Machine Learning and Medical Data: Predicting ICU Mortality and Re-admission Risks by Runia Roy, Ulya Bayram

“…These studies reduce the problem into a binary task of predicting mortality or re-admission only. However, this is unrealistic since both outcomes are highly possible for each patient. …”
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Conjugate Gradient Algorithm Based on Aitken's Process for Training Neural Networks by Khalil Abbo, Hind Mohammed

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Impact of a USMLE Step 2 Prediction Model on Medical Student Motivations by Anthony Shanks, Ben Steckler, Sarah Smith, Debra Rusk, Emily Walvoord, Erin Dafoe, Paul Wallach

“…We also sought to understand how the predicted scores affected student's plans. METHOD Traditional statistical models and machine learning algorithms to identify predictors of Step 2 CK performance were utilized. …”
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Dimension-reduction MUSIC for jointly estimating DOA and polarization using plane polarized arrays by Wei-jian SI, Tong ZHU, Meng-ying ZHANG

Subjects: “…DOA estimation;polarization sensitive array;dimension-reduction MUSIC algorithm;joint estimation…”
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Gutek: Intelligent Revision Algorithms by Lukasz Galka

Subjects: “…Customizable algorithms…”
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The Role of Artificial Intelligence in Transforming Language Learning: Opportunities and Ethical Considerations by Adenike A. Akinsemolu, Helen Onyeaka

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Improved machine learning framework for prediction of phases and crystal structures of high entropy alloys by Debsundar Dey, Suchandan Das, Anik Pal, Santanu Dey, Chandan Kumar Raul, Pritam Mandal, Arghya Chatterjee, Soumya Chatterjee, Manojit Ghosh

“…The important features were selected using the Pearson correlation coefficient matrix, followed by using of five distinct boosting algorithms to predict phases and crystal structures. …”
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A hybrid BOA-SVR approach for predicting aerobic organic and nitrogen removal in a gas-liquid-solid circulating fluidized bed bioreactor by Shaikh Abdur Razzak, Nahid Sultana, S.M. Zakir Hossain, Muhammad Muhitur Rahman, Yue Yuan, Mohammad Mozahar Hossain, Jesse Zhu

“…This study introduces the hybrid of the Bayesian optimization algorithm and support vector regression (BOA-SVR) models to predict the removal of aerobic organic (total chemical oxygen demand, COD) and nitrogen compounds such as total Kjeldahl Nitrogen (TKN), ammonium nitrogen (NH4-N), and nitrate nitrogen (NO3-N) from municipal wastewater in a gas-liquid-solid circulating fluidized bed (GLSCFB) bioreactor. …”
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Effect of a health education program on reduction of pediculosis in school girls at Amphoe Muang, Khon Kaen Province, Thailand. by Manachai Yingklang, Chatchawan Sengthong, Ornuma Haonon, Rungtiwa Dangtakot, Porntip Pinlaor, Chulaporn Sota, Somchai Pinlaor

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Predicting regional tau accumulation with machine learning‐based tau‐PET and advanced radiomics by Saima Rathore, Ixavier A. Higgins, Jian Wang, Ian A. Kennedy, Leonardo Iaccarino, Samantha C. Burnham, Michael J. Pontecorvo, Sergey Shcherbinin

“…Tau prediction in lobar/global regions benefits from diverse multimodal features. …”
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Evaluating soiling effects to optimize solar photovoltaic performance using machine learning algorithms by Muhammad Faizan Tahir, Anthony Tzes, Tarek H.M. El-Fouly, Mohamed Shawky El Moursi, Nauman Ali Larik

“…Additionally, machine learning algorithms such as artificial neural networks, support vector machines, regression trees, ensemble of regression trees, Gaussian process regression, efficient linear regression, and kernel methods are employed to predict power reduction due to soiling and soiling losses across various soiling percentages. …”
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IoT-based prediction model for aquaponic fish pond water quality using multiscale feature fusion with convolutional autoencoder and GRU networks by Suma Christal Mary Sundararajan, Yamini Bhavani Shankar, Sinthia Panneer Selvam, Nalini Manogaran, Koteeswaran Seerangan, Deepa Natesan, Shitharth Selvarajan

Subjects: “…Water quality prediction…”
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Research on Adaptive Education Path Dynamic Programming Algorithm Based on Reinforcement Learning and Cognitive Graphs by Hongli Lou, Pin Yue, Jianwen Chen

“…The proposed method combines dynamic learner models, real-time performance tracking, and predictive analytics, allowing for a highly adaptive educational system. …”
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Decision-Making Model for Life Cycle Management of Aircraft Components by Igor Kabashkin, Vitaly Susanin

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Artificial intelligence-based forecasting models for integrated energy system management planning: An exploration of the prospects for South Africa by Senthil Krishnamurthy, Oludamilare Bode Adewuyi, Emmanuel Luwaca, Mukovhe Ratshitanga, Prathaban Moodley

Subjects: “…Artificial intelligence-based predictive analytics…”
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OKEN: A Supervised Evolutionary Optimizable Dimensionality Reduction Framework for Whole Slide Image Classification by Soroush Oskouei, André Pedersen, Marit Valla, Vibeke Grotnes Dale, Sissel Gyrid Freim Wahl, Mats Dehli Haugum, Thomas Langø, Maria Paula Ramnefjell, Lars Andreas Akslen, Gabriel Kiss, Hanne Sorger

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Prediction of Solar Radiation Data for Garlic Production in Magelang Regency Using Long Short-Term Memory by Muhammad Safrul Safrudin, Imas Sukaesih Sitanggang, Hari Agung Adrianto, Syarifah Aini

“…This study aims to determine the optimal solar radiation for garlic growth using the Long Short-Term Memory (LSTM) algorithm. This algorithm was selected due to its ability to analyze time-series data and predict long-term patterns. …”
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Association of weight-adjusted waist index and body mass index with chronic low back pain in American adults: a retrospective cohort study and predictive model development based on machine learning algorithms (NHANES 2009–2010) by Weiye Zhang, Weiye Zhang, Yan Li, Pengwei Shao, Yuxuan Du, Ke Zhao, Jiawen Zhan, Lee A. Tan

“…Six machine learning algorithms were employed to predict CLBP occurrence and identify the optimal algorithm.ResultsA total of 4,687 participants were included. …”
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GPS Spoofing Detection using CAF Images and Neural Networks Based on the Proposed Peak Mapping Dimensionality Reduction Algorithm and TCNN Model by M. J. Jahantab, S. Tohidi, Mohammad Reza Mosavi, Ahmad Ayatollahi

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A machine learning-based method for predicting the shear behaviors of rock joints by Liu He, Yu Tan, Timothy Copeland, Jiannan Chen, Qiang Tang

“…In this study, machine learning prediction models (MLPMs), including artificial neural network (ANN), support vector regression (SVR), K-nearest neighbors (KNN), and random forest (RF) algorithms, were developed to predict the peak shear stress values and shear stress-displacement curves of rock joints. …”
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