New system for predicting the outcome of secondary peritonitis by Nikolay V. Lebedev, Sariya B. Agrba, Vasily S. Popov, Alexey E. Klimov, Giorgy T. Svanadze

“…We analyzed the effectiveness of several systems of predicting the peritonitis outcomes: the Mannheim’s Peritoneal Index (MPI), World Society for Emergency Surgery Sepsis Severity Score (WSES SSS), Acute Physiology and Chronic Health Evaluation II (APACHE II) system, general Sequential Organ Failure Assessment Score (gSOFA), as well as the Peritonitis Prognosis System (PPS) developed by the authors. …”
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Optimizing Flight Delay Predictions with Scorecard Systems by Ilona Jacyna-Gołda, Krzysztof Cur, Justyna Tomaszewska, Karol Przanowski, Sarka Hoskova-Mayerova, Szymon Świergolik

“…The model was validated using test datasets, and predictive performance was evaluated by comparing forecast delays with actual results. …”
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Pedestrian Vision Language Model for Intentions Prediction by Farzeen Munir, Shoaib Azam, Tsvetomila Mihaylova, Ville Kyrki, Tomasz Piotr Kucner

“…Furthermore, to complement our PedVLM model and further facilitate research, we also publicly release the corresponding dataset, PedPrompt, which includes the prompts in the Question-Answer (QA) template for pedestrian intention prediction. PedVLM is evaluated on PedPrompt, JAAD, and PIE datasets demonstrates its efficacy compared to state-of-the-art methods. …”
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STVMamba: precipitation nowcasting with spatiotemporal prediction model by Maoyang Zou, Longrui Wen, Yuanyuan Huang, Yuan He, Jingzhong Xiao

“…Abstract A lightweight rainfall nowcasting model is required by Sichuan provincial meteorological bureaus. Deep learning methods such as recurrent, convolutional, and Transformer models have been applied to precipitation prediction. …”
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Crypto market betas: the limits of predictability and hedging by Jan Sila, Michael Mark, Ladislav Kristoufek, Thomas A. Weber

“…Abstract This article analyzes the predictability of market betas concerning cryptocurrency assets and evaluates the efficiency of beta-hedged, market-neutral portfolios. …”
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Prediction of Drifter Trajectory Using Evolutionary Computation by Yong-Wook Nam, Yong-Hyuk Kim

“…As the evaluation measure, a method that gives a better score as the Mean Absolute Error (MAE) when the difference between the predicted position in time and the actual position is lower and the Normalized Cumulative Lagrangian Separation (NCLS), which is widely used as a trajectory evaluation method of drifters, were used. …”
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DATA MINING APPROACH FOR PREDICTING STUDENT PERFORMANCE by Edin Osmanbegović, Mirza Suljić

“…Using data mining the aim was to develop a model which can derive the conclusion on students' academic success. Different methods and techniques of data mining were compared during the prediction of students' success, applying the data collected from the surveys conducted during the summer semester at the University of Tuzla, the Faculty of Economics, academic year 2010-2011, among first year students and the data taken during the enrollment. …”
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Real-Time Data Extraction and Prediction of Cryptocurrency by Sanika Chavan, Jahnavi Gundakaram, Sai Dyuti Vaishnavi, Srishti Prasad, K. Deepa

“…Cryptocurrency markets exhibit high volatility, necessitating accurate forecasting methods for effective decision-making. This paper presents an innovative approach that integrates web scraping from cryptocurrency websites with various deep-learning networks to predict cryptocurrency values for the following day. …”
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Prevention or Promotion? Predicting Author's Regulatory Focus by Aswathy Velutharambath, Kai Sassenberg, Roman Klinger

“…Traditionally, text analysis methods using closed-vocabularies are employed to assess the distinctive linguistic patterns associated with the two systems. …”
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Predicting the Multiphotonic Absorption in Graphene by Machine Learning by José Zahid García-Córdova, Jose Alberto Arano-Martinez, Cecilia Mercado-Zúñiga, Claudia Lizbeth Martínez-González, Carlos Torres-Torres

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Factors predicting burn unit length-of-stay by Miguel João Ribeiro Matias, Rubén Malcata Nogueira, Carolina Vasconcelos, Joaquim Bexiga

