Weighted Content Similarity Feature for Software Architecture Anti-Patterns Prediction by Somayeh Kalhor, Mohammad Reza Keyvanpour

“…So, it is more effective than these two features in predicting dependencies between components using machine learning algorithms.…”
Get full text

Machine learning-driven insights into phase prediction for high entropy alloys by Reliance Jain, Sandeep Jain, Sheetal Kumar Dewangan, Lokesh Kumar Boriwal, Sumanta Samal

“…Herein, a method of designing substitutional high entropy alloys with optimization of input features and predict their phase formation, using different ML algorithms are proposed. …”
Get full text

CAREUP: An Integrated Care Platform with Intrinsic Capacity Monitoring and Prediction Capabilities by Marcin Kolakowski, Andrea Lupica, Seif Ben Bader, Vitomir Djaja-Josko, Jerzy Kolakowski, Jacek Cichocki, Jaouhar Ayadi, Luca Gilardi, Angelo Consoli, Irina Georgiana Mocanu, Oana Cramariuc, Lionello Ferrazzini, Eva Reithner, Magdalena Velciu, Barbara Borgogni, Sofia Rivaira, Sara Leonzi, Giacomo Cucchieri, Vera Stara

“…Besides standard functionalities like storing health measurement data or providing users with personalized recommendations, the platform includes novel intrinsic capacity assessment and prediction algorithms. Older adults’ performance is continuously monitored in all five IC domains—locomotion, psychology, cognition, vitality, and sensory capacity—based on measurement results and answers to questionnaires gathered using the platform’s mobile applications. …”
Get full text

Bayesian Model Prediction for Breast Cancer Survival: A Retrospective Analysis by Islam Bani Mohammad, Muayyad M. Ahmad

“…Objective: Over the recent years, machine learning (ML) models have been increasingly used in predicting breast cancer survival because of improvements in ML algorithms. …”
Get full text

Fall Risk Prediction Using Instrumented Footwear in Institutionalized Older Adults by Huanghe Zhang, Chuanyan Wu, Yulong Huang, Rui Song, Damiano Zanotto, Sunil K. Agrawal

“…The importance of each type of data is assessed using a brute-force search method, through which the optimal features are selected. AdaBoost algorithms are then utilized to develop predictive models based on the selected features. …”
Get full text

Feasibility of machine learning–based modeling and prediction to assess osteosarcoma outcomes by Qinfei Zhao, Weiquan Hu, Yu Xia, Shengyun Dai, Xiangsheng Wu, Jing Chen, Xiaoying Yuan, Tianyu Zhong, Xuxiang Xi, Qi Wang

“…However, identifying robust gene signatures to predict osteosarcoma outcomes remains a significant challenge. …”
Get full text

SMART DELAY PREDICTION: SUPERVISED MACHINE LEARNING SOLUTIONS FOR CONSTRUCTION PROJECTS by Pramodini Sahu, Dillip Kumar Bera, Pravat Kumar Parhi, Meenakshi Kandpal

“…Conventional techniques for predicting delays often do not deliver concrete predictions due to the multiplicity and dynamic character of construction tasks. …”
Get full text

Intelligent prediction and oriented design of high-hardness high-entropy ceramics by Anzhe Wang, Jicheng Liu, Linwei Guo, Kejie Qu, Haishen Xie, Yawei Li, Bin Du

“…This work utilizes machine learning and heuristic optimization algorithms to achieve accurate predictions of bulk high-entropy ceramics hardness (with validation set errors <10 %) and the oriented design of high-entropy ceramics with a hardness of 25 GPa (with an average error of 2.6 %). …”
Get full text

Developing advanced datadriven framework to predict the bearing capacity of piles on rock by Kennedy C. Onyelowe, Shadi Hanandeh, Viroon Kamchoom, Ahmed M. Ebid, Fabián Danilo Reyes Silva, José Luis Allauca Palta, José Luis Llamuca Llamuca, Siva Avudaiappan

“…This research presents an advanced data-driven framework that integrates multiple machine learning algorithms to predict the bearing capacity of piles based on geotechnical and in-situ test parameters. …”
Get full text

An interpretable electrocardiogram-based model for predicting arrhythmia and ischemia in cardiovascular disease by Tanjila Alam Sathi, Rafsan Jany, Razia Zaman Ela, AKM Azad, Salem Ali Alyami, Md Azam Hossain, Iqram Hussain

“…This aggregated dataset was employed to train multiple machine learning (ML) models aimed at automatically classifying heart conditions, including arrhythmia, ischemia, and healthy states. We designed a predictive framework utilizing boosting ML algorithms, enhanced by explainable artificial intelligence (XAI) techniques, to ensure high predictive performance in model interpretation. …”
Get full text

