Metrology of convex-shaped nanoparticles via soft classification machine learning of TEM images

Nanoscale Advances ◽

10.1039/d1na00524c ◽

2021 ◽

Author(s):

Haotian Wen ◽

Xiaoxue Xu ◽

Soshan Cheong ◽

Shen-Chuan Lo ◽

Jung-Hsuan Chen ◽

...

Keyword(s):

Machine Learning ◽

Performance Parameter ◽

Shaped Nanoparticles ◽

Soft Classification ◽

Shape Of Nanoparticles ◽

Laboratory Synthesis

The shape of nanoparticles is a key performance parameter for many applications, ranging from nanophotonics to nanomedicines. However, the unavoidable shape variations, which occur even in precision-controlled laboratory synthesis, can...

Download Full-text

Implementing Machine Learning in Laboratory Synthesis by Hybrid of SVR Model and Optimization Algorithms

Advanced Theory and Simulations ◽

10.1002/adts.202100225 ◽

2021 ◽

pp. 2100225

Author(s):

Tolou Pourashraf ◽

Saeid Shokri ◽

Mohammad Yousefi ◽

Abbas Ahmadi ◽

Parviz Aberoomand Azar

Keyword(s):

Machine Learning ◽

Optimization Algorithms ◽

Laboratory Synthesis

Download Full-text

Prediction of Iranian EFL Teachers' Burnout Level Using Machine Learning Algorithms and Maslach Burnout Inventory

10.21203/rs.3.rs-810170/v2 ◽

2021 ◽

Author(s):

Shima Baniadam Dizaj ◽

Shahla Baniadam Dizaj

Keyword(s):

Machine Learning ◽

Emotional Exhaustion ◽

Maslach Burnout Inventory ◽

Performance Parameter ◽

Machine Learning Algorithms ◽

Personal Accomplishment ◽

Lower Back ◽

Efl Teachers ◽

Overall Performance ◽

Coronary Failure

Abstract Burnout is that the end result of excessive work-associated pressure and it is categorized through emotional, psychological, and bodily exhaustion. we can remember it as a cutting-edge epidemic withinside the coaching career likewise as distinct professions. The measures that could have an impact on a lecturer and may reason burnout are categorized as Depersonalization, Emotional Exhaustion, Personal Accomplishment. alevin though articles on educator burnout range of their approach, they incline to specialize withinside the reasons of burnout, the manner to evaluate the symptoms and symptoms of burnout, strategies to forestall burnout, and/or subsequent steps for administrators and teachers. The principal problem that has been incomprehensible is the prediction of acquiring burned out. Through prediction, we can reduce lower back the fees of the burnout effect on teachers, students, schools, and society and forestall its results like depression, coronary failure, or possibly suicide. Our goal is to investigate and are expecting the burnout stage in English as a faraway Language (EFL) instructor. we're going to use Maslach Burnout Inventory (MBI) to collect a dataset and stay the three subscales of instructor burnout: emotional exhaustion, depersonalization, and decreased non-public accomplishment. whilst studying the amassed dataset that consisted of 1433 teachers' data, nine system getting to know type algorithms through enforcing exploitation python programing language is implemented and accuracy is hired as an overall performance parameter.

Download Full-text

Contextual Soft Classification Approaches for Crops Identification Using Multi-sensory Remote Sensing Data: Machine Learning Perspective for Satellite Images

Advances in Intelligent Systems and Computing - Artificial Intelligence Perspectives and Applications ◽

10.1007/978-3-319-18476-0_33 ◽

2015 ◽

pp. 333-346 ◽

Cited By ~ 1

Author(s):

Anand N. Khobragade ◽

Mukesh M. Raghuwanshi

Keyword(s):

Machine Learning ◽

Remote Sensing ◽

Satellite Images ◽

Remote Sensing Data ◽

Sensing Data ◽

Soft Classification

Download Full-text

Mind wandering as data augmentation: How mental travel supports abstraction

Behavioral and Brain Sciences ◽

10.1017/s0140525x1900311x ◽

2020 ◽

Vol 43 ◽

Author(s):

Myrthe Faber

Keyword(s):

Machine Learning ◽

Data Augmentation ◽

Mental Content ◽

Mind Wandering ◽

Theoretical Framework ◽

Important Addition

Abstract Gilead et al. state that abstraction supports mental travel, and that mental travel critically relies on abstraction. I propose an important addition to this theoretical framework, namely that mental travel might also support abstraction. Specifically, I argue that spontaneous mental travel (mind wandering), much like data augmentation in machine learning, provides variability in mental content and context necessary for abstraction.

Download Full-text