, Dimitrios Rallis, Mariana Tsekova, Nikolay Yanev

“…Our discussion spotlights the implementation of convolutional neural networks for enhanced pathogen identification, the advancements in point-of-care diagnostics, and the emergence of new antimicrobials to tackle resistant strains. …”
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Artificial intelligence in the diagnosis of endocrine disorders: A focus on diabetes and thyroid diseases by Kimi Milić Marko, Sinanović Šćepan, Prodović Tanja, Ilanković Tanja

“…Methodologically, the study relies on a systematic review of the existing literature and case studies analyzing the use of algorithms such as convolutional neural networks (CNN) and support vector machines (SVM). …”
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Automated identification of autism spectrum disorder from facial images using explainable deep learning models by El-Sayed Atlam, Khulood O. Aljuhani, Ibrahim Gad, Elsaid Md. Abdelrahim, Ahmed E. Mansour Atwa, Ali Ahmed

“…This study introduces an effective deep learning framework for automated ASD detection that leverages pre-trained convolutional neural networks (CNNs), including VGG16, VGG19, InceptionV3, VGGFace, and MobileNet. …”
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Automatic Identification Model for Landslide Disaster Using Remote Sensing Images Based on Improved Multiresunet by Zhenyu Zhao, Shucheng Tan, Qinghua Zhang, Hui Chen

“…Despite significant progress in semantic segmentation achieved by convolutional neural networks (CNNs), the local sensory field of CNNs poses difficulties in differentiating between landslides and bare surfaces. …”
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Advances in Liver Tumour Diagnosis and Treatment: Etiology, Classification, and the Emerging Role of Machine Learning by Yana Gaur, Abhay Chaudhary, Ajith Abraham

“…The review also explores architectures such as Convolutional Neural Networks, U-Net and its variants, and Transformer-based models, which have been pivotal in segmentation, classification and prognosis. …”
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Unsupervised Deep Clustering on Spatiotemporal Objects Extracted from 4D Point Clouds for Automatic Identification of Topographic Processes in Natural Environments by J. Wang, K. Anders

“…By leveraging the representation learning capability of autoencoders, especially using convolutional neural networks (CNNs) as feature extractors, our approach implements the dimensionality reduction of the original inputs to uniform low-dimensional vectors in latent space. …”
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Environmental Risk Mitigation via Deep Learning Modeling of Compressive Strength in Green Concrete Incorporating Incinerator Ash by Amin Amraee, Seyed Azim Hosseini, Farshid Farokhizadeh, Mohammad Hassan Haeri

“…A database for deep learning modeling was created using Convolutional Neural Networks (CNNs) and the Multi-Verse Optimizer (MVO) algorithm. …”
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AI post-intervention operational and functional outcomes prediction in ischemic stroke patients using MRIs by Emily Wittrup, John Reavey-Cantwell, Aditya S. Pandey, Dennis J. Rivet II, Kayvan Najarian

“…Results Results on a comprehensive dataset of AIS patients demonstrate that our autoencoder-based method has comparable performance to traditional convolutional neural networks image fusion methods and clinical data alone (LOS $$ > $$ 8 days: AUC 0.817, AUPRC 0.573, F1-Score 0.552; 90-day mRS $$>$$ 2: AUC 0.754, AUPRC 0.685, F1-Score 0.626). …”
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Reliable Breast Cancer Diagnosis with Deep Learning: DCGAN-Driven Mammogram Synthesis and Validity Assessment by Dilawar Shah, Mohammad Asmat Ullah Khan, Mohammad Abrar

“…This paper proposes a novel approach that utilizes deep convolutional generative adversarial networks (DCGANs) to effectively tackle the issue of limited data availability. …”
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AI-Based Point Cloud Upsampling for Autonomous Driving Systems by Nicolás Salomón, Claudio A. Delrieux, Damián A. Morero, Leandro E. Borgnino

“…This work is based on 3 main axes: firstly, the analysis of available LiDAR data and its representation; secondly, the development and implementation of an interpolation technique based on 1D convolutional layers integrated with fully connected layers, in order to analyse data coming from a sliding window; and finally, the comparative evaluation of the results between different state-of-the-art interpolation techniques, using object detection networks in point clouds. …”
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Deep learning-based electrical impedance spectroscopy analysis for malignant and potentially malignant oral disorder detection by Zhicheng Lin, Zi-Qiang Lang, Lingzhong Guo, Dawn C Walker, Malwina Matella, Mengxiao Wang, Craig Murdoch

“…Here we developed a novel deep learning convolutional neural network (CNN)-based method to automatically differentiate normal, OPMD and malignant oral tissues using EIS measurements. …”
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