Evolutionary fuzzy learning for Chinese medicine liver syndrome differentiation by Jia-Yu Yan, Peng-Wei Zhang, Wei-Guo Sheng, Jun-Ping Shi, Wei Ni, Li Li, Yu-Jun Zheng

“…To effectively overcome the uncertainty and improve the interpretability, we proposed an evolutionary fuzzy learning method for syndrome differentiation of the TCM liver system, which used neuro-fuzzy inference systems to infer the severity of six typical syndromes (liver depression, liver blood deficiency, liver yin deficiency, liver-fire, liver-cold, and damp-heat liver-gallbladder) based on a wide set of symptoms as input features. …”
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Online variational Gaussian process for time series data by Weidong Wang, Mian Muhammad Yasir Khalil, Leta Yobsan Bayisa

“…In this paper, we propose the OnLine Variational Gaussian Process (OLVGP) algorithm, which introduces a novel approach for dynamically managing the number of inducing points based on the concept of eigenfunction inducing features. …”
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Analysis and Validation of a Diagnostic Nomogram for Predicting the Risk of Acute Respiratory Failure for Non-HIV Related Pneumocystis Jirovecii Pneumonia Patients by Bian W, Xin Y, Bao J, Gong P, Li R, Wang K, Xi W, Chen Y, Ni W, Gao Z

“…A total of 49 patients from Peking University People’s Hospital were collected for external validation. Crucial clinical features of these patients are selected applying univariate and multivariate logistic regression analysis. …”
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Effects of catnip (Nepeta cataria L.) and Mexican sunflower (Tithonia diversifolia L.) density on growth, yield, and proximate composition of jute mallow (Corchorus olitorius L.) by О.П. Айоделе, О.А. Алуко, О. Д. Адегбаджу

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Exploring Applications of Convolutional Neural Networks in Analyzing Multispectral Satellite Imagery: A Systematic Review by Antonia Ivanda, Ljiljana Šerić, Maja Braović

“…, RQ2: “What are the commonly utilized MSI datasets for training CNN models in the context of processing multispectral satellite imagery?”…”
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Social participation challenges and facilitators among Chinese stroke survivors: a qualitative descriptive study by Xiaojuan Wan, Dorothy Ngo Sheung Chan, Janita Pak Chun Chau, Yu Zhang, Zhi’e Gu, Limei Xu

“…Results Three investigator-derived categories and 14 subcategories based on data-derived responses were identified. …”
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Inonotus hispidus (Bull.) P. Karst from the Inner Western Anatolia part of Türkiye with a molecular analysis, phenotypic characters study, genus of host determination, and geo-hydr... by Ahmed Badri Abed, Kaan Hürkan, Safiye Elif Korcan, Arzu Ünal

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TransformerPayne: Enhancing Spectral Emulation Accuracy and Data Efficiency by Capturing Long-range Correlations by Tomasz Różański, Yuan-Sen Ting, Maja Jabłońska

“…This fine-tuning approach enabled up to a 10-fold reduction in training grid size compared to models trained from scratch. …”
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Detection of honey bees (<em>Apis mellifera</em>) in hypertemporal LiDAR point cloud time series to extract bee activity zones and times by J. S. Meyer, R. Tabernig, R. Tabernig, B. Höfle, B. Höfle

“…We employed an experimental setup of a permanent terrestrial laser scanner (Riegl VZ-600i) to capture point clouds in a region of interest of 3 &times; 2 &times; 5 m at regular intervals (30 s) over ca. 1.8 h. By training a random forest classifier based on local neighbourhood features, the classified points can then be clustered in single and distinct objects of bees/hornets. …”
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Supervised machine learning statistical models for visual outcome prediction in macular hole surgery: a single-surgeon, standardized surgery study by Kanika Godani, Vishma Prabhu, Priyanka Gandhi, Ayushi Choudhary, Shubham Darade, Rupal Kathare, Prathiba Hande, Ramesh Venkatesh

“…Conclusion The RF regression model demonstrated superior predictive accuracy for forecasting postoperative VA, suggesting ML-driven approaches may improve surgical planning and patient counselling by providing reliable insights into expected visual outcomes based on pre-operative OCT features. Clinical trial registration number Not applicable.…”
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Identifying drivers of surface ozone bias in global chemical reanalysis with explainable machine learning by K. Miyazaki, Y. Marchetti, J. Montgomery, S. Lu, K. Bowman

