Photovoltaic output prediction based on VMD disturbance feature extraction and WaveNet by ShouSheng Zhao, Xiaofeng Yang, Kangyi Li, Xijuan Li, Weiwen Qi, Xingxing Huang

“…Then, to reveal power changes, especially the underlying patterns of disturbances and their relationship with weather factors, K-means clustering is applied to the IMF modes representing output disturbances, clustering the disturbance IMFs into different power change feature clusters. …”
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The order of multisensory associative sequences is reinstated as context feature during successful recognition by Marike Christiane Maack, Jan Ostrowski, Michael Rose

“…Furthermore, MVPA successfully decoded neural patterns of different modality sequences, hinting at specific memory traces. …”
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Improving unsupervised pedestrian re‐identification with enhanced feature representation and robust clustering by Jiang Luo, Lingjun Liu

“…A global contrastive pooling (GCP) module is introduced to obtain the global features of the image. Second, a dispersion‐based clustering method, which can effectively evaluate the quality of clustering and discover potential patterns in the data is designed. …”
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Distributed Photovoltaic Communication Anomaly Detection Based on Spatiotemporal Feature Collaborative Modeling by Li Di, Zhuo Lv, Hao Chang, Junfei Cai

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Multi-Function Working Mode Recognition Based on Multi-Feature Joint Learning by Lei Liu, Minghua Wu, Dongyang Cheng, Wei Wang

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Detecting Lameness in Dairy Cows Based on Gait Feature Mapping and Attention Mechanisms by Xi Kang, Junjie Liang, Qian Li, Gang Liu

“…Current computer vision approaches often rely on isolated lameness feature quantification, disregarding critical interdependencies among gait parameters. …”
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XGBoost Algorithm for Cervical Cancer Risk Prediction: Multi-dimensional Feature Analysis by Sudi Suryadi, Masrizal

“…This performance profile indicates adept navigation of the delicate balance between missed diagnoses and unnecessary interventions. Feature importance analysis revealed a multifaceted risk landscape, where screening test results contributed substantial predictive power (approximately 60%), complemented by demographic and behavioral factors, including age, reproductive history, and contraceptive usage patterns. …”
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Adaptive dual-graph learning joint feature selection for EEG emotion recognition by Liangliang Hu, Congming Tan, Yin Tian

“…Domain-invariant feature selection projects EEG data from different domains into a shared subspace, capturing emotion-related features that are domain-independent, thereby effectively mitigating data differences across subjects and sessions. …”
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A Lightweight Tri-Stream Feature Fusion Network for Speech Emotion Recognition by Ronghe Cao, Yunxing Wang, Xiaolong Wu, Shuang Jin, Huiling Niu

“…Tri-Stream integrates three complementary feature streams: spectral patterns extracted via a Swin Transformer, deep acoustic representations from HuBERT, and engineered prosodic features capturing rhythmic information. …”
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Unsupervised feature selection based on generalized regression model with linear discriminant constraints by Xiangguang Dai, Mingyu Guan, Facheng Dai, Wei Zhang, Tingji Zhang, Hangjun Che, Xiangqin Dai

“…Benefited from this, the relationships and patterns within the high-dimensional data are retained in the reduced-dimensional feature space. …”
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Reconstructing coastal ponds functional classification: Integration of multi-feature remote sensing by Yijun Tong, Chen Lin, Ke Song, Tingchen Jiang, Ronghua Ma, Wenzhuo Cui, Danhua Ma, Jianchun Chen, Zhenxing Wang, Xiaofen Bai

“…The functional types of PWS can be categorized as aquaculture, landscaping, water storage, and salt drying. (2) Regarding different PWS functional types, significant differences were demonstrated in terms of remote sensing features and geographical patterns. Remote sensing features revealed that LCAP, MAS, and SP differ greatly across various spectral bands, whereas NP varied substantially in shape characteristics, and LP exhibited distinct spatial distribution. …”
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AFQSeg: An Adaptive Feature Quantization Network for Instance-Level Surface Crack Segmentation by Shaoliang Fang, Lu Lu, Zhu Lin, Zhanyu Yang, Shaosheng Wang

