Hierarchical partition of urban land-use units by unsupervised graph learning from high-resolution satellite images by Mengmeng Li, Xinyi Gai, Kangkai Lou, Alfred Stein

“…A significant challenge remains the accurate partition of fine-grained land-use units from these images. This paper presents a novel method for deriving these units based on unsupervised graph learning techniques using high-resolution satellite images and open street boundaries. …”
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Sustained learned immunosuppression could not prevent local allergic ear swelling in a rat model of contact hypersensitivity by Yasmin Salem, Stephan Leisengang, Marie Jakobs, Kirsten Dombrowski, Julia Bihorac, Laura Heiss-Lückemann, Sebastian Wenzlaff, Lisa Trautmann, Tim Hagernacker, Manfred Schedlowski, Martin Hadamitzky

“…Against this background, the present study applied an established taste-immune associative learning protocol to a rat model of DNFB-induced contact hypersensitivity. …”
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GNN-EADD: Graph Neural Network-Based E-Commerce Anomaly Detection via Dual-Stage Learning by Zhouhang Shao, Xuran Wang, Enkai Ji, Shiyang Chen, Jin Wang

“…E-commerce platforms face significant challenges in detecting anomalous products, including counterfeit goods and fraudulent listings, which can undermine user trust and platform integrity. This paper presents Graph Neural Network-based E-commerce Anomaly Detection via Dual-stage Learning (GNN-EADD), a novel approach leveraging graph neural networks for anomaly detection in large-scale e-commerce ecosystems. …”
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Similarity-Based Retrieval in Process-Oriented Case-Based Reasoning Using Graph Neural Networks and Transfer Learning by Johannes Pauli, Maximilian Hoffmann, Ralph Bergmann

“…Previous work tackles this problem by using Graph Neural Networks (GNNs) to learn pairwise graph similarities. In this paper, we present a novel approach that improves on the GNN-based case retrieval with a Transfer Learning (TL) setup, composed of two phases: First, the pretraining phase trains a model for assessing the similarities between graph nodes and edges and their semantic annotations. …”
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Automation of image processing through ML algorithms of GRASS GIS using embedded Scikit-Learn library of Python by Polina Lemenkova

“…The satellite images were classified into raster maps presenting the land cover types. These include ‘i.cluster’ and ‘i.maxlik’ for non-supervised classification used as training dataset of random pixel seeds, ‘r.random’, ‘r.learn.train’, ‘r.learn.predict’ and ‘r.category’ for ML part of image processing. …”
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Smart Electric Vehicle Charging Algorithm to Reduce the Impact on Power Grids: A Reinforcement Learning Based Methodology by Federico Rossi, Cesar Diaz-Londono, Yang Li, Changfu Zou, Giambattista Gruosso

“…The increasing penetration of electric vehicles (EVs) presents a significant challenge for power grid management, particularly in maintaining network stability and optimizing energy costs. …”
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GOAT: a novel global-local optimized graph transformer framework for predicting student performance in collaborative learning by Tianhao Peng, Qiang Yue, Yu Liang, Jian Ren, Jie Luo, Haitao Yuan, Wenjun Wu

“…Most current methods analyze this complex task solely based on the frequency of student activities, overlooking the rich spatial and temporal features present in these activities, as well as the diverse textual content provided by various learning artifacts. …”
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HOODIE: Hybrid Computation Offloading via Distributed Deep Reinforcement Learning in Delay-Aware Cloud-Edge Continuum by Anastasios E. Giannopoulos, Ilias Paralikas, Sotirios T. Spantideas, Panagiotis Trakadas

“…Cloud-Edge Computing Continuum (CEC) system, where edge and cloud nodes are seamlessly connected, is dedicated to handle substantial computational loads offloaded by end-users. …”
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Intelligent Fault Detection and Self-Healing Mechanisms in Wireless Sensor Networks Using Machine Learning and Flying Fox Optimization by Almamoon Alauthman, Abeer Al-Hyari

“…Extensive performance evaluation of the developed system using a large dataset was presented and compared with the state-of-the-art heuristic-based traditional methods and machine learning models. …”
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Phase Wise Classification of Abnormal Gait in Children With Cerebral Palsy Using Hybrid Neural TEMPODE Deep Learning Techniques by Yelle Kavya, S.Sofana Reka

“…It is essential to analyse and categorise these abnormalities of gait in order to implement tailored therapeutic interventions. The proposed study presents two Hybrid Neural TEMPODE (Temporal Ordinary Differential Equations) architectures for phase-wise classification of abnormal gait in children with cerebral palsy that combines Temporal Convolutional Networks (TCN) with Neural Ordinary Differential Equations (NODE) and Temporal Fusion Transformers (TFT) with Neural Ordinary Differential Equations. …”
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Adaptive Throughput Optimization in Multi-Rate IEEE 802.11 WLANs via Multi-Agent Deep Reinforcement Learning by Ming-Chu Chou, Cheng-Feng Hung, Chin-Ya Huang, Chih-Heng Ke

