Management and Industrial Engineering - Machine Learning in Industry
Latest Publications

TOTAL DOCUMENTS

(FIVE YEARS 9)

H-INDEX

(FIVE YEARS 0)

Published By Springer International Publishing

9783030758462, 9783030758479

Latest Documents Most Cited Documents Contributed Authors Related Sources Related Keywords

Fundamentals of Machine Learning

Management and Industrial Engineering - Machine Learning in Industry ◽

10.1007/978-3-030-75847-9_1 ◽

2021 ◽

pp. 1-27

Author(s):

A. Vinoth ◽

Shubhabrata Datta

Keyword(s):

Machine Learning

Performance Improvement in Hot Rolling Process with Novel Neural Architectural Search

Management and Industrial Engineering - Machine Learning in Industry ◽

10.1007/978-3-030-75847-9_9 ◽

2021 ◽

pp. 177-197

Author(s):

Srinivas Soumitri Miriyala ◽

Itishree Mohanty ◽

Kishalay Mitra

Keyword(s):

Performance Improvement ◽

Hot Rolling ◽

Rolling Process ◽

Hot Rolling Process

Accurate, Real-Time Replication of Governing Equations of Physical Systems with Transpose CNNs — for Industry 4.0 and Digital Twins

Management and Industrial Engineering - Machine Learning in Industry ◽

10.1007/978-3-030-75847-9_7 ◽

2021 ◽

pp. 139-158

Author(s):

Hritik Narayan ◽

Arya K. Bhattacharya

Keyword(s):

Real Time ◽

Industry 4.0 ◽

Governing Equations ◽

Physical Systems ◽

Digital Twins

A Brief Appraisal of Machine Learning in Industrial Sensing Probes

Management and Industrial Engineering - Machine Learning in Industry ◽

10.1007/978-3-030-75847-9_4 ◽

2021 ◽

pp. 83-96

Author(s):

R. Biswas

Keyword(s):

Machine Learning ◽

Industrial Sensing

Deep Learning in Vision-Based Automated Inspection: Current State and Future Prospects

Management and Industrial Engineering - Machine Learning in Industry ◽

10.1007/978-3-030-75847-9_8 ◽

2021 ◽

pp. 159-175

Author(s):

R. Senthilnathan

Keyword(s):

Deep Learning ◽

Automated Inspection ◽

Future Prospects ◽

Current State

Mining the Genesis of Sliver Defects Through Rough and Fuzzy Set Theories

Management and Industrial Engineering - Machine Learning in Industry ◽

10.1007/978-3-030-75847-9_5 ◽

2021 ◽

pp. 97-120

Author(s):

Itishree Mohanty ◽

Partha Dey ◽

Shubhabrata Datta

Keyword(s):

Fuzzy Set

Machine Learning Studies in Materials Science

Management and Industrial Engineering - Machine Learning in Industry ◽

10.1007/978-3-030-75847-9_6 ◽

2021 ◽

pp. 121-137

Author(s):

Barbara Mrzygłód ◽

Krzysztof Regulski ◽

Andrzej Opaliński

Keyword(s):

Machine Learning ◽

Materials Science

Neural Network Model Identification Studies to Predict Residual Stress of a Steel Plate Based on a Non-destructive Barkhausen Noise Measurement

Management and Industrial Engineering - Machine Learning in Industry ◽

10.1007/978-3-030-75847-9_2 ◽

2021 ◽

pp. 29-45

Author(s):

Tero Vuolio ◽

Olli Pesonen ◽

Aki Sorsa ◽

Suvi Santa-aho

Keyword(s):

Neural Network ◽

Residual Stress ◽

Network Model ◽

Neural Network Model ◽

Steel Plate ◽

Model Identification ◽

Noise Measurement ◽

Barkhausen Noise ◽

Non Destructive

Data-Driven Optimization of Blast Furnace Iron Making Process Using Evolutionary Deep Learning

Management and Industrial Engineering - Machine Learning in Industry ◽

10.1007/978-3-030-75847-9_3 ◽

2021 ◽

pp. 47-81

Author(s):

Bashista Kumar Mahanta ◽

Rajesh Jha ◽

Nirupam Chakraborti

Keyword(s):

Blast Furnace ◽

Deep Learning ◽

Data Driven

Management and Industrial Engineering - Machine Learning in IndustryLatest Publications

TOTAL DOCUMENTS

H-INDEX

Published By Springer International Publishing

Fundamentals of Machine Learning

Performance Improvement in Hot Rolling Process with Novel Neural Architectural Search

Accurate, Real-Time Replication of Governing Equations of Physical Systems with Transpose CNNs — for Industry 4.0 and Digital Twins

A Brief Appraisal of Machine Learning in Industrial Sensing Probes

Deep Learning in Vision-Based Automated Inspection: Current State and Future Prospects

Mining the Genesis of Sliver Defects Through Rough and Fuzzy Set Theories

Machine Learning Studies in Materials Science

Neural Network Model Identification Studies to Predict Residual Stress of a Steel Plate Based on a Non-destructive Barkhausen Noise Measurement

Data-Driven Optimization of Blast Furnace Iron Making Process Using Evolutionary Deep Learning

Management and Industrial Engineering - Machine Learning in Industry
Latest Publications