Combining Generative Adversarial Networks (GANs) With Gaussian Noise for Anomaly Detection in Internet of Things (IoT) Traffic by Roya Morshedi, S. Mojtaba Matinkhah

“…The results indicate that this model offers superior performance in learning attack patterns, enhancing detection accuracy, and reducing false positives. …”
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Grayscale Lithography and a Brief Introduction to Other Widely Used Lithographic Methods: A State-of-the-Art Review by Svetlana N. Khonina, Nikolay L. Kazanskiy, Muhammad A. Butt

“…Grayscale lithography (GSL) is highly valued in precision manufacturing and research endeavors because of its unique capacity to create intricate and customizable patterns with varying depths and intensities. Unlike traditional binary lithography, which produces discrete on/off features, GSL offers a spectrum of exposure levels. …”
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Automatic Characterization of the Physiological Condition of the Carotid Artery in 2D Ultrasound Image Sequences Using Spatiotemporal and Spatiospectral 2D Maps by Hamed Hamid Muhammed, Jimmy C. Azar

“…Spatiotemporal and spatiospectral 2D maps describing these patterns (in both the spatial and the frequency domains, resp.) were generated and analyzed by visual inspection as well as automatic feature extraction and classification. …”
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Common brain representations of action and perception investigated with cross-modal classification of newly learned melodies by Yu-Hsin Fiona Chang, Fredrik Ullén, Örjan de Manzano

“…Within-condition multivariate pattern analyses revealed that patterns of activity in auditory-motor regions represent pitch sequences. …”
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Not an Illusion but a Manifestation: Understanding Large Language Model Reasoning Limitations Through Dual-Process Theory by Boris Gorelik

“…In other words, they represent not a bug but a feature of bounded rational systems. I propose empirically testable hypotheses comparing LRM token patterns with human pupillometry data. …”
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A deep learning framework for virtual continuous glucose monitoring and glucose prediction based on life-log data by Min Hyuk Lim, Hyocheol Chae, Jeongwon Yoon, Insik Shin

“…The distribution of latent representations from the encoder showed the potential differentiation for glucose patterns. The model’s ability to maintain predictive accuracy during periods of CGM unavailability has the potential to support intermittent monitoring scenarios for users.…”
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Adaptive multi-scale phase-aware fusion network for EEG seizure recognition by Yanting Liang, Jingyuan Liu, Xinzhou Zhang

“…The framework integrates three novel components: (1) a Dynamic Frequency Selection (DFS) module employing Gumbel-SoftMax for adaptive spectral filtering to enhance seizure-related frequency bands; (2) a Multi-Scale Feature Extraction (MCFE) module using hierarchical downsampling and temperature-controlled multi-head attention to capture both macro-rhythmic and micro-transient EEG patterns; and (3) a Multi-Scale Phase-Aware Fusion (MCPA) module that aligns temporal features across scales through phase-sensitive weighting.ResultsThe AMS-PAFN was evaluated on the CHB-MIT dataset and achieved state-of-the-art performance, with 98.97% accuracy, 99.53% sensitivity, and 95.21% specificity (Subset 1). …”
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Identification of genes related to fatty acid metabolism in type 2 diabetes mellitus by Ji Yang, Yikun Zhou, Jiarui Zhang, Yongqin Zheng, Jundong He

“…Immune cells, including dendritic cells, eosinophils, and neutrophils, may play a role in the progression of T2DM. ceRNA and drug-target network analysis revealed potential interactions, such as RP11-miR-29a-YTHDF3 and BPA-MSANTD1. The expression patterns of the feature genes, except for YTHDF3, were consistently upregulated in T2DM, aligning with trends observed in the training set. …”
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A hybrid learning network with progressive resizing and PCA for diagnosis of cervical cancer on WSI slides by Nitin Kumar Chauhan, Krishna Singh, Amit Kumar, Ashutosh Mishra, Sachin Kumar Gupta, Shubham Mahajan, Seifedine Kadry, Jungeun Kim

“…ResNet-152 and VGG-16, two fine-tuned DL models, are employed together with transfer learning to train on augmented and progressively resized training data with dimensions of 224 × 224, 512 × 512, and 1024 × 1024 pixels for enhanced feature extraction. Principal component analysis (PCA) is subsequently employed to process the combined features extracted from two DL models and reduce the dimensional space of the feature set. …”
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A Comparative Study of Lesion-Centered and Severity-Based Approaches to Diabetic Retinopathy Classification: Improving Interpretability and Performance by Gang-Min Park, Ji-Hoon Moon, Ho-Gil Jung

