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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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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Predictive Framework for Sustainable Engineering through Machine Learning and Cross-Sector Collaboration by Choudhary Abhik, Adhikari Upasana, Roy Dipankar, Gupta Subir, Roy Priyanka, Bhaduri Aparna

“…Unlike traditional rule-based and qualitative evaluations, the proposed method measures diverse parameters like funding streams, policy advocacy, and stakeholder participation to construct a comprehensive model of collaboration at cross-sectoral levels. …”
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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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Predictive Study on the Cutting Energy Efficiency of Dredgers Based on Specific Cutting Energy by Junlang Yuan, Ke Yang, Taiwei Yang, Haoran Xu, Ting Xiong, Shidong Fan

“…Subsequently, five machine learning algorithms, such as RF and XGBoost, are used in combination with a grid search to find the optimal hyperparameters, and Lasso is used as the meta-learner to integrate the prediction results. The experimental results show that the Random Forest and Stacking models have high prediction accuracy for slurry concentration, cutter motor power, and slurry flow rate, verifying the feasibility of this method.…”
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Inflammation‐Derived and Clinical Indicator‐Based Predictive Model for Ischemic Stroke Recovery by Jiao Luo, You Cai, Peng Xiao, Changchun Cao, Meiling Huang, Xiaohua Zhang, Jie Guo, Yongyang Huo, Qiaoyan Tang, Liuyang Zhao, Jiabang Liu, Yaqi Ma, Anqun Yang, Mingchao Zhou, Yulong Wang

“…Conclusions The combined model is a valuable tool for evaluating prognostic outcomes, and the predictive factors can facilitate development of better treatment strategies.…”
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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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Predicting Postoperative Troponin in Patients Undergoing Elective Hip or Knee Arthroplasty: A Comparison of Five Cardiac Risk Prediction Tools by Merih T. Tesfazghi, Anne R. Bass, Noor Al-Hammadi, Scott C. Woller, Scott M. Stevens, Charles S. Eby, Mitchell G. Scott, Lindsey Snyder, Troy S. Wildes, Brian F. Gage

“…Elderly patients undergoing hip or knee arthroplasty are at a risk for myocardial injury after noncardiac surgery (MINS). We evaluated the ability of five common cardiac risk scores, alone or combined with baseline high-sensitivity cardiac troponin I (hs-cTnI), in predicting MINS and postoperative day 2 (POD2) hs-cTnI levels in patients undergoing elective total hip or knee arthroplasty. …”
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Research on wheelchair form design based on Kansei engineering and GWO-BP neural network by Weilin Cai, Zhengyu Wang, Yi Wang, Meiyu Zhou

“…This study proposes a wheelchair form design method based on the Kansei engineering approach, which integrates the evaluation grid method (EGM), grey wolf optimization (GWO) algorithm, and back propagation neural network (BPNN) technology. …”
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Predicting Mortality in Severe Burns: A Comparison of Four Mortality Prediction Scores and the Role of Organizational Changes in the Croatian Burn Center by Agata Skunca, Ana Mesic, Dorotea Zagorac, Mirela Dobric, Vedran Lokosek, Morana Banic, Aleksandra Munjiza, Aisa Muratovic

“…A secondary aim was to compare patient outcomes before and after the organizational and protocol changes. Methods: A retrospective study and comparison of four prediction scores was conducted over a nine-year period in burn patients with ≥20% total body surface area (TBSA) burned. …”
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The Improved-EFI Score: A Multi-Omics-Based Novel Efficacy Predictive Tool for Predicting the Natural Fertility of Endometriosis Patients by He Q, Zhang C, Hu Y, Deng J, Zhang S

“…This study aims to establish a prediction model to accurately predict the natural pregnancy outcome of patients with EM, providing valuable information for clinical decision-making.Methods: We retrospectively selected a total of 496 patients who underwent their first laparoscopic surgery for infertility at the Obstetrics and Gynecology Department of Jingzhou Central Hospital from January 2016 to June 2023. …”
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Machine learning vehicle fuel efficiency prediction by So-rin Yoo, Jae-woo Shin, Seoung-Ho Choi

“…The proposed method comprises a predictive model and analysis framework utilizing key vehicle attributes, such as fuel type, engine displacement, and vehicle grade, to enhance prediction accuracy. …”
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