Classification of Metro Facilities with Deep Neural Networks by Deqiang He, Zhou Jiang, Jiyong Chen, Jianren Liu, Jian Miao, Abid Shah

“…Intelligent monitoring systems based on computer vision not only complete safeguarding tasks efficiently but also save a great deal of human labor. Deep convolutional neural networks (DCNNs) are the most state-of-the-art technology in computer vision tasks. …”
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A Multi-Branch Attention Fusion Method for Semantic Segmentation of Remote Sensing Images by Kaibo Li, Zhenping Qiang, Hong Lin, Xiaorui Wang

“…However, due to the inherent complexity of remote sensing images, most attention mechanisms designed for natural images underperform when applied to remote sensing data. …”
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Comparative Analysis of Data Visualization and Deep Learning Models in Air Quality Forecasting by Bihter Daş, Damla Mengus

“…However, RNN emerged as the most accurate model, achieving an R² of 0.97 for both PM10 and SO2 forecasts. …”
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Using Deep Learning to Predict Sentiments: Case Study in Tourism by C. A. Martín, J. M. Torres, R. M. Aguilar, S. Diaz

“…An analysis of our findings shows that the most accurate and robust estimators are those based on LSTM recurrent neural networks.…”
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Scenario modeling of the drug prescription process for children: application of machine learning methods by А. А. Kondrashov, М. М. Kurashov, Е. Е. Loskutova

“…Objective: determining the most appropriate machine learning method to solve the problem of drug prescribtion for children, evaluating its performance and potential for implementation into scenario modeling systems of the pharmaceutical care structure.Material and methods. …”
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Keywords, morpheme parsing and syntactic trees: features for text complexity assessment by Dmitry A. Morozov, Ivan A. Smal, Timur A. Garipov, Anna V. Glazkova

“…The use of an extensive set of syntactic features allowed, in most cases, to improve the quality of work of neural network models in comparison with the previously described set.…”
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Literature Review: A Comparative Study of Waste Classification using Deep Learning Algorithms by Ariza Ikhlas, Billy Hendrik

“…The main objectives are to identify the most appropriate algorithms for waste type classification, determine the most suitable model architectures, and examine the correlation between dataset size, number of classes, and classification accuracy. …”
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Computer-Aided Diagnosis Techniques for Brain Tumor Segmentation and Classification Using MRI by Gadicha A. B., Kale Prachi V., Dalvi G. D., Mohod M. M., Pakhale S. C., Khan S. M.

“…Brain tumors are among the most life-threatening neurological conditions, characterized by the abnormal and uncontrolled growth of brain cells. …”
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A New and Tested Ionospheric TEC Prediction Method Based on SegED-ConvLSTM by Yuanhang Liu, Yingkui Gong, Hao Zhang, Ziyue Hu, Guang Yang, Hong Yuan

“…We compared our model with traditional image-based models such as convolutional neural networks (CNNs), convolutional long short-term memory networks (ConvLSTMs), a self-attention mechanism-integrated ConvLSTM (SAM-ConvLSTM) model, and one-day predicted ionospheric products (C1PG) provided by the Center for Orbit Determination in Europe (CODE). …”
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S²RCFormer: Spatial-Spectral Residual Cross-Attention Transformer for Multimodal Remote Sensing Data Classification by Yifei Xu, Lingming Cao, Jialu Li, Wenlong Li, Yaochen Li, Yingjie Zong, Aichen Wang, Yuan Rao, Shuiguang Deng

“…It mainly consists of a patchwise convolutional module (PTConv), pixelwise convolutional module (PXConv), residual cross-attention tokenization module (RCTM), and transformer feature fusion module (TFFM). …”
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Deep FS: A Deep Learning Approach for Surface Solar Radiation by Fatih Kihtir, Kasim Oztoprak

“…The method extracted and provided the selected features that are most appropriate for predicting the surface exposure. …”
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Glaucoma identification with retinal fundus images using deep learning: Systematic review by Dulani Meedeniya, Thisara Shyamalee, Gilbert Lim, Pratheepan Yogarajah

“…The findings of this study, including comparisons of existing methods and key insights, will assist researchers and developers in identifying the most suitable techniques for glaucoma detection.…”
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Modelling on Car-Sharing Serial Prediction Based on Machine Learning and Deep Learning by Nihad Brahimi, Huaping Zhang, Lin Dai, Jianzi Zhang

“…Meanwhile, the model using all the different feature categories results in the most precise prediction than any of the models using one feature category at a time…”
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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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SKIN RASH CLASSIFICATION SYSTEM USING MODIFIED DENSENET201 THROUGH RANDOM SEARCH FOR HYPERPARAMETER TUNING by Fayza Nayla Riyana Putri, R.Rizal Isnanto, Aris Sugiharto

“…The proposed modified model, optimized using Random Search, improved overall accuracy to 80%, with enhanced precision, recall, and F1-score across most classes. The model’s performance was particularly notable in the HFMD and normal skin classes, although further improvements are needed for challenging classes like Cowpox and Measles. …”
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Digital mapping of soil electrical conductivity for paddy field by The Anh Anh, Luu Trong Hieu, Chi Ngon Nguyen

“…Using 228 data samples, the study found that the Gaussian model within Kriging was the most effective for interpolating soil EC, achieving the highest R-squared values (0.79 with test data and 0.96 with full data) and the lowest RMSE values (0.049 with test data and 0.022 with full data). …”
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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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A Multimodel Fusion Method for Cardiovascular Disease Detection Using ECG by Guanghui Song, Jiajian Zhang, Dandan Mao, Genlang Chen, Chaoyi Pang

“…Owing to a lack of well-labeled ECG record databases, most of this work has focused on heartbeat arrhythmia detection based on ECG signal quality. …”
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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 applied sciences, the most common ML-based approach for developing stochastic simulation schemes is the use of generative adversarial networks (GANs), which consist of two sub-models, that is, a generator and a discriminator. …”
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