“…Transfer learning from NMC to APTOS notably improved severity classification, achieving performance gains of 15.2% in mild cases and 66.3% in severe cases through feature fusion using Bidirectional Feature Pyramid Network (BiFPN) and Feature Pyramid Network (FPN). …”
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Federated Deep Learning for Scalable and Privacy-Preserving Distributed Denial-of-Service Attack Detection in Internet of Things Networks by Abdulrahman A. Alshdadi, Abdulwahab Ali Almazroi, Nasir Ayub, Miltiadis D. Lytras, Eesa Alsolami, Faisal S. Alsubaei, Riad Alharbey

“…This paper presents a Federated-Learning (FL) framework using ResVGG-SwinNet, a hybrid deep-learning architecture, for multi-label DDoS attack detection. ResNet improves feature extraction, VGGNet optimises feature refining, and Swin-Transformer captures contextual dependencies, making the model sensitive to complicated attack patterns across varied network circumstances. …”
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Genetic algorithm–optimized support vector machine for real-time activity recognition in health smart home by Yan Hu, Bingce Wang, Yuyan Sun, Jing An, Zhiliang Wang

“…This technology aims to recognize the activity patterns of users from a series of observations on the user’ actions and the environmental conditions, so as to avoid distress situations as much as possible. …”
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Deep learning driven prediction and comparative study of surrounding rock deformation in high speed railway tunnels by Zeping Yang, Zhikai Cheng, Da Wu

“…Using 300-hour continuous deformation records from multiple cross-sections of the G Tunnel (March 2023), a novel WOA-CNN-GRU model is developed, integrating data preprocessing, feature extraction, and prediction. The methodology incorporates quadratic exponential smoothing for outlier mitigation, followed by sequential feature extraction using convolutional neural networks (CNNs) and bidirectional gated recurrent units (GRUs). …”
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Adaptive Weighted Diversity Ensemble Learning Approach for Fetal Health Classification on Cardiotocography Data by K. Aditya Shastry, Mohan Sellappa Gounder, T. G. Mohan Kumar, D. U. Karthik, V. Sushma, D. Subashree

“…EDA provided insights into class distribution and feature correlations, guiding subsequent analysis. …”
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Wind Power Short-Term Prediction Method Based on Time-Domain Dual-Channel Adaptive Learning Model by Haotian Guo, Keng-Weng Lao, Junkun Hao, Xiaorui Hu

“…The ACON adaptive activation function autonomously learns optimal activation patterns, with fused features visualized through visualization techniques. …”
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Multimodal image fusion for ich detection and classification using parallel Dl models by Sri Sangepu Nagaraju, S. Prince Mary, V. Pavani Chandra, Nandam Gayatri

“…The model employs an encoder-decoder structure and uses a DFA attention module for efficient image feature extraction and easy channel feature weight integration. …”
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Multi-dimensional water quality indicators forecasting from IoT sensors: A tensor decomposition and multi-head self-attention mechanism. by Li Bo, Lv Junrui, Luo Xuegang

“…To overcome these limitations, we propose TGMHA (Tensor Decomposition and Gated Neural Network with Multi-Head Self-Attention), a novel hybrid model that integrates three key innovations: 1) Tensor-based Feature Extraction: We combine Standard Delay Embedding Transformation (SDET) with Tucker tensor decomposition to reconstruct raw time series into low-rank tensor representations, capturing latent spatio-temporal patterns while suppressing sensor noise. 2) Multi-Head Self-Attention for Inter-Indicator Dependencies: A multi-head self-attention mechanism explicitly models complex inter-dependencies among diverse water quality indicators (e.g., pH, dissolved oxygen, conductivity) via parallel feature subspace learning. 3) Efficient Long-Term Dependency Modeling: An encoder-decoder architecture with gated recurrent units (GRUs), optimized by adaptive rank selection, ensures efficient modeling of long-term dependencies without compromising computational performance. …”
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An Explainable Machine Learning Model for Predicting Macroseismic Intensity for Emergency Management by Federico Mori, Giuseppe Naso

“…Predicting macroseismic intensity from instrumental ground motion parameters remains a complex task due to the nonlinear relationship with observed damage patterns. An explainable machine learning model based on the XGBoost algorithm was developed to address the challenge. …”
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Effective Detection of Cloud Masks in Remote Sensing Images by Yichen Cui, Hong Shen, Chan-Tong Lam

“…The model uses an improved residual module to capture the multi-scale features of clouds more effectively. MDU-Net first extracts the feature maps using four residual modules at different scales, and then sends them to the context information full flow module for the first up-sampling. …”
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The Making of a Market: The Relational Aspects of Credit Card Installments in Turkey by Muhammed Fatih Karakaya

“…Installment sale as a century-old buy-on-credit pattern has allowed a new market device to take part in the polyadic relation of sale and debt until forming a hybrid pattern of buying on credit. …”
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