Using machine learning to predict the rupture risk of multiple intracranial aneurysms by Junqiang Feng, Chunyi Wang, Yu Wang, He Liu, He Liu

“…By analyzing detailed morphological and anatomical parameters, our model provides a tailored approach to rupture risk assessment in MIAs, offering potential improvements over existing methods.MethodsTo address dataset imbalance, we conducted five-fold cross-validation. …”
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Embedded physical constraints in machine learning to enhance vegetation phenology prediction by Mengying Cao, Qihao Weng

“…The results show that the overall root mean square error (RMSE) with physical constraints is reduced to 12.37 days, higher by 2.6 days than the machine learning method without physical constraints. We compared four vegetation types using different machine learning and traditional rule-based methods, deciduous vegetation (DV) exhibited the most favorable prediction results, with the RMSE and mean bias error (MBE) as low as 5.71 days and 4.06 days in PCNNs model, respectively. …”
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AN INTEGRATED SYSTEM FOR PREDICTION OF ADVERSE OUTCOME DURING ANTI-TUBERCULOSIS TREATMENT by Tatiana V. Pianzova, Elena S. Kagan, Anastasia A. Abros'kina

“…Тo develop a comprehensive method for evaluating the risk of an adverse outcome during anti-tuberculosis (anti-TB) treatment. …”
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The reporting quality and methodological quality of dynamic prediction models for cancer prognosis by Peijing Yan, Zhengxing Xu, Xu Hui, Xiajing Chu, Yizhuo Chen, Chao Yang, Shixi Xu, Huijie Cui, Li Zhang, Wenqiang Zhang, Liqun Wang, Yanqiu Zou, Yan Ren, Jiaqiang Liao, Qin Zhang, Kehu Yang, Ling Zhang, Yunjie Liu, Jiayuan Li, Chunxia Yang, Yuqin Yao, Zhenmi Liu, Xia Jiang, Ben Zhang

“…Abstract Background To evaluate the reporting quality and methodological quality of dynamic prediction model (DPM) studies on cancer prognosis. …”
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Enhancing the Prediction of Dam Deformations: A Novel Data-Driven Approach by Jonas Ziemer, Gideon Stein, Carolin Wicker, Jannik Jänichen, Daniel Klöpper, Katja Last, Joachim Denzler, Christiane Schmullius, Maha Shadaydeh, Clémence Dubois

“…Through a comprehensive evaluation of advanced data-driven approaches, we demonstrated considerable improvements in predicting dam deformations and evaluating their drivers. …”
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Machine learning model for random forest acute oral toxicity prediction by A.M. Elsayad, M.M. Zeghid, K.A. Elsayad, A.N. Khan, ِA.K.M. Baareh, A. Sadiq, S.A. Mukhtar, H.F. Ali, S. Abd El-kader

“…The focus of this study is to establish the most efficient method for constructing reliable and precise models for toxicity prediction.METHODS: The random forests were evaluated, a robust ensemble method, for predicting acute oral toxicity using a comprehensive dataset from National Toxicology Program/Interagency Center for the Evaluation of Alternative Toxicological Methods and Environmental Protection Agency/National Center for Competency Testing, which presented significant class imbalance, 8 percent very toxic 92 percent not very toxic. …”
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In silico metabolism and toxicity prediction using a knowledge-based approach by Emilio Mateev, Maya Georgieva, Alexandrina Mateeva, Alexander Zlatkov

“…The application of in silico approaches for predicting metabolic pathways and toxicity profiles has significantly advanced drug discovery and chemical risk evaluation. …”
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Predicting the efficacy of rehabilitation in patients with type 2 diabetes and diabetic polyneuropathy by T.H. Bakaliuk, N.R. Makarchuk, H.O. Stelmakh, V.I. Pankiv, I.I. Kamyshna

“…Materials and methods. Ninety-five patients with type 2 diabetes and DPN were examined to construct a predictive model of rehabilitation effectiveness using multiple regression analysis. …”
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Multiple Machine Learning Algorithms-based NBA Team Playoffs Prediction by Yeung Manho

“…With the rapid development of data analytics in sports, it is vital to use machine learning methods to make decisions and predictions. This study focuses on predicting NBA playoff qualifications using machine learning techniques. …”
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Prediction of fracture strength factor for the fracture behavior of 304 stainless steel by Bin Cai, Zhengyang Yu

