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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Personal recognition using finger knuckle shape oriented features and texture analysis by K. Usha, M. Ezhilarasan

“…Whereas, the knuckle texture feature analysis is carried out by means of multi-resolution transform known as Curvelet transform. …”
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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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Type-specific human papillomavirus biological features: validated model-based estimates. by Iacopo Baussano, K Miriam Elfström, Fulvio Lazzarato, Anna Gillio-Tos, Laura De Marco, Francesca Carozzi, Francesca Carozzi, Annarosa Del Mistro, Joakim Dillner, Silvia Franceschi, Guglielmo Ronco

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Radiological Features of Changes in the Lungs Caused by Fast- or Slow-Growing Nontuberculous Mycobacteria by R. B. Amansakhedov, L. I. Dmitrieva, T. G. Smirnova, A. A. Veshkin, A. E. Ergeshov

“…Despite the similarities of clinical and radiological patterns of NTM, we established some distinguished features for slow-growing and fast-growing types.…”
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A Biopsy as a Rescue in a Case of Pancreatoblastoma with Vague Radiology Features by Paheli Maru, Dhaval Jetly, Rujuta Shah, Sanjiban Patra, Udaya S

“…Because they present with vague clinical features, it is important to recognise them and make correct pathological diagnoses using radiological suggestions and histopathology. …”
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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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Features of the functioning of regional airports in the context of the development of the Russian air transportation market by I. N. Rykova, A. A. Alaev, R. S. Gubanov

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Machine Learning-Based Diabetes Risk Prediction Using Associated Behavioral Features by Ayodeji O. J. Ibitoye, Joseph D. Akinyemi, Olufade F. W. Onifade

“…With different uncertainties in human lifestyles, it is difficult to predict diabetes while assuming that the risk patterns are the same for all. The likelihood of diabetes in a patient is mostly predicted using machine learning (ML) models on features explicitly available in datasets, while the intrinsic relationship between features viz-a-viz their potential relevance to the presence of diabetes is oftentimes neglected. …”
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SOME FEATURES OF CHANGING TELOMERE LENGTH IN LYMPHOCYTES FROM THE PATIENTS WITH BRONCHIAL ASTHMA by V. I. Borisov, S. A. Demakov, V. M. Nepomnyashchikh, M. I. Leonova, D. V. Demina, N. A. Barovskaya, V. S. Kozhevnikov

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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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The Specific Features of Congenital and Acquired Forms of Behavior in the Postresuscitative Period (Experimental Study) by Yu. V. Zarzhetsky, A. V. Volkov, M. Sh. Avrushchenko

“…Postresuscitatively, the rats were found to preserve their individual typological features of behavior. At the same time the animals with baseline various behavioral patterns showed great differences in becoming familiar with a new environment. …”
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OSFS‐Vague: Online streaming feature selection algorithm based on vague set by Jie Yang, Zhijun Wang, Guoyin Wang, Yanmin Liu, Yi He, Di Wu

“…In real big data‐related applications, the patterns and distributions of streaming features constantly change over time due to dynamic data generation environments. …”
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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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MLCRP: ML-Based GPU Cache Performance Modeling Featured With Reuse Profiles by Minjung Cho, Eui-Young Chung

“…In the data preparation stage, synthetic RP-based traces are generated from parameterized distributions to simulate diverse and non-stationary memory patterns. In the training stage, a regression-based ML model is trained to capture the relationship between RP features, cache configurations, and performance metrics such as miss rate and miss status holding register (MSHR) merge rate. …”
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