Advances in Neural Network assisted Tool Pressure Prediction by Göltl Florian, Harst Felix, Birkert Arndt, Stache Nicolaj C.

“…It has been demonstrated that convolutional neural networks (CNNs) can predict pressure distributions from spotting patterns. …”
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A novel robust network construction and analysis workflow for mining infant microbiota relationships by Wei Jiang, Yue Zhai, Dongbo Chen, Qinghua Yu

“…Understanding the relationships within the microbiota is essential to linking its maturation process to these outcomes. Several network-based methods have been developed to analyze the developing patterns of infant microbiota, but evaluating the reliability and effectiveness of these approaches remains a challenge. …”
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A Review of Link Prediction Algorithms in Dynamic Networks by Mengdi Sun, Minghu Tang

“…After summarizing and comparing the common datasets and evaluation indicators for dynamic network link prediction, we briefly review classic related algorithms in recent years, and classify them according to the network changes, sampling methods, underlying principles of algorithms, and other classification methods. …”
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Klasifikasi Ekspresi Wajah Menggunakan Covolutional Neural Network by Ahmad Taufiq Akbar, Shoffan Saifullah, Hari Prapcoyo

“…Penelitian ini mengusulkan arsitektur sederhana Convolutional Neural Network (CNN) untuk meningkatkan efisiensi klasifikasi emosi pada dataset kecil. …”
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Clustering and Detecting Network Intrusion Based on Fuzzy Algorithms by Manar Kashmola, Bayda Khaleel

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Graph Neural Network Learning on the Pediatric Structural Connectome by Anand Srinivasan, Rajikha Raja, John O. Glass, Melissa M. Hudson, Noah D. Sabin, Kevin R. Krull, Wilburn E. Reddick

“…Architecture exploration experiments were conducted to evaluate the impact of network depth, pooling techniques, and skip connections on the ability of GNN models to capture connectomic patterns. …”
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Convolutional Neural Networks for Predicting Power Outages in Baghdad by Saja Jafar Jawad, Shaymaa. W. Al-Shammari

“…The research uses this dataset to train one-dimensional Convolutional Neural Networks (1D CNN) to find patterns and relationships that can accurately predict when power outages can happen in the long term and short term to improve the management of the Baghdad electricity grid through data-driven networks. …”
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Dual-Path Adversarial Denoising Network Based on UNet by Jinchi Yu, Yu Zhou, Mingchen Sun, Dadong Wang

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Binarized Neural Networks for Resource-Efficient Spike Sorting by Luca M. Meyer, Majid Zamani, Andreas Demosthenous

“…This enables the generation of precise modulatory patterns of neuronal circuits. Deep learning-based spike sorting algorithms are based on power-intensive dot products, which poses challenges for on-chip processing with resource-constrained devices. …”
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Grinding wheel wear evaluation with the PMSCNN model by Sumei Si, Zekai Si, Deqiang Mu, Hailiang Tang

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Evaluation of gridded precipitation datasets over Iran by Mohammad Saeed Najafi, Omid Alizadeh, Tobias Sauter

“…The accuracy of these datasets in representing precipitation patterns is assessed from 2000 to 2017, with a focus on their performance across diverse climate zones in Iran. …”
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LIQUEFACTION POTENTIAL ASSESSMENT USING MULTILAYER ARTIFICIAL NEURAL NETWORK

“…In this paper, the classification operation, in which an input pattern is passed to the network and the network produces a representative class as output, is considered for evaluation of liquefaction hazard potential. …”
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SankeyNetwork: A clear and concise visualization tool for bibliometric data by Sher-Wei Lim, Willy Chou, Lifan Chen

“…This study proposes a novel framework to overcome the limitations of traditional bibliometric visualizations—such as co-word network charts—by integrating Sankey diagrams with author collaborations and co-word occurrences to better identify key contributors and themes. …”
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Quantum Neural Network Based Machine Translator for Hindi to English by Ravi Narayan, V. P. Singh, S. Chakraverty

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Control of Linear-Threshold Brain Networks via Reservoir Computing by Michael McCreesh, Jorge Cortes

“…Given the impracticality of evaluating closed-form control signals, particularly with growing network complexity, we provide a framework where a reservoir of a larger size than the network is trained to drive the activity to the desired pattern. …”
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Modulating salience network connectivity through olfactory nerve stimulation by Carina Heller, Maria Geisler, Nicolas L. Mayer, Annabelle Thierfelder, Martin Walter, Thomas Hummel, Ilona Croy

“…Modification of this clinical pattern is challenging. Leveraging the direct neural pathways from olfactory processing regions to the salience network, we explored the effects of electrical stimulation of the olfactory mucosa on brain connectivity. …”
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Research on disaster information dissemination based on social sensor networks by Shanshan Wan, Zhuo Chen, Cheng Lyu, Ruofan Li, Yuntao Yue, Ying Liu

“…Previous studies mainly focused on the impact of human mobility patterns on social sensor networks. In this article, based on the inherent autonomy property of human individuals, we propose a social sensor information dissemination model, which mainly focuses on the impact of the individual characteristics, social characteristics, and group information dissemination mode on social sensor networks. …”
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Graph Neural Networks for Pressure Estimation in Water Distribution Systems by Huy Truong, Andrés Tello, Alexander Lazovik, Victoria Degeler

“…Second, a realistic evaluation protocol that considers real temporal patterns and noise injection to mimic the uncertainties intrinsic to real‐world scenarios. …”
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Generative Adversarial Networks for Dynamic Cybersecurity Threat Detection and Mitigation by William Villegas-Ch, Rommel Gutierrez, Jaime Govea

“…The model is designed to detect anomalies in network traffic and generate malicious synthetic patterns to strengthen system defenses. …”
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Osteoarthritis Classification Using Hybrid Quantum Convolutional Neural Network by Devansh Tikariha, Abdul Moomin, D. Jeyamani, P. Rukmani

“…Using a QCNN, this model harnesses the ability of quantum computing to represent high-dimensional data transformations, a novel approach that complements classical CNN layers by exploring patterns that are not captured in traditional networks. …”
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