Research on Investment Estimation of Prefabricated Buildings Based on Genetic Algorithm Optimization Neural Network by Jin Gao, Wanhua Zhao

“…Starting from the investment decision-making stage of construction projects, this paper analyses the characteristics of prefabricated investment estimation and the relevant literature on the characteristics of prefabricated construction projects, uses the rough set attribute reduction algorithm to screen the key engineering characteristic factors, and establishes a BP neural network model optimized by genetic algorithm to estimate and analyze the investment of completed prefabricated construction projects. …”
Get full text

Leveraging AlphaFold2 structural space exploration for generating drug target structures in structure-based virtual screening by Keisuke Uchikawa, Kairi Furui, Masahito Ohue

“…Computational virtual screening (VS) plays a vital role in early-stage drug discovery by enabling the efficient selection of candidate compounds and reducing associated costs. …”
Get full text

Potential use of saliva infrared spectra and machine learning for a minimally invasive screening test for congenital syphilis in infants by Deise Cristina Dal’Ongaro, Cicero Cena, Bruno Spolon Marangoni, Daniele A. Soares-Marangoni

“…When applying Quadratic Standard Normal Variate preprocessing with LOOCV, the model achieved 90% accuracy, 100% sensitivity, and 80% specificity. …”
Get full text

Two-Step Screening for Depression and Anxiety in Patients with Cancer: A Retrospective Validation Study Using Real-World Data by Bryan Gascon, Joel Elman, Alyssa Macedo, Yvonne Leung, Gary Rodin, Madeline Li

“…<b>Conclusions:</b> The present study is among the first to demonstrate that a two-step screening algorithm for depression may improve depression screening in cancer using real-world data. …”
Get full text

Does advancement in marker-less pose-estimation mean more quality research? A systematic review by Shivam Bhola, Shivam Bhola, Hyun-Bin Kim, Hyeon Su Kim, BonSang Gu, Jun-Il Yoo, Jun-Il Yoo

“…Despite the emergence of state-of-the-art keypoint-detection algorithms, the extent to which these tools are employed and the nature of their application in scientific research has yet to be systematically documented. …”
Get full text

Identification and validation of prognostic genes associated with T-cell exhaustion and macrophage polarization in breast cancer by Fengqiang Cui, Changjiao Yan, Jiang Wu, Yuqing Yang, Jixin Yang, Jialing Luo, Nanlin Li

“…Next, 101 combinations of 10 machine learning algorithms and univariate Cox analysis were utilized to screen for prognostic genes. …”
Get full text

Prospective external validation of the automated PIPRA multivariable prediction model for postoperative delirium on real-world data from a consecutive cohort of non-cardiac surgery... by Mary-Anne Kedda, Kelly A Reeve, Nayeli Schmutz Gelsomino, Michela Venturini, Felix Buddeberg, Martin Zozman, Reto Stocker, Philipp Meier, Marius Möller, Simone Pascale Wildhaber, Benjamin T Dodsworth

“…The study highlighted the model’s applicability across diverse clinical environments, despite differences in patient populations and screening protocols.Conclusions The PIPRA algorithm is a reliable tool for identifying surgical patients at risk of POD, supporting early intervention strategies to improve patient outcomes. …”
Get full text

Construction of a machine learning-based risk prediction model for depression in middle-aged and elderly patients with cardiovascular metabolic diseases in China: a longitudinal st... by Gege Zhang, Sijie Dong, Li Wang

“…LASSO regression was used to screen for risk factors, and three machine learning algorithms—logistic regression (LR), random forest (RF), and XGBoost—were employed to build predictive models. …”
Get full text

Development of a postoperative recurrence prediction model for stage Ⅰ non-small cell lung cancer patients using multimodal data based on machine learning by ZHANG Di, WU Yi, XU Yu

“…A combined model was further constructed by integrating both feature sets, and model performance was compared to identify the optimal predictive model.Results‍　‍This study screened the features from non-contrast CT images and ultimately selected 7 radiomic features for constructing radiomic model. …”
Get full text

A Literature Analysis-Based Study on Advances in Underwater Multi-Robot Pursuit-Evasion Problems by Zhenkun LEI, Mingzhi CHEN, Daqi ZHU

“…The principles, advantages, disadvantages, and applicability of four intelligent pursuit-evasion methods, including reinforcement learning, model predictive control, Apollonius circle, and artificial potential field, were analyzed in depth. …”
Get full text

Construction of a Prediction Model for Sleep Quality in Embryo Repeated Implantation Failure Patients Undergoing Assisted Reproductive Technology Based on Machine Learning: A Singl... by Zhao Y, Xu C, Qin N, Bai L, Wang X, Wang K

“…Use Lasso regression to screen variables and construct a risk prediction model using six machine learning algorithms. …”
Get full text

