MultiSenseNet: Multi-Modal Deep Learning for Machine Failure Risk Prediction by Mostafijur Rahman, Md Sabbir Hossain, Uland Rozario, Satyabrata Roy, M. F. Mridha, Nilanjan Dey

“…It also excelled in precision, recall, F1-score, and AUC metrics compared to traditional machine learning models and recent deep learning architectures. …”
Get full text

Application of Genetic Algorithms for Finding Edit Distance between Process Models by Anna A. Kalenkova, Danil A. Kolesnikov

“…Finding graph-edit distance (graph similarity) is an important task in many computer science areas, such as image analysis, machine learning, chemicalinformatics. …”
Get full text

Time series prediction for monitoring cardiovascular health in autistic patients by Congsha Ma, Ming Lei

“…IntroductionMonitoring cardiovascular health in autistic patients presents unique challenges due to atypical sensory profiles, altered autonomic regulation, and communication difficulties. …”
Get full text

GeoFAN: Point Pattern Recognition in Spatial Vector Data by Zhuoyi Yang, Zeyi Li, Haitao Zhang, Wei Zhang, Yanwei Wang, Yihang Huang

“…In this article, we propose a geometric feature attention scheme to overcome the above challenges. We also present an implementation of the scheme based on the graph method, termed GeoFAN, to extract and classify point patterns simultaneously in spatial vector data. …”
Get full text

Influence of Explanatory Variable Distributions on the Behavior of the Impurity Measures Used in Classification Tree Learning by Krzysztof Gajowniczek, Marcin Dudziński

“…The remaining graphs present distinct impurity measures with different parameters. …”
Get full text

Deep Learning for Sector-Specific Labor Market Forecasting: Integrating Job Postings and Macroeconomic Indicators by Haojun Ding

“…This paper presents a sector-specific employment forecasting framework that integrates deep learning with heterogeneous labor market data, including job postings and macroeconomic indicators. …”
Get full text

Active learning accelerated exploration of single-atom local environments in multimetallic systems for oxygen electrocatalysis by Hoje Chun, Jaclyn R. Lunger, Jeung Ku Kang, Rafael Gómez-Bombarelli, Byungchan Han

“…Abstract Single-atom catalysts (SACs) with multiple active sites exhibit high activity for a wide range of sluggish reactions, but identifying optimal multimetallic SAC is challenging due to the vast design space. Here, we present a self-driving computational strategy that combines first-principles calculations and equivariant graph neural network (GNN) to explore over 30,000 binary metallic sites with varying combinations of 3d transition metals and different ligand environments for oxygen reduction and evolution reactions (ORR/OER). …”
Get full text

A Systematic Model of an Adaptive Teaching, Learning and Assessment Environment Designed Using Genetic Algorithms by Doru Anastasiu Popescu, Nicolae Bold, Michail Stefanidakis

Get full text

Integration of deep learning and railway big data for environmental risk prediction models and analysis of their limitations by Liuhui Quan, Minjie Wang, Lyu Baihang, Zhang Ziwen, Zhang Ziwen, Zhang Ziwen

“…To address these gaps, we propose a novel framework leveraging deep learning techniques tailored to railway big data. Our method integrates temporal encoders and spatial graph neural networks, combined with domain-specific knowledge and contextual awareness, to achieve robust anomaly detection, predictive maintenance, and passenger demand forecasting. …”
Get full text

Utilizing deep learning for intelligent monitoring and early warning of slope disasters in public space design by Wang Ting, Ying Wang

“…IntroductionThe increasing frequency of slope disasters in urban and recreational public spaces, driven by climate change, presents significant risks to public safety and sustainable urban design. …”
Get full text

Beyond the Backbone: A Quantitative Review of Deep-Learning Architectures for Tropical Cyclone Track Forecasting by He Huang, Difei Deng, Liang Hu, Yawen Chen, Nan Sun

