A Deep Graph-Embedded LSTM Neural Network Approach for Airport Delay Prediction by Weili Zeng, Juan Li, Zhibin Quan, Xiaobo Lu

“…On this basis, a diffusion convolution kernel is constructed to capture characteristics of delay propagation between airports, and it is further integrated into the sequence-to-sequence LSTM neural network to establish a deep learning framework for delay prediction. We name this model as deep graph-embedded LSTM (DGLSTM). …”
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Cooperative UAV Scheduling for Power Grid Deicing Using Fuzzy Learning and Evolutionary Optimization by Yu-Jun Zheng, Zhi-Yuan Zhang, Jia-Yu Yan, Wei-Guo Sheng

“…Uncertain outage risk, collapse risk, and deicing workload of each power line are modeled as fuzzy values predicted by fuzzy deep learning models, and we transform the fuzzy optimization problem into a crisp optimization problem based on fuzzy arithmetics and uncertain theory. …”
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Algorithmic emergence? Epistemic in/justice in AI-directed transformations of healthcare by Imo Emah, SJ Bennett

“…Moves toward integration of Artificial Intelligence (AI), particularly deep learning and generative AI-based technologies, into the domains of healthcare and public health have recently intensified, with a growing body of literature tackling the ethico-political implications of this. …”
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The Improvement of Automated Crack Segmentation on Concrete Pavement with Graph Network by Jiang Chen, Ye Yuan, Hong Lang, Shuo Ding, Jian John Lu

“…Recent research on pavement crack detection based on deep learning has laid a good foundation for automated crack segmentation, but there can still be improvements. …”
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Comparison of Fully Convolutional Networks and U-Net for Optic Disc and Optic Cup Segmentation by Jin Zixiao

“…This work presents the contribution of the deep learning models in improving glaucoma screening and therefore helping in avoiding blindness.…”
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Salient object detection with non-local feature enhancement and edge reconstruction by Tao Xu, Jingyao Jiang, Lei Cai, Haojie Chai, Hanjun Ma

“…Abstract The salient object detection task based on deep learning has made significant advances. However, the existing methods struggle to capture long-range dependencies and edge information in complex images, which hinders precise prediction of salient objects. …”
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Domain-adaptive neural networks improve supervised machine learning based on simulated population genetic data. by Ziyi Mo, Adam Siepel

“…We focus our analysis on two state-of-the-art deep-learning population genetic methods-SIA, which infers positive selection from features of the ancestral recombination graph (ARG), and ReLERNN, which infers recombination rates from genotype matrices. …”
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Biologically Inspired Spatial–Temporal Perceiving Strategies for Spiking Neural Network by Yu Zheng, Jingfeng Xue, Jing Liu, Yanjun Zhang

“…However, current mainstream DNN (deep learning neural network)-based AI (artificial intelligence) is a ‘black box’. …”
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Evaluating Large Language Models for Optimized Intent Translation and Contradiction Detection Using KNN in IBN by Muhammad Asif, Talha Ahmed Khan, Wang-Cheol Song

“…This research addresses these gaps by evaluating advanced Large Language Models (LLMs) such as BERT-base uncased (BERT-bu), GPT2, LLaMA3, Claude2 and small deep learning model BiLSTM with attention for translating intents and detecting contradictions. …”
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Construction of multi-modal social media dataset for fake news detection by Guopeng GAO, Yaodong FANG, Yanfang HAN, Zhenxing QIAN, Chuan QIN

“…The advent of social media has brought about significant changes in people’s lives.While social media allows for easy access and sharing of news, it has also become a breeding ground for the dissemination of fake news, posing a serious threat to social security and stability.Consequently, researchers have shifted their focus towards fake news detection.Although several deep learning-based solutions have been proposed, these methods heavily rely on large amounts of supporting data.Currently, there is a scarcity of existing datasets, particularly in Chinese, and the collected news articles are often limited to the same category.To enhance the detection of fake news, a new multi-modal fake news dataset (MFND) was developed, which comprised Chinese and English news data from ten diverse categories: politics, economy, entertainment, sports, international affairs, technology, military, education, health, and social life.The word frequencies and categories of the proposed fake news dataset were analyzed and compared with existing fake news datasets in terms of number of news, news categories, modal information and news languages.The results of the comparison demonstrate that the MFND dataset excels in terms of category information and news languages.Moreover, training and validating existing typical fake news detection methods with MFND dataset, the experimental results show an improvement of approximately 10% in model performance compared to existing mainstream fake news datasets.…”
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Driver Distraction Identification with an Ensemble of Convolutional Neural Networks by Hesham M. Eraqi, Yehya Abouelnaga, Mohamed H. Saad, Mohamed N. Moustafa

“…In addition, we propose a reliable deep learning-based solution that achieves a 90% accuracy. …”
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Accelerating charge estimation in molecular dynamics simulations using physics-informed neural networks: corrosion applications by Aditya Venkatraman, Mark A. Wilson, David Montes de Oca Zapiain

