Predicting biomass transportation costs: A machine learning approach for enhanced biofuel competitiveness by Ali Omidkar, Razieh Es’haghian, Hua Song

“…Departing from the prevalent reliance on regression analysis in previous studies, this research demonstrates the limitations of multiple linear regression for accurately predicting transportation costs. Consequently, this study explores the predictive capabilities of two alternative machine learning algorithms: random forests and artificial neural networks. …”
Get full text

Precise prediction of choke oil rate in critical flow condition via surface data by Qing Wang, Muntadher Abed Hussein, Bhavesh Kanabar, Anupam Yadav, Asha Rajiv, Aman Shankhyan, Sachin Jaidka, Mehul Manu, Issa Mohammed Kadhim, Zainab Jamal Hamoodah, Fadhil Faez, Mohammad Mahtab Alam, Hojjat Abbasi

“…This approach represents a significant advancement over previous efforts in the literature, offering more accurate and reliable predictions for oil production forecasting.…”
Get full text

Polyamine metabolism related gene index prediction of prognosis and immunotherapy response in breast cancer by Ruoya Wang, Shouliang Cai, Qing Gao, Yidong Chen, Xue Han, Fangjian Shang, Chunyan Liang, Guolian Zhu, Bo Chen

“…Additionally, we analyzed the immune microenvironment and enriched pathways across different subtypes using multiple algorithms. Finally, the “oncoPredict” R package was used to assess potential drug sensitivities in high-risk and low-risk groups.ResultsSeventeen polyamine metabolism genes were identified. …”
Get full text

Optimization of the Canopy Three-Dimensional Reconstruction Method for Intercropped Soybeans and Early Yield Prediction by Xiuni Li, Menggen Chen, Shuyuan He, Xiangyao Xu, Panxia Shao, Yahan Su, Lingxiao He, Jia Qiao, Mei Xu, Yao Zhao, Wenyu Yang, Wouter H. Maes, Weiguo Liu

“…Accurate early-stage yield prediction of intercropped soybeans is essential for the rapid screening and breeding of high-yield soybean varieties. …”
Get full text

Bioinformatics analysis of ferroptosis-related biomarkers and potential drug predictions in doxorubicin-induced cardiotoxicity by Jian Yu, Jian Yu, Jiangtao Wang, Xinya Liu, Xinya Liu, Cancan Wang, Cancan Wang, Li Wu, Yuanming Zhang, Yuanming Zhang

“…Constructed an mRNA-miRNA-lncRNA network diagram, and performed immune cell composition analysis using CIBERSORT. Finally, predicted potential drugs for key genes using the DSigDB database.ResultsWe obtained 119 genes after intersecting 1380 Differentially Expressed Genes (DEGs) with Ferroptosis-Related Genes (FRGs). …”
Get full text

Multi-task aquatic toxicity prediction model based on multi-level features fusion by Xin Yang, Jianqiang Sun, Bingyu Jin, Yuer Lu, Jinyan Cheng, Jiaju Jiang, Qi Zhao, Jianwei Shuai

“…Furthermore, in comparison with previous algorithms, ATFPGT-multi outperforms comparative methods, emphasizing that our approach exhibits higher accuracy and reliability in predicting aquatic toxicity. …”
Get full text

Dynamic and interpretable deep learning model for predicting respiratory failure following cardiac surgery by Man Xu, Hao Liu, Anran Dai, Qilian Tan, Xinlong Zhang, Rui Ding, Chen Chen, Jianjun Zou, Yongjun Li, Yanna Si

“…Abstract Background Postoperative respiratory failure following cardiac surgery (CS-PRF) remains a critical complication with substantial morbidity and mortality. Current risk prediction models are limited by static assessments and suboptimal accuracy. …”
Get full text

Analysis and prediction of land use changes: the case study of coastal areas of Gilan province by Sahar Abdollahi, Hashem Dadashpoor

“…Land use changes and then modeling the transmission potential were explored using multilayer perceptron algorithm of artificial neural network using 13 independent variables and obtained 7 sub-models for modeling land use change for 2016 and then using Markov chain method, land use map for the year 2016 was predicted with a coefficient of Kappa 0.98. …”
Get full text

ARIMA-Kriging and GWO-BiLSTM Multi-Model Coupling in Greenhouse Temperature Prediction by Wei Zhou, Shuo Liu, Junxian Guo, Na Liu, Zhenglin Li, Chang Xie

“…Utilizing the high-quality data processed by this model, this study proposes and constructs a novel Grey Wolf Optimizer and Bidirectional Long Short-Term Memory (GWO-BiLSTM) temperature prediction framework, which combines a Grey Wolf Optimizer (GWO)-enhanced algorithm with a Bidirectional Long Short-Term Memory (BiLSTM) network. …”
Get full text

Predicting the Risk of Preterm Birth Throughout Pregnancy Based on a Novel Transcriptomic Signature by Yang Pan, Yuxin Ran, Dongni Huang, Nanlin Yin, Yanqing Wen, Yan Jiang, Yamin Liu, Hongbo Qi

“…This study focused on the prediction of preterm birth (PTB). It aimed to identify the transcriptomic signature essential for the occurrence of PTB and evaluate its predictive value in early, mid, and late pregnancy and in women with threatened preterm labor (TPTL). …”
Get full text

Interpretable machine learning analysis of immunoinflammatory biomarkers for predicting CHD among NAFLD patients by Wenyuan Dong, Hongcheng Jiang, Yu Li, Luo Lv, Yuxin Gong, Bao Li, Hongjie Wang, Hesong Zeng

