Energy Efficiency Optimization for UAV-RIS-Assisted Wireless Powered Communication Networks by Xianhao Shen, Ling Gu, Jiazhi Yang, Shuangqin Shen

“…In urban environments, unmanned aerial vehicles (UAVs) can significantly enhance the performance of wireless powered communication networks (WPCNs), enabling reliable communication and efficient energy transfer for urban Internet of Things (IoTs) nodes. However, the complex urban landscape characterized by dense building structures and node distributions severely hampers the efficiency of wireless power transmission. …”
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WirelessNet: An Efficient Radio Access Network Model Based on Heterogeneous Graph Neural Networks by Jose Perdomo, M. A. Gutierrez-Estevez, Chan Zhou, Jose F. Monserrat

“…WirelessNet represents network nodes and the underlying wireless phenomena between them as nodes and edges of different type in a heterogeneous graph. …”
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SineKAN: Kolmogorov-Arnold Networks using sinusoidal activation functions by Eric Reinhardt, Dinesh Ramakrishnan, Sergei Gleyzer

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UWB and GNSS Sensor Fusion Using ML-Based Positioning Uncertainty Estimation by Mihkel Tommingas, Taavi Laadung, Sander Varbla, Ivo Muursepp, Muhammad Mahtab Alam

“…This article presents a novel machine learning (ML) augmented sensor fusion scheme for seamless indoor-outdoor positioning using ultra-wideband (UWB) and global navigation satellite system (GNSS) sensors. …”
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DFL topology optimization based on peer weighting mechanism and graph neural network in digital twin platform by Nguyen Anh Tuan, Atif Rizwan, Sa Jim Soe Moe, Anam Nawaz Khan, Do Hyeun Kim

“…Abstract Decentralized federated learning (DFL) represents a distributed learning framework where participating nodes independently train local models and exchange model updates with proximate peers, circumventing the reliance on a centralized orchestrator. …”
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Approach to knowledge management and the development of a multi-agent knowledge representation and processing system by E. I. Zaytsev, E. V. Nurmatova

“…The aim of the present work is to identify models and methods, as well as software modules and tools, for use in developing a highly efficient MKRPS.Methods. …”
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Efficient Edge AI for Next Generation Smart Mirror Applications by Nils Kucza, Florian Porrmann, Christian Stollenwerk, Jens Hagemeyer

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ASIC Design for Real-Time CAN-Bus Intrusion Detection and Prevention System Using Random Forest by Junseok Lee, Sangmin Park, Sua Shin, Hyungchul Im, Joosock Lee, Seongsoo Lee

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Enhancing cloud security and deduplication efficiency with SALIGP and cryptographic authentication by J. K. Periasamy, S. Prabhakar, A. Vanathi, Liu Yu

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Multi-Attribute Graph Estimation With Sparse-Group Non-Convex Penalties by Jitendra K. Tugnait

“…Most existing methods for graph estimation are based on single-attribute models where one associates a scalar random variable with each node. In multi-attribute graphical models, each node represents a random vector. …”
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Combined compression and encryption of linear wireless sensor network data using autoencoders by N. Shylashree, Sachin Kumar, Hong Min

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Scaling of hardware-compatible perturbative training algorithms by B. G. Oripov, A. Dienstfrey, A. N. McCaughan, S. M. Buckley

“…Furthermore, we demonstrate that MGD can be used to calculate a drop-in replacement for the gradient in stochastic gradient descent, and therefore, optimization accelerators such as momentum can be used alongside MGD, ensuring compatibility with existing machine learning practices. Our results indicate that MGD can efficiently train large networks on hardware, achieving accuracy comparable with backpropagation, thus presenting a practical solution for future neuromorphic computing systems.…”
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Analyse von Begleittexten am Beispiel von 103 Ethnografien by Fabienne Kilchör, Jörg Lehmann

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Fusing multiplex heterogeneous networks using graph attention-aware fusion networks by Ziynet Nesibe Kesimoglu, Serdar Bozdag

“…Abstract Graph Neural Networks (GNN) emerged as a deep learning framework to generate node and graph embeddings for downstream machine learning tasks. …”
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Graph Neural Networks for Digital Pathology by Vincenzo DELLA MEA, Hafsa AKEBLI, Kevin ROITERO

“…The graph learning tasks can be either node-level, edge-level or graph-level. …”
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Optimizing IoT Intrusion Detection—A Graph Neural Network Approach with Attribute-Based Graph Construction by Tien Ngo, Jiao Yin, Yong-Feng Ge, Hua Wang

“…While graph deep-learning-based methods have shown promise in cybersecurity applications, existing approaches primarily construct graphs based on physical network connections, which may not effectively capture node representations. …”
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Consensus-based sparse signal reconstruction algorithm for wireless sensor networks by Bao Peng, Zhi Zhao, Guangjie Han, Jian Shen

“…This article presents a distributed Bayesian reconstruction algorithm for wireless sensor networks to reconstruct the sparse signals based on variational sparse Bayesian learning and consensus filter. …”
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CNN Based Fault Classification and Predition of 33kw Solar PV System with IoT Based Smart Data Collection Setup by K. Punitha, G. Sivapriya, T. Jayachitra

“…This research presents a unique machine learning model based fault diagnosis and detection method for a 33 KW solar PV system at P.S.R. …”
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Feature-enriched hyperbolic network geometry by Roya Aliakbarisani, M. Ángeles Serrano, Marián Boguñá

“…Notably, node features are at the core of deep learning techniques, such as graph convolutional neural networks (GCNs), offering great utility in downstream tasks. …”
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Child-Sum (N2E2N)Tree-LSTMs: An interactive Child-Sum Tree-LSTMs to extract biomedical event by Lei Wang, Han Cao, Liu Yuan

“…However, the original Child-Sum Tree-LSTMs ignores edge features during aggregating the sub-nodes hidden states. To enhance node representation, we propose an interaction mechanism that can alternately updating nodes and edges vectors, thus the model can learn the richer nodes vectors. …”
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