DeepFusion: Fusing User-Generated Content and Item Raw Content towards Personalized Product Recommendation by Mingxin Gan, Hang Zhang

“…In this framework, we utilize multiple types of deep neural networks that are best suited for each type of heterogeneous inputs and introduce an extra layer to obtain the joint representations for users and items. …”
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Reduced Order Probabilistic Emulation for Physics‐Based Thermosphere Models by Richard J. Licata, Piyush M. Mehta

“…Our method leverages principal component analysis to reduce the dimensionality of TIE‐GCM and recurrent neural networks to model the dynamic behavior of the thermosphere much quicker than the numerical model. …”
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On the assessment and reliability of political and ideological education in colleges using deep learning methods by Yongsheng Ma, Xianhui Sun, Aiqun Ma

“…Sophisticated deep learning techniques including artificial neural networks (ANN), convolutional neural networks (CNN), and support vector machines (SVM) were utilized to enhance the reliability of these evaluations. …”
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The classification of absence seizures using power-to-power cross-frequency coupling analysis with a deep learning network by A.V. Medvedev, B. Lehmann

“…New artificial intelligence methods such as deep learning neural networks can provide powerful tools for automated analysis of EEG. …”
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A Large-Scale Spatio-Temporal Multimodal Fusion Framework for Traffic Prediction by Bodong Zhou, Jiahui Liu, Songyi Cui, Yaping Zhao

“…Specifically, we utilize Convolutional Neural Networks (CNNs) for spatial information processing and a combination of Recurrent Neural Networks (RNNs) for final spatio-temporal traffic prediction. …”
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Lightweight resilience: Advancing visual-inertial odometry with deep convolutional networks and an intelligent learnable Kalman filter for defense against laser remote attacks by A. Ebrahimi, M.R. Mosavi, A. Ayatollahi

“…The proposed system integrates lightweight Convolutional Neural Networks (CNNs) to enhance robustness against such attacks. …”
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Fuel Cell Output Current Prediction with a Hybrid Intelligent System by José-Luis Casteleiro-Roca, Antonio Javier Barragán, Francisca Segura, José Luis Calvo-Rolle, José Manuel Andújar

“…This hybrid model uses artificial neural networks to predict the output current of the fuel cell in a very precise way. …”
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Diabetic retinopathy grading using curvelet CNN with optimized SSO activations and wavelet-based image enhancement by N. Mohana Suganthi, M. Arun

“…Method: In this paper, a novel Curvelet convolutional neural networks (CCNN) framework has been proposed to detect DR. …”
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Unifying topological structure and self-attention mechanism for node classification in directed networks by Yue Peng, Jiwen Xia, Dafeng Liu, Miao Liu, Long Xiao, Benyun Shi

“…In this paper, We propose TWC-GNN, a novel graph neural network design, as a solution to this problem. …”
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Traditional guidance mechanism based deep robust watermarking by Xuejing GUO, Yixiang FANG, Yi ZHAO, Tianzhu ZHANG, Wenchao ZENG, Junxiang WANG

“…With the development of network and multimedia technology, multimedia data has gradually become a key source of information for people, making digital media the primary battlefield for copyright protection and anti-counterfeit traceability.Digital watermarking techniques have been widely studied and recognized as important tools for copyright protection.However, the robustness of conventional digital watermarking methods is limited as sensitive digital media can easily be affected by noise and external interference during transmission.Then the existing powerful digital watermarking technology’s comprehensive resistance to all forms of attacks must be enhanced.Moreover, the conventional robust digital watermarking algorithm’s generalizability across a variety of image types is limited due to its embedding method.Deep learning has been widely used in the development of robust digital watermarking systems due to its self-learning abilities.However, current initialization techniques based on deep neural networks rely on random parameters and features, resulting in low-quality model generation, lengthy training times, and potential convergence issues.To address these challenges, a deep robust digital watermarking algorithm based on a traditional bootstrapping mechanism was proposed.It combined the benefits of both traditional digital watermarking techniques and deep neural networks, taking into account their learning abilities and robust characteristics.The algorithm used the classic robust digital watermarking algorithm to make watermarked photos, and the constructed feature guaranteed the resilience of traditional watermarked images.The final dense image was produced by fusing the conventionally watermarked image with the deep network using the U-Net structure.The testing results demonstrate that the technique can increase the stego image’s resistance to various attacks and provide superior visual quality compared to the conventional algorithm.…”
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A Step Towards Neuroplasticity: Capsule Networks with Self-Building Skip Connections by Nikolai A. K. Steur, Friedhelm Schwenker