“…This study evaluated the predictive value of ten immunoinflammatory indexes for CHD risk in NAFLD patients using an interpretable machine learning framework. …”
Get full text

Predicting Pineapple Quality from Hyperspectral Data of Plant Parts Applied to Machine Learning by Vitória Carolina Dantas Alves, Sebastião Ferreira de Lima, Dthenifer Cordeiro Santana, Rafael Ferreira Barreto, Roger Augusto da Cunha, Ana Carina da Silva Cândido Seron, Larissa Pereira Ribeiro Teodoro, Paulo Eduardo Teodoro, Rita de Cássia Félix Alvarez, Cid Naudi Silva Campos, Carlos Antonio da Silva Junior, Fábio Luíz Checchio Mingotte

“…The aim of this study was to verify accurate ML models for predicting pineapple fruit quality and the best inputs for algorithms: Artificial Neural Networks (ANNs), M5P (model tree), REPTree decision trees, Random Forest (RF), Support Vector Machine (SMV) and Zero R. …”
Get full text

PREDICTION AND PREVENTION OF LIVER FAILURE AFTER MAJOR LIVER PRIMARY AND METASTATIC TUMORS RESECTION by A. D. Kaprin, D. V. Sidorov, N. A. Rubtsova, A. V. Leontyev, M. V. Lozhkin, L. O. Petrov, N. A. Grishin, T. N. Lazutina, I. V. Pylova, A. G. Isaeva

“…The first group included 53 patients who carried out 13C-breath test metallimovie and dynamic scintigraphy of the liver in the preoperative stage in addition to the standard algorithm of examination. Patients of the 2nd group (n=35) had a standard clinical and laboratory examination, the patients were not performed the preoperative evaluation of the functional reserve of the liver, the incidences of total bilirubin, albumin and prothrombin time did not reveal a reduction of liver function. …”
Get full text

Prediction of the packaging chemical migration into food and water by cutting-edge machine learning techniques by Behzad Vaferi, Mohsen Dehbashi, Reza Yousefzadeh, Ali Hosin Alibak

“…Optimizing the hyperparameters, evaluating the prediction accuracy, and comparing the performance of these AI models reveal that the gradient boosting regressor (GBR) is the superior method for this simulation. …”
Get full text

A machine learning-based model to predict intravenous immunoglobulin resistance in Kawasaki disease by Yuhan Xia, Yuezhong Huang, Min Gong, Weirong Liu, Yuanhui Meng, Huiyang Wu, Hui Zhang, Hao Zhang, Luyi Weng, Xiao-Li Chen, Huixian Qiu, Xing Rong, Rongzhou Wu, Maoping Chu, Xiu-Feng Huang

“…Summary: Accurate prediction of intravenous immunoglobulin (IVIG) resistance is crucial for the effective treatment of Kawasaki disease(KD). …”
Get full text

Predicting brain age for veterans with traumatic brain injuries and healthy controls: an exploratory analysis by John P. Coetzee, John P. Coetzee, Xiaojian Kang, Xiaojian Kang, Victoria Liou-Johnson, Victoria Liou-Johnson, Victoria Liou-Johnson, Ines Luttenbacher, Srija Seenivasan, Elika Eshghi, Daya Grewal, Siddhi Shah, Frank Hillary, Emily L. Dennis, Emily L. Dennis, Maheen M. Adamson, Maheen M. Adamson, Maheen M. Adamson

“…This may be driven by underlying biological changes resulting from the injury. Machine learning algorithms can use structural MRIs to give a predicted brain age (pBA). …”
Get full text

Rapid Damage Assessment and Bayesian-Based Debris Prediction for Building Clusters Against Earthquakes by Xiaowei Zheng, Yaozu Hou, Jie Cheng, Shuai Xu, Wenming Wang

“…Finally, with the structural response data of maximum floor displacement, a surrogate model for rapidly calculating seismic responses of structures is developed based on the XGBoost algorithm, achieving R² > 0.99 for floor displacements and R² = 0.989 for maximum inter-story drift ratio (MIDR) predictions. …”
Get full text

A machine learning model for predicting anatomical response to Anti-VEGF therapy in diabetic macular edema by Wenrui Lu, Kunhong Xiao, Xuemei Zhang, Yuqing Wang, Wenbin Chen, Xierong Wang, Yunxi Ye, Yan Lou, Li Li

“…SHAP analysis revealed that preoperative retinal edema, DRIL, SRF, and CRT had the strongest positive contributions, while intact EZ was a negative predictor of CRT reduction. A nomogram was developed to facilitate individualized clinical decision-making.ConclusionWe successfully developed a predictive model for anatomical response to anti-VEGF therapy in DME patients. …”
Get full text

Human-Aware Control for Physically Interacting Robots by Reza Sharif Razavian

“…The computational efficiency of the model also makes it suitable for repetitive predictive simulations within a robot’s control algorithm to predict the user’s behavior in human–robot interactions. …”
Get full text

Ways to predict interstitial lung disease in patients with systemic sclerosis: results of an observational study by D. V. Khorolsky, A. A. Klimenko, E. S. Pershina, N. M. Babadeva, A. A. Kondrashov, N. A. Shostak, E. P. Mikheeva, E. V. Zhilyaev

“…Classification trees (CTr) were constructed to predict the development of widespread ILD using the CHAID algorithm (exhaustive). …”
Get full text