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

“…Deep learning has emerged as an effective approach, offering robust capabilities to process large datasets, and uncover intricate relationships and patterns.MethodsIn this systematic literature review, we explore research conducted over the past decade, focusing on the use of deep learning techniques for community detection in social networks. …”
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Research on Creep Constitutive Model for High Temperature Reactor Based on Neural Network by ZHANG Fan, DAI Shoutong

“…This allowed the model to capture the intricate deformation patterns with high precision. For the secondary creep, the neural network was employed to perform reverse parameter calibration of the Norton Bailey’s power law. …”
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Impaired spatial dynamic functional network connectivity and neurophysiological correlates in functional hemiparesis by E. Premi, V. Cantoni, A. Benussi, A. Iraji, V.D. Calhoun, D. Corbo, R. Gasparotti, M. Tinazzi, B. Borroni, M. Magoni

“…The aim of this work was to assess static functional connectivity (large-scale) networks and dynamic brain states, which represent distinct dFNC patterns that reoccur in time and across subjects. …”
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Characterising physical activity patterns in community-dwelling older adults using digital phenotyping: a 2-week observational study protocol by Bruno Bonnechère, Dominique Hansen, Kim Daniels, Sharona Vonck, Jolien Robijns, Annemie Spooren

“…Machine learning will analyse data, employing unsupervised learning (eg, hierarchical clustering) to identify PA behaviour patterns and supervised learning (eg, recurrent neural networks) to predict behavioural influences. …”
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Dual-Path Beat Tracking: Combining Temporal Convolutional Networks and Transformers in Parallel by Nikhil Thapa, Joonwhoan Lee

“…Transformers excel at capturing global, long-range dependencies in sequences, which is valuable for tracking rhythmic patterns over time. Temporal Convolutional Networks (TCNs), with their dilated convolutions, are effective at processing local, temporal patterns with reduced complexity. …”
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Harnessing Large Language Models and Deep Neural Networks for Fake News Detection by Eleftheria Papageorgiou, Iraklis Varlamis, Christos Chronis

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Novel Approach in Vegetation Detection Using Multi-Scale Convolutional Neural Network by Fatema A. Albalooshi

“…Vegetation segmentation plays a crucial role in accurately monitoring and analyzing vegetation cover, growth patterns, and changes over time, which in turn contributes to environmental studies, land management, and assessing the impact of climate change. …”
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A Data-Driven Approach to Mitigate Evolving Volumetric Attacks in Programmable Networks by Muhammad Saqib, Halima Elbiaze, Roch H. Glitho

“…To address these limitations, we propose an automated, data-driven method for identifying novel malicious traffic patterns and updating ML model seamlessly in programmable networks. …”
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ClothGAN: generation of fashionable Dunhuang clothes using generative adversarial networks by Qiang Wu, Baixue Zhu, Binbin Yong, Yongqiang Wei, Xuetao Jiang, Rui Zhou, Qingguo Zhou

“…In this paper, we propose ClothGAN, which is an innovative framework for “designing” new patterns and styles of clothes based on generative adversarial network (GAN) and style transfer algorithm. …”
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GWO-FNN: Fuzzy Neural Network Optimized via Grey Wolf Optimization by Paulo Vitor de Campos Souza, Iman Sayyadzadeh

“…These evaluations demonstrate the model’s strong capability to extract and clearly express intricate patterns within the data. …”
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A hybrid ensemble framework with particle swarm optimization for network anomaly detection by Narinder Verma, Neerendra Kumar, Gourav Kumar, Kuljeet Singh

“…Abstract The increasing complexity of cyber threats necessitates the development of a robust and adaptive Intrusion Detection System (IDS) capable of safeguarding network infrastructures. Traditional IDS approaches often struggle to detect sophisticated attacks due to their reliance on predefined patterns. …”
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Multi-Head Attention-Based Framework with Residual Network for Human Action Recognition by Basheer Al-Tawil, Magnus Jung, Thorsten Hempel, Ayoub Al-Hamadi

“…However, recognizing patterns across different temporal scales remains challenging. …”
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Temporal Logical Attention Network for Log-Based Anomaly Detection in Distributed Systems by Yang Liu, Shaochen Ren, Xuran Wang, Mengjie Zhou

“…This paper introduces a TLAN (Temporal Logical Attention Network), a novel deep learning framework that integrates temporal sequence modeling with logical dependency analysis for robust anomaly detection in distributed system logs. …”
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A multimodal functional structure-based graph neural network for fatigue detection by Dongrui Gao, Zhihong Zhou, Zongyao Peng, Haokai Zhang, Shihong Liu, Manqing Wang, Hongli Chang

“…The graph neural network dynamically generates frequency-band-channel correlation matrices and adaptively assigns channel weights through learnable parameters. …”
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Assessment and Variation of Water Quality in Urban Distribution Networks: From Reservoir to Faucet by Eunhye Jeong, Kyung-Yup Hwang, Sumin Lee, Kwangjun Jung, Hyunjun Kim

“…This study focuses on evaluating the spatiotemporal variations in water quality across a potable water distribution network in D City, South Korea, spanning from a reservoir to a large consumer’s tap. …”
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Epidemic Dynamics and Intervention Measures in Campus Settings Based on Multilayer Temporal Networks by Xianyang Zhang, Ming Tang

“…This study simulates the spread of epidemics on university campuses using a multilayer temporal network model combined with the SEIR (Susceptible–Exposed–Infectious–Recovered) transmission model. …”
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A novel twin time series network for building energy consumption predicting. by Zhixin Sun, Han Cui, Xiangxiang Mei, Hailei Yuan

“…To overcome these issues, the study proposes Twin Time-Series Networks (T2SNET), which incorporates a time-embedding layer and a Temporal Convolutional Network (TCN) to extract patterns from Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN), along with an adaptive fusion gate to combine energy consumption and meteorological data. …”
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Bayesian network modeling of flood cascade and climate risks in the Pearl River Delta by Wen Zhang, Jianglong Cui, Weike Yao, Mariavittoria Guida, Frederick Kwame Yeboah, Xuanru Zhou, Yafei Li, Lixiao Zhang, Gengyuan Liu

“…To evaluate future flood hazards, downscaled climate projections from global climate models and a stochastic weather generator were employed to simulate extreme precipitation patterns. …”
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Wind Speed Forecasting in the Greek Seas Using Hybrid Artificial Neural Networks by Lateef Adesola Afolabi, Takvor Soukissian, Diego Vicinanza, Pasquale Contestabile

“…In this work, various artificial neural networks (ANNs) were developed and evaluated for their wind speed prediction ability using the ERA5 historical reanalysis data for four potential Offshore Wind Farm Organized Development Areas in Greece, selected as suitable for floating wind installations. …”
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Neural network-based symmetric encryption algorithm with encrypted traffic protocol identification by Jiakai Hao, Ming Jin, Yuting Li, Yuxin Yang

“…In this study, we first introduce a plaintext guessing model (SCGM model) based on symmetric encryption algorithms, leveraging the strengths of convolutional neural networks to evaluate the plaintext guessing capabilities of four symmetric encryption algorithms. …”
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