“…Surface ozone observations from the Tropospheric Ozone Assessment Report (TOAR) network and chemical reanalysis outputs from the multi-model multi-constituent chemical (MOMO-Chem) data assimilation (DA) system for the period 2005–2020 were utilized for ML training. A regression-tree-based randomized ensemble ML approach successfully reproduced the spatiotemporal patterns of ozone bias in the chemical reanalysis relative to TOAR observations across North America, Europe, and East Asia. …”
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Characterization of terminated and withdrawn clinical trials for the treatment and prevention of oral mucositis by Alex Reznik, Stephen Sonis, Alessandro Villa

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Forecasting Delivery Time of Goods in Supply Chains Using Machine Learning Methods by V. K. Rezvanov, O. M. Romakina, E. V. Zaytseva

“…The model showed satisfactory results in terms of time spent on training (3.3087 s) and forecasting (0.0051 s). Actual and predicted values almost perfectly matched. …”
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Development of interpretable intelligent frameworks for estimating river water turbidity by Amin Gharehbaghi, Salim Heddam, Saeid Mehdizadeh, Sungwon Kim

“…USGS 14206950 and USGS 14211720) were selected as a case study. 70% and 30% of whole data were utilized as the training and validation datasets when developing the models, respectively. …”
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Leveraging snow probe data, lidar, and machine learning for snow depth estimation in complex-terrain environments by D. Liljestrand, R. Johnson, B. Neilson, P. Strong, E. Cotter

“…This study aims to generate basin-scale snow depth estimates using a multistep, Gaussian-based machine learning model that combines snow probe depth measurements with static lidar terrain features from a single snow-free date, enabling rapid, high-resolution estimation at low institutional cost. …”
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GC-Like LDPC Code Construction and its NN-Aided Decoder Implementation by Yu-Lun Hsu, Li-Wei Liu, Yen-Chin Liao, Hsie-Chia Chang

“…The trade-off between decoding performance and hardware costs has been a long-standing challenge in Low-Density Parity Check (LDPC) decoding. Based on model-driven methodology, the Neural Network-Aided Variable Weight Min-Sum (NN-aided vwMS) algorithm is proposed to address this dilemma in this paper. …”
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On the differential diagnostic prospects of determining the profile of the lateral organization of the personality during assessment of the systematic alcohol abuse by K. A. Bamburov, Z. V. Lukovtseva

“…The researchers made an attempt to highlight the features of PLO of such a «risk group» as those previously examined in a state of alcoholic intoxication (based on data obtained during the study according to the typology of E.D. …”
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Analysis of physicians’ statements on electronic cigarettes in YouTube videos in Türkiye: a thematic content analysis by Ersan Gürsoy, Mehmet Vatansever

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INNOVATIVE COMPETENCE AS A COMPONENT OF THE PROFESSIONAL ACTIVITY OF A MODERN TEACHER by Nadiia О. Vientseva, Olena V. Karapetrova

“…The most characteristic features of innovative competence of the teacher are distinguished, which include: 1) personal orientation of the specialist to learn something new, the willingness to change the ways of professional activity; 2) the subjectivity of goal setting, goal realization and self-realization; 3) the clarity of professional position, awareness of the social significance of innovations, inclusion in social creativity; 4) the compliance of the composition of competence with the structure of innovation activity; 5) the effectiveness of ways to implement a system of knowledge, skills, abilities at all stages of the innovation process; 6) the ability to be creative in solving professional problems; 7) the integrity of the whole set of competencies in the innovative competence of the specialist as a system entity; 8) the high level of professionalism of the specialist, based on the understanding and self-improvement of their professional experience. …”
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Exploration of Fully‐Automated Body Composition Analysis Using Routine CT‐Staging of Lung Cancer Patients for Survival Prognosis by Marc‐David Künnemann, Christian Römer, Anne Helfen, Annalen Bleckmann, Marcel Kemper, Walter Heindel, Tobias J. Brix, Michael Forsting, Johannes Haubold, Marcel Opitz, Martin Schuler, Felix Nensa, Katarzyna Borys, René Hosch

“…The multivariate survival model trained on Hospital A's data demonstrated prognostic differentiation of groups in internal (n = 209, p ≤ 0.001) and external (Hospital B, n = 361, p = 0.044) validation, with SI feature importance (0.037) ranking below ECOG (0.082) and M‐status (0.078), outperforming all other features including conventional L3‐single‐slice measurements. …”
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