Faster Dynamic Graph CNN: Faster Deep Learning on 3D Point Cloud Data by Jinseok Hong, Keeyoung Kim, Hongchul Lee

“…Geometric data are commonly expressed using point clouds, with most 3D data collection devices outputting data in this form. …”
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Multi-dimensional visual information processing under complex light environments using time-evolved polarization-sensitive synaptic electronics by Yaqian Yang, Wenhao Ran, Ying Li, Yancheng Chen, Di Chen, Guozhen Shen

“…Abstract Biological vision system-inspired optoelectronic synapses integrate sensing, memory, and processing for external information perception. However, most efforts focus on spatial expansion while overlooking critical dimensions like polarization and temporal evolution, which are critical for information extraction in complex environments. …”
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CNN Performance Improvement for Classifying Stunted Facial Images Using Early Stopping Approach by Yunidar Yunidar, Y Yusni, N Nasaruddin, Fitri Arnia

“…The main aim of this research is to identify the CNN model that is most effective in differentiating facial images of stunted children from normal children. …”
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Protein homodimers structure prediction based on deep neural network by A. Y. Hadarovich, A. A. Kalinouski, A. V. Tuzikov

“…Homodimers (complexes which consist of two identical proteins) are the most common type of protein complexes in nature but there is still no universal algorithm to predict their 3D structures. …”
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Predicting the Temperature of a Permanent Magnet Synchronous Motor: A Comparative Study of Artificial Neural Network Algorithms by Nabil El Bazi, Nasr Guennouni, Mohcin Mekhfioui, Adil Goudzi, Ahmed Chebak, Mustapha Mabrouki

“…The intent is to identify the most favorable model that balances high accuracy with low computational cost.…”
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CMPF-UNet: a ConvNeXt multi-scale pyramid fusion U-shaped network for multi-category segmentation of remote sensing images by Ning Li, Xiaopeng Yu, Miao Yu

“…Most U-shaped convolutional neural network (CNN) methods have the problems of insufficient feature extraction and fail to fully utilize global/multi-scale context information, which makes it difficult to distinguish similar objects and shadow occluded objects in remote sensing images. …”
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An Investigation on Prediction of Infrastructure Asset Defect with CNN and ViT Algorithms by Nam Lethanh, Tu Anh Trinh, Mir Tahmid Hossain

“…Convolutional Neural Networks (CNNs) have been demonstrated to be one of the most powerful methods for image recognition, being applied in many fields, including civil and structural health monitoring in infrastructure asset management. …”
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Analyzing spatiotemporal variation in suspended particulate matter in lakes using remote sensing by WEI Junyan, ZHAO Yiming, HAO Yanling, JIA Xiaoxue, MA Xinyan

“…The most accurate model was selected to estimate SPM concentrations across the lake.…”
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Digitization of Medical Device Displays Using Deep Learning Models: A Comparative Study by Pedro Ferreira, Pedro Lobo, Filipa Reis, João L. Vilaça, Pedro Morais

“…Since most of these devices display clinical information on a screen, this research explores how a standard smartphone camera, combined with artificial intelligence, can be used to automatically extract the displayed data in a simple and non-intrusive way. …”
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Maize and soybean yield prediction using machine learning methods: a systematic literature review by Ramandeep Kumar Sharma, Jasleen Kaur, Gary Feng, Yanbo Huang, Chandan Kumar, Yi Wang, Sandhir Sharma, Johnie Jenkins, Jagmandeep Dhillon

“…The Random Forest (RF), Artificial Neural Networks (ANNs), Support Vector Machines (SVMs), and Extreme Gradient Boosting (XG-Boost) were identified as the mostly used ML algorithms. Most often applied deep learning techniques include long short-term memory (LSTM) and convolutional neural networks (CNNs). …”
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Machine Learning in the Management of Patients Undergoing Catheter Ablation for Atrial Fibrillation: Scoping Review by Aijing Luo, Wei Chen, Hongtao Zhu, Wenzhao Xie, Xi Chen, Zhenjiang Liu, Zirui Xin

