Results 201 to 210 of about 461,060 (258)

Differentiating Human Falls from Daily Activities Using Machine Learning Methods Based on Accelerometer and Altimeter Sensor Fusion Feature Engineering. [PDF]

Sensors (Basel)
Jurčić K, Magjarević R.
europepmc   +1 more source

Versatile vector tools for efficient protein screening across multiple expression systems

FEBS Open Bio, EarlyView.
A unified vector toolkit enables rapid protein expression screening across E. coli, insect, and mammalian cells. A single primer pair amplifies the target gene, which is inserted into any vector via a standardized interface. This streamlined workflow eliminates repeated cloning steps, accelerating the identification of optimal expression conditions for
Zhimin Zhu, Yaqing Liu, Lei Qin, Feng Yu, Qisheng Wang, Yahui Liu   +5 more
wiley   +1 more source

Advanced feature engineering in Acute:Chronic Workload Ratio (ACWR) calculation for injury forecasting in elite soccer. [PDF]

PLoS One
Matas-Bustos JB, Mora-García AM, de Hoyo Lora M, Nieto-Alarcón A, Gonzalez-Fernández FT.   +4 more
europepmc   +1 more source

Multi-Sensor Data Fusion and Vibro-Acoustic Feature Engineering for Health Monitoring and Remaining Useful Life Prediction of Hydraulic Valves. [PDF]

Sensors (Basel)
Li X, Zhang L, Tan T, Wang X, Zhao X, Xu Y.   +5 more
europepmc   +1 more source

Graph-Based Deep Learning Models for Predicting p<i>K</i>_a Values of Protein-Ionizable Residues via Physically Inspired Feature Engineering. [PDF]

J Chem Inf Model
Song Z, Wang R, Jiao X, Huang Z.
europepmc   +1 more source

Optimizing unsupervised feature engineering and classification pipelines for differentiated thyroid cancer recurrence prediction. [PDF]

BMC Med Inform Decis Mak
Onah E, Eze UJ, Abdulraheem AS, Ezigbo UG, Amorha KC, Ntie-Kang F.   +5 more
europepmc   +1 more source

UM-CPP: A Universal Model for Efficient Classification of Protein Particles in cryo-EM Micrographs with Feature Engineering. [PDF]

ACS Omega
Yao Z, Wang H, Luo W, Zhan Y, Wu X, Dai Y, Pei Y, Zhang G, Wang Z.   +8 more
europepmc   +1 more source

Paraconsistent Feature Engineering

, 2019
openaire   +2 more sources

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Feature Selection and Feature Engineering

2019
Feature selection and engineering are important steps in a machine learning pipeline and involves all the techniques adopted to reduce their dimensionality. Most of the time, these steps come after cleaning the dataset.
Hisham El-Amir, Mahmoud Hamdy
openaire   +1 more source

Intelligent Feature Engineering for Cybersecurity

2019 IEEE International Conference on Big Data (Big Data), 2019
Feature engineering and selection is a critical step in the implementation of any machine learning system. In application areas such as intrusion detection for cybersecurity, this task is made more complicated by the diverse data types and ranges presented in both raw data packets and derived data fields.
Paul Maxwell, Elie Alhajjar, Nathaniel D. Bastian   +2 more
openaire   +1 more source