Evaluating Leaf Water Potential of Maize Through Multi-Cultivar Dehydration Experiments and Segmentation Thresholding by Shuanghui Zhao, Yanqun Zhang, Pancen Feng, Xinlong Hu, Yan Mo, Hao Li, Jiusheng Li

“…In this study, leaf dehydration experiments of three maize cultivars were applied to provide a dataset covering a wide range of <i>Ψ_leaf</i> variations, which is often challenging to obtain in field trials. The analysis screened published VIs highly correlated with <i>Ψ_leaf</i> and constructed a model for <i>Ψ_leaf</i> estimation based on three algorithms—partial least squares regression (PLSR), random forest (RF), and multiple linear stepwise regression (MLR)—for each cultivar and all three cultivars. …”
Get full text

Deep Learning-Based Detection of Aflatoxin B1 Contamination in Almonds Using Hyperspectral Imaging: A Focus on Optimized 3D Inception–ResNet Model by Md. Ahasan Kabir, Ivan Lee, Sang-Heon Lee

“…A feature selection algorithm was employed to enhance processing efficiency and reduce spectral dimensionality while maintaining high classification accuracy. …”
Get full text

Cervical cancer demystified: exploring epidemiology, risk factors, screening, treatment modalities, preventive measures, and the role of artificial intelligence by N. Mohammad, M. Khan, M. Maqsood, A. H. K. Naseeb

“…However, disparities persist due to limited healthcare infrastructure and access to routine screening. AI-driven technologies, including deep learning algorithms and machine learning models, are emerging as valuable tools in cervical cancer detection, risk assessment, and treatment planning. …”
Get full text

Interpretable machine learning algorithms reveal gut microbiome features associated with atopic dermatitis by Jingtai Ma, Jingtai Ma, Yiting Fang, Yiting Fang, Shiqi Li, Shiqi Li, Lilian Zeng, Siyi Chen, Zhifeng Li, Guiyuan Ji, Xingfen Yang, Wei Wu, Wei Wu

Get full text

Artificial Intelligence With Neural Network Algorithms in Pediatric Astrocytoma Diagnosis: A Systematic Review by Floresya K. Farmawati, Della W.A. Nurwakhid, Tifani A. Pradhea, Rayyan Fitriasa, Hutami H. Arrahmi, Muhana F. Ilyas, Fadhilah T. Nur

“…The AI models exhibited performance levels comparable to or exceeding that of expert radiologists, with metrics such as tumor classification accuracy of 92% and high values of the area under the receiver operating characteristic curve.Conclusions: AI with neural network algorithms shows significant promise in enhancing accuracy of pediatric astrocytoma diagnosis. …”
Get full text

Machine learning prediction of non-attendance to postpartum glucose screening and subsequent risk of type 2 diabetes following gestational diabetes. by Nishanthi Periyathambi, Durga Parkhi, Yonas Ghebremichael-Weldeselassie, Vinod Patel, Nithya Sukumar, Rahul Siddharthan, Leelavati Narlikar, Ponnusamy Saravanan

“…<h4>Objective</h4>The aim of the present study was to identify the factors associated with non-attendance of immediate postpartum glucose test using a machine learning algorithm following gestational diabetes mellitus (GDM) pregnancy.…”
Get full text

Evaluating the generalisability of region-naïve machine learning algorithms for the identification of epilepsy in low-resource settings. by Ioana Duta, Symon M Kariuki, Anthony K Ngugi, Angelina Kakooza Mwesige, Honorati Masanja, Daniel M Mwanga, Seth Owusu-Agyei, Ryan Wagner, J Helen Cross, Josemir W Sander, Charles R Newton, Arjune Sen, Gabriel Davis Jones

Get full text

Saliva-derived transcriptomic signature for gastric cancer detection using machine learning and leveraging publicly available datasets by Catarina Lopes, Andreia Brandão, Manuel R. Teixeira, Mário Dinis-Ribeiro, Carina Pereira

“…Leveraging transcriptomic data from the Gene Expression Omnibus (GEO), we constructed and validated predictive models through machine learning algorithms within the tidymodels framework. …”
Get full text

Multi-model machine learning framework for lung cancer risk prediction: A comparative analysis of nine classifiers with hybrid and ensemble approaches using behavioral and hematolo... by Vinod Kumar, Chander prabha, Deepali Gupta, Sapna Juneja, Swati Kumari, Ali Nauman

“…The present study investigates 34 demographic, behavioral, and hematological risk factors based on a sample of 2,000 patient data records. A multi-model machine learning approach compares nine algorithms: KNN, AdaBoost (AB), logistic regression (LR), random forest (RF), SVM, naive Bayes (NB), decision tree (DT), gradient boosting (GB), and stochastic gradient descent (SGD). …”
Get full text