“…In terms of model type, deep learning, represented by convolutional neural networks, was most frequently applied (14/23, 61%). …”
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Deep learning with data transformation improves cancer risk prediction in oral precancerous conditions by John Adeoye, Yuxiong Su

“…Background: Oral cancer is the most common head and neck malignancy and may develop from oral leukoplakia (OL) and oral lichenoid disease (OLD). …”
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An Automated Image-Based Dietary Assessment System for Mediterranean Foods by Fotios S. Konstantakopoulos, Eleni I. Georga, Dimitrios I. Fotiadis

“…<italic>Results:</italic> The classification accuracy where true class matches with the most probable class predicted by the model (Top-1 accuracy) is 83.8&#x0025;, while the accuracy where true class matches with any one of the 5 most probable classes predicted by the model (Top-5 accuracy) is 97.6&#x0025;, for the food classification subsystem. …”
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Acoustic cues for person identification using cough sounds by Van-Thuan Tran, Ting-Hao You, Wei-Ho Tsai

“…While recent works have demonstrated the feasibility of cough-based PID (CPID), most report accuracies around 80–90 % and could face limitations in terms of model efficiency, generalization, or robustness. …”
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AI-Driven Ensemble Classifier for Jamming Attack Detection in VANETs to Enhance Security in Smart Cities by Walid El-Shafai, Ahmad Taher Azar, Saim Ahmed

“…Subsequently, we proposed a voting-based ensemble AI classifier combining the most accurate ML and DL classifiers, namely Random Forest (RF), Extra Tree (ET), and fine-tuned Convolutional Neural Network (CNN). …”
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Opinion Mining and Analysis Using Hybrid Deep Neural Networks by Adel Hidri, Suleiman Ali Alsaif, Muteeb Alahmari, Eman AlShehri, Minyar Sassi Hidri

“…Text-based opinions are the most structured, hence playing an important role in sentiment analysis. …”
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Predictive modeling of air quality in the Tehran megacity via deep learning techniques by Abdullah Kaviani Rad, Mohammad Javad Nematollahi, Abbas Pak, Mohammadreza Mahmoudi

“…In terms of operational speed, the FCNN model exhibited the most efficiency, with a minimum and maximum runtime of 13 and 28 s, respectively. …”
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A systematic review of deep learning methods for community detection in social networks by Mohamed El-Moussaoui, Mohamed Hanine, Ali Kartit, Monica Garcia Villar, Monica Garcia Villar, Monica Garcia Villar, Helena Garay, Helena Garay, Helena Garay, Isabel de la Torre Díez

“…This review investigates the employed methodologies, evaluates their effectiveness, and discusses the challenges identified in these works.ResultsOur review shows that models like graph neural networks (GNNs), autoencoders, and convolutional neural networks (CNNs) are some of the most commonly used approaches for community detection. …”
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Deep-learning based morphological segmentation of canine diffuse large B-cell lymphoma by Kenneth Ancheta, Androniki Psifidi, Andrew D. Yale, Sophie Le Calvez, Jonathan Williams

“…Diffuse large B-cell lymphoma is the most common type of non-Hodgkin lymphoma (NHL) in humans, accounting for about 30–40% of NHL cases worldwide. …”
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Deep learning-based dual optimization framework for accurate thyroid disease diagnosis using CNN architectures by Zeeshan Ali Haider, Nasser A Alsadhan, Fida Muhammad Khan, Waleed Al-Azzawi, Inam Ullah Khan, Inam Ullah

“…Thyroid diseases, including hypothyroidism, hyperthyroidism, thyroid nodules, thyroiditis, and thyroid cancer, are among the most prevalent endocrine disorders, posing significant health risks, which need to be diagnosed and treated promptly. …”
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