Prediction models for cognitive impairment in middle-aged patients with cerebral small vessel disease by Wei Zheng, Xiaoyan Qin, Ronghua Mu, Peng Yang, Bingqin Huang, Bingqin Huang, Zhixuan Song, Xiqi Zhu, Xiqi Zhu

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Predicting Residual Rectal Adenocarcinoma in the Surgical Specimen after Preoperative Brachytherapy with Endoscopic Ultrasound by Joseph Romagnuolo, Josée Parent, Té Vuong, Mélanie Bélanger, René P Michel, Paul J Belliveau, Judith L Trudel

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Increased Expression of the Pro-Protein Convertase Furin Predicts Decreased Survival in Ovarian Cancer by Robert E. Page, Andrés J. P. Klein-Szanto, Samuel Litwin, Emmanuelle Nicolas, Raid Al-Jumaily, Peter Alexander, Andrew K. Godwin, Eric A. Ross, Russell J. Schilder, Daniel E. Bassi

“…In summary, furin may constitute a marker for ovarian tumor progression and could contribute to predict the outcome of this disease.…”
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Prediction of Unconfined Compression Strength for Saline-Alkaline Soils Mixed with Cement and Wheat Straw by Guoqi Xing, Changjiang Liu, Wei Xuan, Yueyue Pan, Bing Zhang, Yue Zhao

“…In addition, based on the results from unconfined compression tests, a formula for predicting the unconfined compression strength of specimens related to cement content, wheat straw content, curing periods, etc., was determined, and comparing with the results from unconfined compression tests, it had higher precision in predicting the unconfined compression strength of specimens.…”
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Deep Learning Models to Predict Fatal Pneumonia Using Chest X-Ray Images by Satoshi Anai, Junko Hisasue, Yoichi Takaki, Naohiko Hara

“…We evaluated the performance of our deep learning models using sensitivity, specificity, PPV, negative predictive value (NPV), accuracy, and F1 score. Using the external validation test dataset of 100 CXR images, the sensitivity, specificity, accuracy, and F1 score were 68.0%, 86.0%, 77.0%, and 74.7%, respectively. …”
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ML-Based Quantitative Analysis of Linguistic and Speech Features Relevant in Predicting Alzheimer’s Disease by Tripti Tripathi, Rakesh Kumar

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An ensemble prediction approach to weekly Dengue cases forecasting based on climatic and terrain conditions by Sougata Deb, Cleta Milagros Libre Acebedo, Gomathypriya Dhanapal, Chua Matthew Chin Heng

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Predicting Depression: a comparative study of machine learning approaches based on language usage. by Luciana Mariñelarena-Dondena, Edgardo Ferretti, Manolis Maragoudakis, Maximiliano Sapino, Marcelo Luis Errecalde

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Methodology for the Analysis and Assessment of the Reliability of the State and Prediction the Service Life of Reinforced Concrete Structures by A. G. Tamrazian

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Prediction method of gas emission in working face based on feature selection and BO-GBDT by MA Wenwei

Subjects: “…gas emission prediction…”
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An Effective Self-Attention-Based Hybrid Model for Short-Term Traffic Flow Prediction by Zhihong Li, Xiaoyu Wang, Kairan Yang

“…To improve the prediction accuracy and ensure fine traffic management, an effective self-attention-based hybrid model is proposed to predict the short-term traffic flow. …”
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Constructing a fall risk prediction model for hospitalized patients using machine learning by Cheng-Wei Kang, Zhao-Kui Yan, Jia-Liang Tian, Xiao-Bing Pu, Li-Xue Wu

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Predictability of the gingival margin of the upper incisors with transparent aligners: a pilot retrospective study by Antonio Bach-Álvarez, Lourdes Abellán-Ferris, María-Dolores Oteo-Calatayud, Susana de la Cruz-Vigo, Wenceslao Piedra-Gascón, Alba Pérez-Jardón, Naresh Kewalramani, Ángel-Orión Salgado-Peralvo, Carlos Oteo-Morilla

“…This retrospective study aims to analyse the predictability of the gingival margin of the upper incisors designed in the ClinCheck, comparing it with real intraoral photographs of the patients.Materials and methods Thirty-one patients between 16 and 59 years old were selected from the dental clinics of the Complutense University of Madrid and the position of the gingival margin in the ClinCheck was compared with its millimetre template by superimposing it on intraoral photographs taken with a millimetre ruler under the incisors in order to make the match between both.Results In 71% of the upper incisors there were differences between the ClinCheck and the final photographs, being these differences statistically significant (p = 0.001).Conclusion This is a pilot retrospective study with short-term results and a small sample size. …”
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Comprehensive Evaluation of Bankruptcy Prediction in Taiwanese Firms Using Multiple Machine Learning Models by Hung V. Pham, Tuan Chu, Tuan M. Le, Hieu M. Tran, Huong T.K. Tran, Khanh N. Yen, Son V. T. Dao

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An Artificial Neural Network Based Prediction of Mechanical and Durability Characteristics of Sustainable Geopolymer Composite by P. Manikandan, K. Selija, V. Vasugi, V. Prem Kumar, L. Natrayan, M. Helen Santhi, G. Senthil Kumaran

“…The RHA substitution proportion, sodium hydroxide (NaOH) liquid concentration, and polypropylene fiber content have been employed as input parameters in the construction of ANN framework. The predicted strength values of mechanical and durability tests achieved from the ANN framework agree well with experiment results. …”
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Direct Interval Prediction of Landslide Displacements Using Least Squares Support Vector Machines by Yankun Wang, Huiming Tang, Tao Wen, Junwei Ma

“…Accurate and reliable predictions of landslide displacements are difficult to perform using traditional point prediction approaches due to the associated uncertainty. …”
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Spatial-temporal load prediction of electric bus charging station based on S2TAT by Guangnian Xiao, Hailin Tong, Yaqing Shu, Anning Ni

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Temperature Prediction of Photovoltaic Panels Based on Support Vector Machine with Pigeon-Inspired Optimization by Siyuan Fan, Shengxian Cao, Yanhui Zhang

“…In this paper, a novel temperature prediction method of PV panels with support vector machine (SVM) is proposed, which can solve the temperature prediction problem in a complex environment. …”
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DMSS: An Attention-Based Deep Learning Model for High-Quality Mass Spectrometry Prediction by Yihui Ren, Yu Wang, Wenkai Han, Yikang Huang, Xiaoyang Hou, Chunming Zhang, Dongbo Bu, Xin Gao, Shiwei Sun

“…Accurate prediction of peptide spectra is crucial for improving the efficiency and reliability of proteomic analysis, as well as for gaining insight into various biological processes. …”
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EEG Alpha Band Synchrony Predicts Cognitive and Motor Performance in Patients with Ischemic Stroke by Sviatlana Dubovik, Radek Ptak, Tatiana Aboulafia, Cécile Magnin, Nicole Gillabert, Lara Allet, Jean-Michel Pignat, Armin Schnider, Adrian G. Guggisberg

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