“…This trend is closely related to the behaviors represented by the stress concentration factor and strain rate concentration factor. The proposed method for predicting ductile fracture behavior has made significant advancements in this field, enabling accurate estimations while minimizing costs and reliance on a wide range of experimental methods.…”
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Comparative performance analysis of neoepitope prediction algorithms in head and neck cancer by Leila Y. Chihab, Leila Y. Chihab, Julie G. Burel, Aaron M. Miller, Aaron M. Miller, Luise Westernberg, Brandee Brown, Jason Greenbaum, Michael J. Korrer, Stephen P. Schoenberger, Sebastian Joyce, Sebastian Joyce, Young J. Kim, Zeynep Koşaloğlu-Yalçin, Bjoern Peters, Bjoern Peters

“…This was primarily due to the inclusion of longer peptides in IPV compared to TESLA.ConclusionsOur work underscores the improved predictive ability of IPV in comparison to TESLA in this assay system and highlights the need to clearly define which experimental metrics are used to evaluate bioinformatic epitope predictions.…”
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Analysis and spatial prediction of water retention curves in two types of soil by Diego L. Cortés-Delgadillo, Jesús H. Camacho Tamayo, Ramón Giraldo

“…The objective of this work was to evaluate the capacity of functional geostatistics in predicting the spatial distribution of water retention curves in two types of soils. …”
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Prediction Model of Music Popular Trend Based on NNS and DM Technology by Yichen Xu, Mingxun Wang, Hao Chen, Fan Hu

“…Therefore, this paper will apply data mining (DM) technology, introduce neural network (NNS) theory, establish a prediction model of music fashion trend, predict and evaluate the music fashion trend according to the selected evaluation index, find the change of music fashion trend in time, and provide decision-making basis for music fashion trend. …”
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A systematic review of the factors predicting the interest in cosmetic plastic surgery by Panagiotis Milothridis, Leonidas Pavlidis, Anna-Bettina Haidich, Efharis Panagopoulou

“…Background: A systematic review of the literature was performed to clarify the psychosocial characteristics of patients who have an interest in cosmetic plastic surgery. Methods: Medical literature was reviewed by two independent researchers, and a third reviewer evaluated their results. …”
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BIMLP Model Based on Deep Learning for Predicting Electrical Load Demand by Somayeh Talebzadeh, Reza Radfar, Abbas Toloei Ashlaghi

“…To address this challenge, this research proposes a novel hybrid machine-learning approach for predicting electricity demand. In this research, first, different regression methods were investigated to solve the problem, the results showed that the multi-layer perceptron (MLP) regression model has the best performance in predicting electricity demand. …”
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Disease candidate genes prediction using positive labeled and unlabeled instances by Sepideh Molaei, Saeed Jalili

“…Consequently, disease genes are predicted and ranked. The proposed method evaluation of various six diseases and Cancer class indicates better results than other studies.…”
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PM2.5 prediction using population-based centrality weight by Hee Joon Choi, Won Kyung Lee, So Young Sohn

“…Abstract The particulate matter (PM)2.5 forecasting has been being advanced with the development of deep learning methods. However, most of them do not consider the active population exposed to air pollution. …”
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A spatiotemporal graph wavelet neural network for traffic flow prediction by Linjie Zhang, Jianfeng Ma

“…In view of capturing precise environment characteristics, the context could have a precise effect on the prediction results while previous methods rarely took this into account. …”
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A Multilayer Residual Dendritic Neural Model for Predicting Stroke Prognosis by Yuxi Wang, Haochang Jin, Maocheng Cao, Xiong Xiao, Li Wang

“…Residual connections are added between layers to address the vanishing gradient problem. Evaluations using one public and two private stroke prognosis datasets demonstrate that RDNM significantly outperforms original DNM and state-of-the-art deep-learning methods, highlighting its potential for clinical applications. …”
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A Systematic Review on Intelligent Prediction of Inorganic Building Materials Performance by Mengru Li, Zhenya Zhang, Xianyi Zeng, Zhenglei He

“…Computational intelligent methods are effective for accurately predicting product quality. …”
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