Towards a multi-modal Deep Learning Architecture for User Modeling by Ange Tato, Roger Nkambou

“…The architecture combines a Long Short-Term Memory, a Convolutional Neural Network, and multiple Deep Neu-ral Networks to handle the multi-modality of data. …”
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Deep learning of structural morphology imaged by scanning X-ray diffraction microscopy by Aileen Luo, Tao Zhou, Martin V. Holt, Andrej Singer, Mathew J. Cherukara

“…In this study, we implement NanobeamNN, a convolutional neural network specifically tailored to the analysis of scanning probe X-ray microscopy data. …”
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Evaluation of data driven low-rank matrix factorization for accelerated solutions of the Vlasov equation. by Bhavana Jonnalagadda, Stephen Becker

“…We propose a data-driven factorization method using artificial neural networks, specifically with convolutional layer architecture, that trains on existing simulation data. …”
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Machine Learning Monitoring Model for Fertilization and Irrigation to Support Sustainable Cassava Production: Systematic Literature Review by Ahmad Chusyairi, Yeni Herdiyeni, Heru Sukoco, Edi Santosa

“…The UAV is processed by building an orthomosaic, labeling, extracting features, and Convolutional Neural Network (CNN) modeling. The outcomes are then analyzed to determine the requirements for air pressure and fertilization. …”
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Leveraging sentiment analysis of food delivery services reviews using deep learning and word embedding by Dheya Mustafa, Safaa M. Khabour, Mousa Al-kfairy, Ahmed Shatnawi

“…It does this by utilizing word embedding models, deep learning techniques, and natural language processing to extract subjective opinions, determine polarity, and recognize customers’ feelings in the FDS domain. Convolutional neural network (CNN), bidirectional long short-term memory recurrent neural network (BiLSTM), and an LSTM-CNN hybrid model were among the deep learning approaches to classification that we evaluated. …”
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Long-term forecasting of shield tunnel position and attitude deviation using the 1DCNN-informer method by Jiajie Zhen, Ming Huang, Shuang Li, Kai Xu, Qianghu Zhao

“…This study introduces a novel deep learning model, termed 1DCNN-Informer, which integrates the one-dimensional convolutional neural network (1DCNN) and the Informer model. …”
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Software refactoring prediction evaluation method based on deep learning models by Yichi ZHANG, Yang ZHANG, Yanlei LI, Kun ZHENG, Wei LIU

“…Firstly, refactoring and non-refactoring labeled instances were collected from 303 Java projects using static analysis tools, and seven datasets comprising source code metrics were constructed for seven refactoring operations: extracting class, extracting subclass, extracting super class, extracting interface, moving class, renaming class, and moving and renaming class. Secondly, convolutional neural network (CNN), long short-term memory (LSTM) network, gated recurrent unit (GRU) model, multilayer perceptron(MLP), and autoencoder(AE) were trained and tested on the datasets. …”
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Rapid learning with phase-change memory-based in-memory computing through learning-to-learn by Thomas Ortner, Horst Petschenig, Athanasios Vasilopoulos, Roland Renner, Špela Brglez, Thomas Limbacher, Enrique Piñero, Alejandro Linares-Barranco, Angeliki Pantazi, Robert Legenstein

“…We demonstrate the versatility of our approach in two scenarios: a convolutional neural network performing image classification and a biologically-inspired spiking neural network generating motor commands for a real robotic arm. …”
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An Efficient and Automated Classification System for Rocks Based on Visually Explainable Deep Learning by Shan Lin, Quanke Hu, Hongwei Guo, Miao Dong, Kaiyang Zhao, Hong Zheng, Zhijun Liu

“…Traditional methods rely heavily on manual expertise, which makes them susceptible to human errors due to their reliance on individual skills and experience. Although current machine learning models have mitigated some drawbacks by classifying rock images, their generalization and predictive performance are limited by suboptimal network structures, image data quality, and quantity. …”
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A multimodal model in the prediction of the delivery mode using data from a digital twin-empowered labor monitoring system by Jieyun Bai, Xue Kang, Weishan Wang, Ziduo Yang, Weiguang Ou, Yuxin Huang, Yaosheng Lu

