Slipping Trend Prediction Based on Improved Informer by Jingchun Huang, Sheng He, Haoxiang Feng, Yongjiang Yu

“…The transformer-based Informer algorithm performs well in time series prediction and analysis. …”
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Modify possibilities of the secondary structures prediction method by Alvydas Špokas, Albertas Timinskas

“… It was analyzed dependence of the average accuracy of secondary protein structure prediction on various GOR algorithm modifications. In essence new modification has expanded informational parameter set by taking into account secondary structure of neighboring amino acid. …”
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Heuristic Global Optimization for Thermal Model Reduction and Correlation in Aerospace Applications by João P. Castanheira, Beltran N. Arribas, Rui Melicio, Paulo Gordo, André R. R. Silva

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Outcome prediction of the measles vaccination in healthcare employees by A. A. Ereshchenko, O. A. Gusyakova, N. B. Migacheva, F. N. Gilmiyarova, A. V. Lyamin

“…These models allowed to develop algorithm for predicting failures of the measles vaccination in healthcare workers that can be used for detection of persons at risk for non-forming specific humoral immunity. …”
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Explainable machine learning to predict the cost of capital by Niklas Bussmann, Paolo Giudici, Paolo Giudici, Alessandra Tanda, Alessandra Tanda, Ellen Pei-Yi Yu

“…Our findings pave the way for future investigations on the impact of ESG and country factors in predicting the cost of capital.…”
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Prediction of amphipathic helix-membrane interactions with Rosetta. by Alican Gulsevin, Jens Meiler

“…The AmphiScan protocol predicted the coordinates of amphipathic helices within less than 3Å of the reference structures and identified membrane-embedded residues with a Matthews Correlation Constant (MCC) of up to 0.57. …”
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Application of federated learning in predicting breast cancer by Chai Jiarui

“…The prediction and diagnosis of breast cancer relies on multimodal data, such as imaging, genetic information, and patient lifestyle habits. …”
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Development and Validation of Predictive Models for Non-Adherence to Antihypertensive Medication by Cristian Daniel Marineci, Andrei Valeanu, Cornel Chiriță, Simona Negreș, Claudiu Stoicescu, Valentin Chioncel

“…This study aimed to develop and validate several predictive models for non-adherence, using patient-reported data collected via a structured questionnaire. …”
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The Prediction of the Compaction Curves and Energy of Bituminous Mixtures by Filippo Giammaria Praticò, Giusi Perri

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Comparing prediction efficiency in the BTW and Manna sandpiles by Denis Sapozhnikov, Alexander Shapoval, Mikhail Shnirman

“…The existence of the inactivity allows for the prediction of these events in advance. In this work, we explore the predictability of the Bak–Tang–Wiesenfeld (BTW) and Manna models on the square lattice as a function of the lattice length. …”
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Multimodal deep learning for allergenic proteins prediction by Lezheng Yu, Yuxin Luo, Shiqi Wu, Siyi Chen, Li Xue, Runyu Jing, Jiesi Luo

“…Results Here, we present Multimodal-AlgPro, a unified framework based on a multimodal deep learning algorithm designed to predict allergens by integrating multiple dimensions, including physicochemical properties, amino acid sequences, and evolutionary information. …”
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Prediction of Drifter Trajectory Using Evolutionary Computation by Yong-Wook Nam, Yong-Hyuk Kim

“…In contrast to existing numerical models that use the Lagrangian method, we used an optimization algorithm to predict the trajectory. 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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Machine Learning with Voting Committee for Frost Prediction by Vinícius Albuquerque de Almeida, Juliana Aparecida Anochi, José Roberto Rozante, Haroldo Fraga de Campos Velho

“…A machine learning (ML)-based methodology for predicting frosts was applied to the southern and southeastern regions of Brazil, as well as to other countries including Uruguay, Paraguay, northern Argentina, and southeastern Bolivia. …”
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Predicting surgical risk in morbidly obese patients by K. A. Anisimova, D. I. Vasilevsky, S. G. Balandov, E. T. Berulava, A. V. Zinchenko, N. V. Markov, I. G. Buhankov, E. V. Blinov, G. V. Semikova

“…The results obtained during the study made it possible to integrate the developed tactics of preoperative examination and preparation for surgical intervention in morbidly obese patients into a practical algorithm. Application of the developed tools for predicting the risk of complications in bariatric surgeries allowed to reduce the complication rate from 12.2 % to 2.0 %, and the mortality rate from 2.0 % to 0 %.CONCLUSION. …”
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Machine Learning for Health Insurance Prediction in Nigeria by Victor Enemona Ochigbo, Oluwasogo Adekunle Okunade, Emmanuel Gbenga Dada, Oluyemi Mikail Olaniyi, Oluwatoyosi Victoria Oyewande

“…This paper focused on predicting the likelihood of medical insurance coverage among individuals in Nigeria by employing four prominent Machine learning techniques: Logistic Regression, Random Forest, Decision Tree, and Support Vector Machine classifiers. …”
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Population FBA predicts metabolic phenotypes in yeast. by Piyush Labhsetwar, Marcelo C R Melo, John A Cole, Zaida Luthey-Schulten

“…We find the partitioning of flux between fermentation and respiration predicted by our model agrees with recent 13C fluxomics experiments, and that our model largely recovers the Crabtree effect (the experimentally known bias among certain yeast species toward fermentation with the production of ethanol even in the presence of oxygen), while FBA without proteomics constraints predicts respirative metabolism almost exclusively. …”
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STEP-BY-STEP PREDICTION OF LATE SPONTANEOUS MISCARRIAGE by Юлия Алексеевна Дударева, Татьяна Викторовна Раченкова, Сергей Вадимович Дронов

“…The aim of the study was to develop a step-by-step system for predicting late spontaneous miscarriages, taking into account clinical, anamnestic, echographic data, and the cytokine status of pregnant women. …”
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AI model for predicting asthma prognosis in children by Elham Sagheb, MS, Chung-Il Wi, MD, Katherine S. King, MS, Bhavani Singh Agnikula Kshatriya, MS, Euijung Ryu, PhD, Hongfang Liu, PhD, Miguel A. Park, MD, Hee Yun Seol, MD, Shauna M. Overgaard, PhD, Deepak K. Sharma, PhD, Young J. Juhn, MD, Sunghwan Sohn, PhD

“…Utilizing electronic health records (EHRs) to predict asthma prognosis can aid health care providers and patients in developing effective prioritized care plans. …”
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Prediction models for COVID-19 disease outcomes by Cynthia Y. Tang, Cheng Gao, Kritika Prasai, Tao Li, Shreya Dash, Jane A. McElroy, Jun Hang, Xiu-Feng Wan

“…Using the Virus-Human Outcomes Prediction (ViHOP) algorithm, we aim to utilize the individual’s clinical characteristics, the individual’s location, and the infecting SARS-CoV-2 virus characteristics obtained by whole genome sequencing to determine their likelihood of admission to the hospital, admission to the intensive care unit (ICU), or experiencing long COVID. …”
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Smart diabetes management: remote monitoring and predictive health insights by K.S. Smelyakov, I.A. Lurin, K.V. Misiura, A.S. Chupryna, T.V. Tyzhnenko, O.D. Dolhanenko, V.M. Repikhov

“…The use of deep learning and neural network algorithms enhances the accuracy of these predictions by capturing complex data trends over time. …”
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