Resource Optimization Method Based on Spatio-Temporal Modeling in a Complex Cluster Environment for Electric Vehicle Charging Scenarios by Hongwei Wang, Wei Liu, Chenghui Wang, Kao Guo, Zihao Wang

“…Its dilation rate grows exponentially with the layer depth, allowing it to effectively capture the time trends of graph nodes and handle long time series data. For spatial modeling, an innovative dual-view dynamic graph convolutional network architecture is utilized to accurately explore the static and dynamic correlation information of the spatial layout of charging piles. …”
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Urban Functional Zone Mapping by Integrating Multi-Source Data and Spatial Relationship Characteristics by Daoyou Zhu, Xu Dang, Wenjia Shi, Yixiang Chen, Wenmei Li

“…This framework leverages the OpenStreetMap (OSM) road network to partition the study area into functional units, employs a graph model to represent urban functional nodes and their intricate spatial topological relationships, and harnesses the capabilities of Graph Convolutional Network (GCN) to fuse these multi-dimensional features through end-to-end learning for accurate urban function discrimination. …”
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Predicting Student Dropout Through Text and Media Content Analysis of VKontakte Profiles by Sergei S. Gorshkov, Dmitry I. Ignatov, Anastasia Yu. Chernysheva

“…This paper presents a novel approach to predicting student dropout by analyzing publicly available data from VKontakte social network profiles. …”
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Unmasking insider threats using a robust hybrid optimized generative pretrained neural network approach by P. Lavanya, H. Anila Glory, Manuj Aggarwal, V. S. Shankar Sriram

“…The structure of the proposed approach involves three phases: (1) Chebyshev Graph Laplacian Eigenmaps solver (CGLE) for selecting the user-designated samples by reducing the dimensionality of the data and Insider State clustering via Density-Based Spatial Clustering of Applications with Noise (IS-DBSCAN) (2) The EHI of multi-objective Bayesian optimization for optimizing the sensitive learning rate hyperparameter to ensure the stability of the Adabelief optimized WGAN and improve the quality of the generated adversarial samples. (3) The L2-SP regularization technique effectively fine-tunes the pretrained AGCN, which identifies the user behavioural pattern to detect the insiders. …”
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Advancing cardiac motion estimation with emerging AI techniques for enhanced echocardiographic image registration by M. Rajesh, S. Balakrishnan, R. Elankavi

“…Adversarial and self-supervised contrastive learning enhance picture quality and generalisability across adult and foetal echocardiography, while a graph neural network (GNN)-based anatomical constraint maintains heart shape. …”
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A Novel Remote Sensing Recognition Using Modified GMM Segmentation and DenseNet by Muhammad Waqas Ahmed, Moneerah Alotaibi, Sultan Refa Alotaibi, Dina Abdulaziz Alhammadi, Asaad Algarni, Ahmad Jalal, Jeongho Cho

“…To address these challenges, we present an innovative framework that combines advanced segmentation techniques, diverse feature extraction methods, optimization algorithms, and deep learning. …”
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Knowledge Improved Hybrid DNN–KAN Framework for Intrusion Detection in Wireless Sensor Networks by M. Sriraghavendra, Muna Elsadig, Ines Hilali Jaghdam, S. Abdel-Khalek, B. Galeebathullah, Salem Alkhalaf

“…Wireless Sensor Networks (WSNs) are increasingly vulnerable to sophisticated cyber threats, necessitating advanced intrusion detection systems (IDS) that balance high accuracy with interpretability. This paper presents a Knowledge-Improved Hybrid Deep Neural Network-Kolmogorov Arnold Network (DNN-KAN) Framework for intrusion detection in WSNs, integrating data-driven learning with domain-specific knowledge to enhance detection performance. …”
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PLL-VO： An Efficient and Robust Visual Odometry Integrating Point-Line Features and Neural Networks by L. Zhao, Y. Yang, D. Ma, X. Lin, W. Wang

“…This paper proposes PLL-VO, which integrates point-line features and deep learning. To overcome the impact of complex lighting conditions, a self-supervised learning method for interest point detection and a line detection algorithm that combines line optical flow tracking with cross-constraints is presented. …”
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Prediction of Post-Diagnostic Decisions for Tested Hand Grenades’ Fuzes Using Decision Trees by Dariusz AMPUŁA