“…Objective This study aims to address the limitations of current clinical methods in predicting delivery mode by constructing a multimodal neural network-based model. …”
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A Self-Supervised Specific Emitter Identification Method Based on Contrastive Asymmetric Masked Learning by Dong Wang, Yonghui Huang, Tianshu Cui, Yan Zhu

“…The proposed method constructs an asymmetric auto-encoder architecture, comprising an encoder network based on channel squeeze-and-excitation residual blocks to capture radio frequency fingerprint (RFF) features embedded in signals, while employing a lightweight single-layer convolutional decoder for masked signal reconstruction. …”
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Deep Learning with Dual-Channel Feature Fusion for Epileptic EEG Signal Classification by Bingbing Yu, Mingliang Zuo, Li Sui

“…Channel 2 employs a dual-branch convolutional neural network (CNN) to extract deeper and distinct features. …”
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Rep-MobileViT: Texture and Color Classification of Solid Wood Floors Based on a Re-Parameterized CNN-Transformer Hybrid Model by Anning Duanmu, Sheng Xue, Zhenye Li, Yajun Zhang, Chao Ni

“…Specifically, the RepAIRB module is introduced, incorporating an asymmetric convolutional block (ACB) and a re-parameterized structure within the inverted residual block (IRB) module to enhance the network’s receptive field without increasing computational costs. …”
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SMANet: A Model Combining SincNet, Multi-Branch Spatial—Temporal CNN, and Attention Mechanism for Motor Imagery BCI by Danjie Wang, Qingguo Wei

“…We propose an end-to-end deep learning model, Sinc-multibranch-attention network (SMANet), which combines a SincNet, a multibranch spatial-temporal convolutional neural network (MBSTCNN), and an attention mechanism for MI-BCI classification. …”
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Data-Driven Proactive Early Warning of Grid Congestion Probability Based on Multiple Time Scales by Haobo Fu, Ruizhuo Wang, Bingxu Zhai, Yuanzhuo Li, Pengyuan Li, Rui Zhang, Haoyuan He, Siyang Liao

“…Then, a multi-time-scale prediction model based on a convolutional neural network and a bi-directional long and short-term memory network is constructed to realize the active early warning of the power system in the face of grid congestion events. …”
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Identification of Earthquake Precursors Origin and AI Framework for Automatic Classification for One of These Precursors by Ghada Ali, Lotfy Samy, Omar M. Saad, Ali G. Hafez, El-Sayed Hasaneen, Kamal AbdElrahman, Ibrahim Salah, Mohammed S. Fnais, Hamed Nofel, Ahmed M. Mohamed

“…By automating the classification of these patterns, the true P-wave arrival can be determined in real-time processing, reducing the error in P-wave arrival timing. The current study also introduces this automatic classification by developing various machine learning (ML) and Convolutional Neural Network (CNN) models to highlight the features characterizing each pattern. …”
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Development and validation of a deep learning-enhanced prediction model for the likelihood of pulmonary embolism by Yu Tian, Yu Tian, Jingjie Liu, Shan Wu, Yucong Zheng, Rongye Han, Qianhui Bao, Lei Li, Lei Li, Tao Yang

“…Our prediction model uses a convolutional neural network (CNN), enhanced with three custom-designed modules for better performance. …”
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Software Defect Prediction Based On Deep Learning Algorithms : A Systematic Literature Review by Akhlas Hasan, Shayma Mohi-Aldeen

“…The top three DL algorithms used in building SDP models and used in predicting software bugs were convolutional neural network (CNN), long-short-term memory (LSTM), and bidirectional LSTM. …”
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Combining Deep Learning and Street View Images for Urban Building Color Research by Wenjing Li, Qian Ma, Zhiyong Lin

“…In the “deep learning” phase, a deep convolutional neural network (DCNN)-based color extraction model is designed to automatically learn building color information from street view images; in the “quantitative analysis” phase, building color is quantitatively analyzed at the overall and local levels, and a color clustering model is designed to quantitatively display the color relationship to comprehensively understand the current status of urban building color. …”
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Integrating Color and Contour Analysis with Deep Learning for Robust Fire and Smoke Detection by Abror Shavkatovich Buriboev, Akmal Abduvaitov, Heung Seok Jeon

“…This study suggests a unique concatenated convolutional neural network (CNN) model that combines deep learning with hybrid preprocessing methods, such as contour-based algorithms and color characteristics analysis, to provide reliable and accurate fire and smoke detection. …”
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