Artificial intelligence for surgical outcome prediction in glaucoma: a systematic review by Zeena Kailani, Lauren Kim, Joshua Bierbrier, Michael Balas, David J. Mathew, David J. Mathew

“…Artificial intelligence (AI) has emerged as a promising tool for enhancing predictive accuracy in clinical decision-making.MethodsThis systematic review was conducted to evaluate the current evidence on the use of AI to predict surgical outcomes in glaucoma patients. …”
Get full text

Machine learning for predicting medical outcomes associated with acute lithium poisoning by Omid Mehrpour, Varun Vohra, Samaneh Nakhaee, Seyed Ali Mohtarami, Farshad M. Shirazi

“…Abstract The use of machine learning algorithms and artificial intelligence in medicine has attracted significant interest due to its ability to aid in predicting medical outcomes. …”
Get full text

Machine learning for predicting strength properties of waste iron slag concrete by Matiur Rahman Raju, Syed Ishtiaq Ahmad, Md Mehedi Hasan, Noor Md. Sadiqul Hasan, Md Monirul Islam, Md. Abdul Basit, Ishraq Tasnim Hossain, Saif Ahmed Santo, Md Shahrior Alam, Mahfuzur Rahman

“…The experimental investigation of WIS-incorporated concrete focused on compressive and tensile strength with machine learning (ML) models for prediction. Among the tested ML algorithms, Decision Tree (DT) and XGBoost showed the highest accuracy (R2 = 0.95135) in predicting concrete strength properties, while models like SVM and Symbolic Regression underperformed. …”
Get full text

Use machine learning to predict treatment outcome of early childhood caries by Yafei Wu, Maoni Jia, Ya Fang, Duangporn Duangthip, Chun Hung Chu, Sherry Shiqian Gao

“…Machine learning algorithms including Naive Bayes, logistic regression, decision tree, random forest, support vector machine, and extreme gradient boosting were adopted to predict the caries-arresting outcome of ECC at 30-month follow-up after receiving fluoride and silver therapy. …”
Get full text

Controlled Fault Current Interruption Scheme for Improved Fault Prediction Accuracy by Xu Yang, Qi Long, Hao Li, Dachao Huang, Shupeng Xue, Jiajie Huang, Hongzhang Liang, Xiongying Duan

“…To enhance the accuracy and efficiency of controlled fault current interruption (CFI) in short-circuit current processing within power systems, a half-cycle elimination prediction algorithm and a double-sampling CFI sequence method are proposed in this study. …”
Get full text

Different artificial neural networks for predicting burnout risk in Italian anesthesiologists by Marco Cascella, Alessandro Simonini, Sergio Coluccia, Elena Giovanna Bignami, Gilberto Fiore, Emiliano Petrucci, Alessandro Vergallo, Giacomo Sollecchia, Franco Marinangeli, Roberto Pedone, Alessandro Vittori

“…Despite substantial differences among the six implemented algorithms, no significant variation in prediction performance was observed. …”
Get full text

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

“…Therefore, we constructed a risk prediction model for the rupture of MIAs by machine learning algorithms.…”
Get full text

Interpretable prediction of stroke prognosis: SHAP for SVM and nomogram for logistic regression by Kun Guo, Kun Guo, Bo Zhu, Lei Zha, Yuan Shao, Zhiqin Liu, Naibing Gu, Kongbo Chen

“…Machine Learning (ML) models have emerged as promising tools for predicting stroke prognosis, surpassing traditional methods in accuracy and speed.ObjectiveThe aim of this study was to develop and validate ML algorithms for predicting the 6-month prognosis of patients with Acute Cerebral Infarction, using clinical data from two medical centers in China, and to assess the feasibility of implementing Explainable ML in clinical settings.MethodsA retrospective observational cohort study was conducted involving 398 patients diagnosed with Acute Cerebral Infarction from January 2023 to February 2024. …”
Get full text

Identification and validation of ubiquitination-related genes for predicting cervical cancer outcome by Ge Jin, Xiaomei Fan, Xiaoliang Liang, Honghong Dai, Jun Wang

“…The risk score model constructed based on these biomarkers could effectively predict the survival rate of cervical cancer patients (AUC &gt;0.6 for 1/3/5 years). …”
Get full text

Methodic approaches to prediction of a positive corporate image of pharmaceutical organizations by A. G. Petrov, O. I. Knysh, G. P. Petrov

“…The technique includes the algorithm for construction of estimation and prediction tables and rules of their usage.…”
Get full text