“…A sheet with risk assessment and standard error for the learning sample and the v-fold cross-check were presented. …”
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Orientation of ambiguous image sequences with similar and repeated structures by G. Perda, L. Morelli, F. Remondino

“…The paper reviews various state-of-the-art approaches to orient ambiguous image sequences and determination correct camera orientation parameters. We also present an in-house graph-based approach to reliably and precisely orient sets of images with doppelgangers. …”
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Predicting Facial Biotypes Using Continuous Bayesian Network Classifiers by Gonzalo A. Ruz, Pamela Araya-Díaz

“…Bayesian networks are useful machine learning techniques that are able to combine quantitative modeling, through probability theory, with qualitative modeling, through graph theory for visualization. …”
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EFFICIENCY OF THE EXPLANATORY AND ILLUSTRATIVE METHOD IN THE COURSE OF TEACHING THERMODYNAMICS WITHIN THE CURRICULUM OF THE TRAINING OF BACHELORS MAJORING IN 184 MINING by Kozinets Inna, Trofymova Olena

“…Tables and diagrams, graphs and schemes are an integral part of studying the discipline “Thermodynamics”. …”
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Microservice Workflow Scheduling with a Resource Configuration Model Under Deadline and Reliability Constraints by Wenzheng Li, Xiaoping Li, Long Chen, Mingjing Wang

“…We introduce a graph deep learning model (DeepMCC) that automatically configures containers to meet various service quality (QoS) requirements. …”
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Recent Developments in Path Planning for Unmanned Ground Vehicles in Underground Mining Environment by Abdurauf Abdukodirov, Jörg Benndorf

“…This review examines both global and local path-planning techniques, encompassing traditional graph-based methods, sampling-based approaches, nature-inspired algorithms, and reinforcement learning strategies. …”
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AI Driven Fraud Detection Models in Financial Networks: A Comprehensive Systematic Review by Nusrat Jahan Sarna, Farzana Ahmed Rithen, Umme Salma Jui, Sayma Belal, Al Amin, Tasnim Kabir Oishee, A. K. M. Muzahidul Islam

“…Techniques such as supervised and unsupervised learning, along with advanced approaches like Graph Neural Networks (GNNs), have proven particularly effective in detecting various types of financial fraud, including payment fraud, identity theft, and money laundering. …”
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An Ontology-Based Framework for Complex Urban Object Recognition through Integrating Visual Features and Interpretable Semantics by Xiao Xie, Xiran Zhou, Jingzhong Li, Weijiang Dai

“…This work is conducive to the development of complex urban object recognition toward the fields including multilayer learning algorithms and knowledge graph-based relational reinforcement learning.…”
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Sculpting molecules in text-3D space: a flexible substructure aware framework for text-oriented molecular optimization by Kaiwei Zhang, Yange Lin, Guangcheng Wu, Yuxiang Ren, Xuecang Zhang, Bo Wang, Xiao-Yu Zhang, Weitao Du

“…Abstract The integration of deep learning, particularly AI-Generated Content, with high-quality data derived from ab initio calculations has emerged as a promising avenue for transforming the landscape of scientific research. …”
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A Novel Skeletonization Algorithm for Topologically Complex Structures: Comparative Analysis and Application to Renal Arterial Trees by Katarzyna Heryan, Stefan-Daniel Caliman

“…Without proper reconstruction, skeletonization, and graph representation, raw <inline-formula> <tex-math notation="LaTeX">$\mu $ </tex-math></inline-formula>-CT data remains an unstructured point cloud, unsuitable for quantitative analysis. …”
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GMM-searcher: efficient object search in large-scale scenes using large language models by Lanxiang Zheng, Ruidong Mei, Bingzhi Zou, Zhijun Zhao, Mingxin Wei, Xu Siliu

“…For large-scale environments, an adaptive-resolution topological graph (ARTG) is combined with Gaussian Mixture Models (GMM) to optimize memory usage while preserving high environmental fidelity. …”
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Attention-Based Framework for Automated Symbol Recognition and Wiring Design in Electrical Diagrams by Ikenna Ekeke, Carlos Francisco Moreno-García, Eyad Elyan

“…These challenges are aggravated by the diversity of symbols, high inter-class similarities, and the inherent complexities of wiring layouts, which require advanced recognition and efficient wiring design. This paper presents a deep learning framework that integrates an attention mechanism for symbol recognition, followed by a graph-based algorithm for fully automated wiring design. …”
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