“…However, the heterogeneity of nodes and transmission conditions presents significant challenges to existing wireless strategies and traditional centralized AI methods, making it difficult to meet user demands for network throughput. …”
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Machine learning-optimized dual-band wearable antenna for real-time remote patient monitoring in biomedical IoT systems by Umar Musa, Amor Smida, Muhammad S. Yahya, Mohamed I. Waly, Jun Jiat Tiang, Nazih Khaddaj Mallat, Surajo Muhammad, Abubakar Salisu

“…Abstract This work presents a machine learning (ML)-optimized dual-band wearable antenna designed specifically for biomedical applications in healthcare monitoring. …”
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AFF_CGE: Combined Attention-Aware Feature Fusion and Communication Graph Embedding Learning for Detecting Encrypted Malicious Traffic by Junhao Liu, Guolin Shao, Hong Rao, Xiangjun Li, Xuan Huang

“…While encryption enhances data security, it also presents significant challenges for network traffic analysis, especially in detecting malicious activities. …”
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TriageHD: A Hyper-Dimensional Learning-to-Rank Framework for Dynamic Micro-Segmentation in Zero-Trust Network Security by Ryozo Masukawa, Sanggeon Yun, Sungheon Jeong, Nathaniel D. Bastian, Mohsen Imani

“…To address these challenges, micro-segmentation has proven to be an effective defense strategy for isolating network components and limiting breach propagation. This paper presents TriageHD, a novel framework that integrates graph-based Hyper-Dimensional Computing (HDC) with a learning-to-rank algorithm to strengthen zero-trust network security. …”
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An end-to-end real-time pollutants spilling recognition in wastewater based on the IoT-ready SENSIPLUS platform by Luca Gerevini, Gianni Cerro, Alessandro Bria, Claudio Marrocco, Luigi Ferrigno, Michele Vitelli, Andrea Ria, Mario Molinara

“…Accordingly, an End-to-End IoT-ready node for the sensing, local processing, and transmission of the data collected on the pollutants in the wastewater is presented here. …”
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Anomaly Detection Over Multi-Relational Graphs Using Graph Structure Learning and Multi-Scale Meta-Path Graph Aggregation by Chi Zhang, Junho Jeong, Jin-Woo Jung

“…To address these limitations, we introduce a graph structure learning layer designed to refine the original, noisy graph structure, enhancing the representation of node relationships. …”
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Enhancing security and efficiency in Mobile Ad Hoc Networks using a hybrid deep learning model for flooding attack detection by Pramodh Krishna D., E. Sandhya, Khaja Shareef Sk, Srihari Varma Mantena, Venkata Subbaiah Desanamukula, Ch Koteswararao, Srinivasa Rao Vemula, Maruthi Vemula

“…However, their wireless architecture makes them highly vulnerable to flooding attacks, which can disrupt communication, deplete energy resources, and degrade network performance. This study presents a novel hybrid deep learning approach integrating Convolutional Neural Networks (CNN) with Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) architectures to effectively detect and mitigate flooding attacks in MANETs. …”
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A Trust Based Anomaly Detection Scheme Using a Hybrid Deep Learning Model for IoT Routing Attacks Mitigation by Khatereh Ahmadi, Reza Javidan

“…In order to evaluate the efficiency and effectiveness of the proposed model in timely detection of RPL–specific routing attacks, we have implemented the proposed model on several RPL–based IoT scenarios simulated using Contiki Cooja simulator separately, and the results have been compared in details. According to the presented results, the implemented detection scheme on all attack scenarios, demonstrated that the trend of estimated anomaly between real and predicted routing behavior is similar to the evaluated attack frequency of malicious nodes during the RPL process and in contrast, analyzed trust scores represent an opposite pattern, which shows high accurate and timely detection of attack incidences using our proposed trust scheme.…”
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DeepGFT: identifying spatial domains in spatial transcriptomics of complex and 3D tissue using deep learning and graph Fourier transform by Shuli Sun, Jixin Liu, Guojun Li, Bingqiang Liu

“…However, high dropout rates and noise hinder accurate spatial domain identification for understanding tissue architecture. We present DeepGFT, a method that simultaneously models spot-wise and gene-wise relationships by integrating deep learning with graph Fourier transform for spatial domain identification. …”
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GAPO: A Graph Attention-Based Reinforcement Learning Algorithm for Congestion-Aware Task Offloading in Multi-Hop Vehicular Edge Computing by Hongwei Zhao, Xuyan Li, Chengrui Li, Lu Yao

“…To address these issues, this paper proposes a graph attention-based reinforcement learning algorithm, named GAPO. The algorithm models the dynamic VEC network as an attributed graph and utilizes a graph neural network (GNN) to learn a network state representation that captures the global topological structure and node contextual information. …”
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