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Predicting violence in veterans with posttraumatic stress disorder by Jovanović Aleksandar A., Lečić-Toševski Dušica, Ivković Maja, Damjanović Aleksandar, Jašović-Gašić Miroslava

“…The aim of this study was to evaluate the accuracy of clinical prediction of violence in combat veterans suffering from PTSD. …”
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Ultrasonographic Prediction of Difficult Laryngoscopy in Obese Patients by Amit Sharma, Swaran Bhalla

“…We hypothesized that there is an association between ultrasonographic measurements of the anterior neck soft-tissue thickness at level of vocal cords (VCs) and hyoid bone (HB) in predicting difficult laryngoscopy in the obese patients and evaluated the feasibility of ultrasound in predicting difficult laryngoscopy in Indian population. …”
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Prediction of prognosis and renal outcome in lupus nephritis by Farah Tamirou, Ioannis Parodis, Frédéric A Houssiau

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PREDICTING THE LEARNING PATH TO LEARNER’S OPTIMUM COMPREHENSION by Ifeanyi Isaiah Achi, Chukwuemeka Odi Agwu, Christopher Chizoba Nnamene, Sylvester C. Aniobi, Ifebude Barnabas C., Kelechi Christian Oketa, Godson Kenechukwu Ezeh, John Otozi Ugah

“…This is due to the present systems' inability to model the learner to determine the best methods for achieving maximum comprehension. Hence, this research paper focuses on deriving an improved mathematical model for predicting the learning path to a learner’s optimum comprehension. …”
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Predicting Object Communication Errors in Constructor Development by Abdul Majid Soomro, Awad Bin Naeem, Susama Bagchi, Babul Salam KSM Kader Ibrahim, Sanjoy Kumar Debnath

“…Earlier researchers studied various methods for predicting and mitigating software defects in object-oriented programming. …”
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Predicting abatacept retention using machine learning by Rieke Alten, Claire Behar, Pierre Merckaert, Ebenezer Afari, Virginie Vannier-Moreau, Anael Ohayon, Sean E. Connolly, Aurélie Najm, Pierre-Antoine Juge, Gengyuan Liu, Angshu Rai, Yedid Elbez, Karissa Lozenski

“…The pooled dataset was split into a training/validation cohort for model development and a test cohort for an unbiased evaluation of performance. SHapley Additive exPlanation (SHAP) values determined the level of importance and directionality for key patient features predicting abatacept retention. …”
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Comparison of machine learning models for coronavirus prediction by B. K. Amos, I. V. Smirnov, M. M. Hermann

“…The study objective is to build a model based on machine learning that can predict the detection of SARS-CoV-2 from medical data. …”
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Predict+Optimize Problem in Renewable Energy Scheduling by Christoph Bergmeir, Frits de Nijs, Evgenii Genov, Abishek Sriramulu, Mahdi Abolghasemi, Richard Bean, John Betts, Quang Bui, Nam Trong Dinh, Nils Einecke, Rasul Esmaeilbeigi, Scott Ferraro, Priya Galketiya, Robert Glasgow, Rakshitha Godahewa, Yanfei Kang, Steffen Limmer, Luis Magdalena, Pablo Montero-Manso, Daniel Peralta, Yogesh Pipada Sunil Kumar, Alejandro Rosales-Perez, Julian Ruddick, Akylas Stratigakos, Peter Stuckey, Guido Tack, Isaac Triguero, Rui Yuan

“…The novelty of this work lies in its comprehensive evaluation of Predict+Optimize methodologies applied to a real-world renewable energy scheduling problem, providing insights into the scalability, generalizability, and effectiveness of the proposed solutions. …”
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Application of Radiomics in Predicting the Prognosis of Medulloblastoma in Children by Jiashu Chen, Wei Yang, Zesheng Ying, Ping Yang, Yuting Liang, Chen Liang, Baojin Shang, Hong Zhang, Yingjie Cai, Xiaojiao Peng, Hailang Sun, Wenping Ma, Ming Ge

“…Then, the clinical model, radiomics model, and clinical–radiomics model were compared to validate the improvement of radiomics in predicting the prognosis of medulloblastoma. The performance of the three models was evaluated with the C-index and the time-dependent AUC. …”
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