“…Specifically, the maximum soft pooling module improves the continuity and integrity of detected cracks. The adaptive crack feature quantization module enhances the contrast between cracks and background features and strengthens the model’s focus on critical regions through spatial feature fusion. …”
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PFVnet, a feature enhancement network for low recognition coal and rock images by Cai Han, Zhenwen Liu, Shenglei Zhao, Yubo Li, Yanwei Duan, Xinzhou Yang, Chuanbo Hao

“…We characterized the grayscale and texture feature patterns of coal-rock media under varying degrees of interference and established a comprehensive multi-element image training sample library. …”
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Machine Learning-Driven Acoustic Feature Classification and Pronunciation Assessment for Mandarin Learners by Gulnur Arkin, Tangnur Abdukelim, Hankiz Yilahun, Askar Hamdulla

“…Based on acoustic feature analysis, this study systematically examines the differences in vowel pronunciation characteristics among Mandarin learners at various proficiency levels. …”
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Lightweight ECG signal classification via linear law-based feature extraction by Péter Pósfay, Marcell T Kurbucz, Péter Kovács, Antal Jakovác

“…The method identifies linear laws that capture shared patterns within a reference class, enabling compact and verifiable representations of time series data. …”
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EEG-Based Emotion Detection Using Roberts Similarity and PSO Feature Selection by Mustafa Hussein Mohammed, Mustafa Noaman Kadhim, Dhiah Al-Shammary, Ayman Ibaida

“…The proposed classifier addresses these challenges by segmenting EEG signals into block sizes categorized as small (1 to 10 samples), medium (20 to 100 samples), and large (200 to 1,000 samples), demonstrating particularly strong performance with medium and large block sizes to capture essential features. Integration of Particle Swarm Optimization (PSO) for feature selection, with Robert’s similarity as the fitness function, effectively refines the feature set, boosting classification accuracy and computational efficiency. …”
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Employee Turnover Prediction Model Based on Feature Selection and Imbalanced Data Handling by Yuan Fang, Zhongqiu Zhang

“…The dataset underwent rigorous preprocessing and exploratory data analysis (EDA) to identify key patterns and relationships. Feature selection was performed using correlation matrix analysis, Chi-Square tests, and Recursive Feature Elimination (RFE) to identify the most relevant features. …”
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N6-methyladenine identification using deep learning and discriminative feature integration by Salman Khan, Islam Uddin, Sumaiya Noor, Salman A. AlQahtani, Nijad Ahmad

“…In this study, we present Deep-N6mA, a novel Deep Neural Network (DNN) model incorporating optimal hybrid features for precise 6 mA site identification. The proposed framework captures complex patterns from DNA sequences through a comprehensive feature extraction process, leveraging k-mer, Dinucleotide-based Cross Covariance (DCC), Trinucleotide-based Auto Covariance (TAC), Pseudo Single Nucleotide Composition (PseSNC), Pseudo Dinucleotide Composition (PseDNC), and Pseudo Trinucleotide Composition (PseTNC). …”
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A novel Swin transformer based framework for speech recognition for dysarthria by Rabbia Mahum, Ismaila Ganiyu, Lotfi Hidri, Ahmed M. El-Sherbeeny, Haseeb Hassan

“…Firstly, the speech is converted into mel-spectrograms to reflect the maximum patterns of voice signals. Despite the ST’s initial aim to effectively extract the local and global visual features, it still prioritizes global features. …”
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An Agglomerative Clustering Combined with an Unsupervised Feature Selection Approach for Structural Health Monitoring by Tales Boratto, Heder Soares Bernardino, Alex Borges Vieira, Tiago Silveira Gontijo, Matteo Bodini, Dmitriy A. Martyushev, Camila Martins Saporetti, Alexandre Cury, Flávio Barbosa, Leonardo Goliatt

“…The results obtained for the four analyzed cases provide clear insights into the patterns of behavior and structural anomalies.…”
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