Machine Learning‐Enhanced Nanoparticle Design for Precision Cancer Drug Delivery by Qingquan Wang, Yujian Liu, Chenchen Li, Bin Xu, Shidang Xu, Bin Liu

“…The synthesis of nanomedicines involves numerous parameters, and the complexity of nano–bio interactions in vivo presents further difficulties. …”
Get full text

Hybrid extreme learning machine for real-time rate of penetration prediction by Abdelhamid Kenioua, Omar Djebili, Ammar Touati Brahim

“…The methodology involves a formation-specific modelling approach, where separate ELM models are trained for each formation using surface drilling parameters such as weight on bit (WOB), rotary speed (RPM), and flow rate. …”
Get full text

Enhanced water saturation estimation in hydrocarbon reservoirs using machine learning by Ali Akbari, Ali Ranjbar, Yousef Kazemzadeh, Dmitriy A. Martyushev

“…Traditional Sw estimation approaches often face limitations due to idealized assumptions, dependency on core-derived parameters, and geological heterogeneity. In this study, a comprehensive dataset consisting of 30,660 independent data points was utilized to develop machine learning (ML) models for Sw prediction. …”
Get full text

A New Approach for Brain Tumor Detection Using Machine Learning by Elsadek Hussien Ibrahim, Shaaban Ebrahim Abo-Youssef, Khaled El-Bahnasy, Khaled Ahmed Mohamed Fathy

Get full text

Online Learning of Entrainment Closures in a Hybrid Machine Learning Parameterization by Costa Christopoulos, Ignacio Lopez‐Gomez, Tom Beucler, Yair Cohen, Charles Kawczynski, Oliver R. A. Dunbar, Tapio Schneider

“…Abstract This work integrates machine learning into an atmospheric parameterization to target uncertain mixing processes while maintaining interpretable, predictive, and well‐established physical equations. …”
Get full text

Data-driven insights into groundwater quality: machine and deep learning approaches by Gift Mbuzi, Abdur Rashid Sangi, Baha Ihnaini, Anil Carie, Sruthi Sivarajan, Satish Anamalamudi

“…Mapping a five-year time series historical dataset (2016–2021) of important physicochemical parameters such as conductivity, pH, BOD, fluoride, arsenic, and nitrate, this paper compares some machine learning and deep learning models. …”
Get full text

Mobile machine design through dynamic load simulation on their drive units by S. A. Partko, L. M. Groshev, A. N. Sirotenko

“…Introduction. The mobile machine design is impossible without considering the vibration parameters of their units. …”
Get full text

Research on rock strength prediction model based on machine learning algorithm by Xiang Ding, Mengyun Dong, Wanqing Shen

“…By selecting different features, the optimal feature combination for predicting rock compressive strength was obtained, and the optimal parameters for different models were obtained through the Sparrow Search Algorithm (SSA). …”
Get full text

Real-time classification of EEG signals using Machine Learning deployment by Swati CHOWDHURI, Satadip SAHA, Samadrita KARMAKAR, Ankur CHANDA

“…Machine learning has emerged as a powerful tool for simplifying the analysis of complex variables, enabling the effective assessment of the students' concentration levels based on specific parameters. …”
Get full text

Single Transit Detection in Kepler with Machine Learning and Onboard Spacecraft Diagnostics by Matthew T. Hansen, Jason A. Dittmann

Get full text

NUMERICO-ANALYTICAL INVESTIGATION OF DYNAMIC CONTACT PROCESSES OF CYLINDRICAL MACHINE PARTS by L. P. VOVK, B. V. SOBOL

Get full text

Machine Learning Reconstruction of Left Ventricular Pressure From Peripheral Waveforms by Alessio Tamborini, PhD, Arian Aghilinejad, PhD, Ray V. Matthews, MD, Morteza Gharib, PhD

“…Objectives: This study aimed to develop a cuff-based machine learning (cuff-ML) approach for reconstructing LV pressure from noninvasive brachial waveforms as a bedside assessment of cardiac function. …”
Get full text

Detection of cardiac amyloidosis using machine learning on routine echocardiographic measurements by I-Min Chiu, David Ouyang, James Zou, Rachel Si-Wen Chang, Phillip Tacon, Michael Abiragi, Louie Cao, Gloria Hong, Jonathan Le, Chathuri Daluwatte, Piero Ricchiuto

“…Features that were large contributors to the model included mitral A-wave velocity, global longitudinal strain (GLS), left ventricle posterior wall diameter end diastolic (LVPWd) and left atrial area.Conclusion Machine learning on echocardiographic parameters can detect patients with CA with accuracy. …”
Get full text

Extracting Knowledge from Machine Learning Models to Diagnose Breast Cancer by José Manuel Martínez-Ramírez, Cristobal Carmona, María Jesús Ramírez-Expósito, José Manuel Martínez-Martos

“…This study discusses the roles of the identified parameters in cancer development, thus underscoring the potential of explainable machine learning models for enhancing early breast cancer diagnosis by focusing on explainability and the use of serum biomarkers.The combination of both can lead to improved early detection and personalized treatments, emphasizing the potential of these methods in clinical settings. …”
Get full text

Nanoscale Polishing of TC4 Titanium Alloy Surface Based on Dual-Pole Magnetic Abrasive Finishing Method by Zhenfeng Zhou, Xu Sun, Shibing Liang, Ying Fang, Yanzhen Yang, Yongjian Fu, Shiqing Zou

Subjects: Get full text

Phase diagram construction and prediction method based on machine learning algorithms by Shengkun Xi, Jiahui Li, Longke Bao, Rongpei Shi, Haijun Zhang, Xiaoyu Chong, Zhou Li, Cuiping Wang, Xingjun Liu

“…The fast-growing machine learning technique opens a new pathway to deal with tons of data and parameters. …”
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

Machine Learning Techniques to Model and Predict Airflow Requirements in Underground Mining by Maria Karagianni, Andreas Benardos

“…This paper analyzes the airflow requirements of underground operations and the accurate assessment of future conditions so as to effectively adjust ventilation parameters. More particularly, ML techniques are utilized to capture patterns or prevailing conditions and to be able to generalize/predict future conditions managed via the ventilation system. …”
Get full text

Aero Engine Fault Diagnosis Using an Optimized Extreme Learning Machine by Xinyi Yang, Shan Pang, Wei Shen, Xuesen Lin, Keyi Jiang, Yonghua Wang

“…A new extreme learning machine optimized by quantum-behaved particle swarm optimization (QPSO) is developed in this paper. …”
Get full text

INFLUENCE OF MACHINING TECHNOLOGIES ON VALUES OF RESIDUAL STRESSES OF OXIDE CUTTING CERAMICS by Jakub Němeček, Kamil Kolařík, Jiří Čapek, Nikolaj Ganev

“…The influence of the parameters of machining to residual stresses was studied and the resulting values were compared with each other.…”
Get full text