Using Cuckoo Search Algorithm to Predict Corporate Financial Risks and Alleviate Economic Uncertainty by Muqiao Cai

“…The system’s dynamic learning capabilities identify patterns connected to company failures and financial decline. …”
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Hybrid boosted attention-based LightGBM framework for enhanced credit risk assessment in digital finance by Chengwei Ying, Anlu Shi, Xiongyi Li

“…The HBA-LGBM model introduces four key innovations: (1) a multi-stage feature selection mechanism that dynamically filters key borrower attributes; (2) an attention-based feature enhancement layer, which prioritizes critical financial risk factors dynamically based on contextual importance; (3) a hybrid boosting strategy, integrating LightGBM with an adaptive neural network, enabling the model to capture complex borrower behavior and non-linear credit risk patterns; and (4) an advanced imbalanced learning strategy, combining synthetic data augmentation and cost-sensitive learning to mitigate class imbalance and enhance minority class predictions. …”
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Temporal and spatial self supervised learning methods for electrocardiograms by Wenping Chen, Huibin Wang, Lili Zhang, Min Zhang

“…This highlights TSSL’s ability to provide deeper insights and enhanced feature extraction beyond mere performance improvements. …”
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Potential of EnMAP Hyperspectral Imagery for Regional-Scale Soil Organic Matter Mapping by Yassine Bouslihim, Abdelkrim Bouasria

“…The combination of SNV with feature selection successfully identified significant wavelengths for SOM prediction, particularly around 550 nm in the Vis–NIR region, 1570–1630 nm, and 1600 nm and 2200 nm in the SWIR region. …”
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LGFusion: Frequency-Aware Dual-Branch Integration Network for Infrared and Visible Image Fusion by Ruizhe Shang, Jianan Liu, Xinhuai Wang

“…To address these challenges, we propose LGFusion, a dual-branch parallel fusion framework that enables structure-aware and detail-preserving feature extraction and fusion. We introduce a frequency-based structural assumption: low-frequency components across modalities are correlated and represent shared background and layout, while high-frequency components are modality-specific, capturing details like thermal patterns or textures. …”
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Incorporating edge convolution and correlative self-attention into graph neural network for material properties prediction by Zexi Yang, Qi Yu, Yapeng Zhan, Jiying Liu

“…Indeed, even the periodic patterns of the crystal structures are not seriously considered. …”
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Supramodal and cross-modal representations of working memory in higher-order cortex by Doyoung Park, Seong-Hwan Hwang, Keonwoo Lee, Yeeun Ryoo, Hyoung F. Kim, Sue-Hyun Lee

“…While numerous studies have shown the involvement of sensory areas in maintaining working memory in a feature-specific manner, the challenge of utilizing retained sensory representations without interference from incoming stimuli of the same feature remains unresolved. …”
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Enhancing student success prediction in higher education with swarm optimized enhanced efficientNet attention mechanism. by Meshari Alazmi, Nasir Ayub

“…In addition, we developed a novel hybrid feature selection model that combined correlation filtering with mutual information, Cross-Validation (CV) along with Recursive Feature Eliminatio (RFE) (R, and stability selection to identify the most impactful features. …”
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Low-frequency local field potentials reveal integration of spatial and non-spatial information in prefrontal cortex by Mohammad Aliramezani, Balbir Singh, Christos Constantinidis, Mohammad Reza Daliri

“…The similar LFP power patterns in the PFC subdivisions for spatial and feature stimuli throughout the analysis suggested that spatial and non-spatial inputs are integrated by the PFC, revealed by the low-frequency components of the LFP.…”
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An Enhanced Approach for Remaining Useful Life Prediction of Bearings Using Incomplete Lifecycle Data by Xunmeng An, Chao Zhang, Caiye Liu, Wentao Zhao

“…To address these challenges, we propose the Incomplete Lifecycle Data Enhanced Network (ILDENet), a self-supervised learning network incorporating contrastive learning to improve feature discrimination. The network operates in three distinct phases: first, feature parameters indicative of bearing degradation trends are extracted across time, frequency, and time-frequency domains. …”
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Integrating hybrid bald eagle crow search algorithm and deep learning for enhanced malicious node detection in secure distributed systems by Feras Mohammed Al-Matarneh

“…Malicious node recognition is a crucial feature of safeguarding the safety and reliability of distributed methods. …”
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Predicting biking preferences in Kigali city: A comparative study of traditional statistical models and ensemble machine learning models by Jean Marie Vianney Ntamwiza, Hannibal Bwire

“…Ensemble models classified better biking preferences (shared, non-shared, and both categories), significantly improving precision and recall across all three groups. Feature importance indicated that day and month are critical factors in bike preference prediction, reflecting significant daily and seasonal patterns. …”
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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

“…Their approach combines advanced techniques, including convolutional neural networks (CNNs) for feature extraction, long short-term memory networks (LSTMs) for temporal patterns, transformer-based attention mechanisms for critical feature identification, and graph neural networks (GNNs) for modeling sensor-machine relationships. …”
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Arsitektur Convolutional Neural Network untuk Model Klasifikasi Citra Batik Yogyakarta by Arya Prayoga, Maimunah, Pristi Sukmasetya, Muhammad Resa Arif Yudianto, Rofi Abul Hasani

“…The CNN architecture consists of two stages, namely Convolutional for feature extraction and Neural Network for classification. …”
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LSEVGG: An attention mechanism and lightweight-improved VGG network for remote sensing landscape image classification by Yao Lu

“…The model exhibits particularly strong performance in identifying natural landscape categories, achieving 88%–92% accuracy for classes with uniform spatial patterns such as forests, beaches, and meadows, where the SE attention mechanism effectively captures distinctive textural and spatial features characteristic of different landscape types. …”
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Mapping gender networks of smartphone addiction and academic procrastination: a network analysis study by Lu Song, Zhilin Liu, Yujia Yang, Shuangshuang Yuan

“…In addition, within the SA-AP interaction network, the core feature of the male network is academic procrastination, reflecting deficiencies in time management and self-regulation. …”
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ADA-NAF: Semi-Supervised Anomaly Detection Based on the Neural Attention Forest by Andrey Ageev, Andrei Konstantinov, Lev Utkin

“…First, a proposed end-to-end training methodology over normal data minimizes the reconstruction errors while learning and optimizing neural attention weights to focus on hidden features. Second, a novel encoding mechanism leverages NAF’s hierarchical structure to capture complex data patterns. …”
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Meta-Learning With Relation Embedding for Few-Shot Deepfake Detection by Xiaoyong Liu, Pengcheng Song, Pei Lu, Yanjun Wang

“…Initially, we employ an embedding function to generate feature representations of the images. Subsequently, we convert the basic representations of feature maps into their corresponding self-correlation tensors, enabling us to learn the structural patterns inherent in these tensors. …”
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IoT-Based Traffic Prediction for Smart Cities by Zhinong Miao, Qilong Liao

“…Traffic data collected from IoT sensors were processed to extract relevant spatial features using CNNs, and PSO was employed to fine-tune the CNN hyperparameters and feature selection. …”
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Time-domain brain: temporal mechanisms for brain functions using time-delay nets, holographic processes, radio communications, and emergent oscillatory sequences by Janet M. Baker, Peter Cariani

“…It adopts a signal-centric perspective in which neural assemblies produce circulating and propagating characteristic temporally patterned signals for each attribute (feature). Temporal precision is essential for temporal coding and processing. …”
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