Using Hybrid Neural Networks to Improve Traffic Prediction and Congestion Management by Ali Abd Samir

“…The proposed Materials and methods integrates Diffusion Convolutional Recurrent Neural Network (DCRNN) with graph-based models, allowing information to be shared among related sensors over large distances. …”
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Multi-task aquatic toxicity prediction model based on multi-level features fusion by Xin Yang, Jianqiang Sun, Bingyu Jin, Yuer Lu, Jinyan Cheng, Jiaju Jiang, Qi Zhao, Jianwei Shuai

“…Objectives: This article presents ATFPGT-multi, an advanced multi-task deep neural network prediction model for organic toxicity. …”
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Multi-Label Feature Selection with Graph-based Ant Colony Optimization and Generalized Jaccard Similarity by Sabah Robitan Mahmood, Tahsin Ali Mohammed Amin, Khalid Hassan Ahmed, Rebar Dara Mohammed, Pshtiwan Jabar Karim

“…The findings demonstrate that the proposed method outperforms most of existing and advanced approaches. This paper presents a novel feature selection approach for multi-label learning based on ACO. …”
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From sound to story: GAS-Saudi’s graph-based solution for audio summarization in the deaf community by Raed Alharbi, Khalid Almalki

“…This paper presents GAS-Saudi, a proof-of-concept novel graph-based framework designed to enhance summarization for the deaf community by leveraging complex relationships within acoustic signals. …”
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An Advanced Spatio-Temporal Graph Neural Network Framework for the Concurrent Prediction of Transient and Voltage Stability by Chaoping Deng, Liyu Dai, Wujie Chao, Junwei Huang, Jinke Wang, Lanxin Lin, Wenyu Qin, Shengquan Lai, Xin Chen

“…In real-world scenarios, these two types of instability often co-occur, necessitating distinct and coordinated control strategies. This paper presents a novel concurrent prediction framework for transient and voltage stability using a spatio-temporal embedding graph neural network (STEGNN). …”
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Dynamic load balancing in cloud computing using predictive graph networks and adaptive neural scheduling by K. Rajammal, M. Chinnadurai

“…To overcome these issues, a novel approach is presented in this research work utilizing Spiking Neural Networks (SNNs) for adaptive decision-making and Temporal Graph Neural Networks (TGNNs) for dynamic resource state modeling. …”
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Advancing Hate Speech Detection in Indonesian Language Using Graph Neural Networks and TF-IDF by Syaikha Amirah Zikrina, Fitriyani

“…Most of the hate speech and abusive content on social media, particularly in the Indonesian language, presents significant challenges for content moderation systems. …”
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Synthetic Sentiment Cue Enhanced Graph Relation-Attention Network for Aspect-Level Sentiment Analysis by Hongwei Tang, Haining Yan, Ran Song

“…To address these limitations, this paper presents a novel Synthetic Sentiment Cue Enhanced Graph Relation-Attention Network (SSC-GRAN), a hybrid framework that synergistically integrates large language models (LLMs) with graph neural networks (GNNs). …”
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Comparison of ensemble and correlation graphs in the task of classifying brain states based on fMRI data by Vlasenko, Daniil Vladimirovich, Ushakov, Vadim Gennadevich, Zaikin, Aleksei Anatolevich, Zakharov, Denis Gennadevich

“…Methods. This paper presents a novel method for representing fMRI data in graph form based on ensemble learning. …”
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Fairness-Aware Graph Neural Networks for ICU Length of Stay Prediction in IoT-Enabled Environments by Angelos Christos Maroudis, Konstantina Karathanasopoulou, Charithea C. Stylianides, George Dimitrakopoulos, Andreas S. Panayides

“…To address the loss of static information, we introduce a custom graph neural network that dynamically reconstructs patient relationships over time, adapting from static demographics to evolving inter-patient correlations via multi-modal embeddings (e.g., medications, procedures, vitals, conditions) and learned feature-driven edge formation. …”
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A Graph Isomorphic Network with Attention Mechanism for Intelligent Fault Diagnosis of Axial Piston Pump by Kai Li, Bofan Wu, Shiqi Xia, Xianshi Jia

“…Subsequently, a spatio-temporal attention-based module used to learn the graph representation of piston pump faults is presented, where a novel READOUT function and Transformer encoder provide spatial and temporal interpretability, respectively. …”
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Crack Identification for Bridge Condition Monitoring Combining Graph Attention Networks and Convolutional Neural Networks by Feiyu Chen, Tong Tong, Jiadong Hua, Chun Cui

“…In order to ensure the life span of bridges, methods for automatic crack identification are needed. In this paper, we present a novel approach for crack detection and bridge condition monitoring by integrating convolutional neural networks (CNNs) with graph attention networks (GATs). …”
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SGRiT: Non-Negative Matrix Factorization via Subspace Graph Regularization and Riemannian-Based Trust Region Algorithm by Mohsen Nokhodchian, Mohammad Hossein Moattar, Mehrdad Jalali

“…Furthermore, this paper incorporates a novel subspace graph regularization term that considers high-order geometric information and introduces a sparsity term for the factor matrices. …”
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Exploring Gait Recognition in Wild Nighttime Scenes by Haotian Li, Wenjuan Gong, Yutong Li, Yikai Wu, Kechen Li, Jordi Gonzàlez

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An integrated AI-driven framework for maximizing the efficiency of heterostructured nanomaterials in photocatalytic hydrogen production by Pramod N. Belkhode, Shrikant M. Awatade, Chander Prakash, Sagar D. Shelare, Deepali Marghade, Sameer Sheshrao Gajghate, Muhamad M. Noor, Milon Selvam Dennison

“…Traditional synthesis methods often rely on trial-and-error, resulting in inefficiencies in material discovery and optimization. This work presents a new AI-driven framework that overcomes these challenges by integrating advanced machine-learning techniques specific to heterostructured nanomaterials. …”
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A Novel Discrete Time Series Representation With De Bruijn Graphs for Enhanced Forecasting Using TimesNet by Mert Onur Cakiroglu, Hasan Kurban, Elham Buxton, Mehmet Dalkilic

“…In this paper, we present a novel method for advancing time series forecasting by representing discretized time series data through de Bruijn Graphs (dBGs). …”
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On Traffic Prediction With Knowledge-Driven Spatial–Temporal Graph Convolutional Network Aided by Selected Attention Mechanism by Yuwen Qian, Tianyang Qiu, Chuan Ma, Yiyang Ni, Long Yuan, Xiangwei Zhou, Jun Li

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An Unmanned Delivery Vehicle Path-Planning Method Based on Point-Graph Joint Embedding and Dual Decoders by Jiale Cheng, Zhiwei Ni, Wentao Liu, Qian Chen, Rui Yan

“…In addition, the model is trained offline using a deep reinforcement-learning strategy in combination with pseudo-label learning. …”
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Glaucoma detection from retinal fundus images using graph convolution based multi-task model by Satyabrata Lenka, Zefree Lazarus Mayaluri, Ganapati Panda

“…The intended objective of the present study is to come up with and train a distinctive multi-task deep learning model for automated fundus image segmentation and classification. …”
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Financial risk forecasting with RGCT-prerisk: a relational graph and cross-temporal contrastive pretraining framework by Liyu Chen, Xiangwei Fan

“…Our approach achieves state-of-the-art predictive performance while providing human-interpretable insights into why a firm is predicted to be at risk. This work presents a new direction for interpretable financial risk forecasting by integrating graph-based representation learning, contrastive pretraining, and case-based reasoning.…”
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