“…This paper presents a comprehensive review of DL-based approaches for TC track forecasting. …”
Get full text

Embedded Anchors Coupled Low-Rank Tensor Learning for Multi-View Intrinsic Subspace Clustering by Yueyao Li, Yanying Mei, Zhenwen Ren, Bin Wu

“…Besides, these methods do not reveal the high-order relationships concealed behind multi-view data and recover the global low-rank of the anchor graphs. Given this, we present a new approach called embedded anchors coupled low-rank tensor learning for multi-view intrinsic subspace clustering (ALTMSC). …”
Get full text

Power transmission system’s fault location, detection, and classification: Pay close attention to transmission nodes by Chiagoziem C. Ukwuoma, Dongsheng Cai, Olusola Bamisile, Ejiyi J. Chukwuebuka, Ekong Favour, Gyarteng S.A. Emmanuel, Acen Caroline, Sabirin F. Abdi

“…The model makes use of a deep graph neural network with multi-scale attention and multi-linear perceptron block which accounts for the power network's structural composition during learning. …”
Get full text

MLFoMpy: A post-processing tool for semiconductor TCAD data with machine-learning capabilities by Enrique Comesaña, Julian G. Fernández, Natalia Seoane, Antonio García-Loureiro

“…We present MLFoMpy, a Python package for post-processing data from semiconductor device simulations. …”
Get full text

Effects of Rhythmic and Simple Auditory Stimulations on Learning the Timing of Sequential Motor Task in Children With DCD by Ahmad Dehnavi, Alireza Saberi Kakhaki, Hamidreza Taheri Torbati, Mohammadreza Shahabi Kaeb

“…Introduction: Children and adolescents with Developmental Coordination Disorder (DCD) usually fail to understand spatial awareness and motor timing. The present study assessed Rhythmic Auditory Stimulations (RAS) and Simple Auditory Stimulations (SAS) to facilitate the learning of timing in sequential motor task and recorded the results of their relative and absolute timing errors. …”
Get full text

Measuring. Monitoring. Management. Control by V.R. Aleksandrov, A.A. Shchetkin, A.S. Bevz

A Spatial Long-Term Load Forecast Using a Multiple Delineated Machine Learning Approach by Terence Kibula Lukong, Derick Nganyu Tanyu, Yannick Nkongtchou, Thomas Tamo Tatietse, Detlef Schulz

Get full text

Unified physio-thermodynamic descriptors via learned CO2 adsorption properties in metal-organic frameworks by Emily Lin, Yang Zhong, Gang Chen, Sili Deng

“…Herein, we present IsothermNet, a high-throughput graph neural network designed to estimate uptake and $$\Delta {H}_{{\rm{ads}}}$$ Δ H ads over 0–50 bars, enabling high-quality full isotherm reconstruction (PCC: 0.73–0.95 [uptake], 0.76–0.88 [ $$\Delta {H}_{{\rm{ads}}}$$ Δ H ads ]). …”
Get full text

NMFGOT: a multi-view learning framework for the microbiome and metabolome integrative analysis with optimal transport plan by Yuanyuan Ma, Lifang Liu

“…NMFGOT is an unsupervised learning framework based on nonnegative matrix factorization with graph regularized optimal transport, where it utilizes the optimal transport plan to measure the probability distance between microbiome samples, which better dealt with the nonlinear high-order interactions among microbial taxa and metabolites. …”
Get full text

Hybrid machine learning-based 3-dimensional UAV node localization for UAV-assisted wireless networks by Workeneh Geleta Negassa, Demissie J. Gelmecha, Ram Sewak Singh, Davinder Singh Rathee

“…This paper presents a hybrid machine-learning framework for optimizing 3-Dimensional (3D) Unmanned Aerial Vehicles (UAV) node localization and resource distribution in UAV-assisted THz 6G networks to ensure efficient coverage in dynamic, high-density environments. …”
Get full text