“…<b>Background:</b> Integrating nonlinear behavior into the architecture of artificial neural networks is regarded as essential requirement to constitute their effectual learning capacity for solving complex tasks. …”
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The Neural Frontier of Future Medical Imaging: A Review of Deep Learning for Brain Tumor Detection by Tarek Berghout

“…The results revealed that research primarily focuses on Convolutional Neural Networks (CNNs) and their variants, with a strong emphasis on transfer learning using pre-trained models. …”
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Advanced Deep Learning Algorithms for Energy Optimization of Smart Cities by Izabela Rojek, Dariusz Mikołajewski, Krzysztof Galas, Adrianna Piszcz

“…Reinforcement learning models optimize power distribution by learning from historical patterns and adapting to changes in energy usage in real time. Convolutional neural networks (CNNs) and recurrent neural networks (RNNs) facilitate detailed analysis of spatial and temporal data to better predict energy usage. …”
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Neuronal Ensemble Decoding Using a Dynamical Maximum Entropy Model by Duho Sin, Jinsoo Kim, Jee Hyun Choi, Sung-Phil Kim

“…This decoder blends a dynamical system model of neural networks into the maximum entropy model to better suit for nonstationary circumstances. …”
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Enhanced Landslide Susceptibility Assessment in Western Sichuan Utilizing DCGAN-Generated Samples by Yuanxin Tong, Hongxia Luo, Zili Qin, Hua Xia, Xinyao Zhou

“…To address this issue, this study introduces a novel approach leveraging a deep convolutional generative adversarial network (DCGAN) for data augmentation aimed at enhancing the efficacy of various machine learning methods in LSA, including support vector machines (SVMs), convolutional neural networks (CNNs), and residual neural networks (ResNets). …”
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The classification of concentration of mixture of analytes using total principal component regression by Romas Baronas, Feliksas Ivanauskas, Robertas Paulauskas, Pranas Vaitkus

“…The results are compared with the results obtained using artificial neural networks. …”
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Prefrontal Abilities by D. F. Benson

“…While the prefrontal cortex does not appear to contain the neural networks that carry out cognitive activities, the management of these high level manipulations, so uniquely characteristic of the human, appears dependent upon the prefrontal cortex.…”
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Enhanced Heart Disease Classification Using Dual Attention Mechanisms and 3D-Echo Fusion Algorithm in Echocardiogram Videos by S Deepika, N. Jaisankar

“…In this paper, we present a novel hybrid deep learning framework that integrates convolutional neural networks (CNNs) with recurrent neural networks (RNNs) alongside a 3D-Echo Fusion approach and a Dual Attention Model for heart valve disease classification using echocardiogram videos. …”
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Robust Data-Driven Fault Detection: An Application to Aircraft Air Data Sensors by Yunmei Zhao, Hang Zhao, Jianliang Ai, Yiqun Dong

“…Convolutional neural networks (CNN) and long-short time memory (LSTM) blocks are used in the DNN scheme for accurate FD performances. …”
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Forecasting Models for Time and Cost Performance Predicting of Infrastructural Projects by Aldhamad Saja Hadi Raheem, Maya Rana, Alazawy Suha Falih Mahdi, Alzwainy Faiq M.

“…The efficacy of residential property investment projects will be assessed through the implementation of models that incorporate Artificial Neural Networks and Multiple Linear Regression. Historical information of thirteen boundaries for twenty finished Private Property Venture Tasks were separated from the records of the Directorate of Lodging, then four models were created by utilizing Multiple Linear Regression strategy and Artificial Neural Networks method. …”
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