Clinical Application of Artificial Intelligence in Breast MRI by Jong-Min Kim, Su Min Ha

“…Breast MRI is the most sensitive imaging modality for detecting breast cancer. …”
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Digital mapping of soil electrical conductivity for paddy field by The Anh Anh, Luu Trong Hieu, Chi Ngon Nguyen

“…This study aims to address this gap by using the common interpolation method —K-Nearest Neighbors (KNN), Inverse Distance Weighting (IDW), Kriging interpolation, and Convolutional Neural Networks (CNN)—to map soil EC over an area of approximately 1.4 hectares. …”
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Arrhythmia Detection by Data Fusion of ECG Scalograms and Phasograms by Michele Scarpiniti

“…To this aim, several deep learning approaches have been recently proposed to automatically classify heartbeats in a small number of classes. Most of these approaches use convolutional neural networks (CNNs), exploiting some bi-dimensional representation of the ECG signal, such as spectrograms, scalograms, or similar. …”
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Early Heart Attack Detection Using Hybrid Deep Learning Techniques by Niga Amanj Hussain, Aree Ali Mohammed

“…The proposed model combines a Convolutional Neural Network (CNN) with self-attention, leveraging the self-attention mechanism to focus on the most critical aspects of the sequence. …”
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Just a Single-Layer CNN for Stochastic Modeling: A Discriminator-Free Approach by Evangelos Rozos

“…In this study, we propose a simpler stochastic scheme based on a single convolutional neural network (CNN) used as a generator, replacing the discriminator component of the GAN with a specifically designed cost function. …”
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DOA Estimation Based on CNNs Embedded With Mamba by Zhang Ziyan, Yi Shichao, Wang Chengyi

“…The convolutional neural networks (CNN) have been proved to be more efficient in Direction of Arrival (DOA) estimation of underwater acoustic array signals. …”
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Comparative analysis of data-driven models for spatially resolved thermometry using emission spectroscopy. by Ruiyuan Kang, Dimitrios C Kyritsis, Panos Liatsis

“…Two categories of data-driven methods are analyzed: (i) Feature engineering and classical machine learning algorithms, and (ii) end-to-end convolutional neural networks (CNN). In total, combinations of fifteen feature groups and fifteen classical machine learning models, and eleven CNN models are considered and their performances explored. …”
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PM2.5 prediction using population-based centrality weight by Hee Joon Choi, Won Kyung Lee, So Young Sohn

“…Abstract The particulate matter (PM)2.5 forecasting has been being advanced with the development of deep learning methods. However, most of them do not consider the active population exposed to air pollution. …”
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Generative Adversarial Network for Damage Identification in Civil Structures by Zahra Rastin, Gholamreza Ghodrati Amiri, Ehsan Darvishan

“…In the first stage, a deep convolutional GAN (DCGAN) is used to detect and quantify structural damages; the detected damages are then localized in the second stage using a conditional GAN (CGAN). …”
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Method of Non-Destructive Control of Single-Phase and Three-Phase Transformers's Condition on the Basis of Frequency Characteristics by I. L. Hramyka, V. N. Galushko

“…The obtained characteristics in the form of pictures are the initial data for the convolutional neural network, which determines the type of defect. …”
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A Comparative Analysis of Deep Learning Models for Prediction of Microsatellite Instability in Colorectal Cancer by Ziynet Pamuk, Hüseyin Erikçi

“…Colorectal cancer remains one of the most prevalent and fatal malignancies worldwide, underscoring the necessity for early and precise diagnostic approaches to enhance patient prognoses. …”
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Application of Transformer Models for Classification of Chest X-rays by Enoel Arrokho Ernandes

“…Chest X-raying is the most well-known and widespread clinical method of diagnosing pneumonia. …”
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Artificial intelligence in degenerative cervical disease: A systematic review of MRI-based diagnostic models by Qian Du, Xinxin Shao, Minbo Zhang, Guangru Cao

“…Accuracy ranged from 81.58% to 98%, sensitivities from 84% to 98%, specificities from 90% to 100%, and AUC values reached up to 0.97. Convolutional neural networks (CNN) were the most frequently used models (four studies), followed by support vector machines (three studies). …”
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TSDCA-BA: An Ultra-Lightweight Speech Enhancement Model for Real-Time Hearing Aids with Multi-Scale STFT Fusion by Zujie Fan, Zikun Guo, Yanxing Lai, Jaesoo Kim

“…To address this challenge, we propose a lightweight hybrid module, Temporal Statistics Enhancement, Squeeze-and-Excitation-based Dual Convolutional Attention, and Band-wise Attention (TSE, SDCA, BA) Module. …”
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Analytical Methods and Determinants of Frequency and Severity of Road Accidents: A 20-Year Systematic Literature Review by Carlos M. Ferreira-Vanegas, Jorge I. Vélez, Guisselle A. García-Llinás

“…We identified Accident Analysis and Prevention as the most important journal, Fred Mannering as the main author, and The Statistical Analysis of Crash-Frequency Data: A Review and Assessment of Methodological Alternatives as the most cited publication. …”
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Fingerprint Classification Based on Multilayer Extreme Learning Machines by Axel Quinteros, David Zabala-Blanco

“…Fingerprint recognition is one of the most effective and widely adopted methods for person identification. …”
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Modeling Equatorial to Mid‐Latitudinal Global Night Time Ionospheric Plasma Irregularities Using Machine Learning by Ephrem Beshir Seba, Giovanni Lapenta

“…We utilize Random Forest (RF) and a one‐dimensional Convolutional Neural Network (1D‐CNN) model, incorporating data from the Swarm A, B, and C satellites, space weather data from the OMNIWeb data center, as well as zonal and meridional wind model data. …”
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A Dynamic Spatio-Temporal Deep Learning Model for Lane-Level Traffic Prediction by Bao Li, Quan Yang, Jianjiang Chen, Dongjin Yu, Dongjing Wang, Feng Wan

“…Specifically, we take advantage of the graph convolutional network (GCN) with a data-driven adjacent matrix for spatial feature modeling and treat different lanes of the same road segment as different nodes. …”
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An In-depth Investigation of OBIA Classification with High-Resolution Imagery: Unravelling the Explanations Behind Deep Learning and Machine Learning by E. O. Yilmaz, T. Kavzoglu

“…The SHAP analysis indicated that the HSI transform was the most influential factor in the XGBoost algorithm’s decision-making process whereas the average DN values of the green band were the most effective feature for the CNN model. …”
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Comparative Analysis of Data Visualization and Deep Learning Models in Air Quality Forecasting by Bihter Daş, Damla Mengus

“…This study utilizes air pollution data from the Continuous Monitoring Center of the Ministry of Environment, Urbanization, and Climate Change in Turkey to predict various pollutants using three advanced deep learning approaches: LSTM (Long Short-Term Memory), CNN (Convolutional Neural Network), and RNN (Recurrent Neural Network). …”
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