Explainable AI supported hybrid deep learnig method for layer 2 intrusion detection by Ilhan Firat Kilincer

“…This study proposes the creation of a Comprehensive Layer 2 − IDS (CL2-IDS) dataset for the development of IDS systems utilised in the local network structures of organisations, in conjunction with a hybrid deep learning (DL) model for the detection of attack vectors in the proposed dataset. …”
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Scheme for identifying malware traffic with TLS data based on machine learning by Ziming LUO, Shubin XU, Xiaodong LIU

Subjects: “…transport layer security…”
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ADF-SL: An Adaptive and Fair Scheme for Smart Learning Task Distribution by Ahmed A. Al-Saedi, Veselka Boeva

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Age and gender-related changes in choroidal thickness: Insights from deep learning analysis of swept-source OCT images by Dan Song, Guanzheng Wang, Guangfeng Liu, Chengxia Zhang, Bin Lv, Yuan Ni, Guotong Xie

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A multilayer deep autoencoder approach for cross layer IoT attack detection using deep learning algorithms by K. Saranya, A. Valarmathi

“…This work presents the Multi-Layer Deep Autoencoder (M-LDAE), especially tailored for cross-layer IoT threat detection, to solve these difficulties. …”
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LayerFold: A Python library to reduce the depth of neural networks by Giommaria Pilo, Nour Hezbri, André Pereira e Ferreira, Victor Quétu, Enzo Tartaglione

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Assessment of Landslide Susceptibility Based on the Two-Layer Stacking Model—A Case Study of Jiacha County, China by Zhihan Wang, Tao Wen, Ningsheng Chen, Ruixuan Tang

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Quantitative evaluation of grooves in fuel ice layers of ICF based on deep learning and x-ray phase retrieval by Kaijun Shi, Kai Wang, Xin Wang, Ji Yan, Baolu Yang, Cheng Yang, Mingtao Li, Mingxun Wang, Jie Xu, Fei Dai, Xing Zhang, Zhanshan Wang, Baozhong Mu

“…This study presents a quantitative evaluation method for grooves based on deep learning and phase retrieval. The XPC images of a capsule were first processed by phase retrieval, and the angular distribution of the inner interface of the ice layer was extracted. …”
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Mixed Layer Depth Estimation From Multisource Remote Sensing Data Using Clustering-Machine Learning Method by Zengxin Guan, Kaijun Ren, Senliang Bao, Hengqian Yan, Huizan Wang, Yanlai Zhao, Jianbin Liu

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MeDML: Med-Dynamic Meta Learning - A multi-layered representation to identify provider fraud in healthcare by Nitish Kumar, Deepak Chaurasiya, Alok Singh, Siddhartha Asthana, Kushagra Agarwal, Ankur Arora

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On the Sufficiency of a Single Hidden Layer in Feed-Forward Neural Networks Used for Machine Learning of Materials Properties by Ye Min Thant, Sergei Manzhos, Manabu Ihara, Methawee Nukunudompanich

Subjects: “…machine learning…”
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VTA projections to M1 are essential for reorganization of layer 2-3 network dynamics underlying motor learning by Amir Ghanayim, Hadas Benisty, Avigail Cohen Rimon, Sivan Schwartz, Sally Dabdoob, Shira Lifshitz, Ronen Talmon, Jackie Schiller

“…Using dexterity task, calcium imaging, chemogenetic inhibiting, and geometric data analysis, we demonstrate VTA-dependent reorganization of M1 layer 2-3 during motor learning. While average activity and average functional connectivity of layer 2-3 network remain stable during learning, activity kinetics, correlational configuration of functional connectivity, and average connectivity strength of layer 2-3 neurons gradually transform towards an expert configuration. …”
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Uncertainty-Aware Deep Learning for Robust and Interpretable MI EEG Using Channel Dropout and LayerCAM Integration by Óscar Wladimir Gómez-Morales, Sofia Escalante-Escobar, Diego Fabian Collazos-Huertas, Andrés Marino Álvarez-Meza, German Castellanos-Dominguez

“…We evaluate three DL architectures (<i>ShallowConvNet</i>, <i>EEGNet</i>, <i>TCNet Fusion</i>) on a 52-subject MI-EEG dataset, applying channel dropout to simulate structural variability and LayerCAM to visualize spatiotemporal patterns. Results demonstrate that among the three evaluated deep learning models for MI-EEG classification, <i>TCNet Fusion</i> achieved the highest peak accuracy of 74.4% using 32 EEG channels. …”
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HETMCL: High-Frequency Enhancement Transformer and Multi-Layer Context Learning Network for Remote Sensing Scene Classification by Haiyan Xu, Yanni Song, Gang Xu, Ke Wu, Jianguang Wen

“…To solve this problem, we propose a novel method based on High-Frequency Enhanced Vision Transformer and Multi-Layer Context Learning (HETMCL), which can effectively learn the comprehensive features of high-frequency and low-frequency information in visual data. …”
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Entorhinal cortex layer III Adgrl2 expression controls topographical circuit connectivity required for sequence learning by Jordan D. Donohue, Crisylle Blanton, Anna Chen, Amna Ahmad, Elizabeth D. Liu, Lisette Saab, Rajbir Kaur, Woojin Yang, Garret R. Anderson

“…These neural connectivity impacts include MECIII axon projections to contralateral MEC layer I, and presubiculum axons to ipsilateral MEC layer III. …”
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Aero Engine Component Fault Diagnosis Using Multi-Hidden-Layer Extreme Learning Machine with Optimized Structure by Shan Pang, Xinyi Yang, Xiaofeng Zhang

“…A new aero gas turbine engine gas path component fault diagnosis method based on multi-hidden-layer extreme learning machine with optimized structure (OM-ELM) was proposed. …”
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Traffic identification of network business based on multi-layer cascade algorithm by Feng HUANG, Yao WANG, Li HUANG, Jiming YAO, Shidong LIU

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Securing Blockchain Systems: A Layer-Oriented Survey of Threats, Vulnerability Taxonomy, and Detection Methods by Mohammad Jaminur Islam, Saminur Islam, Mahmud Hossain, Shahid Noor, S. M. Riazul Islam

“…Additionally, the layer-based framework demonstrates that vulnerabilities span all layers of a blockchain system, with attacks frequently targeting the consensus process, network integrity, and smart contract code. …”
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Short-term photovoltaic power forecasting based on a new hybrid deep learning model incorporating transfer learning strategy by Tiandong Ma, Feng Li, Renlong Gao, Siyu Hu, Wenwen Ma

“…First, the processed data are input into the DCNN layer, and the dilation convolution mechanism captures the spatial features of the wide sensory field of the input data. …”
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Evaluating Security Anomalies by Classifying Traffic Using a Multi-Layered Model by Mohammadreza Samadzadeh, Najmeh Farajipour Ghohroud

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