“…The atomic charges predicted by the deep learning model trained on this work were obtained two orders of magnitude faster than those from molecular dynamics (MD) simulations, with an error of less than 3% compared to the MD-obtained charges, even in extrapolative scenarios, while adhering to physical constraints. …”
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A Model for Estimating Resiliency of AI-Based Classifiers Defending Against Cyber Attacks by Kousik Barik, Sanjay Misra, Luis Fernandez-Sanz

“…The study proposes an Egyptian Vulture Optimized Adaptive Elman Recurrent Neural Networks (EVO-AERNN) model to assess cybersecurity resilience and compare it with machine learning and deep learning-based classifiers. It illustrates the potential of using adversary-aware feature sampling to build more robust classifiers and use an optimized algorithm to maintain inherent resilience. …”
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Graph neural network driven traffic prediction technology:review and challenge by Yi ZHOU, Shuting HU, Wei LI, Nan CHENG, Ning LU, Xuemin(Sherman) SHEN

“…With the rapid development of Internet of things and artificial intelligence technology, accurate analysis and prediction of traffic data have become the primary target of intelligent transportations.In recent years, the method of traffic forecasting has gradually changed from the classical model-driven type to the data-driven type.However, how to effectively analyze the spatial-temporal characteristics of road networks through big data is one of the key issues in the traffic prediction process.Spatiotemporal big data analysis is a powerful tool for the traffic prediction.The traffic network can be modeled as a graph network, while the deep learning method can be extended on the graph network.Utilizing graph neural networks, we can build the spatiotemporal prediction model, and obtain the spatial-temporal correlation between the sensor nodes in road networks effectively by using graph convolution, which can significantly improve the accuracy of traffic prediction models.The traffic forecasting technology driven by graph neural networks was explored, and two kinds of traffic prediction models based on the analysis of deep spatial-temporal characteristics were extracted.The actual cases were analyzed and evaluated to discuss the technical advantages and key challenges of graph neural networks in the traffic prediction.The potential issues of graph neural network driven prediction mechanisms were also excavated.…”
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A Comprehensive Review of Facial Beauty Prediction Using Multi-task Learning and Facial Attributes by Ali H. Ibrahem, Adnan M. Abdulazeez

“…This review addresses the pressing need to develop robust and fair predictive models for facial beauty assessments by leveraging deep learning techniques. Using facial attributes such as symmetry, skin complexion, and hairstyle, we explore how these features influence perceptions of attractiveness. …”
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A hybrid stock prediction method based on periodic/non-periodic features analyses by Cheng Zhao, Junyi Cai, Shuyi Yang

“…Stock price prediction is a challenging task due to the nonlinearity and high volatility of stock time series. Existing deep learning models may not capture the periodic and non-periodic features of stock data effectively. …”
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A Novel Approach to Discriminate Between Structural and Non-Structural Post-Earthquake Damage in RC Structures by Beyza Gultekin, Gamze Dogan

“…For the damage classification model, a deep learning algorithm was developed using the 9680 damage images obtained from field studies after the recent earthquakes of Mw ≥ 5; Istanbul-Silivri (Mw: 5.8), Elazığ-Sivrice (Mw: 6.8) and Izmir-Seferihisar (Mw: 6.6) in Turkey. …”
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PGN: Progressively Guided Network with Pixel-Wise Attention for Underwater Image Enhancement by Huidi Jia, Qiang Wang, Bo Fu, Zhimin Zheng, Yandong Tang

“…Light scattering and attenuation in water degrade underwater images with low visibility and color distortion, which often interfere with the high-level visual tasks of underwater autonomous robots. Most existing deep learning methods for underwater image enhancement only supervise the final output of network and ignore the promotion effect of the intermediate results on the final feature representation. …”
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Analytical, Dynamic, and Interactive Platform for Generation and Managing Predictive Models Focused on Energy Sector by Inés Romero, Alberto Ochoa-Zezzati

“…In this investigation, components related to the generation of electrical energy in this area are identified and a centralized system is proposed, with information segmentation, management of 3 user profiles, 6 KPIs, 5 configurable parameters, 7 different forecast models using statistical techniques, support vector machines, and automatic and deep learning, with 2 ways of visualization, to carry out analyses at 3 different time horizons. …”
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Investigation of Coal Preparation for Life Cycle by Using Building Information Modeling (BIM): A Case Study by Jian Chen, Da-Lin Guo, Xiao-Long Dong, Xiao-Bo Hou, Zhi-Feng Wang

“…In this paper, the kappa big data processing architecture is used to realize the integration and unification of stream data and batch data processing process. By using deep learning method and multimodal data fusion method, the multimodal data association fusion is realized, and Bentley software is adopted for verification and integration. …”
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